The quantification of driving risk is an important basis for ensuring the driving safety of autonomous vehicles. In order to make the autonomous vehicle have more accurate risk cognition ability, we propose a driving risk quantification method from the perspective of anthropomorphism, which draws on the driver’s risk perception experience. In this method, the intensity of the driver’s avoidance operation is used to reflect their perceived level of environmental danger; therefore, the GPR algorithm is used to construct driver’s avoidance operation intensity prediction model, which can predict the driver’s avoidance operation intensity based on the vehicles’ historical interaction information. Finally, an exponential model of driving risk and driver’s avoidance operation intensity is constructed to achieve real-time quantification of driving risk. The risk quantification method proposed in this paper proves advantageous for autonomous vehicles, empowering them to adopt more adaptable behavioral decisions without compromising driving safety.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    Driving Risk Quantification of Autonomous Vehicles Based on Drivers’ Risk Perception Experience


    Contributors:
    Wei, Zehong (author) / Zhao, Lan (author) / Ren, Yuanyuan (author)

    Conference:

    24th COTA International Conference of Transportation Professionals ; 2024 ; Shenzhen, China


    Published in:

    CICTP 2024 ; 2274-2282


    Publication date :

    2024-12-11




    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



    RISK PERCEPTION AMONG DRIVERS WITH SUSPENDED DRIVING LICENSES

    Čičevič, Svetlana / Lipovac, Krsto / Antić, Boris et al. | TIBKAT | 2016

    Free access

    Effect of drivers risk perception on safe driving attitude

    Ram,T. / Chand,K. / Guru Jambheshwar Univ.of Science a.Technol.,IN | Automotive engineering | 2015



    Modeling Drivers' Risk Perception via Attention to Improve Driving Assistance

    Biswas, Abhijat / Gideon, John / Tamura, Kimimasa et al. | ArXiv | 2024

    Free access