Analyzing and reconstructing driving scenarios is crucial for testing and evaluating highly automated vehicles (HAVs). This research analyzed left-turn / straight-driving conflicts at unprotected intersections by extracting actual vehicle motion data from a naturalistic driving database collected by the University of Michigan. Nearly 7,000 left turn across path — opposite direction (LTAP/OD) events involving heavy trucks and light vehicles were extracted and used to build a stochastic model of such LTAP/OD scenario, which is among the top priority light-vehicle pre-crash scenarios identified by National Highway Traffic Safety Administration (NHTSA). Statistical analysis showed that vehicle type is a significant factor, whereas the change of season seems to have limited influence on the statistical nature of the conflict. The results can be used to build testing environments for HAVs to simulate the LTAP/OD crash cases in a stochastic manner.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    Analysis of unprotected intersection left-turn conflicts based on naturalistic driving data


    Contributors:
    Wang, Xinpeng (author) / Zhao, Ding (author) / Peng, Huei (author) / LeBlanc, David J. (author)


    Publication date :

    2017-06-01


    Size :

    1508215 byte




    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English




    Geometric Models to Calculate Intersection Sight Distance for Unprotected Left-Turn Traffic

    Yan, Xuedong / Radwan, Essam | Transportation Research Record | 2004



    Geometric Models to Calculate Intersection Sight Distance for Unprotected Left-Turn Traffic

    Yan, X. / Radwan, E. / National Research Council (U.S.) | British Library Conference Proceedings | 2004


    Unprotected left-turn driving control method based on deep reinforcement learning

    ZHAO MIN / SUN DIHUA / CHEN JIN | European Patent Office | 2021

    Free access