This paper proposes a risk assessment method based on trajectory data which are used to quantify the risk faced by drivers for application in autonomous vehicles. A risk field is derived from the field theory of traffic flow, based on which the risk repulsion indicator of car-following is determined. By describing the repulsion force perceived by drivers in the process of car-following, the risk faced by drivers is assessed. The validity of the indicator is established from crash trajectory data obtained by simulation, and a binary logit model is employed to predict the crash. The result shows that the risk repulsion indicator based on risk field theory can distinguish crash states and non-crash states significantly. The prediction accuracy of binary logit model based on risk repulsion performs better than that of crash prediction model based on loop detector data.


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    Titel :

    A longitudinal car-following risk assessment model based on risk field theory for autonomous vehicles


    Beteiligte:
    Bing Wu (Autor:in) / Yan Yan (Autor:in) / Daiheng Ni (Autor:in) / Linbo Li (Autor:in)


    Erscheinungsdatum :

    2021




    Medientyp :

    Aufsatz (Zeitschrift)


    Format :

    Elektronische Ressource


    Sprache :

    Unbekannt




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