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Comprehensive Evaluation of Freeway Driving Risks Based on Fuzzy Logic

Author

Listed:
  • Lian Xie

    (Intelligent Transportation Systems Research Center, Wuhan University of Technology, Wuhan 430063, China
    Guangxi Key Laboratory of ITS, Guilin University of Electronic Technology, Guilin 541004, China)

  • Jiaxin Zhang

    (Guangxi Key Laboratory of ITS, Guilin University of Electronic Technology, Guilin 541004, China)

  • Rui Cheng

    (Guangxi Key Laboratory of ITS, Guilin University of Electronic Technology, Guilin 541004, China)

Abstract

The quantitative evaluation of driving risk is a crucial prerequisite for intelligent vehicle accident warning, and it is necessary to predict it comprehensively and accurately. Therefore, a simulated driving experiment was conducted with 16 experimental scenarios designed through an orthogonal design, and 44 subjects were recruited to explore the driving risks in different situations. A two-layer fuzzy integrated evaluation model was constructed, which considered the workload as an important element for balancing driving risk and driving behavior. Workload and road environment indicators were taken as the underlying input variables. The results show that the comprehensive evaluation model is well-suited to identify the risks of each scenario. The effectiveness of the proposed method is further confirmed by comparing the results with those of the technique for order preference by similarity to an ideal solution (TOPSIS) model. The proposed method could be used for real-time vehicle safety warning and provide a reference for accident prevention.

Suggested Citation

  • Lian Xie & Jiaxin Zhang & Rui Cheng, 2023. "Comprehensive Evaluation of Freeway Driving Risks Based on Fuzzy Logic," Sustainability, MDPI, vol. 15(1), pages 1-20, January.
    • Handle: RePEc:gam:jsusta:v:15:y:2023:i:1:p:810-:d:1022804
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    References listed on IDEAS

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    1. Nengchao Lyu & Lian Xie & Chaozhong Wu & Qiang Fu & Chao Deng, 2017. "Driver’s Cognitive Workload and Driving Performance under Traffic Sign Information Exposure in Complex Environments: A Case Study of the Highways in China," IJERPH, MDPI, vol. 14(2), pages 1-25, February.
    2. Golob, Thomas F. & Recker, Wilfred W., 2004. "A method for relating type of crash to traffic flow characteristics on urban freeways," Transportation Research Part A: Policy and Practice, Elsevier, vol. 38(1), pages 53-80, January.
    3. Robert Tibshirani & Guenther Walther & Trevor Hastie, 2001. "Estimating the number of clusters in a data set via the gap statistic," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 63(2), pages 411-423.
    4. Lei Han & Zhigang Du & Shoushuo Wang & Ying Chen, 2022. "Analysis of Traffic Signs Information Volume Affecting Driver’s Visual Characteristics and Driving Safety," IJERPH, MDPI, vol. 19(16), pages 1-23, August.
    5. Huan Liu & Jinliang Xu & Xiaodong Zhang & Chao Gao & Rishuang Sun, 2022. "Evaluation Method of the Driving Workload in the Horizontal Curve Section Based on the Human Model of Information Processing," IJERPH, MDPI, vol. 19(12), pages 1-18, June.
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    Cited by:

    1. Yadav, Sunita & Redhu, Poonam, 2024. "Impact of driving prediction on headway and velocity in car-following model under V2X environment," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 635(C).

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