Distributed Drive Autonomous Vehicle Trajectory Tracking Control Based on Multi-Agent Deep Reinforcement Learning

Yalei Liu, Weiping Ding, Mingliang Yang (), Honglin Zhu, Liyuan Liu and Tianshi Jin
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Yalei Liu: School of Mechanical Engineering, Southwest Jiaotong University, Chengdu 610031, China
Weiping Ding: School of Mechanical Engineering, Southwest Jiaotong University, Chengdu 610031, China
Mingliang Yang: School of Mechanical Engineering, Southwest Jiaotong University, Chengdu 610031, China
Honglin Zhu: School of Mechanical Engineering, Southwest Jiaotong University, Chengdu 610031, China
Liyuan Liu: School of Mechanical Engineering, Southwest Jiaotong University, Chengdu 610031, China
Tianshi Jin: School of Mechanical Engineering, Southwest Jiaotong University, Chengdu 610031, China

Mathematics, 2024, vol. 12, issue 11, 1-21

Abstract: In order to enhance the trajectory tracking accuracy of distributed-driven intelligent vehicles, this paper formulates the tasks of torque output control for longitudinal dynamics and steering angle output control for lateral dynamics as Markov decision processes. To dissect the requirements of action output continuity for longitudinal and lateral control, this paper adopts the deep deterministic policy gradient algorithm (DDPG) for longitudinal velocity control and the deep Q-network algorithm (DQN) for lateral motion control. Multi-agent reinforcement learning methods are applied to the task of trajectory tracking in distributed-driven vehicle autonomous driving. By contrasting with two classical trajectory tracking control methods, the proposed approach in this paper is validated to exhibit superior trajectory tracking performance, ensuring that both longitudinal velocity deviation and lateral position deviation of the vehicle remain at lower levels. Compared with classical control methods, the maximum lateral position deviation is improved by up to 90.5% and the maximum longitudinal velocity deviation is improved by up to 97%. Furthermore, it demonstrates excellent generalization and high computational efficiency, and the running time can be reduced by up to 93.7%.

Keywords: distributed drive autonomous vehicles; trajectory tracking; multi-agent deep reinforcement learning; deep deterministic policy gradient; deep Q-network (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2024
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