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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    Titel :

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


    Weitere Titelangaben:

    Lect. Notes Electrical Eng.


    Beteiligte:
    Liu, Lianqing (Herausgeber:in) / Niu, Yifeng (Herausgeber:in) / Fu, Wenxing (Herausgeber:in) / Qu, Yi (Herausgeber:in) / Yan, Wang (Autor:in) / Xia, Wu (Autor:in) / Jiadong, Li (Autor:in) / Dengrong, Li (Autor:in)

    Kongress:

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



    Erscheinungsdatum :

    02.04.2025


    Format / Umfang :

    11 pages





    Medientyp :

    Aufsatz/Kapitel (Buch)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch