Volume 41 Issue 4
Aug.  2023
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CUI Bingyan, LI He, CUI Zhe, JI Haojie, GUAN Yuxin. A Review of Safety Studies on Lane Change Decision-makings for Connected Automated Vehicles[J]. Journal of Transport Information and Safety, 2023, 41(4): 1-13. doi: 10.3963/j.jssn.1674-4861.2023.04.001
Citation: CUI Bingyan, LI He, CUI Zhe, JI Haojie, GUAN Yuxin. A Review of Safety Studies on Lane Change Decision-makings for Connected Automated Vehicles[J]. Journal of Transport Information and Safety, 2023, 41(4): 1-13. doi: 10.3963/j.jssn.1674-4861.2023.04.001

A Review of Safety Studies on Lane Change Decision-makings for Connected Automated Vehicles

doi: 10.3963/j.jssn.1674-4861.2023.04.001
  • Received Date: 2023-03-13
    Available Online: 2023-11-23
  • Safety of lane change decision-makings for connected automated vehicles (CAVs) is a key task to improve traffic safety and enhance road mobility. In this paper, the safety issues related to lane changing of CAVs are investigated. From the perspective of driving safety, the adverse impacts of extreme lane-changing behavior and emergency lane-changing behavior on traffic safety are analyzed, emphasizing the importance of risk assessment. The various risk assessment methods of lane changing are reviewed, including the use of environmental sensors, traffic conflict indicators, and vehicle-level micro-trajectory data. Identifying risks through risk assessment and taking corresponding measures can significantly reduce traffic accidents caused by dangerous lane-changing behavior. Furthermore, the methods for CAVs to make lane-changing decisions by obtaining environmental information in both traditional and vehicle to everything (V2X) environments are elaborated. Particularly focusing on the CAVs in V2X environment, the decision-making through the environment perception and recognition, targets detection, and data processing is analyzed. Reasonable recommendations are proposed for achieving safe decision-making by CAVs in V2X environment in the future. Then the existing models for decision-making of lane-changing are analyzed and categorized into four types: rule-based models, discrete choice models, artificial intelligence models, and game theory models. The status of research and application, existing problems, and prospects of decision-making models in the field of road traffic safety are systematically summarized, both domestically and internationally. In summary, despite significant research achievements in lane-changing technologies for CAVs, there are still many challenges ahead. To tackle the existing problems in research, such as ensuring safe and reliable decisions in low-level automated driving environments, making more efficient and intelligent driving decisions for CAVs in low-penetration scenarios, achieving safe decision-making in situations with incomplete information, and improving the optimization of the algorithms for lane-changing decision-making, feasible solutions are proposed accordingly.

     

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  • [1]
    ZHENG Z D. Recent developments and research needs in modeling lane changing[J]. Transportation Research Part B: Methodological, 2014, 60(1): 16-32.
    [2]
    WINSUM W, WAARD D, BROOKHUIS K A. Lane change manoeuvres and safety margins[J]. Transportation Research Part F: Traffic Psychology and Behaviour, 1999, 2 (3): 139-149. doi: 10.1016/S1369-8478(99)00011-X
    [3]
    PANDE A, ABDELATY M. Assessment of freeway traffic parameters leading to lane-change related collisions[J]. Accident Analysis & Prevention, 2006, 38(5): 936-948.
    [4]
    ZHU S, AKSUN-GUVENC B. Trajectory planning of autonomous vehicles based on parameterized control optimization in dynamic on-road environments[J]. Journal of Intelligent and Robotic Systems, 2020, 100(4): 1055-1067.
    [5]
    靳雅寓, 何赏璐, 苏宁, 等. 智能网联车专用车道对人工驾驶车的影响分析[C]. 2022世界交通运输大会. 武汉: 中国公路学会, 2022.

    JIN Y Y, HE S L, SU N, et al. Analysis of the influence of intelligent connected vehicle special lane on artificial driving vehicles[C]. 2022 World Transport Congress. Wuhan, CHINA: China Highway Society, 2022. (in Chinese)
    [6]
    YU H T, TSENG H E, LANGARI R. A human-like game theory-based controller for automatic lane changing[J]. Transportation Research Part C: Emerging Technologies, 2018, 88(1): 140-158.
    [7]
    张羽翔, 何钢磊, 李鑫, 等. 基于参数描述的换道场景自动驾驶精确决策学习[J]. 同济大学学报(自然科学版), 2021, 49(增刊1): 132-140. doi: 10.11908/j.issn.0253-374x.22787

    ZHANG Y X, HE G L, LI X, et al. Precise decision-making learning for autonomous driving in lane change scenarios based on parameter description[J]. Journal of Tongji University (Natural Science Edition), 2021, 49(S1): 132-140. (in Chinese) doi: 10.11908/j.issn.0253-374x.22787
    [8]
    杨龙海, 罗沂, 徐洪. 基于GPS定位数据的高速公路换道特征分析与行为识别[J]. 北京交通大学学报, 2017, 41(3): 39-46. doi: 10.3969/j.issn.1672-8106.2017.03.005

