This work develops a motion planner that compensates the deficiencies from perception modules by exploiting the reaction capabilities of a vehicle. The work analyzes present uncertainties and defines driving objectives together with constraints that ensure safety. The resulting problem is solved in real-time, in two distinct ways: first, with nonlinear optimization, and secondly, by framing it as a partially observable Markov decision process and approximating the solution with sampling


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    Titel :

    Motion Planning for Autonomous Vehicles in Partially Observable Environments


    Beteiligte:


    Erscheinungsdatum :

    2023


    Format / Umfang :

    1 Online-Ressource (222 p.)



    Medientyp :

    Buch


    Format :

    Elektronische Ressource


    Sprache :

    Unbekannt






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