We present a motion planning framework for autonomous on-road driving considering both the uncertainty caused by an autonomous vehicle and other traffic participants. The future motion of traffic participants is predicted using a local planner, and the uncertainty along the predicted trajectory is computed based on Gaussian propagation. For the autonomous vehicle, the uncertainty from localization and control is estimated based on a Linear-Quadratic Gaussian (LQG) framework. Compared with other safety assessment methods, our framework allows the planner to avoid unsafe situations more efficiently, thanks to the direct uncertainty information feedback to the planner. We also demonstrate our planner's ability to generate safer trajectories compared to planning only with a LQG framework.


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    Title :

    Motion planning under uncertainty for on-road autonomous driving


    Contributors:
    Xu, Wenda (author) / Pan, Jia (author) / Wei, Junqing (author) / Dolan, John M. (author)


    Publication date :

    2014-05-01


    Size :

    307615 byte




    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



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