Enhancing Digital Twin Model for Mixed-Autonomy Traffic in Stability Analysis

doi:10.19678/j.issn.1000-3428.0070245

Abstract

Abstract:

In a hybrid autonomous traffic scenario, where intelligent Connected Automated Vehicles (CAV) and Human-driven Vehicles (HV) coexist in a 6G network environment, vehicles automatically form a queue. By reducing the distance between vehicles, the traffic volume on the road can be increased, and the stability of the resulting fleet is worth studying. Fleet stability ensures driving safety between vehicles and alleviates traffic congestion. A hybrid autonomous traffic stability analysis method based on Digital Twin (DT) technology is proposed using an enhanced DT model to evaluate system performance without interrupting the current traffic state. First, considering environmental and vehicle transmission system factors such as weather conditions, road conditions, loads, and transmissions, as well as communication delays between CAV and their DT, based on the vehicle transmission system and longitudinal dynamics, an accurate and interpretable enhanced DT model is constructed in a model-driven manner. This model improves the efficiency, reliability, and safety of intelligent transportation. Subsequently, stability and series stability analyses are conducted on the constructed enhanced DT system, and the critical delay for the stability of the hybrid autonomous transportation system and the control gain conditions for the series stability of the CAV are derived. Finally, we analyze the impact of environmental data bias on enhanced DT systems in different traffic states and determine the effective parameter range for DT predictability. The numerical simulation results show that the proposed method can quickly determine the stability of hybrid autonomous transportation systems and obtain an effective parameter range for DT predictability.

Key words: mixed-autonomy traffic, Digital Twin (DT), string stability, complexity, critical delay

摘要：

在6G网络环境中的智能网联车(CAV)和人工驾驶车(HV)共存的混合自治交通场景下, 车辆自动形成跟驰车队列, 通过缩小车辆的间距可以提高道路的通行量, 而车队的稳定性问题值得研究。车队的稳定性既能保证车辆间的驾驶安全, 又能缓解交通拥堵。基于数字孪生(DT)技术, 提出一种增强型DT模型的混合自治交通稳定性分析方法, 在不中断正在进行的交通状态的情况下评估系统性能。首先, 考虑环境和车辆传动系统因素, 如天气情况、路面情况、载荷、传动等, 以及CAV与DT的通信延迟, 基于车辆传动系统和纵向动力学, 以模型驱动的方式构建精确化、可解释的增强型DT模型, 从而提高智能交通的通行效率、可靠性和安全性。然后, 对所构建的增强型DT系统进行稳定性和串稳定性分析, 推导出混合自治交通系统稳定性的临界时延和串稳定性的CAV的控制增益条件。最后, 分析环境数据的偏差对不同状态的增强型DT系统的影响, 判断DT可预测性的有效参数范围。数值仿真实验结果表明, 该方法能快速判断混合自治交通系统的稳定性, 并获得DT可预测性的有效参数范围。

关键词: 混合自治交通, 数字孪生, 串稳定性, 复杂性, 临界时延

WANG Xiaoxu, WU Maoqiang, KANG Jiawen, YU Rong. Enhancing Digital Twin Model for Mixed-Autonomy Traffic in Stability Analysis[J]. Computer Engineering, 2026, 52(4): 398-408.

王晓旭, 吴茂强, 康嘉文, 余荣. 增强型数字孪生模型的混合自治交通的稳定性分析[J]. 计算机工程, 2026, 52(4): 398-408.

/ Recommend / Download Citations

URL: https://www.ecice06.com/EN/10.19678/j.issn.1000-3428.0070245

https://www.ecice06.com/EN/Y2026/V52/I4/398

Figures/Tables 13

Fig.1 The enhancing DT system framework for mixed-autonomy traffic

Fig.2 The enhancing DT mixed-autonomy traffic system under string stability states

Fig.3 The enhancing DT mixed-autonomy traffic system under boundary string stability states

Fig.4 The enhancing DT mixed-autonomy traffic system under complexity characteristic states

Fig.5 Trajectory state of the enhancing DT mixed-autonomy traffic system in string stability with (k₁-δ)

Fig.6 Trajectory state of the enhancing DT mixed-autonomy traffic system in string stability with (k₁+δ)

Fig.7 Trajectory state of the enhancing DT mixed-autonomy traffic system in boundary string stability with (k₁-δ)

