基于多车状态变化特征的网联车跟驰模型

doi:10.19562/j.chinasae.qcgc.2023.08.002

Abstract

Abstract:

For the unstable problem of traffic flow caused by the unidirectional and bidirectional abrupt change of speed of the preceding vehicle， a car following model for connected vehicles based on Multiple Vehicles with State Change Features （MVSCF） is proposed. Firstly， the acceleration difference change characteristics of multiple preceding vehicles and the optimized estimation of speed expectation are introduced in to improve the model of Multiple Vehicles Changes with Memory （MVCM）. Secondly， the critical stability conditions of MVSCF model are obtained by utilizing the micro perturbation method and the reductive perturbation method， respectively. Meanwhile， for the multiple preceding vehicles， the acceleration difference coefficient k， the preceding vehicle quantity q and the optimal speed weight ε are deduced in the circular road scenario， respectively. Finally， the traffic flow stability effect of the MVSCF model is simulated and analyzed in the straight road scenario under the influence of non-stationary change of the preceding vehicle speed. The simulation results show that when the speed of the preceding vehicle is influenced by either unidirectional or bidirectional abrupt change， the MVSCF model can better absorb the disturbance from the preceding vehicle， with the peak-to-valley difference of speed change and the fluctuation amplitude of acceleration reduced， which is conducive to improving the stability of traffic flow.

Key words: connected vehicles, car-following model, optimal speed, the micro perturbation method, the reductive perturbation method

Xin Shi,Jian Zhu,Xiangmo Zhao,Fei Hui,Junyan Ma. Car-Following Model for Connected Vehicles Based on Multiple Vehicles with State Change Features[J].Automotive Engineering, 2023, 45(8): 1309-1319.

Figures/Tables 13

参数名称及符号	数值或取值范围
安全车头间距h_c	4 m
时间步长Δt	0.1 s
最优速度敏感系数α	0.41
速度差敏感系数 $λ'$	0.6
最优速度记忆敏感系数 $γ i$	0.2
多前车数q	［1， 2， 3， 4］
多前车最优速度权重ε	［1.0， 0.9， 0.8， 0.7］
多前车加速度差敏感系数k	［0， 0.1， 0.2， 0.3， 0.4， 0.5］

k值	$v m a x$ / （m·s^-1）	$v a v e$ / （m·s^-1）	$v m i n$ / （m·s^-1）	$R u p$ /%	$R d n$ /%
k=0	2.19	1.26	0.69	73.81	45.24
k=0.1	1.96	1.22	0.70	60.66	42.62
k=0.2	1.75	1.17	0.72	49.57	38.46
k=0.3	1.28	1.08	0.87	18.52	19.44
k=0.4	1.46	1.11	0.80	31.53	27.93
k=0.5	1.45	1.09	0.79	33.03	27.52

