init commit

EgoState_pji.csv

@@ -0,0 +1,2998 @@
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accurate.py

@@ -0,0 +1,296 @@
+#!/usr/bin/env python
+# -*- coding: utf-8 -*-
+##################################################################
+#
+# Copyright (c) 2023 CICV, Inc. All Rights Reserved
+#
+##################################################################
+"""
+@Authors:           xieguijin(xieguijin@china-icv.cn), yangzihao(yangzihao@china-icv.cn)
+@Data:              2023/08/03
+@Last Modified:     2023/08/03
+@Summary:           Functionality metrics
+"""
+
+import math
+import numpy as np
+import pandas as pd
+
+from score_weight import cal_score_with_priority, cal_weight_from_80
+from common import score_grade, string_concatenate, replace_key_with_value
+
+
+class Accurate(object):
+    """
+    Class for achieving accurate metrics for autonomous driving.
+
+    Attributes:
+        df: Vehicle driving data, stored in dataframe format.
+    """
+
+    def __init__(self, data_processed, scoreModel, resultPath):
+        # self.eval_data = pd.DataFrame()
+        # self.data_processed = data_processed
+        self.scoreModel = scoreModel
+        self.resultPath = resultPath
+
+        self.df = data_processed.ego_df
+        self.df_trajectory = data_processed.trajectory_df  # 读取轨迹的数据
+
+        self.config = data_processed.config
+        accurate_config = data_processed.accurate_config
+        self.accurate_config = accurate_config
+
+        # common data
+        self.builtin_metric_list = self.config.builtinMetricList
+
+        # dimension data
+        self.weight_custom = accurate_config['weightCustom']
+        self.metric_list = accurate_config['metric']
+        self.type_list = accurate_config['type']
+        self.type_name_dict = accurate_config['typeName']
+        self.name_dict = accurate_config['name']
+        self.unit_dict = accurate_config['unit']
+
+        # custom metric data
+        self.customMetricParam = accurate_config['customMetricParam']
+        self.custom_metric_list = list(self.customMetricParam.keys())
+        self.custom_data = {}
+        self.custom_param_dict = {}
+
+        # score data
+        self.weight = accurate_config['weightDimension']
+
+        self.weight_type_dict = accurate_config['typeWeight']
+        self.weight_type_list = accurate_config['typeWeightList']
+
+        self.weight_dict = accurate_config['weight']
+        self.weight_list = accurate_config['weightList']
+
+        self.priority_dict = accurate_config['priority']
+        self.priority_list = accurate_config['priorityList']
+
+        self.kind_dict = accurate_config['kind']
+        self.optimal_dict = accurate_config['optimal']
+        self.multiple_dict = accurate_config['multiple']
+        self.kind_list = accurate_config['kindList']
+        self.optimal_list = accurate_config['optimalList']
+        self.multiple_list = accurate_config['multipleList']
+
+        # metric data
+        self.metric_dict = accurate_config['typeMetricDict']
+        # self.drive_metric_list = self.metric_dict['accurateDrive']
+        # self.stop_metric_list = self.metric_dict['accurateStop']
+        # self.drive_metric_list = ["averageSpeed"]
+        # self.stop_metric_list = ["stopDuration", "stopCount"]
+
+        # metric value
+        self.positionError_list = []
+        self.executeAccurateError_list = []
+
+        self.positionError_dict = {}
+        self.positionError_sum = 0
+        # self.executeAccurateError_count = 0
+
+        self.positionError = 0
+        self.executeAccurateError = 0
+
+    def _accurate_metric_cal(self):
+        self._positionError_cal()
+        self._executeAccurateError_cal()
+
+    def _executeAccurateError_cal(self):
+        # 用于记录段数的变量
+        error_segment_count = 0
+
+        # 标记当前是否在目标数字的段中
+        in_segment = False
+        targets = [21201000300, 21201000001, 21201000002, 21202000000, 21203000200, 21203000300]
+        task_error_code_list = self.df.task_error_code.tolist()
+        for target in targets:
+            for number in task_error_code_list:
+                # 如果当前数字是目标数字，并且我们之前不在段中
+                if number == target and not in_segment:
+                    # 开始一个新的段
+                    error_segment_count += 1
+                    in_segment = True
+                    # 如果当前数字不是目标数字，并且我们之前在段中
+                elif number != target and in_segment:
+                    # 结束当前的段
+                    in_segment = False
+
+                    # 注意：如果列表以目标数字结束，并且没有额外的非目标数字来结束段，
+            # 则上面的循环将不会将最后一个段计数。我们需要在这里检查它。
+            if task_error_code_list and (task_error_code_list[-1] == target) and in_segment:
+                error_segment_count += 1
+
+        self.executeAccurateError = error_segment_count
+
+    def _positionError_cal(self):
+        self.df_trajectory['ego_pos'] = self.df_trajectory.apply(lambda row: (row['ego_posX'], row['ego_posY']), axis=1)
+        self.df_trajectory['target_pos'] = self.df_trajectory.apply(lambda row: (row['TargetX'], row['TargetY']),
+                                                                    axis=1)
+
+        # 初始化存储最小距离的列表
+        min_distances = []
+
+        # 遍历第一组坐标
+        for P in self.df_trajectory['ego_pos']:
+            min_dist = float('inf')  # 初始化为正无穷大
+            # 遍历第二组坐标
+            for Q in self.df_trajectory['target_pos']:
+                # 计算两点间的距离
+                dist = math.sqrt((P[0] - Q[0]) ** 2 + (P[1] - Q[1]) ** 2)
+                # 更新最小距离
+                if dist < min_dist:
+                    min_dist = dist
+                    # 存储最小距离
+            min_distances.append(min_dist)
+        # print("min_distances is", min_distances)
+
+        self.positionError = np.std(np.array(min_distances))
+
+    def _cal_max_min_avg(self, num_list):
+        maxx = round(max(num_list), 4) if num_list else "-"
+        minn = round(min(num_list), 4) if num_list else "-"
+        avg = round(sum(num_list) / len(num_list), 4) if num_list else "-"
+
+        result = {
+            "max": maxx,
+            "min": minn,
+            "avg": avg
+        }
+
+        return result
+
+    def _accurate_statistic(self):
+        """
+
+        """
+        self._accurate_metric_cal()
+        self.positionError_dict = self._cal_max_min_avg(self.positionError_list)
+        arr_accurate = [[self.positionError, self.executeAccurateError]]
+        return arr_accurate
+
+    def _score_cal(self):
+        """
+        """
+        arr_accurate = self._accurate_statistic()
+        print("\n[准确性表现及得分情况]")
+        print("准确性各指标值：", [[round(num, 2) for num in row] for row in arr_accurate])
+
+        arr_accurate = np.array(arr_accurate)
+        score_model = self.scoreModel(self.kind_list, self.optimal_list, self.multiple_list, arr_accurate)
+        score_sub = score_model.cal_score()
+        score_sub = list(map(lambda x: 80 if np.isnan(x) else x, score_sub))
+        score_metric = [round(num, 2) for num in score_sub]
+
+        metric_list = [x for x in self.metric_list if x in self.config.builtinMetricList]
+        score_metric_dict = {key: value for key, value in zip(metric_list, score_metric)}
+
+        score_metric_dict = {key: score_metric_dict[key] for key in self.metric_list}
+        score_metric = list(score_metric_dict.values())
+
+        score_type_dict = {}
+        if self.weight_custom:  # 自定义权重
+            score_metric_with_weight_dict = {key: score_metric_dict[key] * self.weight_dict[key] for key in
+                                             self.weight_dict}
+
+            for type in self.type_list:
+                type_score = sum(
+                    value for key, value in score_metric_with_weight_dict.items() if key in self.metric_dict[type])
+                score_type_dict[type] = round(type_score, 2)
+
+            score_type_with_weight_dict = {key: score_type_dict[key] * self.weight_type_dict[key] for key in
+                                           score_type_dict}
+            score_accurate = sum(score_type_with_weight_dict.values())
+
+        else:  # 客观赋权
+            self.weight_list = cal_weight_from_80(score_metric)
+            self.weight_dict = {key: value for key, value in zip(self.metric_list, self.weight_list)}
+            score_accurate = cal_score_with_priority(score_metric, self.weight_list, self.priority_list)
+
+            for type in self.type_list:
+                type_weight = sum(value for key, value in self.weight_dict.items() if key in self.metric_dict[type])
+                self.weight_dict = {key: round(value / type_weight, 4) for key, value in self.weight_dict.items() if
+                                    key in self.metric_dict[type]}
+                type_score_metric = [value for key, value in score_metric_dict.items() if key in self.metric_dict[type]]
+                type_weight_list = [value for key, value in self.weight_dict.items() if key in self.metric_dict[type]]
+                type_priority_list = [value for key, value in self.priority_dict.items() if
+                                      key in self.metric_dict[type]]
+
+                type_score = cal_score_with_priority(type_score_metric, type_weight_list, type_priority_list)
+                score_type_dict[type] = round(type_score, 2)
+
+        score_accurate = round(score_accurate, 2)
+
+        print("准确性各指标基准值：", self.optimal_list)
+        print(f"准确性得分为：{score_accurate:.2f}分。")
+        print(f"准确性各类型得分为：{score_type_dict}。")
+        print(f"准确性各指标得分为：{score_metric_dict}。")
+        return score_accurate, score_type_dict, score_metric_dict
+
+    def report_statistic(self):
+        """
+
+        Returns:
+
+        """
+
+        report_dict = {
+            "name": "准确性",
+            "weight": f"{self.weight * 100:.2f}%",
+        }
+
+        score_accurate, score_type_dict, score_metric_dict = self._score_cal()
+
+        # score_accurate, score_metric = self.effi_score()
+        score_accurate = int(score_accurate) if int(score_accurate) == score_accurate else round(score_accurate, 2)
+        grade_accurate = score_grade(score_accurate)
+        report_dict["score"] = score_accurate
+        report_dict["level"] = grade_accurate
+
+        description = "· 在准确性方面，"
+        is_good = True
+
+        if self.positionError_sum > 1:
+            is_good = False
+            description += f"行驶过程中，位置偏移总误差为{self.positionError_sum}米，需重点优化。"
+
+        if self.executeAccurateError > 0:
+            is_good = False
+            description += f"出现{self.executeAccurateError}次任务执行状态错误，需重点优化。"
+
+        if is_good:
+            description += f"行驶准确且任务执行准确，算法表现优秀。"
+
+        report_dict["description"] = description
+
+        description1 = f"最大值：{self.positionError_dict['max']}m/s；" \
+                       f"最小值：{self.positionError_dict['min']}m/s；" \
+                       f"平均值：{self.positionError_dict['avg']}m/s"
+
+        description2 = f"次数：{self.executeAccurateError}次；"
+
+        positionError_index = {
+            "weight": self.weight_dict['positionError'],
+            "score": score_metric_dict['positionError'],
+            "description": description1
+        }
+
+        executeAccurateError_index = {
+            "weight": self.weight_dict['executeAccurateError'],
+            "score": score_metric_dict['executeAccurateError'],
+            "description": description2
+        }
+
+        indexes_dict = {
+            "positionError": positionError_index,
+            "executeAccurateError": executeAccurateError_index
+        }
+
+        report_dict["indexes"] = indexes_dict
+
+        print(report_dict)
+
+        return report_dict

comfort.py

@@ -0,0 +1,833 @@
+#!/usr/bin/env python
+# -*- coding: utf-8 -*-
+##################################################################
+#
+# Copyright (c) 2023 CICV, Inc. All Rights Reserved
+#
+##################################################################
+"""
+@Authors:           zhanghaiwen(zhanghaiwen@china-icv.cn), yangzihao(yangzihao@china-icv.cn)
+@Data:              2023/06/25
+@Last Modified:     2023/06/25
+@Summary:           Comfort metrics
+"""
+
+import os
+import sys
+import math
+import pandas as pd
+import numpy as np
+import scipy.signal
+
+sys.path.append('../common')
+sys.path.append('../modules')
+sys.path.append('../results')
+
+from data_info import DataInfoList
+from score_weight import cal_score_with_priority, cal_weight_from_80
+from common import get_interpolation, score_grade, string_concatenate, replace_key_with_value, get_frame_with_time
+import matplotlib.pyplot as plt
+
+
+def peak_valley_decorator(method):
+    def wrapper(self, *args, **kwargs):
+        peak_valley = self._peak_valley_determination(self.df)
+        pv_list = self.df.loc[peak_valley, ['simTime', 'speedH']].values.tolist()
+        if len(pv_list) != 0:
+            flag = True
+            p_last = pv_list[0]
+
+            for i in range(1, len(pv_list)):
+                p_curr = pv_list[i]
+
+                if self._peak_valley_judgment(p_last, p_curr):
+                    # method(self, p_curr, p_last)
+                    method(self, p_curr, p_last, flag, *args, **kwargs)
+                else:
+                    p_last = p_curr
+
+            return method
+        else:
+            flag = False
+            p_curr = [0, 0]
+            p_last = [0, 0]
+            method(self, p_curr, p_last, flag, *args, **kwargs)
+            return method
+
+    return wrapper
+
+
+class Comfort(object):
+    """
+    Class for achieving comfort metrics for autonomous driving.
+
+    Attributes:
+        dataframe: Vehicle driving data, stored in dataframe format.
+    """
+
+    def __init__(self, data_processed, scoreModel, resultPath):
+        self.eval_data = pd.DataFrame()
+        self.data_processed = data_processed
+        self.scoreModel = scoreModel
+        self.resultPath = resultPath
+
+        # self.data = data_processed.obj_data[1]
+        self.data = data_processed.ego_df
+        self.mileage = data_processed.report_info['mileage']
+        self.ego_df = pd.DataFrame()
+        # self.discomfort_df = pd.DataFrame(columns=['start_time', 'end_time', 'start_frame', 'end_frame', 'type'])
+
+        self.config = data_processed.config
+        comfort_config = data_processed.comfort_config
+        self.comfort_config = comfort_config
+
+        # common data
+        self.bulitin_metric_list = self.config.builtinMetricList
+
+        # dimension data
+        self.weight_custom = comfort_config['weightCustom']
+        self.metric_list = comfort_config['metric']
+        self.type_list = comfort_config['type']
+        self.type_name_dict = comfort_config['typeName']
+        self.name_dict = comfort_config['name']
+        self.unit_dict = comfort_config['unit']
+
+        # custom metric data
+        # self.customMetricParam = comfort_config['customMetricParam']
+        # self.custom_metric_list = list(self.customMetricParam.keys())
+        # self.custom_data = custom_data
+        # self.custom_param_dict = {}
+
+        # score data
+        self.weight = comfort_config['weightDimension']
+
+        self.weight_type_dict = comfort_config['typeWeight']
+        self.weight_type_list = comfort_config['typeWeightList']
+
+        self.weight_dict = comfort_config['weight']
+        self.weight_list = comfort_config['weightList']
+
+        self.priority_dict = comfort_config['priority']
+        self.priority_list = comfort_config['priorityList']
+
+        self.kind_dict = comfort_config['kind']
+        self.optimal_dict = comfort_config['optimal']
+        # self.optimal1_dict = self.optimal_dict[0]
+        # self.optimal2_dict = self.optimal_dict[1]
+        # self.optimal3_dict = self.optimal_dict[2]
+        self.multiple_dict = comfort_config['multiple']
+        self.kind_list = comfort_config['kindList']
+        self.optimal_list = comfort_config['optimalList']
+        self.multiple_list = comfort_config['multipleList']
+
+        # metric data
+        self.metric_dict = comfort_config['typeMetricDict']
+        self.lat_metric_list = self.metric_dict['comfortLat']
+        self.lon_metric_list = self.metric_dict['comfortLon']
+        # self.lat_metric_list = ["zigzag", "shake"]
+        # self.lon_metric_list = ["cadence", "slamBrake", "slamAccelerate"]
+
+        self.time_list = data_processed.driver_ctrl_data['time_list']
+        self.frame_list = data_processed.driver_ctrl_data['frame_list']
+        self.speed_list = []
+        self.commandSpeed_list = []
+        self.accel_list = []
+        self.accelH_list = []
+
+        self.linear_accel_dict = dict()
+        self.angular_accel_dict = dict()
+        self.speed_instruction_dict = dict()
+
+        self.count_dict = {}
+        self.duration_dict = {}
+        self.strength_dict = {}
+
+        self.discomfort_count = 0
+        self.zigzag_count = 0
+        # self.shake_count = 0
+        self.cadence_count = 0
+        self.slam_brake_count = 0
+        self.slam_accel_count = 0
+        self.speed_instruction_jump_count = 0
+
+        # self.zigzag_strength = 0
+        # self.shake_strength = 0
+        # self.cadence_strength = 0
+        # self.slam_brake_strength = 0
+        # self.slam_accel_strength = 0
+        #
+        self.discomfort_duration = 0
+        # self.zigzag_duration = 0
+        # self.shake_duration = 0
+        # self.cadence_duration = 0
+        # self.slam_brake_duration = 0
+        # self.slam_accel_duration = 0
+
+        self.zigzag_time_list = []
+        # self.zigzag_frame_list = []
+        self.zigzag_stre_list = []
+        self.cur_ego_path_list = []
+        self.curvature_list = []
+
+        self._get_data()
+        self._comf_param_cal()
+
+    def _get_data(self):
+        """
+
+        """
+        comfort_info_list = DataInfoList.COMFORT_INFO
+        self.ego_df = self.data[comfort_info_list].copy()
+        # self.df = self.ego_df.set_index('simFrame')  # 索引是csv原索引
+        self.df = self.ego_df.reset_index(drop=True)  # 索引是csv原索引
+
+    def _cal_cur_ego_path(self, row):
+        try:
+            divide = (row['speedX'] ** 2 + row['speedY'] ** 2) ** (3 / 2)
+            if not divide:
+                res = None
+            else:
+                res = (row['speedX'] * row['accelY'] - row['speedY'] * row['accelX']) / divide
+        except:
+            res = None
+        return res
+
+    def _cal_max_min_avg(self, num_list):
+        maxx = round(max(num_list), 4) if num_list else "-"
+        minn = round(min(num_list), 4) if num_list else "-"
+        avg = round(sum(num_list) / len(num_list), 4) if num_list else "-"
+
+        result = {
+            "max": maxx,
+            "min": minn,
+            "avg": avg
+        }
+
+        return result
+
+    def _comf_param_cal(self):
+        """
+
+        """
+        # for i in range(len(self.optimal_list)):
+        #     if i % 3 == 2:
+        #         continue
+        #     else:
+        #         self.optimal_list[i] = round(self.optimal_list[i] * self.mileage / 100000, 8)
+
+        # self.optimal_list = [round(self.optimal_list[i] * self.mileage / 100000, 8) for i in range(len(self.optimal_list))]
+        self.optimal_dict = {key: value * self.mileage / 100000 for key, value in self.optimal_dict.copy().items()}
+        # self.optimal1_dict = {key: value * self.mileage / 100000 for key, value in self.optimal1_dict.copy().items()}
+        # self.optimal2_dict = {key: value * self.mileage / 100000 for key, value in self.optimal2_dict.copy().items()}
+
+        # [log]
+        self.ego_df['ip_acc'] = self.ego_df['v'].apply(get_interpolation, point1=[18, 4], point2=[72, 2])
+        self.ego_df['ip_dec'] = self.ego_df['v'].apply(get_interpolation, point1=[18, -5], point2=[72, -3.5])
+        self.ego_df['slam_brake'] = self.ego_df.apply(
+            lambda row: self._slam_brake_process(row['lon_acc'], row['ip_dec']), axis=1)
+        self.ego_df['slam_accel'] = self.ego_df.apply(
+            lambda row: self._slam_accelerate_process(row['lon_acc'], row['ip_acc']), axis=1)
+
+        self.ego_df['cadence'] = self.ego_df.apply(
+            lambda row: self._cadence_process_new(row['lon_acc'], row['ip_acc'], row['ip_dec']), axis=1)
+
+        self.speed_list = self.ego_df['v'].values.tolist()
+        self.commandSpeed_list = self.ego_df['cmd_lon_v'].values.tolist()
+        self.accel_list = self.ego_df['accel'].values.tolist()
+        self.accelH_list = self.ego_df['accelH'].values.tolist()
+
+        v_jump_threshold = 0.5
+        self.ego_df['cmd_lon_v_diff'] = self.ego_df['cmd_lon_v'].diff()
+        self.ego_df['cmd_v_jump'] = (abs(self.ego_df['cmd_lon_v_diff']) > v_jump_threshold).astype(int)
+
+        self.linear_accel_dict = self._cal_max_min_avg(self.ego_df['accel'].dropna().values.tolist())
+        self.angular_accel_dict = self._cal_max_min_avg(self.ego_df['accelH'].dropna().values.tolist())
+        self.speed_instruction_dict = self._cal_max_min_avg(self.ego_df['cmd_lon_v_diff'].dropna().values.tolist())
+
+    def _peak_valley_determination(self, df):
+        """
+        Determine the peak and valley of the vehicle based on its current angular velocity.
+
+        Parameters:
+            df: Dataframe containing the vehicle angular velocity.
+
+        Returns:
+            peak_valley: List of indices representing peaks and valleys.
+        """
+
+        peaks, _ = scipy.signal.find_peaks(df['speedH'], height=0.01, distance=1, prominence=0.01)
+        valleys, _ = scipy.signal.find_peaks(-df['speedH'], height=0.01, distance=1, prominence=0.01)
+        peak_valley = sorted(list(peaks) + list(valleys))
+
+        return peak_valley
+
+    def _peak_valley_judgment(self, p_last, p_curr, tw=6000, avg=0.4):
+        """
+        Determine if the given peaks and valleys satisfy certain conditions.
+
+        Parameters:
+            p_last: Previous peak or valley data point.
+            p_curr: Current peak or valley data point.
+            tw: Threshold time difference between peaks and valleys.
+            avg: Angular velocity gap threshold.
+
+        Returns:
+            Boolean indicating whether the conditions are satisfied.
+        """
+        t_diff = p_curr[0] - p_last[0]
+        v_diff = abs(p_curr[1] - p_last[1])
+        s = p_curr[1] * p_last[1]
+
+        zigzag_flag = t_diff < tw and v_diff > avg and s < 0
+        if zigzag_flag and ([p_last[0], p_curr[0]] not in self.zigzag_time_list):
+            self.zigzag_time_list.append([p_last[0], p_curr[0]])
+        return zigzag_flag
+
+    @peak_valley_decorator
+    def zigzag_count_func(self, p_curr, p_last, flag=True):
+        """
+        Count the number of zigzag movements.
+
+        Parameters:
+            df: Input dataframe data.
+
+        Returns:
+            zigzag_count: Number of zigzag movements.
+        """
+        if flag:
+            self.zigzag_count += 1
+        else:
+            self.zigzag_count += 0
+
+    @peak_valley_decorator
+    def cal_zigzag_strength_strength(self, p_curr, p_last, flag=True):
+        """
+        Calculate various strength statistics.
+
+        Returns:
+            Tuple containing maximum strength, minimum strength,
+            average strength, and 99th percentile strength.
+        """
+        if flag:
+            v_diff = abs(p_curr[1] - p_last[1])
+            t_diff = p_curr[0] - p_last[0]
+            self.zigzag_stre_list.append(v_diff / t_diff)  # 平均角加速度
+        else:
+            self.zigzag_stre_list = []
+
+    def _cadence_process(self, lon_acc_roc, ip_dec_roc):
+        if abs(lon_acc_roc) >= abs(ip_dec_roc) or abs(lon_acc_roc) < 1:
+            return np.nan
+        # elif abs(lon_acc_roc) == 0:
+        elif abs(lon_acc_roc) == 0:
+            return 0
+        elif lon_acc_roc > 0 and lon_acc_roc < -ip_dec_roc:
+            return 1
+        elif lon_acc_roc < 0 and lon_acc_roc > ip_dec_roc:
+            return -1
+
+    def _slam_brake_process(self, lon_acc, ip_dec):
+        if lon_acc - ip_dec < 0:
+            return 1
+        else:
+            return 0
+
+    def _slam_accelerate_process(self, lon_acc, ip_acc):
+        if lon_acc - ip_acc > 0:
+            return 1
+        else:
+            return 0
+
+    def _cadence_process_new(self, lon_acc, ip_acc, ip_dec):
+        if abs(lon_acc) < 1 or lon_acc > ip_acc or lon_acc < ip_dec:
+            return np.nan
+        # elif abs(lon_acc_roc) == 0:
+        elif abs(lon_acc) == 0:
+            return 0
+        elif lon_acc > 0 and lon_acc < ip_acc:
+            return 1
+        elif lon_acc < 0 and lon_acc > ip_dec:
+            return -1
+
+    def _cadence_detector(self):
+        """
+        # 加速度突变：先加后减，先减后加，先加然后停，先减然后停
+        # 顿挫：2s内多次加速度变化率突变
+        # 求出每一个特征点，然后提取，然后将每一个特征点后面的2s做一个窗口，统计频率，避免无效运算
+
+        # 将特征点筛选出来
+        # 将特征点时间作为聚类标准，大于1s的pass，小于等于1s的聚类到一个分组
+        # 去掉小于3个特征点的分组
+        """
+        # data = self.ego_df[['simTime', 'simFrame', 'lon_acc_roc', 'cadence']].copy()
+        data = self.ego_df[['simTime', 'simFrame', 'lon_acc', 'lon_acc_roc', 'cadence']].copy()
+        time_list = data['simTime'].values.tolist()
+
+        data = data[data['cadence'] != np.nan]
+        data['cadence_diff'] = data['cadence'].diff()
+        data.dropna(subset='cadence_diff', inplace=True)
+        data = data[data['cadence_diff'] != 0]
+
+        t_list = data['simTime'].values.tolist()
+        f_list = data['simFrame'].values.tolist()
+
+        group_time = []
+        group_frame = []
+        sub_group_time = []
+        sub_group_frame = []
+        for i in range(len(f_list)):
+            if not sub_group_time or t_list[i] - t_list[i - 1] <= 1:  # 特征点相邻一秒内的，算作同一组顿挫
+                sub_group_time.append(t_list[i])
+                sub_group_frame.append(f_list[i])
+            else:
+                group_time.append(sub_group_time)
+                group_frame.append(sub_group_frame)
+                sub_group_time = [t_list[i]]
+                sub_group_frame = [f_list[i]]
+
+        group_time.append(sub_group_time)
+        group_frame.append(sub_group_frame)
+        group_time = [g for g in group_time if len(g) >= 1]  # 有一次特征点则算作一次顿挫
+        # group_frame = [g for g in group_frame if len(g) >= 1]
+
+        # 输出图表值
+        cadence_time = [[g[0], g[-1]] for g in group_time]
+        # cadence_frame = [[g[0], g[-1]] for g in group_frame]
+
+        # 将顿挫组的起始时间为组重新统计时间
+        cadence_time_list = [time for pair in cadence_time for time in time_list if pair[0] <= time <= pair[1]]
+
+        # stre_list = []
+        freq_list = []
+        for g in group_time:
+            # calculate strength
+            g_df = data[data['simTime'].isin(g)]
+            # strength = g_df['lon_acc'].abs().mean()
+            # stre_list.append(strength)
+
+            # calculate frequency
+            cnt = len(g)
+            t_start = g_df['simTime'].iloc[0]
+            t_end = g_df['simTime'].iloc[-1]
+            t_delta = t_end - t_start
+            frequency = cnt / t_delta
+            freq_list.append(frequency)
+
+        self.cadence_count = len(freq_list)
+        # cadence_stre = sum(stre_list) / len(stre_list) if stre_list else 0
+
+        return cadence_time_list
+
+    def _slam_brake_detector(self):
+        # 统计急刹全为1的分段的个数，记录分段开头的frame_ID
+        # data = self.ego_df[['simTime', 'simFrame', 'lon_acc_roc', 'ip_dec_roc', 'slam_brake']].copy()
+        data = self.ego_df[['simTime', 'simFrame', 'lon_acc', 'lon_acc_roc', 'ip_dec', 'slam_brake']].copy()
+        # data['slam_diff'] = data['slam_brake'].diff()
+        # res_df = data[data['slam_diff'] == 1]
+
+        res_df = data[data['slam_brake'] == 1]
+        t_list = res_df['simTime'].values
+        f_list = res_df['simFrame'].values.tolist()
+
+        group_time = []
+        group_frame = []
+        sub_group_time = []
+        sub_group_frame = []
+        for i in range(len(f_list)):
+            if not sub_group_time or f_list[i] - f_list[i - 1] <= 1:  # 连续帧的算作同一组急刹
+                sub_group_time.append(t_list[i])
+                sub_group_frame.append(f_list[i])
+            else:
+                group_time.append(sub_group_time)
+                group_frame.append(sub_group_frame)
+                sub_group_time = [t_list[i]]
+                sub_group_frame = [f_list[i]]
+
+        group_time.append(sub_group_time)
+        group_frame.append(sub_group_frame)
+        group_time = [g for g in group_time if len(g) >= 2]  # 达到两帧算作一次急刹
+        # group_frame = [g for g in group_frame if len(g) >= 2]
+
+        time_list = [element for sublist in group_time for element in sublist]
+        self.slam_brake_count = len(group_time)  # / self.mileage  # * 1000000
+        return time_list
+
+    def _slam_accel_detector(self):
+        # 统计急刹全为1的分段的个数，记录分段开头的frame_ID
+        # data = self.ego_df[['simTime', 'simFrame', 'lon_acc_roc', 'ip_acc_roc', 'slam_accel']].copy()
+        data = self.ego_df[['simTime', 'simFrame', 'lon_acc', 'ip_acc', 'slam_accel']].copy()
+        # data['slam_diff'] = data['slam_accel'].diff()
+        # res_df = data.loc[data['slam_diff'] == 1]
+
+        res_df = data.loc[data['slam_accel'] == 1]
+        t_list = res_df['simTime'].values
+        f_list = res_df['simFrame'].values.tolist()
+
+        group_time = []
+        group_frame = []
+        sub_group_time = []
+        sub_group_frame = []
+        for i in range(len(f_list)):
+            if not group_time or f_list[i] - f_list[i - 1] <= 1:  # 连续帧的算作同一组急加速
+                sub_group_time.append(t_list[i])
+                sub_group_frame.append(f_list[i])
+            else:
+                group_time.append(sub_group_time)
+                group_frame.append(sub_group_frame)
+                sub_group_time = [t_list[i]]
+                sub_group_frame = [f_list[i]]
+
+        group_time.append(sub_group_time)
+        group_frame.append(sub_group_frame)
+        group_time = [g for g in group_time if len(g) >= 2]
+        # group_frame = [g for g in group_frame if len(g) >= 2]
+
+        time_list = [element for sublist in group_time for element in sublist]
+        self.slam_accel_count = len(group_time)  # / self.mileage  # * 1000000
+        return time_list
+
+    def _speed_instruction_jump_detector(self):
+
+        data = self.ego_df[['simTime', 'simFrame', 'cmd_lon_v', 'cmd_lon_v_diff', 'cmd_v_jump']].copy()
+        # data['slam_diff'] = data['slam_accel'].diff()
+        # res_df = data.loc[data['slam_diff'] == 1]
+
+        res_df = data.loc[data['cmd_v_jump'] == 1]
+        t_list = res_df['simTime'].values
+        f_list = res_df['simFrame'].values.tolist()
+
+        group_time = []
+        group_frame = []
+        sub_group_time = []
+        sub_group_frame = []
+        for i in range(len(f_list)):
+            if not group_time or f_list[i] - f_list[i - 1] <= 10:  # 连续帧的算作同一组跳变
+                sub_group_time.append(t_list[i])
+                sub_group_frame.append(f_list[i])
+            else:
+                group_time.append(sub_group_time)
+                group_frame.append(sub_group_frame)
+                sub_group_time = [t_list[i]]
+                sub_group_frame = [f_list[i]]
+
+        group_time.append(sub_group_time)
+        group_frame.append(sub_group_frame)
+        group_time = [g for g in group_time if len(g) >= 2]
+        # group_frame = [g for g in group_frame if len(g) >= 2]
+
+        time_list = [element for sublist in group_time for element in sublist]
+        self.speed_instruction_jump_count = len(group_time)  # / self.mileage  # * 1000000
+        return time_list
+
+    def comf_statistic(self):
+        """
+
+        """
+        # df = self.ego_df[['simTime', 'cur_diff', 'lon_acc', 'lon_acc_roc', 'accelH']].copy()
+        df = self.ego_df[['simTime', 'lon_acc', 'lon_acc_roc', 'accelH']].copy()
+
+        self.zigzag_count_func()
+        self.cal_zigzag_strength_strength()
+        # if self.zigzag_time_list:
+        #     zigzag_df = pd.DataFrame(self.zigzag_time_list, columns=['start_time', 'end_time'])
+        #     zigzag_df = get_frame_with_time(zigzag_df, self.ego_df)
+        #     zigzag_df['type'] = 'zigzag'
+            # self.discomfort_df = pd.concat([self.discomfort_df, zigzag_df], ignore_index=True)
+            # discomfort_df = pd.concat([time_df, frame_df], axis=1)
+            # self.discomfort_df = pd.concat([self.discomfort_df, discomfort_df], ignore_index=True)
+
+        zigzag_t_list = []
+        # 只有[t_start, t_end]数对，要提取为完整time list
+        t_list = df['simTime'].values.tolist()
+        for t_start, t_end in self.zigzag_time_list:
+            index_1 = t_list.index(t_start)
+            index_2 = t_list.index(t_end)
+            zigzag_t_list.extend(t_list[index_1:index_2 + 1])
+        zigzag_t_list = list(set(zigzag_t_list))
+        # shake_t_list = self._shake_detector()
+        cadence_t_list = self._cadence_detector()
+        slam_brake_t_list = self._slam_brake_detector()
+        slam_accel_t_list = self._slam_accel_detector()
+        speed_instruction_jump_t_list = self._speed_instruction_jump_detector()
+
+        discomfort_time_list = zigzag_t_list + cadence_t_list + slam_brake_t_list + slam_accel_t_list + speed_instruction_jump_t_list
+        discomfort_time_list = sorted(discomfort_time_list)  # 排序
+        discomfort_time_list = list(set(discomfort_time_list))  # 去重
+
+        time_diff = self.time_list[3] - self.time_list[2]
+        # time_diff = 0.4
+
+        self.discomfort_duration = len(discomfort_time_list) * time_diff
+
+        self.count_dict = {
+            "zigzag": self.zigzag_count,
+            # "shake": self.shake_count,
+            "cadence": self.cadence_count,
+            "slamBrake": self.slam_brake_count,
+            "slamAccelerate": self.slam_accel_count,
+            "speedInstructionJump": self.speed_instruction_jump_count
+        }
+
+        tmp_comf_arr = [self.zigzag_count, self.cadence_count, self.slam_brake_count, self.slam_accel_count,
+                        self.speed_instruction_jump_count]
+        self.discomfort_count = sum(tmp_comf_arr)
+
+        comf_arr = [tmp_comf_arr]
+        return comf_arr
+
+    # def _nan_detect(self, num):
+    #     if math.isnan(num):
+    #         return 0
+    #     return num
+
+    def comf_score_new(self):
+        arr_comf = self.comf_statistic()
+        print("\n[平顺性表现及得分情况]")
+        print("平顺性各指标值：", [[round(num, 2) for num in row] for row in arr_comf])
+        arr_comf = np.array(arr_comf)
+
+        score_model = self.scoreModel(self.kind_list, self.optimal_list, self.multiple_list, arr_comf)
+        score_sub = score_model.cal_score()
+        score_metric = list(map(lambda x: 80 if np.isnan(x) else x, score_sub))
+
+        metric_list = [x for x in self.metric_list if x in self.config.builtinMetricList]
+        score_metric_dict = {key: value for key, value in zip(metric_list, score_metric)}
+
+        # custom_metric_list = list(self.customMetricParam.keys())
+        # for metric in custom_metric_list:
+        #     value = self.custom_data[metric]['value']
+        #     param_list = self.customMetricParam[metric]
+        #     score = self.custom_metric_score(metric, value, param_list)
+        #     score_metric_dict[metric] = round(score, 2)
+
+        # score_metric_dict = {key: score_metric_dict[key] for key in self.metric_list}
+        # score_metric = list(score_metric_dict.values())
+
+        score_type_dict = {}
+        if self.weight_custom:  # 自定义权重
+            score_metric_with_weight_dict = {key: score_metric_dict[key] * self.weight_dict[key] for key in
+                                             self.weight_dict}
+
+            for type in self.type_list:
+                type_score = sum(
+                    value for key, value in score_metric_with_weight_dict.items() if key in self.metric_dict[type])
+                score_type_dict[type] = round(type_score, 2)
+
+            score_type_with_weight_dict = {key: score_type_dict[key] * self.weight_type_dict[key] for key in
+                                           score_type_dict}
+
+            score_comfort = sum(score_type_with_weight_dict.values())
+        else:  # 客观赋权
+            self.weight_list = cal_weight_from_80(score_metric)
+            self.weight_dict = {key: value for key, value in zip(self.metric_list, self.weight_list)}
+            score_comfort = cal_score_with_priority(score_metric, self.weight_list, self.priority_list)
+
+            for type in self.type_list:
+                type_weight = sum(value for key, value in self.weight_dict.items() if key in self.metric_dict[type])
+                self.weight_dict = {key: round(value / type_weight, 4) for key, value in self.weight_dict.items() if
+                                    key in self.metric_dict[type]}
+                type_score_metric = [value for key, value in score_metric_dict.items() if key in self.metric_dict[type]]
+                type_weight_list = [value for key, value in self.weight_dict.items() if key in self.metric_dict[type]]
+                type_priority_list = [value for key, value in self.priority_dict.items() if
+                                      key in self.metric_dict[type]]
+
+                type_score = cal_score_with_priority(type_score_metric, type_weight_list, type_priority_list)
+                score_type_dict[type] = round(type_score, 2)
+
+        score_comfort = round(score_comfort, 2)
+
+        print("平顺性各指标基准值：", self.optimal_list)
+        print(f"平顺性得分为：{score_comfort:.2f}分。")
+        print(f"平顺性各类型得分为：{score_type_dict}。")
+        print(f"平顺性各指标得分为：{score_metric_dict}。")
+        return score_comfort, score_type_dict, score_metric_dict
+
+    def zip_time_pairs(self, zip_list, upper_limit=9999):
+        zip_time_pairs = zip(self.time_list, zip_list)
+        zip_vs_time = [[x, upper_limit if y > upper_limit else y] for x, y in zip_time_pairs if not math.isnan(y)]
+        return zip_vs_time
+
+    def report_statistic(self):
+        """
+
+        Returns:
+
+        """
+        # report_dict = {
+        #     "name": "平顺性",
+        #     "weight": f"{self.weight * 100:.2f}%",
+        #     "weightDistribution": weight_distribution,
+        #     "score": score_comfort,
+        #     "level": grade_comfort,
+
+        #     'discomfortCount': self.discomfort_count,
+        #     "description1": comf_description1,
+        #     "description2": comf_description2,
+        #     "description3": comf_description3,
+        #     "description4": comf_description4,
+        #
+        #     "comfortLat": lat_dict,
+        #     "comfortLon": lon_dict,
+        #
+        #     "speData": ego_speed_vs_time,
+        #     "speMarkLine": discomfort_slices,
+        #
+        #     "accData": lon_acc_vs_time,
+        #     "accMarkLine": discomfort_acce_slices,
+        #
+        #     "anvData": yawrate_vs_time,
+        #     "anvMarkLine": discomfort_zigzag_slices,
+        #
+        #     "anaData": yawrate_roc_vs_time,
+        #     "anaMarkLine": discomfort_zigzag_slices,
+        #
+        #     "curData": [cur_ego_path_vs_time, curvature_vs_time],
+        #     "curMarkLine": discomfort_shake_slices,
+        # }
+        # brakePedal_list = self.data_processed.driver_ctrl_data['brakePedal_list']
+        # throttlePedal_list = self.data_processed.driver_ctrl_data['throttlePedal_list']
+        # steeringWheel_list = self.data_processed.driver_ctrl_data['steeringWheel_list']
+        #
+        # # common parameter calculate
+        # brake_vs_time = self.zip_time_pairs(brakePedal_list, 100)
+        # throttle_vs_time = self.zip_time_pairs(throttlePedal_list, 100)
+        # steering_vs_time = self.zip_time_pairs(steeringWheel_list)
+
+        report_dict = {
+            "name": "平顺性",
+            "weight": f"{self.weight * 100:.2f}%",
+
+            # 'discomfortCount': self.discomfort_count,
+        }
+
+        score_comfort, score_type_dict, score_metric_dict = self.comf_score_new()
+        score_comfort = int(score_comfort) if int(score_comfort) == score_comfort else round(score_comfort, 2)
+        grade_comfort = score_grade(score_comfort)
+        report_dict["score"] = score_comfort
+        report_dict["level"] = grade_comfort
+
+        description = f"· 在平顺性方面，得分{score_comfort}分，表现{grade_comfort}，"
+        is_good = True
+
+        if any(score_metric_dict[metric] < 80 for metric in self.lon_metric_list):
+            is_good = False
+            description += "线加速度变化剧烈，"
+            tmp = [metric for metric in self.lon_metric_list if score_metric_dict[metric] < 80]
+            str_tmp = "、".join(tmp)
+            description += f"有{str_tmp}情况，需重点优化。"
+
+        if any(score_metric_dict[metric] < 80 for metric in self.lat_metric_list):
+            is_good = False
+            description += "角加速度变化剧烈，"
+            tmp = [metric for metric in self.lat_metric_list if score_metric_dict[metric] < 80]
+            str_tmp = "、".join(tmp)
+            description += f"有{str_tmp}情况，需重点优化。"
+
+        if is_good:
+            description += f"线加速度和角加速度变化平顺，表现{grade_comfort}。"
+
+        report_dict["description"] = replace_key_with_value(description, self.name_dict)
+
+        # indexes
+
+        description1 = f"最大值：{self.linear_accel_dict['max']}m/s²；" \
+                       f"最小值：{self.linear_accel_dict['min']}m/s²；" \
+                       f"平均值：{self.linear_accel_dict['avg']}m/s²"
+
+        description2 = f"最大值：{self.angular_accel_dict['max']}rad/s²；" \
+                       f"最小值：{self.angular_accel_dict['min']}rad/s²；" \
+                       f"平均值：{self.angular_accel_dict['avg']}rad/s²"
+
+        description3 = f"最大值：{self.speed_instruction_dict['max']}m/s；" \
+                       f"最小值：{self.speed_instruction_dict['min']}m/s；" \
+                       f"平均值：{self.speed_instruction_dict['avg']}m/s"
+
+        linearAccelerate_index = {
+            "weight": self.weight_type_dict['comfortLon'],
+            "score": score_type_dict['comfortLon'],
+            "description": description1
+        }
+
+        angularAccelerate_index = {
+            "weight": self.weight_type_dict['comfortLat'],
+            "score": score_type_dict['comfortLat'],
+            "description": description2
+        }
+
+        speedInstruction_index = {
+            "weight": self.weight_type_dict['comfortSpeed'],
+            "score": score_type_dict['comfortSpeed'],
+            "description": description3
+        }
+
+        indexes_dict = {
+            "comfortLat": linearAccelerate_index,
+            "comfortLon": angularAccelerate_index,
+            "comfortSpeed": speedInstruction_index
+        }
+
+        report_dict["indexes"] = indexes_dict
+
+        # LinearAccelerate.png
+        plt.figure(figsize=(12, 3))
+        plt.plot(self.time_list, self.accel_list, label='Linear Accelerate')
+
+        plt.xlabel('Time(s)')
+        plt.ylabel('Linear Accelerate(m/s^2)')
+        plt.legend()
+
+        # 调整布局，消除空白边界
+        plt.tight_layout()
+
+        plt.savefig(os.path.join(self.resultPath, "LinearAccelerate.png"))
+        plt.close()
+
+        # AngularAccelerate.png
+        plt.figure(figsize=(12, 3))
+        plt.plot(self.time_list, self.accelH_list, label='Angular Accelerate')
+
+        plt.xlabel('Time(s)')
+        plt.ylabel('Angular Accelerate(rad/s^2)')
+        plt.legend()
+
+        # 调整布局，消除空白边界
+        plt.tight_layout()
+
+        plt.savefig(os.path.join(self.resultPath, "AngularAccelerate.png"))
+        plt.close()
+
+        # Speed.png
+        plt.figure(figsize=(12, 3))
+        plt.plot(self.time_list, self.speed_list, label='Speed')
+
+        plt.xlabel('Time(s)')
+        plt.ylabel('Speed(m/s)')
+        plt.legend()
+
+        # 调整布局，消除空白边界
+        plt.tight_layout()
+
+        plt.savefig(os.path.join(self.resultPath, "Speed.png"))
+        plt.close()
+
+        # commandSpeed.png draw
+        plt.figure(figsize=(12, 3))
+        plt.plot(self.time_list, self.commandSpeed_list, label='commandSpeed')
+
+        plt.xlabel('Time(s)')
+        plt.ylabel('commandSpeed(m/s)')
+        plt.legend()
+
+        # 调整布局，消除空白边界
+        plt.tight_layout()
+
+        plt.savefig(os.path.join(self.resultPath, "CommandSpeed.png"))
+        plt.close()
+
+        print(report_dict)
+        return report_dict
+
+    # def get_eval_data(self):
+    #     df = self.eval_data[
+    #         ['simTime', 'simFrame', 'playerId', 'ip_acc', 'ip_dec', 'slam_brake', 'slam_accel', 'cadence']].copy()
+    #     return df

