The trajectory prediction of traffic agents plays an important role regarding to the safety of autonomous driving. Structured by gate recurrent unit (GRU), this paper proposes a new predict model with the combination of trajectory mapping method. The experimental results show that the proposed model can feasibly predict the future trajectories of the surrounding traffic agents in a mixed flow including vehicles, cyclists, and pedestrians.


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    Titel :

    Traffic Agent Trajectory Prediction Using a Time Sequence Deep Learning Model with Trajectory Mapping for Autonomous Driving


    Beteiligte:
    Hsu, Pei-Yun (Autor:in) / Huang, Mei-Lin (Autor:in) / Wang, Wei-Yen (Autor:in) / Chiang, Hsin-Han (Autor:in)


    Erscheinungsdatum :

    2021-09-15


    Format / Umfang :

    976715 byte





    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



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