References 21

1	赵祥模，惠飞，史昕，等. 泛在交通信息服务系统的概念，架构与关键技术［J］. 交通运输工程学报， 2014， 14（4）： 105-115.
	ZHAO X M， HUI F， SHI X， et al. Concept， architecture and key technology of ubiquitous traffic information service system［J］. Journal of Traffic and Transportation Engineering， 2014， 14（4）： 105-115.
2	梁军，王军，杨云庆，等. 网联车对抗神经网络跟驰模型［J］. 汽车工程， 2021， 43（4）： 588-600.
	LIANG J， WANG J， YANG Y Q， et al. A connected and autonomous vehicle following model based on generative adversarial network［J］. Automotive Engineering， 2021， 43（4）： 588-600.
3	BANDO M， HASEBE K， NAKAYAMA A， et al. Dynamical model of traffic congestion and numerical simulation［J］. Physical Review E， 1995， 51（2）： 1035-1042.
4	HELBING D， TILCH B. Generalized force model of traffic dynamics［J］. Physical Review E， 1998， 58（1）： 133-138.
5	JIANG R， WU Q， ZHU Z. Full velocity difference model for a car-following theory［J］. Physical Review E， 2001， 64： 017101.
6	PENG G， LU W， HE H， et al. Nonlinear analysis of a new car-following model accounting for the optimal velocity changes with memory［J］. Communications in Nonlinear Science and Numerical Simulation， 2016， 40： 197-205.
7	MA M H， MA G Y， LIANG S D. Density waves in car-following model for autonomous vehicles with backward looking effect［J］. Applied Mathematical Modelling， 2021， 94： 1-12.
8	安树科，徐良杰，钱良辉，等. 考虑前方多车优化速度信息的车辆跟驰模型［J］. 东南大学学报， 2020， 50（6）： 1156-1162.
	AN S K， XU L J， QIAN L H， et al. Car-following model with optimal velocity information of multiple-vehicle ahead［J］. Journal of Southeast University， 2020， 50（6）： 1156-1162.
9	WANG S Y， YU B， WU M Y. MVCM car-following model for connected vehicles and simulation-based traffic analysis in mixed traffic flow［J］. IEEE Transactions on Intelligent Transportation Systems， 2021， 99： 1-8.
10	PENG G H. Stabilization analysis of multiple car-following model in traffic flow［J］. Chinese Physics B， 2010， 19（5）： 434-441.
11	WANG L， HORN B K. On the stability analysis of mixed traffic with vehicles under car-following and bilateral control［J］. IEEE Transactions on Automatic Control， 2020， 65： 3076-3083.
12	GONG H， LIU H， WANG B H. An asymmetric full velocity difference car-following model［J］. Physica A： Statistical Mechanics and its Applications， 2008， 387（11）： 2595-2602.
13	宗芳，石佩鑫，王猛，等. 考虑前后多车的网联自动驾驶车辆混流跟驰模型［J］. 中国公路学报， 2021， 34（7）： 105-117.
	ZONG F， SHI P X， WANG M， et al. Connected and automated vehicle mixed-traffic car-following model considering states of multiple front and rear vehicles［J］. China Journal of Highway and Transport， 2021， 34（7）： 105-117.
14	孙亮，于建均，龚道雄. 线性系统理论基础［M］. 北京：北京工业大学出版社， 2006： 136-168.
	SUN L， YU J J， GONG D X. Theoretical basis of linear system［M］. Beijing： Beijing University of Technology Press， 2006： 136-168.
15	GE H X， CHENG R J， LI Z P. Two velocity difference model for a car following theory［J］. Physica A： Statistical Mechanics and its Applications， 2012， 387（21）： 5239-5245.
16	ZHANG J J， LU G Q， YU H Y， et al. Effect of the uncertainty level of vehicle-position information on the stability and safety of the car-following process ［J］. IEEE Transactions on Intelligent Transportation Systems， 2020， 99： 1-15.
17	HUANG X， SUN J， SUN J. A car-following model considering asymmetric driving behavior based on long short-term memory neural networks［J］. Transportation Research Part C， 2018， 95： 346-362.
18	李修云，周桐，杨智勇. 基于最紧邻前车加速度信息的跟驰模型［J］. 重庆大学学报， 2015， 38（1）： 153-158.
	LI X Y， ZHOU T， YANG Z Y. Car-following model based on the information of the nearest-neighbor leading car's acceleration［J］. Journal of Chongqing University， 2015， 38（1）： 153-158.
19	纪艺，史昕，赵祥模. 基于多前车信息融合的智能网联车辆跟驰模型［J］. 计算机应用， 2019， 39（12）： 3685-3690.
	JI Y， SHI X， ZHAO X M. Car-following model for intelligent connected vehicles based on multiple headway information fusion［J］. Journal of Computer Applications， 2019， 30（12）： 3685-3690.
20	MA G Y， MA M H， LIANG S D， et al. Nonlinear analysis of the car-following model considering headway changes with memory and backward looking effect［J］. Physica A： Statistical Mechanics and its Applications， 2021， 562： 125303.
21	ZONG F， WANG M， TANG M， et al. An improved intelligent driver model considering the information of multiple front and rear vehicles［J］. IEEE Access， 2021， 9： 66241-66252.