common.py

@@ -0,0 +1,340 @@
+#!/usr/bin/env python
+# -*- coding: utf-8 -*-
+##################################################################
+#
+# Copyright (c) 2023 CICV, Inc. All Rights Reserved
+#
+##################################################################
+"""
+@Authors:           yangzihao(yangzihao@china-icv.cn)
+@Data:              2023/07/24
+@Last Modified:     2023/07/24
+@Summary:           Evaluateion functions
+"""
+
+import sys
+
+sys.path.append('../common')
+sys.path.append('../modules')
+
+import os
+import time
+import json
+import math
+import pandas as pd
+import numpy as np
+import zipfile
+import importlib
+import matplotlib.pyplot as plt
+
+
+def save_line_chart(list1, list2, save_path):
+    plt.plot(list1, label='列表1')
+    plt.plot(list2, label='列表2')
+
+    plt.xlabel('索引')
+    plt.ylabel('值')
+    plt.title('折线图')
+    plt.legend()
+
+    plt.savefig(save_path)
+    plt.close()
+
+
+def import_class(path, file):
+    sys.path.append(path)  # 将path添加到Python的搜索路径中
+    # module_name = file[:-3]  # 去掉文件名的".py"后缀
+    module = __import__(file, fromlist=['ScoreModel'])
+    class_instance = module.ScoreModel
+    return class_instance
+
+
+def import_class_lib(path, file):
+    sys.path.append(path)  # 将path添加到Python的搜索路径中
+    module = importlib.import_module(file)
+    class_name = "ScoreModel"
+    class_instance = getattr(module, class_name)
+    return class_instance
+
+
+def score_over_100(score):
+    if score > 100:
+        return 100
+    return score
+
+
+def normalize_dict_values(dictionary):
+    # 计算字典中键的数量
+    n = len(dictionary)
+
+    # 初始化总和为0
+    total_sum = 0
+
+    # 遍历字典，对每个值进行赋值，并累加总和
+    for key, value in dictionary.items():
+        # 计算当前值，除了最后一个值外都四舍五入到两位小数
+        if key != list(dictionary.keys())[-1]:
+            new_value = round(1 / n, 2)
+        else:
+            # 最后一个值的计算：1减去之前所有值的和
+            new_value = round(1 - total_sum, 2)
+
+            # 更新字典的值
+        dictionary[key] = new_value
+
+        # 累加当前值到总和
+        total_sum += new_value
+
+    return dictionary
+
+def cal_velocity(lat_v, lon_v):
+    """
+    The function can calculate the velocity with lateral velocity and longitudinal velocity.
+
+    Args:
+        lat_v: lateral velocity, m/s
+        lon_v: longitudinal velocity, m/s
+
+    Returns:
+        v: the resultant velocity, km/h
+
+    """
+    v = (lat_v ** 2 + lon_v ** 2) ** 0.5
+    return v
+
+
+def json2dict(json_file):
+    with open(json_file, 'r', encoding='utf-8') as f:
+        json_dict = json.load(f)
+    return json_dict
+
+
+def get_subfolders_name(path):
+    return [name for name in os.listdir(path) if os.path.isdir(os.path.join(path, name))]
+
+
+def zip_dir(dir_path, zip_file_path):
+    """
+    This function can zip the files in dir_path as a zipfile.
+
+    Arguments:
+        dir_path: A str of the path of the files to be ziped.
+        zip_file_path: A str of the path of the zipfile to be stored.
+
+    Returns:
+        None
+    """
+    with zipfile.ZipFile(zip_file_path, 'w', zipfile.ZIP_DEFLATED) as zipf:
+        for root, dirs, files in os.walk(dir_path):
+            for file in files:
+                file_path = os.path.join(root, file)
+                zipf.write(file_path)
+
+
+def score_grade(score):
+    """
+    Returns the corresponding grade based on the input score.
+
+    Arguments:
+        score: An integer or float representing the score.
+
+    Returns:
+        grade: A string representing the grade.
+    """
+    if score >= 90:
+        grade = '优秀'
+    elif score >= 80:
+        grade = '良好'
+    elif score > 60:
+        grade = '一般'
+    else:
+        grade = '较差'
+
+    return grade
+
+
+def mileage_format(mileage):
+    if mileage < 1000:
+        return f"{mileage:.2f}米"
+    else:
+        mileage = mileage / 1000
+        return f"{mileage:.2f}公里"
+
+
+def duration_format(duration):
+    if duration < 60:
+        return f"{duration:.2f}秒"
+    elif duration < 3600:
+        minute = int(duration / 60)
+        second = int(duration % 60)
+        return f"{minute}分{second}秒" if second != 0 else f"{minute}分"
+    else:
+        hour = int(duration / 3600)
+        minute = int((duration % 3600) / 60)
+        second = int(duration % 60)
+        ans = f"{hour:d}时"
+        ans = ans + f"{minute}分" if minute != 0 else ans
+        ans = ans + f"{second}秒" if second != 0 else ans
+        return ans
+
+
+def continuous_group(df):
+    time_list = df['simTime'].values.tolist()
+    frame_list = df['simFrame'].values.tolist()
+
+    group_time = []
+    group_frame = []
+    sub_group_time = []
+    sub_group_frame = []
+
+    for i in range(len(frame_list)):
+        if not sub_group_time or frame_list[i] - frame_list[i - 1] <= 1:
+            sub_group_time.append(time_list[i])
+            sub_group_frame.append(frame_list[i])
+        else:
+            group_time.append(sub_group_time)
+            group_frame.append(sub_group_frame)
+            sub_group_time = [time_list[i]]
+            sub_group_frame = [frame_list[i]]
+
+    group_time.append(sub_group_time)
+    group_frame.append(sub_group_frame)
+    group_time = [g for g in group_time if len(g) >= 2]
+    group_frame = [g for g in group_frame if len(g) >= 2]
+
+    # 输出图表值
+    time = [[g[0], g[-1]] for g in group_time]
+    frame = [[g[0], g[-1]] for g in group_frame]
+
+    time_df = pd.DataFrame(time, columns=['start_time', 'end_time'])
+    frame_df = pd.DataFrame(frame, columns=['start_frame', 'end_frame'])
+
+    result_df = pd.concat([time_df, frame_df], axis=1)
+
+    return result_df
+
+
+def continuous_group_old(df):
+    time_list = df['simTime'].values.tolist()
+    frame_list = df['simFrame'].values.tolist()
+
+    group = []
+    sub_group = []
+
+    for i in range(len(frame_list)):
+        if not sub_group or frame_list[i] - frame_list[i - 1] <= 1:
+            sub_group.append(time_list[i])
+        else:
+            group.append(sub_group)
+            sub_group = [time_list[i]]
+
+    group.append(sub_group)
+    group = [g for g in group if len(g) >= 2]
+
+    # 输出图表值
+    time = [[g[0], g[-1]] for g in group]
+    unsafe_df = pd.DataFrame(time, columns=['start_time', 'end_time'])
+
+    return unsafe_df
+
+
+def get_frame_with_time(df1, df2):
+    # 将dataframe1按照start_time与simTime进行合并
+    df1_start = df1.merge(df2[['simTime', 'simFrame']], left_on='start_time', right_on='simTime')
+    df1_start = df1_start[['start_time', 'simFrame']].copy()
+    df1_start.rename(columns={'simFrame': 'start_frame'}, inplace=True)
+
+    # 将dataframe1按照end_time与simTime进行合并
+    df1_end = df1.merge(df2[['simTime', 'simFrame']], left_on='end_time', right_on='simTime')
+    df1_end = df1_end[['end_time', 'simFrame']].copy()
+    df1_end.rename(columns={'simFrame': 'end_frame'}, inplace=True)
+
+    # 将两个合并后的数据框按照行索引进行拼接
+    result = pd.concat([df1_start, df1_end], axis=1)
+    return result
+
+
+def string_concatenate(str_list):
+    """
+    This function concatenates the input string list to generate a new string.
+    If str_list is empty, an empty string is returned.
+    If str_list has only one element, return that element.
+    If str_list has multiple elements, concatenate all the elements except the last element with ', ', and then concatenate the resulting string with the last element with 'and'.
+
+    Arguments:
+        str_list: A list of strings.
+
+    Returns:
+        ans_str: A concatenated string.
+    """
+    if not str_list:
+        return ""
+    ans_str = '、'.join(str_list[:-1])
+    ans_str = ans_str + "和" + str_list[-1] if len(str_list) > 1 else ans_str + str_list[-1]
+    return ans_str
+
+
+def zip_time_pairs(time_list, zip_list, upper_limit=9999):
+    zip_time_pairs = zip(time_list, zip_list)
+    zip_vs_time = [[x, upper_limit if y > upper_limit else y] for x, y in zip_time_pairs if not math.isnan(y)]
+    return zip_vs_time
+
+
+def replace_key_with_value(input_string, replacement_dict):
+    """
+    替换字符串中的关键字为给定的字典中的值。
+
+    :param input_string: 需要进行替换的字符串
+    :param replacement_dict: 替换规则，格式为 {key: value}
+    :return: 替换后的字符串
+    """
+    # 遍历字典中的键值对，并用值替换字符串中的键
+    for key, value in replacement_dict.items():
+        if key in input_string:
+            input_string = input_string.replace(key, value)
+
+    return input_string
+
+
+def continous_judge(frame_list):
+    if not frame_list:
+        return 0
+
+    cnt = 1
+    for i in range(1, len(frame_list)):
+        if frame_list[i] - frame_list[i - 1] <= 3:
+            continue
+        cnt += 1
+    return cnt
+
+
+def get_interpolation(x, point1, point2):
+    """
+    According to the two extreme value points, the equation of one variable is determined,
+    and the solution of the equation is obtained in the domain of definition.
+
+    Arguments:
+        x: A float number of the independent variable.
+        point1: A set of the coordinate extreme point.
+        point2: A set of the other coordinate extreme point.
+
+    Returns:
+        y: A float number of the dependent variable.
+    """
+    try:
+        k = (point1[1] - point2[1]) / (point1[0] - point2[0])
+        b = (point1[0] * point2[1] - point1[1] * point2[0]) / (point1[0] - point2[0])
+        y = x * k + b
+        return y
+    except Exception as e:
+        return f"Error: {str(e)}"
+
+
+def statistic_analysis(data_list):
+    sorted_data_list = sorted(data_list)
+    maxx = sorted_data_list[-1]
+    minn = sorted_data_list[0]
+    meann = sum(sorted_data_list) / len(sorted_data_list)
+    percentile_99 = np.percentile(sorted_data_list, 99)
+    ans_list = [maxx, minn, meann, percentile_99]
+    return ans_list

config.json

@@ -0,0 +1,152 @@
+{
+  "comfort": {
+    "comfortLat": {
+      "zigzag": {
+        "name": "画龙",
+        "unit": "次",
+        "weight": 1.0,
+        "priority": "0",
+        "paramList": [
+          {
+            "kind": "-1",
+            "optimal": "1",
+            "multiple": [
+              "0.2",
+              "5"
+            ],
+            "spare": [
+              {
+                "param": null
+              },
+              {
+                "param": null
+              }
+            ]
+          }
+        ]
+      }
+    },
+    "comfortLon": {
+      "cadence": {
+        "name": "顿挫",
+        "unit": "次",
+        "weight": 0.34,
+        "priority": "0",
+        "paramList": [
+          {
+            "kind": "-1",
+            "optimal": "1",
+            "multiple": [
+              "0.2",
+              "5"
+            ],
+            "spare": [
+              {
+                "param": null
+              },
+              {
+                "param": null
+              }
+            ]
+          }
+        ]
+      },
+      "slamBrake": {
+        "name": "急刹车",
+        "unit": "次",
+        "weight": 0.33,
+        "priority": "1",
+        "paramList": [
+          {
+            "kind": "-1",
+            "optimal": "1",
+            "multiple": [
+              "0.2",
+              "5"
+            ],
+            "spare": [
+              {
+                "param": null
+              },
+              {
+                "param": null
+              }
+            ]
+          }
+        ]
+      },
+      "slamAccelerate": {
+        "name": "急加速",
+        "unit": "次",
+        "weight": 0.33,
+        "priority": "1",
+        "paramList": [
+          {
+            "kind": "-1",
+            "optimal": "1",
+            "multiple": [
+              "0.2",
+              "5"
+            ],
+            "spare": [
+              {
+                "param": null
+              },
+              {
+                "param": null
+              }
+            ]
+          }
+        ]
+      }
+    },
+    "comfortSpeed": {
+      "speedInstructionJump": {
+        "name": "速度指令跳变",
+        "unit": "次",
+        "weight": 1.0,
+        "priority": "0",
+        "paramList": [
+          {
+            "kind": "-1",
+            "optimal": "1",
+            "multiple": [
+              "0.2",
+              "5"
+            ],
+            "spare": [
+              {
+                "param": null
+              },
+              {
+                "param": null
+              }
+            ]
+          }
+        ]
+      }
+    }
+  },
+  "typeWeight": {
+    "comfort": {
+      "comfortLat": 0.33,
+      "comfortLon": 0.33,
+      "comfortSpeed": 0.34
+    }
+  },
+  "typeName": {
+    "comfort": {
+      "comfortLat": "横向平顺性",
+      "comfortLon": "纵向平顺性",
+      "comfortSpeed": "速度控制指令"
+    }
+  },
+  "dimensionWeight": {
+    "comfort": 0.15
+  },
+  "dimensionName": {
+    "comfort": "平顺性"
+  },
+  "scoreModel": "builtin",
+  "scoreModel1": "linear_score"
+}

config.py

@@ -0,0 +1,23 @@
+#!/usr/bin/env python
+# -*- coding: utf-8 -*-
+##################################################################
+#
+# Copyright (c) 2023 CICV, Inc. All Rights Reserved
+#
+##################################################################
+
+# not sure
+
+# accurate.py
+
+# comfort.py
+
+# safe.py
+DANGEROUS_DIST = 0.10  # 与障碍物距离小于此值即为危险
+LONGITUDINAL_DISTANCE_FRONT = 100
+LONGITUDINAL_DISTANCE_REAR = 100
+LATERAL_DISTANCE_ABS = 1
+OVERSPEED_THRESHOLD = 1.17
+
+# report_generate.py
+HEIGHT_LIMIT = 300

config_0627.json

@@ -0,0 +1,324 @@
+{
+  "safe": {
+    "safeCollision": {
+      "collisionCount": {
+        "name": "碰撞次数",
+        "unit": "次",
+        "weight": 0.6,
+        "priority": "0",
+        "paramList": [
+          {
+            "kind": "-1",
+            "optimal": "1",
+            "multiple": [
+              "0.2",
+              "1"
+            ],
+            "spare": [
+              {
+                "param": null
+              },
+              {
+                "param": null
+              }
+            ]
+          }
+        ]
+      },
+      "collisionRisk": {
+        "name": "碰撞风险概率",
+        "unit": "%",
+        "weight": 0.05,
+        "priority": "0",
+        "paramList": [
+          {
+            "kind": "-1",
+            "optimal": "10",
+            "multiple": [
+              "0.5",
+              "5"
+            ],
+            "spare": [
+              {
+                "param": null
+              },
+              {
+                "param": null
+              }
+            ]
+          }
+        ]
+      },
+      "collisionSeverity": {
+        "name": "碰撞严重程度",
+        "unit": "%",
+        "weight": 0.05,
+        "priority": "0",
+        "paramList": [
+          {
+            "kind": "-1",
+            "optimal": "10",
+            "multiple": [
+              "0.5",
+              "5"
+            ],
+            "spare": [
+              {
+                "param": null
+              },
+              {
+                "param": null
+              }
+            ]
+          }
+        ]
+      }
+    },
+    "safeSpeed": {
+      "overSpeed": {
+        "name": "超速比例",
+        "unit": "%",
+        "weight": 0.05,
+        "priority": "1",
+        "paramList": [
+          {
+            "kind": "-1",
+            "optimal": "1.17",
+            "multiple": [
+              "0.99",
+              "1.2"
+            ],
+            "spare": [
+              {
+                "param": null
+              },
+              {
+                "param": null
+              }
+            ]
+          }
+        ]
+      }
+    }
+  },
+  "comfort": {
+    "comfortLat": {
+      "zigzag": {
+        "name": "画龙",
+        "unit": "次",
+        "weight": 0.05,
+        "priority": "0",
+        "paramList": [
+          {
+            "kind": "-1",
+            "optimal": "1",
+            "multiple": [
+              "0.2",
+              "5"
+            ],
+            "spare": [
+              {
+                "param": null
+              },
+              {
+                "param": null
+              }
+            ]
+          }
+        ]
+      }
+    },
+    "comfortLon": {
+      "cadence": {
+        "name": "顿挫",
+        "unit": "次",
+        "weight": 0.02,
+        "priority": "0",
+        "paramList": [
+          {
+            "kind": "-1",
+            "optimal": "1",
+            "multiple": [
+              "0.2",
+              "5"
+            ],
+            "spare": [
+              {
+                "param": null
+              },
+              {
+                "param": null
+              }
+            ]
+          }
+        ]
+      },
+      "slamBrake": {
+        "name": "急刹车",
+        "unit": "次",
+        "weight": 0.02,
+        "priority": "1",
+        "paramList": [
+          {
+            "kind": "-1",
+            "optimal": "1",
+            "multiple": [
+              "0.2",
+              "5"
+            ],
+            "spare": [
+              {
+                "param": null
+              },
+              {
+                "param": null
+              }
+            ]
+          }
+        ]
+      },
+      "slamAccelerate": {
+        "name": "急加速",
+        "unit": "次",
+        "weight": 0.01,
+        "priority": "1",
+        "paramList": [
+          {
+            "kind": "-1",
+            "optimal": "1",
+            "multiple": [
+              "0.2",
+              "5"
+            ],
+            "spare": [
+              {
+                "param": null
+              },
+              {
+                "param": null
+              }
+            ]
+          }
+        ]
+      }
+    },
+    "comfortSpeed": {
+      "speedInstructionJump": {
+        "name": "速度指令跳变",
+        "unit": "次",
+        "weight": 0.05,
+        "priority": "0",
+        "paramList": [
+          {
+            "kind": "-1",
+            "optimal": "1",
+            "multiple": [
+              "0.2",
+              "5"
+            ],
+            "spare": [
+              {
+                "param": null
+              },
+              {
+                "param": null
+              }
+            ]
+          }
+        ]
+      }
+    }
+  },
+  "accurate": {
+    "accurateExecute": {
+      "positionError": {
+        "name": "位置偏移误差",
+        "unit": "米",
+        "weight": 0.05,
+        "priority": "0",
+        "paramList": [
+          {
+            "kind": "-1",
+            "optimal": "0.3",
+            "multiple": [
+              "0.2",
+              "2"
+            ],
+            "spare": [
+              {
+                "param": null
+              },
+              {
+                "param": null
+              }
+            ]
+          }
+        ]
+      },
+      "executeAccurateError": {
+        "name": "任务执行状态错误次数",
+        "unit": "次",
+        "weight": 0.05,
+        "priority": "0",
+        "paramList": [
+          {
+            "kind": "-1",
+            "optimal": "1",
+            "multiple": [
+              "0.2",
+              "5"
+            ],
+            "spare": [
+              {
+                "param": null
+              },
+              {
+                "param": null
+              }
+            ]
+          }
+        ]
+      }
+    }
+  },
+  "typeWeight": {
+    "safe": {
+      "safeCollision": 0.7,
+      "safeSpeed": 0.05
+    },
+    "comfort": {
+      "comfortLat": 0.05,
+      "comfortLon": 0.05,
+      "comfortSpeed": 0.05
+    },
+    "accurate": {
+      "accurateExecute": 0.1
+    }
+  },
+  "typeName": {
+    "safe": {
+      "safeCollision": "碰撞安全性",
+      "safeSpeed": "超速安全性"
+    },
+    "comfort": {
+      "comfortLat": "横向平顺性",
+      "comfortLon": "纵向平顺性",
+      "comfortSpeed": "速度控制指令"
+    },
+    "accurate": {
+      "accurateExecute": "执行准确性"
+    }
+  },
+  "dimensionWeight": {
+    "safe": 0.75,
+    "comfort": 0.15,
+    "accurate": 0.1
+  },
+  "dimensionName": {
+    "safe": "安全性",
+    "comfort": "平顺性",
+    "accurate": "准确性"
+  },
+  "scoreModel": "builtin",
+  "scoreModel1": "linear_score"
+}

config_accurate.json

@@ -0,0 +1,72 @@
+{
+  "accurate": {
+    "accurateExecute": {
+      "positionError": {
+        "name": "位置偏移误差",
+        "unit": "米",
+        "weight": 0.05,
+        "priority": "0",
+        "paramList": [
+          {
+            "kind": "-1",
+            "optimal": "0.3",
+            "multiple": [
+              "0.2",
+              "2"
+            ],
+            "spare": [
+              {
+                "param": null
+              },
+              {
+                "param": null
+              }
+            ]
+          }
+        ]
+      },
+      "executeAccurateError": {
+        "name": "任务执行状态错误次数",
+        "unit": "次",
+        "weight": 0.05,
+        "priority": "0",
+        "paramList": [
+          {
+            "kind": "-1",
+            "optimal": "1",
+            "multiple": [
+              "0.2",
+              "5"
+            ],
+            "spare": [
+              {
+                "param": null
+              },
+              {
+                "param": null
+              }
+            ]
+          }
+        ]
+      }
+    }
+  },
+  "typeWeight": {
+    "accurate": {
+      "accurateExecute": 0.1
+    }
+  },
+  "typeName": {
+    "accurate": {
+      "accurateExecute": "执行准确性"
+    }
+  },
+  "dimensionWeight": {
+    "accurate": 0.1
+  },
+  "dimensionName": {
+    "accurate": "准确性"
+  },
+  "scoreModel": "builtin",
+  "scoreModel1": "linear_score"
+}

config_comfort.json

@@ -0,0 +1,152 @@
+{
+  "comfort": {
+    "comfortLat": {
+      "zigzag": {
+        "name": "画龙",
+        "unit": "次",
+        "weight": 1.0,
+        "priority": "0",
+        "paramList": [
+          {
+            "kind": "-1",
+            "optimal": "1",
+            "multiple": [
+              "0.2",
+              "5"
+            ],
+            "spare": [
+              {
+                "param": null
+              },
+              {
+                "param": null
+              }
+            ]
+          }
+        ]
+      }
+    },
+    "comfortLon": {
+      "cadence": {
+        "name": "顿挫",
+        "unit": "次",
+        "weight": 0.34,
+        "priority": "0",
+        "paramList": [
+          {
+            "kind": "-1",
+            "optimal": "1",
+            "multiple": [
+              "0.2",
+              "5"
+            ],
+            "spare": [
+              {
+                "param": null
+              },
+              {
+                "param": null
+              }
+            ]
+          }
+        ]
+      },
+      "slamBrake": {
+        "name": "急刹车",
+        "unit": "次",
+        "weight": 0.33,
+        "priority": "1",
+        "paramList": [
+          {
+            "kind": "-1",
+            "optimal": "1",
+            "multiple": [
+              "0.2",
+              "5"
+            ],
+            "spare": [
+              {
+                "param": null
+              },
+              {
+                "param": null
+              }
+            ]
+          }
+        ]
+      },
+      "slamAccelerate": {
+        "name": "急加速",
+        "unit": "次",
+        "weight": 0.33,
+        "priority": "1",
+        "paramList": [
+          {
+            "kind": "-1",
+            "optimal": "1",
+            "multiple": [
+              "0.2",
+              "5"
+            ],
+            "spare": [
+              {
+                "param": null
+              },
+              {
+                "param": null
+              }
+            ]
+          }
+        ]
+      }
+    },
+    "comfortSpeed": {
+      "speedInstructionJump": {
+        "name": "速度指令跳变",
+        "unit": "次",
+        "weight": 1.0,
+        "priority": "0",
+        "paramList": [
+          {
+            "kind": "-1",
+            "optimal": "1",
+            "multiple": [
+              "0.2",
+              "5"
+            ],
+            "spare": [
+              {
+                "param": null
+              },
+              {
+                "param": null
+              }
+            ]
+          }
+        ]
+      }
+    }
+  },
+  "typeWeight": {
+    "comfort": {
+      "comfortLat": 0.33,
+      "comfortLon": 0.33,
+      "comfortSpeed": 0.34
+    }
+  },
+  "typeName": {
+    "comfort": {
+      "comfortLat": "横向平顺性",
+      "comfortLon": "纵向平顺性",
+      "comfortSpeed": "速度控制指令"
+    }
+  },
+  "dimensionWeight": {
+    "comfort": 0.15
+  },
+  "dimensionName": {
+    "comfort": "平顺性"
+  },
+  "scoreModel": "builtin",
+  "scoreModel1": "linear_score"
+}

config_parser.py

@@ -0,0 +1,192 @@
+#!/usr/bin/env python
+# -*- coding: utf-8 -*-
+##################################################################
+#
+# Copyright (c) 2023 CICV, Inc. All Rights Reserved
+#
+##################################################################
+"""
+@Authors:           yangzihao(yangzihao@china-icv.cn)
+@Data:              2023/11/02
+@Last Modified:     2023/11/02
+@Summary:           This module provides the function to parse the config json file.
+"""
+
+import pandas as pd
+from common import json2dict
+
+
+class ConfigParse(object):
+    """
+
+    """
+
+    def __init__(self, json_file):
+        # weight info
+        self.scoreModel = "builtin"
+        self.dimension_weight = {}
+        self.dimension_list = []
+        self.dimension_name = {}
+        self.type_weight = {}
+        self.type_list = []
+        self.type_name = {}
+        self.metric_list = []
+        self.metric_dict = {}
+        self.name_dict = {}
+        self.unit_dict = {}
+
+        self.builtinDimensionList = ["accurate", "comfort", "safe"]
+        self.builtinTypeList = []
+        self.builtinMetricList = ["zigzag", "shake", "cadence", "slamBrake", "slamAccelerate", "speedInstructionJump",
+                                  "collisionCount", "collisionRisk", "collisionSeverity", "overSpeed",
+                                  "positionError", "executeAccurateError"]
+
+        # score info
+        self.config = {}
+
+        # initialization
+        self.config_dict = json2dict(json_file)
+        self._config_parse(self.config_dict)
+
+    def _config_parse(self, config_dict):
+
+        # dimension info
+        dimensionWeight = config_dict['dimensionWeight']
+        self.dimension_weight = dimensionWeight
+        self.dimension_name = config_dict['dimensionName']
+        self.dimension_list = list(self.dimension_weight.keys())
+
+        # type info
+        typeWeight = config_dict['typeWeight']
+        self.type_weight = typeWeight
+        self.type_name = config_dict["typeName"]
+
+        for dimension in self.dimension_list:
+            self.config[dimension] = self._dimension_config_parse(dimension, config_dict, typeWeight, dimensionWeight)
+
+            self.name_dict.update(self.config[dimension]['name'])
+            self.unit_dict.update(self.config[dimension]['unit'])
+
+            self.metric_dict[dimension] = self.config[dimension]['typeMetricDict']
+            self.metric_list.extend(self.config[dimension]['metric'])
+            self.type_list.extend(self.config[dimension]['type'])
+
+        print()
+
+    def _dimension_config_parse(self, dimension, config_dict, typeWeight, dimensionWeight):
+
+        # get weight type
+        typeWeightDimension = typeWeight[dimension]
+        typeDimension = list(typeWeightDimension.keys())  # get type list
+        typeWeightDimensionList = list(typeWeightDimension.values())
+        flagCustomDimension = not all(x is None for x in typeWeightDimensionList)
+
+        # get type name
+        typeNameDimension = self.type_name[dimension]
+
+        # Dimension
+        dimension_dict = config_dict[dimension]
+        dimension_value_dict = {}
+        typeMetricDict = {}
+
+        for type in typeDimension:
+            dimension_value_dict.update(dimension_dict[type])
+            typeMetricDict[type] = list(dimension_dict[type].keys())
+
+        df_dimension_value = pd.DataFrame(dimension_value_dict).T
+
+        # get metric list
+        metricDimension = df_dimension_value.index.tolist()
+
+        # get name list
+        nameDimension = df_dimension_value['name'].to_dict()
+        # nameDimensionList = list(nameDimension.values())
+
+        # get unit list
+        unitDimension = df_dimension_value['unit'].to_dict()
+        # unitDimensionList = list(unitDimension.values())
+
+        # get weight list
+        weightDimension = df_dimension_value['weight'].astype(float).to_dict()
+        weightDimensionList = list(weightDimension.values())
+
+        # get priority list
+        priorityDimension = df_dimension_value['priority'].astype(int).to_dict()
+        priorityDimensionList = list(priorityDimension.values())
+
+        # get paramList
+        paramDimension = df_dimension_value['paramList'].to_dict()
+
+        bulitin_first_key = next((key for key in paramDimension if key in self.builtinMetricList), None)
+        first_key = next(iter(paramDimension)) if not bulitin_first_key else bulitin_first_key
+        paramNum = len(paramDimension[first_key])
+
+        kindDimension = [{} for _ in range(paramNum)]
+        optimalDimension = [{} for _ in range(paramNum)]
+        multipleDimension = [{} for _ in range(paramNum)]
+        spareDimension = [{} for _ in range(paramNum)]
+        # spare1Dimension = [{} for _ in range(paramNum)]
+        # spare2Dimension = [{} for _ in range(paramNum)]
+
+        customMetricParam = {}  # custiom metric paramList
+
+        for key, value_list in paramDimension.items():
+            if key in self.builtinMetricList:
+                for i in range(len(value_list)):
+                    kindDimension[i][key] = int(value_list[i]['kind'])
+                    optimalDimension[i][key] = float(value_list[i]['optimal'])
+                    multipleDimension[i][key] = [float(x) for x in value_list[i]['multiple']]
+                    spareDimension[i][key] = [item["param"] for item in value_list[i]["spare"]]
+                    # spareDimension[i][key] = [float(item["param"]) for item in value_list[i]["spare"]]
+                    # spare1Dimension[i][key] = (value_list[i]['spare1'])
+                    # spare2Dimension[i][key] = (value_list[i]['spare2'])
+            else:
+                customMetricParam[key] = value_list
+
+        kindDimensionList = [value for dict_val in kindDimension for value in dict_val.values()]
+        optimalDimensionList = [value for dict_val in optimalDimension for value in dict_val.values()]
+        multipleDimensionList = [value for dict_val in multipleDimension for value in dict_val.values()]
+        spareDimensionList = [value for dict_val in spareDimension for value in dict_val.values()]
+        # spare1DimensionList = [value for dict_val in spare1Dimension for value in dict_val.values()]
+        # spare2DimensionList = [value for dict_val in spare2Dimension for value in dict_val.values()]
+
+        if paramNum == 1:
+            kindDimension = kindDimension[0]
+            optimalDimension = optimalDimension[0]
+            multipleDimension = multipleDimension[0]
+            spareDimension = spareDimension[0]
+            # spare1Dimension = spare1Dimension[0]
+            # spare2Dimension = spare2Dimension[0]
+
+        result = {
+            "weightDimension": float(dimensionWeight[dimension]),
+            "weightCustom": flagCustomDimension,
+            "type": typeDimension,
+            "typeWeight": typeWeightDimension,
+            "typeWeightList": typeWeightDimensionList,
+            "typeName": typeNameDimension,
+            "customMetricParam": customMetricParam,
+            "metric": metricDimension,
+            "typeMetricDict": typeMetricDict,
+            "name": nameDimension,
+            # "nameList": nameDimensionList,
+            "unit": unitDimension,
+            # "unitList": unitDimensionList,
+            "weight": weightDimension,
+            "weightList": weightDimensionList,
+            "priority": priorityDimension,
+            "priorityList": priorityDimensionList,
+            "kind": kindDimension,
+            "kindList": kindDimensionList,
+            "optimal": optimalDimension,
+            "optimalList": optimalDimensionList,
+            "multiple": multipleDimension,
+            "multipleList": multipleDimensionList,
+            "spare": spareDimension,
+            "spareList": spareDimensionList,
+            # "spare1": spare1Dimension,
+            # "spare1List": spare1DimensionList,
+            # "spare2": spare2Dimension,
+            # "spare2List": spare2DimensionList
+        }
+        return result

config_safe.json

@@ -0,0 +1,124 @@
+{
+  "safe": {
+    "safeCollision": {
+      "collisionCount": {
+        "name": "碰撞次数",
+        "unit": "次",
+        "weight": 0.6,
+        "priority": "0",
+        "paramList": [
+          {
+            "kind": "-1",
+            "optimal": "1",
+            "multiple": [
+              "0.2",
+              "1"
+            ],
+            "spare": [
+              {
+                "param": null
+              },
+              {
+                "param": null
+              }
+            ]
+          }
+        ]
+      },
+      "collisionRisk": {
+        "name": "碰撞风险概率",
+        "unit": "%",
+        "weight": 0.05,
+        "priority": "0",
+        "paramList": [
+          {
+            "kind": "-1",
+            "optimal": "10",
+            "multiple": [
+              "0.5",
+              "5"
+            ],
+            "spare": [
+              {
+                "param": null
+              },
+              {
+                "param": null
+              }
+            ]
+          }
+        ]
+      },
+      "collisionSeverity": {
+        "name": "碰撞严重程度",
+        "unit": "%",
+        "weight": 0.05,
+        "priority": "0",
+        "paramList": [
+          {
+            "kind": "-1",
+            "optimal": "10",
+            "multiple": [
+              "0.5",
+              "5"
+            ],
+            "spare": [
+              {
+                "param": null
+              },
+              {
+                "param": null
+              }
+            ]
+          }
+        ]
+      }
+    },
+    "safeSpeed": {
+      "overSpeed": {
+        "name": "超速比例",
+        "unit": "%",
+        "weight": 0.05,
+        "priority": "1",
+        "paramList": [
+          {
+            "kind": "-1",
+            "optimal": "1.17",
+            "multiple": [
+              "0.99",
+              "1.2"
+            ],
+            "spare": [
+              {
+                "param": null
+              },
+              {
+                "param": null
+              }
+            ]
+          }
+        ]
+      }
+    }
+  },
+  "typeWeight": {
+    "safe": {
+      "safeCollision": 0.7,
+      "safeSpeed": 0.05
+    }
+  },
+  "typeName": {
+    "safe": {
+      "safeCollision": "碰撞安全性",
+      "safeSpeed": "超速安全性"
+    }
+  },
+  "dimensionWeight": {
+    "safe": 0.75
+  },
+  "dimensionName": {
+    "safe": "安全性"
+  },
+  "scoreModel": "builtin",
+  "scoreModel1": "linear_score"
+}

data_info.py

@@ -0,0 +1,75 @@
+#!/usr/bin/env python
+# -*- coding: utf-8 -*-
+##################################################################
+#
+# Copyright (c) 2023 CICV, Inc. All Rights Reserved
+#
+##################################################################
+"""
+@Authors:           zhanghaiwen(zhanghaiwen@china-icv.cn)
+@Data:              2023/12/01
+@Last Modified:     2023/12/01
+@Summary:           This module provides the evaluation data information.
+"""
+
+
+class CsvData(object):
+    OBJECT_INFO = [
+        'time',
+        'frame_ID',
+        'speed_x'
+    ]
+
+
+class DataInfoList(object):
+    """
+    Data info for dimensions.
+    """
+    COMFORT_INFO = [  # 'unit_scene_ID',
+        'simTime',
+        'simFrame',
+        'lon_v',
+        'lat_v',
+        'v',
+        'lat_acc',
+        'lon_acc',
+        'accel',
+        'time_diff',
+        'lon_acc_diff',
+        'lon_acc_roc',
+        'speedH',
+        'accelH',
+        'cmd_lon_v']
+
+    SAFE_INFO = [  # 'unit_scene_ID',
+        'simTime',
+        'simFrame',
+        'playerId',
+        'v',
+        'posX',
+        'posY',
+        'posZ',
+        'posH',
+        'speedX',
+        'speedY',
+        'accelX',
+        'accelY',
+        'dimX',
+        'offX']
+
+    FUNCTION = [  # 'unit_scene_ID',
+        'simTime',
+        'simFrame',
+        'v']
+
+    EFFICIENT_INFO = [  # 'unit_scene_ID',
+        'simTime',
+        'simFrame',
+        'playerId',
+        'v',
+        'obstacle']
+
+    COMPLIANCE_INFO = [  # 'unit_scene_ID',
+        'simTime',
+        'simFrame',
+        'v']

