智能网联车环境下交叉口车流轨迹优化模型

交通运输系统工程与信息 ›› 2021, Vol. 21 ›› Issue (2): 91-97.

所属专题：车路协同与智能化技术

• 智能交通系统与信息技术 • 上一篇下一篇

智能网联车环境下交叉口车流轨迹优化模型

高志波¹，吴志周^*1，郝威²，杨玥¹，龙科军_²，邹清全³

1. 同济大学，道路与交通工程教育部重点实验室，上海 201804；2. 长沙理工大学，智能道路与车路协同湖南省重点实验室，长沙 410004；3. 上海汽车集团股份有限公司，上海 201804

收稿日期:2020-07-16 修回日期:2021-01-06 出版日期:2021-04-25 发布日期:2021-04-25
作者简介:高志波(1991- )，男，江西上饶人，博士生。
基金资助:
国家自然科学基金/National Natural Science Foundation of China (61773288, 51678076)；上海汽车工业科技发展基金/Shanghai Automobile Industry Technology Development Fund(1916)。

Vehicle Trajectory Optimization Model for Intersection under the Connected and Automated Vehicles Environment

GAO Zhi-bo¹ , WU Zhi-zhou^*1 , HAO Wei² , YANG Yue¹ , LONG Ke-jun² , ZOU Qing-quan³

1. The Key Laboratory of Road and Traffic Engineering, Ministry of Education, Tongji University, Shanghai 201804, China; 2. Hunan Key Laboratory of Smart Roadway and Cooperative Vehicle-Infrastructure System, Changsha University of Science and Technology, Changsha 410004, China; 3. SAIC Motor Corp. LTD., Shanghai 201804, China

Received:2020-07-16 Revised:2021-01-06 Online:2021-04-25 Published:2021-04-25

摘要/Abstract

摘要：

在智能网联环境下，车辆可通过相互穿插和协作通过交叉口，无需信号灯控制。为保证车辆安全高效运行，建立车辆到达时序和速度协同优化的交叉口车流轨迹优化模型。提出车辆到达时序优化模型和车辆速度优化模型，建立车辆到达时刻与速度的函数关系；在此基础上，模型以所有车辆在控制区域的行程时间与油耗加权最小为目标，车辆路径、到达时刻和速度等关键参数为决策变量，设计迭代式算法求解，实现同时优化车辆到达时刻和速度且交叉口运行效益最大的目的。实验结果表明，与车辆时序和轨迹分别优化的两阶段模型相比，本文模型降低车均延误 32.1%，减少车均油耗9.9%，说明该模型具有良好的主动性和适应性，在降低车辆延误的同时也节省了油耗。

关键词: 城市交通, 智能网联车, 交叉口, 时序优化, 轨迹优化

Abstract:

Under the connected and automated driving environment, vehicles can cross the intersection with good coordination and minimal controls from traditional traffic signals. To ensure the safe and efficient vehicle operations at intersections, this study proposes a trajectory optimization model to optimize vehicle arrival time and speed. The vehicle arrival time sequence optimization model and the vehicle speed optimization model are developed to establish the functional relation between vehicle arrival time and speed. Then, the weighted sum of all vehicle travel time and fuel consumptions are set as the objective of the proposed model. The decision variables include vehicle route, arrival time, and speed. An iterative algorithm is designed to optimize both the vehicle arrival time and speed, and maximize the operation benefit at the intersection. Compared with the results from the two-level trajectory optimization model, the proposed model reduced the average delay by 32.0% and reduced the fuel consumption by 9.9% . The proposed model has good flexibility and mobility, which can reduce both vehicle delays and fuel consumptions.

Key words: urban traffic, connected and automated vehicles, intersection, time sequence optimization, trajectory optimization

中图分类号:

U491

高志波，吴志周，郝威，杨玥，龙科军，邹清全. 智能网联车环境下交叉口车流轨迹优化模型[J]. 交通运输系统工程与信息, 2021, 21(2): 91-97.

GAO Zhi-bo,WU Zhi-zhou,HAO Wei, YANG Yue, LONG Ke-jun,ZOU Qing-quan. Vehicle Trajectory Optimization Model for Intersection under the Connected and Automated Vehicles Environment[J]. Journal of Transportation Systems Engineering and Information Technology, 2021, 21(2): 91-97.

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链接本文: http://www.tseit.org.cn/CN/abstract/abstract30214.shtml

http://www.tseit.org.cn/CN/Y2021/V21/I2/91

参考文献

[1] FAYAZI S A, VAHIDI A. Mixed- integer linear programming for optimal scheduling of autonomous vehicle intersection crossing[J]. IEEE Transactions on Intelligent Vehicles, 2018, 3(3): 287-299.

