Effective driving style analysis is critical to developing human-centered intelligent driving systems that consider drivers’ preferences. However, the approaches and conclusions of most related studies are diverse and inconsistent because no unified datasets tagged with driving styles exist as a reliable benchmark. The absence of explicit driving style labels makes verifying different approaches and algorithms difficult. This paper provides a new benchmark by constructing a natural dataset of Driving Style (100-DrivingStyle) tagged with the subjective evaluation of 100 drivers’ driving styles. In this dataset, the subjective quantification of each driver’s driving style is from themselves and an expert according to the Likert-scale questionnaire. The testing routes are selected to cover various driving scenarios, including highways, urban, highway ramps, and signalized traffic. The collected driving data consists of lateral and longitudinal manipulation information, including steering angle, steering speed, lateral acceleration, throttle position, throttle rate, brake pressure, etc. This dataset is the first to provide detailed manipulation data with driving-style tags, and we demonstrate its benchmark function using six classifiers. The 100-DrivingStyle dataset is available via https://github.com/chaopengzhang/100-DrivingStyle-Dataset


    Zugriff

    Zugriff prüfen

    Verfügbarkeit in meiner Bibliothek prüfen

    Bestellung bei Subito €


    Exportieren, teilen und zitieren



    Titel :

    100 Drivers, 2200 km: A Natural Dataset of Driving Style toward Human-centered Intelligent Driving Systems


    Beteiligte:
    Zhang, Chaopeng (Autor:in) / Wang, Wenshuo (Autor:in) / Chen, Zhaokun (Autor:in) / Xi, Junqiang (Autor:in)


    Erscheinungsdatum :

    02.06.2024


    Format / Umfang :

    3119209 byte





    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



    Intelligent driving style identification method based on natural driving data

    ZHANG SIYANG / ZHANG ZHERUI / ZHAO CHI | Europäisches Patentamt | 2025

    Freier Zugriff



    SYSTEM FOR DRIVING STYLE DESCRIPTION OF VEHICLE DRIVERS

    IAKINI FRANKO | Europäisches Patentamt | 2016

    Freier Zugriff

    Driving style recognition system based on natural driving database

    ZHU TIANJUN / LI JIANYING / QIAN YUANZHI et al. | Europäisches Patentamt | 2025

    Freier Zugriff