Currently, many automated vehicle systems primarily perceive the environment from a single perspective and as a result are unable to leverage additional scene information from the viewpoint of other vehicles on the road using vehicleto-vehicle communication technologies. We study how increased data sharing can improve the perception capabilities of automated vehicles. Our methodology shares sensor measurements and objects detected by state-of-the-art deep learning networks between vehicles to increase the automated driving systems confidence of detecting objects using a 3D sensor fusion algorithm. This approach can benefit scenarios where an object may be occluded (fully or partially) or located too far away to classify accurately by a single vehicle alone.


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    Titel :

    Collaborative Perception for Automated Vehicles Leveraging Vehicle-to-Vehicle Communications


    Beteiligte:
    Yee, Ryan (Autor:in) / Chan, Ellick (Autor:in) / Cheng, Bin (Autor:in) / Bansal, Gaurav (Autor:in)


    Erscheinungsdatum :

    2018-06-01


    Format / Umfang :

    4205904 byte




    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



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