Highlights We present a warning device to prevent the roadway departure of light vehicles while cornering. A probabilistic strategy is adopted to assess the roadway departure risk. The infrastructure-based methodology enables the real curve characteristics to be considered. Two safety criteria are tested and the potential safety benefits are evaluated. The proposed approach does not require extra equipment for vehicles.

    Abstract This paper presents a warning device to prevent the roadway departure of light vehicles while cornering. The proposed risk assessment methodology is based on recent works from the authors (Rey et al., 2011b,a; Rey, 2010). Given the random variability arising from the driver, the vehicle and the infrastructure at the entrance to the curve, a probabilistic strategy is adopted to assess the roadway departure risk. The infrastructure-based methodology enables the real curve characteristics to be considered and an alarm triggering decision to be made. Two safety criteria are tested and the potential safety benefits of the proposed warning device are evaluated. Contrary to existing roadway departure warning systems, the proposed approach does not require extra equipment for vehicles; it only requires that the measuring and warning devices be part of the road infrastructure, which is a great advantage in terms of economic cost.


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    Title :

    Infrastructure based approach for roadway departure risk assessment


    Contributors:


    Publication date :

    2014-08-31


    Size :

    11 pages




    Type of media :

    Article (Journal)


    Type of material :

    Electronic Resource


    Language :

    English





    Probabilistic assessment of roadway departure risk in a curve

    Rey, G. / Clair, D. / Fogli, M. et al. | Tema Archive | 2011


    Probabilistic assessment of roadway departure risk in a curve

    Rey,G. / Clair,D. / Fogli,M. et al. | Automotive engineering | 2011



    Probabilistic assessment of roadway departure risk in a curve

    Rey, G. / Clair, D. / Fogli, M. et al. | Taylor & Francis Verlag | 2011