In urban driving scenarios, it is a key component for autonomous vehicles to generate a smooth, kinodynamically feasible, and collision-free path. We present an optimization-based path planning method for autonomous vehicles navigating in cluttered environment, e.g., roads partially blocked by static or moving obstacles. Our method first computes a collision-free reference line using quadratic programming(QP), and then using the reference line as initial guess to generate a smooth and feasible path by iterative optimization using sequential quadratic programming(SQP). It works within a fractions of a second, thus permitting efficient regeneration.


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    Titel :

    Robust Online Path Planning for Autonomous Vehicle Using Sequential Quadratic Programming


    Beteiligte:
    Jiang, Yuncheng (Autor:in) / Liu, Zenghui (Autor:in) / Qian, Danjian (Autor:in) / Zuo, Hao (Autor:in) / He, Weiliang (Autor:in) / Wang, Jun (Autor:in)


    Erscheinungsdatum :

    05.06.2022


    Format / Umfang :

    1217646 byte




    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch