基于多智能体一致性的多车道车辆集群编队控制

doi:10.3969/j.issn.1674-8484.2024.05.012

汽车安全与节能学报 ›› 2024, Vol. 15 ›› Issue (5): 742-752.DOI: 10.3969/j.issn.1674-8484.2024.05.012

基于多智能体一致性的多车道车辆集群编队控制

姬鹏霄¹(), 孔伟伟¹^,^*(), 罗禹贡², 于杰², 刘彦斌², 汪俊杰¹, 朱洧震¹, 梁伟铭³

1.中国农业大学工学院，北京 100083，中国
2.清华大学车辆与运载学院，北京 100084，中国
3.上海汽车集团股份有限公司技术中心，上海 201804，中国

收稿日期:2023-11-13 修回日期:2024-08-21 出版日期:2024-10-31 发布日期:2024-11-07
通讯作者: *孔伟伟，副教授。E-mail：kongweiwei@cau.edu.cn。
作者简介:姬鹏霄（2000—），男（汉），山东，博士研究生。E-mail：2316013818@qq.com。
基金资助:
国家自然科学基金创新研究群体项目(52221005);北京市科技新星计划(20220484040);上海汽车工业科技发展基金会项目(2301);汽车安全与节能国家重点实验室开放基金课题(KFY2210)

Formation control of vehicle clusters in multi-lane scenarios based on Multi-Agent System consensus

JI Pengxiao¹(), KONG Weiwei¹^,^*(), LUO Yugong², YU Jie², LIU Yanbin², WANG Junjie¹, ZHU Weizhen¹, LIANG Weiming³

1. College of Engineering, China Agricultural University, Beijing 100083, China
2. School of Vehicle and Transportation, Tsinghua University, Beijing 100084, China
3. Technical Center of Shanghai Automotive Group Co. Ltd, Shanghai 201804, China

Received:2023-11-13 Revised:2024-08-21 Online:2024-10-31 Published:2024-11-07

摘要/Abstract

摘要：

为安全、高效地控制智能网联汽车（CAV）编队，研究了基于多智能体系统（MAS）一致性的多车道、零散随机分布的车辆集群编队控制策略，并进行数值仿真验证。建立了基于交错式结构的车群期望几何拓扑，提出了包括间距调整阶段、变道控制阶段和队形收敛阶段的3阶段式编队控制流程。基于一致性理论，设计了车群编队控制器，并进行稳定性论证。选取2种典型场景，对三车道场景中初始分布较为极端的车群开展编队研究。结果表明：在该策略的控制过程中，纵向车间距与期望间距误差在0.5 m以内，横向位置与期望位置误差在5 cm以内，且速度在变化后能快速收敛至期望值。因而，该策略能有效控制车群车辆安全、高效地达到车群期望队形。

关键词: 智能网联汽车（CAV）, 车辆集群编队控制, 一致性控制, 多智能体系统（MAS）, 道路受限场景

Abstract:

A control strategy with multi-lane vehicle platoon formation was investigated for Connected-Automated-Vehicles (CAV) based on Multi-Agent System (MAS) consensus to solve the problem of safe and efficient formation control of multi-lane, randomly distributed vehicle clusters. A platoon's desired geometric topology was established based on an interleaved structure for the vehicle cluster with multiple lanes and scattered random distribution. A three-stage formation control process was proposed, including the spacing adjustment stage, lane change control stage, and formation convergence stage. A platoon formation controller was designed based on consensus theory, and the stability of the controller is demonstrated. Formation experiment of vehicle clusters was conducted in extreme initial distribution scenarios within a three-lane environment, with two typical scenarios being selected to perform numerical simulations and vehicle dynamics simulations of the formation process. The results show that for highly uneven and irregularly distributed vehicle clusters on multiple lanes, all vehicles are effectively controlled to achieve the desired formation in a safe and efficient manner. Throughout the control process, the longitudinal distance error between vehicles and the desired spacing is within 0.5 m, and the lateral position error relative to the desired position is within 5 cm, and the velocity quickly converges to the desired value after changes. Therefore, the strategy can effectively control the vehicles of the cluster to safely and efficiently achieve the desired formation.

