Autonomous Intersection Control Method Based on Maximum Clique Theory

NAVIGATE

Table of Contents

STATISTICS

Viewed403

Downloads318

Autonomous Intersection Control Method Based on Maximum Clique Theory

[HTML] PDF下载 (318)

[1]LIU Mingjian,ZHU Yunhe,ZHANG Sijia,et al.Autonomous Intersection Control Method Based on Maximum Clique Theory[J].Journal of Zhengzhou University (Engineering Science),2024,45(02):114-120.[doi:10.13705/j.issn.1671-6833.2023.05.004]

Copy

Journal of Zhengzhou University (Engineering Science)[ISSN 1671-6833/CN 41-1339/T] Volume: 45 Number of periods: 2024 02 Page number: 114-120 Column: Public date: 2024-03-06

Title:: Autonomous Intersection Control Method Based on Maximum Clique Theory

Author(s):: LIU Mingjian; ZHU Yunhe; ZHANG Sijia; SUN Hua; College of Information Engineering, Dalian Ocean University, Dalian 116023, China

Keywords:: traffic engineering; unsignalized intersection; vehicle-infrastructure cooperative; maximum clique; trajectory prediction

CLC:: -

DOI:: 10.13705/j.issn.1671-6833.2023.05.004

Abstract:: The existing autonomous intersection control strategies lack foresight and are prone to deadlock, resulting in low execution efficiency of the control system. To address this issue, a vehicle road collaborative autonomous intersection control strategy based on maximum clique theory is designed. Firstly, the spatiotemporal trajectory of vehicles was modeled, and the conflict matrix describing the driving conflict relationship between vehicles was constructed. Secondly, the conflict matrix was transformed into a conflict relation graph. Through three established solution stages, the complement of the maximum clique in the conflict relation graph was solved as the set of accepted vehicle reservation requests, which could ensure more successful vehicle reservation requests passing through the intersection within each batch processing cycle and could improve the efficiency of intersection passage while ensuring the safety of vehicle driving at the intersection. Simulation results showed that compared with the first come first served control strategy, traffic signal control strategy, and Tabu-based control strategy, the average waiting time was reduced by 40%, 17%, and 8%, and the number of vehicles passing through the intersection per unit time was increased by 30%, 18%, and 9%, respectively. This proved the effectiveness of the strategy, which not only could improved the throughput of the intersection but also could effectively reduce the average waiting time of vehicles.

