Automated vehicles can change the society by improved safety, mobility, and fuel efficiency. However, due to the higher cost and change in business model, over the coming decades, the highly automated vehicles likely will continue to interact with many human-driven vehicles. In the past, the control/design of the highly automated (robotic) vehicles mainly considers safety and efficiency but failed to address the “driving culture” of surrounding human-driven vehicles. Thus, the robotic vehicles may demonstrate behaviors very different from other vehicles. We study this “driving etiquette” problem in this paper. As the first step, we report the key behavior parameters of human driven vehicles derived from a large naturalistic driving database. The results can be used to guide future algorithm design of highly automated vehicles or to develop realistic human-driven vehicle behavior model in simulations.


    Zugriff

    Zugriff prüfen

    Verfügbarkeit in meiner Bibliothek prüfen

    Bestellung bei Subito €


    Exportieren, teilen und zitieren



    Titel :

    Developing Robot Driver Etiquette Based on Naturalistic Human Driving Behavior


    Beteiligte:
    Huang, Xianan (Autor:in) / Zhang, Songan (Autor:in) / Peng, Huei (Autor:in)


    Erscheinungsdatum :

    01.04.2020


    Format / Umfang :

    4285795 byte




    Medientyp :

    Aufsatz (Zeitschrift)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



    Developing Robot Driver Etiquette Based on Naturalistic Human Driving Behavior

    Huang, Xianan / Zhang, Songan / Peng, Huei | ArXiv | 2018

    Freier Zugriff

    An Unsupervised Approach for Inferring Driver Behavior From Naturalistic Driving Data

    Bender, Asher / Agamennoni, Gabriel / Ward, James R. et al. | IEEE | 2015



    Driver Behavior as Affected by Static Objects: A Naturalistic Driving Approach

    Dutta, Bhupali / Vasudevan, Vinod | Springer Verlag | 2019


    Investigation of Driver Lane Keeping Behavior in Normal Driving based on Naturalistic Driving Study Data

    Johnson, Taylor / Chen, Rong / Sherony, Rini et al. | British Library Conference Proceedings | 2016