This paper proposes a novel hybrid model for learning discrete and continuous dynamics of car-following behaviors. Multiple modes representing driving patterns are identified by partitioning the model into groups of states. The model is visualizable and interpretable for car-following behavior recognition, traffic simulation, and human-like cruise control. The experimental results using the next generation simulation datasets demonstrate its superior fitting accuracy over conventional models.


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    Titel :

    MOHA: A Multi-Mode Hybrid Automaton Model for Learning Car-Following Behaviors


    Beteiligte:
    Lin, Qin (Autor:in) / Zhang, Yihuan (Autor:in) / Verwer, Sicco (Autor:in) / Wang, Jun (Autor:in)


    Erscheinungsdatum :

    01.02.2019


    Format / Umfang :

    2447071 byte




    Medientyp :

    Aufsatz (Zeitschrift)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch




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