Understanding of the driving scenario represents a necessary condition for autonomous driving. Within the control routine of an autonomous vehicle, it represents the preliminary step for the motion planning system. Estimation algorithms hence need to handle a considerable number of information coming from multiple sensors, to provide estimates regarding the motion of ego-vehicle and surrounding obstacles. Furthermore, tracking is crucial in obstacles state estimation, because it ensures obstacles recognition during time. This paper presents an integrated algorithm for the estimation of ego-vehicle and obstacles’ positioning and motion along a given road, modeled in curvilinear coordinates. Sensor fusion deals with information coming from two Radars and a Lidar to identify and track obstacles. The algorithm has been validated through experimental tests carried on a prototype of an autonomous vehicle.


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    Titel :

    An integrated algorithm for ego-vehicle and obstacles state estimation for autonomous driving


    Beteiligte:
    Bersani M. (Autor:in) / Mentasti S. (Autor:in) / Dahal P. (Autor:in) / Arrigoni S. (Autor:in) / Vignati M. (Autor:in) / Cheli F. (Autor:in) / Matteucci M. (Autor:in) / Bersani, M. / Mentasti, S. / Dahal, P.

    Erscheinungsdatum :

    2021-01-01



    Medientyp :

    Aufsatz (Zeitschrift)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



    Klassifikation :

    DDC:    629



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