According to the analysis of car accidents many casualties occur at intersections. As ongoing research demonstrates, Advanced Driver Assistance Systems that aim at preventing this type of accident, need to reliably predict the turning maneuver of all relevant participants in the scene. In this work an approach is introduced, which models human drivers as the optimizer of an optimal control problem with an unknown terminal state. Tracking the cost-to-go gradient to the terminal state of each driving option leads to the most plausible hypothesis. The optimal control problem itself is formulated with costs that minimize jerk, time and steering effort with good resemblance to typical human driving behavior. In combination with a simplified vehicle model this leads to a nonlinear constrained dynamic optimization problem, which is solved numerically. The performance of the proposed approach is evaluated on data obtained in a field test with promising results.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    Maneuver prediction at intersections using cost-to-go gradients


    Contributors:


    Publication date :

    2013-10-01


    Size :

    1027085 byte




    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



    Personalized Maneuver Prediction at Intersections

    Losing, Viktor / Hammer, Barbara / Wersing, Heiko | BASE | 2017

    Free access

    Personalized maneuver prediction at intersections

    Losing, Viktor / Hammer, Barbara / Wersing, Heiko | IEEE | 2017


    Infrastructure-based vehicle maneuver estimation at urban intersections

    Schendzielorz, T. / Mathias, P. / Busch, F. | IEEE | 2013



    Maneuver Coordination Using V2I to Improve Traffic Efficiency at Intersections

    Farina, Lorenzo / Rapelli, Marco / Masini, Barbara Mavi et al. | IEEE | 2024