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.


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    Titel :

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


    Beteiligte:
    Wiest, Jurgen (Autor:in) / Karg, Matthias (Autor:in) / Kunz, Felix (Autor:in) / Reuter, Stephan (Autor:in) / Kresel, Ulrich (Autor:in) / Dietmayer, Klaus (Autor:in)


    Erscheinungsdatum :

    01.06.2015


    Format / Umfang :

    1248160 byte




    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



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