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Open Access

A Digital Twin and Consensus Empowered Cooperative Control Framework for Platoon-Based Autonomous Driving

School of Information and Communication Engineering, University of Electronic Science and Technology of China, Chengdu 611731, China; and also with Shenzhen Institute for Advanced Study, University of Electronic Science and Technology of China, Shenzhen 518110, China
College of Computer Science and Technology and also with College of Data Science, Taiyuan University of Technology, Taiyuan 030024, China
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Abstract

Platoon-based autonomous driving is indispensable for traffic automation, but it confronts substantial constraints in rugged terrains with unreliable links and scarce communication resources. This paper proposes a novel hierarchical Digital Twin (DT) and consensus empowered cooperative control framework for safe driving in harsh areas. Specifically, leveraging intra-platoon information exchange, one platoon-level DT is constructed on the leader and multiple vehicle-level DTs are distributed among platoon members. The leader first makes critical platoon-driving decisions based on the platoon-level DT. Then, considering the impact of unreliable links on the platoon-level DT accuracy and the consequent risk of unsafe decision-making, a distributed consensus scheme is proposed to negotiate critical decisions efficiently. Upon successful negotiation, vehicles proceed to execute critical decisions, relying on their vehicle-level DTs. Otherwise, a Space-Air-Ground-Integrated-Network (SAGIN) enabled information exchange is utilized to update the platoon-level DT for subsequent safe decision-making in scenarios with unreliable links, no roadside units, and obstructed platoons. Furthermore, based on this framework, an adaptive platooning scheme is designed to minimize total delay and ensure driving safety. Simulation results indicate that our proposed scheme improves driving safety by 21.1% and reduces total delay by 24.2% in harsh areas compared with existing approaches.

References

[1]

J. Wang, J. Liu, and N. Kato, Networking and communications in autonomous driving: A survey, IEEE Commun. Surv. Tutor., vol. 21, no. 2, pp. 1243–1274, 2019.

[2]

K. Xiong, S. Leng, X. Chen, C. Huang, C. Yuen, and Y. L. Guan, Communication and computing resource optimization for connected autonomous driving, IEEE Trans. Veh. Technol., vol. 69, no. 11, pp. 12652–12663, 2020.

[3]

K. Li, W. Ni, E. Tovar, and M. Guizani, Optimal rate-adaptive data dissemination in vehicular platoons, IEEE Trans. Intell. Transp. Syst., vol. 21, no. 10, pp. 4241–4251, 2020.

[4]

Y. Fu, C. Li, F. R. Yu, T. H. Luan, and Y. Zhang, A survey of driving safety with sensing, vehicular communications, and artificial intelligence-based collision avoidance, IEEE Trans. Intell. Transp. Syst., vol. 23, no. 7, pp. 6142–6163, 2022.

[5]
N. Wu, X. Wang, B. Lin, and K. Zhang, A CNN-based end-to-end learning framework toward intelligent communication systems, IEEE Access, vol. 7, pp. 110197–110204, 2019.
[6]

H. Seo, J. Park, M. Bennis, and W. Choi, Communication and consensus co-design for distributed, low-latency, and reliable wireless systems, IEEE Internet Things J., vol. 8, no. 1, pp. 129–143, 2021.

[7]

D. Yu, W. Li, H. Xu, and L. Zhang, Low reliable and low latency communications for mission critical distributed industrial Internet of Things, IEEE Commun. Lett., vol. 25, no. 1, pp. 313–317, 2021.

[8]

L. Zhang, H. Xu, O. Onireti, M. Ali Imran, and B. Cao, How much communication resource is needed to Run a wireless blockchain network, IEEE Netw., vol. 36, no. 1, pp. 128–135, 2022.

[9]

Z. Na, C. Ji, B. Lin, and N. Zhang, Joint optimization of trajectory and resource allocation in secure UAV relaying communications for Internet of Things, IEEE Internet Things J., vol. 9, no. 17, pp. 16284–16296, 2022.

[10]

Z. Niu, X. S. Shen, Q. Zhang, and Y. Tang, Space-air-ground integrated vehicular network for connected and automated vehicles: Challenges and solutions, Intell. Converged Netw., vol. 1, no. 2, pp. 142–169, 2020.

