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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    Title :

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


    Contributors:
    Bing Wu (author) / Yan Yan (author) / Daiheng Ni (author) / Linbo Li (author)


    Publication date :

    2021




    Type of media :

    Article (Journal)


    Type of material :

    Electronic Resource


    Language :

    Unknown




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