data_process.py

@@ -0,0 +1,321 @@
+#!/usr/bin/env python
+# -*- coding: utf-8 -*-
+##################################################################
+#
+# Copyright (c) 2023 CICV, Inc. All Rights Reserved
+#
+##################################################################
+"""
+@Authors:           yangzihao(yangzihao@china-icv.cn)
+@Data:              2023/11/27
+@Last Modified:     2023/11/27
+@Summary:           Csv data process functions
+"""
+
+import os
+import numpy as np
+import pandas as pd
+from common import cal_velocity
+from data_info import CsvData
+import matplotlib.pyplot as plt
+
+
+class DataProcess(object):
+    """
+    The data process class. It is a template to get evaluation raw data and process the raw data.
+
+    Attributes:
+    """
+
+    def __init__(self, data_path, config):
+        self.data_path = data_path
+        self.casePath = data_path
+
+        # config info
+        self.config = config
+        self.safe_config = config.config['safe']
+        self.comfort_config = config.config['comfort']
+        self.accurate_config = config.config['accurate']
+
+        # data process
+        self.ego_df = pd.DataFrame()
+        self.object_df = pd.DataFrame()
+        self.trajectory_df = pd.DataFrame()
+
+        # self.obj_data = {}
+        # self.ego_data = {}
+        self.obj_id_list = {}
+
+        self.car_info = {}
+        self.report_info = {}
+        self.driver_ctrl_data = {}
+
+        self._process()
+
+    def _process(self):
+        # self._merge_csv()
+        self._read_csv()
+        self._columns_process()
+        self._draw_track()
+        # self._signal_mapping()
+        # self.car_info = self._get_car_info(self.object_df)
+        # self._compact_data()
+        # self._abnormal_detect()
+        # self._status_map(self.object_df)
+        self._ego_df_process()
+        self._object_df_process()
+        self.report_info = self._get_report_info(self.ego_df)
+        self.driver_ctrl_data = self._get_driver_ctrl_data(self.ego_df)
+
+    def _read_csv(self):
+        """
+        Read csv files to dataframe.
+
+        """
+        # self.object_df = pd.read_csv(os.path.join(self.data_path, 'merged_ObjState.csv'))
+        self.ego_df = pd.read_csv(os.path.join(self.data_path, 'EgoState_pji.csv'))
+        self.object_df = pd.read_csv(os.path.join(self.data_path, 'objects.csv'))
+        self.trajectory_df = pd.read_csv(os.path.join(self.data_path, 'trajectory_pji.csv'))
+
+        # self.driver_ctrl_df = pd.read_csv(os.path.join(self.data_path, 'DriverCtrl.csv'))
+        # self.road_mark_df = pd.read_csv(os.path.join(self.data_path, 'RoadMark.csv'))
+        # self.road_pos_df = pd.read_csv(os.path.join(self.data_path, 'RoadPos.csv'))
+        # self.traffic_light_df = pd.read_csv(os.path.join(self.data_path, 'TrafficLight.csv'))
+        # self.traffic_signal_df = pd.read_csv(os.path.join(self.data_path, 'TrafficSign.csv'))
+
+        # self.lane_info_df = pd.read_csv(os.path.join(self.data_path, 'LaneInfo.csv')).drop_duplicates()
+        # self.status_df = pd.read_csv(os.path.join(self.data_path, 'VehicleState.csv'))
+        # self.vehicle_sys_df = pd.read_csv(
+        #     os.path.join(self.data_path, 'VehicleSystems.csv')).drop_duplicates()  # 车灯信息
+
+    def _columns_process(self):
+        # ego data process
+        self.ego_df.rename(columns={"speed_linear": "lon_v", "speed_angular": "speedH", "cmd_speed_linear": "cmd_lon_v",
+                                    "cmd_speed_angular": "cmd_speedH", }, inplace=True)
+
+        self.ego_df['playerId'] = 1
+        self.ego_df['lat_v'] = 0
+        self.ego_df['lat_acc'] = 0
+        # self.ego_df['lon_acc'] = 0
+
+        # object data process
+        self.object_df.rename(columns={"simtime": "simTime", "FrameID": "simFrame", "id": "playerId",
+                                       "poseX": "posX", "poseY": "posY", "poseZ": "posZ",
+                                       "velocity_linear_x": "lon_v", "velocity_linear_y": "lat_v",
+                                       "velocity_angular_x": "speedH",
+                                       "acceleration_linear_x": "lon_acc", "acceleration_linear_y": "lat_acc",
+                                       "acceleration_angular_x": "accelH"}, inplace=True)
+
+        # Time, simtime, FrameID, id, HeadingAngle, poseX, poseY, poseZ, label, dimX, dimY, dimZ,
+        #  velocity_linear_x, velocity_linear_y, velocity_linear_z, velocity_angular_x, velocity_angular_y, velocity_angular_z,
+        #  acceleration_linear_x, acceleration_linear_y, acceleration_linear_z, acceleration_angular_x, acceleration_angular_y, acceleration_angular_z
+
+    def _draw_track(self):
+        """
+
+        """
+        df = self.ego_df.copy()
+        plt.scatter(df['posX'], df['posY'], c=df['simTime'], s=0.1)
+        plt.axis('equal')
+
+        # 添加坐标轴标签和标题
+        plt.xlabel('posX')
+        plt.ylabel('posY')
+        plt.title('Trajectory')
+
+        # 显示图形
+        plt.savefig(os.path.join(self.casePath, "./track.png"))
+        plt.close()
+
+    def _get_car_info(self, df):
+        """
+
+        Args:
+            df:
+
+        Returns:
+
+        """
+        first_row = df[df['playerId'] == 1].iloc[0].to_dict()
+
+        length = first_row['dimX']
+        width = first_row['dimY']
+        height = first_row['dimZ']
+        offset = first_row['offX']
+
+        car_info = {
+            "length": length,
+            "width": width,
+            "height": height,
+            "offset": offset
+        }
+
+        return car_info
+
+    def _mileage_cal(self, df):
+        """
+        Calculate mileage of given df.
+
+        Args:
+            df: A dataframe of driving data.
+
+        Returns:
+            mileage: A float of the mileage(meter) of the driving data.
+
+        """
+        travelDist = df['traveledDist'].values.tolist()
+        travelDist = [x for x in travelDist if not np.isnan(x)]
+
+        # mile_start = df['travelDist'].iloc[0]
+        mile_start = travelDist[0]
+        mile_end = travelDist[-1]
+        mileage = mile_end - mile_start
+        return mileage
+
+    def _duration_cal(self, df):
+        """
+        Calculate duration of given df.
+        Args:
+            df: A dataframe of driving data.
+
+        Returns:
+            duration: A float of the duration(second) of the driving data.
+
+        """
+        time_start = df['simTime'].iloc[0]
+        time_end = df['simTime'].iloc[-1]
+        duration = time_end - time_start
+        return duration
+
+    def _get_report_info(self, df):
+        """
+        Get report infomation from dataframe.
+
+        Args:
+            df: A dataframe of driving data.
+
+        Returns:
+            report_info: A dict of report infomation.
+
+        """
+        mileage = self._mileage_cal(df)
+        duration = self._duration_cal(df)
+        report_info = {
+            "mileage": mileage,
+            "duration": duration
+        }
+        return report_info
+
+    def _ego_df_process(self):
+        # ego_df process
+        self.ego_df['time_diff'] = self.ego_df['simTime'].diff()
+        self.ego_df['lon_v_diff'] = self.ego_df['lon_v'].diff()
+        self.ego_df['lon_acc'] = self.ego_df['lon_v_diff'] / self.ego_df['time_diff']
+
+        data = self.ego_df.copy()
+        data.rename(
+            columns={"speedY": "lat_v", "speedX": "lon_v", "accelY": "lat_acc", "accelX": "lon_acc", "dimZ": "speedH"},
+            inplace=True)
+
+        # calculate common parameters
+        # data['lat_v'] = data['speedX'] * np.sin(data['posH']) * -1 + data['speedY'] * np.cos(data['posH'])
+        # data['lon_v'] = data['speedX'] * np.cos(data['posH']) + data['speedY'] * np.sin(data['posH'])
+        data['v'] = data.apply(lambda row: cal_velocity(row['lat_v'], row['lon_v']), axis=1)
+
+        data['time_diff'] = data['simTime'].diff()
+        data['avg_speed'] = (data['v'] + data['v'].shift()) / 2  # 计算每个时间间隔的平均速度
+        data['distance_increment'] = data['avg_speed'] * data['time_diff']  # 计算每个时间间隔的距离增量
+
+        # 计算当前里程
+        data['traveledDist'] = data['distance_increment'].cumsum()
+        data['traveledDist'] = data['traveledDist'].fillna(0)
+
+        # calculate acceleraton components
+        # data['lat_acc'] = data['accelX'] * np.sin(data['posH']) * -1 + data['accelY'] * np.cos(data['posH'])
+        # data['lon_acc'] = data['accelX'] * np.cos(data['posH']) + data['accelY'] * np.sin(data['posH'])
+        # data['accel'] = data.apply(lambda row: cal_velocity(row['lat_acc'], row['lon_acc']), axis=1)
+        data['accel'] = data['lon_acc']
+
+        data['lat_acc_diff'] = data['lat_acc'].diff()
+        data['lon_acc_diff'] = data['lon_acc'].diff()
+        data['speedH_diff'] = data['speedH'].diff()
+
+        data['time_diff'] = data['simTime'].diff()
+        # self.obj_data['avg_speed'] = (self.obj_data['v'] + self.obj_data['v'].shift()) / 2  # 计算每个时间间隔的平均速度
+        # self.obj_data['distance_increment'] = self.obj_data['avg_speed'] * self.obj_data['time_diff'] / 3.6  # 计算每个时间间隔的距离增量
+        #
+        # # 计算当前里程
+        # self.obj_data['travelDist'] = self.obj_data['distance_increment'].cumsum()
+        # self.obj_data['travelDist'] = self.obj_data['travelDist'].fillna(0)
+
+        data['lat_acc_roc'] = data['lat_acc_diff'] / data['time_diff']
+        data['lon_acc_roc'] = data['lon_acc_diff'] / data['time_diff']
+        data['accelH'] = data['speedH_diff'] / data['time_diff']
+
+        self.ego_df = data.copy()
+
+    def _object_df_process(self):
+        """
+        Process the data of object dataframe. Save the data groupby object_ID.
+
+        Returns:
+            No returns.
+
+        """
+        self.obj_id_list = list(set(self.object_df.playerId))
+
+        # self.object_df = pd.merge(self.object_df, self.ego_df, how='left')
+
+        # calculate respective parameters
+        # for obj_id, obj_data in self.object_df.groupby("playerId"):
+        #     self.obj_data[obj_id] = obj_data
+        #     self.obj_data[obj_id]['lat_acc_diff'] = self.obj_data[obj_id]['lat_acc'].diff()
+        #     self.obj_data[obj_id]['lon_acc_diff'] = self.obj_data[obj_id]['lon_acc'].diff()
+        #     self.obj_data[obj_id]['speedH_diff'] = self.obj_data[obj_id]['speedH'].diff()
+        #
+        #     self.obj_data[obj_id]['time_diff'] = self.obj_data[obj_id]['simTime'].diff()
+        #     # self.obj_data['avg_speed'] = (self.obj_data['v'] + self.obj_data['v'].shift()) / 2  # 计算每个时间间隔的平均速度
+        #     # self.obj_data['distance_increment'] = self.obj_data['avg_speed'] * self.obj_data['time_diff'] / 3.6  # 计算每个时间间隔的距离增量
+        #     #
+        #     # # 计算当前里程
+        #     # self.obj_data['travelDist'] = self.obj_data['distance_increment'].cumsum()
+        #     # self.obj_data['travelDist'] = self.obj_data['travelDist'].fillna(0)
+        #
+        #     self.obj_data[obj_id]['lat_acc_roc'] = self.obj_data[obj_id]['lat_acc_diff'] / self.obj_data[obj_id][
+        #         'time_diff']
+        #     self.obj_data[obj_id]['lon_acc_roc'] = self.obj_data[obj_id]['lon_acc_diff'] / self.obj_data[obj_id][
+        #         'time_diff']
+        #     self.obj_data[obj_id]['accelH'] = self.obj_data[obj_id]['speedH_diff'] / self.obj_data[obj_id][
+        #         'time_diff']
+
+        # get object id list
+        # self.obj_id_list = list(self.obj_data.keys())
+        # self.ego_data = self.obj_data[1]
+
+    def _get_driver_ctrl_data(self, df):
+        """
+        Process and get drive ctrl information. Such as brake pedal, throttle pedal and steering wheel.
+
+        Args:
+            df: A dataframe of driver ctrl data.
+
+        Returns:
+            driver_ctrl_data: A dict of driver ctrl info.
+
+        """
+        time_list = df['simTime'].values.tolist()
+        frame_list = df['simFrame'].values.tolist()
+        # df['brakePedal'] = df['brakePedal'] * 100
+        # brakePedal_list = df['brakePedal'].values.tolist()
+        # df['throttlePedal'] = df['throttlePedal'] * 100
+        # throttlePedal_list = df['throttlePedal'].values.tolist()
+        # steeringWheel_list = df['steeringWheel'].values.tolist()
+
+        driver_ctrl_data = {
+            "time_list": time_list,
+            "frame_list": frame_list,
+            # "brakePedal_list": brakePedal_list,
+            # "throttlePedal_list": throttlePedal_list,
+            # "steeringWheel_list": steeringWheel_list
+        }
+        return driver_ctrl_data

demo.py

@@ -0,0 +1,12 @@
+def get_trajectory_image(height, width=400):
+    aspect = height / float(width)
+
+    if height > 300:
+        height = 300
+        width = height / aspect
+
+    return height, width
+
+
+height, width = get_trajectory_image(600, 300)
+print(height, width)

efficient.py

@@ -0,0 +1,651 @@
+#!/usr/bin/env python
+# -*- coding: utf-8 -*-
+##################################################################
+#
+# Copyright (c) 2023 CICV, Inc. All Rights Reserved
+#
+##################################################################
+"""
+@Authors:           xieguijin(xieguijin@china-icv.cn), yangzihao(yangzihao@china-icv.cn)
+@Data:              2023/08/03
+@Last Modified:     2023/08/03
+@Summary:           Functionality metrics
+"""
+
+import os
+import sys
+
+sys.path.append('../common')
+sys.path.append('../modules')
+sys.path.append('../results')
+
+import numpy as np
+import pandas as pd
+
+from data_info import DataInfoList
+from score_weight import cal_score_with_priority, cal_weight_from_80
+from common import score_grade, string_concatenate, replace_key_with_value
+import matplotlib.pyplot as plt
+
+
+class Efficient(object):
+    """
+    Class for achieving efficient metrics for autonomous driving.
+
+    Attributes:
+        df: Vehicle driving data, stored in dataframe format.
+    """
+
+    def __init__(self, data_processed, custom_data, scoreModel, resultPath):
+        self.eval_data = pd.DataFrame()
+        self.data_processed = data_processed
+        self.scoreModel = scoreModel
+        self.resultPath = resultPath
+
+        self.data = data_processed.obj_data[1]
+        self.mileage = data_processed.report_info['mileage']
+        self.df = pd.DataFrame()
+
+        self.speed_dict = dict()
+
+        self.config = data_processed.config
+        efficient_config = data_processed.efficient_config
+        self.efficient_config = efficient_config
+
+        # common data
+        self.bulitin_metric_list = self.config.builtinMetricList
+
+        # dimension data
+        self.weight_custom = efficient_config['weightCustom']
+        self.metric_list = efficient_config['metric']
+        self.type_list = efficient_config['type']
+        self.type_name_dict = efficient_config['typeName']
+        self.name_dict = efficient_config['name']
+        self.unit_dict = efficient_config['unit']
+
+        # custom metric data
+        self.customMetricParam = efficient_config['customMetricParam']
+        self.custom_metric_list = list(self.customMetricParam.keys())
+        self.custom_data = custom_data
+        self.custom_param_dict = {}
+
+        # score data
+        self.weight = efficient_config['weightDimension']
+
+        self.weight_type_dict = efficient_config['typeWeight']
+        self.weight_type_list = efficient_config['typeWeightList']
+
+        self.weight_dict = efficient_config['weight']
+        self.weight_list = efficient_config['weightList']
+
+        self.priority_dict = efficient_config['priority']
+        self.priority_list = efficient_config['priorityList']
+
+        self.kind_dict = efficient_config['kind']
+        self.optimal_dict = efficient_config['optimal']
+        self.multiple_dict = efficient_config['multiple']
+        self.kind_list = efficient_config['kindList']
+        self.optimal_list = efficient_config['optimalList']
+        self.multiple_list = efficient_config['multipleList']
+
+        # metric data
+        self.metric_dict = efficient_config['typeMetricDict']
+        self.drive_metric_list = self.metric_dict['efficientDrive']
+        self.stop_metric_list = self.metric_dict['efficientStop']
+        # self.drive_metric_list = ["averageSpeed"]
+        # self.stop_metric_list = ["stopDuration", "stopCount"]
+
+        self._get_data()
+        self._effi_param_cal()
+        self.time_list = self.data['simTime'].values.tolist()
+        self.speed_list = list()
+        self.value_dict = {}
+        self.average_v = 0
+        self.stop_count = 0
+        self.stop_duration = 0
+        self.sum_stop_time = 0
+        self.pass_junction_time = 0
+
+        self.score = 0
+
+    def _get_data(self):
+        """
+        Get the data required for efficient evaluation according to efficient_INFO in DataInfoList.
+
+        Parameters:
+            df: Dataframe containing the vehicle running data.
+
+        Returns:
+            peak_valley: List of indices representing peaks and valleys.
+        """
+        efficient_info_list = DataInfoList.EFFICIENT_INFO
+        self.df = self.data[efficient_info_list].copy()
+
+    def _effi_param_cal(self):
+        """
+
+        """
+        self.df = self.df[self.df['playerId'] == 1]
+        # self.df = self.df[self.df['id'] == 1]
+        # self.df['v'] = self.df.apply(lambda row: self.velocity(row['speed_x'], row['speed_y']), axis=1)
+        # self.df['v'] = self.df.apply(lambda row: self.velocity(row['speedX'], row['speedY']), axis=1)
+
+    def _cal_max_min_avg(self, num_list):
+        maxx = max(num_list) if num_list else "-"
+        minn = min(num_list) if num_list else "-"
+        avg = sum(num_list) / len(num_list) if num_list else "-"
+
+        result = {
+            "max": maxx,
+            "min": minn,
+            "avg": avg
+        }
+
+        return result
+
+    def average_velocity(self):
+        """
+        1.速度的平均值
+        2.平均速度 = 里程/时间
+        速度不为0为开始时间，速度为0为结束时间
+        """
+        self.average_v = self.df['v'].mean()
+        self.speed_list = self.df['v'].values.tolist()
+        self.speed_dict = self._cal_max_min_avg(self.df['v'].dropna().values.tolist())
+
+    def stop_duration_and_count(self):
+        """
+        百公里停车次数
+        平均每次停车时长
+        驾照科目二考试判定为停车的标准：在考试项目区域内，汽车停顿2秒以上都算中途停车。
+        """
+        stop_v_threshold = 0.01
+        stop_time_threshold = 0.5  # 12.5帧
+
+        without_obstacle = self.df[self.df['obstacle'] == 0].copy()
+        stop_time_list = without_obstacle[without_obstacle['v'] <= stop_v_threshold]['simTime'].values.tolist()
+        # stop_time_list = self.df[self.df['v'] == 0]['simTime'].values.tolist()
+        stop_frame_list = without_obstacle[without_obstacle['v'] <= stop_v_threshold]['simFrame'].values.tolist()
+        # stop_frame_list = self.df[self.df['v'] == 0]['simFrame'].values.tolist()
+
+        stop_frame_group = []
+        stop_time_group = []
+        f1 = stop_frame_list[0] if stop_frame_list else 0
+        t1 = stop_time_list[0] if stop_time_list else 0
+        sum_stop_time = 0
+        for i in range(1, len(stop_frame_list)):
+            if stop_frame_list[i] - stop_frame_list[i - 1] != 1:
+                f2 = stop_frame_list[i - 1]
+                # 当帧数大于13时才确定为停车
+                # 当时长大于0.5s才确定为停车
+                if f2 - f1 >= 13:
+                    t2 = stop_time_list[i - 1]
+                    stop_frame_group.append((f1, f2))
+                    stop_time_group.append((t1, t2))
+                    sum_stop_time += (t2 - t1)
+                    self.stop_count += 1
+                # update f1, t1
+                f1 = stop_frame_list[i]
+                t1 = stop_time_list[i]
+
+        f2 = stop_frame_list[-1] if stop_frame_list else 0
+        # 如果最后一段的帧数差大于13，且停车统计列表中的最后一帧不为用例的最后一帧
+        if f2 - f1 >= 13 and f2 != self.df['simFrame'].values.tolist()[-1]:
+            t2 = stop_time_list[-1]
+            stop_frame_group.append((f1, f2))
+            stop_time_group.append((t1, t2))
+            sum_stop_time += (t2 - t1)
+            self.stop_count += 1
+
+        self.sum_stop_time = sum_stop_time
+        self.stop_duration = sum_stop_time / self.stop_count if self.stop_count != 0 else 0
+        # self.stop_count = self.stop_count / self.mileage * 1000000
+
+    def efficient_statistic(self):
+        arr_effi = []
+        self.average_velocity()
+        self.stop_duration_and_count()
+
+        if "averageSpeed" in self.metric_list:
+            average_v = self.average_v
+            arr_effi.append(average_v)
+            self.value_dict["averageSpeed"] = average_v
+
+        if "stopDuration" in self.metric_list:
+            stop_duration = self.stop_duration
+            arr_effi.append(stop_duration)
+            self.value_dict["stopDuration"] = stop_duration
+
+        if "stopCount" in self.metric_list:
+            stop_count = self.stop_count
+            arr_effi.append(stop_count)
+            self.value_dict["stopCount"] = stop_count
+
+        # arr_effi = [average_v, stop_count, stop_time]
+        return arr_effi
+
+    def custom_metric_param_parser(self, param_list):
+        """
+        param_dict = {
+            "paramA" [
+                {
+                    "kind": "-1",
+                    "optimal": "1",
+                    "multiple": ["0.5","5"],
+                    "spare1": null,
+                    "spare2": null
+                }
+            ]
+        }
+        """
+        kind_list = []
+        optimal_list = []
+        multiple_list = []
+        spare_list = []
+        # spare1_list = []
+        # spare2_list = []
+
+        for i in range(len(param_list)):
+            kind_list.append(int(param_list[i]['kind']))
+            optimal_list.append(float(param_list[i]['optimal']))
+            multiple_list.append([float(x) for x in param_list[i]['multiple']])
+            spare_list.append([item["param"] for item in param_list[i]["spare"]])
+            # spare1_list.append(param_list[i]['spare1'])
+            # spare2_list.append(param_list[i]['spare2'])
+
+        result = {
+            "kind": kind_list,
+            "optimal": optimal_list,
+            "multiple": multiple_list,
+            "spare": spare_list,
+            # "spare1": spare1_list,
+            # "spare2": spare2_list
+        }
+        return result
+
+    def custom_metric_score(self, metric, value, param_list):
+        """
+
+        """
+        param = self.custom_metric_param_parser(param_list)
+        self.custom_param_dict[metric] = param
+
+        score_model = self.scoreModel(param['kind'], param['optimal'], param['multiple'], np.array([value]))
+        score_sub = score_model.cal_score()
+        score = sum(score_sub) / len(score_sub)
+        return score
+
+    def effi_score(self):
+        arr_effi = self.efficient_statistic()
+        print("\n[高效性表现及得分情况]")
+        print("高效性各指标值：", [round(num, 2) for num in arr_effi])
+        arr_effi = np.array([arr_effi])
+
+        score_model = self.scoreModel(self.kind_list, self.optimal_list, self.multiple_list, arr_effi)
+        score_sub = score_model.cal_score()
+        score_sub = list(map(lambda x: 80 if np.isnan(x) else x, score_sub))
+
+        score_metric = [round(num, 2) for num in score_sub]
+
+        metric_list = [x for x in self.metric_list if x in self.config.builtinMetricList]
+        score_metric_dict = {key: value for key, value in zip(metric_list, score_metric)}
+
+        custom_metric_list = list(self.customMetricParam.keys())
+        for metric in custom_metric_list:
+            value = self.custom_data[metric]['value']
+            param_list = self.customMetricParam[metric]
+            score = self.custom_metric_score(metric, value, param_list)
+            score_metric_dict[metric] = round(score, 2)
+
+        score_metric_dict = {key: score_metric_dict[key] for key in self.metric_list}
+        score_metric = list(score_metric_dict.values())
+
+        score_type_dict = {}
+        if self.weight_custom:  # 自定义权重
+            score_metric_with_weight_dict = {key: score_metric_dict[key] * self.weight_dict[key] for key in
+                                             self.weight_dict}
+
+            for type in self.type_list:
+                type_score = sum(
+                    value for key, value in score_metric_with_weight_dict.items() if key in self.metric_dict[type])
+                score_type_dict[type] = round(type_score, 2)
+
+            score_type_with_weight_dict = {key: score_type_dict[key] * self.weight_type_dict[key] for key in
+                                           score_type_dict}
+            score_efficient = sum(score_type_with_weight_dict.values())
+
+        else:  # 客观赋权
+            self.weight_list = cal_weight_from_80(score_metric)
+            self.weight_dict = {key: value for key, value in zip(self.metric_list, self.weight_list)}
+            score_efficient = cal_score_with_priority(score_metric, self.weight_list, self.priority_list)
+
+            for type in self.type_list:
+                type_weight = sum(value for key, value in self.weight_dict.items() if key in self.metric_dict[type])
+                self.weight_dict = {key: round(value / type_weight, 4) for key, value in self.weight_dict.items() if
+                                    key in self.metric_dict[type]}
+                type_score_metric = [value for key, value in score_metric_dict.items() if key in self.metric_dict[type]]
+                type_weight_list = [value for key, value in self.weight_dict.items() if key in self.metric_dict[type]]
+                type_priority_list = [value for key, value in self.priority_dict.items() if
+                                      key in self.metric_dict[type]]
+
+                type_score = cal_score_with_priority(type_score_metric, type_weight_list, type_priority_list)
+                score_type_dict[type] = round(type_score, 2)
+
+        score_efficient = round(score_efficient, 2)
+
+        print("高效性各指标基准值：", self.optimal_list)
+        print(f"高效性得分为：{score_efficient:.2f}分。")
+        print(f"高效性各类型得分为：{score_type_dict}。")
+        print(f"高效性各指标得分为：{score_metric_dict}。")
+        return score_efficient, score_type_dict, score_metric_dict
+
+    def effi_weight_distribution(self):
+        # get weight distribution
+        weight_distribution = {}
+        weight_distribution["name"] = "高效性"
+
+        if "efficientDrive" in self.type_list:
+            drive_weight_indexes_dict = {key: f"{key}({value * 100:.2f}%)" for key, value in self.weight_dict.items() if
+                                         key in self.drive_metric_list}
+
+            weight_distribution_drive = {
+                "driveWeight": f"行驶({self.weight_type_dict['efficientDrive'] * 100:.2f}%)",
+                "indexes": drive_weight_indexes_dict
+            }
+            weight_distribution['efficientDrive'] = weight_distribution_drive
+
+        if "efficientStop" in self.type_list:
+            stop_weight_indexes_dict = {key: f"{key}({value * 100:.2f}%)" for key, value in self.weight_dict.items() if
+                                        key in self.stop_metric_list}
+
+            weight_distribution_stop = {
+                "stopWeight": f"停车({self.weight_type_dict['efficientStop'] * 100:.2f}%)",
+                "indexes": stop_weight_indexes_dict
+            }
+            weight_distribution['efficientStop'] = weight_distribution_stop
+
+        return weight_distribution
+
+    def _get_weight_distribution(self, dimension):
+        # get weight distribution
+        weight_distribution = {}
+        weight_distribution["name"] = self.config.dimension_name[dimension]
+
+        for type in self.type_list:
+            type_weight_indexes_dict = {key: f"{self.name_dict[key]}({value * 100:.2f}%)" for key, value in
+                                        self.weight_dict.items() if
+                                        key in self.metric_dict[type]}
+
+            weight_distribution_type = {
+                "weight": f"{self.type_name_dict[type]}({self.weight_type_dict[type] * 100:.2f}%)",
+                "indexes": type_weight_indexes_dict
+            }
+            weight_distribution[type] = weight_distribution_type
+
+        return weight_distribution
+
+    def report_statistic(self):
+        """
+
+        Returns:
+
+        """
+
+        # report_dict = {
+        #     "name": "高效性",
+        #     "weight": f"{self.weight * 100:.2f}%",
+        #     "weightDistribution": weight_distribution,
+        #     "score": score_efficient,
+        #     "level": grade_efficient,
+        #     'score_type': score_type,
+        #     'score_metric': score_metric,
+        #
+        #     "description1": effi_description1,
+        #     "description2": effi_description2,
+        #     "description3": effi_description3,
+        #
+        #     "efficientDrive": drive_dict,
+        #     "efficientStop": stop_dict,
+        # }
+
+        # brakePedal_list = self.data_processed.driver_ctrl_data['brakePedal_list']
+        # throttlePedal_list = self.data_processed.driver_ctrl_data['throttlePedal_list']
+        # steeringWheel_list = self.data_processed.driver_ctrl_data['steeringWheel_list']
+        #
+        # # common parameter calculate
+        # brake_vs_time = self.zip_time_pairs(brakePedal_list, 100)
+        # throttle_vs_time = self.zip_time_pairs(throttlePedal_list, 100)
+        # steering_vs_time = self.zip_time_pairs(steeringWheel_list)
+
+        report_dict = {
+            "name": "高效性",
+            # "weight": f"{self.weight * 100:.2f}%",
+        }
+
+        # len_time = len(self.time_list)
+        # duration = self.time_list[-1]
+        # duration_minute = round(duration / 60, 2)
+        score_efficient, score_type_dict, score_metric_dict = self.effi_score()
+
+        # get weight distribution
+        # report_dict["weightDistribution"] = self._get_weight_distribution("efficient")
+
+        # score_efficient, score_metric = self.effi_score()
+        score_efficient = int(score_efficient) if int(score_efficient) == score_efficient else round(score_efficient, 2)
+        grade_efficient = score_grade(score_efficient)
+        report_dict["score"] = score_efficient
+        report_dict["level"] = grade_efficient
+
+        # for description
+        # good_type_list = []
+        # bad_type_list = []
+
+        # good_metric_list = []
+        # bad_metric_list = []
+
+        # str for description
+        # str_over_optimal = ""
+        # type_details_dict = {}
+
+        # for type in self.type_list:
+        #     bad_type_list.append(type) if score_type_dict[type] < 80 else good_type_list.append(type)
+        #
+        #     type_dict = {
+        #         "name": type,
+        #     }
+        #
+        #     builtin_graph_dict = {}
+        #     custom_graph_dict = {}
+        #
+        #     score_type = score_type_dict[type]
+        #     grade_type = score_grade(score_type)
+        #     type_dict["score"] = score_type
+        #     type_dict["level"] = grade_type
+        #
+        #     type_dict_indexes = {}
+        #     for metric in self.metric_dict[type]:
+        #         bad_metric_list.append(metric) if score_metric_dict[metric] < 80 else good_metric_list.append(metric)
+        #
+        #         if metric in self.bulitin_metric_list:
+        #             # for indexes
+        #             type_dict_indexes[metric] = {
+        #                 "name": f"{self.name_dict[metric]}({self.unit_dict[metric]})",
+        #                 "score": score_metric_dict[metric],
+        #                 "value": f'{self.value_dict[metric]:.2f}',
+        #                 # "range": f"[0, {self.optimal_dict['averageSpeed']})",
+        #                 # "deviation": f"+{avv_deviation:.2f}%" if avv_deviation > 0 else f"{avv_deviation:.2f}%"
+        #             }
+        #
+        #             if self.kind_dict[metric] == -1:
+        #                 type_dict_indexes[metric]["range"] = f"[0, {self.optimal_dict[metric]}]"
+        #                 metric_over_optimal = ((self.value_dict[metric] - self.optimal_dict[metric]) /
+        #                                        self.optimal_dict[
+        #                                            metric]) * 100
+        #                 type_dict_indexes[metric][
+        #                     "deviation"] = f"+{metric_over_optimal:.2f}%" if metric_over_optimal > 0 else f"0%"
+        #
+        #             elif self.kind_dict[metric] == 1:
+        #                 type_dict_indexes[metric]["range"] = f"[{self.optimal_dict[metric]}, inf)"
+        #                 metric_over_optimal = ((self.value_dict[metric] - self.optimal_dict[metric]) /
+        #                                        self.optimal_dict[
+        #                                            metric]) * 100
+        #                 type_dict_indexes[metric][
+        #                     "deviation"] = f"0%" if metric_over_optimal > 0 else f"{metric_over_optimal:.2f}%"
+        #
+        #             elif self.kind_dict[metric] == 0:
+        #                 value = self.value_dict[metric]
+        #                 minn = self.optimal_dict[metric] * self.multiple_dict[metric][0]
+        #                 maxx = self.optimal_dict[metric] * self.multiple_dict[metric][1]
+        #
+        #                 type_dict_indexes[metric]["range"] = f"[{minn}, {maxx}]"
+        #
+        #                 if value < minn:
+        #                     metric_over_optimal = (value - minn) / minn * 100
+        #                 elif value > maxx:
+        #                     metric_over_optimal = (value - maxx) / maxx * 100
+        #                 else:
+        #                     metric_over_optimal = 0
+        #
+        #                 type_dict_indexes[metric][
+        #                     "deviation"] = f"+{metric_over_optimal:.2f}%" if metric_over_optimal > 0 else f"{metric_over_optimal:.2f}%"
+        #
+        #             # metric_over_optimal = ((self.value_dict[metric] - self.optimal_dict[metric]) / self.optimal_dict[
+        #             #     metric]) * 100
+        #             # type_dict_indexes[metric][
+        #             #     "deviation"] = f"+{metric_over_optimal:.2f}%" if metric_over_optimal > 0 else f"{metric_over_optimal:.2f}%"
+        #
+        #             if score_metric_dict[metric] < 80:
+        #                 str_over_optimal += f'{self.name_dict[metric]}为{round(self.value_dict[metric], 2)}{self.unit_dict[metric]}，超过合理范围{metric_over_optimal}%；'
+        #
+        #         else:
+        #             # for indexes
+        #             type_dict_indexes[metric] = {
+        #                 "name": f"{self.name_dict[metric]}({self.unit_dict[metric]})",
+        #                 "score": score_metric_dict[metric],
+        #                 "value": f'{self.custom_data[metric]["value"][0]:.2f}',
+        #                 # "range": f"[0, {self.optimal_dict['averageSpeed']})",
+        #                 # "deviation": f"+{avv_deviation}%" if avv_deviation > 0 else f"{avv_deviation}%"
+        #             }
+        #
+        #             value = self.custom_data[metric]["value"][0]
+        #             optimal = self.custom_param_dict[metric]['optimal'][0]
+        #
+        #             if self.custom_param_dict[metric]['kind'][0] == -1:
+        #                 type_dict_indexes[metric][
+        #                     "range"] = f"[0, {self.custom_param_dict[metric]['optimal'][0]}]"
+        #
+        #                 metric_over_optimal = (value - optimal) / optimal * 100
+        #                 type_dict_indexes[metric][
+        #                     "deviation"] = f"+{metric_over_optimal:.2f}%" if metric_over_optimal > 0 else f"0%"
+        #
+        #             elif self.custom_param_dict[metric]['kind'][0] == 1:
+        #                 type_dict_indexes[metric][
+        #                     "range"] = f"[{self.custom_param_dict[metric]['optimal'][0]}, inf)"
+        #
+        #                 metric_over_optimal = (value - optimal) / optimal * 100
+        #                 type_dict_indexes[metric][
+        #                     "deviation"] = f"0%" if metric_over_optimal > 0 else f"{metric_over_optimal:.2f}%"
+        #
+        #             elif self.custom_param_dict[metric]['kind'][0] == 0:
+        #                 minn = value * self.custom_param_dict[metric]['multiple'][0][0]
+        #                 maxx = value * self.custom_param_dict[metric]['multiple'][0][1]
+        #
+        #                 type_dict_indexes[metric]["range"] = f"[{minn}, {maxx}]"
+        #
+        #                 if value < minn:
+        #                     metric_over_optimal = (value - minn) / minn * 100
+        #                 elif value > maxx:
+        #                     metric_over_optimal = (value - maxx) / maxx * 100
+        #                 else:
+        #                     metric_over_optimal = 0
+        #
+        #                 type_dict_indexes[metric][
+        #                     "deviation"] = f"+{metric_over_optimal:.2f}%" if metric_over_optimal > 0 else f"{metric_over_optimal:.2f}%"
+        #
+        #             # metric_over_optimal = ((self.custom_param_dict[metric]['optimal'][0] -
+        #             #                         self.custom_data[metric]["value"][0]) /
+        #             #                        self.custom_param_dict[metric]['optimal'][0]) * 100
+        #             # type_dict_indexes[metric][
+        #             #     "deviation"] = f"+{metric_over_optimal:.2f}%" if metric_over_optimal > 0 else f"{metric_over_optimal:.2f}%"
+        #
+        #             if score_metric_dict[metric] < 80:
+        #                 str_over_optimal += f'{self.name_dict[metric]}为{self.custom_data[metric]["value"][0]:.2f}{self.unit_dict[metric]}，超过合理范围{metric_over_optimal}%；'
+        #
+        #             custom_graph_dict[metric] = self.custom_data[metric]['reportData']
+        #
+        #         # str_uncomf_over_optimal = str_uncomf_over_optimal[:-1] + "；"
+        #         type_dict["indexes"] = type_dict_indexes
+        #         type_dict["builtin"] = builtin_graph_dict
+        #         type_dict["custom"] = custom_graph_dict
+        #
+        #     type_details_dict[type] = type_dict
+
+        # report_dict["details"] = type_details_dict
+        # efficient description
+        # str_over_optimal = str_over_optimal[:-1]
+
+        # if grade_efficient == '优秀':
+        #     effi_description1 = '机器人行驶效率高；'
+        # elif grade_efficient == '良好':
+        #     effi_description1 = '机器人在本轮测试中的表现满足设计指标要求；'
+        # elif grade_efficient == '一般':
+        #     str_ineffi_type = string_concatenate(bad_metric_list)
+        #     effi_description1 = f'机器人需要在{str_ineffi_type}指标上进一步优化。其中，{str_over_optimal}；'
+        # elif grade_efficient == '较差':
+        #     str_ineffi_type = string_concatenate(bad_metric_list)
+        #     effi_description1 = f'需要提高机器人在{str_ineffi_type}指标上的表现。其中，{str_over_optimal}；'
+        #
+        # if not bad_metric_list:
+        #     effi_description2 = '高效性在各个指标上的表现俱佳'
+        #     effi_description3 = "机器人的规划控制能力良好，通行效率高"
+        # else:
+        #     str_effi_type = string_concatenate(good_metric_list)
+        #     str_ineffi_type = string_concatenate(bad_metric_list)
+        #
+        #     effi_description2 = f"{str_effi_type}指标表现良好，{str_ineffi_type}指标表现不佳。其中，{str_over_optimal}"
+        #     effi_description3 = "应该优化机器人的规划控制逻辑，提高机器人的通行效率"
+
+        # report_dict["description1"] = replace_key_with_value(effi_description1, self.name_dict)
+        # report_dict["description2"] = replace_key_with_value(effi_description2, self.name_dict)
+        # report_dict["description3"] = effi_description3
+
+        description = "· 在高效性方面，"
+
+        if self.stop_count > 0:
+            description += f"出现{self.stop_count}次无障碍物停止，需重点优化。"
+        else:
+            description += f"平均速度{self.speed_dict['avg']:.4f}m/s，未出现无障碍物停止，表现{grade_efficient}。"
+
+        report_dict["description"] = description
+
+        description1 = f"次数：{self.stop_count}次；" \
+                       f"总时长：{self.sum_stop_time:.4f}s；" \
+                       f"平均时长：{self.stop_duration:.4f}s"
+
+        description2 = f"最大值：{self.speed_dict['max']:.4f}m/s；" \
+                       f"最小值：{self.speed_dict['min']:.4f}m/s；" \
+                       f"平均值：{self.speed_dict['avg']:.4f}m/s"
+
+        report_dict["description1"] = description1
+        report_dict["description2"] = description2
+
+        plt.figure(figsize=(12, 3))
+        plt.plot(self.time_list, self.speed_list, label='Speed')
+
+        plt.xlabel('Time(s)')
+        plt.ylabel('Speed(m/s)')
+        plt.legend()
+
+        # 调整布局，消除空白边界
+        plt.tight_layout()
+
+        plt.savefig(os.path.join(self.resultPath, "Speed.png"))
+        plt.close()
+
+        print(report_dict)
+
+        return report_dict
+
+    def get_eval_data(self):
+        df = self.eval_data
+        return df

evaluate.sh

@@ -0,0 +1,17 @@
+#!/bin/bash  
+  
+# 检查是否提供了参数  
+if [ "$#" -ne 1 ]; then  
+    echo "错误：请提供参数, bag文件的路径。"  
+    exit 1  
+fi  
+  
+# 读取参数  
+BAG_FILE="$1"  
+# PNG_FILE="$2"  
+  
+# 运行rosrun命令  
+# rosrun trajectory demo_node map.bag "$BAG_FILE" "$PNG_FILE"
+# python /home/server/桌面/virtualEnv_yzh/virtual_pji/PJI_robot_evaluate_sim/pji_run.py "/home/server/桌面/virtualEnv_yzh/virtual_pji/PJI_robot_evaluate_sim/result/demo0430/$BAG_FILE" /home/server/桌面/virtualEnv_yzh/virtual_pji/PJI_robot_evaluate_sim/result/demo0430 /home/server/桌面/virtualEnv_yzh/virtual_pji/PJI_robot_evaluate_sim/result/demo0430/track_map.png
+
+python /home/server/桌面/virtualEnv_yzh/virtual_pji/PJI_robot_evaluate_sim/pji_run.py "/home/server/桌面/virtualEnv_yzh/virtual_pji/PJI_robot_evaluate_sim/result/demo0430/$BAG_FILE" /home/server/桌面/virtualEnv_yzh/virtual_pji/PJI_robot_evaluate_sim/result/demo0430 /home/server/桌面/virtualEnv_yzh/virtual_pji/PJI_robot_evaluate_sim/result/demo0430/track_map.png

log.py

@@ -0,0 +1,39 @@
+# -*- coding: utf-8 -*-
+
+import logging
+import os
+
+# 全局日志记录器变量
+logger = None
+
+
+def setup_logger(log_file_path):
+    """
+    设置全局日志记录器。
+    """
+    global logger
+
+    # 确保日志文件路径存在
+    os.makedirs(os.path.dirname(log_file_path), exist_ok=True)
+
+    # 配置日志记录器
+    logger = logging.getLogger(__name__)
+    logger.setLevel(logging.DEBUG)
+
+    # 创建一个文件处理器，将日志输出到文件
+    file_handler = logging.FileHandler(filename=log_file_path, mode='a', encoding='utf-8')
+    file_handler.setLevel(logging.DEBUG)
+
+    # 创建一个格式器，定义日志输出的格式
+    formatter = logging.Formatter("[%(asctime)s] - [%(levelname)s] [%(filename)s] [line:%(lineno)d] - [%(message)s]")
+    file_handler.setFormatter(formatter)
+
+    # 将处理器添加到记录器
+    logger.addHandler(file_handler)
+
+
+def get_logger():
+    """
+    返回已配置的全局日志记录器。
+    """
+    return logger

pji_run.py

@@ -0,0 +1,93 @@
+#!/usr/bin/env python
+# -*- coding: utf-8 -*-
+##################################################################
+#
+# Copyright (c) 2023 CICV, Inc. All Rights Reserved
+#
+##################################################################
+"""
+@Authors:           yangzihao(yangzihao@china-icv.cn)
+@Data:              2024/04/21
+@Last Modified:     2024/08/01
+@Summary:           Pji outdoor robot evaluate entrance
+"""
+
+import os
+import sys
+import time
+from single_case_evaluate import single_case_eval
+from common import get_subfolders_name
+from pjirebot_evaluate_csv import parsebag
+import log
+
+if __name__ == "__main__":
+
+    # 屏蔽warning，减少非必要问题的干扰
+    from warnings import simplefilter
+
+    # simplefilter(action="ignore", category=FutureWarning)
+    simplefilter(action="ignore")
+
+    # TODO：优化输入为dict
+    # input_dict = {
+    #     "data_path": dataPath,
+    #     "resultPath": resultPath,
+    #     "trackPath": trackPath
+    # }
+
+    # 判断参数数量是否足够
+    if len(sys.argv) >= 3:
+        dataPath = sys.argv[1]  # 数据地址路径，包含多个csv
+        resultPath = sys.argv[2]  # 结果文件保存路径，结果文件包括几个过程图片、报告等
+        trackPath = sys.argv[3]  # 轨迹图片地址路径，用于报告展示
+
+        # 用例名称，可选参数，如果传输则选用，不传输则选择文件路径名作为用例名称
+        if len(sys.argv) > 4:
+            case_name = sys.argv[4]
+        else:
+            case_name = dataPath.split('/')[-1]  # .split('.')[0]
+
+        configPath = r"./config.json"  # 配置信息，包含指标列表及评分系数
+
+        # logFileName = f'{resultPath}/log.log'
+        logFileName = os.path.join(resultPath, 'log.log')  # 日志文件名
+        log.setup_logger(logFileName)  # 日志设置
+
+        # 获取日志记录器
+        logger = log.get_logger()
+
+        # 如果数据路径不存在，报错，存入日志，退出
+        if not os.path.exists(dataPath):
+            print('Invalid dataPath!')
+            logger.error(f"[case:{case_name}] SINGLE_CASE_EVAL: Invalid dataPath!")
+            sys.exit(-1)
+        else:
+            try:
+                logger.info(f"[case:{case_name}] SINGLE_CASE_EVAL: Start evaluating:")
+                print("SINGLE_CASE_EVAL程序开始运行：")
+                print(
+                    f"   configPath: {configPath},\n   dataPath: {dataPath},\n   resultPath: {resultPath},\n   trackPath: {trackPath}")
+                # 统计运行时间
+                t1 = time.time()
+                # 将输入的bag进行解析
+                # parsebag(dataPath, resultPath)  # EgoState_pji.csv存放resultPath下
+                # 进入主要模块进行单用例评价分析
+                single_case_eval(configPath, resultPath, resultPath, trackPath, case_name)
+                t2 = time.time()
+                print(f"程序结束，执行时间：{int(t2 - t1)} s")
+                logger.info(f"[case:{case_name}] SINGLE_CASE_EVAL: End.")
+                # 正常运行完成，返回状态0
+                sys.exit(0)
+            # 异常打印，退出
+            except Exception as e:
+                print("异常，退出...")
+                print(repr(e))
+                sys.exit(-1)
+            # 增加兜底策略，输出问题报告（内容暂定为日志及问题总结）
+            # finally:
+            #     pass
+
+    # 参数量不够，报错退出
+    else:
+        print('No enough arguments!')
+        sys.exit(-1)

