Volume 22 Issue 3
Jun.  2022
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Article Navigation >  Journal of Traffic and Transportation Engineering  > 2022  >  22(3): 174-183
PEI Hua-xin, YANG Jing-xuan, HU Jian-ming, ZHANG Yi. Distributed cooperative decision-making method for vehicle swarms in large-scale road networks[J]. Journal of Traffic and Transportation Engineering, 2022, 22(3): 174-183. doi: 10.19818/j.cnki.1671-1637.2022.03.014
Citation: PEI Hua-xin, YANG Jing-xuan, HU Jian-ming, ZHANG Yi. Distributed cooperative decision-making method for vehicle swarms in large-scale road networks[J]. Journal of Traffic and Transportation Engineering, 2022, 22(3): 174-183. doi: 10.19818/j.cnki.1671-1637.2022.03.014
shu

Distributed cooperative decision-making method for vehicle swarms in large-scale road networks

doi: 10.19818/j.cnki.1671-1637.2022.03.014
  • 1.

    School of Inoformation Science and Technology, Tsinghua University, Beijing 100084, China

  • 2.

    Beijing National Research Center for Information Science and Technology (BNRist), Tsinghua University, Beijing 100084, China

  • 3.

    Tsinghua-Berkeley Shenzhen Institute (TBSI), Shenzhen 518055, Guangdong, China

  • 4.

    Collaborative Innovation Center of Modern Urban Traffic Technologies, Southest University, Nanjing 210096, Jiangsu, China

Funds:

National Key Research and Development Program of China 2018YFB1600600

More Information
    Abstract
  • To resolve the cooperative decision-making problem for vehicle swarms in large-scale road networks under the vehicle-infrastructure cooperative environment, a distributed cooperative decision-making method for vehicle swarms was proposed. On the basis of the in-depth analysis on the traffic control characteristics, the road network decomposition model was built to decompose the large-scale cooperative decision-making problem into several homogeneous small-scale sub-problems, each covering three different types of traffic areas: the upstream road segment, intersection, and downstream road segment. By the virtual vehicle mapping technique, the cooperative decision-making model of vehicle swarms was constructed to transform the two-dimensional cooperative decision-making problem of vehicle swarms at intersections into a one-dimensional problem. Similar to the cooperative decision-making method for vehicle swarms in the road segment areas, the interaction and conflict resolution between vehicles at intersections were accomplished by controlling the equivalent time headway of vehicles in the virtual vehicle platoon, and then the unified cooperative decision-making parameters were used to solve the cooperative decision-making problem of vehicle swarms in different areas of each sub-problem. Upon the unification of the cooperative decision-making parameters of vehicle swarms in different areas, the cooperative mechanism between the upstream and downstream areas was designed to ensure that the appropriate driving decisions could be made by the upstream vehicles under the full consideration of the downstream traffic states. Simulation results show that under different traffic demand settings, smooth spatiotemporal trajectories are presented by all vehicles while passing through the conflict areas after the proposed method is adopted, and the violent fluctuations in vehicle spatiotemporal trajectories are avoided. Compared with the purely distributed method, the fuel consumption of vehicles reduces by up to 14% with the proposed method under the given simulation conditions. Therefore, the proposed distributed cooperative decision-making method for vehicle swarms is effective in reducing the impact of conflict areas on the traffic flow continuity after being implemented in large-scale road networks, and thus ensuring the safe, smooth, and environmentally friendly driving of vehicles. 7 figs, 30 refs.

     

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  • [1]
    李克强, 戴一凡, 李升波, 等. 智能网联汽车(ICV)技术的发展现状及趋势[J]. 汽车安全与节能学报, 2017, 8(1): 1-14. doi: 10.3969/j.issn.1674-8484.2017.01.001

    LI Ke-qiang, DAI Yi-fan, LI Sheng-bo, et al. State-of-the-art and technical trends of intelligent and connected vehicles[J]. Journal of Automotive Safety and Energy, 2017, 8(1): 1-14. (in Chinese) doi: 10.3969/j.issn.1674-8484.2017.01.001
    [2]
    张毅, 姚丹亚, 李力, 等. 智能车路协同系统关键技术与应用[J]. 交通运输系统工程与信息, 2021, 21(5): 40-51. https://www.cnki.com.cn/Article/CJFDTOTAL-YSXT202105006.htm

