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

Author

Listed:
  • 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)

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%.

Suggested Citation

  • Yalei Liu & Weiping Ding & Mingliang Yang & Honglin Zhu & Liyuan Liu & Tianshi Jin, 2024. "Distributed Drive Autonomous Vehicle Trajectory Tracking Control Based on Multi-Agent Deep Reinforcement Learning," Mathematics, MDPI, vol. 12(11), pages 1-21, May.
    • Handle: RePEc:gam:jmathe:v:12:y:2024:i:11:p:1614-:d:1398765
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    References listed on IDEAS

    as
    1. Wei, Hongqian & Zhang, Nan & Liang, Jun & Ai, Qiang & Zhao, Wenqiang & Huang, Tianyi & Zhang, Youtong, 2022. "Deep reinforcement learning based direct torque control strategy for distributed drive electric vehicles considering active safety and energy saving performance," Energy, Elsevier, vol. 238(PB).
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