Prediction of the movement of all traffic participants is a very important task in autonomous driving. Well-predicted behavior of other cars and actors is crucial for safety. A sequence of bird’s-eye view artificially rasterized frames are used as input to neural networks which are trained to predict the future behavior of the participants. The Lyft Motion Prediction for Autonomous Vehicles dataset is explored and adapted for this task. We developed and applied a novel approach where the prediction problem is viewed as a problem of spatiotemporal prediction and we use methods based on convolutional recurrent neural networks.


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    Title :

    Spatiotemporal Prediction of Vehicle Movement Using Artificial Neural Networks


    Contributors:
    Pihrt, Jiri (author) / Simanek, Petr (author)


    Publication date :

    2022-06-05


    Size :

    2866330 byte




    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English




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