Autonomous driving in urban environments requires safe control policies that account for the non-determinism of moving obstacles, such as the position other vehicles will take while crossing an uncontrolled intersection. We address this problem by proposing a stochastic model predictive control (MPC) approach with robust collision avoidance constraints to guarantee safety. By adopting a stochastic formulation, the quality of closed-loop tracking is increased by avoiding giving excessive importance to future obstacle configurations that are unlikely to occur. We compute the probabilities associated with different obstacle trajectories by learning a classifier on a realistic dataset generated by the microscopic traffic simulator SUMO and show the benefits of the proposed stochastic MPC formulation on a simulated realistic intersection.


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    Titel :

    Learning-Based Stochastic Model Predictive Control for Autonomous Driving at Uncontrolled Intersections


    Beteiligte:
    Soman, Surya (Autor:in) / Zanon, Mario (Autor:in) / Bemporad, Alberto (Autor:in)


    Erscheinungsdatum :

    01.02.2025


    Format / Umfang :

    6556305 byte




    Medientyp :

    Aufsatz (Zeitschrift)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



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