Key words: connected-automated-vehicles (CAV), vehicle clusters formation control, consensus control, multi-agent system (MAS), restricted road scenario

中图分类号:

U461.99

姬鹏霄, 孔伟伟, 罗禹贡, 于杰, 刘彦斌, 汪俊杰, 朱洧震, 梁伟铭. 基于多智能体一致性的多车道车辆集群编队控制[J]. 汽车安全与节能学报, 2024, 15(5): 742-752.

JI Pengxiao, KONG Weiwei, LUO Yugong, YU Jie, LIU Yanbin, WANG Junjie, ZHU Weizhen, LIANG Weiming. Formation control of vehicle clusters in multi-lane scenarios based on Multi-Agent System consensus[J]. Journal of Automotive Safety and Energy, 2024, 15(5): 742-752.

图/表 15

参考文献 17

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车道内车辆数目(场景a)	N₁= 4、N₂= 3、N₃= 1
车道内车辆数目(场景b)	N₁= 4、N₂= 0、N₃= 4
车群期望车速	v_x_,exp= 20 m / s
初始纵向位置*	x_lane,_m(i) / m = 150 - (i - 1)rand([30,50])
初始横向位置*	y_lane,_m(i) / m = 4(m - 1).
初始纵向速度*	v_x_,lane,_m(i) / (m·s) = 20 + 5rand([-1,1])
初始横向速度*	v_y_,lane,_m(i) / (m·s) = 0.
车道宽度	W_lane= 4 m
间距调整、变道控制阶段期望车间距	D₁/ m = 3.2[v_x_,lane,_m / (m·s)]
收敛阶段期望车间距	D₂/ m = 1.7[v_x_,lane,_m / (m·s)]

车道内车辆数目(场景a)	N₁= 4、N₂= 3、N₃= 1
车道内车辆数目(场景b)	N₁= 4、N₂= 0、N₃= 4
车群期望车速	v_x_,exp= 20 m / s
初始纵向位置*	x_lane,_m(i) / m = 150 - (i - 1)rand([30,50])
初始横向位置*	y_lane,_m(i) / m = 4(m - 1).
初始纵向速度*	v_x_,lane,_m(i) / (m·s) = 20 + 5rand([-1,1])
初始横向速度*	v_y_,lane,_m(i) / (m·s) = 0.
车道宽度	W_lane= 4 m
间距调整、变道控制阶段期望车间距	D₁/ m = 3.2[v_x_,lane,_m / (m·s)]
收敛阶段期望车间距	D₂/ m = 1.7[v_x_,lane,_m / (m·s)]

仿真步长	h = 10 ms
初始纵向位置*	x_lane,_m(i) / m = 210 - 70(i - 1)
初始横向位置*	y_lane,_m(i) / m = 4(m - 1)
初始纵向速度*	v_x_,lane,_m(i) / (m·s) = 20 + rand([-1,1])
初始横向速度*	v_y_,lane,_m(i) / (m·s) = 0
控制器参数	k₀_x = 0.8，k₁_x = 0.8，k₀_y = 0.561，k₁_y = 1.4
车辆参数	C_f = C_r = 52 480，i_sw = 16.34，m_car = 1.575 t I_Z = 1.992 t m/s^-2，a = 1.2 m，b = 1.6 m

仿真步长	h = 10 ms
初始纵向位置*	x_lane,_m(i) / m = 210 - 70(i - 1)
初始横向位置*	y_lane,_m(i) / m = 4(m - 1)
初始纵向速度*	v_x_,lane,_m(i) / (m·s) = 20 + rand([-1,1])
初始横向速度*	v_y_,lane,_m(i) / (m·s) = 0
控制器参数	k₀_x = 0.8，k₁_x = 0.8，k₀_y = 0.561，k₁_y = 1.4
车辆参数	C_f = C_r = 52 480，i_sw = 16.34，m_car = 1.575 t I_Z = 1.992 t m/s^-2，a = 1.2 m，b = 1.6 m

基于多智能体一致性的多车道车辆集群编队控制

Formation control of vehicle clusters in multi-lane scenarios based on Multi-Agent System consensus

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