Straight-Going Priority in Hierarchical Control Framework for Right-Turning Vehicle Merging Based on Cooperative Game

doi:10.1007/s12204-023-2577-z

Abstract

Abstract: With the development of connected and automated vehicles (CAVs), forming strategies could extend from the typically used first-come-first-served rules. It is necessary to consider passing priorities when crossing intersections to prevent conflicts. In this study, a hierarchical strategy based on a cooperative game was developed to improve safety and efficiency during right-turning merging. A right-turn merging conflict model was established to analyze the right-turning vehicle characteristics of the traffic flow. The proposed three-layered hierarchical strategy includes a decision-making layer, a task layer, and an operation layer. A decision-making-layer cooperative game strategy was used to determine the merging priority of straight-going traffic and right-turning flows. In addition, a task-layer cooperative game strategy was designed for the merging sequence. A modified consensus algorithm was utilized to optimize the speed of vehicles in the virtual platoon of the operation layer. Traffic simulations were performed on the PYTHON-SUMO integrated platform to verify the proposed strategy. The simulation results show that, compared with other methods, the proposed hierarchical strategy has the shortest travel time and loss time and performs better than other methods when the straight-going traffic flow increases during right-turning merging at the intersection. The proposed method shows superiority under a significant traffic flow with a threshold of 900 vehicle/(h · lane). This satisfactory application of right-turning merging might be extended to ramps, lane-changing, and other scenarios in the future.

Key words: cooperative game, right-turning merging, coordinated decision-making

摘要： 随着智能网联车辆技术的发展，合流策略大多数扩展于先到先得规则。在实际的交叉口通行过程中需要考虑通行优先级以避免发生冲突。本文提出了一种基于合作博弈的分层合流策略，提高了右转合流时的安全性和效率。首先建立右转并道冲突模型，分析交通流右转车辆特性。接着，提出包括决策层、任务层和操作层的三层分层策略，采用基于合作博弈策略在决策层确定直行车流和右转车流的合流优先级，在任务层确定合并序列，在操作层使用改进的一致性算法来优化有序队列中的车辆速度。最后在PYTHON-SUMO仿真平台上验证所提出的策略。结果表明，所提出的分层策略具有最短的旅行时间和损失时间，并且在直行交通流量增加时的表现优于其他方法。此外，所提出的方法在交通流量900 时效果最好。这种右转汇入策略的成功应用可以在未来扩展到匝道、换道和其他场景。

关键词: 合作博弈, 右转合流, 协同决策

CLC Number:

U121

YANG Jingwena (杨静文), ZHANG Libina (张立彬), WANG Pinga (王萍), YAO Junfengb∗ (姚俊峰), ZHAO Xiangmob (赵祥模). Straight-Going Priority in Hierarchical Control Framework for Right-Turning Vehicle Merging Based on Cooperative Game[J]. J Shanghai Jiaotong Univ Sci, 2023, 28(1): 150-160.

