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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    • References(26)
  • [1]
    LI Q W, LI X P, MANNERING F. Assessment of discretionary lane-changing decisions using a random parameters approach with heterogeneity in means and variances[J]. Transportation Research Record, 2021 (6): 330-338.
    [2]
    GIPPS P G. A model for the structure of lane-changing decisions[J]. Transportation Research Part B: Methodological, 1986, 20 (5): 403-414. doi: 10.1016/0191-2615(86)90012-3
    [3]
    王慧然, 陈无畏, 王其东, 等. 基于相邻车道安全态势划分的换道决策[J]. 机械工程学报, 2023, 59 (2): 233-244. https://www.cnki.com.cn/Article/CJFDTOTAL-JXXB202302020.htm

    WANG H R, CHEN W W, WANG Q D, et al. Lane change decision based on the safety state division of the adjacent lanes[J]. Journal of Mechanical Engineering, 2023, 59(2): 233-244. (in Chinese) https://www.cnki.com.cn/Article/CJFDTOTAL-JXXB202302020.htm
    [4]
    WANG J J, ZHANG Q C, ZHAO D B, et al. Lane change decision-making through deep reinforcement learning with rule-based constraints[C]. 2019 International Joint Conference on Neural Networks(IJCNN), Budapest, Hungary : IEEE, 2019.
    [5]
    SHENG Z H, LIU L, XUE S B, et al. A cooperation-aware lane change method for autonomous vehicles[J]. IEEE Transactions on Intelligent Transportation Systems, 2022, 24(3): 3236-3251.
    [6]
    WANG W D, QIE T Q, YANG C, et al. An intelligent lane-changing behavior prediction and decision-making strategy for an autonomous vehicle[J]. IEEE Transactions on Industrial Electronics, 2021, 69 (3): 2927-2937.
    [7]
    SATHYAN A, MA J Q, COHEN K. Decentralized cooperative driving automation: a reinforcement learning framework using genetic fuzzy systems[J]. Transportmetrica B: Transport Dynamics, 2021, 9 (1): 775-797. doi: 10.1080/21680566.2021.1951394
    [8]
    聂琳真, 管家意, 卢炽华, 等. 基于模糊逻辑的高速公路微观换道行为[J]. 北京工业大学学报, 2018, 44 (3): 424-432. https://www.cnki.com.cn/Article/CJFDTOTAL-BJGD201803015.htm

    NIE L Z, GUAN J Y, LU C H, et al. Micro-lane changing behavior of expressways based on fuzzy logic[J]. Journal of Beijing University of Technology, 2018, 44(3): 424-432. (in Chinese) https://www.cnki.com.cn/Article/CJFDTOTAL-BJGD201803015.htm
    [9]
    GU X P, HAN Y P, YU J F. A novel lane-changing decision model for autonomous vehicles based on deep autoencoder network and XGBoost[J]. IEEE Access, 2020, (8) : 9846-9863.
    [10]
    FAN B, WU Y, HE Z B, et al. Digital twin empowered mobile edge computing for intelligent vehicular lane-changing[J]. IEEE Network, 2021, 35 (6): 194-201. doi: 10.1109/MNET.201.2000768
    [11]
    CHENG S, YANG B, WANG Z, et al. Spatio-temporal image representation and deep-learning-based decision framework for automated vehicles[J]. IEEE Transactions on Intelligent Transportation Systems, 2022, 23 (12): 24866-24875. doi: 10.1109/TITS.2022.3195213
    [12]
    ZHOU W, CHEN D, YAN J, et al. Multi-agent reinforcement learning for cooperative lane changing of connected and autonomous vehicles in mixed traffic[J]. Autonomous Intelligent Systems, 2022, 2 (1): 1-11. doi: 10.1007/s43684-021-00019-7
    [13]
    ZHANG X H, SUN J, QI X, et al. Simultaneous modeling of car-following and lane-changing behaviors using deep learning[J]. Transportation Research Part C: Emerging Technologies, 2019 (104): 287-304.
    [14]
    XIE D F, FANG Z Z, JIA B, et al. A data-driven lane-changing model based on deep learning[J]. Transportation Research Part C: Emerging Technologies, 2019, (106): 41-60.
    [15]
    GAO J, MURPHEY Y L, ZHU H H. Multivariate time series prediction of lane changing behavior using deep neural network[J]. Applied Intelligence, 2018, 48 (10): 3523-3537.
    [16]
    WANG X Y, GUO Y Q, BAI C L, et al. Driver's intention identification with the involvement of emotional factors in two-lane roads[J]. IEEE Transactions on Intelligent Transportation Systems, 2020, 22 (11): 6866-6874.
    [17]
    LIU Y Q, WANG X Y. The analysis of driver's behavioral tendency under different emotional states based on a Bayesian Network[J]. IEEE Transactions on Affective Computing, 2020, 14 (1): 165-177.
    [18]
    LING J, LI J, TEI K, et al. Towards personalized autonomous driving: an emotion preference style adaptation framework[C]. 2021 IEEE International Conference on Agents (ICA), Kyoto, Japan: IEEE, 2021.
    [19]
    李青, 景云超, 朱彤, 等. 基于LightGBM的驾驶人风险感知能力判别方法[J]. 交通信息与安全, 2021, 39 (4): 16-25. doi: 10.3963/j.jssn.1674-4861.2021.04.003

    LI Q, JING Y C, ZHU T, et al. Driver risk perception ability identification method based on LightGBM[J]. Journal of Transport Information and Safety, 2021, 39(4): 16-25. (in Chinese) doi: 10.3963/j.jssn.1674-4861.2021.04.003
    [20]
    GAO H B, QIN Y C, HU C, et al. An interacting multiple model for trajectory prediction of intelligent vehicles in typical road traffic scenario[J]. IEEE Transactions on Neural Networks and Learning Systems, 2021, 34 (9): 1-12.
    [21]
    PAK J M. Hybrid interacting multiple model filtering for improving the reliability of radar-based forward collision warning systems[J]. Sensors, 2022, 22 (3): 875.
    [22]
    GOMAA M A K, DE SILVA O, MANN G K I, et al. Observability-constrained VINS for MAVs using interacting multiple model algorithm[J]. IEEE Transactions on Aerospace and Electronic Systems, 2020, 57 (3): 1423-1442.
    [23]
    FAN X X, WANG G, HAN J C, et al. Interacting multiple model based on maximum correntropy Kalman filter[J]. IEEE Transactions on Circuits and Systems Ⅱ: Express Briefs, 2021, 68 (8): 3017-3021.
    [24]
    LI Y, WANG J Q, WU J. Model calibration concerning risk coefficients of driving safety field model[J]. Journal of Central South University, 2017, 24 (6): 1494-1502.
    [25]
    郭子彬, 陈慧, 夏韬锴, 等. 弯道工况下驾驶人主观风险感知的量化研究[J]. 汽车工程, 2022, 44 (9): 1447-1455.

    GUO Z B, CHEN H, XIA T K, et al. Quantitative study of driver's subjective risk perceptionunder curved working conditions[J]. Automotive Engineering, 2022, 44(9): 1447-1455. (in Chinese)
    [26]
    公安部交通管理局. 中国道路交通事故统计年报[R]. 北京: 公安部交通管理局, 2017.

    Ministry of Public Security, Transportation Bureau. The road traffic accidents statistics report in China[R]. Beijing: Ministry of Public Security, Transportation Bureau, 2017. (in Chinese)
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