This paper presents online-capable deep learning model for probabilistic vehicle trajectory prediction. We propose a simple encoder-decoder architecture based on multihead attention. The proposed model generates the distribution of the predicted trajectories for multiple vehicles in parallel. Our approach to model the interactions can learn to attend to a few influential vehicles in an unsupervised manner, which can improve the interpretability of the network. The experiments using naturalistic trajectories at highway show the clear improvement in terms of positional error on both longitudinal and lateral direction.


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    Title :

    Multi-Head Attention based Probabilistic Vehicle Trajectory Prediction


    Contributors:
    Kim, Hayoung (author) / Kim, Dongchan (author) / Kim, Gihoon (author) / Cho, Jeongmin (author) / Huh, Kunsoo (author)


    Publication date :

    2020-10-19


    Size :

    1555648 byte





    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



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