pji_run_test.py

@@ -0,0 +1,117 @@
+#!/usr/bin/env python
+# -*- coding: utf-8 -*-
+##################################################################
+#
+# Copyright (c) 2023 CICV, Inc. All Rights Reserved
+#
+##################################################################
+"""
+@Authors:           yangzihao(yangzihao@china-icv.cn)
+@Data:              2024/04/21
+@Last Modified:     2024/04/21
+@Summary:           Evaluation entrance
+"""
+
+import os
+import sys
+import time
+from single_case_eval import single_case_eval
+from common import get_subfolders_name
+# from pjirebot_evaluate_csv import parsebag
+import log
+
+if __name__ == "__main__":
+
+    from warnings import simplefilter
+
+    # simplefilter(action="ignore", category=FutureWarning)
+    simplefilter(action="ignore")
+
+    # TODO：优化输入为dict
+    # input_dict = {
+    #     "data_path": dataPath,
+    #     "resultPath": resultPath,
+    #     "trackPath": trackPath
+    # }
+
+    # if len(sys.argv) >= 3:
+    #     dataPath = sys.argv[1]
+    #     resultPath = sys.argv[2]
+    #     trackPath = sys.argv[3]
+    #     if len(sys.argv) > 4:
+    #         case_name = sys.argv[4]
+    #     else:
+    #         case_name = dataPath.split('/')[-1]  # .split('.')[0]
+    #
+    #     # logFileName = f'{resultPath}/log.log'
+    #     logFileName = os.path.join(resultPath, 'log.log')
+    #     log.setup_logger(logFileName)
+    #     logger = log.get_logger()
+    #
+    #     if not os.path.exists(dataPath):
+    #         print('Invalid dataPath!')
+    #         logger.error(f"[case:{case_name}] SINGLE_CASE_EVAL: Invalid dataPath!")
+    #         sys.exit(-1)
+    #     else:
+    #         try:
+    #             logger.info(f"[case:{case_name}] SINGLE_CASE_EVAL: Start evaluating:")
+    #             print("SINGLE_CASE_EVAL程序开始运行：")
+    #             configPath = r"./config.json"
+    #             print(
+    #                 f"   configPath: {configPath},\n   dataPath: {dataPath},\n   resultPath: {resultPath},\n   trackPath: {trackPath}")
+    #             t1 = time.time()
+    #             parsebag(dataPath, resultPath)  # EgoState_pji.csv存放resultPath下
+    #             single_case_eval(configPath, resultPath, resultPath, trackPath, case_name)
+    #             t2 = time.time()
+    #             print(f"程序结束，执行时间：{int(t2 - t1)} s")
+    #             logger.info(f"[case:{case_name}] SINGLE_CASE_EVAL: End.")
+    #             sys.exit(0)
+    #         except Exception as e:
+    #             print("异常，退出...")
+    #             print(repr(e))
+    #             sys.exit(-1)
+    # else:
+    #     print('No enough arguments!')
+    #     sys.exit(-1)
+
+    # dataPath = './bag/2024-04-22-08-48-30_obstacledetection_30.bag'
+    # dataPath = './task/case0613'
+    dataPath = './task/case0730'
+
+    # resultPath = './result/integration'
+    resultPath = dataPath
+    trackPath = f'{dataPath}/trajectory.png'
+
+    logFileName = f'{resultPath}/log.log'
+    log.setup_logger(logFileName)
+    logger = log.get_logger()
+
+    case_name = dataPath.split('/')[-1].split('.')[0]
+    # case_name = os.path.basename(os.path.dirname(dataPath))
+
+    if not os.path.exists(dataPath):
+        print('Invalid dataPath!')
+        logger.error(f"[case:{case_name}] SINGLE_CASE_EVAL: Invalid dataPath!")
+        sys.exit(-1)
+    else:
+        try:
+            logger.info(f"[case:{case_name}] SINGLE_CASE_EVAL: Start evaluating:")
+            print("SINGLE_CASE_EVAL程序开始运行：")
+
+            # configPath = r"./config_comfort.json"
+            # configPath = r"./config_safe.json"
+            # configPath = r"./config_accurate.json"
+            configPath = r"./config_0627.json"
+
+            print(f"   configPath: {configPath},\n   resultPath: {resultPath}")
+            t1 = time.time()
+            # parsebag(dataPath, resultPath)
+            single_case_eval(configPath, resultPath, resultPath, trackPath, case_name)
+            t2 = time.time()
+            print(f"程序结束，执行时间：{int(t2 - t1)} s")
+            logger.info(f"[case:{case_name}] SINGLE_CASE_EVAL: End.")
+            sys.exit(0)
+        except Exception as e:
+            print("异常，退出...")
+            print(repr(e))
+            sys.exit(-1)

pjirebot_evaluate_csv.py

@@ -0,0 +1,125 @@
+#!/usr/bin/env python3
+# -*- coding: utf-8 -*-
+"""
+Created on Mon Dec 25 17:35:56 2023
+
+@author: dell
+"""
+
+# coding: utf-8
+# !/usr/bin/env python2
+import os
+import rosbag
+import csv
+import math
+
+import sys
+import time
+from datetime import datetime
+import rospy
+
+
+def parsebag(input_dir, output_dir):
+    dic_EgoState = ['Time', 'simTime', 'simFrame', 'posX', 'posY', 'posZ', 'speedX', 'speedY', 'speedZ', 'accelX',
+                    'accelY', 'accelZ', 'dimX', 'dimY', 'dimZ', 'obstacle', 'traveledDist']
+    # dic_DriverCtrl=['Time','simTime','simFrame','tarspeedX','tarspeedY','tarspeedZ','tardimX','tardimY','tardimZ']
+
+    EgoState_file = open(output_dir + "/" + "EgoState_pji.csv", 'w')
+    # DriverCtrl_file = open(output_dir + "/"+"DriverCtrl_pji.csv", 'w')
+    writer_EgoState = csv.writer(EgoState_file)
+    writer_EgoState.writerow(dic_EgoState)
+    # writer_DriverCtrl = csv.writer(DriverCtrl_file)
+    # writer_DriverCtrl.writerow(dic_DriverCtrl)
+
+    frame_max = sys.maxsize
+    count = 1
+    with rosbag.Bag(input_dir, 'r') as bag:
+        odom_flag = False
+        first_message_time = None
+        Frame_imu = 1
+        Frame_cmd_vel = 1
+        obstacle_state = 0
+        cur_mileage = ''
+
+        for topic, msg, t in bag.read_messages():  # t代表时间
+
+            if first_message_time is None:
+                first_message_time = t
+                first_message_time = rospy.Time.to_sec(first_message_time)
+                first_message_time = datetime.fromtimestamp(first_message_time)
+
+            if topic == "/obstacle_detection":
+                obstacle_state = msg.data
+                # print(msg.data)
+
+            if topic == "/sys_info":
+                cur_mileage = msg.cur_mileage
+
+            if topic == "/odom":
+                odom_flag = True
+                posX = msg.pose.pose.position.x
+                posY = msg.pose.pose.position.y
+                posZ = msg.pose.pose.position.z
+                speedX = msg.twist.twist.linear.x * 3.6
+                speedY = msg.twist.twist.linear.y * 3.6
+                speedZ = msg.twist.twist.linear.z * 3.6
+                dimX = msg.twist.twist.angular.x
+                dimY = msg.twist.twist.angular.y
+                dimZ = msg.twist.twist.angular.z
+
+            if topic == "/imu":
+
+                if odom_flag:
+                    accelX = msg.linear_acceleration.x
+                    accelY = msg.linear_acceleration.y
+                    accelZ = msg.linear_acceleration.z
+                    timestamp = rospy.Time.to_sec(t)
+                    date_time_imu = datetime.fromtimestamp(timestamp)
+                    simtime_imu = (date_time_imu - first_message_time).total_seconds()
+
+                    message_EgoState = [date_time_imu, simtime_imu, Frame_imu, posX, posY, posZ, speedX, speedY, speedZ,
+                                        accelX, accelY, accelZ, dimX, dimY, dimZ, obstacle_state, cur_mileage]
+
+                    writer_EgoState.writerow(message_EgoState)
+                    Frame_imu += 1
+                else:
+                    print('6666')
+
+            '''        
+            if topic =='/cmd_vel':
+                timestamp = rospy.Time.to_sec(t)
+                date_time_cmd_vel = datetime.fromtimestamp(timestamp)
+                simtime_cmd_vel=(date_time_cmd_vel-first_message_time).total_seconds()
+                message_DriverCtrl =[date_time_cmd_vel,simtime_cmd_vel,Frame_cmd_vel,msg.linear.x*3.6,msg.linear.y*3.6,msg.linear.z*3.6,
+                                     msg.angular.x,msg.angular.y,msg.angular.z]
+                
+                                     
+                writer_DriverCtrl.writerow(message_DriverCtrl)
+                Frame_cmd_vel+=1
+            '''
+
+        EgoState_file.close()
+        # DriverCtrl_file.close()
+
+
+if __name__ == "__main__":
+    #    print("Input the path of file...."+'\n')
+
+    # input_dir = sys.argv[1]
+    input_dir = './bag/2024-04-22-08-48-30_obstacledetection_30.bag'
+    bagname = input_dir.split('/')[-1].split('.')[0]
+    # output_dir = sys.argv[2]
+    output_dir = './'
+    output_dir = os.path.join(output_dir, bagname)
+
+    if not os.path.exists(output_dir):
+        os.makedirs(output_dir)
+    parsebag(input_dir, output_dir)
+
+    '''
+    try:
+        parsebag(input_dir, output_dir)
+        print('successfully analysis '+input_dir)
+    except Exception as e:
+        print(e)
+    '''

readme.txt

@@ -0,0 +1,18 @@
+#### PJI_EVALUATE
+#### yangzihao
+
+
+调用方式：
+python pji_run.py dataPath resultPath trackPath
+
+参数解释：
+pji_run.py：程序调用入口
+dataPath：含EgoState_pji.csv的路径，文件目录
+resultPath：生成PDF文件的路径，同时也是生成图片和log日志的路径，文件目录
+trackPath：轨迹图片的路径，文件路径，含文件名
+
+
+示例：
+python pji_run.py ./task_0422/2024-04-21-15-11-49_obstacledetection_30 ./task_0422/2024-04-21-15-11-49_obstacledetection_30 ./task_0422/2024-04-21-15-11-49_obstacledetection_30/track.png
+
+

report_generate.py

@@ -0,0 +1,326 @@
+#!/usr/bin/env python
+# -*- coding: utf-8 -*-
+##################################################################
+#
+# Copyright (c) 2023 CICV, Inc. All Rights Reserved
+#
+##################################################################
+"""
+@Authors:           yangzihao(yangzihao@china-icv.cn)
+@Data:              2024/04/17
+@Last Modified:     2024/08/01
+@Summary:           This file is for PDF report generate.
+                    Refer to the CSDN: https://blog.csdn.net/cainiao_python/article/details/128928298
+"""
+
+from reportlab.pdfbase import pdfmetrics  # 注册字体
+from reportlab.pdfbase.ttfonts import TTFont  # 字体类
+from reportlab.platypus import Table, SimpleDocTemplate, Paragraph, Image  # 报告内容相关类
+from reportlab.lib.pagesizes import A4  # 页面的标志尺寸(8.5*inch, 11*inch)
+from reportlab.lib.styles import getSampleStyleSheet  # 文本样式
+from reportlab.lib import colors  # 颜色模块
+from reportlab.graphics.charts.barcharts import VerticalBarChart  # 图表类
+from reportlab.graphics.charts.legends import Legend  # 图例类
+from reportlab.graphics.shapes import Drawing  # 绘图工具
+from reportlab.lib.units import cm  # 单位：cm
+from reportlab.lib import utils
+
+import json
+import pandas as pd
+
+# 注册字体(提前准备好字体文件, 如果同一个文件需要多种字体可以注册多个)
+pdfmetrics.registerFont(TTFont('SimSun', 'SimSun.ttf'))
+
+
+class ReportPDF:
+    """
+    This class is for PDF report generate.
+    """
+
+    # 绘制标题
+    @staticmethod
+    def draw_title(title: str):
+        # 获取所有样式表
+        style = getSampleStyleSheet()
+        # 拿到标题样式
+        ct = style['Heading4']
+        # 单独设置样式相关属性
+        ct.fontName = 'SimSun'  # 字体名
+        ct.fontSize = 18  # 字体大小
+        ct.leading = 30  # 行间距
+        ct.textColor = colors.black  # 字体颜色
+        ct.alignment = 1  # 居中
+        ct.bold = True
+        # 创建标题对应的段落，并且返回
+        return Paragraph(title, ct)
+
+    # 绘制小标题
+    @staticmethod
+    def draw_little_title(title: str):
+        # 获取所有样式表
+        style = getSampleStyleSheet()
+        # 拿到标题样式
+        ct = style['Normal']
+        # 单独设置样式相关属性
+        ct.fontName = 'SimSun'  # 字体名
+        ct.fontSize = 15  # 字体大小
+        # ct.leading = 15  # 行间距
+        ct.textColor = colors.darkblue  # 字体颜色
+        # 创建标题对应的段落，并且返回
+        return Paragraph(title, ct)
+
+    # 绘制普通段落内容
+    @staticmethod
+    def draw_text(text: str):
+        # 获取所有样式表
+        style = getSampleStyleSheet()
+        # 获取普通样式
+        ct = style['Normal']
+        ct.fontName = 'SimSun'
+        ct.fontSize = 10
+        # ct.wordWrap = 'CJK'  # 设置自动换行
+        ct.alignment = 0  # 左对齐
+        # ct.firstLineIndent = 32  # 第一行开头空格
+        ct.leading = 15
+        return Paragraph(text, ct)
+
+    # 绘制普通段落内容
+    @staticmethod
+    def draw_line_feed(text: str):
+        # 获取所有样式表
+        style = getSampleStyleSheet()
+        # 获取普通样式
+        ct = style['Normal']
+        ct.fontName = 'SimSun'
+        ct.fontSize = 10
+        ct.textColor = colors.white
+        # ct.wordWrap = 'CJK'  # 设置自动换行
+        ct.alignment = 0  # 左对齐
+        # ct.firstLineIndent = 32  # 第一行开头空格
+        ct.leading = 15
+        return Paragraph(text, ct)
+
+    # 绘制表格
+    @staticmethod
+    def draw_table(*args):
+        # 列宽度
+        # col_width = 120
+        style = [
+            ('FONTNAME', (0, 0), (-1, -1), 'SimSun'),  # 字体
+            ('FONTSIZE', (0, 0), (-1, 0), 9),  # 第一行的字体大小
+            ('FONTSIZE', (0, 1), (-1, -1), 9),  # 第二行到最后一行的字体大小
+            ('BACKGROUND', (0, 0), (-1, 0), '#d5dae6'),  # 设置第一行背景颜色
+            ('ALIGN', (0, 0), (-1, -1), 'CENTER'),  # 第一行水平居中
+            ('ALIGN', (0, 1), (-1, -1), 'CENTER'),  # 第二行到最后一行左右左对齐
+            ('VALIGN', (0, 0), (-1, -1), 'MIDDLE'),  # 所有表格上下居中对齐
+            ('TEXTCOLOR', (0, 0), (-1, -1), colors.darkslategray),  # 设置表格内文字颜色
+            ('GRID', (0, 0), (-1, -1), 0.5, colors.grey),  # 设置表格框线为grey色，线宽为0.5
+            # ('SPAN', (0, 1), (0, 2)),  # 合并第一列二三行
+            # ('SPAN', (0, 3), (0, 4)),  # 合并第一列三四行
+            # ('SPAN', (0, 5), (0, 6)),  # 合并第一列五六行
+            # ('SPAN', (0, 7), (0, 8)),  # 合并第一列五六行
+        ]
+        # table = Table(args, colWidths=col_width, style=style)
+        table = Table(args, style=style)
+        return table
+
+    # 创建图表
+    @staticmethod
+    def draw_bar(bar_data: list, ax: list, items: list):
+        drawing = Drawing(500, 250)
+        bc = VerticalBarChart()
+        bc.x = 45  # 整个图表的x坐标
+        bc.y = 45  # 整个图表的y坐标
+        bc.height = 200  # 图表的高度
+        bc.width = 350  # 图表的宽度
+        bc.data = bar_data
+        bc.strokeColor = colors.black  # 顶部和右边轴线的颜色
+        bc.valueAxis.valueMin = 5000  # 设置y坐标的最小值
+        bc.valueAxis.valueMax = 26000  # 设置y坐标的最大值
+        bc.valueAxis.valueStep = 2000  # 设置y坐标的步长
+        bc.categoryAxis.labels.dx = 2
+        bc.categoryAxis.labels.dy = -8
+        bc.categoryAxis.labels.angle = 20
+        bc.categoryAxis.categoryNames = ax
+
+        # 图示
+        leg = Legend()
+        leg.fontName = 'SimSun'
+        leg.alignment = 'right'
+        leg.boxAnchor = 'ne'
+        leg.x = 475  # 图例的x坐标
+        leg.y = 240
+        leg.dxTextSpace = 10
+        leg.columnMaximum = 3
+        leg.colorNamePairs = items
+        drawing.add(leg)
+        drawing.add(bc)
+        return drawing
+
+    # 绘制图片
+    @staticmethod
+    def draw_img(path):
+        img = Image(path)  # 读取指定路径下的图片
+        img.drawWidth = 0.8 * cm  # 设置图片的宽度
+        img.drawHeight = 0.8 * cm  # 设置图片的高度
+        img.hAlign = 0
+        return img
+
+
+def get_trajectory_image(path, width=400):
+    img = utils.ImageReader(path)
+    iw, ih = img.getSize()
+    aspect = ih / float(iw)
+    # 按照图片限宽锁定纵横比之后计算高度
+    height = (width * aspect)
+    # 如果高度过高，报告可能超出此页，则限高300，宽度按比例调整
+    HEIGHT_LIMIT = 300
+    if height > HEIGHT_LIMIT:
+        height = HEIGHT_LIMIT
+        width = height / aspect
+
+    return Image(path, width=width, height=height)
+
+
+def report_generate(reportDict, reportPdf, trackPath):
+    # with open(f'{reportJson}', 'r', encoding='utf-8') as f:
+    #     data = json.load(f)
+
+    data = reportDict  # dict
+    # 创建PDF文档
+    styles = getSampleStyleSheet()
+
+    # 提取数据
+    name = data["name"]
+    # algorithm_score = data["algorithmComprehensiveScore"]
+    # algorithm_level = data["algorithmLevel"]
+    test_mileage = data["testMileage"]
+    test_duration = data["testDuration"]
+    algorithm_result_description = data["algorithmResultDescription"]
+
+    # dimension description
+    safe_description = data["details"]["safe"]["description"]
+    comfort_description = data["details"]["comfort"]["description"]
+    accurate_description = data["details"]["accurate"]["description"]
+
+    # weight
+    collisionCount_weight = data["details"]["safe"]["indexes"]["collisionCount"]["weight"]
+    collisionRisk_weight = data["details"]["safe"]["indexes"]["collisionRisk"]["weight"]
+    collisionSeverity_weight = data["details"]["safe"]["indexes"]["collisionSeverity"]["weight"]
+    overSpeed_weight = data["details"]["safe"]["indexes"]["overSpeed"]["weight"]
+
+    comfortLat_weight = data["details"]["comfort"]["indexes"]["comfortLat"]["weight"]
+    comfortLon_weight = data["details"]["comfort"]["indexes"]["comfortLon"]["weight"]
+    comfortSpeed_weight = data["details"]["comfort"]["indexes"]["comfortSpeed"]["weight"]
+
+    positionError_weight = data["details"]["accurate"]["indexes"]["positionError"]["weight"]
+    executeAccurateError_weight = data["details"]["accurate"]["indexes"]["executeAccurateError"]["weight"]
+
+    # metric description
+    collisionCount_description = data["details"]["safe"]["indexes"]["collisionCount"]["description"]
+    collisionRisk_description = data["details"]["safe"]["indexes"]["collisionRisk"]["description"]
+    collisionSeverity_description = data["details"]["safe"]["indexes"]["collisionSeverity"]["description"]
+    overSpeed_description = data["details"]["safe"]["indexes"]["overSpeed"]["description"]
+
+    comfortLat_description = data["details"]["comfort"]["indexes"]["comfortLat"]["description"]
+    comfortLon_description = data["details"]["comfort"]["indexes"]["comfortLon"]["description"]
+    comfortSpeed_description = data["details"]["comfort"]["indexes"]["comfortSpeed"]["description"]
+
+    positionError_description = data["details"]["accurate"]["indexes"]["positionError"]["description"]
+    executeAccurateError_description = data["details"]["accurate"]["indexes"]["executeAccurateError"]["description"]
+
+    # score level instruction
+    level_instruction = "优秀（90≤分值≤100），良好（80≤分值<90），一般（60≤分值<80），较差（0≤分值<60）。"
+
+    # 创建表格数据
+    table_data = [
+        ("评价维度", "评价类型", "评价指标", "权重", "指标描述"),
+        ("安全性", "碰撞安全性", "碰撞次数", collisionCount_weight, collisionCount_description),
+        ("安全性", "碰撞安全性", "碰撞风险概率", collisionRisk_weight, collisionRisk_description),
+        ("安全性", "碰撞安全性", "碰撞严重程度", collisionSeverity_weight, collisionSeverity_description),
+        ("安全性", "超速安全性", "超速比例", overSpeed_weight, overSpeed_description),
+        ("平顺性", "纵向平顺性", "线加速度", comfortLat_weight, comfortLat_description),
+        ("平顺性", "横向平顺性", "角加速度", comfortLon_weight, comfortLon_description),
+        ("平顺性", "速度控制指令", "速度控制指令跳变次数", comfortSpeed_weight, comfortSpeed_description),
+        ("准确性", "执行准确性", "位置偏移误差", positionError_weight, positionError_description),
+        ("准确性", "执行准确性", "任务执行状态错误次数", executeAccurateError_weight, executeAccurateError_description)
+    ]
+
+    # 创建内容对应的空列表
+    content = list()
+    content.append(ReportPDF.draw_img('Pji.png'))
+    content.append(ReportPDF.draw_title('朴津AMR算法评价报告'))
+
+    # content.append(ReportPDF.draw_line_feed("换行"))
+    # content.append(ReportPDF.draw_line_feed("换行"))
+
+    ### 报告内容填充
+    # 综述
+    base_info = f"数据包: {name}, 行驶时长: {test_duration}, 行驶里程: {test_mileage}"
+    content.append(ReportPDF.draw_text(base_info))
+    content.append(ReportPDF.draw_text(algorithm_result_description))
+    content.append(ReportPDF.draw_text(safe_description))
+    content.append(ReportPDF.draw_text(comfort_description))
+    content.append(ReportPDF.draw_text(accurate_description))
+
+    # 表格
+    content.append(ReportPDF.draw_line_feed("换行"))
+    content.append(ReportPDF.draw_table(*table_data))
+    content.append(ReportPDF.draw_text(level_instruction))
+    content.append(ReportPDF.draw_line_feed("换行"))
+
+    # 图片
+    # 添加图片到PDF文档
+    for key, value in data["graphPath"].items():
+        # 特判轨迹图片情况
+        if "track.png" in value:  # 评价工具生成轨迹图片
+            trackPath = value
+            continue
+
+        if "trajectory.png" in value:  # ros生成带地图轨迹图片
+            continue
+
+        content.append(Paragraph(key, styles['Heading5']))
+        content.append(Image(value, width=480, height=120))
+        content.append(ReportPDF.draw_line_feed("换行"))
+
+    # content.append(ReportPDF.draw_line_feed("换行"))
+    # content.append(ReportPDF.draw_line_feed("换行"))
+    # content.append(ReportPDF.draw_line_feed("换行"))
+
+    # 添加轨迹图片
+    # content.append(Image(trackPath, width=300, height=200))
+    content.append(Paragraph("trajectory", styles['Heading5']))
+    content.append(get_trajectory_image(trackPath))
+    content.append(ReportPDF.draw_line_feed("换行"))
+
+    if "track.png" in trackPath:
+        content.append(
+            Paragraph("The trajectory starts with the yellow dot, then the color deepened.", styles['Normal']))
+    else:
+        content.append(
+            Paragraph("The trajectory starts with the green dot and ends with the red dot.", styles['Normal']))
+
+    # content.append(ReportPDF.draw_line_feed("换行"))
+    # content.append(ReportPDF.draw_line_feed("换行"))
+
+    # 创建PDF文档
+
+    # pdf_file = "./report123.pdf"
+    pdf_file = reportPdf
+    doc = SimpleDocTemplate(pdf_file, pagesize=A4)
+
+    doc.build(content)
+    print("PDF报告已生成！")
+
+# if __name__ == '__main__':
+#     reportJson = './report.json'
+#     reportJson = '/home/server/桌面/virtualEnv_yzh/virtual_pji/PJI_outdoor_robot_evaluate_real/task/case0613/report.json'
+#     reportPdf = "./report0730.pdf"
+#     trackPath = './track.png'
+#
+#     with open(reportJson, 'r', encoding='utf-8') as f:
+#         reportJson = json.load(f)
+#
+#     report_generate(reportJson, reportPdf, trackPath)
+#     print('over')

report_template.json

@@ -0,0 +1,96 @@
+{
+  "name": "name",
+  "testMileage": "testMileage",
+  "testDuration": "testDuration",
+  "algorithmComprehensiveScore": "algorithmComprehensiveScore",
+  "algorithmLevel": "algorithmLevel",
+  "algorithmResultDescription": "algorithmResultDescription",
+  "details": {
+    "safe": {
+      "weight": "weight",
+      "score": "score",
+      "level": "level",
+      "description": "description",
+      "indexes": {
+        "safeCollision": {
+          "collisionCount": {
+            "weight": "weight",
+            "score": "score",
+            "description": "description"
+          },
+          "collisionRisk": {
+            "weight": "weight",
+            "score": "score",
+            "description": "description"
+          },
+          "collisionSeverity": {
+            "weight": "weight",
+            "score": "score",
+            "description": "description"
+          }
+        },
+        "safeSpeed": {
+          "overSpeed": {
+            "weight": "weight",
+            "score": "score",
+            "description": "description"
+          }
+        }
+      }
+    },
+    "comfort": {
+      "weight": "weight",
+      "score": "score",
+      "level": "level",
+      "description": "description",
+      "indexes": {
+        "comfortLat": {
+          "linearAccelerate": {
+            "weight": "weight",
+            "score": "score",
+            "description": "description"
+          }
+        },
+        "comfortLon": {
+          "angularAccelerate": {
+            "weight": "weight",
+            "score": "score",
+            "description": "description"
+          }
+        },
+        "comfortSpeed": {
+          "speedInstructionJump": {
+            "weight": "weight",
+            "score": "score",
+            "description": "description"
+          }
+        }
+      }
+    },
+    "accurate": {
+      "weight": "weight",
+      "score": "score",
+      "level": "level",
+      "description": "description",
+      "indexes": {
+        "accurateExecute": {
+          "positionError": {
+            "weight": "weight",
+            "score": "score",
+            "description": "description"
+          },
+          "executeAccurateError": {
+            "weight": "weight",
+            "score": "score",
+            "description": "description"
+          }
+        }
+      }
+    }
+  },
+  "graphPath": {
+    "speed": "/home/server/桌面/virtualEnv_yzh/virtual_pji/PJI_robot_evaluate_sim/result/test_0425/Speed.png",
+    "linearAccelerate": "/home/server/桌面/virtualEnv_yzh/virtual_pji/PJI_robot_evaluate_sim/result/test_0425/LinearAccelerate.png",
+    "angularAccelerate": "/home/server/桌面/virtualEnv_yzh/virtual_pji/PJI_robot_evaluate_sim/result/test_0425/AngularAccelerate.png"
+  }
+}

run.spec

@@ -0,0 +1,44 @@
+# -*- mode: python ; coding: utf-8 -*-
+
+
+a = Analysis(
+    ['pji_run.py', 'comfort.py', 'common.py', 'config_parser.py', 'data_info.py', 'data_process.py', 'efficient.py',
+    'log.py', 'pjirebot_evaluate_csv.py', 'score_weight.py', 'single_case_eval.py', 'single_case_evaluate.py'],
+    pathex=[],
+    binaries=[],
+    datas=[],
+    hiddenimports=['scipy', 'scipy.special._cdflib'],
+    hookspath=[],
+    hooksconfig={},
+    runtime_hooks=[],
+    excludes=[],
+    noarchive=False,
+)
+pyz = PYZ(a.pure)
+
+exe = EXE(
+    pyz,
+    a.scripts,
+    [],
+    exclude_binaries=True,
+    name='pji_single',
+    debug=False,
+    bootloader_ignore_signals=False,
+    strip=False,
+    upx=True,
+    console=True,
+    disable_windowed_traceback=False,
+    argv_emulation=False,
+    target_arch=None,
+    codesign_identity=None,
+    entitlements_file=None,
+)
+coll = COLLECT(
+    exe,
+    a.binaries,
+    a.datas,
+    strip=False,
+    upx=True,
+    upx_exclude=[],
+    name='run',
+)