[2] CHEN L, ENGLUND C. Cooperative intersection management: a survey[J]. IEEE Transactions on Intelligent Transportation Systems, 2016, 17(2): 570- 586.

[3] QIAN X J, ALTCHÉ, F, GRÉGOIRE J, et al. Autonomous intersection management systems: criteria, implementation and evaluation[J]. IET Intelligent Transport Systems, 2017, 11(3): 182-189

[4] DRESNER K, STONE P. Multiagent traffic management: a reservation- based intersection control mechanism[C]. Proceedings of the Third International Joint Conference on Autonomous Agents and Multiagent Systems, IEEE, 2004.

[5] DRESNER K M, STONE P. A multiagent approach to autonomous intersection management[J]. Articial Intelligence Research, 2008, 31(3): 591-656.

[6] LEVIN M W, BOYLES S D, PATEL R. Paradoxes of reservation-based intersection controls in traffic networks [J]. Transportation Research Part A: Policy Practice, 2016, 90(2016): 14-25.

[7] 姚志洪, 蒋阳升, 胡蓉, 等. 基于混合整数规划的智能网联车冲突区时序优化模型[J/OL]. 控制与决策, 2019, 1- 6. https://doi.org/10.13195/j.kzyjc.2019.0886. [YAO Z H, JIANG Y S, HU R, et al. Mixed integer programming model of scheduling for connected automated vehicles in a conflict zone[J]. Control and Decision, 2019, 1- 6. https: //doi. org/10.13195/j.kzyjc. 2019. 0886.

[8] 吴伟, 刘洋, 刘威, 等. 自动驾驶环境下交叉口车辆路径规划与最优控制模型[J]. 自动化学报, 2020, 46(9): 1971- 1985. [WU W, LIU Y, LIU W, et al. A novel autonomous vehicle trajectory planning and control model for connected-and-autonomous intersections[J]. Acta Automatica Sinica, 2020, 46(9): 1971-1985.]

[9] 吴伟, 刘洋, 马万经. 自动驾驶环境下面向交叉口自由转向车道的交通控制模型[J]. 中国公路学报, 2019, 32 (12): 25-35. [WU W, LIU Y, MA W J, et al. Intersection traffic control model for free- turning lane in connected and autonomous vehicle environment[J]. China Journal Highway Transportation, 2019, 32(12): 25-35.]

[10] MA J Q, LI X P, FANG Z, et al. Parsimonious shooting heuristic for trajectory design of connected automated traffic part II: Computational issues and optimization[J]. Transportation Research Part B: Methodological, 2017, 95(2017): 421-441.

[11] GHIASIA A, LI X P, MA J Q. A mixed traffic speed harmonization model with connected autonomous vehicles [J]. Transportation Research Part C: Emerging Technologies, 2019, 104(2019): 210-233.

[12] 罗孝羚, 蒋阳升. 智能网联车环境下高速匝道汇入车流轨迹优化模型 [J]. 交通运输系统工程与信息, 2019, 19(4): 94-100. [LUO X L, JIANG Y S. Vehicle Trajectory optimization model for ramp based on connect automatically vehicles[J]. Journal of Transportation Systems Engineering and Information Technology, 2019, 19(4): 94-100.]

[13] ELHENAWY M, ELBERY A A, HASSAN A A, et al. An intersection game-theory- based traffic control algorithm in a connected vehicle environment[C]. 2015 IEEE 18th International Conference on Intelligent Transportation Systems, IEEE, 2015.

[14] WU Y Y, CHEN H P, ZHU F. DCL-AIM: Decentralized coordination learning of autonomous intersection management for connected and automated vehicles[J]. Transportation Research Part C: Emerging Technologies, 2019, 103(2019): 246-260.

[15] YU C H, FENG C, LIU H X. Integrated optimization of traffic signals and vehicle trajectories at isolated urban intersections[J]. Transportation Research Part B: Methodological, 2018, 112(2018): 89-112.

[16] FENG C, YU C H, LIU H X. Spatiotemporal intersection control in a connected and automated vehicle environment[J]. Transportation Research Part C: Emerging Technologies, 2018, 89(2018): 364-383.

[17] Akcelik Y H. Efficiency and drag in the power-based model of fuel consumption[J]. Transportation Research Part B: Methodological, 1989, 23(5): 376-385.

智能网联车环境下交叉口车流轨迹优化模型

Vehicle Trajectory Optimization Model for Intersection under the Connected and Automated Vehicles Environment

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