With the rise of more and more L3 Highway Pilots, it is necessary for autonomous vehicles to make trajectory predictions of surrounding vehicles for improved driving comfort and safety. However, it remains challenging since full-coverage HD maps in highway scenarios is very expensive, so trajectory prediction has to handle coordinate-based inputs, without HD maps. Inspired by human reasoning, we tackle these problems by using the attention mechanism and the Transformer architecture. In this article, we develop an Enhanced Social Trajectory Prediction Transformer (ESTPT) framework for L3 Highway Pilots, which adds the Social Encoder module based on the Transformer network to effectively predict the agent vehicle's trajectories considering the interaction features without using RNN. Specifically, the three main parts of ESTPT are based on the multi-head attention mechanism, which can make accurate predictions based on coordinate-based trajectories and can deal with missing observations, to improve driving safety, comfort, and robustness. We demonstrate the performance on the NGSIM dataset and results show great improvement in terms of the positional error. Some valuable ablation analyses are conducted and the result visualization is given.


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    Titel :

    Enhanced Social Trajectory Prediction Transformer for L3 Highway Pilots


    Beteiligte:
    Du, Jiatong (Autor:in) / Luo, Erpai (Autor:in) / Bai, Yulong (Autor:in) / Huang, Yanjun (Autor:in)


    Erscheinungsdatum :

    24.09.2023


    Format / Umfang :

    543449 byte





    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch





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