With the rapid advancement of intelligent connected vehicle technology, ensuring safe and efficient travel in highway weaving areas has become a significant research topic. Traditional trajectory prediction methods inadequately consider real-time traffic situation changes and lack generalizability. Consequently, this study introduces a trajectory prediction method for mixed vehicles in highway weaving areas based on Cyber-Physical Systems (CPS). Moreover, an enhanced Transformer model is employed to model the dynamic spatial interactions between vehicles. The well-trained model is deployed in the cyber layer, and by training with data from the physical layer, it facilitates real-time prediction of manually driven vehicle trajectories. Experimental results demonstrate that compared to prevalent approaches, our method offers more accurate and efficient trajectory prediction in complex weaving area scenarios.


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    Titel :

    CPS-Based Vehicle Trajectory Prediction in Highway Interchanges under Mixed Traffic Conditions


    Beteiligte:
    Gu, Zhenyu (Autor:in) / Jiang, Wei (Autor:in) / Li, Dongyang (Autor:in) / Liu, Rentao (Autor:in)

    Kongress:

    24th COTA International Conference of Transportation Professionals ; 2024 ; Shenzhen, China


    Erschienen in:

    CICTP 2024 ; 131-140


    Erscheinungsdatum :

    11.12.2024




    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



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