智能网联环境下无信号交叉口车辆通行控制策略

doi:10.13229/j.cnki.jdxbgxb.20221102

摘要/Abstract

摘要：

为实现智能网联环境下无信号交叉口网联自动驾驶车辆的智能管控，提高交叉口通行效率，提出了一种基于间隙理论的车辆通行控制策略。依据交叉口区域功能、用途等，将其划分为变道区、调控区、缓冲区、物理区和恢复区；考虑实车物理大小建立了物理区车辆冲突区域计算模型，并通过优化左转车辆轨迹为椭圆轨迹，开发了直行-直行、直行-左转和左转-左转车辆行车间隙控制的数学模型；基于三角函数加速度控制策略建立了调控区和缓冲区的车速诱导模型；运用Vissim和Matlab联合仿真对控制策略及模型的高效性和合理性进行了对比验证。结果表明：本文控制策略及模型能使相冲突的车辆安全不停车地依次穿插通过冲突区域；对比信号控制策略，在交通量为1 600 pcu/h情况下，该控制策略及模型可使车辆通过交叉口的平均延误时间缩短55.97%，平均行程时间缩短41.87%，车辆能耗减少33.31%，且交通流量越大，改善效果越显著。

关键词: 交通运输系统工程, 智能网联, 无信号交叉口, 间隙理论, 控制策略, 冲突区域

Abstract:

In order to realize the intelligent control of connected and autonomous driving vehicle in the intersection without signal in the intelligent network connection environment and improve the intersection passage efficiency， a vehicle passage control strategy based on the gap theory was proposed. According to the function and usage of the intersection area， the intersection area was divided into a change zone， a regulation zone， a buffer zone， a physical zone and a recovery zone. A vehicle conflict zone calculation model for the physical zone was established by considering the physical size of real vehicles， and a mathematical model for the clearance control of straight-straight， straight-left-turn and left-turn-left-turn vehicles was developed by optimising the trajectory of left-turn vehicles as an elliptical trajectory. A vehicle speed induction model for the regulation zone and buffer zone was established based on the trigonometric acceleration control strategy. The use of the efficiency and rationality of the control strategy and model were compared and verified by using joint simulation of Vissim and Matlab. The results show that the proposed control strategy and model can enable the conflicting vehicles to pass through the conflicting area sequentially without stopping； comparing with the signal control strategy， the average delay time of vehicles through the intersection is reduced by 55.97%， the average travel time is reduced by 41.87%， and the vehicle energy consumption is reduced by 33.31% under this control strategy and model at a traffic volume of 1 600 pcu/h， and the higher the traffic volume， the more significant the improvement effect is.

Key words: transport systems engineering, intelligent network links, signal-free intersections, gap theory, control strategies, conflict zones

中图分类号:

U491.2

潘福全,牛远征,张丽霞,杨金顺,陈秀锋,陈德启. 智能网联环境下无信号交叉口车辆通行控制策略[J]. 吉林大学学报(工学版), 2025, 55(6): 1948-1962.

Fu-quan PAN,Yuan-zheng NIU,Li-xia ZHANG,Jin-shun YANG,Xiu-feng CHEN,De-qi CHEN. Strategies for controlling vehicle movements at signal⁃free intersections in intelligent networked environment[J]. Journal of Jilin University(Engineering and Technology Edition), 2025, 55(6): 1948-1962.

图/表 20

图1

图2

图3

表1

CACC模型的参数取值"

CACC参数	取值
控制参数k₁	0.45
控制参数k₂	0.25
控制间隔 $Δ t$ /s	0.01
安全车头时距T_c/s	0.6
最小停车间距s₀/m	2
车身长L/m	5

