Trajectory planning in urban automated driving is challenging because of the high uncertainty resulting from the unknown future motion of other traffic participants. Robust approaches guarantee safety, but tend to result in overly conservative motion planning. Hence, we propose to use Stochastic Model Predictive Control for vehicle control in urban driving, allowing to efficiently plan the vehicle trajectory, while maintaining the risk probability sufficiently low. For motion optimization, we propose to use a two-stage hierarchical structure that plans the trajectory and the maneuver separately. A high-level layer takes advantage of a long prediction horizon and of an abstract model to plan the optimal maneuver, and a lower level is in charge of executing the selected maneuver by properly planning the vehicle's trajectory. Numerical simulations are included, showing the potential of our proposal.


    Zugriff

    Zugriff prüfen

    Verfügbarkeit in meiner Bibliothek prüfen

    Bestellung bei Subito €


    Exportieren, teilen und zitieren



    Titel :

    Multistage Stochastic Model Predictive Control for Urban Automated Driving


    Beteiligte:


    Erscheinungsdatum :

    2021-09-19


    Format / Umfang :

    437933 byte




    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



    Stochastic Model Predictive Control With a Safety Guarantee for Automated Driving

    Brudigam, Tim / Olbrich, Michael / Wollherr, Dirk et al. | IEEE | 2023


    Model Predictive Trajectory Planning for Automated Driving

    Yi, Boliang / Bender, Philipp / Bonarens, Frank et al. | IEEE | 2019


    Predictive manoeuvre generation for automated driving

    Nilsson, Julia / Ali, Mohammad / Falcone, Paolo et al. | IEEE | 2013


    Predictive Driving: Fusing Prediction and Planning for Automated Highway Driving

    Lienke, Christian / Wissing, Christian / Keller, Martin et al. | IEEE | 2019