A Learning-Based Controller for Trajectory Tracking of Autonomous Vehicles in Complex and Uncertain Scenarios

被引:0
|
作者
Gong, Cheng [1 ]
Qiu, Runqi [1 ]
Lin, Yunlong [1 ]
Li, Zirui [1 ,2 ]
Gong, Jianwei [1 ]
Lu, Chao [1 ]
机构
[1] Beijing Inst Technol, Sch Mech Engn, Beijing 100081, Peoples R China
[2] Tech Univ Dresden, Friedrich List Fac Transport & Traff Sci, Chair Traff Proc Automat, D-01069 Dresden, Germany
来源
2023 IEEE 26TH INTERNATIONAL CONFERENCE ON INTELLIGENT TRANSPORTATION SYSTEMS, ITSC | 2023年
关键词
D O I
10.1109/ITSC57777.2023.10422503
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
This paper proposes a learning-based controller for autonomous driving in dynamic and uncertain environments. The controller network parameters from demonstrations of human expert drivers and then updates the policy with online data samples using incremental learning methods. The controller aims to fit the vehicle's inverse dynamics and cope with external disturbances such as varying adhesion coefficients. To avoid catastrophic forgetting and fit the optimal policy, a knowledge evaluation method and a gradient constraint scheme are introduced. The effectiveness and robustness of the controller are demonstrated by a vehicle dynamics simulation model in MATLAB/Simulink. The experimental results show that the proposed method can adapt to complex curve environments with varying adhesion coefficients under high-speed driving conditions and is able to continuously improve control performance through incremental learning.
引用
收藏
页码:5040 / 5046
页数:7
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