    ZHANG Yi, YAO Dan-ya, LI Li, et al. Technologies and applications for intelligent vehicle-infrastructure cooperation systems[J]. Journal of Transportation Systems Engineering and Information Technology, 2021, 21(5): 40-51. (in Chinese) https://www.cnki.com.cn/Article/CJFDTOTAL-YSXT202105006.htm
    [3]
    MAHBUB A M I, MALIKOPOULOS A A. Conditions to provable system-wide optimal coordination of connected and automated vehicles[J]. Automatica, 2021, 131: 109751. doi: 10.1016/j.automatica.2021.109751
    [4]
    LI Li, WANG Fei-yue. Cooperative driving at blind crossings using intervehicle communication[J]. IEEE Transactions on Vehicular Technology, 2006, 55(6): 1712-1724. doi: 10.1109/TVT.2006.878730
    [5]
    FENG Shuo, ZHANG Yi, LI S E, et al. String stability for vehicular platoon control: definitions and analysis methods[J]. Annual Reviews in Control, 2019, 47: 81-97. doi: 10.1016/j.arcontrol.2019.03.001
    [6]
    GONG Si-yuan, SHEN Jing-lai, DU Li-li. Constrained optimization and distributed computation based car following control of a connected and autonomous vehicle platoon[J]. Transportation Research Part B: Methodological, 2016, 94: 314-334. doi: 10.1016/j.trb.2016.09.016
    [7]
    CUI Lian, CHEN Zheng, WANG A, et al. Development of a robust cooperative adaptive cruise control with dynamic topology[J]. IEEE Transactions on Intelligent Transportation Systems, 2022, 23(5): 4279-4290. doi: 10.1109/TITS.2020.3043194
    [8]
    WON M. L-platooning: a protocol for managing a long platoon with DSRC[J]. IEEE Transactions on Intelligent Transportation Systems, 2022, 23(6): 5777-5790. doi: 10.1109/TITS.2021.3057956
    [9]
    DRESNER K, STONE P. Multiagent traffic management: a reservation-based intersection control mechanism[C]//IEEE. Proceedings of the Third International Joint Conference on Autonomous Agents and Multiagent Systems. New York: IEEE, 2004: 530-537.
    [10]
    DRESNER K, STONE P. A multiagent approach to autonomous intersection management[J]. Journal of Artificial Intelligence Research, 2008, 31: 591-656. doi: 10.1613/jair.2502
    [11]
    LI Li, WEN Ding, YAO Dan-ya. A survey of traffic control with vehicular communications[J]. IEEE Transactions on Intelligent Transportation Systems, 2014, 15(1): 425-432. doi: 10.1109/TITS.2013.2277737
    [12]
    MENG Yue, LI Li, WANG Fei-yue, et al. Analysis of cooperative driving strategies for nonsignalized intersections[J]. IEEE Transactions on Vehicular Technology, 2018, 67(4): 2900-2911. doi: 10.1109/TVT.2017.2780269
    [13]
    XU Biao, LI S E, BIAN You-gang, et al. Distributed conflict-free cooperation for multiple connected vehicles at unsignalized intersections[J]. Transportation Research Part C: Emerging Technologies, 2018, 93: 322-334. doi: 10.1016/j.trc.2018.06.004
    [14]
    PEI Hua-xin, FENG Shuo, ZHANG Yi, et al. A cooperative driving strategy for merging at on-ramps based on dynamic programming[J]. IEEE Transactions on Vehicular Technology, 2019, 68(12): 11646-11656. doi: 10.1109/TVT.2019.2947192
    [15]
    PEI Hua-xin, ZHANG Yu-xiao, ZHANG Yi, et al. Optimal cooperative driving at signal-free intersections with polynomial- time complexity[J]. IEEE Transactions on Intelligent Transportation Systems, 2021, DOI: 10.1109/TITS.2021.3118592.
    [16]
    ZHANG Y J, MALIKOPOULOS A A, CASSANDRAS C G. Optimal control and coordination of connected and automated vehicles at urban traffic intersections[C]//IEEE. 2016 American Control Conference (ACC). New York: IEEE, 2016: 6227-6232.
