面向强制换道场景的智能网联汽车协同换道策略

doi:10.19562/j.chinasae.qcgc.2024.02.002

Abstract

Abstract:

Collaborative lane change technology for intelligent connected vehicles has been widely studied， but existing strategies can hardly solve the problem of vehicle collaboration in mandatory lane change scenarios or may cause notable impact on upstream traffic. For mandatory lane change scenarios demand， a two-stage cooperative lane change strategy considering theoretical minimal safety space is proposed in this paper. Firstly， the control architecture for a two-vehicle cooperative lane change system is proposed and a collaborative lane change scheme is developed for mandatory lane change scenarios. Then， a two-stage receding-horizon trajectory planning strategy of spacing adjustment and collaborative lane change is designed， where the theoretical minimum safe distance is embedded as a constraint of spacing adjustment stage， to solve the problem of conservative spacing strategies in existing research. Finally， numerical simulation and hardware in-loop experiments are performed to verify the effectiveness， advantages and computational real-time performance of the proposed strategy. The results show that the proposed strategy can effectively improve the success rate of lane change， reduce the negative traffic impact while ensuring lane change safety， and is also applicable in real time computing and communication environment of actual edge cloud platform.

Key words: intelligent and connected vehicles, cooperative lane change, mandatory lane change, trajectory planning

Shurui Guan,Keqiang Li,Junyu Zhou,Jia Shi,Weiwei Kong,Yugong Luo. A Cooperative Lane Change Strategy for Intelligent Connected Vehicles Oriented to Mandatory Lane Change Scenarios[J].Automotive Engineering, 2024, 46(2): 201-210.

Figures/Tables 18

参数符号	参数定义	参数取值
$T s$ /s	仿真步长	0.05
$T d$ /s	滚动时域步长	1.0
$t l c$ /s	换道时间	6.0
$ε$ /m	安全裕度	5.0
$a x, m a x$ /（m·s^-2）	加速度阈值	4.0
$j x, m a x$ /（m·s^-3）	加加速度阈值	2.0
$L c a r$ /m	轿车车长	5.2
$W c a r$ /m	轿车车宽	2.0
$L t r u c k$ /m	货车车长	6.0
$W t r u c k$ /m	货车车宽	2.4
$W l a n e$ /m	车道宽	3.5
$w v$	权重系数	0.1
$w t$	权重系数	0.05
$w p$	权重系数	0.01
$v d e s$ /（km·h^-1）	期望速度	40

参数	工况1	工况2	工况3
$? v$ /（km·h^-1）	20	20	5
$O L H$ /m	50	50	30
$T L H$ /m	20	30	20
$d C 1 - C 2$ /m	20	10	0
$d C 1 - H 3$ /m	0	0	25
$d H 3 - H 4$ /m	15	15	50

符号	参数定义	取值范围	数量
$O L H$	原车道初始车距	［30 m， 80 m］	10
$T L H$	目标车道初始车距	［15 m， 40 m］	10
$? v$	C2与前车初始速度差	［0， 20 km/h］	4
$d C 1 - C 2$	C1、C2初始纵向距离	［0， 30 m］	10

参数	$T S C L C$	$P C L C$	改善
$v l o s s$ /（km·h^-1）	19.5	2.82	85.5%
$a m i n$ /（m·s^-2）	1.086 8	0.400 5	63.1%
$v m e a n$ /（km·h^-1）	30.57	33.38	9.2%

