[1]

National Development and Reform Commission. 2020. Notice on printing and distributing the strategy for innovative development of intelligent cars. www.ndrc.gov.cn/xxgk/zcfb/tz/202002/t20200224_1221077_ext.html (in Chinese)

[2]

General Office of the Ministry of Transport. 2023. Notice on printing and distributing the safety service guide for self-driving cars. https://xxgk.mot.gov.cn/2020/jigou/ysfws/202312/t20231205_3962490.html (in Chinese)

[3]

Ran Q, Liang C, Liu P. 2025. A safe lane-changing strategy for autonomous vehicles based on deep Q-networks and prioritized experience replay. Digital Transportation and Safety 4(3):170−174

doi: 10.48130/dts-0025-0013
[4]

Wang J, Zheng X, Huang H. 2020. Decision-making mechanism of the drivers following the principle of least action. China Journal of Highway and Transport 33(4):155−168

[5]

Chen C, Liu B, Wan S, Qiao P, Pei Q. 2021. An edge traffic flow detection scheme based on deep learning in an intelligent transportation system. IEEE Transactions on Intelligent Transportation Systems 22(3):1840−1852

doi: 10.1109/TITS.2020.3025687
[6]

Lai J T, Hu J, Cui L, Chen Z, Yang X. 2020. A generic simulation platform for cooperative adaptive cruise control under partially connected and automated environment. Transportation Research Part C: Emerging Technologies 121:102874

doi: 10.1016/j.trc.2020.102874
[7]

Hu J, Zhang Z H, Xiong L, Wang H, Wu G. 2021. Cut through traffic to catch green light: eco approach with overtaking capability. Transportation Research Part C: Emerging Technologies 123:102927

doi: 10.1016/j.trc.2020.102927
[8]

Sun Z, Jia X, Cai Y, Ji A, Lin X, et al. 2025. Joint control of traffic signal phase sequence and timing: a deep reinforcement learning method. Digital Transportation and Safety 4(2):118−126

doi: 10.48130/dts-0025-0008
[9]

On-Road Automated Driving (ORAD). 2024. J3016_202104 Taxonomy and definitions for terms related to driving automation systems for on-road motor vehicles. US: SAE International. doi: 10.4271/J3016_202104

[10]

Zhu B, Jia S, Zhao J, Han J, Zhang P, et al. 2024. Review of research on decision-making and planning for automated Vehicles. China Journal of Highway and Transport 7(1):15−240 (in Chinese)

doi: 10.19721/j.cnki.1001-7372.2024.01.018
[11]

Gipps PG. 1986. A model for the structure of lane-changing decisions. Transportation Research Part B: Methodological 20(5):403−414

doi: 10.1016/0191-2615(86)90012-3
[12]

Jin CJ, Knoop VL, Li D, Meng LY, Wang H. 2019. Discretionary lane-changing behavior: empirical validation for one realistic rule-based model. Transportmetrica A: Transport Science 15(2):224−262

doi: 10.1080/23249935.2018.1464526
[13]

Jia H, Liu P, Zhang L, Wang Z. 2022. Lane-changing decision model development by combining rules abstract and machine learning technique. Journal of Mechanical Engineering 58(4):212−221 (in Chinese)

doi: 10.3901/jme.2022.04.212
[14]

Shao Y, Deng X, Song J, Wu H. 2022. Lane-changing model of intelligent connected vehicle considering the factor of turn signal. Journal of Advanced Transportation 2022(1):4357954

doi: 10.1155/2022/4357954
[15]

Pei Y, Fu B, Wang Z, Zhang J. 2024. Comprehensive competitiveness-based autonomous driving human-imitative lane-changing model under gravity theory. Journal of Transportation Systems Engineering and Information Technology 24(1):66−80 (in Chinese)

doi: 10.16097/j.cnki.1009-6744.2024.01.007
[16]

Mahajan V, Katrakazas C, Antoniou C. 2020. Prediction of lane-changing maneuvers with automatic labeling and deep learning. Transportation Research Record 2674(7):336−347

doi: 10.1177/0361198120922210
[17]

Han L, Zhang L, Guo W. 2023. Lane-changing decision-making model for connected and automated vehicles based on MSIF-DRL in a mixed traffic environment. Journal of Beijing Jiaotong University 47(5):148−161 (in Chinese)

doi: 10.11860/j.issn.1673-0291.20230049
[18]

Zhang XF, Wang ZY. 2023. Decision model based on dueling automatic lane change double deep Q-network. Journal of Northeastern University (Natural Science) 44(10):1369−1376 (in Chinese)

doi: 10.12068/j.issn.1005-3026.2023.10.001
[19]

Cheng G, Wang W, Chen Y, Xu L. 2025. Decision method of CAV lane change in expressway merging area based on DQN. Journal of Harbin Institute of Technology 57(3):98−109 (in Chinese)

doi: 10.11918/202403056
[20]

