#!/usr/bin/env python # -*- coding: utf-8 -*- import math import pandas as pd import numpy as np import scipy.signal from modules.lib.score import Score from modules.lib.common import get_interpolation, get_frame_with_time from modules.config import config from modules.lib.log_manager import LogManager 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, 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): self.data_processed = data_processed self.logger = LogManager().get_logger() self.data = data_processed.ego_data.copy() self.ego_df = pd.DataFrame() self.discomfort_df = pd.DataFrame(columns=['start_time', 'end_time', 'start_frame', 'end_frame', 'type']) self.calculated_value = { 'weaving': 0, 'shake': 0, 'cadence': 0, 'slamBrake': 0, 'slamAccelerate': 0, } self.time_list = self.data['simTime'].values.tolist() self.frame_list = self.data['simFrame'].values.tolist() # 移除未使用的字典 self.zigzag_count = 0 self.shake_count = 0 self.cadence_count = 0 self.slam_brake_count = 0 self.slam_accel_count = 0 self.zigzag_time_list = [] self.zigzag_stre_list = [] self.cur_ego_path_list = [] self.curvature_list = [] self._get_data() self._comf_param_cal() def _get_data(self): """获取舒适性评估所需数据""" self.ego_df = self.data[config.COMFORT_INFO].copy() self.df = self.ego_df.reset_index(drop=True) def _cal_cur_ego_path(self, row): """计算车辆轨迹曲率""" try: # 计算速度平方和，判断是否接近零 speed_sq = row['speedX']**2 + row['speedY']**2 if speed_sq < 1e-6: # 阈值根据实际场景调整 return 1e5 # 速度接近零时返回极大曲率 divide = speed_sq ** (3/2) res = (row['speedX'] * row['accelY'] - row['speedY'] * row['accelX']) / divide return res except Exception: return 1e5 # 异常时也返回极大值（如除零、缺失值等） def _comf_param_cal(self): """计算舒适性相关参数""" # 加减速阈值计算 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['lon_acc'] - self.ego_df['ip_dec']).apply( lambda x: 1 if x < 0 else 0) self.ego_df['slam_accel'] = (self.ego_df['lon_acc'] - self.ego_df['ip_acc']).apply( lambda x: 1 if x > 0 else 0) 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) # 晃动检测相关参数 self.ego_df['cur_ego_path'] = self.ego_df.apply(self._cal_cur_ego_path, axis=1) self.ego_df['curvHor'] = self.ego_df['curvHor'].astype('float') self.ego_df['cur_diff'] = (self.ego_df['cur_ego_path'] - self.ego_df['curvHor']).abs() self.ego_df['R'] = self.ego_df['curvHor'].apply(lambda x: 10000 if x == 0 else 1 / x) self.ego_df['R_ego'] = self.ego_df['cur_ego_path'].apply(lambda x: 10000 if x == 0 else 1 / x) self.ego_df['R_diff'] = (self.ego_df['R_ego'] - self.ego_df['R']).abs() self.cur_ego_path_list = self.ego_df['cur_ego_path'].values.tolist() self.curvature_list = self.ego_df['curvHor'].values.tolist() def _peak_valley_determination(self, df): """ 确定车辆角速度的峰值和谷值 """ # 调整参数以减少噪音干扰 peaks, _ = scipy.signal.find_peaks(df['speedH'], height=0.03, distance=3, prominence=0.03, width=1) valleys, _ = scipy.signal.find_peaks(-df['speedH'], height=0.03, distance=3, prominence=0.03, width=1) peak_valley = sorted(list(peaks) + list(valleys)) return peak_valley def _peak_valley_judgment(self, p_last, p_curr, tw=100, avg=0.06): """ 判断给定的峰值和谷值是否满足特定条件 """ 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): """计算曲折行驶次数""" if flag: self.zigzag_count += 1 @peak_valley_decorator def cal_zigzag_strength_strength(self, p_curr, p_last, flag=True): """计算曲折行驶强度""" if flag: v_diff = abs(p_curr[1] - p_last[1]) t_diff = p_curr[0] - p_last[0] if t_diff > 0: self.zigzag_stre_list.append(v_diff / t_diff) # 平均角加速度 else: self.zigzag_stre_list = [] def _shake_detector(self, Cr_diff=0.05, T_diff=0.39): """检测晃动事件""" time_list = [] frame_list = [] df = self.ego_df.copy() df = df[df['cur_diff'] > Cr_diff] df['frame_ID_diff'] = df['simFrame'].diff() # 找出行车轨迹曲率与道路曲率之差大于阈值的数据段 filtered_df = df[df.frame_ID_diff > T_diff] # 此处是用大间隔区分多次晃动情景 row_numbers = filtered_df.index.tolist() cut_column = pd.cut(df.index, bins=row_numbers) grouped = df.groupby(cut_column) dfs = {} for name, group in grouped: dfs[name] = group.reset_index(drop=True) for name, df_group in dfs.items(): # 直道，未主动换道 df_group['curvHor'] = df_group['curvHor'].abs() df_group_straight = df_group[(df_group.lightMask == 0) & (df_group.curvHor < 0.001)] if not df_group_straight.empty: time_list.extend(df_group_straight['simTime'].values) frame_list.extend(df_group_straight['simFrame'].values) self.shake_count = self.shake_count + 1 # 打转向灯，道路为直道，此时晃动判断标准车辆曲率变化率为一个更大的阈值 df_group_change_lane = df_group[(df_group['lightMask'] != 0) & (df_group['curvHor'] < 0.001)] df_group_change_lane_data = df_group_change_lane[df_group_change_lane.cur_diff > Cr_diff + 0.2] if not df_group_change_lane_data.empty: time_list.extend(df_group_change_lane_data['simTime'].values) frame_list.extend(df_group_change_lane_data['simFrame'].values) self.shake_count = self.shake_count + 1 # 转弯，打转向灯 df_group_turn = df_group[(df_group['lightMask'] != 