[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]	XU Cheng-Shan, JIANG Fa-Chao, SONG Sen-Nan, TIAN Guang-Yu. [J]. , 2017, 39(1): 9 -14 .
[4]	ZENG Bi-Qiang, GAO Ji-Dong, PENG Wei, SUN Zhen-Dong. [J]. , 2016, 38(12): 1446 -1451 .
[5]	DONG Zhu-Rong, ZHANG Xin, HU Song-Hua, QIU Hao. [J]. , 2017, 39(1): 79 -85 .
[6]	YU Zhuo-Ping, HAN Wei, XIONG Lu. [J]. , 2017, 39(1): 52 -60 .
[7]	ZUO Wen-Jie, CHEN Ji-Shun, LI Yi-Wen, LIU Nian. [J]. , 2017, 39(2): 145 -149 .
[8]	ZHANG Guan-Jun, ZHAO Xin-Feng, CAO Li-Bo. [J]. , 2017, 39(2): 150 -158 .
[9]	Chen-Xin, FENG Xiao, SHEN Chuan-Liang, WANG Shuo, HU Cui-Song, LI Yan-Yang, YANG Chang-Hai-　. [J]. , 2017, 39(2): 206 -213 .
[10]	ZHANG Yong, GU Zheng-Qi, LIU Shui-Chang, MI Cheng-Ji. [J]. , 2017, 39(3): 275 -280 .

模型	v_ave/ （m·s^-1）	v_pt/ （m·s^-1）	D_v/ （m·s^-1）	a_ave/ （m·s^-2）	D_a/ （m·s^-2）
OVCM	5.21	3.59	3.92	-2.72	1.85
ITVDM	4.58	2.87	2.71	-2.04	1.36
MVCM	4.17	1.94	2.53	-1.75	0.92
MFRHVAD	3.76	1.25	1.83	-1.16	0.65
MVSCF	3.49	0.86	1.45	-0.92	0.48

模型	v_ave/ （m·s^-1）	v_pt/ （m·s^-1）	D_v/ （m·s^-1）	a_ave/ （m·s^-2）	D_a/ （m·s^-2）
OVCM	9.23	12.31	1.29	2.13	2.08
ITVDM	10.85	12.03	0.83	1.62	1.74
MVCM	11.67	11.75	0.68	1.34	1.16
MFRHVAD	11.80	10.96	0.35	1.18	0.61
MVSCF	11.89	10.84	0.15	0.96	0.35

Car-Following Model for Connected Vehicles Based on Multiple Vehicles with State Change Features

RichHTML

PDF (PC)

Like

Knowledge

Abstract

Cite this article

share this article

Figures/Tables 13

References 21

Related Articles 9

Metrics

Comments

Recommended 10

[1]	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.
[2]	Pangwei Wang,Cheng Liu,Yunfeng Wang,Mingfang Zhang. Multi-lane Trajectory Optimization for Intelligent Connected Vehicles in Urban Road Network [J]. Automotive Engineering, 2024, 46(2): 241-252.
[3]	Zheng Zuo,Yunpeng Wang,Bin Ma,Bosong Zou,Yaoguang Cao,Shichun Yang. Quantitative Evaluation and Analysis of On-board Network Components Risk Rate Based on AFC-TARA [J]. Automotive Engineering, 2023, 45(9): 1553-1562.
[4]	Wenqin Zhong,Weiwei Kong,Zhiheng Li,Jie Yu,Yugong Luo. Reservation Based Multi-Vehicle Cooperative Control at Traffic-Lightless Intersection Under Different Penetration of Mixed Traffic Flow [J]. Automotive Engineering, 2022, 44(8): 1144-1152.
[5]	Pengfei Li,Yugong Luo,Chang Liu,Weiwei Kong. Control Strategies Design of Intelligent and Connected Vehicle Platoon Under Emergency Conditions [J]. Automotive Engineering, 2022, 44(3): 299-307.
[6]	Yisong Chen,Yunxiang Xing,Xiaoqin Xiong,Libo Lan,Ying Cao,Yongtao Liu. Research on Technical and Economic Evaluation System of Intelligent Connected Vehicles Based on Patent Analysis [J]. Automotive Engineering, 2021, 43(9): 1271-1277.
[7]	Biao Yang,Fucheng Fan,Jicheng Yang,Yingfeng Cai,Hai Wang. Recognition of Pedestrians’ Street⁃crossing Intentions Based on Action Prediction and Environment Context [J]. Automotive Engineering, 2021, 43(7): 1066-1076.
[8]	Li Keqiang, Chang Xueyang, Li Jiawen, Xu Qing, Gao Bolin, Pan Jian. Cloud Control System for Intelligent and Connected Vehicles and Its Application [J]. Automotive Engineering, 2020, 42(12): 1595-1605.
[9]	Yan Gang, Xiao Kun, Chu Wenbo. Research on Virtualization Technology for ComputingPlatform of Intelligent Connected Vehicles [J]. Automotive Engineering, 2020, 42(1): 33-37.