This contribution proposes a novel algorithm for predicting maneuvers at intersections. With applicability to driver assistance systems and autonomous driving, the presented methodology estimates a maneuver probability for every possible direction at an intersection. For this purpose, a generic intersection-feature, space-based representation is defined which combines static and dynamic intersection information with the dynamic properties of the observed vehicle, provided by a tracking module. A statistical behavior model is learned from previously recorded patterns by approximating the resulting feature space. Because the feature space consists of different types of features (mixed-feature space), a Bernoulli-Gaussian Mixture Model is applied as approximating function. Further, an online learning extension is proposed to adapt the model to the characteristics of different intersections.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    A probabilistic maneuver prediction framework for self-learning vehicles with application to intersections


    Contributors:


    Publication date :

    2015-06-01


    Size :

    1248160 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


    Cooperative Maneuver Planning for Mixed Traffic at Unsignalized Intersections Using Probabilistic Predictions

    Mertens, Max Bastian / Muller, Johannes / Buchholz, Michael | IEEE | 2022


    Maneuver prediction at intersections using cost-to-go gradients

    von Eichhorn, Andreas / Werling, Moritz / Zahn, Peter et al. | IEEE | 2013