References

[1] LI L, XU Z G, ZHAO X M, et al. Review of motion planning methods of intelligent connected vehicles [J]. China Journal of Highway and Transport, 2019, 32(6): 20-33 (in Chinese).
[2] HU Y K, WANG C X, YANG M. Decision-making method of intelligent vehicles: A survey [J]. Journal of Shanghai Jiao Tong University, 2021, 55(8): 1035-1048 (in Chinese).
[3] HUA Y D, GONG J F, RONG H, et al. Intelligent vehicle human-simulated steering characteristics access and control strategy [J]. Journal of Shanghai Jiao Tong University (Science), 2018, 23(1): 117-123.
[4] PAN F Q, ZHANG L X, LU J, et al. Calculation models of conflict points for motorized vehicles at unsignalized intersections [J]. Journal of Shanghai Jiao Tong University, 2013, 47(2): 259-263 (in Chinese).
[5] REN G, HUA J Y, ZHANG Z Y, et al. Optimal evacuation network design with contraflow and conflict elimination strategy [J]. China Journal of Highway and Transport, 2015, 28(3): 88-93 (in Chinese). [6] HU J B, HE L C, WANG R H. Review of safety evaluation of freeway interchange [J]. China Journal of Highway and Transport, 2020, 33(7): 17-28 (in Chinese).
[7] MOHEBIFARD R, HAJBABAIE A. Connected automated vehicle control in single lane roundabouts [J]. Transportation Research Part C : Emerging Technologies, 2021, 131: 103308.
[8] WU W, LIU Y, LIU W, et al. A novel autonomous vehicle trajectory planning and control model for connected-and-autonomous intersection [J]. Acta Automatica Sinica, 2020, 46(9): 1971-1985 (in Chinese).
[9] QIAN G M, GUO M, ZHANG L H, et al. Traffic scheduling and control in fully connected and automated networks [J]. Transportation Research Part C : Emerging Technologies, 2021, 126(4): 103011.
[10] ZHANG J, JIANG X, LIU Z Y, et al. A study on autonomous intersection management: Planning-based strategy improved by convolutional neural network [J]. KSCE Journal of Civil Engineering, 2021, 25(10): 3995-4004.
[11] ZHAO Z Q, WU G Y, BARTH M. Corridor-wise eco-friendly cooperative ramp management system for connected and automated vehicles [J]. Sustainability, 2021, 13(15): 8557.
[12] WANG J W, MA F W, YU Y, et al. Optimization design of the decentralized multi-vehicle cooperative controller for freeway ramp entrance [J]. International Journal of Automotive Technology, 2021, 22(3): 799-810.
[13] CHEN N, VAN AREM B, ALKIM T, et al. A hierarchical model-based optimization control approach for cooperative merging by connected automated vehicles [J]. IEEE Transactions on Intelligent Transportation Systems, 2021, 22(12): 7712-7725.
[14] RIOS-TORRES J, MALIKOPOULOS A A. Automated and cooperative vehicle merging at highway on-ramps [J]. IEEE Transactions on Intelligent Transportation Systems, 2017, 18(4): 780-789.
[15] GU J F, FANG Y, SHENG Z C, et al. Double deep Q-network with a dual-agent for traffic signal control [J]. Applied Sciences, 2020, 10(5): 1622.
[16] TACHET R, SANTI P, SOBOLEVSKY S, et al. Revisiting street intersections using slot-based systems [J]. PLoS ONE, 2016, 11(3): e0149607.
[17] KUMARAVEL S D, MALIKOPOULOS A A, AYYAGARI R. Decentralized cooperative merging of platoons of connected and automated vehicles at highway on-ramps [C]//2021 American Control Conference (ACC ). New Orleans, LA: IEEE, 2021: 2055-2060.
[18] KUMARAVEL S D, MALIKOPOULOS A A, AYYAGARI R. Optimal coordination of platoons of connected and automated vehicles at signal-free intersections [J]. IEEE Transactions on Intelligent Vehicles, 2022, 7(2): 186-197.
[19] MORALES MEDINA A I, VAN DE WOUW N, NIJMEIJER H. Cooperative intersection control based on virtual platooning [J]. IEEE Transactions on Intelligent Transportation Systems, 2018, 19(6): 1727-1740.
[20] XIN Q, FU R, UKKUSYURI S V, et al. Modeling and impact analysis of connected vehicle merging accounting for mainline random length tight-platoon [J]. Physica A: Statistical Mechanics and Its Applications, 2021, 563: 125452.
[21] JIANG Y S, JIANG H R, YAO Z H, et al. Vehicle schedule optimization model for autonomous intersection based on virtual platoon [J]. China Journal of Highway and Transport, 2022(8): 291-303 (in Chinese).
[22] CAIAZZO B, LUI D G, PETRILLO A, et al. Distributed double-layer control for coordination of multiplatoons approaching road restriction in the presence of IoV communication delays [J]. IEEE Internet of Things Journal, 2022, 9(6): 4090-4109.
[23] XU B, LI S E, BIAN Y, et al. Distributed conflictfree cooperation for multiple connected vehicles at unsignalized intersections [J]. Transportation Research Part C : Emerging Technologies, 2018, 93: 322-334.
[24] CHEN T Y, WANG M, GONG S, et al. Connected and automated vehicle distributed control for on-ramp merging scenario: A virtual rotation approach [J]. Transportation Research Part C : Emerging Technologies, 2021, 133: 103451.
[25] WANG S F, ZHANG J X, ZHANG J Y. Intelligent vehicles formation control based on artificial potential field and virtual leader [J]. Journal of Shanghai Jiao Tong University, 2020, 54(3): 305-311 (in Chinese).
[26] LIAO X S, ZHAO X P, WU G Y, et al. A game theory based ramp merging strategy for connected and automated vehicles in the mixed traffic: A unity-SUMO integrated platform [EB/OL]. (2021-01-27) [2021-12-20]. https://arxiv.org/abs/2101.11237.
[27] AKTI S, ERDAGI I G, SILGU M A, et al. A gametheoretical approach for lane-changing maneuvers on freeway merging segments [C]//2020 IEEE 23rd International Conference on Intelligent Transportation Systems. Rhodes: IEEE, 2020: 1-6.
[28] KOOPMANN B, PUCH S, EHMEN G, et al. Cooperative maneuvers of highly automated vehicles at urban intersections: A game-theoretic approach [C]//6th International Conference on Vehicle Technology and Intelligent Transport Systems. Prague: SCITEPRESS – Science and Technology Publications, 2020: 15-26.
[29] MIN H G, FANG Y K, WANG R M, et al. A novel onramp merging strategy for connected and automated vehicles based on game theory [J]. Journal of Advanced Transportation, 2020, 2020: 2529856.
[30] HU Y K, ZHUANG H Y, WANG C M, et al. Stackelberg-game-based intelligent vehicle decision method for merging scenarios [J]. Journal of Shanghai Jiao Tong University, 2021, 55(8): 1027-1034 (in Chinese).
[31] YANG M, WANG L C, ZHANG J, et al. Collaborative method of vehicle conflict resolution in merging area for intelligent expressway [J]. Journal of Traffic and Transportation Engineering, 2020, 20(3): 217-224 (in Chinese).
[32] XU Y W, LI D W, XI Y G. A game-based adaptive traffic signal control policy using the vehicle to infrastructure (V2I) [J]. IEEE Transactions on Vehicular Technology, 2019, 68(10): 9425-9437.
[33] KRAJZEWICZ D. Traffic simulation with SUMO – simulation of urban mobility [M]//Fundamentals of traffic simulation. New York: Springer, 2010: 269-293.
[34] RADDON S. SUMO V2X communication research (platooning and CIM) [EB/OL]. (2022-01-06)[2022-02-20]. https://github.com/sraddon/SUMOV2X-Communication-Research-Platooning-and-CIM.