A Molecular Dynamics-based Car-following Model for Connected and
Automated Vehicles Considering Impact of Multiple Vehicles

doi:10.16097/j.cnki.1009-6744.2022.01.005

Abstract

Abstract: The car- following control of connected and automated vehicles (CAV) is studied under mixed traffic flow with regular vehicles. Considering the factors of velocity, headway, and the velocity and acceleration difference of multiple front and rear vehicles, this paper constructed a car- following model for CAVs by using the molecular dynamics to express the impacts of different surrounding vehicles on the host one. The car-following process of CAVs was then described when driving in the traffic mixed with CAVs and regular human- driving vehicles. The results of stability analysis show that, compared with the full speed difference model, the proposed model is more conducive to improving the stability of traffic flow due to the consideration of multiple front and rear vehicles' information. The simulation results indicate that, compared with the Cooperative adaptive cruise control (CACC) model provided by PATH laboratory, the average maximum error of our model is reduced by 0.19 m/s, the average error is reduced by 26.79%, and the fitting accuracy is improved by 0.91%. Besides, in the traffic flow mixed with CAV and RV, with the increase of CAV penetration, the average velocity of the platoon and volume increase gradually. The results of Hysteresis loops show that compared with the Full Velocity Difference (FVD) model, the stability of traffic flow under the CAV model in this paper is better. The proposed model can serve as an effective method for CAV control under both homogeneous traffic flow or heterogeneous flow mixed with CAVs and human- driving vehicles. Under the situation that it is difficult to carry out the field test of heterogeneous flow mixed with CAVs and human-driving vehicles, this study provides a theoretical basis and model support for vehicle control as well as infrastructure planning and design.

Key words: traffic engineering, traffic simulation, molecular dynamics, connected and automated vehicle, multiple front and rear vehicles, stability analysis

摘要： 为研究含智能网联汽车(Connected and Automated Vehicle, CAV)和人工驾驶汽车(Regular Vehicle, RV)混行交通流下CAV跟驰行为的控制问题，考虑前后多车的速度、车头间距、速度差、加速差等参数，采用分子动力学定量表达不同周边车辆对主体车的影响，得到可用于描述CAV在混行交通流中的跟驰过程。稳定性分析结果表明，与全速度差模型相比，本文提出的考虑前后多车信息的CAV跟驰模型有利于提高交通流的稳定性。数值仿真与模型验证结果表明，与PATH 实验室的CACC(Cooperative Adaptive Cruise Control)模型相比，本文建立的CAV跟驰模型平均速度最大误差减小了0.19 m∙s-1 ，平均误差减小26.79%，拟合精度提高了0.91%。同时，在CAV和 RV组成的混行交通流中，随着CAV比例的逐渐增加，车队的平均速度和交通流量逐渐增加。迟滞回环曲线表明，与全速度差(Full Velocity Difference, FVD)模型相比，本文提出的CAV模型控制下的交通流稳定性更强。该模型可用于同质流或CAV与人工驾驶车辆等混行环境下的CAV跟驰控制，在目前开展混行实车实验困难的情况下，为混行交通流场景下的车辆控制及交通设施规划设计提供理论依据和模型支持。

关键词: 交通工程, 交通仿真, 分子动力学, 智能网联汽车, 前后多车, 稳定性分析

CLC Number:

U491.1

ZONG Fang , WANG Meng, HE Zheng-bing. A Molecular Dynamics-based Car-following Model for Connected and Automated Vehicles Considering Impact of Multiple Vehicles[J]. Journal of Transportation Systems Engineering and Information Technology, 2022, 22(1): 37-48.

宗芳, 王猛, 贺正冰. 考虑多车影响的分子动力学智能网联跟驰模型[J]. 交通运输系统工程与信息, 2022, 22(1): 37-48.

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URL: http://www.tseit.org.cn/EN/10.16097/j.cnki.1009-6744.2022.01.005

http://www.tseit.org.cn/EN/Y2022/V22/I1/37

References

[1] 贺正冰. 微观交通模型: 智能网联化转型与通用驾驶人模型框架[J/OL]. 交通运输工程与信息学报: 1-17, [2022- 01- 21]. DOI:10.19961/j.cnki.1672- 4747. 2021. 11.009. [HE Z B. A review of microscopic traffic models enhanced by connected environment and a generalized driver model[J]. Journal of Transportation Engineering and Information: 1-17, [2022-01-21]. DOI:10.19961/j. cnki.1672-4747.2021.11.009].

