Abstract The extraction and analysis of driving style are essential for a comprehensive understanding of human driving behaviours. Most existing studies rely on subjective questionnaires and specific experiments, posing challenges in accurately capturing authentic characteristics of group drivers in naturalistic driving scenarios. As scenario-oriented naturalistic driving data collected by advanced sensors becomes increasingly available, the application of data-driven methods allows for a exhaustive analysis of driving styles across multiple drivers. Following a theoretical differentiation of driving ability, driving performance, and driving style with essential clarifications, this paper proposes a quantitative determination method grounded in large-scale naturalistic driving data. Initially, this paper defines and derives driving ability and driving performance through trajectory optimisation modelling considering various cost indicators. Subsequently, this paper proposes an objective driving style extraction method grounded in the Gaussian mixture model. In the experimental phase, this study employs the proposed framework to extract both driving abilities and performances from the Waymo motion dataset, subsequently determining driving styles. This determination is accomplished through the establishment of quantifiable statistical distributions designed to mirror data characteristics. Furthermore, the paper investigates the distinctions between driving styles in different scenarios, utilising the Jensen–Shannon divergence and the Wilcoxon rank-sum test. The empirical findings substantiate correlations between driving styles and specific scenarios, encompassing both congestion and non-congestion as well as intersection and non-intersection scenarios.

    Highlights A quantitative method of driving performance through trajectory costs is proposed. A general driving style extraction method for the large-scale dataset is proposed. Driving styles of different traffic scenarios are significantly different.


    Zugriff

    Zugriff prüfen

    Verfügbarkeit in meiner Bibliothek prüfen

    Bestellung bei Subito €


    Exportieren, teilen und zitieren



    Titel :

    How drivers perform under different scenarios: Ability-related driving style extraction for large-scale dataset


    Beteiligte:
    Zang, Yingbang (Autor:in) / Wen, Licheng (Autor:in) / Cai, Pinlong (Autor:in) / Fu, Daocheng (Autor:in) / Mao, Song (Autor:in) / Shi, Botian (Autor:in) / Li, Yikang (Autor:in) / Lu, Guangquan (Autor:in)


    Erscheinungsdatum :

    2023-12-23




    Medientyp :

    Aufsatz (Zeitschrift)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch






    Research on the Critical Response Ability of Elderly Drivers under Risk Scenarios

    Zhou, Yanning / Guo, Fengxiang / Lin, Fudi | ASCE | 2020



    Research on the Critical Response Ability of Elderly Drivers under Risk Scenarios

    Zhou, Yanning / Guo, Fengxiang / Lin, Fudi | TIBKAT | 2020