References:: [1] 高德软件有限公司. 2021年度中国主要城市交通分析报告[R]. 北京:高德地图交通大数据,2022.
AMAP. 2021 China′s major urban traffic analysis report[R]. Beijing: Gaode Transportation Big Data, 2022.
[2] 张雪妍, 贺锋. 基于累积前景理论的组合出行交通分配模型[J]. 郑州大学学报(工学版), 2020, 41(3): 91-96.
ZHANG X Y, HE F. Traffic assignment model with combined modes based on cumulative prospect theory[J]. Journal of Zhengzhou University (Engineering Science), 2020, 41(3): 91-96.
[3] 龙雪琴, 王瑞璇, 王晗. 考虑出行者不同理性程度的拥堵交通流分配方法[J]. 交通运输系统工程与信息, 2023, 23(1): 216-223.
LONG X Q, WANG R X, WANG H. Traffic flow assignment method considering travelers′ different rational degree under congestions[J]. Journal of Transportation Systems Engineering and Information Technology, 2023, 23(1): 216-223.
[4] 岳昊, 刘晓玲, 孟晓雨, 等. 拥堵道路网的静态交通流非均衡分配方法[J]. 北京交通大学学报, 2017, 41(3): 1-6.
YUE H, LIU X L, MENG X Y, et al. A non-equilibrium method to solve static traffic assignment problem at the congested road network[J]. Journal of Beijing Jiaotong University, 2017, 41(3): 1-6.
[5] 张新洁, 关宏志, 赵磊, 等. 有限理性视野下出行者出行方式选择分层Logit模型研究[J]. 交通运输系统工程与信息, 2018, 18(6): 110-116.
ZHANG X J, GUAN H Z, ZHAO L, et al. Nested logit model on travel mode choice under boundebly rational view[J]. Journal of Transportation Systems Engineering and Information Technology, 2018, 18(6): 110-116.
[6] HOU Y F, SELIMAN S M S, WANG E S, et al. Cooperative and integrated vehicle and intersection control for energy efficiency (CIVIC-E2)[J]. IEEE Transactions on Intelligent Transportation Systems, 2018, 19(7): 2325-2337.
[7] LI L, WEN D, YAO D Y. A survey of traffic control with vehicular communications[J]. IEEE Transactions on Intelligent Transportation Systems, 2014, 15(1): 425-432.
[8] GUO Q Q, LI L, BAN X G. Urban traffic signal control with connected and automated vehicles: a survey[J]. Transportation Research Part C: Emerging Technologies, 2019, 101: 313-334.
[9] DRESNER K, STONE P. A multiagent approach to autonomous intersection management[J]. Journal of Artificial Intelligence Research, 2008, 31: 591-656.
[10] CARLINO D, BOYLES S D, STONE P. Auction-based autonomous intersection management[C]∥16th International IEEE Conference on Intelligent Transportation Systems (ITSC 2013). Piscataway:IEEE, 2014: 529-534.
[11] VASIRANI M, OSSOWSKI S. A market-inspired approach for intersection management in urban road traffic networks[J]. Journal of Artificial Intelligence Research, 2012, 43: 621-659.
[12] MAHBUB A M I, MALIKOPOULOS A A. Conditions for state and control constraint activation in coordination of connected and automated vehicles[C]∥2020 American Control Conference (ACC). Piscataway:IEEE, 2020: 436-441.
[13] XU H L, ZHANG Y, LI L, et al. Cooperative driving at unsignalized intersections using tree search[J]. IEEE Transactions on Intelligent Transportation Systems, 2020, 21(11): 4563-4571.
[14] ZHU M J, LI X, HUANG H Y, et al. LICP: a look-ahead intersection control policy with intelligent vehicles[C]∥2009 IEEE 6th International Conference on Mobile Adhoc and Sensor Systems. Piscataway:IEEE, 2009: 633-638.
[15] 刘明剑, 谭国真, 魏欣, 等. 基于禁忌搜索的交叉路口自治车辆调度方法[J]. 中国公路学报, 2016, 29(2): 123-129.
LIU M J, TAN G Z, WEI X, et al. Autonomous vehicle scheduling method based on tabu search at intersections[J]. China Journal of Highway and Transport, 2016, 29(2): 123-129.
[16] 常玉林, 汪小渟, 张鹏. 改进蚁群算法在交通分配模型中的应用[J]. 郑州大学学报(工学版), 2017, 38(2): 41-44, 49.
CHANG Y L, WANG X T, ZHANG P. Modified ant colony algorithm and its application on traffic assignment model[J]. Journal of Zhengzhou University (Engineering Science), 2017, 38(2): 41-44, 49.
[17] PEI H X, FENG S, ZHANG Y, 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.
[18] 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. Intersection traffic control model for free-turning lane in connected and autonomous vehicle environment[J]. China Journal of Highway and Transport, 2019, 32(12): 25-35.
[19] 蒋阳升, 蒋浩然, 姚志洪, 等. 基于虚拟车队的自动交叉路口车辆时序优化模型[J]. 中国公路学报, 2022, 35(8): 291-303.
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, 35(8): 291-303.
[20] GONZwidth=8,height=11,dpi=110LEZ C L, ZAPOTECATL J L, GERSHENSON C, et al. A robustness approach to the distributed management of traffic intersections[J]. Journal of Ambient Intelligence and Humanized Computing, 2020, 11(11): 4501-4512.
[21] YESILYURT A Y, TUNC I, SOYLEMEZ M T. A reservation method for multi-agent system intersection management with energy consumption considerations[J]. IFAC-PapersOnLine, 2021, 54(2): 246-251.
[22] 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.
[23] PULLAN W. Approximating the maximum vertex/edge weighted clique using local search[J]. Journal of Heuristics, 2008, 14(2): 117-134.
[24] PULLAN W. Approximating the maximum vertex/edge weighted clique using local search[J]. Journal of Heuristics, 2008, 14(2): 117-134.

Similar References:

Memo

Last Update: 2024-03-08