| 381 | 2 | 243 |
| 下载次数 | 被引频次 | 阅读次数 |
【背景】随着车路协同相关技术的不断发展，通过车辆速度引导实现车辆在交叉口的安全、高效通行成为交叉口管理的重要手段之一，受到广泛关注。【目标】充分利用无信号交叉口灵活性，实现网联人类驾驶车辆路权优化，提供协同式车辆速度引导，缓解交通拥堵，降低车辆燃油消耗。【方法】首先，提出一种考虑车辆驾驶意图的车组划分规则，并基于此考虑车辆动力学、车组占用汇流区时间以及车组间冲突关系等多重约束，建立以规划区域内车辆总延误最小为目标的优化模型。随后，提出一种考虑与障碍车辆间碰撞风险的在线协同速度引导方法，进一步提高交叉口利用率，形成车组划分、路权优化、速度引导的分层异步无信号交叉口一体化管理框架。【结果】利用Matlab软件开展仿真实验，结果表明，基于分层优化方法的平均延误优于其他方法，总体延误处于较低水平。例如，当交通流量为2 000 veh/h时，所提方法相比于基于先到先服务的方法和基于时间接近度的速度引导方法，分别可降低45%以上和16.86%的平均延误。【结论】提出一种考虑网联车辆意图的分层式协同车速引导方法，可有效提高网联无信号交叉口车路协同方法的精细化程度，提高系统通行效率。
Abstract：[Background] With the continuous development of vehicle-infrastructure cooperation technologies, the realization of safe and efficient passage of vehicles at intersections through vehicle speed guidance has become one of the important means of intersection management and has received wide attention. [Objective] The aim is to make full use of the flexibility of unsignalized intersections to achieve right-of-way optimization for connected vehicles, provide collaborative vehicle speed guidance, alleviate traffic congestion, and reduce vehicle fuel consumption. [Methods] A vehicle group division rule based on the vehicle motion state is proposed, based on the vehicle intention. The division scheme is combined with the right-of-way cooperative optimization model, the objective of which is to minimize vehicle delay in the planning area. Necessary constraints like vehicle dynamics,time constraints of vehicle groups occupying the convergence area, and vehicle group conflict constraints are incorporated. According to the right-of-way of vehicle groups, an online speed guidance method considering the collision risk is proposed, further improving the efficiency. Then, a hierarchical asynchronous automatic intersection management framework with vehicle group division, rightof-way optimization, and speed guidance is established. [Results] Numerical experiments are conducted using Matlab and the results show that the proposed method outperforms the other methods in the average delay, remaining at a low level. Specifically, when the traffic flow rate is 2000pcu/h,compared to the first-come-first-served method, the proposed method can reduce delays by more than 45%, and compared to the speed guidance method based on temporal proximity, it can further reduce the average delay by 16.86%. [Conclusions] A hierarchical cooperative speed guidance method considering the intention of connected vehicles is proposed, which can effectively improve the refinement of the coordination between vehicles and infrastructure for unsignalized intersections and enhance the system efficiency.
[1] LIORIS J, PEDARSANI R, TASCIKARAOGLU F Y, et al. Platoons of connected vehicles can double throughput in urban roads[J]. Transportation Research Part C:Emerging Technologies, 2017, 77:292-305.
[2] HUANG Y, WANG Y, YAN X, et al. Behavior model and guidance strategies of the crossing behavior at unsignalized intersections in the connected vehicle environment[J]. Transportation Research Part F:Traffic Psychology and Behaviour, 2022, 88:13-24.
[3]蒋明智,吴天昊,张琳.基于深度强化学习的无信号交叉口车辆协同控制算法[J].交通运输工程与信息学报,2022, 20(2):14-24.JIANG Mingzhi, WU Tianhao, ZHANG Lin. Deep reinforcement learning based vehicular cooperative control algorithm at signal-free intersection[J]. Journal of Transportation Engineering and Information, 2022, 20(2):14-24.