Fig.8 Trajectory state of the enhancing DT mixed-autonomy traffic system in boundary string stability with (k₁+δ)

Fig.9 Trajectory state of the enhancing DT mixed-autonomy traffic system in complexity characteristic states with (k₁-δ)

Fig.10 Trajectory state of the enhancing DT mixed-autonomy traffic system in complexity characteristic states with (k₁+δ)

Fig.11 Comparison of velocity error between the enhancing DT system and the physical system under different states with air resistance coefficient k₁ bias

Fig.12 Comparison of velocity error between the enhancing DT system and the physical system under different states with rolling resistance coefficient r₁ bias

References 30

1	FAN B , WU Y , HE Z B , et al. Digital twin empowered mobile edge computing for intelligent vehicular lane-changing. IEEE Network, 2021, 35 (6): 194- 201. doi: 10.1109/MNET.201.2000768
2	KUŠIĆ K , SCHUMANN R , IVANJKO E . A digital twin in transportation: real-time synergy of traffic data streams and simulation for virtualizing motorway dynamics. Advanced Engineering Informatics, 2023, 55, 101858. doi: 10.1016/j.aei.2022.101858
3	WANG Z R , GUPTA R , HAN K , et al. Mobility digital twin: concept, architecture, case study, and future challenges. IEEE Internet of Things Journal, 2022, 9 (18): 17452- 17467. doi: 10.1109/JIOT.2022.3156028
4	毛竞争, 胡潇锐, 徐庚辰, 等. 数字孪生在工控安全中的应用进展综述. 计算机工程, 2025, 51 (2): 1- 17. doi: 10.19678/j.issn.1000-3428.0068374
	MAO J Z , HU X R , XU G C , et al. Review on the application of digital twinning in industrial safety. Computer Engineering, 2025, 51 (2): 1- 17. doi: 10.19678/j.issn.1000-3428.0068374
5	GRIEVES M. Digital twin: manufacturing excellence through virtual factory replication[EB/OL]. [2024-08-05]. https://www.researchgate.net/publication/275211047_Digital_Twin_Manufacturing_Excellence_through_Virtual_Factory_Replication.
6	CHAI X K , YAN J Q , ZHANG W , et al. Recent progress on digital twins in intelligent connected vehicles: a review. Elektronika Ir Elektrotechnika, 2024, 30 (2): 4- 17. doi: 10.5755/j02.eie.36209
7	WANG Y Z , LIU M Z , CHEN Y . Safety-guaranteed control of coupled longitudinal and lateral vehicle dynamics by considering tire stability via CDBFs. IFAC-PapersOnLine, 2022, 55 (37): 500- 505. doi: 10.1016/j.ifacol.2022.11.232
8	FAN B , SU Z X , CHEN Y Y , et al. Ubiquitous control over heterogeneous vehicles: a digital twin empowered edge AI approach. IEEE Wireless Communications, 2023, 30 (1): 166- 173. doi: 10.1109/MWC.012.2100587
9	LIAO X S , ZHAO X P , WANG Z R , et al. Driver digital twin for online prediction of personalized lane-change behavior. IEEE Internet of Things Journal, 2023, 10 (15): 13235- 13246. doi: 10.1109/JIOT.2023.3262484
10	SHOUKAT M U, YU S Y, SHI S M, et al. Evaluate the connected autonomous vehicles infrastructure using digital twin model based on cyber-physical combination of intelligent network[C]//Proceedings of the 5th CAA International Conference on Vehicular Control and Intelligence (CVCI). Washington D.C., USA: IEEE Press, 2021: 1-6.
11	CHEN X B , MIN X H , LI N N , et al. Dynamic safety measurement-control technology for intelligent connected vehicles based on digital twin system. Vibroengineering Procedia, 2021, 37, 78- 85. doi: 10.21595/vp.2021.21990
12	李亚楠. 智能网联汽车数字孪生测试理论和技术研究[D]. 长春: 吉林大学, 2020.
	LI Y N. Research on digital twin testing theory and technology of intelligent connected vehicle[D]. Changchun: Jilin University, 2020. (in Chinese)
13	祖晖, 龙洋, 韩庆文, 等. 智能网联汽车数字孪生测试关键场景提取和识别. 重庆理工大学学报, 2023, 37 (1): 75- 84.
	ZU H , LONG Y , HAN Q W , et al. Extraction and recognition of key scenes in intelligent networked digital twin test of automobile. Journal of Chongqing Institute of Technology, 2023, 37 (1): 75- 84.
14	WANG X X, YU R, YE M X, et al. Enhancing digital twin model for connected vehicles by powertrain and longitudinal dynamics[C]//Proceedings of the IEEE/CIC International Conference on Communications in China (ICCC). Washington D.C., USA: IEEE Press, 2023: 1-6.