References 16

1	LI T， WU J， CHAN C Y， et al. A cooperative lane change model for connected and automated vehicles［J］. IEEE Access， 2020， 8： 54940-54951.
2	杨澜，赵祥模，吴国垣，等.智能网联汽车协同生态驾驶策略综述［J］.交通运输工程学报，2020，20（5）：58-72.
	YANG L， ZHAO X M， WU G Y， et al. Review on connected and automated vehicles based cooperative eco-driving strategies［J］. Journal of Traffic and Transportation Engineering， 2020，20（5）：58-72.
3	DU R， CHEN S， LI Y， et al. A cooperative control framework for CAV lane change in a mixed traffic environment［J］. arXiv preprint arXiv：， 2020.
4	BAI Y， ZHANG Y， HU J. A motion planner enabling cooperative lane changing： reducing congestion under partially connected and automated environment［J］. Journal of Intelligent Transportation Systems， 2021， 25（5）： 469-481.
5	WANG D， HU M， WANG Y， et al. Model predictive control–based cooperative lane change strategy for improving traffic flow［J］. Advances in Mechanical Engineering， 2016， 8（2）： 1687814016632992.
6	WANG Z， ZHAO X， CHEN Z， et al. A dynamic cooperative lane-changing model for connected and autonomous vehicles with possible accelerations of a preceding vehicle［J］. Expert Systems with Applications， 2021， 173： 114675.
7	SUN K， ZHAO X， WU X. A cooperative lane change model for connected and autonomous vehicles on two lanes highway by considering the traffic efficiency on both lanes［J］. Transportation Research Interdisciplinary Perspectives， 2021， 9： 100310.
8	杨刚，张东好，李克强，等.基于车车通信的车辆并行协同自动换道控制［J］.公路交通科技，2017，34（1）：120-129，136.
	YANG G， ZHANG D H， LI K Q， et al. Cooperative same-direction automated lane-changing based on vehicle-to-vehicle communication［J］. Journal of Highway and Transportation Research， 2017，34（1）：120-129，136.
9	李娟，曲大义，万孟飞，等.基于最小安全距离的车辆交叉换道模型研究［J］.青岛理工大学学报，2017，38（1）：106-112.
	LI J， QU D Y， WAN M F， et al. Research on cross lane-changing model of vehicle based on minimum safety spacing［J］. Journal of Qingdao University of Technology， 2017，38（1）：106-112.
10	LI B， ZHANG Y， FENG Y， et al. Balancing computation speed and quality： a decentralized motion planning method for cooperative lane changes of connected and automated vehicles［J］. IEEE Transactions on Intelligent Vehicles， 2018， 3（3）： 340-350.
11	刘志强，韩静文，倪捷.智能网联环境下的多车协同换道策略研究［J］.汽车工程，2020，42（3）：299-306.
	LIU Z Q， HAN J W， NI J. Study on multi-vehicle coordinated lane change strategy under network conditions［J］. Automotive Engineering， 2020，42（3）：299-306.
12	柏海舰，申剑峰，卫立阳.无人车“三阶段”换道轨迹规划过程分析［J］.合肥工业大学学报（自然科学版），2019，42（5）：577-584，676.
	BAI H J， SHEN J F， WEI L Y. Three-stage lane-changing trajectory planning method for automated vehicles［J］. Journal of Hefei University of Technology（Natural Science）， 2019，42（5）：577-584，676.
13	李克强，常雪阳，李家文，等.智能网联汽车云控系统及其实现［J］.汽车工程，2020，42（12）：1595-1605.
	LI K Q， CHANG X Y， LI J W， et al. Cloud control system for intelligent and connected vehicles and its application［J］. Automotive Engineering， 2020，42（12）：1595-1605.
14	YOU F， ZHANG R， LIE G， et al. Trajectory planning and tracking control for autonomous lane change maneuver based on the cooperative vehicle infrastructure system［J］. Expert Systems with Applications， 2015， 42（14）： 5932-5946.
15	FHWA， Department of Transportation， America. NGSIM： next generation simulation［EB/OL］.（2007-5-5）［2023-04-23］.http：//www.ngsim-community.org/.
16	WANG J， ZHENG Y， LI K， et al. DeeP-LCC： data-enabled predictive leading cruise control in mixed traffic flow［J］. arXiv preprint arXiv：， 2022.

[1]	ZHOU Wei, ZHANG Cheng-Ning, LI Jun-Qiu. [J]. , 2016, 38(12): 1407 -1414 .
[2]	HAO Han, WANG Si-南, LI Xiao, LIU Zong-Wei, ZHAO Fu-Quan. [J]. , 2017, 39(1): 1 -8 .
[3]	ZONG Yi-Qi, GU Zheng-Qi, LUO Ze-Min, JIANG Cai-Mao, ZHANG Qi-Dong, YANG Zhen-Dong. [J]. , 2016, 38(12): 1427 -1433 .
[4]	XU Cheng-Shan, JIANG Fa-Chao, SONG Sen-Nan, TIAN Guang-Yu. [J]. , 2017, 39(1): 9 -14 .
[5]	ZENG Bi-Qiang, GAO Ji-Dong, PENG Wei, SUN Zhen-Dong. [J]. , 2016, 38(12): 1446 -1451 .
[6]	LI Ling, MA Li, MOU Yu, XU Chao, LI Wen-Ru, SHI Shu-Ming. [J]. , 2016, 38(12): 1508 -1514 .
[7]	CUI An, LI Bin, WANG Xue-Liang, ZHANG Shi-Zhan. [J]. , 2016, 38(12): 1521 -1525 .
[8]	QIN Xiao-Hui, WANG Jian-Qiang, XIE Bo-Yuan, HU Man-Jiang, LI Ke-Qiang. [J]. , 2017, 39(1): 73 -78 .
[9]	ZHAO Liang, ZHANG Qiang, GUO Kong-Hui. [J]. , 2017, 39(1): 86 -91 .
[10]	YU Zhuo-Ping, HAN Wei, XIONG Lu. [J]. , 2017, 39(1): 52 -60 .

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A Cooperative Lane Change Strategy for Intelligent Connected Vehicles Oriented to Mandatory Lane Change Scenarios

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