0) & (df_group['curvHor'].abs() > 0.001)] df_group_turn_data = df_group_turn[df_group_turn.cur_diff.abs() > Cr_diff + 0.1] if not df_group_turn_data.empty: time_list.extend(df_group_turn_data['simTime'].values) frame_list.extend(df_group_turn_data['simFrame'].values) self.shake_count = self.shake_count + 1 TIME_RANGE = 1 t_list = time_list f_list = frame_list 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] <= TIME_RANGE: 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]] # 输出图表值 shake_time = [[g[0], g[-1]] for g in group_time] shake_frame = [[g[0], g[-1]] for g in group_frame] self.shake_count = len(shake_time) if shake_time: # 保存晃动事件摘要 time_df = pd.DataFrame(shake_time, columns=['start_time', 'end_time']) frame_df = pd.DataFrame(shake_frame, columns=['start_frame', 'end_frame']) discomfort_df = pd.concat([time_df, frame_df], axis=1) discomfort_df['type'] = 'shake' self.discomfort_df = pd.concat([self.discomfort_df, discomfort_df], ignore_index=True) return time_list def _cadence_process_new(self, lon_acc, ip_acc, ip_dec): """处理顿挫数据""" if abs(lon_acc) < 1 or lon_acc > ip_acc or lon_acc < ip_dec: return np.nan 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 else: return 0 def _cadence_detector(self): """检测顿挫事件""" data = self.ego_df[['simTime', 'simFrame', 'lon_acc', 'lon_acc_roc', 'cadence', 'v']].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() TIME_RANGE = 1 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] <= TIME_RANGE: # 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] if cadence_time: # 保存顿挫事件摘要 time_df = pd.DataFrame(cadence_time, columns=['start_time', 'end_time']) frame_df = pd.DataFrame(cadence_frame, columns=['start_frame', 'end_frame']) discomfort_df = pd.concat([time_df, frame_df], axis=1) discomfort_df['type'] = 'cadence' self.discomfort_df = pd.concat([self.discomfort_df, discomfort_df], ignore_index=True) # 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 self.ego_df['simTime'].values if pair[0] <= time <= pair[1]] self.cadence_count = len(cadence_time) return cadence_time_list def _slam_brake_detector(self): """检测急刹车事件""" data = self.ego_df[['simTime', 'simFrame', 'lon_acc', 'lon_acc_roc', 'ip_dec', 'slam_brake', 'v']].copy() res_df = data[data['slam_brake'] == 1] t_list = res_df['simTime'].values f_list = res_df['simFrame'].values.tolist() TIME_RANGE = 1 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] <= TIME_RANGE: # 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] # 输出图表值 slam_brake_time = [[g[0], g[-1]] for g in group_time] slam_brake_frame = [[g[0], g[-1]] for g in group_frame] if slam_brake_time: # 保存事件摘要 time_df = pd.DataFrame(slam_brake_time, columns=['start_time', 'end_time']) frame_df = pd.DataFrame(slam_brake_frame, columns=['start_frame', 'end_frame']) discomfort_df = pd.concat([time_df, frame_df], axis=1) discomfort_df['type'] = 'slam_brake' self.discomfort_df = pd.concat([self.discomfort_df, discomfort_df], ignore_index=True) time_list = [element for sublist in group_time for element in sublist] self.slam_brake_count = len(group_time) return time_list def _slam_accel_detector(self): """检测急加速事件""" data = self.ego_df[['simTime', 'simFrame', 'lon_acc', 'ip_acc', 'slam_accel', 'v']].copy() 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] # 输出图表值 slam_accel_time = [[g[0], g[-1]] for g in group_time] slam_accel_frame = [[g[0], g[-1]] for g in group_frame] if slam_accel_time: # 保存事件摘要 time_df = pd.DataFrame(slam_accel_time, columns=['start_time', 'end_time']) frame_df = pd.DataFrame(slam_accel_frame, columns=['start_frame', 'end_frame']) discomfort_df = pd.concat([time_df, frame_df], axis=1) discomfort_df['type'] = 'slam_accel' self.discomfort_df = pd.concat([self.discomfort_df, discomfort_df], ignore_index=True) time_list = [element for sublist in group_time for element in sublist] self.slam_accel_count = len(group_time) return time_list def comf_statistic(self): """统计舒适性指标""" df = self.ego_df[['simTime', 'simFrame', 'cur_diff', 'lon_acc', 'lon_acc_roc', 'accelH', 'speedH', 'lat_acc', 'v']].copy() self.zigzag_count_func() self.cal_zigzag_strength_strength() if self.zigzag_time_list: # 保存 Weaving (zigzag) 事件摘要 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) 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() # 统计结果 self.calculated_value = { "weaving": self.zigzag_count, "shake": self.shake_count, "cadence": self.cadence_count, "slamBrake": self.slam_brake_count, "slamAccelerate": self.slam_accel_count } self.logger.info(f"舒适性计算完成，统计结果：{self.calculated_value}") return self.calculated_value def report_statistic(self): """生成舒适性评估报告""" comfort_result = self.comf_statistic() evaluator = Score(self.data_processed.comfort_config) result = evaluator.evaluate(comfort_result) print("\n[舒适性表现及得分情况]") return result