Pedestrian protection is an important issue for intelligent vehicles. This paper proposes a new approach for predicting the possibility of a collision between a vehicle and a pedestrian. Almost all pedestrian behavior toward vehicles observed in the real world is considered safe. Therefore, pedestrian behavior that deviates from usual pedestrian behavior indicates a possibility where the vehicle must take urgent evasive action to avoid collision with the pedestrian. From such a viewpoint, this paper proposes a method for predicting whether the pedestrian behavior deviates from usual pedestrian behavior. Usual pedestrian behavior as observed from the vehicle is modeled with machine learning to detect whether a new observed behavior deviates from the model of the usual pedestrian behavior. The effectiveness of the proposed method is demonstrated with an experiment conducted in a simple road environment.


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    Titel :

    Detecting unusual pedestrian behavior toward own vehicle for vehicle-to-pedestrian collision avoidance


    Beteiligte:
    Nakatsubo, K (Autor:in) / Yamada, K (Autor:in)


    Erscheinungsdatum :

    01.06.2010


    Format / Umfang :

    593164 byte





    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



    Detecting Unusual Pedestrian Behavior Toward Own Vehicle for Vehicle-To-Pedestrian Collision Avoidance, pp. 401-405

    Nakatsubo, K. / Yamada, K. / Institute of Electrical and Electronics Engineers | British Library Conference Proceedings | 2010


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