Ke M, Wang H. 2020. Lane-changing decision model for connected and automated vehicle based on back-propagation neural network. International Conference on Transportation and Development, Washington, D.C., USA. 2020. USA: IEEE. pp. 163−173 doi: 10.1061/9780784483138.015

[21]

Dong C, Liu Y, Wang H, Ni D, Li Y. 2022. Modeling lane-changing behavior based on a joint neural network. Machines 10(2):109−126

doi: 10.3390/machines10020109
[22]

Ye F, Wang P, Chan CY, Zhang J. 2021. Meta reinforcement learning-based lane change strategy for autonomous vehicles. 2021 IEEE Intelligent Vehicles Symposium (IV), Nagoya, Japan, 2021. pp. 223−230 doi: 10.1109/IV48863.2021.9575379

[23]

Huang Z, Fang H, Lin Y, Hu X. 2025. A review of vehicle lane change decisions in human–machine mixed driving environments. Digital Transportation and Safety 4(4):298−311

doi: 10.48130/dts-0025-0028
[24]

Dai C, Yang S, Ye S, Fan W. 2025. V2G optimization strategy from the perspective of game between supply and demand sides. Journal of Southwest Jiaotong University 60(1):166−174,193 (in Chinese)

doi: 10.3969/j.issn.0258-2724.20230097
[25]

Zhang K, Li Y, Liu T, Chen X. 2025. Cooperative lane change control for autonomous vehicles in lane reduction sections. Control and Decision 40(9):2759−2768 (in Chinese)

doi: 10.13195/j.kzyjc.2024.0845
[26]

Lin D, Li L, Jabari SE. 2019. Pay to change lanes: a cooperative lane-changing strategy for connected/automated driving. Transportation Research Part C: Emerging Technologies 105:550−564

doi: 10.1016/j.trc.2019.06.006
[27]

Qu D, Zhang K, Song H, Jia Y, Dai S. 2022. Analysis and modeling for lane changing game strategy of autonomous vehicles. IEEE Access 10(2):69531−69542

doi: 10.1109/access.2022.3187431
[28]

Wang M, Hoogendoorn SP, Daamen W, van Arem B, Happee R. 2015. Game theoretic approach for predictive lane-changing and car-following control. Transportation Research Part C: Emerging Technologies 58:73−92

doi: 10.1016/j.trc.2015.07.009
[29]

Dai S, Qu D, Meng Y, Yang Y, Wang Q. 2024. Evolutionary game mechanisms of lane changing for intelligent connected vehicles on traffic flow frequently changing sections. Complex Systems and Complexity Science 21(3):128−135

doi: 10.13306/j.1672-3813.2024.03.017
[30]

Du X, Yao R. 2024. Evolutionary game mechanism of mandatory lane changing for exciting for intelligent connected bus. Journal of Jilin University (Engineering and Technology Edition) 54(1):124−135

doi: 10.13229/j.cnki.jdxbgxb.20220207
[31]

Zheng Y, Ding W, Ran B, Qu X, Zhang Y. 2020. Coordinated decisions of discretionary lane change between connected and automated vehicles on freeways: a game theory-based lane change strategy. IET Intelligent Transport Systems 14(13):1864−1870

doi: 10.1049/iet-its.2020.0146
[32]

Hang P, Lv C, Xing Y, Huang C, Hu Z. 2021. Human-like decision making for autonomous driving: a noncooperative game theoretic approach. IEEE Transactions on Intelligent Transportation Systems 22(4):2076−2087

doi: 10.1109/TITS.2020.3036984
[33]

Zhai C, Wu W. 2021. A macro traffic flow model with headway variation tendency and bounded rationality. Modern Physics Letters B 35(2):2150054

doi: 10.1142/S0217984921500548
[34]

Yan F, Liu M, Ding C, Wang Y, Yan L. 2019. Driving style recognition based on electroencephalography data from a simulated driving experiment. Frontiers in Psychology 10:1254

doi: 10.3389/fpsyg.2019.01254
[35]

Huo D, Ma J, Chang R. 2020. Lane-changing- decision characteristics and the allocation of visual attention of drivers with an angry driving style. Transportation Research Part F: Traffic Psychology and Behaviour 71:62−75

doi: 10.1016/j.trf.2020.03.008
[36]

Yao RH, Wang XY, Zhao SC, Xu HF. 2015. An optimization model of signal timing for isolated intersections based on vehicle specific power. Journal of Transportation Systems Engineering and Information Technology 15(5):89−95

[37]

Cheng S, Liang Y, Zhao X, Hu J, Wu B, et al. 2024. Prediction of carbon dioxide emissions from on-road light vehicles based on speed interval. Science Technology and Engineering 24(35):15268−15275 (in Chinese)

doi: 10.12404/j.issn.1671-1815.2401797