表1

表2

图4

图5

图6

图7

图8

图9

图10

图11

图12

图13

表3

图14

图15

表4

图16

参考文献 20

[1]	Zhong G, Zhang J, Yin T T, et al. A cooperative management strategy designed for unsignalized intersections under a connected vehicle environment[J]. Coat International Conference of Transportation Professionals, 2015, 2015: 233-245.
[2]	胡永辉, 金旭峰, 王亦兵, 等. 智能网联混行动力异构交通流生态驾驶[J].中国公路学报, 2022, 35(3): 15-27.
	Hu Yong-hui, Jin Xu-feng, Wang Yi-bing, et al. Intelligent networked hybrid mobility for heterogeneous traffic flow eco-driving[J]. Chinese Journal of Highways, 2022, 35(3): 15-27.
[3]	Lee J, Park B. Development and evaluation of a cooperative vehicle intersection control algorithm under the connected vehicles environment[J]. IEEE Intelligent Transportation System, 2012, 13(1): 81-90.
[4]	张游, 潘福全, 张丽霞, 等. 车路协同环境下智能交叉口车速控制[J]. 控制理论与应用, 2022, 39(6): 1057-1064.
	Zhang You, Pan Fu-quan, Zhang Li-xia,et al. Intelligent intersection speed control in a vehicle-road cooperative environment[J]. Control Theory and Applications, 2022, 39(6): 1057-1064.
[5]	潘福全, 张游, 张丽霞, 等. 车路协同下基于间隙理论的交叉口智能控制策略[J]. 重庆交通大学学报: 自然科学版, 2022, 41(1): 44-52.
	Pan Fu-quan, Zhang You, Zhang Li-xia, et al. Intelligent control strategy of intersection based on gap theory under vehicle-road cooperation[J]. Journal of Chongqing Jiaotong University (Natural Science Edition), 2022, 41(1): 44-52.
[6]	Chen W, Liu Y. Gap-based automated vehicular speed guidance towards eco-driving at an unsignalized intersection[J]. Transportmetrica B: Transport Dynamics, 2017, 2017(3): 1-22.
[7]	Chai L G, Cai B G, Wei S G, et al. Connected and autonomous vehicles coordinating approach at intersection based on space-time slot[J]. Transportmetrica A: Transport Science, 2018, 14(10): 929-951.
[8]	刘显贵, 王晖年, 洪经纬, 等. 网联环境下信号交叉口车速控制策略及优化[J]. 交通运输系统工程与信息, 2021, 21(2): 82-90.
	Liu Xian-gui, Wang Hui-nian, Hong Jing-wei, et al. Signal intersection speed control strategy and optimization in a network-linked environment[J]. Transportation Systems Engineering and Information, 2021, 21(2): 82-90.
[9]	Mahyar A, Mehdi N, Oliver G. Optimal traffic control at smart intersections: automated network fundamental diagram[J]. Transportation Research Part B, 2019, 137: 2-18.
[10]	Zhang Y, Cassandras C G. Decentralized optimal control of connected automated vehicles at signal-free intersections including comfort-constrained turns and safety guarantees[J]. Automatica, 2019, 109: No.108563.
[11]	常玉林, 张成祥, 张鹏, 等. 车联网环境下基于间隙优化的无信号交叉口车速控制方法[J]. 重庆理工大学学报: 自然科学, 2021, 35(3): 10-17, 60.
	Chang Yu-lin, Zhang Cheng-xiang, Zhang Peng, et al. A gap optimization-based speed control method for signal-free intersections in a connected vehicle environment[J]. Journal of Chongqing University of Technology (Natural Sciences), 2021, 35(3): 10-17, 60.
[12]	潘福全, 张丽霞, 陆键, 等. 接入管理技术在公路交叉口安全改善中的运用[J]. 北京工业大学学报, 2011, 37(2): 237-242.
	Pan Fu-quan, Zhang Li-xia, Lu Jian, et al. The application of access management technology in highway intersection safety improvement[J]. Journal of Beijing University of Technology, 2011, 37(2): 237-242.
[13]	Milanés V, Shladover S E, Spring J, et al. Cooperative adaptive cruise control in real traffic situations[J]. IEEE Transactions on Intelligent Transportation Systems, 2013, 15(1): 296-305.
[14]	Milanés V, Shladover S E. Modeling cooperative and autonomous adaptive cruise control dynamic responses using experimental data[J]. Transportation Research Part C: Emerging Technologies, 2014, 48: 285-300.
[15]	Xiao L, Wang M, Schakel W, et al. Unravelling effects of cooperative adaptive cruise control deactivation on traffic flow characteristics at merging bottlenecks[J]. Transportation Research Part C: emerging technologies, 2018, 96: 380-397.
[16]	Wu X, Freese D, Cabrera A, et al. Electric vehicles' energy consumption measurement and estimation[J]. Transportation Research Part D: Transport and Environment, 2015, 34: 52-67.
[17]	Altan O D, Wu G, Barth M J, et al. GlidePath: eco-friendly automated approach and departure at signalized intersections[J]. IEEE Transactions on Intelligent Vehicles, 2017, 2(4): 266-277.
[18]	张健, 吴坤润, 杨敏, 等. 智能网联环境下交叉口双环自适应控制模型[J]. 吉林大学学报: 工学版, 2021, 51(2): 541-548.
	Zhang Jian, Wu Kun-run, Yang Min, et al. Dual-loop adaptive control model for intersections in an intelligent network link environment[J]. Journal of Jilin University (Engineering and Technology Edition), 2021, 51(2): 541-548.
[19]	Stevanovic J, Stevanovic A, Martin P T, et al. Stochastic optimization of traffic control and transit priority settings in VISSIM[J]. Transportation Research Part C: Emerging Technologies, 2008, 16(3): 332-349.
[20]	Mahmassani H S. 50th Anniversary invited article—autonomous vehicles and connected vehicle systems: flow and operations considerations[J]. Transportation Science, 2016, 50(4): 1140-1162.

Metrics

Viewed

Full text

Abstract

Cited

Shared

Discussed

参数	取值
车辆质量（包括驾驶员）M/kg	1 266
滚动阻力系数f_rl	0.026 9
电枢常数和磁通量的乘积K/（kg·m^-1）	0.35
电动机电阻r/Ω	10.08
车轮半径R/m	0.5

指标		流量/（pcu·h^-1）
指标		800	1 200	1 600
信号控制	平均延误/s	17.82	30.48	41.29
信号控制	平均行程时间/s	63.17	75.68	118
智能控制	平均延误/s	15.48	16.98	18.03
智能控制	平均行程时间/s	44.9	58.84	68.59
降低比率	延误/%	12.79	43.05	55.97
降低比率	行程时间/%	28.93	32.82	41.87

指标	流量/（pcu·h^-1）
指标	800	1 200	1 600
信号控制能耗/（kw·h^-1）	2.31	4.78	8.4
智能控制能耗/（kw·h^-1）	1.89	3.59	5.63
能耗降低比率/%	17.69	24.83	33.11