[4] NAMAZI E, LI J, LU C. Intelligent intersection management systems considering autonomous vehicles:a systematic literature review[J]. IEEE Access, 2019, 7:91946-91965.
[5] DRESNER K, STONE P. Multiagent traffic management:a reservation-based intersection control mechanism[C]//Autonomous Agents and Multiagent Systems, International Joint Conference on IEEE Computer Society. New York:IEEE,2004:530-537.
[6] XU B, LI S E, BIAN Y, et al. Distributed conflict-free cooperation for multiple connected vehicles at unsignalized intersections[J]. Transportation Research Part C:Emerging Technologies, 2018, 93:322-334.
[7]李磊,王文格,彭景阳.无信号交叉口网联车调度与分布式控制策略[J].计算机应用研究, 2022, 39(11):3346-3350.LI Lei, WANG Wenge, PENG Jingyang. Combined vehicle scheduling and distributed control strategy at signalless intersection[J]. Application Research of Computers,2022, 39(11):3346-3350.
[8]冯红艳,康雷雷,刘澜.智能网联环境下单交叉口车辆轨迹优化[J].交通运输工程与信息学报, 2024, 22(1):25-38.FENG Hongyan, KANG Leilei, LIU Lan. Trajectory optimization of vehicles at isolated intersection in a connected and automated environment[J]. Journal of Transportation Engineering and Information, 2024, 22(1):25-38.
[9] LEVIN M W, REY D. Conflict-point formulation of intersection control for autonomous vehicles[J]. Transportation Research Part C:Emerging Technologies, 2017, 85:528-547.
[10] MüLLER E R, CARLSON R C, JUNIOR W K. Intersection control for automated vehicles with MILP[J]. IFAC-PapersOnLine, 2016, 49(3):37-42.
[11] LI H, DONG W, LU L, et al. Distributed cooperative driving strategy for connected automated vehicles at unsignalized intersections based on Monte Carlo method[J]. Journal of Advanced Transportation, 2024, 2024:6586774.
[12] YAO N, ZHANG F. Resolving contentions for intelligent traffic intersections using optimal priority assignment and model predictive control[C]//2018 IEEE Conference on Control Technology and Applications(CCTA). Copenhagen:IEEE, 2018:632-637.
[13] ZHONG Z, NEJAD M, LEE E E. Autonomous and semiautonomous intersection management:a survey[J]. IEEE Intelligent Transportation Systems Magazine, 2021, 13(2):53-70.
[14] JIANG S, PAN T, ZHONG R, et al. Coordination of mixed platoons and eco-driving strategy for a signal-free intersection[J]. IEEE Transactions on Intelligent Transportation Systems, 2023, 24(6):6597-6613.
[15] ZHAO W, NGODUY D, SHEPHERD S, et al. A platoon based cooperative eco-driving model for mixed automated and human-driven vehicles at a signalised intersection[J]. Transportation Research Part C:Emerging Technologies, 2018, 95:802-821.
[16] QIN Z, JI A, SUN Z, et al. Game theoretic application to intersection management:a literature review[M/OL]//IEEE, IEEE Transactions on Intelligent Vehicles, 2024:1-19.
[17] 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, 2021, 7(2):186-197.
[18] TALLAPRAGADA P, CORTéS J. Hierarchical-distributed optimized coordination of intersection traffic[J].IEEE Transactions on Intelligent Transportation Systems, 2019, 21(5):2100-2113.
[19] YANG K, GULER S I, MENENDEZ M. Isolated intersection control for various levels of vehicle technology:conventional, connected, and automated vehicles[J].Transportation Research Part C:Emerging Technologies, 2016, 72:109-129.
[20] BASHIRI M, FLEMING C H. A platoon-based intersection management system for autonomous vehicles[C]//2017 IEEE Intelligent Vehicles Symposium(Ⅳ). Los Angeles:IEEE, 2017:667-672.
