考虑驾驶员模糊感知的深度学习跟驰模型

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

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.