Driver Car-Following Model Based on Deep Reinforcement Learning

被引：0

作者：

Guo J. ^{[1
]}

Li W. ^{[1
,4
]}

Luo Y. ^{[2
]}

Chen T. ^{[3
]}

Li K. ^{[2
]}

机构：

[1] School of Aerospace Engineering, Xiamen University, Xiamen

[2] School of Vehicle and Mobility, Tsinghua University, Beijing

[3] China Automotive Engineering Research Institute Co., Ltd., Chongqing

[4] School of Automotive Studies, Tongji University, Shanghai

来源：

Guo, Jinghua (guojh@xmu.edu.cn) | 1600年 / SAE-China卷 / 43期

关键词：

Car following; Deep reinforcement learning; Driver model; Intelligent driving;

D O I：

10.19562/j.chinasae.qcgc.2021.04.015

中图分类号：

学科分类号：

摘要：

To enhance the longitudinal car-following performance of intelligent driving system, a driver's car-following model based on deep reinforcement learning is constructed in this paper. Firstly, according to the selection rule defined of car following scenes, typical car-following scenes conforming to conditions are selected from the natural driving data, on which a statistical analysis is then conducted to analyze the influence mechanism of the factors of car spacing, relative speed and time headway on the car following behavior of driver by using correlation coefficient method, with the behavior characteristic and its affecting factors of driver's car following driving process obtained. Then a car following model of driver is established based on the deep deterministic policy gradient algorithm, and the driver's data set of car following trajectory is input into the simulated car following environment so that the intelligent agent can learn the decision-making behavior of driver from the empirical data. Finally, with the original data as the reference base, a comparative simulation verification is performed on the deep reinforcement learning-based car following model, with a result showing that the driver's car following model constructed has good tracking performance and can truly reproducing the car following behavior of driver. © 2021, Society of Automotive Engineers of China. All right reserved.

引用

页码：571 / 579

页数：8

共 17 条

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