[2] 祁宏生, 应雨燕, 林俊山, 等. 混合自动驾驶场景多换道需求下的主动间隙适配和换道序列规划[J]. 交通运输工程与信息学报, 2021, 4(31): 1-19. [QI H S, YING Y Y, LIN J S, et al. Proactive gap adaption and sequence planning or multiple lane changing requests under mixed autonomous vehicle flow[J]. Journal of Transportation Engineering and Information, 2021, 4(31): 1-19].

[3] 慈玉生, 韩应轩, 吴丽娜. 车联网环境下快速路出口匝道与地面衔接区多阶段控制方法[J].交通运输工程与信息学报, 2021, 4(33): 1-11. [CI Y S, HAN Y X, WU L N. Multi-stage control method for the urban expressway off-ramp and round road adjacent area under connected vehicle environment[J]. Journal of TransportationEngineering and Information, 2021, 4(33): 1-11].

[4] 李克强, 戴一凡, 李升波, 等. 智能网联汽车(ICV)技术的发展现状及趋势[J]. 汽车安全与节能学报, 2017, 8 (1): 1-14. [LI K Q, DAI Y F, LI S B, et al. State-of-theart and technical trends of intelligent and connected vehicles[J]. Automotive Safety and Energy, 2017, 8(1): 1- 14.]

[5] 郭戈, 许阳光, 徐涛, 等. 网联共享车路协同智能交通统综述[J]. 控制与决策, 2019, 34(11): 2375-2389. [GUO G, XU Y G, XU T, et al. A survey of connected shared vehicle-road cooperative intelligent transportation systems[J]. Control and Decision, 2019, 34(11): 2375- 2389.]

[6] YANG Y M, JIANG R, HU M B, et al. Traffic flow characteristics in a mixed traffic system consisting of ACC vehicles and manual vehicles: A hybrid modelling approach[J]. Physica A, 2009, 388: 2483-2491.

[7] ZHENG Y, LI S E, LI K, et al. Platooning of connected vehicles with undirected topologies: robustness analysis and distributed h-infinity controller synthesis[J]. IEEE Transactions on Intelligent Transportation Systems, 2018, 19(5): 1353-1364.

[8] MILAN´ES V, SHLADOVER S E, SPRING J, et al. Cooperative adaptive cruise control in real traffic situations[J]. IEEE Transactions on Intelligent Transportation Systems, 2014, 15(1): 296-305.

[9] 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 (1): 66241-66252.

[10] XIE D F, ZHAO X M, HE Z B. Heterogeneous traffic mixing regular and connected vehicles: Modeling and stabilization[J]. IEEE Transactions on Intelligent Transportation Systems, 2019, 20(6): 2060-2071.

[11] ZHANG X, JARRETT D F. Stability analysis of the classical car-following model[J]. Transportation Research B, 1997, 31: 441-462.

[12] 曲大义, 邴其春, 贾彦峰, 等. 基于分子动力学的车辆换道交互行为特性及其模型[J]. 交通运输系统工程与信息, 2019, 19(3): 68-74. [QU D Y, BING Q C, JIA Y F, et al. Molecular dynamics characteristics and models of vehicle lane changing interaction behavior[J]. Journal of Transportation Systems Engineering and Information Technology, 2019, 19(3): 68-74.]

[13] 李娟, 曲大义, 刘聪, 等. 基于分子动力学的跟驰特性及其模型[J]. 公路交通科技, 2018, 35(3): 130-135. [LI J, QU D Y, LIU C, et al. Car- following characteristics and its models based on molecular dynamics[J]. Journal of Highway and Transportation Research and Development, 2018, 35(3): 130-135.]

[14] KNORR F, SCHRECKENBERG M. Influence of intervehicle communication on peak hour traffic flow[J]. Physica A: Statistical Mechanics and its Applications, 2012, 391(6): 2225-2231.

[15] 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 carfollowing model considering the states of multiple front and rear vehicles[J]. China Journal of Highway and Transport, 2021, 34(7): 105-117.]

[16] 唐亮, 孙棣华, 彭光含. 基于多车信息的交通流跟驰模型与数值仿真[J]. 系统仿真报, 2012, 24(2): 293-296. [TANG L, SUN D H, PENG G H. Car-following model of traffic flow and numerical simulation based on multiple information of preceding cars[J]. Journal of System Simulation, 2012, 24 (2): 293-296.]

[17] SUN D H, LIAO X Y, PENG G H. Effect of looking backward on traffic flow in an extended multiple carfollowing model[J]. Physica A, 2011, 390: 631-635.