    LUO L H, LUO Y, XU H. Highway lane change feature analysis and behavior recognition based on GPS positioning data[J]. Journal of Beijing Jiaotong University, 2017, 41(3): 39-46. (in Chinese) doi: 10.3969/j.issn.1672-8106.2017.03.005
    [9]
    SUN D, ELEFTERIADOU L. Lane-changing behavior on urban streets: an "in-vehicle" field experiment-based study[J]. Computer-aided Civil and Infrastructure Engineering, 2012, 27(7): 525-542. doi: 10.1111/j.1467-8667.2011.00747.x
    [10]
    SCHMIDT K, BEGGIATO M, HOFFMANN HEINZ K, et al. A mathematical model for predicting lane changes using the steering wheel angle[J]. Journal of Safety Research, 2014, 49(1): 85-90.
    [11]
    AHMED I, KARR AF, ROUPHAIL NM, et al. Characterizing lane changing behavior and identifying extreme lane changing traits[J]. Transportation Letters the International Journal of Transportation Research, 2022, 15(5): 450-464.
    [12]
    WINSUM V, WAARD D, BROOKHUIS K. Lane change manoeuvres and safety margins[J]. Transportation Research Part F: Traffic Psychology and Behaviour, 1999, 2(3): 139-149. doi: 10.1016/S1369-8478(99)00011-X
    [13]
    MAI T, JIANG R, CHUNG E. A cooperative intelligent transport systems(C-ITS)-based lane-changing advisory for weaving sections[J]. Journal of Advanced Transportation, 2016, 50(5): 752-768. doi: 10.1002/atr.1373
    [14]
    张艺还. 车路协同环境下突发事件救援车辆路径规划算法研究[D]. 北京: 北京交通大学, 2019.

