Advanced driver assistance systems require accurate knowledge on the current and future states of all surrounding vehicles. Current approaches use the information on actual vehicle dynamics and a motion model, leading to short-time pre-dictions. In this work, we developed a framework to determine long-term predictions of the future vehicle trajectory for extra-urban traffic scenarios in dependence on the present situation. Scene analysis is used to yield the probability of occurrence on a set of maneuvers. Trajectory prediction is accomplished by an approach of Case-based reasoning. Based on the current observation, similar trajectories are retrieved from a database, resulting in long-term predictions for a given maneuver. The information of scene analysis is combined with these trajectory predictions to yield the final situation-adaptive long-term vehicle trajectory prediction. The performance of our method is verified with a comparison to conventional approaches in terms of error analysis over the prediction horizon.


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    Titel :

    Situation-adaptive Vehicle Trajectory Prediction


    Beteiligte:
    Omerbegovic, Said (Autor:in) / Firl, Jonas (Autor:in)

    Kongress:

    AmE 2013 - 4. GMM-Fachtagung ; 2013 ; Dortmund, Deutschland


    Erschienen in:

    Erscheinungsdatum :

    01.01.2013


    Format / Umfang :

    6 pages



    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



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