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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    Title :

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


    Contributors:


    Publication date :

    2021-09-15


    Size :

    976715 byte





    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English




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