    ZHANG Y H. Research on route planning algorithm for emergency rescue vehicles under the environment of connected vehicle[D]. Beijing: Beijing Jiaotong University, 2019. (in Chinese)
    [15]
    ZHENG Z D. Recent developments and research needs in modeling lane changing[J]. Transportation Research Part B: Methodological, 2014, 60(1): 16-32.
    [16]
    CICCHINO J B. Effects of blind spot monitoring systems on police-reported lane-change crashes[J]. Traffic Injury Prevention, 2018, 19(6): 615-622. doi: 10.1080/15389588.2018.1476973
    [17]
    YOO J, LANGARI R, A predictive perception model and control strategy for collision-free autonomous driving[J]. IEEE Transactions on Intelligent Transportation Systems, 2019, 20(11): 4078-4091. doi: 10.1109/TITS.2018.2880409
    [18]
    NI J, HAN J W, LIU Z Q, et al. Situation assessment for lane-changing risk based on driver's perception of adjacent rear vehicles[J]. International Journal of Automotive Technology, 2020, 21(2): 427-439. doi: 10.1007/s12239-020-0040-9
    [19]
    HUANG H Y, WANG J Q, FEI C, et al. A probabilistic risk assessment framework considering lane-changing behavior interaction[J]. Science China Information Sciences, 2020, 63 (9): 1-19.
    [20]
    JOO Y J, PARK H C, KHO S Y, et al. Reliability-based assessment of potential risk for lane-changing maneuvers[J]. Transportation Research Record, 2021, 2675(10): 161-173. doi: 10.1177/03611981211010800
    [21]
    LI G F, YANG Y F, ZHANG T R, et al. Risk assessment based collision avoidance decision-making for autonomous vehicles in multi-scenarios[J]. Transportation Research Part C: Emerging Technologies, 2021, 122(1): 102820.
    [22]
    ARUN A, HAQUE M M, WASHINGTON S, et al. How many are enough? Investigating the effectiveness of multiple conflict indicators for crash frequency-by-severity estimation by automated traffic conflict analysis[J]. Transportation Research Part C: Emerging Technologies, 2022, 138(1): 103653.
    [23]
    JIANG R X, ZHU S Y, CHANG H G, et al. Determining an improved traffic conflict indicator for highway safety estimation based on vehicle trajectory data[J]. Sustainability, 2021, 13(16): 9278. doi: 10.3390/su13169278
    [24]
    XING L, HE J, ABDEL M, et al. Examining traffic conflicts of upstream toll plaza area using vehicles' trajectory data[J]. Accident Analysis & Prevention, 2019, 125(1): 174-187.
    [25]
    XING L, HE J, LI Y, et al. Comparison of different models for evaluating vehicle collision risks at upstream diverging area of toll plaza[J]. Accident Analysis & Prevention, 2020, 135(1): 105343.
    [26]
    HARDING J, POWELL G, YOON R, et al. Vehicle-to-vehicle communications: readiness of V2V technology for application[M]. United States: National Highway Traffic Safety Administration, 2014.
    [27]
    REN S J, HE K M, GIRSHICK R, et al. Faster R-CNN: towards real-time object detection with region proposal networks[J]. Advances in Neural Information Processing Systems, 2015, 15(18): 137-146.
    [28]
    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
    [29]
    YANG Q I, KOUTSOPOULOS H N. A microscopic traffic simulator for evaluation of dynamic traffic management systems[J]. Transportation Research Part C: Emerging Technologies, 1996, 4(3): 113-129. doi: 10.1016/S0968-090X(96)00006-X
    [30]
    DING J Y, LI L, PENG H, et al. A rule-based cooperative merging strategy for connected and automated vehicles[J]. IEEE Transactions on Intelligent Transportation Systems, 2019, 21(8): 3436-3446.
    [31]
    JIN C J, KNOOP V L, LI D W, et al. Discretionary lane-changing behavior: empirical validation for one realistic rule-based model[J]. Transportmetrica A-Transport Science, 2019, 15 (2): 224-262.
    [32]
    SHAO Y, DENG X F, SONG J X, et al. Lane-changing model of intelligent connected vehicle considering the factor of turn signal[J]. Journal of Advanced Transportation, 2022, 11 (1): 4357954.
    [33]
    ZHANG J Y, LIAO Y P, HAN J, et al. Lane changing models based on artificial potential field[C]. IEEE 2017 Chinese Automation Congress, Jinan, CHINA: IEEE, 2017.
    [34]
    AHMED K I, BENAKIVA M E, KOUTSOPOULOS, et al. Models of freeway lane changing and gap acceptance behavior[J]. Transportation and Traffic Theory, 1996, 1(13): 501-515.
    [35]
    TOLEDO T, KOUTSOPOULOS H. BEN-AKIVA M. Modeling integrated lane-changing behavior[J]. Transportation Research Record Journal of the Transportation Research Board, 2003, 1(1857): 30-38.
    [36]
    SUN D J, ELEFTERIADOU L. Lane-changing behavior on urban streets: an"in-vehicle"field experiment-based study[J]. Computer Aided Civil and Infrastructure Engineering, 2012, 27(7): 525-542.
    [37]
    SUN D J. A lane-changing model for urban arterial streets[D]. Gainesville: University of Florida, 2009.
    [38]
    SINGH K, LI B B. Discrete choice modelling for traffic densities with lane-change behaviour[C]. 8th International Conference on Traffic and Transportation Studies, Beijing, CHINA: IEEE, 2012.
    [39]
    WANG Z H, CUI S M, YU T Y. Automatic lane change control for intelligent connected vehicles[C]. 4th International Conference on Electromechanical Control Technology and Transportation, Guilin, CHINA: IEEE, 2019.
    [40]
    ZHENG J, SUZUKI K, FUJITA M. Predicting driver's lane-changing decisions using a neural network model[J]. Simulation Modelling Practice and Theory, 2014, 42(1): 73-83.
    [41]
    PENG J S, GUO Y S, FU R, et al. Multi-parameter prediction of drivers' lane-changing behaviourwith neural network model[J]. Applied Ergonomics, 2015, 50(1): 207-217.
    [42]
    MA K, WANG H. Lane-changing decision model for connected and automated vehicle based on back-propagation neural network[C]. International Conference on Transportation and Development, Washington, D. C., USA: IEEE, 2020.
    [43]
    DONG C Y, LIU Y J, WANG H, et al. Modeling lane-changing behavior based on a joint neural network[J]. Machines, 2022, 10(2): 109-126.
    [44]
    LI Y M, ZHANG J, SUN H. Lane change decision-making for autonomous vehicles based on a hybrid model of deep learning and rule-based methods[J]. IEEE Transactions on Intelligent Transportation Systems, 2020, 21(2): 702-712.
    [45]
    REN G Q, ZHANG Y, LIU H, et al. A new lane-changing model with consideration of driving style[J]. International Journal of Intelligent Transportation Systems Research, 2019, 17(3): 181-189.
    [46]
    CHEN J Y, FENG Y H, YAN M Y. A deep reinforcement learning-based lane change system for autonomous vehicles[J]. IEEE Transactions on Intelligent Transportation Systems, 2020, 21(5): 1980-1991.
    [47]
    KANG K, RAKHA H A. A repeated game freeway lane changing model[J]. Sensors, 2020, 20(6): 1554.
    [48]
    DING N, MARIA A, MENG X H, et al. Mandatory lane change strategy in vanet based on coordinated stackelberg game model[C]. 2020 Chinese Control and Decision Conference, Hefei, CHINA: IEEE, 2021.
    [49]
    ZHENG Y, DING W T, RAN B, et al. Coordinated decisions of discretionary lane change between connected and automated vehicles on freeways: a game theory-based lane change strategy[J]. IET Intelligent Transport Systems, 2020, 14(13): 1864-1870.
    [50]
    QU D Y, ZHANG K K, SONG H, et al. Analysis and modeling of lane-changing game strategy for autonomous driving vehicles[J]. IEEE Access, 2022, 10(1): 69531-69542.
    [51]
    JI Y P, ZHANG J, WU K R, et al. Optional lane-changing of intelligent vehicles based on vehicle-to-vehicle communication[J]. Journal of Command and Control, 2022, 7(4): 389-396.
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