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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    Title :

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


    Contributors:
    Lin, Qin (author) / Zhang, Yihuan (author) / Verwer, Sicco (author) / Wang, Jun (author)


    Publication date :

    2019-02-01


    Size :

    2447071 byte




    Type of media :

    Article (Journal)


    Type of material :

    Electronic Resource


    Language :

    English




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