Variable speed limit (VSL) control plays a crucial role in optimizing traffic efficiency. The connected and autonomous vehicles, as a controllable agent, can offer a potential method for multi-agent reinforcement learning-based variable speed limit strategy. To pursue the target between both traffic efficiency and safety, a multi-agent proximal policy optimization-based variable speed optimization strategy is proposed by leveraging connected and autonomous vehicles to form moving bottlenecks proactively (MAPPO-MVSL) in this paper. First, we construct a positive artificial moving bottleneck using connected and autonomous vehicles on a two-lane highway. Then, a Markov Process-based VSL strategy is presented, and MAPPO algorithm is applied to solve the problem. Compared with the baseline and the traditional single-agent variable speed control strategy, the proposed strategy improves traffic efficiency by 40%. Additionally, it enhances safety in the merge area, reducing danger by 16%.


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    Title :

    Multi-agent Reinforcement Learning-Based Variable Speed Limit Strategy by Leveraging Connected and Autonomous Vehicles in Mixed Traffic Flow


    Additional title:

    Lect. Notes Electrical Eng.


    Contributors:
    Liu, Lianqing (editor) / Niu, Yifeng (editor) / Fu, Wenxing (editor) / Qu, Yi (editor) / Yan, Wang (author) / Xia, Wu (author) / Jiadong, Li (author) / Dengrong, Li (author)

    Conference:

    International Conference on Autonomous Unmanned Systems ; 2024 ; Shenyang, China September 19, 2024 - September 21, 2024



    Publication date :

    2025-04-02


    Size :

    11 pages





    Type of media :

    Article/Chapter (Book)


    Type of material :

    Electronic Resource


    Language :

    English