Accurately predicting the trajectory of pedestrians helps autonomous vehicles to drive safely. In this paper, a work of predicting the trajectory of pedestrians by considering their posture is described. Two seconds of historical data are used to predict the pedestrian's actions in the next second based on a long short-term memory approach. The purpose of this experiment is to estimate whether pedestrians will cross the road in a mid-block setting without crosswalks and what paths they will take. The scene of this experiment is located on a street near the campus of the University of California at Berkeley.


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    Titel :

    A Posture Features Based Pedestrian Trajectory Prediction with LSTM


    Beteiligte:
    Kao, I-Hsi (Autor:in) / Zhou, Xiao (Autor:in) / Chen, I-Ming (Autor:in) / Wang, Pin (Autor:in) / Chan, Ching-Yao (Autor:in)


    Erscheinungsdatum :

    15.09.2021


    Format / Umfang :

    1032683 byte





    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



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