DOI: 10.11908/j.issn.0253-374x.20345

作者:

作者单位: 同济大学 道路与交通工程教育部重点实验室， 上海 201804


作者简介: 李林波（1974—），男，副教授，博士生导师，工学博士，主要研究方向为交通规划、交通拥挤管理等。 E-mail:llinbo@tongji.edu.cn


通讯作者:

中图分类号: U491.1


基金项目: 国家重点研发计划（2018YFE0102800）；同济大学大型仪器设备开放测试基金（2021GX106）




Modeling of Car-Following Behaviors Considering Driver’s Fuzzy Perception Using Deep Learning
Author:

Affiliation: Key Laboratory of Road and Traffic Engineering of the Ministry of Education，Tongji University，Shanghai 201084，China


Fund Project:




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摘要:为模拟驾驶人记忆效应以及模糊感知特性，设计了基于模糊感知时间窗的深度学习跟驰模型。提取highD数据集跟驰轨迹，以0.2 s最小时间间隔，连续3 s本车速度、前后车速度差、车头间距的时序数据作为模型输入，模拟驾驶记忆。训练深度学习跟驰模型，得出单层32个输出维度的门控循环单元（GRU）网络可以很好拟合实际数据。在每次输入模型的时序数据中，用模型预测值替换部分真实跟驰状态值，作为驾驶员对场景的估计，即模糊感知。实验得出对同一场景的不同模糊感知，可产生不同跟驰行为，模拟了驾驶行为的异质性，可为异质交通行为仿真提供方法。



Abstract:In order to simulate driver's memory effects and fuzzy perception characteristics， a deep learning car-following model was designed based on fuzzy perception time window. Taking 3 s continuous speed， leading-following car speed difference and headway distance as model inputs with a minimum time interval of 0.2 s，the driving memory was simulated. A gated recurrent unit （GRU） network with a single layer of 32 output dimensions could fit the actual data well by training multiple groups of deep learning car-following models. In each input time series data of the model， part of the real car-following state value was replaced by the predicted value of the model as the driver’s estimation of the scenario， that is， fuzzy perception. The experiment results show that different fuzzy perceptions to the same scenario can produce different car following behaviors， and the heterogeneity of driving behaviors can be simulated， which provide a method for heterogeneous traffic behavior simulation.





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