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Vehicle Lane-Changing Safety Pre-Warning Model under the Environment of the Vehicle Networking

Author

Listed:
  • Qiang Luo

    (School of Civil Engineering, Guangzhou University, Guangzhou 510006, China)

  • Xiaodong Zang

    (School of Civil Engineering, Guangzhou University, Guangzhou 510006, China)

  • Xu Cai

    (School of Civil Engineering, Guangzhou University, Guangzhou 510006, China)

  • Huawei Gong

    (School of Civil Engineering, Guangzhou University, Guangzhou 510006, China)

  • Jie Yuan

    (School of Civil Engineering, Guangzhou University, Guangzhou 510006, China)

  • Junheng Yang

    (School of Civil Engineering, Guangzhou University, Guangzhou 510006, China)

Abstract

Lane-changing behavior is one of the most common driving behaviors while driving. Due to the complexity of its operation, vehicle collision accidents are prone to occur when changing lanes. Under the environment of vehicle networking, drivers can obtain more accurate traffic information in time, which can be of great help in terms of improving lane-changing safety. This paper analyzes the core factors that affect the safety of vehicles changing lanes, establishes the weight model of influencing factors of lane-changing behavior using the analytic hierarchy process (AHP), and obtains the calculation method of lane-changing behavior factors (LCBFs). Based on the fuzzy reasoning theory, the headway between the lane-changing vehicle and adjacent vehicles in the target lane was examined, and fuzzy logic lane-changing models were established for both situations (i.e., change to the left and change to the right lane). The fuzzy logic lane-changing models were tested via simulation experiments, and the test results showed that the models have a better warning effect on lane changing (LCBF = 1.5), with an accuracy of more than 90%. Thus, the established model in this paper can provide theoretical support for safety warnings when changing lanes and theoretical support for the sustainable development of transportation safety.

Suggested Citation

  • Qiang Luo & Xiaodong Zang & Xu Cai & Huawei Gong & Jie Yuan & Junheng Yang, 2021. "Vehicle Lane-Changing Safety Pre-Warning Model under the Environment of the Vehicle Networking," Sustainability, MDPI, vol. 13(9), pages 1-16, May.
  • Handle: RePEc:gam:jsusta:v:13:y:2021:i:9:p:5146-:d:548773
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    References listed on IDEAS

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    1. Zhufei Huang & Zihan Zhang & Haijian Li & Lingqiao Qin & Jian Rong, 2019. "Determining Appropriate Lane-Changing Spacing for Off-Ramp Areas of Urban Expressways," Sustainability, MDPI, vol. 11(7), pages 1-15, April.
    2. Quantao Yang & Feng Lu & Jingsheng Wang & Dan Zhao & Lijie Yu, 2020. "Analysis of the Insertion Angle of Lane-Changing Vehicles in Nearly Saturated Fast Road Segments," Sustainability, MDPI, vol. 12(3), pages 1-17, January.
    3. Pierfrancesco Fiore & Giuseppe Donnarumma & Carmelo Falce & Emanuela D’Andria & Claudia Sicignano, 2020. "An AHP-Based Methodology for Decision Support in Integrated Interventions in School Buildings," Sustainability, MDPI, vol. 12(23), pages 1-20, December.
    4. Xinqiang Chen & Jinquan Lu & Jiansen Zhao & Zhijian Qu & Yongsheng Yang & Jiangfeng Xian, 2020. "Traffic Flow Prediction at Varied Time Scales via Ensemble Empirical Mode Decomposition and Artificial Neural Network," Sustainability, MDPI, vol. 12(9), pages 1-17, May.
    5. Danish Farooq & Janos Juhasz, 2019. "Simulation-Based Analysis of the Effect of Significant Traffic Parameters on Lane Changing for Driving Logic “Cautious” on a Freeway," Sustainability, MDPI, vol. 11(21), pages 1-15, October.
    6. Qiang Luo & Xiaodong Zang & Jie Yuan & Xinqiang Chen & Junheng Yang & Shubo Wu, 2020. "Research of Vehicle Rear-End Collision Model considering Multiple Factors," Mathematical Problems in Engineering, Hindawi, vol. 2020, pages 1-11, April.
    7. Xingping Zhang & Rao Rao & Jian Xie & Yanni Liang, 2014. "The Current Dilemma and Future Path of China’s Electric Vehicles," Sustainability, MDPI, vol. 6(3), pages 1-27, March.
    8. Zheng, Zuduo, 2014. "Recent developments and research needs in modeling lane changing," Transportation Research Part B: Methodological, Elsevier, vol. 60(C), pages 16-32.
    9. Shang, Xue-Cheng & Li, Xin-Gang & Xie, Dong-Fan & Jia, Bin & Jiang, Rui, 2020. "Two-lane traffic flow model based on regular hexagonal cells with realistic lane changing behavior," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 560(C).
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    Cited by:

    1. Li, Huamin & Zhang, Shun, 2022. "Lane change behavior with uncertainty and fuzziness for human driving vehicles and its simulation in mixed traffic," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 606(C).

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