[18] GE H X, ZHU H B, DAI S Q. Effect of looking backward on traffic flow in a cooperative driving car following model [J]. Eur Physica B, 2006, 54(4): 503-507.

[19] JIANG R, WU Q S, ZHU Z J. Full velocity difference model for a car-following theory[J]. Physica. Review, E, 2001, 64(1): 017101.

[20] 蔡晓禹, 蔡明, 张有节, 等. 基于车联网环境的驾驶员反应时间研究[J]. 计算机应用, 2017, 37(2): 270-273. [CAI X Y, CAI M, ZHANG Y J, et al. Research on driver reaction time in Internet of vehicles environment[J]. Journal of Computer Applications, 2017, 37(2): 270- 273.]

[21] PENG G. Stabilisation analysis of multiple car-following model in traffic flow[J]. Chinese Physics B, 2021, 19(5): 434-441.

[22] WANG J, WU J, LI Y. Concept, principle and modeling of driving risk field based on driver-vehicleroad interaction[J]. China Journal of Highway and Transport, 2016, 29(1): 105-114.

[23] 陈秀锋. 基子分子动力学的车辆运行安全特性研究 [D]. 长春: 吉林大学, 2013. [CHEN X F. A study on vehicle operating safety characteristics based on molecular dynamics[D]. Changchun: Jilin University, 2013.]

[24] 曲大义, 杨建, 陈秀锋, 等. 车辆跟驰的分子动力学特性及其模型[J]. 吉林大学学报(工学版), 2012, 42 (5): 1198-1202. [QU D Y, YANG J, CHEN X F, et al. Molecualar kinetics behavior of car-following and its model[J]. Journal of Jilin University (Engineering and Technology Edition), 2012, 42(5): 1198-1202.]

[25] 李萌萌. 微通道气体流动的分子动力学模拟[D]. 西安: 西安电子科技大学, 2005. [LI M M. Molecule dynamics simulation on gas flow in microchannels[D]. Xi'an: Xidian University, 2005.]

[26] 石蕊. 信号控制交叉口行车场建立及车辆通行行为优化[D]. 长春: 吉林大学, 2021. [SHI R. The construction of risk field and optimization of driving behaviors for signalized intersections[D]. Changchun: Jilin University, 2021.]

[27] 李林恒, 甘婧, 曲栩, 等. 智能网联环境下基于安全势场理论的车辆跟驰模型[J]. 中国公路学报, 2019, 32 (12): 80-91. [LI L H, GAN J, QU X, et al. Car-following model based on safety potential field theory under connected and automated vehicle environment[J]. China Journal of Highway and Transport, 2019, 32 (12): 80-91.]

[28] 曲昭伟, 潘昭天, 陈永恒, 等. 基于最优速度模型的改进安全距离跟驰模型[J]. 吉林大学学报(工学版), 2019, 49(4): 1092-1099. [QU S W, PAN S T, CHEN Y H, et al. Car-following model with improving safety distance based on optimal velocity model[J]. Journal of Jilin University (Engineering and Technology Edition), 2019, 49(4): 1092-1099.]

[29] 赵顺. 基于修正优化速度函数的多前车跟驰模型研究 [D]. 哈尔滨: 哈尔滨工业大学, 2016. [ZHAO S. Carfollowing model based on optimal velocity function and multiple proceeding vehicles[D]. Harbin: Harbin Institute of Technology, 2016.]

[30] LI Y F, SUN D H, LIU W N, et al. Modeling and simulation for microscopic traffic flow based on multiple headway, velocity and acceleration difference[J]. Nonlinear Dynamics, 2011, 66(1/2): 15-28.]

[31] 秦严严, 王昊, 王炜. 自适应巡航控制车辆跟驰模型综述[J]. 交通运输工程学报, 2017, 17(3): 121-130. [QIN Y Y, WANG H, WANG W. Review of car- following models of adaptive cruise control[J]. Journal of Traffic and Transportation Engineering, 2017, 17(3): 121-130.]

[32] SHLADOVER S E, SU D Y, LU X Y. Impacts of cooperative adaptive cruise control on freeway traffic flow [J]. Transportation Research Record, 2012, 2324(1): 63- 70.

[33] VANDERWERF J, SHLADOVRE S, KOURJANSKAIA N. Modeling effects of driver control assistance systems on traffic[J]. Transportation Research Record, 2001, 1748 (1): 167-174.

A Molecular Dynamics-based Car-following Model for Connected and Automated Vehicles Considering Impact of Multiple Vehicles

考虑多车影响的分子动力学智能网联跟驰模型

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