Most of the traditional automatic driving behaviours are based on mathematical formulae or vehicle dynamics and kinematics models, which ignore the driver's perception and decision-making ability in the actual driving process, and the generated trajectories are very different from the actual lane-changing trajectories. In this paper, we adopt an automatic driving system based on multi-intelligence reinforcement learning to optimise the lane-changing decision of the vehicle by combining multi-intelligence reinforcement learning algorithms and avoiding the interference of inter-vehicle information interaction. In this way, the self-driving vehicle can have the functions of complex environmental perception, path planning and behavioural decision making. Combined with the highway mixed-flow environment scenario, the vehicle lane-changing behaviour model based on the HMM model is carried out for comparative experiments, and the results show that the lane-changing intention model based on multi-intelligence body reinforcement learning performs better in lane-changing detection accuracy and can be adapted to traffic scenarios with different traffic flow densities.


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    Titel :

    Research on intelligent lane-changing model based on multi-intelligent reinforcement learning


    Beteiligte:
    Fang, Jing (Autor:in) / Zhang, Yongjie (Autor:in)


    Erscheinungsdatum :

    06.12.2024


    Format / Umfang :

    1042660 byte




    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



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