[21] BASHIRI M, JAFARZADEH H, FLEMING C H.PAIM:platoon-based autonomous intersection management[C]//2018 21st International Conference on Intelligent Transportation Systems(ITSC). Maui:IEEE,2018:374-380.
[22] DENG Z, YANG K, SHEN W, et al. Cooperative platoon formation of connected and autonomous vehicles:toward efficient merging coordination at unsignalized intersections[J]. IEEE Transactions on Intelligent Transportation Systems, 2023, 24(5):5625-5639.
[23]王润民,张心睿,赵祥模,等.混行环境下网联信号交叉口车路协同控制方法[J].交通运输工程学报, 2022,22(3):139-151.WANG Runmin, ZHANG Xinrui, ZHAO Xiangmo, et al.Vehicle-infrastructure cooperative control method of connected and signalized intersection in mixed traffic environment[J]. Journal of Traffic and Transportation Engineering, 2022, 22(3):139-151.
[24] GONG X, WANG B, LIANG S. Collision-free cooperative motion planning and decision-making for connected and automated vehicles at unsignalized intersections[J].IEEE Transactions on Systems, Man, and Cybernetics:Systems, 2024, 54(5):2744-2756.
[25] FAYAZI S A, VAHIDI A. Vehicle-in-the-loop(VIL)verification of a smart city intersection control scheme for autonomous vehicles[C]//2017 IEEE Conference on Control Technology and Applications(CCTA). Maui:IEEE, 2017:1575-1580.
[26]雷朝阳,高建平,屈俊凯,等.考虑信号灯状态的经济车速规划[J].科学技术与工程, 2020, 20(18):7484-7492.LEI Zhaoyang, GAO Jianping, QU Junkai, et al. Economic speed planning with consideration the state of traffic lights[J]. Science Technology and Engineering,2020, 20(18):7484-7492.
[27] HE X, LIU H X, LIU X. Optimal vehicle speed trajectory on a signalized arterial with consideration of queue[J]. Transportation Research Part C:Emerging Technologies, 2015, 61:106-120.
[28]李振龙,杨磊,张靖思,等.考虑驾驶员有限理性下信号交叉口车速引导模型[J].科学技术与工程, 2022, 22(16):6728-6733.LI Zhenlong, YANG Lei, ZHANG Jingsi, et al. A speed guidance model accounting for the driver’s bounded rationality at signalized intersection[J]. Science Technology and Engineering, 2022, 22(16):6728-6733.
[29]王顺超,李志斌,曹奇,等.面向智能网联车辆碰撞风险规避的互动速度障碍算法[J].交通运输工程学报,2023, 23(5):264-282.WANG Shunchao, LI Zhibin, CAO Qi, et al. Reciprocal velocity obstacle algorithm for collision risk avoidance of intelligent connected vehicles[J]. Journal of Traffic and Transportation Engineering, 2023, 23(5):264-282.
[30] STEBBINS S, HICKMAN M, KIM J, et al. Characterising green light optimal speed advisory trajectories for platoon-based optimisation[J]. Transportation Research Part C:Emerging Technologies, 2017, 82:43-62.
[31] LI L, GAN J, JI X, et al. Dynamic driving risk potential field model under the connected and automated vehicles environment and its application in car-following modeling[J]. IEEE transactions on intelligent transportation systems, 2020, 23(1):122-141.
[32] YU H, JIANG R, Z. HE, 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.
基本信息:
DOI：10.19961/j.cnki.1672-4747.2024.11.004
中图分类号:U495
引用信息:
[1]丁红亮,刘天麟,孔维耀等.考虑网联车辆意图的无信号交叉口协同车速引导方法[J].交通运输工程与信息学报,2025,23(01):59-71.DOI:10.19961/j.cnki.1672-4747.2024.11.004.
基金信息:
国家自然科学基金面上项目（52272343）; 国家自然青年科学基金项目（52102384）; 江苏省自然科学基金面上项目（BK2022022056）; 中央高校基本科研业务费项目（2682024CX089）; 中国博士后科学基金面上项目（2024M762697）