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- import numpy as np
- import pandas as pd
- import warnings
- warnings.filterwarnings("ignore", category=FutureWarning, module='pandas')
- pd.set_option('future.no_silent_downcasting', True)
- SHUT_ACC_STATUS = 0
- ACC_STATUS = 3
- EGO_ID = 1
- OBJ_ID = 2
- class ACC_Trigger():
- def __init__(self, df_vehState):
- self.df_vehState = df_vehState
- def find_start_end_time(self, change_speed_time_list):
- start_end_time_list = []
- # 初始化一个变量来跟踪当前序列的开始值
- # 和一个变量来跟踪当前序列的长度
- start_of_sequence = None
- sequence_length = 0
- # 遍历sim_times列表（从第二个元素开始比较）
- for i in range(1, len(change_speed_time_list)):
- # 检查当前元素与上一个元素的差是否接近0.04（考虑浮点数精度）
- if abs(change_speed_time_list[i] - change_speed_time_list[i - 1] - 0.01) < 0.04:
- # 如果这是序列的开始，则记录开始值
- if start_of_sequence is None:
- start_of_sequence = change_speed_time_list[i - 1]
- # 增加序列长度
- sequence_length += 1
- else:
- # 如果序列中断且长度足够（例如，至少为2），则添加序列到结果列表中
- if start_of_sequence is not None and sequence_length >= 0:
- start_end_time_list.append([start_of_sequence, change_speed_time_list[i - 1]])
- # 重置开始值和序列长度
- start_of_sequence = None
- sequence_length = 0
- # 处理最后一个序列（如果存在且长度足够）
- if start_of_sequence is not None and sequence_length >= 2:
- start_end_time_list.append([start_of_sequence, change_speed_time_list[-1]])
- return start_end_time_list
- def ACC_active_time_statistics(self):
- start_end_time_dict = {}
- ACC_time = self.df_vehState[self.df_vehState['ACC_status'] == ACC_STATUS]['simTime'].tolist()
- ACC_start_end_time_list = self.find_start_end_time(ACC_time)
- start_end_time_dict['ACC_status_active_time'] = ACC_start_end_time_list
- return start_end_time_dict
- if __name__ == "__main__":
- df_vehState = pd.read_csv(r"D:\Cicv\Lantu\数据\ACC_04_Straight_Front_vehicle_biased_driving\data\VehState.csv")
- ACC = ACC_Trigger(df_vehState)
- start_end_time_dict = ACC.ACC_active_time_statistics()
- print(start_end_time_dict)
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