safe.py

@@ -0,0 +1,757 @@
+#!/usr/bin/env python
+# -*- coding: utf-8 -*-
+##################################################################
+#
+# Copyright (c) 2023 CICV, Inc. All Rights Reserved
+#
+##################################################################
+"""
+@Authors:           xieguijin(xieguijin@china-icv.cn), yangzihao(yangzihao@china-icv.cn)
+@Data:              2023/08/03
+@Last Modified:     2023/08/03
+@Summary:           Functionality metrics
+"""
+
+import os
+import math
+import numpy as np
+import pandas as pd
+
+from collections import defaultdict
+import scipy.integrate as spi
+
+from config import DANGEROUS_DIST, LONGITUDINAL_DISTANCE_FRONT, LONGITUDINAL_DISTANCE_REAR, LATERAL_DISTANCE_ABS, \
+    OVERSPEED_THRESHOLD
+from common import score_grade, string_concatenate, replace_key_with_value, score_over_100
+from score_weight import cal_score_with_priority, cal_weight_from_80
+
+
+class Safe(object):
+    """
+    Class for achieving safe metrics for autonomous driving.
+
+    Attributes:
+        df: Vehicle driving data, stored in dataframe format.
+        df_drivectrl: Vehcile driving control data, including brakepedal, throttle pedal and steerignwheel.
+
+    Methods:
+        _safe_param_cal_new: Calculate parameters for evaluateion.
+        _cal_v_ego_projection: Calculate ego car velocity project over distance.
+        _cal_v_projection: Calculate relative velocity project over distance.
+        _cal_a_projection: Calculate relative acceleration project over distance.
+        _calculate_derivative: Calculate derivative.
+        _cal_relative_angular_v: Calculate relative angular velocity.
+        _death_pr: Calculate death probability.
+        _cal_collisionRisk_level: Calculate collisionRisk level.
+        dist: Calculate distance of two cars.
+
+    """
+
+    def __init__(self, data_processed, scoreModel, resultPath):
+        # self.eval_data = pd.DataFrame()
+        # self.data_processed = data_processed
+        self.scoreModel = scoreModel
+        self.resultPath = resultPath
+
+        self.df = data_processed.object_df.copy()
+        self.ego_df = data_processed.ego_df
+        # print("self.ego_df is", self.ego_df)
+        self.obj_id_list = data_processed.obj_id_list
+        # print("self.obj_id_list is", self.obj_id_list)
+        #
+        # # config infos for calculating score
+        self.config = data_processed.config
+        # print("self.config is", self.config)
+        safe_config = self.config.config['safe']
+        self.safe_config = safe_config
+
+        # # common data
+        self.bulitin_metric_list = self.config.builtinMetricList
+        #
+        # # dimension data
+        self.weight_custom = safe_config['weightCustom']
+        self.metric_list = safe_config['metric']
+        self.type_list = safe_config['type']
+        self.type_name_dict = safe_config['typeName']
+        self.name_dict = safe_config['name']
+        self.unit_dict = safe_config['unit']
+
+        # # custom metric data
+        # # self.customMetricParam = safe_config['customMetricParam']
+        # # self.custom_metric_list = list(self.customMetricParam.keys())
+        # # self.custom_data = {}
+        # # self.custom_param_dict = {}
+        #
+        # # score data
+        self.weight = safe_config['weightDimension']
+
+        self.weight_type_dict = safe_config['typeWeight']
+        self.weight_type_list = safe_config['typeWeightList']
+
+        self.weight_dict = safe_config['weight']
+        self.weight_list = safe_config['weightList']
+
+        self.priority_dict = safe_config['priority']
+        self.priority_list = safe_config['priorityList']
+
+        self.kind1_dict = safe_config['kind']
+        # self.kind1_dict = self.kind_dict[0]
+        self.optimal1_dict = safe_config['optimal']
+        # self.optimal1_dict = self.optimal_dict[0]
+        self.multiple_dict = safe_config['multiple']
+        self.kind_list = safe_config['kindList']
+        self.optimal_list = safe_config['optimalList']
+        self.multiple_list = safe_config['multipleList']
+
+        self.metric_dict = safe_config['typeMetricDict']
+        # # self.time_metric_list = self.metric_dict['safeTime']
+        # # self.distance_metric_list = self.metric_dict['safeDistance']
+        #
+        # # lists of drving control info
+        # self.time_list = data_processed.driver_ctrl_data['time_list']
+        self.frame_list = self.df['simFrame'].values.tolist()
+
+        self.collision_dist_list = []
+        self.collisionRisk_list = []
+        self.collisionSeverity_list = []
+        self.overSpeed_list = []
+
+        self.collision_risk_dict = dict()
+        self.collision_severity_dict = dict()
+        self.over_speed_dict = dict()
+
+        self.collisionCount = 0
+        self.collisionRisk = 0
+        self.collisionSeverity = 0
+        self.overSpeed = 0
+
+        self.empty_flag = True
+
+        if len(self.obj_id_list) > 1:
+            # self.most_dangerous = {}
+            # self.pass_percent = {}
+            self._safe_metric_cal()
+            self._overspeed_cal()
+
+        # # no car following scene
+        # if len(self.obj_id_list) > 1:
+        #     self.unsafe_df = pd.DataFrame(columns=['start_time', 'end_time', 'start_frame', 'end_frame', 'type'])
+        #     self.unsafe_time_df = pd.DataFrame(columns=['start_time', 'end_time', 'start_frame', 'end_frame', 'type'])
+        #     self.unsafe_dist_df = pd.DataFrame(columns=['start_time', 'end_time', 'start_frame', 'end_frame', 'type'])
+        #     # self.unsafe_acce_df = pd.DataFrame(columns=['start_time', 'end_time', 'start_frame', 'end_frame', 'type'])
+        #     self.unsafe_acce_drac_df = pd.DataFrame(
+        #         columns=['start_time', 'end_time', 'start_frame', 'end_frame', 'type'])
+        #     self.unsafe_acce_xtn_df = pd.DataFrame(
+        #         columns=['start_time', 'end_time', 'start_frame', 'end_frame', 'type'])
+        #     self.unsafe_prob_df = pd.DataFrame(columns=['start_time', 'end_time', 'start_frame', 'end_frame', 'type'])
+        #     self.most_dangerous = {}
+        #     self.pass_percent = {}
+        #     self._safe_param_cal_new()
+
+    def _overspeed_cal(self):
+        """
+
+        :return:
+        """
+        self.overSpeed_list = self.ego_df[self.ego_df['v'] > OVERSPEED_THRESHOLD]['v'].values.tolist()
+
+        if self.overSpeed_list:
+            max_speed = max(self.overSpeed_list)
+            overspeed = (max_speed - OVERSPEED_THRESHOLD) / OVERSPEED_THRESHOLD * 100
+            self.overSpeed = round(overspeed, 2)
+        else:
+            self.overSpeed = 0
+
+    def _safe_metric_cal(self):
+        """
+
+        """
+        self.collisionRisk_list = []
+        self.collision_dist_list = []
+        self.collisionSeverity_list = []
+
+        Tc = 0.3
+        rho = 0.3  # 驾驶员制动反应时间
+        ego_accel_max = 6  # 自车油门最大加速度
+        obj_decel_max = 8  # 前车刹车最大减速度
+        ego_decel_min = 1  # 自车刹车最小减速度 ug
+        ego_decel_lon_max = 8
+        ego_decel_lat_max = 1
+
+        # 构建双层字典数据结构
+        obj_dict = defaultdict(dict)
+        obj_data_dict = self.df.to_dict('records')
+
+        for item in obj_data_dict:
+            obj_dict[item['simFrame']][item['playerId']] = item
+            # print("obj_dict is", obj_dict)
+
+        df_list = []
+        EGO_PLAYER_ID = 1
+
+        self.empty_flag = True
+        print("obj_dict[1] is", self.frame_list[-1])
+        for frame_num in self.frame_list:
+            # ego_data = obj_dict[frame_num][EGO_PLAYER_ID]
+            ego_data = self.ego_df.iloc[frame_num]
+            v1 = ego_data['v']  # km/h to m/s
+            x1 = ego_data['posX']
+            # print("x1 is", x1)
+            y1 = ego_data['posY']
+            h1 = ego_data['yaw']
+            # len1 = 2.3  # 随手录入的数值
+            # width1 = ego_data['dimY']  #
+            # o_x1 = ego_data['offX']
+            v_x1 = ego_data['lon_v']  # km/h to m/s
+            # v_y1 = ego_data['speedY'] / 3.6  # km/h to m/s
+            v_y1 = 0  # 随手录入的数值
+            a_x1 = ego_data['speedH']
+            a_y1 = 0  # 随手录入的数值
+            # a_y1 = ego_data['accelY']
+            # a1 = ego_data['accel']
+            # print("calculate is beginning...")
+            # print("self.obj_id_list is", self.obj_id_list)
+            for playerId in self.obj_id_list:
+                dist_list = []
+                # print("playerId is", playerId)
+
+                if playerId == EGO_PLAYER_ID:
+                    continue
+
+                try:
+                    obj_data = obj_dict[frame_num][playerId]
+                except KeyError:
+                    continue
+
+                x2 = obj_data['posX']
+                y2 = obj_data['posY']
+                # print("x2 is", y2)
+                dist = self.dist(x1, y1, x2, y2)
+
+                # DANGEROUS_DIST = 0.10  # 与障碍物距离小于此值即为危险
+                if dist <= DANGEROUS_DIST:
+                    dist_list.append(0)
+                else:
+                    dist_list.append(1)
+                obj_data['dist'] = dist
+                # print("obj_data['dist'] is", obj_data['dist'])
+
+                v_x2 = obj_data['lon_v']  # km/h to m/s
+                v_y2 = obj_data['lat_v']  # km/h to m/s
+                v2 = math.sqrt(v_x2 ** 2 + v_y2 ** 2)
+                # v2 = obj_data['v'] / 3.6  # km/h to m/s
+                # h2 = obj_data['posH']
+                # len2 = obj_data['dimX']
+                # width2 = obj_data['dimY']
+                # o_x2 = obj_data['offX']
+                # a_x2 = obj_data['accelX']
+                # a_y2 = obj_data['accelY']
+                a_x2 = obj_data['lon_acc']
+                a_y2 = obj_data['lat_acc']
+                # a2 = obj_data['accel']
+
+                dx, dy = x2 - x1, y2 - y1
+
+                # 定义矢量A和x轴正向向量x
+                A = np.array([dx, dy])
+                # print("A is", A)
+                x = np.array([1, 0])
+
+                # 计算点积和向量长度
+                dot_product = np.dot(A, x)
+                vector_length_A = np.linalg.norm(A)
+                vector_length_x = np.linalg.norm(x)
+
+                # 计算夹角的余弦值
+                cos_theta = dot_product / (vector_length_A * vector_length_x)
+
+                # 将余弦值转换为角度值（弧度制）
+                beta = np.arccos(cos_theta)  # 如何通过theta正负确定方向
+
+                lon_d = dist * math.cos(beta - h1)
+                lat_d = abs(dist * math.sin(beta - h1))  # 需要增加左正右负的判断，但beta取值为[0,pi)
+
+                obj_dict[frame_num][playerId]['lon_d'] = lon_d
+                obj_dict[frame_num][playerId]['lat_d'] = lat_d
+
+                if lon_d > 5 or lon_d < -2 or lat_d > 1:
+                    continue
+
+                self.empty_flag = False
+
+                vx, vy = v_x1 - v_x2, v_y1 - v_y2
+                ax, ay = a_x2 - a_x1, a_y2 - a_y1
+
+                v_ego_p = self._cal_v_ego_projection(dx, dy, v_x1, v_y1)
+                v_obj_p = self._cal_v_ego_projection(dx, dy, v_x2, v_y2)
+                vrel_projection_in_dist = self._cal_v_projection(dx, dy, vx, vy)
+                arel_projection_in_dist = self._cal_a_projection(dx, dy, vx, vy, ax, ay, x1, y1, x2, y2, v_x1, v_y1,
+                                                                 v_x2, v_y2)
+
+                obj_dict[frame_num][playerId]['vrel_projection_in_dist'] = vrel_projection_in_dist
+                obj_dict[frame_num][playerId]['arel_projection_in_dist'] = arel_projection_in_dist
+                obj_dict[frame_num][playerId]['v_ego_projection_in_dist'] = v_ego_p
+                obj_dict[frame_num][playerId]['v_obj_projection_in_dist'] = v_obj_p
+
+                # obj_type = obj_data['type']
+
+                TTC = self._cal_TTC(dist, vrel_projection_in_dist)
+                # MTTC = self._cal_MTTC(TTC, vrel_projection_in_dist, arel_projection_in_dist)
+                # THW = self._cal_THW(dist, v_ego_p)
+
+                # 单车道时可用
+                # LonSD = self._cal_longitudinal_safe_dist(v_ego_p, v_obj_p, rho, ego_accel_max, ego_decel_min,
+                #                                          obj_decel_max)
+
+                # lat_dist = 0.5
+                # v_right = v1
+                # v_left = v2
+                # a_right_lat_brake_min = 1
+                # a_left_lat_brake_min = 1
+                # a_lat_max = 5
+
+                # LatSD = self._cal_lateral_safe_dist(lat_dist, v_right, v_left, rho, a_right_lat_brake_min,
+                #                                     a_left_lat_brake_min,
+                #                                     a_lat_max)
+
+                # DRAC = self._cal_DRAC(dist, vrel_projection_in_dist, len1, len2, width1, width2, o_x1, o_x2)
+
+                lon_a1 = a_x1 * math.cos(h1) + a_y1 * math.sin(h1)
+                lon_a2 = a_x2 * math.cos(h1) + a_y2 * math.sin(h1)
+                # lon_a = abs(lon_a1 - lon_a2)
+                # lon_d = dist * abs(math.cos(beta - h1))
+                # lon_v = v_x1 * math.cos(h1) + v_y1 * math.sin(h1)
+                # BTN = self._cal_BTN_new(lon_a1, lon_a, lon_d, lon_v, ego_decel_lon_max)
+
+                lat_a1 = a_x1 * math.sin(h1) * -1 + a_y1 * math.cos(h1)
+                lat_a2 = a_x2 * math.sin(h1) * -1 + a_y2 * math.cos(h1)
+                lat_a = abs(lat_a1 - lat_a2)
+                lat_d = dist * abs(math.sin(beta - h1))
+                lat_v = v_x1 * math.sin(h1) * -1 + v_y1 * math.cos(h1)
+                # STN = self._cal_STN_new(TTC, lat_a1, lat_a, lat_d, lat_v, ego_decel_lat_max, width1, width2)
+
+                obj_dict[frame_num][playerId]['lat_v_rel'] = v_x1 - v_x2
+                obj_dict[frame_num][playerId]['lon_v_rel'] = v_y1 - v_y2
+
+                # BTN = self.cal_BTN(a_y1, ay, dy, vy, max_ay)
+                # STN = self.cal_STN(TTC, a_x1, ax, dx, vx, max_ax, len1, len2)
+
+                TTC = None if (not TTC or TTC < 0) else TTC
+                # MTTC = None if (not MTTC or MTTC < 0) else MTTC
+                # THW = None if (not THW or THW < 0) else THW
+                #
+                # DRAC = 10 if DRAC >= 10 else DRAC
+                # print("calculate is beginning...")
+                if not TTC or TTC > 4000:  # threshold = 4258.41
+                    collisionSeverity = 0
+                    pr_death = 0
+                    collisionRisk = 0
+                else:
+                    result, error = spi.quad(self._normal_distribution, 0, TTC - Tc)
+                    collisionSeverity = 1 - result
+                    # print("collisionSeverity is", collisionSeverity)
+                    # pr_death = self._death_pr(obj_type, vrel_projection_in_dist)
+                    pr_death = self._death_pr(vrel_projection_in_dist)
+                    collisionRisk = 0.4 * pr_death + 0.6 * collisionSeverity
+                # print("collinsionRisk is", collisionRisk)
+                self.collisionRisk_list.append(collisionRisk)
+                self.collisionSeverity_list.append(collisionSeverity)
+                self.collision_dist_list.append(max(dist_list))
+        # print("obj_data['dist'] is", collision_dist_list)
+
+        prev_value = self.collision_dist_list[0]  # 初始化前一个元素为列表的第一个元素
+
+        # 遍历列表（从第二个元素开始）
+        for i in range(1, len(self.collision_dist_list)):
+            current_value = self.collision_dist_list[i]  # 当前元素
+
+            # 如果当前元素是0且与前一个元素不同，或者这是第一个元素且为0
+            if (current_value == 0 and prev_value != 0) or (i == 0 and current_value == 0):
+                self.collisionCount += 1  # 增加0的段的计数
+
+        self.collisionRisk = max(self.collisionRisk_list)
+        self.collisionSeverity = max(self.collisionSeverity_list)
+
+        print("collision count is", self.collisionCount)
+        print("collisionRisk is", self.collisionRisk)
+        print("collisionSeverity is", self.collisionSeverity)
+
+    def _cal_v_ego_projection(self, dx, dy, v_x1, v_y1):
+        # 计算 AB 连线的向量 AB
+        # dx = x2 - x1
+        # dy = y2 - y1
+
+        # 计算 AB 连线的模长 |AB|
+        AB_mod = math.sqrt(dx ** 2 + dy ** 2)
+
+        # 计算 AB 连线的单位向量 U_AB
+        U_ABx = dx / AB_mod
+        U_ABy = dy / AB_mod
+
+        # 计算 A 在 AB 连线上的速度 V1_on_AB
+        V1_on_AB = v_x1 * U_ABx + v_y1 * U_ABy
+
+        return V1_on_AB
+
+    def _cal_v_projection(self, dx, dy, vx, vy):
+        # 计算 AB 连线的向量 AB
+        # dx = x2 - x1
+        # dy = y2 - y1
+
+        # 计算 AB 连线的模长 |AB|
+        AB_mod = math.sqrt(dx ** 2 + dy ** 2)
+
+        # 计算 AB 连线的单位向量 U_AB
+        U_ABx = dx / AB_mod
+        U_ABy = dy / AB_mod
+
+        # 计算 A 相对于 B 的速度 V_relative
+        # vx = vx1 - vx2
+        # vy = vy1 - vy2
+
+        # 计算 A 相对于 B 在 AB 连线上的速度 V_on_AB
+        V_on_AB = vx * U_ABx + vy * U_ABy
+
+        return V_on_AB
+
+    def _cal_a_projection(self, dx, dy, vx, vy, ax, ay, x1, y1, x2, y2, v_x1, v_y1, v_x2, v_y2):
+        # 计算 AB 连线的向量 AB
+        # dx = x2 - x1
+        # dy = y2 - y1
+
+        # 计算 θ
+        V_mod = math.sqrt(vx ** 2 + vy ** 2)
+        AB_mod = math.sqrt(dx ** 2 + dy ** 2)
+        if V_mod == 0 or AB_mod == 0:
+            return 0
+
+        cos_theta = (vx * dx + vy * dy) / (V_mod * AB_mod)
+        theta = math.acos(cos_theta)
+
+        # 计算 AB 连线的模长 |AB|
+        AB_mod = math.sqrt(dx ** 2 + dy ** 2)
+
+        # 计算 AB 连线的单位向量 U_AB
+        U_ABx = dx / AB_mod
+        U_ABy = dy / AB_mod
+
+        # 计算 A 相对于 B 的加速度 a_relative
+        # ax = ax1 - ax2
+        # ay = ay1 - ay2
+
+        # 计算 A 相对于 B 在 AB 连线上的加速度 a_on_AB
+        a_on_AB = ax * U_ABx + ay * U_ABy
+
+        VA = np.array([v_x1, v_y1])
+        VB = np.array([v_x2, v_y2])
+        D_A = np.array([x1, y1])
+        D_B = np.array([x2, y2])
+        V_r = VA - VB
+        V = np.linalg.norm(V_r)
+        w = self._cal_relative_angular_v(theta, D_A, D_B, VA, VB)
+        a_on_AB_back = self._calculate_derivative(a_on_AB, w, V, theta)
+        return a_on_AB_back
+
+    # 计算相对加速度
+    def _calculate_derivative(self, a, w, V, theta):
+        # 计算(V×cos(θ))'的值
+        # derivative = a * math.cos(theta) - w * V * math.sin(theta)theta
+        derivative = a - w * V * math.sin(theta)
+        return derivative
+
+    def _cal_relative_angular_v(self, theta, A, B, VA, VB):
+        dx = A[0] - B[0]
+        dy = A[1] - B[1]
+        dvx = VA[0] - VB[0]
+        dvy = VA[1] - VB[1]
+        # (dx * dvy - dy * dvx)
+        angular_velocity = math.sqrt(dvx ** 2 + dvy ** 2) * math.sin(theta) / math.sqrt(dx ** 2 + dy ** 2)
+        return angular_velocity
+
+    # def _death_pr(self, obj_type, v_relative):
+    def _death_pr(self, v_relative):
+        # if obj_type == 5:
+        #     p_death = 1 / (1 + np.exp(7.723 - 0.15 * v_relative))
+        # else:
+        p_death = 1 / (1 + np.exp(8.192 - 0.12 * v_relative))
+        return p_death
+
+    # def _cal_collisionRisk_level(self, obj_type, v_relative, collisionSeverity):
+    def _cal_collisionRisk_level(self, v_relative, collisionSeverity):
+        # if obj_type == 5:
+        #     p_death = 1 / (1 + np.exp(7.723 - 0.15 * v_relative))
+        # else:
+        p_death = 1 / (1 + np.exp(8.192 - 0.12 * v_relative))
+        collisionRisk = 0.4 * p_death + 0.6 * collisionSeverity
+        return collisionRisk
+
+    # 求两车之间当前距离
+    def dist(self, x1, y1, x2, y2):
+        dist = np.sqrt((x1 - x2) ** 2 + (y1 - y2) ** 2)
+        return dist
+
+    def _cal_count_(self, dist):
+        count = 0
+
+    # TTC (time to collision)
+    def _cal_TTC(self, dist, vrel_projection_in_dist):
+        if vrel_projection_in_dist == 0:
+            return math.inf
+        TTC = dist / vrel_projection_in_dist
+        return TTC
+
+    def _normal_distribution(self, x):
+        mean = 1.32
+        std_dev = 0.26
+        return (1 / (math.sqrt(std_dev * 2 * math.pi))) * math.exp(-0.5 * (x - mean) ** 2 / std_dev)
+
+    def _cal_max_min_avg(self, num_list):
+        maxx = round(max(num_list), 4) if num_list else "-"
+        minn = round(min(num_list), 4) if num_list else "-"
+        avg = round(sum(num_list) / len(num_list), 4) if num_list else "-"
+
+        result = {
+            "max": maxx,
+            "min": minn,
+            "avg": avg
+        }
+
+        return result
+
+    def _safe_statistic(self):
+        """
+
+        """
+        self.collision_risk_dict = self._cal_max_min_avg(self.collisionRisk_list)
+        self.collision_severity_dict = self._cal_max_min_avg(self.collisionSeverity_list)
+        self.over_speed_dict = self._cal_max_min_avg(self.overSpeed_list)
+
+        arr_safe = [[self.collisionCount, self.collisionRisk, self.collisionSeverity, self.overSpeed]]
+        return arr_safe
+
+    def safe_score_new(self):
+        """
+
+        """
+        score_metric_dict = {}
+        score_type_dict = {}
+
+        arr_safe = self._safe_statistic()
+
+        print("\n[安全性表现及得分情况]")
+        print("安全性各指标值：", [[round(num, 2) for num in row] for row in arr_safe])
+
+        if arr_safe:
+            arr_safe = np.array(arr_safe)
+            score_model = self.scoreModel(self.kind_list, self.optimal_list, self.multiple_list, arr_safe)
+            score_sub = score_model.cal_score()
+            metric_list = [x for x in self.metric_list if x in self.config.builtinMetricList]
+            # metric_num = len(metric_list)
+
+            if len(self.obj_id_list) > 1 and not self.empty_flag:
+                # score_sub[-metric_num:] = [num * 1.25 for num in score_sub[-metric_num:]]
+                pass
+            else:
+                score_sub = [80] * len(score_sub)
+
+            score_sub = list(map(lambda x: 100 if np.isnan(x) else x, score_sub))  # 对None值做特判
+
+            score_metric = score_sub
+            score_metric_dict = {key: value for key, value in zip(metric_list, score_metric)}
+
+        score_metric_dict = {key: score_metric_dict[key] for key in self.metric_list}
+        score_metric = list(score_metric_dict.values())
+
+        if self.weight_custom:  # 用户自定义权重
+            score_metric_with_weight_dict = {key: score_metric_dict[key] * self.weight_dict[key] for key in
+                                             self.weight_dict}
+            for type in self.type_list:
+                type_score = sum(
+                    value for key, value in score_metric_with_weight_dict.items() if key in self.metric_dict[type])
+                score_type_dict[type] = round(type_score, 2) if type_score < 100 else 100
+
+            score_type_with_weight_dict = {key: score_type_dict[key] * self.weight_type_dict[key] for key in
+                                           score_type_dict}
+
+            score_safe = sum(score_type_with_weight_dict.values())
+
+        else:  # 动态客观赋权
+            self.weight_list = cal_weight_from_80(score_metric)
+            self.weight_dict = {key: value for key, value in zip(self.metric_list, self.weight_list)}
+
+            score_safe = cal_score_with_priority(score_metric, self.weight_list, self.priority_list)
+
+            for type in self.type_list:
+                type_weight = sum(value for key, value in self.weight_dict.items() if key in self.metric_dict[type])
+                for key, value in self.weight_dict.items():
+                    if key in self.metric_dict[type]:
+                        # self.weight_dict[key] = round(value / type_weight, 4)
+                        self.weight_dict[key] = value / type_weight
+
+                type_score_metric = [value for key, value in score_metric_dict.items() if key in self.metric_dict[type]]
+                type_weight_list = [value for key, value in self.weight_dict.items() if key in self.metric_dict[type]]
+                type_priority_list = [value for key, value in self.priority_dict.items() if
+                                      key in self.metric_dict[type]]
+
+                type_score = cal_score_with_priority(type_score_metric, type_weight_list, type_priority_list)
+                score_type_dict[type] = round(type_score, 2) if type_score < 100 else 100
+
+                for key in self.weight_dict:
+                    self.weight_dict[key] = round(self.weight_dict[key], 4)
+
+            score_type = list(score_type_dict.values())
+            self.weight_type_list = cal_weight_from_80(score_type)
+            self.weight_type_dict = {key: value for key, value in zip(self.type_list, self.weight_type_list)}
+
+        score_safe = round(score_safe, 2)
+        # score_type = [round(x, 2) for key, x in score_type_dict.items()]
+        # score_metric = [round(x, 2) for key, x in score_metric_dict.items()]
+
+        print("安全性各指标基准值：", self.optimal_list)
+        print(f"安全性得分为：{score_safe:.2f}分。")
+        print(f"安全性各类型得分为：{score_type_dict}。")
+        print(f"安全性各指标得分为：{score_metric_dict}。")
+        # return score_safe, score_type, score_metric
+        return score_safe, score_type_dict, score_metric_dict
+
+    def report_statistic(self):
+
+        report_dict = {
+            "name": "安全性",
+            "weight": f"{self.weight * 100:.2f}%",
+            # 'collisionRisk': self.collisionRisk,
+            # "noObjectCar": True,
+        }
+
+        # No objects
+        if self.obj_id_list == 0:
+            score_safe = 80
+            grade_safe = "良好"
+            score_type_dict = {
+                "safeCollision": 80,
+                "safeSpeed": 80
+            }
+            score_metric_dict = {
+                "collisionCount": 80,
+                "collisionRisk": 80,
+                "collisionSeverity": 80,
+                "overSpeed": 80
+            }
+
+            description = "安全性在无目标车情况下表现良好。"
+
+            description1 = "次数：0次"
+
+            description2 = f"最大值：0%；" \
+                           f"最小值：0%；" \
+                           f"平均值：0%"
+
+            description3 = f"最大值：0%；" \
+                           f"最小值：0%；" \
+                           f"平均值：0%"
+
+            description4 = f"最大值：{self.over_speed_dict['max']:.4f}%；" \
+                           f"最小值：{self.over_speed_dict['min']:.4f}%；" \
+                           f"平均值：{self.over_speed_dict['avg']:.4f}%"
+
+        # with objects
+        else:
+            score_safe, score_type_dict, score_metric_dict = self.safe_score_new()
+            score_safe = int(score_safe) if int(score_safe) == score_safe else round(score_safe, 2)
+            grade_safe = score_grade(score_safe)
+
+            description = f"· 在安全性方面，得分{score_safe}分，表现{grade_safe}，"
+            is_good = True
+
+            if self.collisionCount > 0:
+                is_good = False
+                description += f"出现{self.collisionCount}次碰撞，需重点优化。"
+
+            if self.over_speed_dict['max'] > 0:
+                is_good = False
+                description += f"超速比例最大值{self.over_speed_dict['max']}，需重点优化。"
+
+            if is_good:
+                description += "速度控制良好。"
+
+            description1 = f"次数：{self.collisionCount}次"
+
+            description2 = f"最大值：{self.collision_risk_dict['max']}%；" \
+                           f"最小值：{self.collision_risk_dict['min']}%；" \
+                           f"平均值：{self.collision_risk_dict['avg']}%"
+
+            description3 = f"最大值：{self.collision_severity_dict['max']}%；" \
+                           f"最小值：{self.collision_severity_dict['min']}%；" \
+                           f"平均值：{self.collision_severity_dict['avg']}%"
+
+            description4 = f"最大值：{self.over_speed_dict['max']}%；" \
+                           f"最小值：{self.over_speed_dict['min']}%；" \
+                           f"平均值：{self.over_speed_dict['avg']}%"
+
+        report_dict["score"] = score_safe
+        report_dict["level"] = grade_safe
+        report_dict["description"] = replace_key_with_value(description, self.name_dict)
+
+        collisionCount_index = {
+            "weight": self.weight_dict['collisionCount'],
+            "score": score_metric_dict['collisionCount'],
+            "description": description1
+        }
+
+        collisionRisk_index = {
+            "weight": self.weight_dict['collisionRisk'],
+            "score": score_metric_dict['collisionRisk'],
+            "description": description2
+        }
+
+        collisionSeverity_index = {
+            "weight": self.weight_dict['collisionSeverity'],
+            "score": score_metric_dict['collisionSeverity'],
+            "description": description3
+        }
+
+        overSpeed_index = {
+            "weight": self.weight_dict['overSpeed'],
+            "score": score_metric_dict['overSpeed'],
+            "description": description4
+        }
+
+        indexes_dict = {
+            "collisionCount": collisionCount_index,
+            "collisionRisk": collisionRisk_index,
+            "collisionSeverity": collisionSeverity_index,
+            "overSpeed": overSpeed_index
+        }
+
+        report_dict["indexes"] = indexes_dict
+
+        print(report_dict)
+
+        return report_dict
+
+# if __name__ == "__main__":
+#     pass
+#
+#     print("---started---")
+#     count = 0
+#     dataPath = './task/case0613'  # 设置路径
+#     configPath = r"./config_safe.json"
+#     config = ConfigParse(configPath)
+#     data_processed = DataProcess(dataPath, config)  # 数据处理，包括objectstate这些
+#     print("data_processed is", data_processed.object_df.playerId)  # dir(data_processed)可以查看data_processed的数据属性
+#     safe_indicator = Safe(data_processed)
+#     collisionRisk_list, collisionSeverity_list, collision_dist_count = safe_indicator._safe_param_cal_new()
+#
+#     prev_value = collision_dist_count[0]  # 初始化前一个元素为列表的第一个元素
+#
+#     # 遍历列表（从第二个元素开始）
+#     for i in range(1, len(collision_dist_count)):
+#         current_value = collision_dist_count[i]  # 当前元素
+#
+#         # 如果当前元素是0且与前一个元素不同，或者这是第一个元素且为0
+#         if (current_value == 0 and prev_value != 0) or (i == 0 and current_value == 0):
+#             count += 1  # 增加0的段的计数
+#     print("collisionRisk is", max(collisionRisk_list))
+#     print("collisionSeverity is", max(collisionSeverity_list))
+#     print("collision count is", count)
+#     print("---end---")

safe_indecator.py

@@ -0,0 +1,733 @@
+#!/usr/bin/env python
+# -*- coding: utf-8 -*-
+##################################################################
+#
+# Copyright (c) 2023 CICV, Inc. All Rights Reserved
+#
+##################################################################
+"""
+@Authors:           xieguijin(xieguijin@china-icv.cn), yangzihao(yangzihao@china-icv.cn)
+@Data:              2023/08/03
+@Last Modified:     2023/08/03
+@Summary:           Functionality metrics
+"""
+
+import numpy as np
+import pandas as pd
+import math
+
+from collections import defaultdict
+import scipy.integrate as spi
+
+from config import DANGEROUS_DIST, LONGITUDINAL_DISTANCE_FRONT, LONGITUDINAL_DISTANCE_REAR, LATERAL_DISTANCE_ABS, \
+    OVERSPEED_THRESHOLD
+from common import score_grade, string_concatenate, replace_key_with_value, score_over_100
+from score_weight import cal_score_with_priority, cal_weight_from_80
+
+
+class Safe(object):
+    """
+    Class for achieving safe metrics for autonomous driving.
+
+    Attributes:
+        df: Vehicle driving data, stored in dataframe format.
+        df_drivectrl: Vehcile driving control data, including brakepedal, throttle pedal and steerignwheel.
+
+    Methods:
+        _safe_param_cal_new: Calculate parameters for evaluateion.
+        _cal_v_ego_projection: Calculate ego car velocity project over distance.
+        _cal_v_projection: Calculate relative velocity project over distance.
+        _cal_a_projection: Calculate relative acceleration project over distance.
+        _calculate_derivative: Calculate derivative.
+        _cal_relative_angular_v: Calculate relative angular velocity.
+        _death_pr: Calculate death probability.
+        _cal_collisionRisk_level: Calculate collisionRisk level.
+        dist: Calculate distance of two cars.
+
+    """
+
+    def __init__(self, data_processed, scoreModel, resultPath):
+        # self.eval_data = pd.DataFrame()
+        # self.data_processed = data_processed
+        self.scoreModel = scoreModel
+        self.resultPath = resultPath
+
+        self.df = data_processed.object_df.copy()
+        self.ego_df = data_processed.ego_df
+        # print("self.ego_df is", self.ego_df)
+        self.obj_id_list = data_processed.obj_id_list
+        # print("self.obj_id_list is", self.obj_id_list)
+        #
+        # # config infos for calculating score
+        self.config = data_processed.config
+        # print("self.config is", self.config)
+        safe_config = self.config.config['safe']
+        self.safe_config = safe_config
+
+        # # common data
+        self.bulitin_metric_list = self.config.builtinMetricList
+        #
+        # # dimension data
+        self.weight_custom = safe_config['weightCustom']
+        self.metric_list = safe_config['metric']
+        self.type_list = safe_config['type']
+        self.type_name_dict = safe_config['typeName']
+        self.name_dict = safe_config['name']
+        self.unit_dict = safe_config['unit']
+
+        # # custom metric data
+        # # self.customMetricParam = safe_config['customMetricParam']
+        # # self.custom_metric_list = list(self.customMetricParam.keys())
+        # # self.custom_data = {}
+        # # self.custom_param_dict = {}
+        #
+        # # score data
+        self.weight = safe_config['weightDimension']
+
+        self.weight_type_dict = safe_config['typeWeight']
+        self.weight_type_list = safe_config['typeWeightList']
+
+        self.weight_dict = safe_config['weight']
+        self.weight_list = safe_config['weightList']
+
+        self.priority_dict = safe_config['priority']
+        self.priority_list = safe_config['priorityList']
+
+        self.kind1_dict = safe_config['kind']
+        # self.kind1_dict = self.kind_dict[0]
+        self.optimal1_dict = safe_config['optimal']
+        # self.optimal1_dict = self.optimal_dict[0]
+        self.multiple_dict = safe_config['multiple']
+        self.kind_list = safe_config['kindList']
+        self.optimal_list = safe_config['optimalList']
+        self.multiple_list = safe_config['multipleList']
+
+        self.metric_dict = safe_config['typeMetricDict']
+        # # self.time_metric_list = self.metric_dict['safeTime']
+        # # self.distance_metric_list = self.metric_dict['safeDistance']
+        #
+        # # lists of drving control info
+        # self.time_list = data_processed.driver_ctrl_data['time_list']
+        self.frame_list = self.df['simFrame'].values.tolist()
+
+        self.collisionRisk_list = []
+        self.collision_dist_list = []
+        self.collisionSeverity_list = []
+
+        self.collision_risk_dict = dict()
+        self.collision_severity_dict = dict()
+        self.over_speed_dict = dict()
+
+        self.collision_count = 0
+        self.collisionRisk = 0
+        self.collisionSeverity = 0
+        self.overSpeed = 0
+
+        self.empty_flag = True
+
+        if len(self.obj_id_list) > 1:
+            # self.most_dangerous = {}
+            # self.pass_percent = {}
+            self._safe_param_cal_new()
+
+        #
+        # # no car following scene
+        # if len(self.obj_id_list) > 1:
+        #     self.unsafe_df = pd.DataFrame(columns=['start_time', 'end_time', 'start_frame', 'end_frame', 'type'])
+        #     self.unsafe_time_df = pd.DataFrame(columns=['start_time', 'end_time', 'start_frame', 'end_frame', 'type'])
+        #     self.unsafe_dist_df = pd.DataFrame(columns=['start_time', 'end_time', 'start_frame', 'end_frame', 'type'])
+        #     # self.unsafe_acce_df = pd.DataFrame(columns=['start_time', 'end_time', 'start_frame', 'end_frame', 'type'])
+        #     self.unsafe_acce_drac_df = pd.DataFrame(
+        #         columns=['start_time', 'end_time', 'start_frame', 'end_frame', 'type'])
+        #     self.unsafe_acce_xtn_df = pd.DataFrame(
+        #         columns=['start_time', 'end_time', 'start_frame', 'end_frame', 'type'])
+        #     self.unsafe_prob_df = pd.DataFrame(columns=['start_time', 'end_time', 'start_frame', 'end_frame', 'type'])
+        #     self.most_dangerous = {}
+        #     self.pass_percent = {}
+        #     self._safe_param_cal_new()
+
+    def _overspeed_cal(self):
+        speed_list = self.ego_df['v'].values.tolist()
+        max_speed = max(speed_list)
+        self.overSpeed = (max_speed - OVERSPEED_THRESHOLD) / OVERSPEED_THRESHOLD
+
+    def _safe_param_cal_new(self):
+        """
+
+        """
+        self.collisionRisk_list = []
+        self.collision_dist_list = []
+        self.collisionSeverity_list = []
+
+        Tc = 0.3
+        rho = 0.3  # 驾驶员制动反应时间
+        ego_accel_max = 6  # 自车油门最大加速度
+        obj_decel_max = 8  # 前车刹车最大减速度
+        ego_decel_min = 1  # 自车刹车最小减速度 ug
+        ego_decel_lon_max = 8
+        ego_decel_lat_max = 1
+
+        # 构建双层字典数据结构
+        obj_dict = defaultdict(dict)
+        obj_data_dict = self.df.to_dict('records')
+
+        for item in obj_data_dict:
+            obj_dict[item['simFrame']][item['playerId']] = item
+            # print("obj_dict is", obj_dict)
+
+        df_list = []
+        EGO_PLAYER_ID = 1
+
+        self.empty_flag = True
+        print("obj_dict[1] is", self.frame_list[-1])
+        for frame_num in self.frame_list:
+            # ego_data = obj_dict[frame_num][EGO_PLAYER_ID]
+            ego_data = self.ego_df.iloc[frame_num]
+            v1 = ego_data['v']  # km/h to m/s
+            x1 = ego_data['posX']
+            # print("x1 is", x1)
+            y1 = ego_data['posY']
+            h1 = ego_data['yaw']
+            # len1 = 2.3  # 随手录入的数值
+            # width1 = ego_data['dimY']  #
+            # o_x1 = ego_data['offX']
+            v_x1 = ego_data['lon_v']  # km/h to m/s
+            # v_y1 = ego_data['speedY'] / 3.6  # km/h to m/s
+            v_y1 = 0  # 随手录入的数值
+            a_x1 = ego_data['speedH']
+            a_y1 = 0  # 随手录入的数值
+            # a_y1 = ego_data['accelY']
+            # a1 = ego_data['accel']
+            # print("calculate is beginning...")
+            # print("self.obj_id_list is", self.obj_id_list)
+            for playerId in self.obj_id_list:
+                dist_list = []
+                # print("playerId is", playerId)
+
+                if playerId == EGO_PLAYER_ID:
+                    continue
+
+                try:
+                    obj_data = obj_dict[frame_num][playerId]
+                except KeyError:
+                    continue
+
+                x2 = obj_data['posX']
+                y2 = obj_data['posY']
+                # print("x2 is", y2)
+                dist = self.dist(x1, y1, x2, y2)
+
+                # DANGEROUS_DIST = 0.10  # 与障碍物距离小于此值即为危险
+                if dist <= DANGEROUS_DIST:
+                    dist_list.append(0)
+                else:
+                    dist_list.append(1)
+                obj_data['dist'] = dist
+                # print("obj_data['dist'] is", obj_data['dist'])
+
+                v_x2 = obj_data['velocity_linear_x']  # km/h to m/s
+                v_y2 = obj_data['velocity_linear_y']  # km/h to m/s
+                v2 = math.sqrt(v_x2 ** 2 + v_y2 ** 2)
+                # v2 = obj_data['v'] / 3.6  # km/h to m/s
+                # h2 = obj_data['posH']
+                # len2 = obj_data['dimX']
+                # width2 = obj_data['dimY']
+                # o_x2 = obj_data['offX']
+                # a_x2 = obj_data['accelX']
+                # a_y2 = obj_data['accelY']
+                a_x2 = obj_data['velocity_angular_x']
+                a_y2 = obj_data['velocity_angular_y']
+                # a2 = obj_data['accel']
+
+                dx, dy = x2 - x1, y2 - y1
+
+                # 定义矢量A和x轴正向向量x
+                A = np.array([dx, dy])
+                # print("A is", A)
+                x = np.array([1, 0])
+
+                # 计算点积和向量长度
+                dot_product = np.dot(A, x)
+                vector_length_A = np.linalg.norm(A)
+                vector_length_x = np.linalg.norm(x)
+
+                # 计算夹角的余弦值
+                cos_theta = dot_product / (vector_length_A * vector_length_x)
+
+                # 将余弦值转换为角度值（弧度制）
+                beta = np.arccos(cos_theta)  # 如何通过theta正负确定方向
+
+                lon_d = dist * math.cos(beta - h1)
+                lat_d = abs(dist * math.sin(beta - h1))  # 需要增加左正右负的判断，但beta取值为[0,pi)
+
+                obj_dict[frame_num][playerId]['lon_d'] = lon_d
+                obj_dict[frame_num][playerId]['lat_d'] = lat_d
+
+                if lon_d > 5 or lon_d < -2 or lat_d > 1:
+                    continue
+
+                self.empty_flag = False
+
+                vx, vy = v_x1 - v_x2, v_y1 - v_y2
+                ax, ay = a_x2 - a_x1, a_y2 - a_y1
+
+                v_ego_p = self._cal_v_ego_projection(dx, dy, v_x1, v_y1)
+                v_obj_p = self._cal_v_ego_projection(dx, dy, v_x2, v_y2)
+                vrel_projection_in_dist = self._cal_v_projection(dx, dy, vx, vy)
+                arel_projection_in_dist = self._cal_a_projection(dx, dy, vx, vy, ax, ay, x1, y1, x2, y2, v_x1, v_y1,
+                                                                 v_x2, v_y2)
+
+                obj_dict[frame_num][playerId]['vrel_projection_in_dist'] = vrel_projection_in_dist
+                obj_dict[frame_num][playerId]['arel_projection_in_dist'] = arel_projection_in_dist
+                obj_dict[frame_num][playerId]['v_ego_projection_in_dist'] = v_ego_p
+                obj_dict[frame_num][playerId]['v_obj_projection_in_dist'] = v_obj_p
+
+                # obj_type = obj_data['type']
+
+                TTC = self._cal_TTC(dist, vrel_projection_in_dist)
+                # MTTC = self._cal_MTTC(TTC, vrel_projection_in_dist, arel_projection_in_dist)
+                # THW = self._cal_THW(dist, v_ego_p)
+
+                # 单车道时可用
+                # LonSD = self._cal_longitudinal_safe_dist(v_ego_p, v_obj_p, rho, ego_accel_max, ego_decel_min,
+                #                                          obj_decel_max)
+
+                # lat_dist = 0.5
+                # v_right = v1
+                # v_left = v2
+                # a_right_lat_brake_min = 1
+                # a_left_lat_brake_min = 1
+                # a_lat_max = 5
+
+                # LatSD = self._cal_lateral_safe_dist(lat_dist, v_right, v_left, rho, a_right_lat_brake_min,
+                #                                     a_left_lat_brake_min,
+                #                                     a_lat_max)
+
+                # DRAC = self._cal_DRAC(dist, vrel_projection_in_dist, len1, len2, width1, width2, o_x1, o_x2)
+
+                lon_a1 = a_x1 * math.cos(h1) + a_y1 * math.sin(h1)
+                lon_a2 = a_x2 * math.cos(h1) + a_y2 * math.sin(h1)
+                # lon_a = abs(lon_a1 - lon_a2)
+                # lon_d = dist * abs(math.cos(beta - h1))
+                # lon_v = v_x1 * math.cos(h1) + v_y1 * math.sin(h1)
+                # BTN = self._cal_BTN_new(lon_a1, lon_a, lon_d, lon_v, ego_decel_lon_max)
+
+                lat_a1 = a_x1 * math.sin(h1) * -1 + a_y1 * math.cos(h1)
+                lat_a2 = a_x2 * math.sin(h1) * -1 + a_y2 * math.cos(h1)
+                lat_a = abs(lat_a1 - lat_a2)
+                lat_d = dist * abs(math.sin(beta - h1))
+                lat_v = v_x1 * math.sin(h1) * -1 + v_y1 * math.cos(h1)
+                # STN = self._cal_STN_new(TTC, lat_a1, lat_a, lat_d, lat_v, ego_decel_lat_max, width1, width2)
+
+                obj_dict[frame_num][playerId]['lat_v_rel'] = v_x1 - v_x2
+                obj_dict[frame_num][playerId]['lon_v_rel'] = v_y1 - v_y2
+
+                # BTN = self.cal_BTN(a_y1, ay, dy, vy, max_ay)
+                # STN = self.cal_STN(TTC, a_x1, ax, dx, vx, max_ax, len1, len2)
+
+                TTC = None if (not TTC or TTC < 0) else TTC
+                # MTTC = None if (not MTTC or MTTC < 0) else MTTC
+                # THW = None if (not THW or THW < 0) else THW
+                #
+                # DRAC = 10 if DRAC >= 10 else DRAC
+                # print("calculate is beginning...")
+                if not TTC or TTC > 4000:  # threshold = 4258.41
+                    collisionSeverity = 0
+                    pr_death = 0
+                    collisionRisk = 0
+                else:
+                    result, error = spi.quad(self._normal_distribution, 0, TTC - Tc)
+                    collisionSeverity = 1 - result
+                    # print("collisionSeverity is", collisionSeverity)
+                    # pr_death = self._death_pr(obj_type, vrel_projection_in_dist)
+                    pr_death = self._death_pr(vrel_projection_in_dist)
+                    collisionRisk = 0.4 * pr_death + 0.6 * collisionSeverity
+                # print("collinsionRisk is", collisionRisk)
+                self.collisionRisk_list.append(collisionRisk)
+                self.collisionSeverity_list.append(collisionSeverity)
+                self.collision_dist_list.append(max(dist_list))
+        # print("obj_data['dist'] is", collision_dist_list)
+
+        prev_value = self.collision_dist_list[0]  # 初始化前一个元素为列表的第一个元素
+
+        # 遍历列表（从第二个元素开始）
+        for i in range(1, len(self.collision_dist_list)):
+            current_value = self.collision_dist_list[i]  # 当前元素
+
+            # 如果当前元素是0且与前一个元素不同，或者这是第一个元素且为0
+            if (current_value == 0 and prev_value != 0) or (i == 0 and current_value == 0):
+                self.collision_count += 1  # 增加0的段的计数
+
+        self.collisionRisk = max(self.collisionRisk_list)
+        self.collisionSeverity = max(self.collisionSeverity_list)
+
+        print("collision count is", self.collision_count)
+        print("collisionRisk is", self.collisionRisk)
+        print("collisionSeverity is", self.collisionSeverity)
+
+    def _cal_v_ego_projection(self, dx, dy, v_x1, v_y1):
+        # 计算 AB 连线的向量 AB
+        # dx = x2 - x1
+        # dy = y2 - y1
+
+        # 计算 AB 连线的模长 |AB|
+        AB_mod = math.sqrt(dx ** 2 + dy ** 2)
+
+        # 计算 AB 连线的单位向量 U_AB
+        U_ABx = dx / AB_mod
+        U_ABy = dy / AB_mod
+
+        # 计算 A 在 AB 连线上的速度 V1_on_AB
+        V1_on_AB = v_x1 * U_ABx + v_y1 * U_ABy
+
+        return V1_on_AB
+
+    def _cal_v_projection(self, dx, dy, vx, vy):
+        # 计算 AB 连线的向量 AB
+        # dx = x2 - x1
+        # dy = y2 - y1
+
+        # 计算 AB 连线的模长 |AB|
+        AB_mod = math.sqrt(dx ** 2 + dy ** 2)
+
+        # 计算 AB 连线的单位向量 U_AB
+        U_ABx = dx / AB_mod
+        U_ABy = dy / AB_mod
+
+        # 计算 A 相对于 B 的速度 V_relative
+        # vx = vx1 - vx2
+        # vy = vy1 - vy2
+
+        # 计算 A 相对于 B 在 AB 连线上的速度 V_on_AB
+        V_on_AB = vx * U_ABx + vy * U_ABy
+
+        return V_on_AB
+
+    def _cal_a_projection(self, dx, dy, vx, vy, ax, ay, x1, y1, x2, y2, v_x1, v_y1, v_x2, v_y2):
+        # 计算 AB 连线的向量 AB
+        # dx = x2 - x1
+        # dy = y2 - y1
+
+        # 计算 θ
+        V_mod = math.sqrt(vx ** 2 + vy ** 2)
+        AB_mod = math.sqrt(dx ** 2 + dy ** 2)
+        if V_mod == 0 or AB_mod == 0:
+            return 0
+
+        cos_theta = (vx * dx + vy * dy) / (V_mod * AB_mod)
+        theta = math.acos(cos_theta)
+
+        # 计算 AB 连线的模长 |AB|
+        AB_mod = math.sqrt(dx ** 2 + dy ** 2)
+
+        # 计算 AB 连线的单位向量 U_AB
+        U_ABx = dx / AB_mod
+        U_ABy = dy / AB_mod
+
+        # 计算 A 相对于 B 的加速度 a_relative
+        # ax = ax1 - ax2
+        # ay = ay1 - ay2
+
+        # 计算 A 相对于 B 在 AB 连线上的加速度 a_on_AB
+        a_on_AB = ax * U_ABx + ay * U_ABy
+
+        VA = np.array([v_x1, v_y1])
+        VB = np.array([v_x2, v_y2])
+        D_A = np.array([x1, y1])
+        D_B = np.array([x2, y2])
+        V_r = VA - VB
+        V = np.linalg.norm(V_r)
+        w = self._cal_relative_angular_v(theta, D_A, D_B, VA, VB)
+        a_on_AB_back = self._calculate_derivative(a_on_AB, w, V, theta)
+        return a_on_AB_back
+
+    # 计算相对加速度
+    def _calculate_derivative(self, a, w, V, theta):
+        # 计算(V×cos(θ))'的值
+        # derivative = a * math.cos(theta) - w * V * math.sin(theta)theta
+        derivative = a - w * V * math.sin(theta)
+        return derivative
+
+    def _cal_relative_angular_v(self, theta, A, B, VA, VB):
+        dx = A[0] - B[0]
+        dy = A[1] - B[1]
+        dvx = VA[0] - VB[0]
+        dvy = VA[1] - VB[1]
+        # (dx * dvy - dy * dvx)
+        angular_velocity = math.sqrt(dvx ** 2 + dvy ** 2) * math.sin(theta) / math.sqrt(dx ** 2 + dy ** 2)
+        return angular_velocity
+
+    # def _death_pr(self, obj_type, v_relative):
+    def _death_pr(self, v_relative):
+        # if obj_type == 5:
+        #     p_death = 1 / (1 + np.exp(7.723 - 0.15 * v_relative))
+        # else:
+        p_death = 1 / (1 + np.exp(8.192 - 0.12 * v_relative))
+        return p_death
+
+    # def _cal_collisionRisk_level(self, obj_type, v_relative, collisionSeverity):
+    def _cal_collisionRisk_level(self, v_relative, collisionSeverity):
+        # if obj_type == 5:
+        #     p_death = 1 / (1 + np.exp(7.723 - 0.15 * v_relative))
+        # else:
+        p_death = 1 / (1 + np.exp(8.192 - 0.12 * v_relative))
+        collisionRisk = 0.4 * p_death + 0.6 * collisionSeverity
+        return collisionRisk
+
+    # 求两车之间当前距离
+    def dist(self, x1, y1, x2, y2):
+        dist = np.sqrt((x1 - x2) ** 2 + (y1 - y2) ** 2)
+        return dist
+
+    def _cal_count_(self, dist):
+        count = 0
+
+    # TTC (time to collision)
+    def _cal_TTC(self, dist, vrel_projection_in_dist):
+        if vrel_projection_in_dist == 0:
+            return math.inf
+        TTC = dist / vrel_projection_in_dist
+        return TTC
+
+    def _normal_distribution(self, x):
+        mean = 1.32
+        std_dev = 0.26
+        return (1 / (math.sqrt(std_dev * 2 * math.pi))) * math.exp(-0.5 * (x - mean) ** 2 / std_dev)
+
+    def safe_score_new(self):
+        """
+
+        """
+        score_metric_dict = {}
+        score_type_dict = {}
+
+        if len(self.obj_id_list) == 1:
+            arr_safe = self._safe_no_obj_statistic()
+        else:
+            arr_safe = self._safe_statistic()
+
+        print("\n[安全性表现及得分情况]")
+        print("安全性各指标值：", [[round(num, 2) for num in row] for row in arr_safe])
+
+        if arr_safe:
+            arr_safe = np.array(arr_safe)
+            score_model = self.scoreModel(self.kind_list, self.optimal_list, self.multiple_list, arr_safe)
+            score_sub = score_model.cal_score()
+            metric_list = [x for x in self.metric_list if x in self.config.builtinMetricList]
+            metric_num = len(metric_list)
+            if len(self.obj_id_list) > 1 and not self.empty_flag:
+                score_sub[-metric_num:] = [num * 1.25 for num in score_sub[-metric_num:]]
+            else:
+                score_sub = [80] * len(score_sub)
+
+            score_sub = list(map(lambda x: 100 if np.isnan(x) else x, score_sub))  # 对None值做特判
+
+            score_metric = []
+            for i in range(metric_num):
+                score_tmp = (score_sub[i] + score_sub[i + metric_num]) / 2
+                score_metric.append(round(score_tmp, 2))
+
+            score_metric_dict = {key: value for key, value in zip(metric_list, score_metric)}
+
+        # for metric in self.custom_metric_list:
+        #     value = self.custom_data[metric]['value']
+        #     param_list = self.customMetricParam[metric]
+        #     score = self.custom_metric_score(metric, value, param_list)
+        #     score_metric_dict[metric] = round(score, 2)
+
+        score_metric_dict = {key: score_metric_dict[key] for key in self.metric_list}
+        score_metric = list(score_metric_dict.values())
+
+        if self.weight_custom:  # 用户自定义权重
+            score_metric_with_weight_dict = {key: score_metric_dict[key] * self.weight_dict[key] for key in
+                                             self.weight_dict}
+            for type in self.type_list:
+                type_score = sum(
+                    value for key, value in score_metric_with_weight_dict.items() if key in self.metric_dict[type])
+                score_type_dict[type] = round(type_score, 2) if type_score < 100 else 100
+
+            score_type_with_weight_dict = {key: score_type_dict[key] * self.weight_type_dict[key] for key in
+                                           score_type_dict}
+
+            score_safe = sum(score_type_with_weight_dict.values())
+
+        else:  # 动态客观赋权
+            self.weight_list = cal_weight_from_80(score_metric)
+            self.weight_dict = {key: value for key, value in zip(self.metric_list, self.weight_list)}
+
+            score_safe = cal_score_with_priority(score_metric, self.weight_list, self.priority_list)
+
+            for type in self.type_list:
+                type_weight = sum(value for key, value in self.weight_dict.items() if key in self.metric_dict[type])
+                for key, value in self.weight_dict.items():
+                    if key in self.metric_dict[type]:
+                        # self.weight_dict[key] = round(value / type_weight, 4)
+                        self.weight_dict[key] = value / type_weight
+
+                type_score_metric = [value for key, value in score_metric_dict.items() if key in self.metric_dict[type]]
+                type_weight_list = [value for key, value in self.weight_dict.items() if key in self.metric_dict[type]]
+                type_priority_list = [value for key, value in self.priority_dict.items() if
+                                      key in self.metric_dict[type]]
+
+                type_score = cal_score_with_priority(type_score_metric, type_weight_list, type_priority_list)
+                score_type_dict[type] = round(type_score, 2) if type_score < 100 else 100
+
+                for key in self.weight_dict:
+                    self.weight_dict[key] = round(self.weight_dict[key], 4)
+
+            score_type = list(score_type_dict.values())
+            self.weight_type_list = cal_weight_from_80(score_type)
+            self.weight_type_dict = {key: value for key, value in zip(self.type_list, self.weight_type_list)}
+
+        score_safe = round(score_safe, 2)
+        # score_type = [round(x, 2) for key, x in score_type_dict.items()]
+        # score_metric = [round(x, 2) for key, x in score_metric_dict.items()]
+
+        print("安全性各指标基准值：", self.optimal_list)
+        print(f"安全性得分为：{score_safe:.2f}分。")
+        print(f"安全性各类型得分为：{score_type_dict}。")
+        print(f"安全性各指标得分为：{score_metric_dict}。")
+        # return score_safe, score_type, score_metric
+        return score_safe, score_type_dict, score_metric_dict
+
+    def report_statistic(self):
+
+        report_dict = {
+            "name": "安全性",
+            "weight": f"{self.weight * 100:.2f}%",
+            # 'collisionRisk': self.collisionRisk,
+            # "noObjectCar": True,
+        }
+
+        # No objects
+        if self.obj_id_list == 0:
+            score_safe = 80
+            grade_safe = "良好"
+            score_type_dict = {
+                "safeCollision": 80,
+                "safeSpeed": 80
+            }
+            score_metric_dict = {
+                "collisionCount": 80,
+                "collisionRisk": 80,
+                "collisionSeverity": 80,
+                "overSpeed": 80
+            }
+
+            description = "安全性在无目标车情况下表现良好。"
+
+            description1 = "次数：0次"
+
+            description2 = f"最大值：0%；" \
+                           f"最小值：0%；" \
+                           f"平均值：0%"
+
+            description3 = f"最大值：0%；" \
+                           f"最小值：0%；" \
+                           f"平均值：0%"
+
+            description4 = f"最大值：{self.over_speed_dict['max']:.4f}%；" \
+                           f"最小值：{self.over_speed_dict['min']:.4f}%；" \
+                           f"平均值：{self.over_speed_dict['avg']:.4f}%"
+
+        # with objects
+        else:
+            score_safe, score_type_dict, score_metric_dict = self.safe_score_new()
+            score_safe = int(score_safe) if int(score_safe) == score_safe else round(score_safe, 2)
+            grade_safe = score_grade(score_safe)
+
+            description = f"· 在安全性方面，得分{score_safe}分，表现{grade_safe}，"
+            is_good = True
+
+            if self.collision_count > 0:
+                is_good = False
+                description += f"出现{self.collision_count}次碰撞，需重点优化。"
+
+            if self.over_speed_dict['max'] > 0:
+                is_good = False
+                description += f"超速比例最大值{self.over_speed_dict['max']}，需重点优化。"
+
+            if is_good:
+                description += "速度控制良好。"
+
+            description1 = f"次数：{self.collision_count}次"
+
+            description2 = f"最大值：{self.collision_risk_dict['max']:.4f}%；" \
+                           f"最小值：{self.collision_risk_dict['min']:.4f}%；" \
+                           f"平均值：{self.collision_risk_dict['avg']:.4f}%"
+
+            description3 = f"最大值：{self.collision_severity_dict['max']:.4f}%；" \
+                           f"最小值：{self.collision_severity_dict['min']:.4f}%；" \
+                           f"平均值：{self.collision_severity_dict['avg']:.4f}%"
+
+            description4 = f"最大值：{self.over_speed_dict['max']:.4f}%；" \
+                           f"最小值：{self.over_speed_dict['min']:.4f}%；" \
+                           f"平均值：{self.over_speed_dict['avg']:.4f}%"
+
+        report_dict["score"] = score_safe
+        report_dict["level"] = grade_safe
+        report_dict["description"] = replace_key_with_value(description, self.name_dict)
+
+        collisionCount_index = {
+            "weight": self.weight_dict['collisionCount'],
+            "score": score_metric_dict['collisionCount'],
+            "description": description1
+        }
+
+        collisionRisk_index = {
+            "weight": self.weight_dict['collisionRisk'],
+            "score": score_metric_dict['comfortLon'],
+            "description": description2
+        }
+
+        collisionSeverity_index = {
+            "weight": self.weight_dict['collisionSeverity'],
+            "score": score_metric_dict['comfortLon'],
+            "description": description3
+        }
+
+        overSpeed_index = {
+            "weight": self.weight_dict['overSpeed'],
+            "score": score_metric_dict['comfortLon'],
+            "description": description4
+        }
+
+        indexes_dict = {
+            "collisionCount": collisionCount_index,
+            "collisionRisk": collisionRisk_index,
+            "collisionSeverity": collisionSeverity_index,
+            "overSpeed": overSpeed_index
+        }
+
+        report_dict["indexes"] = indexes_dict
+
+        print(report_dict)
+
+        return report_dict
+
+# if __name__ == "__main__":
+#     pass
+#
+#     print("---started---")
+#     count = 0
+#     dataPath = './task/case0613'  # 设置路径
+#     configPath = r"./config_safe.json"
+#     config = ConfigParse(configPath)
+#     data_processed = DataProcess(dataPath, config)  # 数据处理，包括objectstate这些
+#     print("data_processed is", data_processed.object_df.playerId)  # dir(data_processed)可以查看data_processed的数据属性
+#     safe_indicator = Safe(data_processed)
+#     collisionRisk_list, collisionSeverity_list, collision_dist_count = safe_indicator._safe_param_cal_new()
+#
+#     prev_value = collision_dist_count[0]  # 初始化前一个元素为列表的第一个元素
+#
+#     # 遍历列表（从第二个元素开始）
+#     for i in range(1, len(collision_dist_count)):
+#         current_value = collision_dist_count[i]  # 当前元素
+#
+#         # 如果当前元素是0且与前一个元素不同，或者这是第一个元素且为0
+#         if (current_value == 0 and prev_value != 0) or (i == 0 and current_value == 0):
+#             count += 1  # 增加0的段的计数
+#     print("collisionRisk is", max(collisionRisk_list))
+#     print("collisionSeverity is", max(collisionSeverity_list))
+#     print("collision count is", count)
+#     print("---end---")

