文章基本信息

标题：Tracking Control of Intelligent Vehicle Lane Change Based on RLMPC
本地全文：下载
作者：Quanshan Hou ; Yanan Zhang ; Shuai Zhao 等
期刊名称：E3S Web of Conferences
印刷版ISSN：2267-1242
电子版ISSN：2267-1242
出版年度：2021
卷号：233
页码：4019
DOI：10.1051/e3sconf/202123304019
出版社：EDP Sciences
摘要：Autonomous lane changing, as a key module to realize high-level automatic driving, has important practical significance for improving the driving safety, comfort and commuting efficiency of vehicles. Traditional controllers have disadvantages such as weak scene adaptability and difficulty in balancing multi-objective optimization. In this paper, combined with the excellent self-learning ability of reinforcement learning, an interactive model predictive control algorithm is designed to realize the tracking control of the lane change trajectory. At the same time, two typical scenarios are verified by PreScan and Simulink, and the results show that the control algorithm can significantly improve the tracking accuracy and stability of the lane change trajectory.
其他摘要：Autonomous lane changing, as a key module to realize high-level automatic driving, has important practical significance for improving the driving safety, comfort and commuting efficiency of vehicles. Traditional controllers have disadvantages such as weak scene adaptability and difficulty in balancing multi-objective optimization. In this paper, combined with the excellent self-learning ability of reinforcement learning, an interactive model predictive control algorithm is designed to realize the tracking control of the lane change trajectory. At the same time, two typical scenarios are verified by PreScan and Simulink, and the results show that the control algorithm can significantly improve the tracking accuracy and stability of the lane change trajectory.