15	WANG P W , DENG H , ZHANG J , et al. Model predictive control for connected vehicle platoon under switching communication topology. IEEE Transactions on Intelligent Transportation Systems, 2022, 23 (7): 7817- 7830. doi: 10.1109/TITS.2021.3073012
16	ZHU Y X , LI Y F , ZHU H , et al. Joint sliding-mode controller and observer for vehicle platoon subject to disturbance and acceleration failure of neighboring vehicles. IEEE Transactions on Intelligent Vehicles, 2023, 8 (3): 2345- 2357. doi: 10.1109/TIV.2022.3213074
17	DU H , LENG S P , HE J H , et al. Digital twin empowered cooperative trajectory planning of platoon vehicles for collision avoidance with unexpected obstacles. Digital Communications and Networks, 2024, 10 (6): 1666- 1676. doi: 10.1016/j.dcan.2023.06.002
18	朱永薪, 李永福, 朱浩, 等. 通信延时环境下基于观测器的智能网联车辆队列分层协同纵向控制. 自动化学报, 2023, 49 (8): 1785- 1798.
	ZHU Y X , LI Y F , ZHU H , et al. Hierarchical collaborative longitudinal control of intelligent networked vehicle queue based on observer in communication delay environment. Acta Automatica Sinica, 2023, 49 (8): 1785- 1798.
19	李永福, 王兴全, 黄龙旺, 等. 基于组合间距策略的自适应车辆队列纵向控制. 中国公路学报, 2024, 37 (2): 239- 252.
	LI Y F , WANG X Q , HUANG L W , et al. Adaptive longitudinal control of vehicle queue based on combined spacing strategy. China Journal of Highway and Transport, 2024, 37 (2): 239- 252.
20	ZHOU Z , LI L H , QU X , et al. An autonomous platoon formation strategy to optimize CAV car-following stability under periodic disturbance. Physica A: Statistical Mechanics and Its Applications, 2023, 626, 129096. doi: 10.1016/j.physa.2023.129096
21	HUANG Z Y , YU R , YE M X , et al. Digital twin edge services with proximity-aware longitudinal lane changing model for connected vehicles. IEEE Transactions on Vehicular Technology, 2024, 73 (11): 17457- 17471. doi: 10.1109/TVT.2024.3412119
22	WANG X X , HUANG Z Y , ZHENG S M , et al. Unpredictability of digital twin for connected vehicles. China Communications, 2023, 20 (2): 26- 45. doi: 10.23919/JCC.2023.02.003
23	FENG S , ZHANG Y , LI S E , et al. String stability for vehicular platoon control: definitions and analysis methods. Annual Reviews in Control, 2019, 47, 81- 97. doi: 10.1016/j.arcontrol.2019.03.001
24	MA L J , QU S R , REN J , et al. Mixed traffic flow of human-driven vehicles and connected autonomous vehicles: string stability and fundamental diagram. Mathematical Biosciences and Engineering, 2023, 20 (2): 2280- 2295.
25	WANG X X , HAO M , WU M Q , et al. Digital-twin-assisted safety control for connected automated vehicles in mixed-autonomy traffic. IEEE Internet of Things Journal, 2025, 12 (1): 472- 487. doi: 10.1109/JIOT.2024.3464521
26	WU M Q , YANG R B , HUANG X M , et al. Joint optimization of model partition and resource allocation for split federated learning over vehicular edge networks. IEEE Transactions on Vehicular Technology, 2024, 73 (10): 15860- 15865. doi: 10.1109/TVT.2024.3399011
27	HE C R , GE J , OROSZ G . Fuel efficient connected cruise control for heavy-duty trucks in real traffic. IEEE Transactions on Control Systems Technology, 2020, 28 (6): 2474- 2481. doi: 10.1109/TCST.2019.2925583
28	GUO S C , OROSZ G , MOLNAR T G . Connected cruise and traffic control for pairs of connected automated vehicles. IEEE Transactions on Intelligent Transportation Systems, 2023, 24 (11): 12648- 12658. doi: 10.1109/TITS.2023.3285852
29	HU M J , LI J N , BIAN Y G , et al. Distributed coordinated brake control for longitudinal collision avoidance of multiple connected automated vehicles. IEEE Transactions on Intelligent Vehicles, 2023, 8 (1): 745- 755. doi: 10.1109/TIV.2022.3197951
30	DONÀ R , MATTAS K , HE Y L , et al. Multianticipation for string stable adaptive cruise control and increased motorway capacity without vehicle-to-vehicle communication. Transportation Research Part C: Emerging Technologies, 2022, 140, 103687. doi: 10.1016/j.trc.2022.103687

