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Author:

Wang, Zhuwei (Wang, Zhuwei.) | Xue, Yi (Xue, Yi.) | Liu, Lihan (Liu, Lihan.) | Zhang, Haijun (Zhang, Haijun.) | Qu, Chunhui (Qu, Chunhui.) | Fang, Chao (Fang, Chao.)

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EI Scopus

Abstract:

The development of intelligent transportation systems (ITS) has attracted significant attention to connected and autonomous vehicles (CAVs). It is urgent to investigate multi-CAV intelligent cruise control solutions in mixed traffic environments. In addition, the impact of platoon dynamics and time delays, induced by shared wireless communications, data processing, and actuation cannot be ignored. This article investigates the development of a multi-agent deep reinforcement learning (MADRL) controller tailored for CAVs operating within mixed and dynamic traffic scenarios that involve time delays. Firstly, the error dynamics in the discrete-time domain for each subplatoon is derived by considering the time-varying delays and leading vehicle states, and then the optimal CAV cruise control problem is formulated. Subsequently, the partially observable Markov game (POMG) is used to construct the multi-agent environment, and then a centralized training decentralized execution (CTDE) algorithm framework is proposed based on the multi-agent deep deterministic policy gradient (MADDPG) method. Finally, the computational complexity and the influence of delay are analyzed. The simulation results illustrate the effectiveness of the proposed intelligent algorithm. © 2024 IEEE.

Keyword:

Discrete time control systems Deep reinforcement learning Street traffic control Markov processes Time varying control systems Delay control systems Reinforcement learning Magnetic levitation vehicles Time domain analysis

Author Community:

  • [ 1 ] [Wang, Zhuwei]Beijing University of Technology, School of Information Science and Technology, Beijing; 100124, China
  • [ 2 ] [Xue, Yi]Beijing University of Technology, School of Information Science and Technology, Beijing; 100124, China
  • [ 3 ] [Liu, Lihan]Beijing Wuzi University, School of Statistics and Data Science, Beijing; 101149, China
  • [ 4 ] [Zhang, Haijun]University of Science and Technology Beijing, Beijing Advanced Innovation Center for Materials Genome Engineering and Beijing Engineering, Technology Research Center for Convergence Networks and Ubiquitous Services, Beijing; 100083, China
  • [ 5 ] [Qu, Chunhui]Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing; 100094, China
  • [ 6 ] [Fang, Chao]Beijing University of Technology, School of Information Science and Technology, Beijing; 100124, China

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Source :

IEEE Transactions on Intelligent Transportation Systems

ISSN: 1524-9050

Year: 2024

Issue: 11

Volume: 25

Page: 17676-17688

8 . 5 0 0

JCR@2022

Cited Count:

WoS CC Cited Count:

SCOPUS Cited Count: 4

ESI Highly Cited Papers on the List: 0 Unfold All

WanFang Cited Count:

Chinese Cited Count:

30 Days PV: 2

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