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Lane-Change Risk When the Subject Vehicle Is Faster Than the Following Vehicle: A Case Study on the Lane-Changing Warning Model Considering Different Driving Styles

Author

Listed:
  • Tong Liu

    (College of Traffic & Transportation, Chongqing Jiaotong University, Chongqing 400074, China
    Key Laboratory for Automotive Transportation Safety Enhancement Technology of the Ministry of Transport, Chang’an University, Xi’an 710064, China)

  • Chang Wang

    (Key Laboratory for Automotive Transportation Safety Enhancement Technology of the Ministry of Transport, Chang’an University, Xi’an 710064, China)

  • Rui Fu

    (Key Laboratory for Automotive Transportation Safety Enhancement Technology of the Ministry of Transport, Chang’an University, Xi’an 710064, China)

  • Yong Ma

    (Key Laboratory for Automotive Transportation Safety Enhancement Technology of the Ministry of Transport, Chang’an University, Xi’an 710064, China)

  • Zhuofan Liu

    (Modern Postal School, Xi’an University of Posts & Telecommunications, Xi’an 710061, China)

  • Tangzhi Liu

    (College of Traffic & Transportation, Chongqing Jiaotong University, Chongqing 400074, China)

Abstract

The research of early warning and control strategy considering driving styles during lane changes is a hotspot in the field of automatic driving. However, many lane-changing studies only emphasize the warning analysis when the following vehicle is faster than the subject vehicle, while neglecting the potential risk when the subject vehicle is faster than the following vehicle in the adjacent lane during lane changes. To study the lane-changing characteristics of drivers considering driving styles and to establish a personalized lane-changing warning model under different relative speed conditions, fifty participants (three females and forty-seven males) were recruited to carry out a real road driving test. A novel Gaussian mixture model with the results of k-means clustering was established to classify driving styles based on two-dimensional variables: average time gap and average minimum time to collision. The clustering result was then verified. In addition, by analyzing the relationship between the subject vehicle and the following vehicle in the adjacent lane during lane changes, a lane-changing warning model considering driving styles under different relative speed conditions was established. Results show that the clustering algorithm proposed in this paper has high separability between samples, achieving a much softer clustering result that can provide a reference for the parameter setting of the personalized driver assistance system. Furthermore, the overall recognition accuracy of the hazardous lane-changing behaviors improved after drivers were classified into different driving styles. The established lane-changing warning model has a better recognition performance for aggressive drivers when compared with the other two driver types. The results provide a basis for the algorithm design of the intelligent lane-changing warning system and can improve the user acceptance of an advanced driver assistance system for self-driving vehicles.

Suggested Citation

  • Tong Liu & Chang Wang & Rui Fu & Yong Ma & Zhuofan Liu & Tangzhi Liu, 2022. "Lane-Change Risk When the Subject Vehicle Is Faster Than the Following Vehicle: A Case Study on the Lane-Changing Warning Model Considering Different Driving Styles," Sustainability, MDPI, vol. 14(16), pages 1-20, August.
    • Handle: RePEc:gam:jsusta:v:14:y:2022:i:16:p:9938-:d:885785
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    References listed on IDEAS

    as
    1. Ruoxi Jiang & Shunying Zhu & Hongguang Chang & Jingan Wu & Naikan Ding & Bing Liu & Ji Qiu, 2021. "Determining an Improved Traffic Conflict Indicator for Highway Safety Estimation Based on Vehicle Trajectory Data," Sustainability, MDPI, vol. 13(16), pages 1-30, August.
    2. Huajing Ning & Yunyan Yu & Lu Bai, 2022. "Unsafe Behaviors Analysis of Sideswipe Collision on Urban Expressways Based on Bayesian Network," Sustainability, MDPI, vol. 14(13), pages 1-15, July.
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    Cited by:

    1. Liling Zhu & Da Yang & Zhiwei Cheng & Xiaoyue Yu & Bin Zheng, 2023. "A Model to Manage the Lane-Changing Conflict for Automated Vehicles Based on Game Theory," Sustainability, MDPI, vol. 15(4), pages 1-19, February.

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