    [17]
    PEI Hua-xin, ZHANG Jia-wei, ZHANG Yi, et al. Fault- tolerant cooperative driving at signal-free intersections[J]. IEEE Transactions on Intelligent Vehicles, 2022, DOI: 10.1109/TIV.2022.3159088.
    [18]
    LI Li, WANG Fei-yue. Cooperative driving at adjacent blind intersections[C]//IEEE. 2005 IEEE International Conference on Systems, Man, and Cybernetics. New York: IEEE, 2005: 847-852.
    [19]
    ZHANG Jia-wei, PEI Hua-xin, BAN Xue-gang, et al. Analysis of cooperative driving strategies at road network level with macroscopic fundamental diagram[J]. Transportation Research Part C: Emerging Technologies, 2022, 135: 103503. doi: 10.1016/j.trc.2021.103503
    [20]
    WANG Yun-peng, CAI Pin-long, LU Guang-quan. Cooperative autonomous traffic organization method for connected automated vehicles in multi-intersection road networks[J]. Transportation Research Part C: Emerging Technologies, 2020, 111: 458-476. doi: 10.1016/j.trc.2019.12.018
    [21]
    MAHBUB A M I, MALIKOPOULOS A A, ZHAO Liu-hui. Decentralized optimal coordination of connected and automated vehicles for multiple traffic scenarios[J]. Automatica, 2020, 117: 108958. doi: 10.1016/j.automatica.2020.108958
    [22]
    ASHTIANI F, FAYAZI S A, VAHIDI A. Multi-intersection traffic management for autonomous vehicles via distributed mixed integer linear programming[C]//IEEE. 2018 Annual American Control Conference. New York: IEEE, 2018: 6341-6346.
    [23]
    CHALAKI B, MALIKOPOULOS A A. An optimal coordination framework for connected and automated vehicles in two interconnected intersections[C]//IEEE. 2019 IEEE Conference on Control Technology and Applications. New York: IEEE, 2019: 888-893.
    [24]
    XU Qing, CAI Meng-chi, LI Ke-qiang, et al. Coordinated formation control for intelligent and connected vehicles in multiple traffic scenarios[J]. IET Intelligent Transport Systems, 2021, 15(1): 159-173. doi: 10.1049/itr2.12022
    [25]
    PEI Hua-xin, ZHANG Yi, TAO Qing-hua, et al. Distributed cooperative driving in multi-intersection road networks[J]. IEEE Transactions on Vehicular Technology, 2021, 70(6): 5390-5403. doi: 10.1109/TVT.2021.3079272
    [26]
    YU Hai-yang, JIANG Rui, HE Zheng-bing, et al. Automated vehicle-involved traffic flow studies: a survey of assumptions, models, speculations, and perspectives[J]. Transportation Research Part C: Emerging Technologies, 2021, 127: 103101.
    [27]
    UNO A, SAKAGUCHI T, TSUGAWA S. A merging control algorithm based on inter-vehicle communication[C]//IEEE. Proceedings of the IEEE/IEEJ/JSAI International Conference on Intelligent Transportation Systems. New York: IEEE, 1999: 783-787.
    [28]
    SAKAGUCHI T, UNO A, KATO S, et al. Cooperative driving of automated vehicles with inter-vehicle communications[C]// IEEE. Proceedings of the IEEE Intelligent Vehicles Symposium. New York: IEEE, 2000: 516-521.
    [29]
    DING Ji-shi-yu, LI Li, PENG H, et al. A rule-based cooperative merging strategy for connected and automated vehicles[J]. IEEE Transactions on Intelligent Transportation Systems, 2020, 21(8): 3436-3446. doi: 10.1109/TITS.2019.2928969
    [30]
    KAMAL M A S, MUKAI M, MURATA J, et al. Ecological vehicle control on roads with up-down slopes[J]. IEEE Transactions on Intelligent Transportation Systems, 2011, 12(3): 783-794.
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