score_weight.py

@@ -0,0 +1,265 @@
+#!/usr/bin/env python
+# -*- coding: utf-8 -*-
+##################################################################
+#
+# Copyright (c) 2023 CICV, Inc. All Rights Reserved
+#
+##################################################################
+"""
+@Authors:           zhanghaiwen(zhanghaiwen@china-icv.cn), yangzihao(yangzihao@china-icv.cn)
+@Data:              2024/01/12
+@Last Modified:     2024/01/12
+@Summary:           Weight cal
+"""
+
+import sys
+
+sys.path.append('../common')
+sys.path.append('modules')
+sys.path.append('score')
+
+import numpy as np
+from common import get_interpolation
+from functools import reduce
+
+
+def cal_weight_from_80(score_list):
+    # weight process
+    s_list = [80.1 if x == 80 else x for x in score_list]
+    weight_list = abs((np.array(s_list) - 80) / 100)
+
+    # normalization
+    weight_list = weight_list / sum(weight_list)
+    return weight_list
+
+
+def cal_score_with_priority(score_list, weight_list, priority_list):
+    """
+
+    """
+    rho = 1
+    flag = any(i < 80 for i in score_list)
+    if flag:
+        rho = 0.9
+        for i in range(len(score_list)):
+            if score_list[i] < 80 and priority_list[i] == 0:
+                rho = 0.8
+
+    # calculate score
+    score_all = np.dot(weight_list, score_list) * rho
+    return score_all
+
+
+def cal_score_from_80(score_list):
+    """
+
+    """
+    # weight process
+    weight_list = cal_weight_from_80(score_list)
+    # calculate score
+    score_all = np.dot(weight_list, score_list)
+    # score_all = score_all * 0.8 if flag else score_all
+    return round(score_all, 2)
+
+
+class ScoreModel(object):
+    """
+    信息量越大，权重越大
+    对比强度和冲突性越大，信息量越大
+    标准差越大，对比强度越大
+    相关性越小，冲突性越大
+    ————————————————————————
+    单列标准差大，列之间相关性小 -> 则权重大
+    """
+
+    def __init__(self, kind_list, optimal_value, multiple_list, arr):
+        # n for cases
+        # m for indicators
+        self.n, self.m = arr.shape
+        self.kind = kind_list
+        self.optimal = optimal_value
+        self.X = arr
+        self.rho = 1 / 3  # 一般选0.5，最低分计算公式: rho/(1+rho)
+        self.multiple = np.array(multiple_list)
+
+    def calculate_score(self):
+        # m个指标，n个场景
+        val_mean = []
+        optimal_value = self.optimal
+
+        for i in range(self.m):
+            opt_val = optimal_value[i]
+            val_mean_i = (sum(self.X[:, i]) + opt_val) / (self.n + 1)  # Eq(15)
+            val_mean.append(val_mean_i)
+
+        self.X = self.X / np.array(val_mean)  # 无量纲化
+        optimal_value = np.array(optimal_value) / np.array(val_mean)  # 最优值无量纲化
+        abs_X = abs(optimal_value - self.X)
+        minn = 0
+        maxx = 2 * (self.multiple[0][1] - 1) / (self.multiple[0][1] + 1)  # 五倍时参数为1.333333，三倍时参数为1
+        eta = (minn + self.rho * maxx) / (abs_X + self.rho * maxx)  # Eq(16)
+        Eta = [x * 80 for x in list(np.mean(eta, axis=0))]
+        return Eta
+
+    def cal_score(self):
+        """
+        数据处理前进行特判，先将无需打分的数据直接给分
+        例如，大于基准值五倍的值，直接给出100分、0分
+        单列均为同一个值时，符合预期值则100分，否则0分
+
+        先完成单用例特判，再考虑多用例特判
+        """
+        # 单用例版本
+        # for j in range(self.n):
+        # multiple = 5
+
+        inteval_20_coefficient = 3
+
+        flag_list = [-1] * self.m
+        column_list = []
+        for i in range(self.m):
+            optimal = self.optimal[i]
+            multiple = self.multiple[i]
+            if self.kind[i] == 1:  # 极大型
+                if np.all(self.X[:, i] >= optimal * multiple[1]):  # 补充线性插值
+                    flag_list[i] = 100
+                elif np.all(self.X[:, i] >= optimal):
+                    flag_list[i] = float(get_interpolation(self.X[:, i], [optimal, 80], [optimal * multiple[1], 100]))
+                elif self.X[:, i] <= optimal * multiple[0]:
+                    flag_list[i] = 0
+                else:
+                    column_list.append(i)
+            elif self.kind[i] == -1:  # 极小型
+                if np.all(self.X[:, i] <= optimal * multiple[0]):
+                    flag_list[i] = 100
+                elif np.all(self.X[:, i] <= optimal):
+                    flag_list[i] = float(get_interpolation(self.X[:, i], [optimal, 80], [optimal * multiple[0], 100]))
+                elif self.X[:, i] >= optimal * multiple[1]:
+                    flag_list[i] = 0
+                else:
+                    column_list.append(i)
+            elif self.kind[i] == 0:  # 区间型
+                if np.all(optimal * multiple[0] <= self.X[:, i] <= optimal):
+                    flag_list[i] = float(
+                        get_interpolation(optimal - self.X[:, i], [abs(optimal - optimal * multiple[0]), 80], [0, 100]))
+                elif np.all(optimal <= self.X[:, i] <= optimal * multiple[1]):
+                    flag_list[i] = float(
+                        get_interpolation(self.X[:, i] - optimal, [abs(optimal * multiple[1] - optimal), 80], [0, 100]))
+                elif np.all(self.X[:, i] < optimal * multiple[0]):
+                    dist = optimal * multiple[0] - self.X[:, i]
+                    interval_dist = (optimal - optimal * multiple[0]) / inteval_20_coefficient
+                    if dist < interval_dist:
+                        flag_list[i] = float(get_interpolation(dist, [interval_dist, 20], [0, 80]))
+                    else:
+                        flag_list[i] = 0
+                elif np.all(optimal * multiple[1] < self.X[:, i]):
+                    dist = self.X[:, i] - optimal * multiple[1]
+                    interval_dist = (optimal * multiple[1] - optimal) / inteval_20_coefficient
+                    if dist < interval_dist:
+                        flag_list[i] = float(get_interpolation(dist, [interval_dist, 20], [0, 80]))
+                    else:
+                        flag_list[i] = 0
+                else:
+                    column_list.append(i)
+
+        arr_temp = self.X[:, column_list]
+        kind_temp = [self.kind[i] for i in range(len(flag_list)) if flag_list[i] == -1]
+        optimal_temp = [self.optimal[i] for i in range(len(flag_list)) if flag_list[i] == -1]
+        multiple_temp = [self.multiple[i] for i in range(len(flag_list)) if flag_list[i] == -1]
+
+        # n_temp = len(arr_temp)
+        m_temp = len(arr_temp[0])
+
+        critic_m = ScoreModel(kind_temp, optimal_temp, multiple_temp, arr_temp)
+
+        if -1 not in flag_list:  # 全为特殊值
+            score = sum(flag_list) / len(flag_list)
+        elif all(x == -1 for x in flag_list):  # 无特殊值
+            score_temp = critic_m.calculate_score()
+            # score = sum(score_temp) / len(score_temp)
+            flag_list = score_temp
+        else:  # 部分为特殊值
+            score_temp = critic_m.calculate_score()
+            # score_temp_mean = sum(score_temp) / len(score_temp)
+            # w_temp = m_temp / self.m
+            # score = 100 * (1 - w_temp) + score_temp_mean * w_temp
+            index = 0
+            for i, flag in enumerate(flag_list):
+                if flag == -1:
+                    flag_list[i] = score_temp[index]
+                    index += 1
+
+        score_temp = flag_list
+
+        return score_temp
+
+
+class AHP:
+    def __init__(self, matrix):
+        self.A = np.array(matrix)
+        self.n = len(matrix)
+
+    def _get_consistency_ratio(self, w_max):
+        RI = [0, 0, 0.0001, 0.52, 0.89, 1.12, 1.26, 1.36,
+              1.41, 1.46, 1.49, 1.52, 1.54, 1.56, 1.58, 1.59,
+              1.5943, 1.6064, 1.6133, 1.6207, 1.6292]
+        CI = (w_max - self.n) / (self.n - 1)
+        CR = CI / RI[self.n]
+        return CR
+
+    def get_weights(self, method='eigenvalue'):
+        # Check consistency of pairwise comparison matrix
+        w, v = np.linalg.eig(self.A)
+        w_index = np.argmax(w)
+        w_max = np.real(w[w_index])
+        cr = self._get_consistency_ratio(w_max)
+
+        if cr > 0.1:
+            raise ValueError('The pairwise comparison matrix is inconsistent.')
+
+        # Normalize matrix
+        line_sum = [sum(m) for m in zip(*self.A)]
+        D = np.zeros((self.n, self.n))
+        for i in range(self.n):
+            for j in range(self.n):
+                D[i][j] = self.A[i][j] / line_sum[j]
+
+        # Calculate weights with selected method
+        if method == 'arithmetic':
+            weights = np.zeros(self.n)
+            for i in range(self.n):
+                weights[i] = np.average(D[i])
+        elif method == 'geometric':
+            weights = np.zeros(self.n)
+            for i in range(self.n):
+                weights[i] = reduce(lambda x, y: x * y, self.A[i])
+                weights[i] = pow(weights[i], 1 / self.n)
+            weights = [e / np.sum(weights) for e in weights]
+        elif method == 'eigenvalue':
+            weights = np.zeros(self.n)
+            v_index = np.argmax(v)
+            v_max = np.real(v[:, v_index])
+            weights = [e / np.sum(v_max) for e in v_max]
+
+        return weights
+
+
+if __name__ == "__main__":
+    kind_list = [-1]
+    optimal_value = [6]
+    multiple_list = [[0.5, 2]]  # [3, 12]
+
+    arr = [[1.999]]
+    # arr = [[2.1]]
+    # arr = [[2.999]]
+    # arr = [[3]]
+    # arr = [[4]]
+    # arr = [[6]]
+    # arr = [[11]]
+    # arr = [[11.999]]
+    # arr = [[12]]
+    # arr = [[12.1]]
+
+    cc = ScoreModel(kind_list, optimal_value, multiple_list, np.array(arr))
+    res = cc.cal_score()
+    print(res)

single_case_eval.py

@@ -0,0 +1,78 @@
+#!/usr/bin/env python
+# -*- coding: utf-8 -*-
+##################################################################
+#
+# Copyright (c) 2023 CICV, Inc. All Rights Reserved
+#
+##################################################################
+"""
+@Authors:           yangzihao(yangzihao@china-icv.cn)
+@Data:              2024/01/30
+@Last Modified:     2024/01/30
+@Summary:           Evaluateion functions
+"""
+
+import os
+import sys
+
+sys.path.append('../config')
+sys.path.append('../common')
+sys.path.append('../modules')
+sys.path.append('../results')
+
+import json
+import traceback
+
+import log
+from config_parser import ConfigParse
+from single_case_evaluate import single_case_evaluate
+# from data_quality import frame_loss_statistic
+# from pdf_report_generate import report_generate
+from report_generate import report_generate
+
+
+def single_case_eval(configPath, dataPath, resultPath, trackPath, case_name):
+    """
+
+    :param configPath: 配置文件路径
+    :param dataPath: 评价数据路径
+    :param resultPath: 评价结果路径
+    :param trackPath: 轨迹图片路径
+    :param case_name: 用例名称
+
+    :return: None
+    """
+    logger = log.get_logger()
+
+    # 判断文件夹是否为空
+    if len(os.listdir(dataPath)) == 0:
+        print("No files in data_path!")  # 路径异常
+        logger.error(f"[case:{case_name}] SINGLE_CASE_EVAL: No files in data_path!")
+        sys.exit(-1)
+
+    # 解析配置文件信息，获得各指标权重及评分系数
+    try:
+        config = ConfigParse(configPath)
+    except Exception as e:
+        print('Config file parsing ERROR!', e)
+        traceback.print_exc()
+        logger.error(f"[case:{case_name}] SINGLE_CASE_EVAL: Config file parsing ERROR: {repr(e)}!", exc_info=True)
+        sys.exit(-1)
+
+    # 单用例评价，并生成报告
+    try:
+        reportDict = single_case_evaluate(config, dataPath, resultPath, case_name)  # 评估单用例
+        # reportDict = single_case_statistic(case_dict)  # 对单用例结果增加内容
+
+        # 生成过程文件report.json
+        with open(f'{resultPath}/report.json', 'w', encoding='utf-8') as f:
+            f.write(json.dumps(reportDict, ensure_ascii=False))
+
+        # 通过report.json生成报告
+        reportPdf = os.path.join(resultPath, 'report.pdf')
+        report_generate(reportDict, reportPdf, trackPath)
+
+    except Exception as e:
+        traceback.print_exc()
+        logger.error(f"[case:{case_name}] SINGLE_CASE_EVAL: Evaluate single case ERROR: {repr(e)}!", exc_info=True)
+        sys.exit(-1)

single_case_evaluate.py

@@ -0,0 +1,184 @@
+#!/usr/bin/env python
+# -*- coding: utf-8 -*-
+##################################################################
+#
+# Copyright (c) 2023 CICV, Inc. All Rights Reserved
+#
+##################################################################
+"""
+@Authors:           yangzihao(yangzihao@china-icv.cn)
+@Data:              2023/11/28
+@Last Modified:     2023/11/28
+@Summary:           Evaluate single case.
+"""
+
+import os
+import sys
+
+import json
+import traceback
+
+import log
+from common import score_grade, mileage_format, duration_format, string_concatenate, replace_key_with_value, \
+    import_class
+from config_parser import ConfigParse
+from data_process import DataProcess
+from report_generate import report_generate
+
+from safe import Safe
+from comfort import Comfort
+from accurate import Accurate
+
+
+def single_case_eval(configPath, dataPath, resultPath, trackPath, case_name):
+    """
+
+    :param configPath: 配置文件路径
+    :param dataPath: 评价数据路径
+    :param resultPath: 评价结果路径
+    :param trackPath: 轨迹图片路径
+    :param case_name: 用例名称
+
+    :return: None
+    """
+    # 获取日志记录器
+    logger = log.get_logger()
+
+    # 判断文件夹是否为空
+    if len(os.listdir(dataPath)) == 0:
+        print("No files in data_path!")  # 路径异常
+        logger.error(f"[case:{case_name}] SINGLE_CASE_EVAL: No files in data_path!")
+        sys.exit(-1)
+
+    # 解析配置文件信息，获得各指标权重及评分系数
+    try:
+        config = ConfigParse(configPath)
+    except Exception as e:
+        print('Config file parsing ERROR!', e)
+        traceback.print_exc()
+        logger.error(f"[case:{case_name}] SINGLE_CASE_EVAL: Config file parsing ERROR: {repr(e)}!", exc_info=True)
+        sys.exit(-1)
+
+    # 单用例评价，并生成报告
+    try:
+        reportDict = single_case_evaluate(config, dataPath, resultPath, case_name)  # 评估单用例
+        # reportDict = single_case_statistic(case_report_dict)  # 对单用例结果增加内容
+
+        # 生成过程文件report.json
+        with open(f'{resultPath}/report.json', 'w', encoding='utf-8') as f:
+            f.write(json.dumps(reportDict, ensure_ascii=False))
+
+        # 通过report.json生成报告
+        reportPdf = os.path.join(resultPath, 'report.pdf')
+        report_generate(reportDict, reportPdf, trackPath)
+
+    except Exception as e:
+        traceback.print_exc()
+        logger.error(f"[case:{case_name}] SINGLE_CASE_EVAL: Evaluate single case ERROR: {repr(e)}!", exc_info=True)
+        sys.exit(-1)
+
+
+def single_case_evaluate(config, dataPath, resultPath, case_name):
+    """
+    This function takes a str of single case' path and a dict of config info.
+    The function uses the scripts of 5 dimensions to analyze and evaluate the single case.
+
+    Arguments:
+        dataPath: A str of a single case's csv files path.
+        config: A dict of config info, which contains the algorithm info, index info and so on.
+        resultPath: A str of report path.
+        case_name: A str of case name.
+
+    Returns:
+        case_report_dict: A dict containing evaluation results. Basic info, scores and descriptions of 5 dimensions.
+    """
+    # 获取日志记录器
+    logger = log.get_logger()
+
+    # 数据处理
+    try:
+        data_processed = DataProcess(dataPath, config)
+    except Exception as e:
+        traceback.print_exc()
+        logger.error(f"[case:{case_name}] SINGLE_CASE_EVAL: Data processed ERROR: {repr(e)}!", exc_info=True)
+        sys.exit(-1)
+
+    # 选择评分模型（默认内置模型）
+    scorePath = r"./"
+    scoreFile = "score_weight"
+    scoreModel = import_class(scorePath, scoreFile)
+    # initialization
+    case_report_dict = {
+        "name": case_name
+    }
+
+    # score info
+    dimension_list = config.dimension_list
+    score_dimension_dict = {}
+    bad_dimension_list = []
+    dimension_dict = {}
+
+    # 分维度进行评价
+    try:
+        logger.info(f"[case:{case_name}] SINGLE_CASE_EVAL: Dimension evaluate start: ")
+        for dimension in dimension_list:
+            # 根据维度列表动态调用类
+            dimension_instance = globals()[dimension.capitalize()](data_processed, scoreModel, resultPath)
+            # 运行各维度评价函数，进行分析评价及报告内容统计
+            dimension_report_dict = dimension_instance.report_statistic()
+            dimension_dict[dimension] = dimension_report_dict
+            score_dimension_dict[dimension] = dimension_report_dict['score']
+            # 对各维度分数进行判断，分类表现差的维度
+            if score_dimension_dict[dimension] < 80:
+                bad_dimension_list.append(dimension)
+
+            # if dimension == "comfort":
+            #     discomfortCount = dimension_report_dict['discomfortCount']
+            #     bad_indicator_list.append("不舒适行为") if discomfortCount != 0 else good_indicator_list.append("不舒适行为")
+
+    # 异常打印，并退出
+    except Exception as e:
+        traceback.print_exc()
+        logger.error(f"[case:{case_name}] SINGLE_CASE_EVAL: Dimension evaluate ERROR: {repr(e)}!", exc_info=True)
+        sys.exit(-1)
+
+    # 将评价结果写入报告字典内
+    case_report_dict["details"] = dimension_dict
+    # calculate the score and grade of the case
+    weight_dimension_dict = config.dimension_weight
+    score_dimension = {key: score_dimension_dict[key] * weight_dimension_dict[key] for key in score_dimension_dict}
+
+    # 计算用例分数、等级，写入报告字典
+    score_case = round(sum(score_dimension.values()), 2)
+    grade_case = score_grade(score_case)
+    case_report_dict['algorithmComprehensiveScore'] = score_case
+    case_report_dict['algorithmLevel'] = grade_case
+
+    # 计算用例里程和持续时长
+    # case_report_dict['testMileage'] = data_processed.report_info['mileage']
+    # case_report_dict['testDuration'] = data_processed.report_info['duration']
+    case_report_dict['testMileage'] = mileage_format(data_processed.report_info['mileage'])
+    case_report_dict['testDuration'] = duration_format(data_processed.report_info['duration'])
+
+    # generate algorithmResultDescription2
+    if not bad_dimension_list:
+        algorithmResultDescription2 = '算法在各个维度的表现俱佳。'
+    else:
+        str_bad_dimension = string_concatenate(bad_dimension_list)
+        # replace English to Chinese dimension name
+        algorithmResultDescription2 = f'建议算法优化在{str_bad_dimension}指标上的表现。'
+
+    algorithmResultDescription = f"算法得分{score_case}分，总体表现{grade_case}。{algorithmResultDescription2}"
+    case_report_dict['algorithmResultDescription'] = algorithmResultDescription
+
+    # 生成图片字典，将过程生成图片路径记录，用于报告生成
+    graph_dict = {
+        "speed": os.path.join(resultPath, "Speed.png"),
+        "commandSpeed": os.path.join(resultPath, "CommandSpeed.png"),
+        "linearAccelerate": os.path.join(resultPath, "LinearAccelerate.png"),
+        "angularAccelerate": os.path.join(resultPath, "AngularAccelerate.png"),
+        "trajectory": os.path.join(resultPath, "track.png")
+    }
+
+    case_report_dict['graphPath'] = graph_dict
+    return case_report_dict

single_run.py

@@ -0,0 +1,105 @@
+#!/usr/bin/env python
+# -*- coding: utf-8 -*-
+##################################################################
+#
+# Copyright (c) 2023 CICV, Inc. All Rights Reserved
+#
+##################################################################
+"""
+@Authors:           yangzihao(yangzihao@china-icv.cn)
+@Data:              2024/03/27
+@Last Modified:     2024/03/27
+@Summary:           Evaluation entrance
+"""
+
+import os
+import sys
+import time
+from single_case_eval import single_case_eval
+from common import get_subfolders_name
+import log
+
+if __name__ == "__main__":
+
+    # if len(sys.argv) >= 6:
+    #     configPath = sys.argv[1]
+    #     dataPath = sys.argv[2]
+    #     reportPath = sys.argv[3]
+    #     csvPath = sys.argv[4]
+    #     customMetricPath = sys.argv[5]
+    #     customScorePath = sys.argv[6]
+    #
+    #     case_name = os.path.basename(os.path.dirname(dataPath))
+    #
+    #     if not os.path.exists(configPath):
+    #         print('Invalid configPath!')
+    #         logger.error(f"[case:{case_name}] SINGLE_CASE_EVAL: Invalid configPath!")
+    #         sys.exit(-1)
+    #     elif not os.path.exists(dataPath):
+    #         print('Invalid dataPath!')
+    #         logger.error(f"[case:{case_name}] SINGLE_CASE_EVAL: Invalid dataPath!")
+    #         sys.exit(-1)
+    #     elif not os.path.exists(customMetricPath):
+    #         print('Invalid customMetricPath!')
+    #         logger.error(f"[case:{case_name}] SINGLE_CASE_EVAL: Invalid customMetricPath!")
+    #         sys.exit(-1)
+    #     elif not os.path.exists(customScorePath):
+    #         print('Invalid customScorePath!')
+    #         logger.error(f"[case:{case_name}] SINGLE_CASE_EVAL: Invalid customScorePath!")
+    #         sys.exit(-1)
+    #     else:
+    #         try:
+    #             logger.info(f"[case:{case_name}] SINGLE_CASE_EVAL: Start evaluating:")
+    #             print("SINGLE_CASE_EVAL程序开始运行：")
+    #             print(f"   configPath: {configPath},\n   dataPath: {dataPath},\n   reportPath: {reportPath},\n   "
+    #                   f"csvPath: {csvPath},\n   customMetricPath: {customMetricPath}\n   customScorePath: {customScorePath}")
+    #             t1 = time.time()
+    #             single_case_eval(configPath, dataPath, reportPath, csvPath, customMetricPath, customScorePath)
+    #             t2 = time.time()
+    #             print(f"程序结束，执行时间：{int(t2 - t1)} s")
+    #             logger.info(f"[case:{case_name}] SINGLE_CASE_EVAL: End.")
+    #             sys.exit(0)
+    #         except Exception as e:
+    #             print("异常，退出...")
+    #             print(repr(e))
+    #             sys.exit(-1)
+    # else:
+    #     print('No enough arguments!')
+    #     logger.error(f"SINGLE_CASE_EVAL: No enough arguments!")
+    #     sys.exit(-1)
+
+
+    task_path = r"./task_0416"
+
+    cases = get_subfolders_name(task_path)
+
+    for case in cases:
+        dataPath = os.path.join(task_path, case)
+
+        # dataPath = r"./task/pjibot_pjibot-P1YNYD1M228000127_data_merge_2024-04-07-02-01-35_obstacledetection_9"
+        configPath = r"./config.json"
+
+        # case_name = os.path.basename(os.path.dirname(dataPath))
+        case_name = dataPath.split('/')[-1]
+        casePath = os.path.join("./result", case_name)
+        if not os.path.exists(casePath):
+            os.makedirs(casePath)
+
+        reportPath = rf"{casePath}/report.json"
+        logFileName = './result/log.txt'
+
+        log.setup_logger(logFileName)
+        logger = log.get_logger()
+
+        if not os.path.exists(dataPath):
+            print('Invalid dataPath!')
+            logger.error(f"[case:{case_name}] SINGLE_CASE_EVAL: Invalid dataPath!")
+        else:
+            logger.info(f"[case:{case_name}] SINGLE_CASE_EVAL: Start evaluating:")
+            print("SINGLE_CASE_EVAL程序开始运行：")
+            print(f"  dataPath: {dataPath}, \n  reportPath: {reportPath}")
+            t1 = time.time()
+            single_case_eval(configPath, dataPath, reportPath, casePath)
+            t2 = time.time()
+            print(f"程序结束，执行时间：{int(t2 - t1)} s")
+            logger.info(f"[case:{case_name}] SINGLE_CASE_EVAL: End.")

task/case0613/EgoState_pji.csv

@@ -0,0 +1,3337 @@
+Time,simTime,simFrame,posX,posY,posZ,yaw,speed_linear,speed_angular,cmd_speed_linear,cmd_speed_angular,obstacle
+2024-06-12 15:37:15.410875,0.07701,1,0.2973842918872833,-0.7645921111106873,-1.3997269868850708,0.48832422838576456,2.9664000034332276,-0.04590163379907608,2.4489048957824706,-0.1505475640296936,0
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task/case0613/log.log

