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

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 acti...

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Vydáno v:Mathematics (Basel) Ročník 12; číslo 11; s. 1614
Hlavní autoři: Liu, Yalei, Ding, Weiping, Yang, Mingliang, Zhu, Honglin, Liu, Liyuan, Jin, Tianshi
Médium: Journal Article
Jazyk:angličtina
Vydáno: Basel MDPI AG 01.06.2024
Témata:
Algorithms
Autonomous vehicles
Control algorithms
Control methods
Control systems
Controllers
Decision making
deep deterministic policy gradient
Deep learning
deep Q-network
Deviation
distributed drive autonomous vehicles
Driverless cars
Efficiency
Equipment and supplies
Intelligent vehicles
Kinematics
Lateral control
Markov processes
Methods
Motion control
multi-agent deep reinforcement learning
Multiagent systems
Neural networks
Steering
Tracking control
Trajectory control
trajectory tracking
China
ISSN:2227-7390, 2227-7390
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Shrnutí: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%.
Bibliografie:ObjectType-Article-1
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ISSN:2227-7390
2227-7390
DOI:10.3390/math12111614