Complementing the missing trajectory data in real time is of great significance for the safe driving of autonomous trucks. This paper introduced a spline based long short-term memory network (LSTM) to tackle the positioning signal loss issues by realizing the real-time trajectory prediction of autonomous truck. The cubic B-spline curve is used to fit the trajectory data to obtain control points series. The LSTM is then used to train with historical data. As a result, without relying on the physical information of the road network and the surrounding environment information of the truck, truck trajectory prediction in 1–3 seconds is realized. A case study is conducted based on simulation data. Compared with other common methods, the proposed spline-LSTM shows advantages in both convergence speed and prediction accuracy, and is more suitable for real-time analysis.


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    Titel :

    A spline-LSTM for Autonomous Truck Trajectory Prediction Based on Curve feature extraction


    Beteiligte:
    Liu, Suri (Autor:in) / Xu, Xinglu (Autor:in) / Feng, Tao (Autor:in) / Song, Xiangqun (Autor:in) / Wang, Wenyuan (Autor:in)


    Erscheinungsdatum :

    2021-10-22


    Format / Umfang :

    8633334 byte




    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



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