Highlights A novel method of evaluating the rollover proximity on existing roads is presented. The method uses non-intrusive tracking and estimated trajectories of vehicles. An estimated rollover speed is compared to an actual speed. The method is demonstrated for trucks on a selected roundabout. No excessive rollover risk was detected during a day and night.

    Abstract The rollover propensity of SUVs and trucks has long been recognized as a potential safety issue. The propensity may increase with the growing number of roundabouts that are being built on high-speed roadways designed for 50mi/h or higher. This paper presents a research methodology developed to evaluate the rollover propensity of trucks on existing roundabouts and other roads with tight curves and high-speed traffic. The research objective was accomplished by developing an advanced 3D model of rollover that is applicable to field observations of the undisturbed behavior of multiple vehicles. This model was supplemented with a nonintrusive method of data collection based on recording video from a remote location and a novel method of extracting the data from the video material and processing it to generate the input required by the rollover model. The method is demonstrated in this paper on an example roundabout by evaluating the rollover propensity of semi-trailers in daytime and nighttime conditions. The results indicate that the drivers observed in nighttime conditions compensated well for the challenging conditions by driving more cautiously, which led to their rollover propensity at night being lower than during the day. The method was found useful for timely detection of the potential rollover problem without waiting for crashes.


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    Titel :

    Evaluating the rollover propensity of trucks—A roundabout example


    Beteiligte:

    Erschienen in:

    Erscheinungsdatum :

    2016-02-29


    Format / Umfang :

    8 pages




    Medientyp :

    Aufsatz (Zeitschrift)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch





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