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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    Title :

    Robust Online Path Planning for Autonomous Vehicle Using Sequential Quadratic Programming


    Contributors:
    Jiang, Yuncheng (author) / Liu, Zenghui (author) / Qian, Danjian (author) / Zuo, Hao (author) / He, Weiliang (author) / Wang, Jun (author)


    Publication date :

    2022-06-05


    Size :

    1217646 byte




    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English