@@ -0,0 +1,277 @@
+[2024-06-25 15:58:21,563] - [INFO] [pji_run_test.py] [line:96] - [[case:case0613] SINGLE_CASE_EVAL: Start evaluating:]
+[2024-06-25 16:02:45,368] - [INFO] [pji_run_test.py] [line:96] - [[case:case0613] SINGLE_CASE_EVAL: Start evaluating:]
+[2024-06-27 20:32:02,045] - [INFO] [pji_run_test.py] [line:96] - [[case:case0613] SINGLE_CASE_EVAL: Start evaluating:]
+[2024-06-27 20:32:02,392] - [ERROR] [single_case_evaluate.py] [line:59] - [[case:case0613] SINGLE_CASE_EVAL: Data processed ERROR: KeyError('lon_v')!]
+Traceback (most recent call last):
+  File "/home/server/.local/lib/python3.8/site-packages/pandas/core/indexes/base.py", line 3653, in get_loc
+    return self._engine.get_loc(casted_key)
+  File "pandas/_libs/index.pyx", line 147, in pandas._libs.index.IndexEngine.get_loc
+  File "pandas/_libs/index.pyx", line 176, in pandas._libs.index.IndexEngine.get_loc
+  File "pandas/_libs/hashtable_class_helper.pxi", line 7080, in pandas._libs.hashtable.PyObjectHashTable.get_item
+  File "pandas/_libs/hashtable_class_helper.pxi", line 7088, in pandas._libs.hashtable.PyObjectHashTable.get_item
+KeyError: 'lon_v'
+
+The above exception was the direct cause of the following exception:
+
+Traceback (most recent call last):
+  File "/home/server/桌面/virtualEnv_yzh/virtual_pji/PJI_outdoor_robot_evaluate_sim/single_case_evaluate.py", line 56, in single_case_evaluate
+    data_processed = DataProcess(path, config)
+  File "/home/server/桌面/virtualEnv_yzh/virtual_pji/PJI_outdoor_robot_evaluate_sim/data_process.py", line 61, in __init__
+    self._process()
+  File "/home/server/桌面/virtualEnv_yzh/virtual_pji/PJI_outdoor_robot_evaluate_sim/data_process.py", line 73, in _process
+    self._object_df_process()
+  File "/home/server/桌面/virtualEnv_yzh/virtual_pji/PJI_outdoor_robot_evaluate_sim/data_process.py", line 254, in _object_df_process
+    data['v'] = data.apply(lambda row: cal_velocity(row['lat_v'], row['lon_v']), axis=1)
+  File "/home/server/.local/lib/python3.8/site-packages/pandas/core/frame.py", line 9423, in apply
+    return op.apply().__finalize__(self, method="apply")
+  File "/home/server/.local/lib/python3.8/site-packages/pandas/core/apply.py", line 678, in apply
+    return self.apply_standard()
+  File "/home/server/.local/lib/python3.8/site-packages/pandas/core/apply.py", line 798, in apply_standard
+    results, res_index = self.apply_series_generator()
+  File "/home/server/.local/lib/python3.8/site-packages/pandas/core/apply.py", line 814, in apply_series_generator
+    results[i] = self.f(v)
+  File "/home/server/桌面/virtualEnv_yzh/virtual_pji/PJI_outdoor_robot_evaluate_sim/data_process.py", line 254, in <lambda>
+    data['v'] = data.apply(lambda row: cal_velocity(row['lat_v'], row['lon_v']), axis=1)
+  File "/home/server/.local/lib/python3.8/site-packages/pandas/core/series.py", line 1007, in __getitem__
+    return self._get_value(key)
+  File "/home/server/.local/lib/python3.8/site-packages/pandas/core/series.py", line 1116, in _get_value
+    loc = self.index.get_loc(label)
+  File "/home/server/.local/lib/python3.8/site-packages/pandas/core/indexes/base.py", line 3655, in get_loc
+    raise KeyError(key) from err
+KeyError: 'lon_v'
+[2024-06-27 20:35:49,256] - [INFO] [pji_run_test.py] [line:96] - [[case:case0613] SINGLE_CASE_EVAL: Start evaluating:]
+[2024-06-27 20:35:49,562] - [ERROR] [single_case_evaluate.py] [line:59] - [[case:case0613] SINGLE_CASE_EVAL: Data processed ERROR: KeyError('lon_v')!]
+Traceback (most recent call last):
+  File "/home/server/.local/lib/python3.8/site-packages/pandas/core/indexes/base.py", line 3653, in get_loc
+    return self._engine.get_loc(casted_key)
+  File "pandas/_libs/index.pyx", line 147, in pandas._libs.index.IndexEngine.get_loc
+  File "pandas/_libs/index.pyx", line 176, in pandas._libs.index.IndexEngine.get_loc
+  File "pandas/_libs/hashtable_class_helper.pxi", line 7080, in pandas._libs.hashtable.PyObjectHashTable.get_item
+  File "pandas/_libs/hashtable_class_helper.pxi", line 7088, in pandas._libs.hashtable.PyObjectHashTable.get_item
+KeyError: 'lon_v'
+
+The above exception was the direct cause of the following exception:
+
+Traceback (most recent call last):
+  File "/home/server/桌面/virtualEnv_yzh/virtual_pji/PJI_outdoor_robot_evaluate_sim/single_case_evaluate.py", line 56, in single_case_evaluate
+    data_processed = DataProcess(path, config)
+  File "/home/server/桌面/virtualEnv_yzh/virtual_pji/PJI_outdoor_robot_evaluate_sim/data_process.py", line 61, in __init__
+    self._process()
+  File "/home/server/桌面/virtualEnv_yzh/virtual_pji/PJI_outdoor_robot_evaluate_sim/data_process.py", line 73, in _process
+    self._object_df_process()
+  File "/home/server/桌面/virtualEnv_yzh/virtual_pji/PJI_outdoor_robot_evaluate_sim/data_process.py", line 254, in _object_df_process
+    data['v'] = data.apply(lambda row: cal_velocity(row['lat_v'], row['lon_v']), axis=1)
+  File "/home/server/.local/lib/python3.8/site-packages/pandas/core/frame.py", line 9423, in apply
+    return op.apply().__finalize__(self, method="apply")
+  File "/home/server/.local/lib/python3.8/site-packages/pandas/core/apply.py", line 678, in apply
+    return self.apply_standard()
+  File "/home/server/.local/lib/python3.8/site-packages/pandas/core/apply.py", line 798, in apply_standard
+    results, res_index = self.apply_series_generator()
+  File "/home/server/.local/lib/python3.8/site-packages/pandas/core/apply.py", line 814, in apply_series_generator
+    results[i] = self.f(v)
+  File "/home/server/桌面/virtualEnv_yzh/virtual_pji/PJI_outdoor_robot_evaluate_sim/data_process.py", line 254, in <lambda>
+    data['v'] = data.apply(lambda row: cal_velocity(row['lat_v'], row['lon_v']), axis=1)
+  File "/home/server/.local/lib/python3.8/site-packages/pandas/core/series.py", line 1007, in __getitem__
+    return self._get_value(key)
+  File "/home/server/.local/lib/python3.8/site-packages/pandas/core/series.py", line 1116, in _get_value
+    loc = self.index.get_loc(label)
+  File "/home/server/.local/lib/python3.8/site-packages/pandas/core/indexes/base.py", line 3655, in get_loc
+    raise KeyError(key) from err
+KeyError: 'lon_v'
+[2024-06-27 20:36:30,849] - [INFO] [pji_run_test.py] [line:96] - [[case:case0613] SINGLE_CASE_EVAL: Start evaluating:]
+[2024-06-27 20:37:32,032] - [INFO] [pji_run_test.py] [line:96] - [[case:case0613] SINGLE_CASE_EVAL: Start evaluating:]
+[2024-06-27 20:39:30,568] - [INFO] [pji_run_test.py] [line:96] - [[case:case0613] SINGLE_CASE_EVAL: Start evaluating:]
+[2024-06-27 20:39:38,403] - [INFO] [pji_run_test.py] [line:96] - [[case:case0613] SINGLE_CASE_EVAL: Start evaluating:]
+[2024-06-27 20:39:38,753] - [INFO] [single_case_evaluate.py] [line:116] - [[case:case0613] SINGLE_CASE_EVAL: Dimension evaluate start: ]
+[2024-06-27 20:39:38,753] - [ERROR] [single_case_evaluate.py] [line:149] - [[case:case0613] SINGLE_CASE_EVAL: Dimension evaluate ERROR: TypeError('__init__() takes 4 positional arguments but 5 were given')!]
+Traceback (most recent call last):
+  File "/home/server/桌面/virtualEnv_yzh/virtual_pji/PJI_outdoor_robot_evaluate_sim/single_case_evaluate.py", line 118, in single_case_evaluate
+    dimension_instance = globals()[dimension.capitalize()](data_processed, custom_data, scoreModel, resultPath)
+TypeError: __init__() takes 4 positional arguments but 5 were given
+[2024-06-27 20:41:08,152] - [INFO] [pji_run_test.py] [line:96] - [[case:case0613] SINGLE_CASE_EVAL: Start evaluating:]
+[2024-06-27 20:41:08,819] - [INFO] [single_case_evaluate.py] [line:116] - [[case:case0613] SINGLE_CASE_EVAL: Dimension evaluate start: ]
+[2024-06-27 20:43:28,876] - [INFO] [pji_run_test.py] [line:96] - [[case:case0613] SINGLE_CASE_EVAL: Start evaluating:]
+[2024-06-27 20:43:28,878] - [ERROR] [single_case_evaluate.py] [line:59] - [[case:case0613] SINGLE_CASE_EVAL: Data processed ERROR: KeyError('safe')!]
+Traceback (most recent call last):
+  File "/home/server/桌面/virtualEnv_yzh/virtual_pji/PJI_outdoor_robot_evaluate_sim/single_case_evaluate.py", line 56, in single_case_evaluate
+    data_processed = DataProcess(path, config)
+  File "/home/server/桌面/virtualEnv_yzh/virtual_pji/PJI_outdoor_robot_evaluate_sim/data_process.py", line 36, in __init__
+    self.safe_config = config.config['safe']
+KeyError: 'safe'
+[2024-06-28 09:14:45,242] - [INFO] [pji_run_test.py] [line:96] - [[case:case0613] SINGLE_CASE_EVAL: Start evaluating:]
+[2024-06-28 09:14:45,588] - [INFO] [single_case_evaluate.py] [line:116] - [[case:case0613] SINGLE_CASE_EVAL: Dimension evaluate start: ]
+[2024-06-28 09:14:45,589] - [ERROR] [single_case_evaluate.py] [line:149] - [[case:case0613] SINGLE_CASE_EVAL: Dimension evaluate ERROR: KeyError(0)!]
+Traceback (most recent call last):
+  File "/home/server/桌面/virtualEnv_yzh/virtual_pji/PJI_outdoor_robot_evaluate_sim/single_case_evaluate.py", line 118, in single_case_evaluate
+    dimension_instance = globals()[dimension.capitalize()](data_processed, custom_data, scoreModel, resultPath)
+  File "/home/server/桌面/virtualEnv_yzh/virtual_pji/PJI_outdoor_robot_evaluate_sim/comfort.py", line 115, in __init__
+    self.optimal1_dict = self.optimal_dict[0]
+KeyError: 0
+[2024-06-28 09:14:59,620] - [INFO] [pji_run_test.py] [line:96] - [[case:case0613] SINGLE_CASE_EVAL: Start evaluating:]
+[2024-06-28 09:15:00,285] - [INFO] [single_case_evaluate.py] [line:116] - [[case:case0613] SINGLE_CASE_EVAL: Dimension evaluate start: ]
+[2024-06-28 10:52:31,759] - [INFO] [pji_run_test.py] [line:96] - [[case:case0613] SINGLE_CASE_EVAL: Start evaluating:]
+[2024-06-28 10:52:32,108] - [INFO] [single_case_evaluate.py] [line:116] - [[case:case0613] SINGLE_CASE_EVAL: Dimension evaluate start: ]
+[2024-06-28 10:52:32,111] - [ERROR] [single_case_evaluate.py] [line:149] - [[case:case0613] SINGLE_CASE_EVAL: Dimension evaluate ERROR: AttributeError("'Comfort' object has no attribute 'optimal1_dict'")!]
+Traceback (most recent call last):
+  File "/home/server/桌面/virtualEnv_yzh/virtual_pji/PJI_outdoor_robot_evaluate_sim/single_case_evaluate.py", line 118, in single_case_evaluate
+    dimension_instance = globals()[dimension.capitalize()](data_processed, custom_data, scoreModel, resultPath)
+  File "/home/server/桌面/virtualEnv_yzh/virtual_pji/PJI_outdoor_robot_evaluate_sim/comfort.py", line 167, in __init__
+    self._comf_param_cal()
+  File "/home/server/桌面/virtualEnv_yzh/virtual_pji/PJI_outdoor_robot_evaluate_sim/comfort.py", line 212, in _comf_param_cal
+    self.optimal1_dict = {key: value * self.mileage / 100000 for key, value in self.optimal1_dict.copy().items()}
+AttributeError: 'Comfort' object has no attribute 'optimal1_dict'
+[2024-06-28 10:52:56,744] - [INFO] [pji_run_test.py] [line:96] - [[case:case0613] SINGLE_CASE_EVAL: Start evaluating:]
+[2024-06-28 10:52:57,094] - [INFO] [single_case_evaluate.py] [line:116] - [[case:case0613] SINGLE_CASE_EVAL: Dimension evaluate start: ]
+[2024-06-28 10:52:57,198] - [ERROR] [single_case_evaluate.py] [line:149] - [[case:case0613] SINGLE_CASE_EVAL: Dimension evaluate ERROR: IndexError('list index out of range')!]
+Traceback (most recent call last):
+  File "/home/server/桌面/virtualEnv_yzh/virtual_pji/PJI_outdoor_robot_evaluate_sim/single_case_evaluate.py", line 125, in single_case_evaluate
+    dimension_report_dict = dimension_instance.report_statistic()
+  File "/home/server/桌面/virtualEnv_yzh/virtual_pji/PJI_outdoor_robot_evaluate_sim/comfort.py", line 1022, in report_statistic
+    score_comfort, score_type_dict, score_metric_dict = self.comf_score_new()
+  File "/home/server/桌面/virtualEnv_yzh/virtual_pji/PJI_outdoor_robot_evaluate_sim/comfort.py", line 853, in comf_score_new
+    score_sub = score_model.cal_score()
+  File "/home/server/桌面/virtualEnv_yzh/virtual_pji/PJI_outdoor_robot_evaluate_sim/score_weight.py", line 121, in cal_score
+    optimal = self.optimal[i]
+IndexError: list index out of range
+[2024-07-01 15:00:56,990] - [INFO] [pji_run_test.py] [line:96] - [[case:case0613] SINGLE_CASE_EVAL: Start evaluating:]
+[2024-07-01 15:00:57,637] - [INFO] [single_case_evaluate.py] [line:116] - [[case:case0613] SINGLE_CASE_EVAL: Dimension evaluate start: ]
+[2024-07-01 16:07:53,208] - [INFO] [pji_run_test.py] [line:96] - [[case:case0613] SINGLE_CASE_EVAL: Start evaluating:]
+[2024-07-01 16:33:18,830] - [INFO] [pji_run_test.py] [line:96] - [[case:case0613] SINGLE_CASE_EVAL: Start evaluating:]
+[2024-07-01 16:33:31,800] - [INFO] [pji_run_test.py] [line:96] - [[case:case0613] SINGLE_CASE_EVAL: Start evaluating:]
+[2024-07-01 16:33:32,449] - [INFO] [single_case_evaluate.py] [line:116] - [[case:case0613] SINGLE_CASE_EVAL: Dimension evaluate start: ]
+[2024-07-01 16:36:39,102] - [INFO] [pji_run_test.py] [line:96] - [[case:case0613] SINGLE_CASE_EVAL: Start evaluating:]
+[2024-07-01 16:38:40,395] - [INFO] [pji_run_test.py] [line:96] - [[case:case0613] SINGLE_CASE_EVAL: Start evaluating:]
+[2024-07-01 16:43:18,163] - [INFO] [pji_run_test.py] [line:96] - [[case:case0613] SINGLE_CASE_EVAL: Start evaluating:]
+[2024-07-01 16:43:18,845] - [INFO] [single_case_evaluate.py] [line:116] - [[case:case0613] SINGLE_CASE_EVAL: Dimension evaluate start: ]
+[2024-07-01 16:45:15,280] - [INFO] [pji_run_test.py] [line:96] - [[case:case0613] SINGLE_CASE_EVAL: Start evaluating:]
+[2024-07-01 16:45:15,835] - [INFO] [single_case_evaluate.py] [line:116] - [[case:case0613] SINGLE_CASE_EVAL: Dimension evaluate start: ]
+[2024-07-01 16:46:02,592] - [INFO] [pji_run_test.py] [line:96] - [[case:case0613] SINGLE_CASE_EVAL: Start evaluating:]
+[2024-07-01 16:46:03,116] - [INFO] [single_case_evaluate.py] [line:116] - [[case:case0613] SINGLE_CASE_EVAL: Dimension evaluate start: ]
+[2024-07-02 14:24:47,064] - [INFO] [pji_run_test.py] [line:96] - [[case:case0613] SINGLE_CASE_EVAL: Start evaluating:]
+[2024-07-02 15:00:33,956] - [INFO] [pji_run_test.py] [line:96] - [[case:case0613] SINGLE_CASE_EVAL: Start evaluating:]
+[2024-07-03 15:54:21,969] - [INFO] [pji_run_test.py] [line:96] - [[case:case0613] SINGLE_CASE_EVAL: Start evaluating:]
+[2024-07-03 15:54:22,562] - [INFO] [single_case_evaluate.py] [line:81] - [[case:case0613] SINGLE_CASE_EVAL: Dimension evaluate start: ]
+[2024-07-03 15:54:22,562] - [ERROR] [single_case_evaluate.py] [line:98] - [[case:case0613] SINGLE_CASE_EVAL: Dimension evaluate ERROR: TypeError("__init__() missing 1 required positional argument: 'resultPath'")!]
+Traceback (most recent call last):
+  File "/home/server/桌面/virtualEnv_yzh/virtual_pji/PJI_outdoor_robot_evaluate_sim/single_case_evaluate.py", line 83, in single_case_evaluate
+    dimension_instance = globals()[dimension.capitalize()](data_processed, scoreModel, resultPath)
+TypeError: __init__() missing 1 required positional argument: 'resultPath'
+[2024-07-03 15:55:08,556] - [INFO] [pji_run_test.py] [line:96] - [[case:case0613] SINGLE_CASE_EVAL: Start evaluating:]
+[2024-07-03 15:55:09,123] - [INFO] [single_case_evaluate.py] [line:81] - [[case:case0613] SINGLE_CASE_EVAL: Dimension evaluate start: ]
+[2024-07-03 15:55:09,524] - [ERROR] [single_case_evaluate.py] [line:98] - [[case:case0613] SINGLE_CASE_EVAL: Dimension evaluate ERROR: KeyError('cmd_lon_v_diff')!]
+Traceback (most recent call last):
+  File "/home/server/.local/lib/python3.8/site-packages/pandas/core/indexes/base.py", line 3653, in get_loc
+    return self._engine.get_loc(casted_key)
+  File "pandas/_libs/index.pyx", line 147, in pandas._libs.index.IndexEngine.get_loc
+  File "pandas/_libs/index.pyx", line 176, in pandas._libs.index.IndexEngine.get_loc
+  File "pandas/_libs/hashtable_class_helper.pxi", line 7080, in pandas._libs.hashtable.PyObjectHashTable.get_item
+  File "pandas/_libs/hashtable_class_helper.pxi", line 7088, in pandas._libs.hashtable.PyObjectHashTable.get_item
+KeyError: 'cmd_lon_v_diff'
+
+The above exception was the direct cause of the following exception:
+
+Traceback (most recent call last):
+  File "/home/server/桌面/virtualEnv_yzh/virtual_pji/PJI_outdoor_robot_evaluate_sim/single_case_evaluate.py", line 83, in single_case_evaluate
+    dimension_instance = globals()[dimension.capitalize()](data_processed, scoreModel, resultPath)
+  File "/home/server/桌面/virtualEnv_yzh/virtual_pji/PJI_outdoor_robot_evaluate_sim/comfort.py", line 168, in __init__
+    self._comf_param_cal()
+  File "/home/server/桌面/virtualEnv_yzh/virtual_pji/PJI_outdoor_robot_evaluate_sim/comfort.py", line 233, in _comf_param_cal
+    self.ego_df['cmd_lon_v_diff'] = self.ego_df['cmd_lon_v_diff'].diff()
+  File "/home/server/.local/lib/python3.8/site-packages/pandas/core/frame.py", line 3761, in __getitem__
+    indexer = self.columns.get_loc(key)
+  File "/home/server/.local/lib/python3.8/site-packages/pandas/core/indexes/base.py", line 3655, in get_loc
+    raise KeyError(key) from err
+KeyError: 'cmd_lon_v_diff'
+[2024-07-03 15:55:25,527] - [INFO] [pji_run_test.py] [line:96] - [[case:case0613] SINGLE_CASE_EVAL: Start evaluating:]
+[2024-07-03 15:55:26,112] - [INFO] [single_case_evaluate.py] [line:81] - [[case:case0613] SINGLE_CASE_EVAL: Dimension evaluate start: ]
+[2024-07-03 15:55:26,803] - [ERROR] [single_case_evaluate.py] [line:98] - [[case:case0613] SINGLE_CASE_EVAL: Dimension evaluate ERROR: KeyError('cmd_lon_v')!]
+Traceback (most recent call last):
+  File "/home/server/.local/lib/python3.8/site-packages/pandas/core/indexes/base.py", line 3653, in get_loc
+    return self._engine.get_loc(casted_key)
+  File "pandas/_libs/index.pyx", line 147, in pandas._libs.index.IndexEngine.get_loc
+  File "pandas/_libs/index.pyx", line 176, in pandas._libs.index.IndexEngine.get_loc
+  File "pandas/_libs/hashtable_class_helper.pxi", line 7080, in pandas._libs.hashtable.PyObjectHashTable.get_item
+  File "pandas/_libs/hashtable_class_helper.pxi", line 7088, in pandas._libs.hashtable.PyObjectHashTable.get_item
+KeyError: 'cmd_lon_v'
+
+The above exception was the direct cause of the following exception:
+
+Traceback (most recent call last):
+  File "/home/server/桌面/virtualEnv_yzh/virtual_pji/PJI_outdoor_robot_evaluate_sim/single_case_evaluate.py", line 83, in single_case_evaluate
+    dimension_instance = globals()[dimension.capitalize()](data_processed, scoreModel, resultPath)
+  File "/home/server/桌面/virtualEnv_yzh/virtual_pji/PJI_outdoor_robot_evaluate_sim/comfort.py", line 168, in __init__
+    self._comf_param_cal()
+  File "/home/server/桌面/virtualEnv_yzh/virtual_pji/PJI_outdoor_robot_evaluate_sim/comfort.py", line 233, in _comf_param_cal
+    self.ego_df['cmd_lon_v_diff'] = self.ego_df['cmd_lon_v'].diff()
+  File "/home/server/.local/lib/python3.8/site-packages/pandas/core/frame.py", line 3761, in __getitem__
+    indexer = self.columns.get_loc(key)
+  File "/home/server/.local/lib/python3.8/site-packages/pandas/core/indexes/base.py", line 3655, in get_loc
+    raise KeyError(key) from err
+KeyError: 'cmd_lon_v'
+[2024-07-03 15:56:15,337] - [INFO] [pji_run_test.py] [line:96] - [[case:case0613] SINGLE_CASE_EVAL: Start evaluating:]
+[2024-07-03 15:56:15,881] - [INFO] [single_case_evaluate.py] [line:81] - [[case:case0613] SINGLE_CASE_EVAL: Dimension evaluate start: ]
+[2024-07-03 15:57:42,427] - [INFO] [pji_run_test.py] [line:96] - [[case:case0613] SINGLE_CASE_EVAL: Start evaluating:]
+[2024-07-03 15:57:43,023] - [INFO] [single_case_evaluate.py] [line:81] - [[case:case0613] SINGLE_CASE_EVAL: Dimension evaluate start: ]
+[2024-07-03 16:03:19,315] - [INFO] [pji_run_test.py] [line:96] - [[case:case0613] SINGLE_CASE_EVAL: Start evaluating:]
+[2024-07-03 16:03:19,950] - [INFO] [single_case_evaluate.py] [line:81] - [[case:case0613] SINGLE_CASE_EVAL: Dimension evaluate start: ]
+[2024-07-03 16:13:24,334] - [INFO] [pji_run_test.py] [line:96] - [[case:case0613] SINGLE_CASE_EVAL: Start evaluating:]
+[2024-07-03 16:13:24,963] - [INFO] [single_case_evaluate.py] [line:81] - [[case:case0613] SINGLE_CASE_EVAL: Dimension evaluate start: ]
+[2024-07-03 16:21:01,534] - [ERROR] [single_case_evaluate.py] [line:98] - [[case:case0613] SINGLE_CASE_EVAL: Dimension evaluate ERROR: KeyError('max')!]
+Traceback (most recent call last):
+  File "/home/server/桌面/virtualEnv_yzh/virtual_pji/PJI_outdoor_robot_evaluate_sim/single_case_evaluate.py", line 85, in single_case_evaluate
+    dimension_report_dict = dimension_instance.report_statistic()
+  File "/home/server/桌面/virtualEnv_yzh/virtual_pji/PJI_outdoor_robot_evaluate_sim/comfort.py", line 777, in report_statistic
+    description3 = f"最大值：{self.speed_instruction_dict['max']:.4f}rad/s²；" \
+KeyError: 'max'
+[2024-07-03 16:27:51,765] - [INFO] [pji_run_test.py] [line:96] - [[case:case0613] SINGLE_CASE_EVAL: Start evaluating:]
+[2024-07-03 16:27:52,318] - [INFO] [single_case_evaluate.py] [line:81] - [[case:case0613] SINGLE_CASE_EVAL: Dimension evaluate start: ]
+[2024-07-03 16:31:34,211] - [INFO] [pji_run_test.py] [line:96] - [[case:case0613] SINGLE_CASE_EVAL: Start evaluating:]
+[2024-07-03 16:34:29,435] - [INFO] [pji_run_test.py] [line:96] - [[case:case0613] SINGLE_CASE_EVAL: Start evaluating:]
+[2024-07-03 16:34:29,994] - [INFO] [single_case_evaluate.py] [line:81] - [[case:case0613] SINGLE_CASE_EVAL: Dimension evaluate start: ]
+[2024-07-03 16:44:29,348] - [INFO] [pji_run_test.py] [line:96] - [[case:case0613] SINGLE_CASE_EVAL: Start evaluating:]
+[2024-07-03 16:44:29,981] - [INFO] [single_case_evaluate.py] [line:81] - [[case:case0613] SINGLE_CASE_EVAL: Dimension evaluate start: ]
+[2024-07-03 17:46:50,504] - [INFO] [pji_run_test.py] [line:96] - [[case:case0613] SINGLE_CASE_EVAL: Start evaluating:]
+[2024-07-03 17:46:51,109] - [INFO] [single_case_evaluate.py] [line:81] - [[case:case0613] SINGLE_CASE_EVAL: Dimension evaluate start: ]
+[2024-07-23 10:16:40,211] - [INFO] [pji_run_test.py] [line:96] - [[case:case0613] SINGLE_CASE_EVAL: Start evaluating:]
+[2024-07-23 10:16:40,230] - [ERROR] [single_case_evaluate.py] [line:55] - [[case:case0613] SINGLE_CASE_EVAL: Data processed ERROR: KeyError('comfort')!]
+Traceback (most recent call last):
+  File "/home/server/桌面/virtualEnv_yzh/virtual_pji/PJI_outdoor_robot_evaluate_sim/single_case_evaluate.py", line 52, in single_case_evaluate
+    data_processed = DataProcess(dataPath, config)
+  File "/home/server/桌面/virtualEnv_yzh/virtual_pji/PJI_outdoor_robot_evaluate_sim/data_process.py", line 37, in __init__
+    self.comfort_config = config.config['comfort']
+KeyError: 'comfort'
+[2024-07-23 10:16:51,069] - [INFO] [pji_run_test.py] [line:96] - [[case:case0613] SINGLE_CASE_EVAL: Start evaluating:]
+[2024-07-23 10:16:51,625] - [INFO] [single_case_evaluate.py] [line:81] - [[case:case0613] SINGLE_CASE_EVAL: Dimension evaluate start: ]
+[2024-07-23 10:22:34,778] - [INFO] [pji_run_test.py] [line:96] - [[case:case0613] SINGLE_CASE_EVAL: Start evaluating:]
+[2024-07-23 10:24:05,747] - [INFO] [pji_run_test.py] [line:96] - [[case:case0613] SINGLE_CASE_EVAL: Start evaluating:]
+[2024-07-23 11:07:44,435] - [INFO] [pji_run_test.py] [line:96] - [[case:case0613] SINGLE_CASE_EVAL: Start evaluating:]
+[2024-07-23 11:28:03,864] - [INFO] [pji_run_test.py] [line:96] - [[case:case0613] SINGLE_CASE_EVAL: Start evaluating:]
+[2024-07-23 11:28:04,367] - [ERROR] [single_case_evaluate.py] [line:55] - [[case:case0613] SINGLE_CASE_EVAL: Data processed ERROR: ValueError('You are trying to merge on int64 and object columns. If you wish to proceed you should use pd.concat')!]
+Traceback (most recent call last):
+  File "/home/server/桌面/virtualEnv_yzh/virtual_pji/PJI_outdoor_robot_evaluate_sim/single_case_evaluate.py", line 52, in single_case_evaluate
+    data_processed = DataProcess(dataPath, config)
+  File "/home/server/桌面/virtualEnv_yzh/virtual_pji/PJI_outdoor_robot_evaluate_sim/data_process.py", line 52, in __init__
+    self._process()
+  File "/home/server/桌面/virtualEnv_yzh/virtual_pji/PJI_outdoor_robot_evaluate_sim/data_process.py", line 65, in _process
+    self._object_df_process()
+  File "/home/server/桌面/virtualEnv_yzh/virtual_pji/PJI_outdoor_robot_evaluate_sim/data_process.py", line 270, in _object_df_process
+    self.object_df = pd.merge(self.object_df, self.ego_df, how='left')
+  File "/home/server/.local/lib/python3.8/site-packages/pandas/core/reshape/merge.py", line 148, in merge
+    op = _MergeOperation(
+  File "/home/server/.local/lib/python3.8/site-packages/pandas/core/reshape/merge.py", line 741, in __init__
+    self._maybe_coerce_merge_keys()
+  File "/home/server/.local/lib/python3.8/site-packages/pandas/core/reshape/merge.py", line 1401, in _maybe_coerce_merge_keys
+    raise ValueError(msg)
+ValueError: You are trying to merge on int64 and object columns. If you wish to proceed you should use pd.concat
+[2024-07-23 11:28:37,856] - [INFO] [pji_run_test.py] [line:96] - [[case:case0613] SINGLE_CASE_EVAL: Start evaluating:]
+[2024-07-23 13:22:44,369] - [INFO] [pji_run_test.py] [line:96] - [[case:case0613] SINGLE_CASE_EVAL: Start evaluating:]
+[2024-07-23 17:23:05,971] - [INFO] [pji_run_test.py] [line:96] - [[case:case0613] SINGLE_CASE_EVAL: Start evaluating:]
+[2024-07-23 17:23:06,310] - [ERROR] [single_case_evaluate.py] [line:55] - [[case:case0613] SINGLE_CASE_EVAL: Data processed ERROR: KeyError(1)!]
+Traceback (most recent call last):
+  File "/home/server/桌面/virtualEnv_yzh/virtual_pji/PJI_outdoor_robot_evaluate_sim/single_case_evaluate.py", line 52, in single_case_evaluate
+    data_processed = DataProcess(dataPath, config)
+  File "/home/server/桌面/virtualEnv_yzh/virtual_pji/PJI_outdoor_robot_evaluate_sim/data_process.py", line 52, in __init__
+    self._process()
+  File "/home/server/桌面/virtualEnv_yzh/virtual_pji/PJI_outdoor_robot_evaluate_sim/data_process.py", line 66, in _process
+    self.report_info = self._get_report_info(self.obj_data[1])
+KeyError: 1

task/case0613/objects.csv

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task/case0613/report.json

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+{
+  "name": "case0613",
+  "details": {
+    "comfort": {
+      "name": "平顺性",
+      "weight": "15.00%",
+      "score": 46.8,
+      "level": "较差",
+      "description": "· 在平顺性方面，得分46.8分，表现较差，线加速度变化剧烈，有顿挫情况，需重点优化。角加速度变化剧烈，有画龙情况，需重点优化。",
+      "indexes": {
+        "comfortLat": {
+          "weight": 0.05,
+          "score": 59.4,
+          "description": "最大值：5.0047m/s²；最小值：-7.5916m/s²；平均值：-0.0094m/s²"
+        },
+        "comfortLon": {
+          "weight": 0.05,
+          "score": 0.0,
+          "description": "最大值：2.7512rad/s²；最小值：-2.9821rad/s²；平均值：0.0007rad/s²"
+        },
+        "comfortSpeed": {
+          "weight": 0.05,
+          "score": 80.0,
+          "description": "最大值：1.3640m/s；最小值：-1.4774m/s；平均值：-0.0007m/s"
+        }
+      }
+    },
+    "safe": {
+      "name": "安全性",
+      "weight": "75.00%",
+      "score": 85.8,
+      "level": "良好",
+      "description": "· 在安全性方面，得分85.8分，表现良好，速度控制良好。",
+      "indexes": {
+        "collisionCount": {
+          "weight": 0.60,
+          "score": 86,
+          "description": "次数：0次"
+        },
+        "collisionRisk": {
+          "weight": 0.05,
+          "score": 80,
+          "description": "最大值：0%；最小值：0%；平均值：0%"
+        },
+        "collisionSeverity": {
+          "weight": 0.05,
+          "score": 80,
+          "description": "最大值：0%；最小值：0%；平均值：0%"
+        },
+        "overSpeed": {
+          "weight": 0.05,
+          "score": 80,
+          "description": "最大值：0%；最小值：0%；平均值：0%"
+        }
+      }
+    },
+    "accurate": {
+      "weight": 0.10,
+      "score": 100,
+      "level": "优秀",
+      "description": "· 在准确性方面，得分100分，表现优秀，算法表现较佳。",
+      "indexes": {
+        "positionError": {
+          "weight": 0.05,
+          "score": 80,
+          "description": "最大值：0m；最小值：0m；平均值：0m"
+        },
+        "executeAccurateError": {
+          "weight": 0.05,
+          "score": 80,
+          "description": "最大值：0次；最小值：0次；平均值：0次"
+          }
+      }
+    }
+  },
+  "algorithmComprehensiveScore": 87.02,
+  "algorithmLevel": "良好",
+  "testMileage": "1.20公里",
+  "testDuration": "5分35秒",
+  "algorithmResultDescription": "算法得分87.02分，总体表现良好。",
+  "graphPath": {
+    "speed": "./task/case0613/Speed.png",
+    "commandSpeed": "./task/case0613/CommandSpeed.png",
+    "linearAccelerate": "./task/case0613/LinearAccelerate.png",
+    "angularAccelerate": "./task/case0613/AngularAccelerate.png"
+  }
+}

