Trajectory prediction is very important for autonomous driving vehicles. Deep-learning approaches can capture the interaction information. Therefore more and more researchers are using deep-learning models to accomplish vehicle trajectory prediction. This paper provides a detailed and comparative description of the methods for vehicle trajectory prediction based on deep-learning models. We start by modelling trajectory predictions for vehicles. Several common deep-learning models used in vehicle trajectory prediction are then highlighted. Next, we compare the structural features, salient advantages and applicable scenarios of commonly used deep-learning models. Future directions for vehicle trajectory prediction are discussed at the end.


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    Titel :

    A Survey of Vehicle Trajectory Prediction Based on Deep-Learning


    Beteiligte:
    Yin, Huilin (Autor:in) / Wen, Yurong (Autor:in) / Li, Jiaxiang (Autor:in)


    Erscheinungsdatum :

    24.02.2023


    Format / Umfang :

    1223213 byte




    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



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