Reinforcement learning has great potential in solving complex decision-making problems in autonomous driving, especially in traffic scenarios where autonomous and human driving vehicles are mixed. This article describes the lane changing problem of mixed traffic expressway vehicles as a multi-agent reinforcement learning problem, in which autonomous vehicles learn a strategy adapted to human driving vehicles through collaboration to maximize traffic throughput. This article extends the SAC-Discrete algorithm to the multi-agent reinforcement learning framework and proposes the MASAC-Discrete algorithm. In addition, this article also proposes a motion prediction safety controller that includes a motion predictor and motion replacement module to ensure driving safety during training and testing. This article trains, evaluates, and tests the proposed method on a highway simulator under three different levels of traffic modes. The simulation results show that even in high traffic density situations, this method can significantly reduce collision rates while maintaining high efficiency. In the considered highway scenarios, its performance is superior to several state-of-theart benchmark algorithms.


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    Title :

    Research on automatic vehicle lane changing model based on MASAC-discrete algorithm


    Contributors:
    Yang, Lvqing (editor) / Tan, Wenjun (editor) / Liu, Qi (author) / Hu, Xiaohui (author) / Li, Shaobing (author)

    Conference:

    Sixth International Conference on Advanced Electronic Materials, Computers, and Software Engineering (AEMCSE 2023) ; 2023 ; Shenyang, China


    Published in:

    Proc. SPIE ; 12787


    Publication date :

    2023-08-16





    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



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