task/case0701_fake/EgoState_pji.csv

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task/case0730/log.log

@@ -0,0 +1,513 @@
+[2024-07-31 17:07:47,059] - [INFO] [pji_run_test.py] [line:98] - [[case:case0730] SINGLE_CASE_EVAL: Start evaluating:]
+[2024-07-31 17:07:47,353] - [ERROR] [single_case_evaluate.py] [line:55] - [[case:case0730] SINGLE_CASE_EVAL: Data processed ERROR: KeyError(1)!]
+Traceback (most recent call last):
+  File "/home/server/桌面/virtualEnv_yzh/virtual_pji/PJI_outdoor_robot_evaluate_sim/single_case_evaluate.py", line 52, in single_case_evaluate
+    data_processed = DataProcess(dataPath, config)
+  File "/home/server/桌面/virtualEnv_yzh/virtual_pji/PJI_outdoor_robot_evaluate_sim/data_process.py", line 53, in __init__
+    self._process()
+  File "/home/server/桌面/virtualEnv_yzh/virtual_pji/PJI_outdoor_robot_evaluate_sim/data_process.py", line 67, in _process
+    self.report_info = self._get_report_info(self.obj_data[1])
+KeyError: 1
+[2024-07-31 17:09:13,947] - [INFO] [pji_run_test.py] [line:98] - [[case:case0730] SINGLE_CASE_EVAL: Start evaluating:]
+[2024-07-31 17:09:14,226] - [INFO] [single_case_evaluate.py] [line:81] - [[case:case0730] SINGLE_CASE_EVAL: Dimension evaluate start: ]
+[2024-07-31 17:09:14,229] - [ERROR] [single_case_evaluate.py] [line:98] - [[case:case0730] SINGLE_CASE_EVAL: Dimension evaluate ERROR: KeyError('posX')!]
+Traceback (most recent call last):
+  File "/home/server/桌面/virtualEnv_yzh/virtual_pji/PJI_outdoor_robot_evaluate_sim/single_case_evaluate.py", line 83, in single_case_evaluate
+    dimension_instance = globals()[dimension.capitalize()](data_processed, scoreModel, resultPath)
+  File "/home/server/桌面/virtualEnv_yzh/virtual_pji/PJI_outdoor_robot_evaluate_sim/safe.py", line 133, in __init__
+    self._safe_metric_cal()
+  File "/home/server/桌面/virtualEnv_yzh/virtual_pji/PJI_outdoor_robot_evaluate_sim/safe.py", line 225, in _safe_metric_cal
+    x2 = obj_data['posX']
+KeyError: 'posX'
+[2024-07-31 17:10:58,563] - [INFO] [pji_run_test.py] [line:98] - [[case:case0730] SINGLE_CASE_EVAL: Start evaluating:]
+[2024-07-31 17:10:58,822] - [INFO] [single_case_evaluate.py] [line:81] - [[case:case0730] SINGLE_CASE_EVAL: Dimension evaluate start: ]
+[2024-07-31 17:10:58,825] - [ERROR] [single_case_evaluate.py] [line:98] - [[case:case0730] SINGLE_CASE_EVAL: Dimension evaluate ERROR: KeyError('velocity_linear_x')!]
+Traceback (most recent call last):
+  File "/home/server/桌面/virtualEnv_yzh/virtual_pji/PJI_outdoor_robot_evaluate_sim/single_case_evaluate.py", line 83, in single_case_evaluate
+    dimension_instance = globals()[dimension.capitalize()](data_processed, scoreModel, resultPath)
+  File "/home/server/桌面/virtualEnv_yzh/virtual_pji/PJI_outdoor_robot_evaluate_sim/safe.py", line 133, in __init__
+    self._safe_metric_cal()
+  File "/home/server/桌面/virtualEnv_yzh/virtual_pji/PJI_outdoor_robot_evaluate_sim/safe.py", line 238, in _safe_metric_cal
+    v_x2 = obj_data['velocity_linear_x']  # km/h to m/s
+KeyError: 'velocity_linear_x'
+[2024-07-31 17:11:52,969] - [INFO] [pji_run_test.py] [line:98] - [[case:case0730] SINGLE_CASE_EVAL: Start evaluating:]
+[2024-07-31 17:11:53,239] - [INFO] [single_case_evaluate.py] [line:81] - [[case:case0730] SINGLE_CASE_EVAL: Dimension evaluate start: ]
+[2024-07-31 17:11:53,246] - [ERROR] [single_case_evaluate.py] [line:98] - [[case:case0730] SINGLE_CASE_EVAL: Dimension evaluate ERROR: TypeError("unsupported operand type(s) for -: 'list' and 'float'")!]
+Traceback (most recent call last):
+  File "/home/server/桌面/virtualEnv_yzh/virtual_pji/PJI_outdoor_robot_evaluate_sim/single_case_evaluate.py", line 83, in single_case_evaluate
+    dimension_instance = globals()[dimension.capitalize()](data_processed, scoreModel, resultPath)
+  File "/home/server/桌面/virtualEnv_yzh/virtual_pji/PJI_outdoor_robot_evaluate_sim/safe.py", line 134, in __init__
+    self._overspeed_cal()
+  File "/home/server/桌面/virtualEnv_yzh/virtual_pji/PJI_outdoor_robot_evaluate_sim/safe.py", line 159, in _overspeed_cal
+    overspeed = (max_speed - OVERSPEED_THRESHOLD) / OVERSPEED_THRESHOLD * 100
+TypeError: unsupported operand type(s) for -: 'list' and 'float'
+[2024-07-31 17:12:29,723] - [INFO] [pji_run_test.py] [line:98] - [[case:case0730] SINGLE_CASE_EVAL: Start evaluating:]
+[2024-07-31 17:12:29,979] - [INFO] [single_case_evaluate.py] [line:81] - [[case:case0730] SINGLE_CASE_EVAL: Dimension evaluate start: ]
+[2024-07-31 17:12:29,986] - [ERROR] [single_case_evaluate.py] [line:98] - [[case:case0730] SINGLE_CASE_EVAL: Dimension evaluate ERROR: TypeError("'int' object is not iterable")!]
+Traceback (most recent call last):
+  File "/home/server/桌面/virtualEnv_yzh/virtual_pji/PJI_outdoor_robot_evaluate_sim/single_case_evaluate.py", line 85, in single_case_evaluate
+    dimension_report_dict = dimension_instance.report_statistic()
+  File "/home/server/桌面/virtualEnv_yzh/virtual_pji/PJI_outdoor_robot_evaluate_sim/safe.py", line 650, in report_statistic
+    score_safe, score_type_dict, score_metric_dict = self.safe_score_new()
+  File "/home/server/桌面/virtualEnv_yzh/virtual_pji/PJI_outdoor_robot_evaluate_sim/safe.py", line 525, in safe_score_new
+    print("安全性各指标值：", [[round(num, 2) for num in row] for row in arr_safe])
+  File "/home/server/桌面/virtualEnv_yzh/virtual_pji/PJI_outdoor_robot_evaluate_sim/safe.py", line 525, in <listcomp>
+    print("安全性各指标值：", [[round(num, 2) for num in row] for row in arr_safe])
+TypeError: 'int' object is not iterable
+[2024-07-31 17:13:00,765] - [INFO] [pji_run_test.py] [line:98] - [[case:case0730] SINGLE_CASE_EVAL: Start evaluating:]
+[2024-07-31 17:13:01,023] - [INFO] [single_case_evaluate.py] [line:81] - [[case:case0730] SINGLE_CASE_EVAL: Dimension evaluate start: ]
+[2024-07-31 17:13:01,030] - [ERROR] [single_case_evaluate.py] [line:98] - [[case:case0730] SINGLE_CASE_EVAL: Dimension evaluate ERROR: ValueError('not enough values to unpack (expected 2, got 1)')!]
+Traceback (most recent call last):
+  File "/home/server/桌面/virtualEnv_yzh/virtual_pji/PJI_outdoor_robot_evaluate_sim/single_case_evaluate.py", line 85, in single_case_evaluate
+    dimension_report_dict = dimension_instance.report_statistic()
+  File "/home/server/桌面/virtualEnv_yzh/virtual_pji/PJI_outdoor_robot_evaluate_sim/safe.py", line 650, in report_statistic
+    score_safe, score_type_dict, score_metric_dict = self.safe_score_new()
+  File "/home/server/桌面/virtualEnv_yzh/virtual_pji/PJI_outdoor_robot_evaluate_sim/safe.py", line 529, in safe_score_new
+    score_model = self.scoreModel(self.kind_list, self.optimal_list, self.multiple_list, arr_safe)
+  File "/home/server/桌面/virtualEnv_yzh/virtual_pji/PJI_outdoor_robot_evaluate_sim/score_weight.py", line 78, in __init__
+    self.n, self.m = arr.shape
+ValueError: not enough values to unpack (expected 2, got 1)
+[2024-07-31 17:13:43,112] - [INFO] [pji_run_test.py] [line:98] - [[case:case0730] SINGLE_CASE_EVAL: Start evaluating:]
+[2024-07-31 17:13:43,371] - [INFO] [single_case_evaluate.py] [line:81] - [[case:case0730] SINGLE_CASE_EVAL: Dimension evaluate start: ]
+[2024-07-31 17:13:43,378] - [ERROR] [single_case_evaluate.py] [line:98] - [[case:case0730] SINGLE_CASE_EVAL: Dimension evaluate ERROR: TypeError("type list doesn't define __round__ method")!]
+Traceback (most recent call last):
+  File "/home/server/桌面/virtualEnv_yzh/virtual_pji/PJI_outdoor_robot_evaluate_sim/single_case_evaluate.py", line 85, in single_case_evaluate
+    dimension_report_dict = dimension_instance.report_statistic()
+  File "/home/server/桌面/virtualEnv_yzh/virtual_pji/PJI_outdoor_robot_evaluate_sim/safe.py", line 650, in report_statistic
+    score_safe, score_type_dict, score_metric_dict = self.safe_score_new()
+  File "/home/server/桌面/virtualEnv_yzh/virtual_pji/PJI_outdoor_robot_evaluate_sim/safe.py", line 525, in safe_score_new
+    print("安全性各指标值：", [round(num, 2) for num in arr_safe])
+  File "/home/server/桌面/virtualEnv_yzh/virtual_pji/PJI_outdoor_robot_evaluate_sim/safe.py", line 525, in <listcomp>
+    print("安全性各指标值：", [round(num, 2) for num in arr_safe])
+TypeError: type list doesn't define __round__ method
+[2024-07-31 17:14:35,424] - [INFO] [pji_run_test.py] [line:98] - [[case:case0730] SINGLE_CASE_EVAL: Start evaluating:]
+[2024-07-31 17:14:35,694] - [INFO] [single_case_evaluate.py] [line:81] - [[case:case0730] SINGLE_CASE_EVAL: Dimension evaluate start: ]
+[2024-07-31 17:14:35,701] - [ERROR] [single_case_evaluate.py] [line:98] - [[case:case0730] SINGLE_CASE_EVAL: Dimension evaluate ERROR: IndexError('list index out of range')!]
+Traceback (most recent call last):
+  File "/home/server/桌面/virtualEnv_yzh/virtual_pji/PJI_outdoor_robot_evaluate_sim/single_case_evaluate.py", line 85, in single_case_evaluate
+    dimension_report_dict = dimension_instance.report_statistic()
+  File "/home/server/桌面/virtualEnv_yzh/virtual_pji/PJI_outdoor_robot_evaluate_sim/safe.py", line 650, in report_statistic
+    score_safe, score_type_dict, score_metric_dict = self.safe_score_new()
+  File "/home/server/桌面/virtualEnv_yzh/virtual_pji/PJI_outdoor_robot_evaluate_sim/safe.py", line 542, in safe_score_new
+    score_tmp = (score_sub[i] + score_sub[i + metric_num]) / 2
+IndexError: list index out of range
+[2024-07-31 17:19:41,190] - [INFO] [pji_run_test.py] [line:98] - [[case:case0730] SINGLE_CASE_EVAL: Start evaluating:]
+[2024-07-31 17:19:41,539] - [INFO] [single_case_evaluate.py] [line:81] - [[case:case0730] SINGLE_CASE_EVAL: Dimension evaluate start: ]
+[2024-07-31 17:21:04,448] - [INFO] [pji_run_test.py] [line:98] - [[case:case0730] SINGLE_CASE_EVAL: Start evaluating:]
+[2024-07-31 17:21:04,800] - [INFO] [single_case_evaluate.py] [line:81] - [[case:case0730] SINGLE_CASE_EVAL: Dimension evaluate start: ]
+[2024-07-31 17:25:22,495] - [INFO] [pji_run_test.py] [line:98] - [[case:case0730] SINGLE_CASE_EVAL: Start evaluating:]
+[2024-07-31 17:25:22,876] - [INFO] [single_case_evaluate.py] [line:81] - [[case:case0730] SINGLE_CASE_EVAL: Dimension evaluate start: ]
+[2024-07-31 17:31:00,463] - [ERROR] [single_case_evaluate.py] [line:98] - [[case:case0730] SINGLE_CASE_EVAL: Dimension evaluate ERROR: KeyError('max')!]
+Traceback (most recent call last):
+  File "/home/server/桌面/virtualEnv_yzh/virtual_pji/PJI_outdoor_robot_evaluate_sim/single_case_evaluate.py", line 85, in single_case_evaluate
+    dimension_report_dict = dimension_instance.report_statistic()
+  File "/home/server/桌面/virtualEnv_yzh/virtual_pji/PJI_outdoor_robot_evaluate_sim/safe.py", line 653, in report_statistic
+    if self.over_speed_dict['max'] > 0:
+KeyError: 'max'
+[2024-07-31 17:34:31,300] - [INFO] [pji_run_test.py] [line:98] - [[case:case0730] SINGLE_CASE_EVAL: Start evaluating:]
+[2024-07-31 17:34:31,570] - [INFO] [single_case_evaluate.py] [line:81] - [[case:case0730] SINGLE_CASE_EVAL: Dimension evaluate start: ]
+[2024-07-31 17:34:31,577] - [ERROR] [single_case_evaluate.py] [line:98] - [[case:case0730] SINGLE_CASE_EVAL: Dimension evaluate ERROR: KeyError('comfortLon')!]
+Traceback (most recent call last):
+  File "/home/server/桌面/virtualEnv_yzh/virtual_pji/PJI_outdoor_robot_evaluate_sim/single_case_evaluate.py", line 85, in single_case_evaluate
+    dimension_report_dict = dimension_instance.report_statistic()
+  File "/home/server/桌面/virtualEnv_yzh/virtual_pji/PJI_outdoor_robot_evaluate_sim/safe.py", line 703, in report_statistic
+    "score": score_metric_dict['comfortLon'],
+KeyError: 'comfortLon'
+[2024-07-31 17:34:52,524] - [INFO] [pji_run_test.py] [line:98] - [[case:case0730] SINGLE_CASE_EVAL: Start evaluating:]
+[2024-07-31 17:34:52,788] - [INFO] [single_case_evaluate.py] [line:81] - [[case:case0730] SINGLE_CASE_EVAL: Dimension evaluate start: ]
+[2024-07-31 17:34:52,796] - [ERROR] [single_case_evaluate.py] [line:98] - [[case:case0730] SINGLE_CASE_EVAL: Dimension evaluate ERROR: AttributeError("'DataProcess' object has no attribute 'obj_data'")!]
+Traceback (most recent call last):
+  File "/home/server/桌面/virtualEnv_yzh/virtual_pji/PJI_outdoor_robot_evaluate_sim/single_case_evaluate.py", line 83, in single_case_evaluate
+    dimension_instance = globals()[dimension.capitalize()](data_processed, scoreModel, resultPath)
+  File "/home/server/桌面/virtualEnv_yzh/virtual_pji/PJI_outdoor_robot_evaluate_sim/comfort.py", line 74, in __init__
+    self.data = data_processed.obj_data[1]
+AttributeError: 'DataProcess' object has no attribute 'obj_data'
+[2024-07-31 17:35:20,235] - [INFO] [pji_run_test.py] [line:98] - [[case:case0730] SINGLE_CASE_EVAL: Start evaluating:]
+[2024-07-31 17:35:20,495] - [INFO] [single_case_evaluate.py] [line:81] - [[case:case0730] SINGLE_CASE_EVAL: Dimension evaluate start: ]
+[2024-07-31 17:35:20,503] - [ERROR] [single_case_evaluate.py] [line:98] - [[case:case0730] SINGLE_CASE_EVAL: Dimension evaluate ERROR: AttributeError("'DataProcess' object has no attribute 'comfort_config'")!]
+Traceback (most recent call last):
+  File "/home/server/桌面/virtualEnv_yzh/virtual_pji/PJI_outdoor_robot_evaluate_sim/single_case_evaluate.py", line 83, in single_case_evaluate
+    dimension_instance = globals()[dimension.capitalize()](data_processed, scoreModel, resultPath)
+  File "/home/server/桌面/virtualEnv_yzh/virtual_pji/PJI_outdoor_robot_evaluate_sim/comfort.py", line 81, in __init__
+    comfort_config = data_processed.comfort_config
+AttributeError: 'DataProcess' object has no attribute 'comfort_config'
+[2024-07-31 17:35:33,641] - [INFO] [pji_run_test.py] [line:98] - [[case:case0730] SINGLE_CASE_EVAL: Start evaluating:]
+[2024-07-31 17:35:33,898] - [INFO] [single_case_evaluate.py] [line:81] - [[case:case0730] SINGLE_CASE_EVAL: Dimension evaluate start: ]
+[2024-07-31 17:35:33,916] - [ERROR] [single_case_evaluate.py] [line:98] - [[case:case0730] SINGLE_CASE_EVAL: Dimension evaluate ERROR: KeyError('commandSpeed')!]
+Traceback (most recent call last):
+  File "/home/server/.local/lib/python3.8/site-packages/pandas/core/indexes/base.py", line 3653, in get_loc
+    return self._engine.get_loc(casted_key)
+  File "pandas/_libs/index.pyx", line 147, in pandas._libs.index.IndexEngine.get_loc
+  File "pandas/_libs/index.pyx", line 176, in pandas._libs.index.IndexEngine.get_loc
+  File "pandas/_libs/hashtable_class_helper.pxi", line 7080, in pandas._libs.hashtable.PyObjectHashTable.get_item
+  File "pandas/_libs/hashtable_class_helper.pxi", line 7088, in pandas._libs.hashtable.PyObjectHashTable.get_item
+KeyError: 'commandSpeed'
+
+The above exception was the direct cause of the following exception:
+
+Traceback (most recent call last):
+  File "/home/server/桌面/virtualEnv_yzh/virtual_pji/PJI_outdoor_robot_evaluate_sim/single_case_evaluate.py", line 83, in single_case_evaluate
+    dimension_instance = globals()[dimension.capitalize()](data_processed, scoreModel, resultPath)
+  File "/home/server/桌面/virtualEnv_yzh/virtual_pji/PJI_outdoor_robot_evaluate_sim/comfort.py", line 169, in __init__
+    self._comf_param_cal()
+  File "/home/server/桌面/virtualEnv_yzh/virtual_pji/PJI_outdoor_robot_evaluate_sim/comfort.py", line 231, in _comf_param_cal
+    self.speed_list = self.ego_df['commandSpeed'].values.tolist()
+  File "/home/server/.local/lib/python3.8/site-packages/pandas/core/frame.py", line 3761, in __getitem__
+    indexer = self.columns.get_loc(key)
+  File "/home/server/.local/lib/python3.8/site-packages/pandas/core/indexes/base.py", line 3655, in get_loc
+    raise KeyError(key) from err
+KeyError: 'commandSpeed'
+[2024-07-31 17:36:14,279] - [INFO] [pji_run_test.py] [line:98] - [[case:case0730] SINGLE_CASE_EVAL: Start evaluating:]
+[2024-07-31 17:36:14,535] - [INFO] [single_case_evaluate.py] [line:81] - [[case:case0730] SINGLE_CASE_EVAL: Dimension evaluate start: ]
+[2024-07-31 17:36:14,867] - [ERROR] [single_case_evaluate.py] [line:98] - [[case:case0730] SINGLE_CASE_EVAL: Dimension evaluate ERROR: AttributeError("'Comfort' object has no attribute 'commandSpeed_list'")!]
+Traceback (most recent call last):
+  File "/home/server/桌面/virtualEnv_yzh/virtual_pji/PJI_outdoor_robot_evaluate_sim/single_case_evaluate.py", line 85, in single_case_evaluate
+    dimension_report_dict = dimension_instance.report_statistic()
+  File "/home/server/桌面/virtualEnv_yzh/virtual_pji/PJI_outdoor_robot_evaluate_sim/comfort.py", line 856, in report_statistic
+    plt.plot(self.time_list, self.commandSpeed_list, label='commandSpeed')
+AttributeError: 'Comfort' object has no attribute 'commandSpeed_list'
+[2024-07-31 17:38:26,203] - [INFO] [pji_run_test.py] [line:98] - [[case:case0730] SINGLE_CASE_EVAL: Start evaluating:]
+[2024-07-31 17:38:26,466] - [INFO] [single_case_evaluate.py] [line:81] - [[case:case0730] SINGLE_CASE_EVAL: Dimension evaluate start: ]
+[2024-07-31 17:38:27,010] - [ERROR] [single_case_evaluate.py] [line:98] - [[case:case0730] SINGLE_CASE_EVAL: Dimension evaluate ERROR: ValueError('The truth value of a Series is ambiguous. Use a.empty, a.bool(), a.item(), a.any() or a.all().')!]
+Traceback (most recent call last):
+  File "/home/server/桌面/virtualEnv_yzh/virtual_pji/PJI_outdoor_robot_evaluate_sim/single_case_evaluate.py", line 85, in single_case_evaluate
+    dimension_report_dict = dimension_instance.report_statistic()
+  File "/home/server/桌面/virtualEnv_yzh/virtual_pji/PJI_outdoor_robot_evaluate_sim/accurate.py", line 230, in report_statistic
+    score_accurate, score_type_dict, score_metric_dict = self._score_cal()
+  File "/home/server/桌面/virtualEnv_yzh/virtual_pji/PJI_outdoor_robot_evaluate_sim/accurate.py", line 163, in _score_cal
+    arr_accurate = self._accurate_statistic()
+  File "/home/server/桌面/virtualEnv_yzh/virtual_pji/PJI_outdoor_robot_evaluate_sim/accurate.py", line 156, in _accurate_statistic
+    self._accurate_metric_cal()
+  File "/home/server/桌面/virtualEnv_yzh/virtual_pji/PJI_outdoor_robot_evaluate_sim/accurate.py", line 99, in _accurate_metric_cal
+    self._positionError_cal()
+  File "/home/server/桌面/virtualEnv_yzh/virtual_pji/PJI_outdoor_robot_evaluate_sim/accurate.py", line 123, in _positionError_cal
+    if task_error_code_list and task_error_code_list[-1] == target and in_segment:
+  File "/home/server/.local/lib/python3.8/site-packages/pandas/core/generic.py", line 1466, in __nonzero__
+    raise ValueError(
+ValueError: The truth value of a Series is ambiguous. Use a.empty, a.bool(), a.item(), a.any() or a.all().
+[2024-07-31 17:39:18,492] - [INFO] [pji_run_test.py] [line:98] - [[case:case0730] SINGLE_CASE_EVAL: Start evaluating:]
+[2024-07-31 17:39:18,752] - [INFO] [single_case_evaluate.py] [line:81] - [[case:case0730] SINGLE_CASE_EVAL: Dimension evaluate start: ]
+[2024-07-31 17:39:18,762] - [ERROR] [single_case_evaluate.py] [line:98] - [[case:case0730] SINGLE_CASE_EVAL: Dimension evaluate ERROR: KeyError('v')!]
+Traceback (most recent call last):
+  File "/home/server/桌面/virtualEnv_yzh/virtual_pji/PJI_outdoor_robot_evaluate_sim/single_case_evaluate.py", line 83, in single_case_evaluate
+    dimension_instance = globals()[dimension.capitalize()](data_processed, scoreModel, resultPath)
+  File "/home/server/桌面/virtualEnv_yzh/virtual_pji/PJI_outdoor_robot_evaluate_sim/comfort.py", line 132, in __init__
+    self.speed_list = self.ego_df['v'].values.tolist()
+  File "/home/server/.local/lib/python3.8/site-packages/pandas/core/frame.py", line 3761, in __getitem__
+    indexer = self.columns.get_loc(key)
+  File "/home/server/.local/lib/python3.8/site-packages/pandas/core/indexes/range.py", line 349, in get_loc
+    raise KeyError(key)
+KeyError: 'v'
+[2024-07-31 17:40:34,406] - [INFO] [pji_run_test.py] [line:98] - [[case:case0730] SINGLE_CASE_EVAL: Start evaluating:]
+[2024-07-31 17:40:34,670] - [INFO] [single_case_evaluate.py] [line:81] - [[case:case0730] SINGLE_CASE_EVAL: Dimension evaluate start: ]
+[2024-07-31 17:40:35,187] - [ERROR] [single_case_evaluate.py] [line:98] - [[case:case0730] SINGLE_CASE_EVAL: Dimension evaluate ERROR: ValueError('The truth value of a Series is ambiguous. Use a.empty, a.bool(), a.item(), a.any() or a.all().')!]
+Traceback (most recent call last):
+  File "/home/server/桌面/virtualEnv_yzh/virtual_pji/PJI_outdoor_robot_evaluate_sim/single_case_evaluate.py", line 85, in single_case_evaluate
+    dimension_report_dict = dimension_instance.report_statistic()
+  File "/home/server/桌面/virtualEnv_yzh/virtual_pji/PJI_outdoor_robot_evaluate_sim/accurate.py", line 230, in report_statistic
+    score_accurate, score_type_dict, score_metric_dict = self._score_cal()
+  File "/home/server/桌面/virtualEnv_yzh/virtual_pji/PJI_outdoor_robot_evaluate_sim/accurate.py", line 163, in _score_cal
+    arr_accurate = self._accurate_statistic()
+  File "/home/server/桌面/virtualEnv_yzh/virtual_pji/PJI_outdoor_robot_evaluate_sim/accurate.py", line 156, in _accurate_statistic
+    self._accurate_metric_cal()
+  File "/home/server/桌面/virtualEnv_yzh/virtual_pji/PJI_outdoor_robot_evaluate_sim/accurate.py", line 99, in _accurate_metric_cal
+    self._positionError_cal()
+  File "/home/server/桌面/virtualEnv_yzh/virtual_pji/PJI_outdoor_robot_evaluate_sim/accurate.py", line 123, in _positionError_cal
+    if task_error_code_list and task_error_code_list[-1] == target and in_segment:
+  File "/home/server/.local/lib/python3.8/site-packages/pandas/core/generic.py", line 1466, in __nonzero__
+    raise ValueError(
+ValueError: The truth value of a Series is ambiguous. Use a.empty, a.bool(), a.item(), a.any() or a.all().
+[2024-07-31 17:41:03,566] - [INFO] [pji_run_test.py] [line:98] - [[case:case0730] SINGLE_CASE_EVAL: Start evaluating:]
+[2024-07-31 17:41:03,827] - [INFO] [single_case_evaluate.py] [line:81] - [[case:case0730] SINGLE_CASE_EVAL: Dimension evaluate start: ]
+[2024-07-31 17:41:04,239] - [ERROR] [single_case_evaluate.py] [line:98] - [[case:case0730] SINGLE_CASE_EVAL: Dimension evaluate ERROR: ValueError('The truth value of a Series is ambiguous. Use a.empty, a.bool(), a.item(), a.any() or a.all().')!]
+Traceback (most recent call last):
+  File "/home/server/桌面/virtualEnv_yzh/virtual_pji/PJI_outdoor_robot_evaluate_sim/single_case_evaluate.py", line 85, in single_case_evaluate
+    dimension_report_dict = dimension_instance.report_statistic()
+  File "/home/server/桌面/virtualEnv_yzh/virtual_pji/PJI_outdoor_robot_evaluate_sim/accurate.py", line 230, in report_statistic
+    score_accurate, score_type_dict, score_metric_dict = self._score_cal()
+  File "/home/server/桌面/virtualEnv_yzh/virtual_pji/PJI_outdoor_robot_evaluate_sim/accurate.py", line 163, in _score_cal
+    arr_accurate = self._accurate_statistic()
+  File "/home/server/桌面/virtualEnv_yzh/virtual_pji/PJI_outdoor_robot_evaluate_sim/accurate.py", line 156, in _accurate_statistic
+    self._accurate_metric_cal()
+  File "/home/server/桌面/virtualEnv_yzh/virtual_pji/PJI_outdoor_robot_evaluate_sim/accurate.py", line 99, in _accurate_metric_cal
+    self._positionError_cal()
+  File "/home/server/桌面/virtualEnv_yzh/virtual_pji/PJI_outdoor_robot_evaluate_sim/accurate.py", line 123, in _positionError_cal
+    if task_error_code_list and (task_error_code_list[-1] == target) and in_segment:
+  File "/home/server/.local/lib/python3.8/site-packages/pandas/core/generic.py", line 1466, in __nonzero__
+    raise ValueError(
+ValueError: The truth value of a Series is ambiguous. Use a.empty, a.bool(), a.item(), a.any() or a.all().
+[2024-07-31 17:41:37,282] - [INFO] [pji_run_test.py] [line:98] - [[case:case0730] SINGLE_CASE_EVAL: Start evaluating:]
+[2024-07-31 17:41:37,544] - [INFO] [single_case_evaluate.py] [line:81] - [[case:case0730] SINGLE_CASE_EVAL: Dimension evaluate start: ]
+[2024-07-31 17:41:37,943] - [ERROR] [single_case_evaluate.py] [line:98] - [[case:case0730] SINGLE_CASE_EVAL: Dimension evaluate ERROR: TypeError("'numpy.ndarray' object is not callable")!]
+Traceback (most recent call last):
+  File "/home/server/桌面/virtualEnv_yzh/virtual_pji/PJI_outdoor_robot_evaluate_sim/single_case_evaluate.py", line 85, in single_case_evaluate
+    dimension_report_dict = dimension_instance.report_statistic()
+  File "/home/server/桌面/virtualEnv_yzh/virtual_pji/PJI_outdoor_robot_evaluate_sim/accurate.py", line 230, in report_statistic
+    score_accurate, score_type_dict, score_metric_dict = self._score_cal()
+  File "/home/server/桌面/virtualEnv_yzh/virtual_pji/PJI_outdoor_robot_evaluate_sim/accurate.py", line 163, in _score_cal
+    arr_accurate = self._accurate_statistic()
+  File "/home/server/桌面/virtualEnv_yzh/virtual_pji/PJI_outdoor_robot_evaluate_sim/accurate.py", line 156, in _accurate_statistic
+    self._accurate_metric_cal()
+  File "/home/server/桌面/virtualEnv_yzh/virtual_pji/PJI_outdoor_robot_evaluate_sim/accurate.py", line 99, in _accurate_metric_cal
+    self._positionError_cal()
+  File "/home/server/桌面/virtualEnv_yzh/virtual_pji/PJI_outdoor_robot_evaluate_sim/accurate.py", line 108, in _positionError_cal
+    task_error_code_list = self.df.task_error_code.values().tolist()
+TypeError: 'numpy.ndarray' object is not callable
+[2024-07-31 17:42:23,325] - [INFO] [pji_run_test.py] [line:98] - [[case:case0730] SINGLE_CASE_EVAL: Start evaluating:]
+[2024-07-31 17:42:23,595] - [INFO] [single_case_evaluate.py] [line:81] - [[case:case0730] SINGLE_CASE_EVAL: Dimension evaluate start: ]
+[2024-07-31 17:42:24,064] - [ERROR] [single_case_evaluate.py] [line:98] - [[case:case0730] SINGLE_CASE_EVAL: Dimension evaluate ERROR: ValueError('Cannot set a DataFrame with multiple columns to the single column ego_pos')!]
+Traceback (most recent call last):
+  File "/home/server/桌面/virtualEnv_yzh/virtual_pji/PJI_outdoor_robot_evaluate_sim/single_case_evaluate.py", line 85, in single_case_evaluate
+    dimension_report_dict = dimension_instance.report_statistic()
+  File "/home/server/桌面/virtualEnv_yzh/virtual_pji/PJI_outdoor_robot_evaluate_sim/accurate.py", line 230, in report_statistic
+    score_accurate, score_type_dict, score_metric_dict = self._score_cal()
+  File "/home/server/桌面/virtualEnv_yzh/virtual_pji/PJI_outdoor_robot_evaluate_sim/accurate.py", line 163, in _score_cal
+    arr_accurate = self._accurate_statistic()
+  File "/home/server/桌面/virtualEnv_yzh/virtual_pji/PJI_outdoor_robot_evaluate_sim/accurate.py", line 156, in _accurate_statistic
+    self._accurate_metric_cal()
+  File "/home/server/桌面/virtualEnv_yzh/virtual_pji/PJI_outdoor_robot_evaluate_sim/accurate.py", line 100, in _accurate_metric_cal
+    self._executeAccurateError_cal()
+  File "/home/server/桌面/virtualEnv_yzh/virtual_pji/PJI_outdoor_robot_evaluate_sim/accurate.py", line 129, in _executeAccurateError_cal
+    self.df_trajectory['ego_pos'] = self.df_trajectory.apply(lambda row: (row['ego_posX'], row['ego_posY']), axis=1)
+  File "/home/server/.local/lib/python3.8/site-packages/pandas/core/frame.py", line 3940, in __setitem__
+    self._set_item_frame_value(key, value)
+  File "/home/server/.local/lib/python3.8/site-packages/pandas/core/frame.py", line 4094, in _set_item_frame_value
+    raise ValueError(
+ValueError: Cannot set a DataFrame with multiple columns to the single column ego_pos
+[2024-07-31 17:42:43,890] - [INFO] [pji_run_test.py] [line:98] - [[case:case0730] SINGLE_CASE_EVAL: Start evaluating:]
+[2024-07-31 17:42:44,205] - [INFO] [single_case_evaluate.py] [line:81] - [[case:case0730] SINGLE_CASE_EVAL: Dimension evaluate start: ]
+[2024-07-31 17:44:35,970] - [INFO] [pji_run_test.py] [line:98] - [[case:case0730] SINGLE_CASE_EVAL: Start evaluating:]
+[2024-07-31 17:44:36,229] - [INFO] [single_case_evaluate.py] [line:81] - [[case:case0730] SINGLE_CASE_EVAL: Dimension evaluate start: ]
+[2024-07-31 17:44:36,672] - [ERROR] [single_case_evaluate.py] [line:98] - [[case:case0730] SINGLE_CASE_EVAL: Dimension evaluate ERROR: ValueError('Cannot set a DataFrame with multiple columns to the single column ego_pos')!]
+Traceback (most recent call last):
+  File "/home/server/桌面/virtualEnv_yzh/virtual_pji/PJI_outdoor_robot_evaluate_sim/single_case_evaluate.py", line 85, in single_case_evaluate
+    dimension_report_dict = dimension_instance.report_statistic()
+  File "/home/server/桌面/virtualEnv_yzh/virtual_pji/PJI_outdoor_robot_evaluate_sim/accurate.py", line 230, in report_statistic
+    score_accurate, score_type_dict, score_metric_dict = self._score_cal()
+  File "/home/server/桌面/virtualEnv_yzh/virtual_pji/PJI_outdoor_robot_evaluate_sim/accurate.py", line 163, in _score_cal
+    arr_accurate = self._accurate_statistic()
+  File "/home/server/桌面/virtualEnv_yzh/virtual_pji/PJI_outdoor_robot_evaluate_sim/accurate.py", line 156, in _accurate_statistic
+    self._accurate_metric_cal()
+  File "/home/server/桌面/virtualEnv_yzh/virtual_pji/PJI_outdoor_robot_evaluate_sim/accurate.py", line 100, in _accurate_metric_cal
+    self._executeAccurateError_cal()
+  File "/home/server/桌面/virtualEnv_yzh/virtual_pji/PJI_outdoor_robot_evaluate_sim/accurate.py", line 129, in _executeAccurateError_cal
+    self.df_trajectory['ego_pos'] = self.df_trajectory.apply(lambda row: (row['ego_posX'], row['ego_posY']), axis=1)
+  File "/home/server/.local/lib/python3.8/site-packages/pandas/core/frame.py", line 3940, in __setitem__
+    self._set_item_frame_value(key, value)
+  File "/home/server/.local/lib/python3.8/site-packages/pandas/core/frame.py", line 4094, in _set_item_frame_value
+    raise ValueError(
+ValueError: Cannot set a DataFrame with multiple columns to the single column ego_pos
+[2024-07-31 17:45:08,808] - [INFO] [pji_run_test.py] [line:98] - [[case:case0730] SINGLE_CASE_EVAL: Start evaluating:]
+[2024-07-31 17:45:09,129] - [INFO] [single_case_evaluate.py] [line:81] - [[case:case0730] SINGLE_CASE_EVAL: Dimension evaluate start: ]
+[2024-07-31 17:45:41,515] - [INFO] [pji_run_test.py] [line:98] - [[case:case0730] SINGLE_CASE_EVAL: Start evaluating:]
+[2024-07-31 17:45:41,795] - [INFO] [single_case_evaluate.py] [line:81] - [[case:case0730] SINGLE_CASE_EVAL: Dimension evaluate start: ]
+[2024-07-31 17:45:58,059] - [ERROR] [single_case_evaluate.py] [line:98] - [[case:case0730] SINGLE_CASE_EVAL: Dimension evaluate ERROR: TypeError("type list doesn't define __round__ method")!]
+Traceback (most recent call last):
+  File "/home/server/桌面/virtualEnv_yzh/virtual_pji/PJI_outdoor_robot_evaluate_sim/single_case_evaluate.py", line 85, in single_case_evaluate
+    dimension_report_dict = dimension_instance.report_statistic()
+  File "/home/server/桌面/virtualEnv_yzh/virtual_pji/PJI_outdoor_robot_evaluate_sim/accurate.py", line 230, in report_statistic
+    score_accurate, score_type_dict, score_metric_dict = self._score_cal()
+  File "/home/server/桌面/virtualEnv_yzh/virtual_pji/PJI_outdoor_robot_evaluate_sim/accurate.py", line 165, in _score_cal
+    print("准确性各指标值：", [round(num, 2) for num in arr_accurate])
+  File "/home/server/桌面/virtualEnv_yzh/virtual_pji/PJI_outdoor_robot_evaluate_sim/accurate.py", line 165, in <listcomp>
+    print("准确性各指标值：", [round(num, 2) for num in arr_accurate])
+TypeError: type list doesn't define __round__ method
+[2024-07-31 17:46:54,683] - [INFO] [pji_run_test.py] [line:98] - [[case:case0730] SINGLE_CASE_EVAL: Start evaluating:]
+[2024-07-31 17:46:54,963] - [INFO] [single_case_evaluate.py] [line:81] - [[case:case0730] SINGLE_CASE_EVAL: Dimension evaluate start: ]
+[2024-07-31 17:47:11,939] - [ERROR] [single_case_evaluate.py] [line:98] - [[case:case0730] SINGLE_CASE_EVAL: Dimension evaluate ERROR: AttributeError("'list' object has no attribute 'shape'")!]
+Traceback (most recent call last):
+  File "/home/server/桌面/virtualEnv_yzh/virtual_pji/PJI_outdoor_robot_evaluate_sim/single_case_evaluate.py", line 85, in single_case_evaluate
+    dimension_report_dict = dimension_instance.report_statistic()
+  File "/home/server/桌面/virtualEnv_yzh/virtual_pji/PJI_outdoor_robot_evaluate_sim/accurate.py", line 229, in report_statistic
+    score_accurate, score_type_dict, score_metric_dict = self._score_cal()
+  File "/home/server/桌面/virtualEnv_yzh/virtual_pji/PJI_outdoor_robot_evaluate_sim/accurate.py", line 167, in _score_cal
+    score_model = self.scoreModel(self.kind_list, self.optimal_list, self.multiple_list, arr_accurate)
+  File "/home/server/桌面/virtualEnv_yzh/virtual_pji/PJI_outdoor_robot_evaluate_sim/score_weight.py", line 78, in __init__
+    self.n, self.m = arr.shape
+AttributeError: 'list' object has no attribute 'shape'
+[2024-07-31 17:48:06,140] - [INFO] [pji_run_test.py] [line:98] - [[case:case0730] SINGLE_CASE_EVAL: Start evaluating:]
+[2024-07-31 17:48:06,413] - [INFO] [single_case_evaluate.py] [line:81] - [[case:case0730] SINGLE_CASE_EVAL: Dimension evaluate start: ]
+[2024-07-31 17:48:22,620] - [ERROR] [single_case_evaluate.py] [line:98] - [[case:case0730] SINGLE_CASE_EVAL: Dimension evaluate ERROR: AttributeError("'Accurate' object has no attribute 'executeAccurateError_count'")!]
+Traceback (most recent call last):
+  File "/home/server/桌面/virtualEnv_yzh/virtual_pji/PJI_outdoor_robot_evaluate_sim/single_case_evaluate.py", line 85, in single_case_evaluate
+    dimension_report_dict = dimension_instance.report_statistic()
+  File "/home/server/桌面/virtualEnv_yzh/virtual_pji/PJI_outdoor_robot_evaluate_sim/accurate.py", line 245, in report_statistic
+    if self.executeAccurateError_count > 0:
+AttributeError: 'Accurate' object has no attribute 'executeAccurateError_count'
+[2024-07-31 17:48:53,362] - [INFO] [pji_run_test.py] [line:98] - [[case:case0730] SINGLE_CASE_EVAL: Start evaluating:]
+[2024-07-31 17:48:53,636] - [INFO] [single_case_evaluate.py] [line:81] - [[case:case0730] SINGLE_CASE_EVAL: Dimension evaluate start: ]
+[2024-07-31 17:49:09,422] - [ERROR] [single_case_evaluate.py] [line:98] - [[case:case0730] SINGLE_CASE_EVAL: Dimension evaluate ERROR: KeyError('max')!]
+Traceback (most recent call last):
+  File "/home/server/桌面/virtualEnv_yzh/virtual_pji/PJI_outdoor_robot_evaluate_sim/single_case_evaluate.py", line 85, in single_case_evaluate
+    dimension_report_dict = dimension_instance.report_statistic()
+  File "/home/server/桌面/virtualEnv_yzh/virtual_pji/PJI_outdoor_robot_evaluate_sim/accurate.py", line 254, in report_statistic
+    description1 = f"最大值：{self.positionError_dict['max']:.4f}m/s；" \
+KeyError: 'max'
+[2024-07-31 17:50:24,073] - [INFO] [pji_run_test.py] [line:98] - [[case:case0730] SINGLE_CASE_EVAL: Start evaluating:]
+[2024-07-31 17:50:24,345] - [INFO] [single_case_evaluate.py] [line:81] - [[case:case0730] SINGLE_CASE_EVAL: Dimension evaluate start: ]
+[2024-07-31 17:50:40,350] - [ERROR] [single_case_evaluate.py] [line:98] - [[case:case0730] SINGLE_CASE_EVAL: Dimension evaluate ERROR: ValueError("Unknown format code 'f' for object of type 'str'")!]
+Traceback (most recent call last):
+  File "/home/server/桌面/virtualEnv_yzh/virtual_pji/PJI_outdoor_robot_evaluate_sim/single_case_evaluate.py", line 85, in single_case_evaluate
+    dimension_report_dict = dimension_instance.report_statistic()
+  File "/home/server/桌面/virtualEnv_yzh/virtual_pji/PJI_outdoor_robot_evaluate_sim/accurate.py", line 268, in report_statistic
+    description1 = f"最大值：{self.positionError_dict['max']:.4f}m/s；" \
+ValueError: Unknown format code 'f' for object of type 'str'
+[2024-07-31 17:59:29,713] - [INFO] [pji_run_test.py] [line:98] - [[case:case0730] SINGLE_CASE_EVAL: Start evaluating:]
+[2024-07-31 17:59:29,988] - [INFO] [single_case_evaluate.py] [line:81] - [[case:case0730] SINGLE_CASE_EVAL: Dimension evaluate start: ]
+[2024-07-31 17:59:46,016] - [ERROR] [single_case_evaluate.py] [line:98] - [[case:case0730] SINGLE_CASE_EVAL: Dimension evaluate ERROR: ValueError("Unknown format code 'f' for object of type 'str'")!]
+Traceback (most recent call last):
+  File "/home/server/桌面/virtualEnv_yzh/virtual_pji/PJI_outdoor_robot_evaluate_sim/single_case_evaluate.py", line 85, in single_case_evaluate
+    dimension_report_dict = dimension_instance.report_statistic()
+  File "/home/server/桌面/virtualEnv_yzh/virtual_pji/PJI_outdoor_robot_evaluate_sim/accurate.py", line 268, in report_statistic
+    description1 = f"最大值：{self.positionError_dict['max']:.4f}m/s；" \
+ValueError: Unknown format code 'f' for object of type 'str'
+[2024-07-31 18:08:29,509] - [INFO] [pji_run_test.py] [line:98] - [[case:case0730] SINGLE_CASE_EVAL: Start evaluating:]
+[2024-07-31 18:08:29,785] - [INFO] [single_case_evaluate.py] [line:81] - [[case:case0730] SINGLE_CASE_EVAL: Dimension evaluate start: ]
+[2024-07-31 18:08:45,564] - [ERROR] [single_case_eval.py] [line:67] - [[case:case0730] SINGLE_CASE_EVAL: Evaluate single case ERROR: OSError('Cannot open resource "./task/case0730/trajectory.png"\nfileName=\'./task/case0730/trajectory.png\' identity=[ImageReader@0x7fd48167ef40 filename=\'./task/case0730/trajectory.png\']')!]
+Traceback (most recent call last):
+  File "/usr/lib/python3/dist-packages/reportlab/lib/utils.py", line 655, in open_for_read
+    return open_for_read_by_name(name,mode)
+  File "/usr/lib/python3/dist-packages/reportlab/lib/utils.py", line 599, in open_for_read_by_name
+    return open(name,mode)
+FileNotFoundError: [Errno 2] No such file or directory: './task/case0730/trajectory.png'
+
+During handling of the above exception, another exception occurred:
+
+Traceback (most recent call last):
+  File "/usr/lib/python3/dist-packages/reportlab/lib/utils.py", line 658, in open_for_read
+    return getBytesIO(datareader(name) if name[:5].lower()=='data:' else urlopen(name).read())
+  File "/usr/lib/python3.8/urllib/request.py", line 222, in urlopen
+    return opener.open(url, data, timeout)
+  File "/usr/lib/python3.8/urllib/request.py", line 509, in open
+    req = Request(fullurl, data)
+  File "/usr/lib/python3.8/urllib/request.py", line 328, in __init__
+    self.full_url = url
+  File "/usr/lib/python3.8/urllib/request.py", line 354, in full_url
+    self._parse()
+  File "/usr/lib/python3.8/urllib/request.py", line 383, in _parse
+    raise ValueError("unknown url type: %r" % self.full_url)
+ValueError: unknown url type: './task/case0730/trajectory.png'
+
+During handling of the above exception, another exception occurred:
+
+Traceback (most recent call last):
+  File "/home/server/桌面/virtualEnv_yzh/virtual_pji/PJI_outdoor_robot_evaluate_sim/single_case_eval.py", line 63, in single_case_eval
+    report_generate(reportDict, reportPdf, trackPath)
+  File "/home/server/桌面/virtualEnv_yzh/virtual_pji/PJI_outdoor_robot_evaluate_sim/report_generate.py", line 276, in report_generate
+    content.append(get_image(trackPath))
+  File "/home/server/桌面/virtualEnv_yzh/virtual_pji/PJI_outdoor_robot_evaluate_sim/report_generate.py", line 166, in get_image
+    img = utils.ImageReader(path)
+  File "/usr/lib/python3/dist-packages/reportlab/lib/utils.py", line 813, in __init__
+    annotateException('\nfileName=%r identity=%s'%(fileName,self.identity()))
+  File "/usr/lib/python3/dist-packages/reportlab/lib/utils.py", line 1394, in annotateException
+    rl_reraise(t,v,b)
+  File "/usr/lib/python3/dist-packages/reportlab/lib/utils.py", line 147, in rl_reraise
+    raise v
+  File "/usr/lib/python3/dist-packages/reportlab/lib/utils.py", line 777, in __init__
+    self.fp = open_for_read(fileName,'b')
+  File "/usr/lib/python3/dist-packages/reportlab/lib/utils.py", line 660, in open_for_read
+    raise IOError('Cannot open resource "%s"' % name)
+OSError: Cannot open resource "./task/case0730/trajectory.png"
+fileName='./task/case0730/trajectory.png' identity=[ImageReader@0x7fd48167ef40 filename='./task/case0730/trajectory.png']
+[2024-07-31 18:12:17,214] - [INFO] [pji_run_test.py] [line:98] - [[case:case0730] SINGLE_CASE_EVAL: Start evaluating:]
+[2024-07-31 18:12:17,501] - [INFO] [single_case_evaluate.py] [line:81] - [[case:case0730] SINGLE_CASE_EVAL: Dimension evaluate start: ]
+[2024-07-31 18:12:36,247] - [ERROR] [single_case_eval.py] [line:67] - [[case:case0730] SINGLE_CASE_EVAL: Evaluate single case ERROR: OSError('Cannot open resource "./task/case0730/trajectory.png"\nfileName=\'./task/case0730/trajectory.png\' identity=[ImageReader@0x7fb30100cf40 filename=\'./task/case0730/trajectory.png\']')!]
+Traceback (most recent call last):
+  File "/usr/lib/python3/dist-packages/reportlab/lib/utils.py", line 655, in open_for_read
+    return open_for_read_by_name(name,mode)
+  File "/usr/lib/python3/dist-packages/reportlab/lib/utils.py", line 599, in open_for_read_by_name
+    return open(name,mode)
+FileNotFoundError: [Errno 2] No such file or directory: './task/case0730/trajectory.png'
+
+During handling of the above exception, another exception occurred:
+
+Traceback (most recent call last):
+  File "/usr/lib/python3/dist-packages/reportlab/lib/utils.py", line 658, in open_for_read
+    return getBytesIO(datareader(name) if name[:5].lower()=='data:' else urlopen(name).read())
+  File "/usr/lib/python3.8/urllib/request.py", line 222, in urlopen
+    return opener.open(url, data, timeout)
+  File "/usr/lib/python3.8/urllib/request.py", line 509, in open
+    req = Request(fullurl, data)
+  File "/usr/lib/python3.8/urllib/request.py", line 328, in __init__
+    self.full_url = url
+  File "/usr/lib/python3.8/urllib/request.py", line 354, in full_url
+    self._parse()
+  File "/usr/lib/python3.8/urllib/request.py", line 383, in _parse
+    raise ValueError("unknown url type: %r" % self.full_url)
+ValueError: unknown url type: './task/case0730/trajectory.png'
+
+During handling of the above exception, another exception occurred:
+
+Traceback (most recent call last):
+  File "/home/server/桌面/virtualEnv_yzh/virtual_pji/PJI_outdoor_robot_evaluate_sim/single_case_eval.py", line 63, in single_case_eval
+    report_generate(reportDict, reportPdf, trackPath)
+  File "/home/server/桌面/virtualEnv_yzh/virtual_pji/PJI_outdoor_robot_evaluate_sim/report_generate.py", line 280, in report_generate
+    content.append(get_image(trackPath))
+  File "/home/server/桌面/virtualEnv_yzh/virtual_pji/PJI_outdoor_robot_evaluate_sim/report_generate.py", line 166, in get_image
+    img = utils.ImageReader(path)
+  File "/usr/lib/python3/dist-packages/reportlab/lib/utils.py", line 813, in __init__
+    annotateException('\nfileName=%r identity=%s'%(fileName,self.identity()))
+  File "/usr/lib/python3/dist-packages/reportlab/lib/utils.py", line 1394, in annotateException
+    rl_reraise(t,v,b)
+  File "/usr/lib/python3/dist-packages/reportlab/lib/utils.py", line 147, in rl_reraise
+    raise v
+  File "/usr/lib/python3/dist-packages/reportlab/lib/utils.py", line 777, in __init__
+    self.fp = open_for_read(fileName,'b')
+  File "/usr/lib/python3/dist-packages/reportlab/lib/utils.py", line 660, in open_for_read
+    raise IOError('Cannot open resource "%s"' % name)
+OSError: Cannot open resource "./task/case0730/trajectory.png"
+fileName='./task/case0730/trajectory.png' identity=[ImageReader@0x7fb30100cf40 filename='./task/case0730/trajectory.png']
+[2024-07-31 18:15:59,799] - [INFO] [pji_run_test.py] [line:98] - [[case:case0730] SINGLE_CASE_EVAL: Start evaluating:]
+[2024-07-31 18:16:00,087] - [INFO] [single_case_evaluate.py] [line:81] - [[case:case0730] SINGLE_CASE_EVAL: Dimension evaluate start: ]
+[2024-07-31 18:16:17,235] - [ERROR] [single_case_eval.py] [line:67] - [[case:case0730] SINGLE_CASE_EVAL: Evaluate single case ERROR: OSError('Cannot open resource "./task/case0730/trajectory.png"\nfileName=\'./task/case0730/trajectory.png\' identity=[ImageReader@0x7ff0ae2b0940 filename=\'./task/case0730/trajectory.png\']')!]
+Traceback (most recent call last):
+  File "/usr/lib/python3/dist-packages/reportlab/lib/utils.py", line 655, in open_for_read
+    return open_for_read_by_name(name,mode)
+  File "/usr/lib/python3/dist-packages/reportlab/lib/utils.py", line 599, in open_for_read_by_name
+    return open(name,mode)
+FileNotFoundError: [Errno 2] No such file or directory: './task/case0730/trajectory.png'
+
+During handling of the above exception, another exception occurred:
+
+Traceback (most recent call last):
+  File "/usr/lib/python3/dist-packages/reportlab/lib/utils.py", line 658, in open_for_read
+    return getBytesIO(datareader(name) if name[:5].lower()=='data:' else urlopen(name).read())
+  File "/usr/lib/python3.8/urllib/request.py", line 222, in urlopen
+    return opener.open(url, data, timeout)
+  File "/usr/lib/python3.8/urllib/request.py", line 509, in open
+    req = Request(fullurl, data)
+  File "/usr/lib/python3.8/urllib/request.py", line 328, in __init__
+    self.full_url = url
+  File "/usr/lib/python3.8/urllib/request.py", line 354, in full_url
+    self._parse()
+  File "/usr/lib/python3.8/urllib/request.py", line 383, in _parse
+    raise ValueError("unknown url type: %r" % self.full_url)
+ValueError: unknown url type: './task/case0730/trajectory.png'
+
+During handling of the above exception, another exception occurred:
+
+Traceback (most recent call last):
+  File "/home/server/桌面/virtualEnv_yzh/virtual_pji/PJI_outdoor_robot_evaluate_sim/single_case_eval.py", line 63, in single_case_eval
+    report_generate(reportDict, reportPdf, trackPath)
+  File "/home/server/桌面/virtualEnv_yzh/virtual_pji/PJI_outdoor_robot_evaluate_sim/report_generate.py", line 280, in report_generate
+    content.append(get_image(trackPath))
+  File "/home/server/桌面/virtualEnv_yzh/virtual_pji/PJI_outdoor_robot_evaluate_sim/report_generate.py", line 166, in get_image
+    img = utils.ImageReader(path)
+  File "/usr/lib/python3/dist-packages/reportlab/lib/utils.py", line 813, in __init__
+    annotateException('\nfileName=%r identity=%s'%(fileName,self.identity()))
+  File "/usr/lib/python3/dist-packages/reportlab/lib/utils.py", line 1394, in annotateException
+    rl_reraise(t,v,b)
+  File "/usr/lib/python3/dist-packages/reportlab/lib/utils.py", line 147, in rl_reraise
+    raise v
+  File "/usr/lib/python3/dist-packages/reportlab/lib/utils.py", line 777, in __init__
+    self.fp = open_for_read(fileName,'b')
+  File "/usr/lib/python3/dist-packages/reportlab/lib/utils.py", line 660, in open_for_read
+    raise IOError('Cannot open resource "%s"' % name)
+OSError: Cannot open resource "./task/case0730/trajectory.png"
+fileName='./task/case0730/trajectory.png' identity=[ImageReader@0x7ff0ae2b0940 filename='./task/case0730/trajectory.png']
+[2024-07-31 18:18:43,346] - [INFO] [pji_run_test.py] [line:98] - [[case:case0730] SINGLE_CASE_EVAL: Start evaluating:]
+[2024-07-31 18:18:43,630] - [INFO] [single_case_evaluate.py] [line:81] - [[case:case0730] SINGLE_CASE_EVAL: Dimension evaluate start: ]
+[2024-07-31 18:19:00,778] - [INFO] [pji_run_test.py] [line:112] - [[case:case0730] SINGLE_CASE_EVAL: End.]
+[2024-07-31 18:19:47,451] - [INFO] [pji_run_test.py] [line:98] - [[case:case0730] SINGLE_CASE_EVAL: Start evaluating:]
+[2024-07-31 18:19:47,726] - [INFO] [single_case_evaluate.py] [line:81] - [[case:case0730] SINGLE_CASE_EVAL: Dimension evaluate start: ]
+[2024-07-31 18:20:07,202] - [INFO] [pji_run_test.py] [line:112] - [[case:case0730] SINGLE_CASE_EVAL: End.]

task/case0730/objects.csv

@@ -0,0 +1,41 @@
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task/case0730/trajectory_pji.csv

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