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.


    Zugriff

    Zugriff prüfen

    Verfügbarkeit in meiner Bibliothek prüfen

    Bestellung bei Subito €


    Exportieren, teilen und zitieren



    Titel :

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


    Beteiligte:
    Wang, Xinpeng (Autor:in) / Zhao, Ding (Autor:in) / Peng, Huei (Autor:in) / LeBlanc, David J. (Autor:in)


    Erscheinungsdatum :

    01.06.2017


    Format / Umfang :

    1508215 byte




    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



    Analysis of Unprotected Intersection Left-Turn Conflicts Based on Naturalistic Driving Data

    Wang, Xinpeng / Zhao, Ding / Peng, Huei et al. | British Library Conference Proceedings | 2017




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

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


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

    ZHAO MIN / SUN DIHUA / CHEN JIN | Europäisches Patentamt | 2021

    Freier Zugriff