Volume 41 Issue 6
Dec.  2023
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LI Ke, HAN Tongqun, YANG Zhengcai, GAO Fei, WU Haoran. A Personalized Lane Change Decision Method Based on Driver's Psychological Risk Field Model[J]. Journal of Transport Information and Safety, 2023, 41(6): 32-41. doi: 10.3963/j.jssn.1674-4861.2023.06.004
Citation: LI Ke, HAN Tongqun, YANG Zhengcai, GAO Fei, WU Haoran. A Personalized Lane Change Decision Method Based on Driver's Psychological Risk Field Model[J]. Journal of Transport Information and Safety, 2023, 41(6): 32-41. doi: 10.3963/j.jssn.1674-4861.2023.06.004
shu

A Personalized Lane Change Decision Method Based on Driver's Psychological Risk Field Model

doi: 10.3963/j.jssn.1674-4861.2023.06.004
  • 1.

    School of Automotive Engineering, Hubei University of Automotive Technology, Shiyan 442002, China

  • 2.

    Hubei Key Laboratory of Automotive Power Train and Electronic Control, Hubei University of Automotive Technology, Shiyan, 442002, China

  • 3.

    State Key Laboratory of Automotive Simulation and Control, Jilin University, Changchun 130022, China

  • Received Date: 2023-05-04
    Available Online: 2024-04-03
    Abstract
  • In driving environments, the motion behavior of interacting vehicles can stimulate the psychological and mental state of drivers, subsequently influencing their lane-changing decision behavior. In response to this, a personalized lane-changing decision method based on a driver's psychological risk field model is investigated. Focusing on a three-lane expressway traffic scenario, the vehicles' lateral speed and lateral offset are analyzed by interacting multiple models. Variable lateral speed-related transition probability matrices are introduced to predict the target lane selection of interacting vehicles. A model is established to quantify the impact of the driving environment and interacting vehicles' motion behavior on drivers' psychological risk. The experiment is conducted by establishing mixed traffic scenarios in a SUMO-based driving simulator, and 287 cases of lane-change datasets are collected. Two characteristic parameters, average collision time and driver psychological risk factor, are selected. The K-means algorithm is used for driver style clustering, categorizing drivers into conservative, normal, and aggressive styles. Furthermore, different thresholds for psychological risk at the initial moment of lane-changing are determined for drivers with different styles. Then personalized safe lane-changing decisions for vehicles are implemented. Experimental results show that, for conservative, normal, and aggressive drivers, the actual minimum lane-changing decision times are 3.48, 6.29, and 11.33 s, respectively. The actual maximum lane-changing decision times are 4.65, 7.45, and 12.52 s, respectively. The theoretical lane-changing decision times are 4.09, 6.83, and 11.95 s, respectively. The prediction errors of the personalized lane-changing decision model are all less than 0.62 seconds. This approach accurately assesses the psychological risk of drivers with different styles and achieves personalized lane-changing decisions.

     

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