[1]	KONG Siwei, YE Yongjin, WU Yongzheng, WANG Shi, HOU Jie, NI Ming. Quantum Annealing Algorithm for Solving Unmanned Aerial Vehicle Swarm Trajectory Planning Problem [J]. Computer Engineering, 2026, 52(3): 308-317.
[2]	MAO Jingzheng, HU Xiaorui, XU Gengchen, WU Guodong, SUN Yanbin, TIAN Zhihong. Review of Application Progress of Digital Twins in Industrial Control Security [J]. Computer Engineering, 2025, 51(2): 1-17.
[3]	REN Fang, YANG Yiping, XUE Feiyuan. Reversible Data Hiding Algorithm Based on Pixel Value Ordering and Block Subdivision [J]. Computer Engineering, 2022, 48(10): 130-137.
[4]	HE Yanqi, PENG Daqin, ZHAO Xuezhi. A Recurrent Neural Network Based BP Decoding Algorithm for Polar Codes [J]. Computer Engineering, 2022, 48(1): 197-203.
[5]	YU Heng, WANG Rangding, YAN Diqun, ZHANG Xueyuan. Reversible Audio Steganography Algorithm Based on Sample Value Ordering [J]. Computer Engineering, 2021, 47(1): 123-128,138.
[6]	HE Zhuoheng, LIU Zhiyong, LI Lu, LI Changming, ZHANG Lin. Comparative Study of XML Parsing Methods in Heterogeneous Text Data Conversion [J]. Computer Engineering, 2020, 46(7): 286-293,299.
[7]	LIANG Wentao, KANG Yan, LI Hao, LI Jinyuan, NING Haoyu. Adaptive Remote Sensing Scene Classification Based on Complexity Clustering [J]. Computer Engineering, 2020, 46(12): 254-261,269.
[8]	ZHU Guohui,CHEN Xing. A Low Complexity Precoding Algorithm for Large-scale MIMO System [J]. Computer Engineering, 2019, 45(3): 96-100.
[9]	CAO Haiyan,ZHOU Dong,FANG Xin,WANG Xiumin. Low Complexity Precoding Based on Lanczos Method in Massive MIMO System [J]. Computer Engineering, 2019, 45(2): 87-91.
[10]	WANG Han,WANG Kun,LU Wenjin,WANG Lei. Anchor Node Optimal Deployment Algorithm in Wireless Sensor Network [J]. Computer Engineering, 2018, 44(8): 105-111.
[11]	JIANG An,LI Xiangyang. Tag Anti-collision Algorithm Based on Information Bit Grouping [J]. Computer Engineering, 2018, 44(3): 294-300.
[12]	ZHAO Zhizhou,LU Chang,HE Zhenying,WANG Xiaoyang. An Efficient Text Interval Hot Word Query Algorithm [J]. Computer Engineering, 2018, 44(2): 17-23,30.
[13]	ZHANG Guangna,GUO Mingxi,SHEN Yuehong. Simulation Analysis of Two Iterative Frequency Domain Block Decision Feedback Equalizers for Faster-than-Nyquist System [J]. Computer Engineering, 2018, 44(2): 75-78.
[14]	ZHANG Hongchi,LIU Baixiang,WEN Jie. Research on Quantum Non-locality and Quantum Communication Complexity [J]. Computer Engineering, 2018, 44(12): 28-32.
[15]	QIN Zhenhua,MU Yongmin. Automatic Calculation Method of Ring Complexity for C Program [J]. Computer Engineering, 2018, 44(12): 102-107,114.

Please choose a citation manager

Content to export