Highlights This paper proposed a modified general model and specific model of driving safety field. This modified model is described briefly, which can be applied to cases that are more general in nature. A pre-collision warning algorithm, which used a new index, namely RDSI, to evaluate the driving risk level. The effectiveness is verified by field experiments. The results indicate that this algorithm shows better efficiency in multi-vehicle scenarios than TTCi, which indicates that the proposed algorithm can be applied to multi-vehicle driving scenarios.

    Abstract The concept and contents of driving safety field theory were presented in our previous study. On this basis, this study focus on driving safety field theory modeling and application. First, a general model is presented, which considered the driver-vehicle-road interactions. The model include the following three parts: (i) driver behaviors, which are determined by driver characteristics, such as physical-psychological, cognition, driving skill, and traffic violations; (ii) vehicle characteristics, which are determined by velocity vectors and virtual masses of vehicles; (iii) road conditions, which are determined by virtual mass of on road non-moving objects, types of traffic signs, road adhesion coefficient, road slope, road curvature, and visibility. In order to establish concrete functional forms, the specific model is presented. This specific model provides a method for virtual mass calculation and describes the field strength and field force in detail. After that, a driving safety indicator namely DSI is defined. Finally, a vehicle collision warning algorithm based on driving safety field model is presented. This algorithm used a new index namely RDSI to evaluate the driving risk level. The effectiveness of this collision warning algorithm is verified by field experiments.


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    Title :

    Driving safety field theory modeling and its application in pre-collision warning system


    Contributors:
    Wang, Jianqiang (author) / Wu, Jian (author) / Zheng, Xunjia (author) / Ni, Daiheng (author) / Li, Keqiang (author)


    Publication date :

    2016-10-04


    Size :

    19 pages




    Type of media :

    Article (Journal)


    Type of material :

    Electronic Resource


    Language :

    English





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