Road-users are a critical part of decision-making for both self-driving cars and driver assistance systems. Some road-users, however, are more important for decision-making than others because of their respective intentions, ego-vehicle's intention and their effects on each other. In this paper, we propose a novel architecture for road-user importance estimation which takes advantage of the local and global context of the scene. For local context, the model exploits the appearance of the road users (which captures orientation, intention, etc.) and their location relative to ego-vehicle. The global context in our model is defined based on the feature map of the convolutional layer of the module which predicts the future path of the ego-vehicle and contains rich global information of the scene (e.g., infrastructure, road lanes, etc.), as well as the ego-vehicle's intention information. Moreover, this paper introduces a new data set of real-world driving, concentrated around intersections and includes annotations of important road users. Systematic evaluations of our proposed method against several baselines show promising results.


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    Titel :

    Context Aware Road-user Importance Estimation (iCARE)


    Beteiligte:
    Rahimpour, Alireza (Autor:in) / Martin, Sujitha (Autor:in) / Tawari, Ashish (Autor:in) / Qi, Hairong (Autor:in)


    Erscheinungsdatum :

    01.06.2019


    Format / Umfang :

    3575998 byte





    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



    CONTEXT AWARE ROAD-USER IMPORTANCE ESTIMATION (ICARE)

    Rahimpour, Alireza / Martin, Sujitha / Tawari, Ashish et al. | British Library Conference Proceedings | 2019


    The ICARE Method

    Henke, L. / American Institute of Aeronautics and Astronautics | British Library Conference Proceedings | 2010


    The ICARE Method

    Henke, Luke | AIAA | 2010


    Vendre. Aunction V.A. Icare

    Online Contents | 1997