The riskiness of the roadway environment needs to be known in advance to improve driving safety. Such knowledge brings strong benefit to drivers and could be used to reduce the risk of collision. For example, vehicles can support a driver with guidance before arriving at the risky zones. In this paper, we focus on this use case. We propose risk avoidance by Vehicular Knowledge Networking (VKN). The proposed method mines the maneuver conflicts to determine risky zones. According to identified zones, guidance (e.g., speed and lane change suggestions) is shared with vehicles to help drivers pass these risky regions smoothly. Extensive simulations in different settings have shown that risk avoidance by VKN could decrease the collision risk by approximately 50%.


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    Titel :

    Risk Avoidance by Vehicular Knowledge Networking


    Beteiligte:
    Ucar, Seyhan (Autor:in) / Higuchi, Takamasa (Autor:in) / Altintas, Onur (Autor:in)


    Erscheinungsdatum :

    2022-06-01


    Format / Umfang :

    372745 byte





    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



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