@inproceedings{9346ea4430f14fca981ee09d01770bec,
title = "An adaptive path tracking controller for autonomous vehicles based on the Pure Pursuit algorithm",
abstract = "Aiming at the problem of unmanned vehicle path tracking, this paper proposes an adaptive path tracking controller based on the Pure Pursuit(PP) method. The controller replaces the traditional manual selection of the lookahead distance in the traditional PP algorithm with the Deep Q Network (DQN) algorithm. The controller dynamically adjusts the lookahead distance based on lateral error, heading error, and vehicle speed to adapt to different operating conditions and improve path tracking performance. This paper obtained the Deep Q Network - Pure Pursuit (DQN-PP) adaptive controller model through reinforcement learning training. In order to verify the control effect of the DQN-PP adaptive controller, this paper designed a simulation experiment and analyzed the experimental results. The results show that compared with the traditional method, the DQN-PP adaptive controller has better path tracking performance, and the average error value of path tracking has been reduced by 21%. This paper provides an effective adaptive solution for path tracking control in autonomous vehicles and has practical application value.",
keywords = "Adaptive Controller, Deep Q Network, Path Tracking, Pure Pursuit",
author = "Yuze Wang and Dongguang Li and Xing Zhuang and Yue Wang and Siyuan Yang and Ruoyu Wu",
note = "Publisher Copyright: {\textcopyright} 2023 IEEE.; 2023 China Automation Congress, CAC 2023 ; Conference date: 17-11-2023 Through 19-11-2023",
year = "2023",
doi = "10.1109/CAC59555.2023.10452053",
language = "English",
series = "Proceedings - 2023 China Automation Congress, CAC 2023",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "7508--7512",
booktitle = "Proceedings - 2023 China Automation Congress, CAC 2023",
address = "United States",
}