Collision avoidance is an essential component in advanced driving assistance systems, as it ensures the safety of the vehicle in near crash or crash scenarios. In this study, a collision avoidance system for lane change events is proposed which plans the trajectory based on the level of danger. The danger level is computed by a fuzzy inference system developed with naturalistic driving data to better capture the real-world factors, which may cause an accident. In addition, a fault determination classifier is introduced in order to determine the responsible driver in a near crash event. This system is evaluated on simulated naturalistic near crash events and the results demonstrate good performance of the proposed system.


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    Titel :

    A collision avoidance system with fuzzy danger level detection


    Beteiligte:


    Erscheinungsdatum :

    01.06.2017


    Format / Umfang :

    203488 byte




    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



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