Motion planning for car-like robots is one of the major challenges in automated driving. It requires to solve a two-point boundary value problem that connects a start and a goal configuration with a collision-free trajectory. This paper introduces a novel approach for motion planning under uncertainty resulting in motion plans with higher robustness due to a bounded risk of collision. To that end, the state of the art G1and G2 continuous steering functions, namely Dubins, Reeds-Shepp, Hybrid Curvature, and Continuous Curvature Steer, are extended to belief space, where an expected vehicle state is augmented by its associated uncertainty. The so-called beliefs are obtained by propagating a Gaussian probability distribution along the nominal path of the steering function while taking into account the control and localization uncertainty. A novel concept for collision avoidance in Gaussian belief space is then introduced that considers the shape of the vehicle and the full information about the uncertainty of a state. Together with the sampling-based motion planner RRT*, the proposed realtime capable methodology reduces the collision probability by an order of magnitude in three challenging automated driving scenarios.


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    Titel :

    From Footprints to Beliefprints: Motion Planning under Uncertainty for Maneuvering Automated Vehicles in Dense Scenarios


    Beteiligte:
    Banzhaf, Holger (Autor:in) / Dolgov, Maxim (Autor:in) / Stellet, Jan (Autor:in) / Zollner, J. Marius (Autor:in)


    Erscheinungsdatum :

    2018-11-01


    Format / Umfang :

    2910982 byte





    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch




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