[11]
B. Li, Z. Na, and B. Lin, UAV trajectory planning from a comprehensive energy efficiency perspective in harsh environments, IEEE Netw., vol. 36, no. 4, pp. 62–68, 2022.
[12]

S. Mihai, M. Yaqoob, D. V. Hung, W. Davis, P. Towakel, M. Raza, M. Karamanoglu, B. Barn, D. Shetve, R. V. Prasad et al., Digital twins: A survey on enabling technologies, challenges, trends and future prospects, IEEE Commun. Surv. Tutorials, vol. 24, no. 4, pp. 2255–2291, 2022.

[13]

Y. Dai and Y. Zhang, Adaptive digital twin for vehicular edge computing and networks, J. Commun. Inf. Netw., vol. 7, no. 1, pp. 48–59, 2022.

[14]

Y. Lu, X. Huang, K. Zhang, S. Maharjan, and Y. Zhang, Low-latency federated learning and blockchain for edge association in digital twin empowered 6G networks, IEEE Trans. Ind. Inform., vol. 17, no. 7, pp. 5098–5107, 2021.

[15]
D. Ongaro and J. Ousterhout, In search of an understandable consensus algorithm, In: 2014 USENIX Annual Technical Conference, 2014, pp. 305-319.
[16]
M. Won, L-platooning: A protocol for managing a long platoon with DSRC, IEEE Trans. Intell. Transp. Syst., vol. 23, no. 6, pp. 5777–5790, 2022.
[17]

M. Razzaghpour, R. Valiente, M. Zaman, and Y. P. Fallah, Predictive model-based and control-aware communication strategies for cooperative adaptive cruise control, IEEE Open J. Intell. Transp. Syst., vol. 4, pp. 232–243, 2023.

[18]

X. Chen, S. Leng, J. He, L. Zhou, and H. Liu, The upper bounds of cellular vehicle-to-vehicle communication latency for platoon-based autonomous driving, IEEE Trans. Intell. Transp. Syst., vol. 24, no. 7, pp. 6874–6887, 2023.

[19]

C. Feng, Z. Xu, X. Zhu, P. V. Klaine, and L. Zhang, Wireless distributed consensus in vehicle to vehicle networks for autonomous driving, IEEE Trans. Veh. Technol., vol. 72, no. 6, pp. 8061–8073, 2023.

[20]

H. Xu, Y. Fan, W. Li, and L. Zhang, Wireless distributed consensus for connected autonomous systems, IEEE Internet Things J., vol. 10, no. 9, pp. 7786–7799, 2023.

[21]

B. Lin, X. Wang, W. Yuan, and N. Wu, A novel OFDM autoencoder featuring CNN-based channel estimation for Internet of vessels, IEEE Internet Things J., vol. 7, no. 8, pp. 7601–7611, 2020.

[22]

R. Han, Y. Wen, L. Bai, J. Liu, and J. Choi, Age of information aware UAV deployment for intelligent transportation systems, IEEE Trans. Intell. Transp. Syst., vol. 23, no. 3, pp. 2705–2715, 2022.

[23]

Y. Wang, S. Wu, J. Jiao, P. Yang, and Q. Zhang, On the prediction policy for timely status updates in space-air-ground integrated transportation systems, IEEE Trans. Intell. Transp. Syst., vol. 23, no. 3, pp. 2716–2726, 2022.

[24]

L. Tan, K. Yu, L. Lin, X. Cheng, G. Srivastava, J. C.-W. Lin, and W. Wei, Speech emotion recognition enhanced traffic efficiency solution for autonomous vehicles in a 5G-enabled space–air–ground integrated intelligent transportation system, IEEE Trans. Intell. Transp. Syst., vol. 23, no. 3, pp. 2830–2842, 2022.

[25]

B. Fan, Z. Su, Y. Chen, Y. Wu, C. Xu, and T. Q. S. Quek, Ubiquitous control over heterogeneous vehicles: A digital twin empowered edge AI approach, IEEE Wirel. Commun., vol. 30, no. 1, pp. 166–173, 2023.

[26]

S. Almeaibed, S. Al-Rubaye, A. Tsourdos, and N. P. Avdelidis, Digital twin analysis to promote safety and security in autonomous vehicles, IEEE Commun. Stand. Mag., vol. 5, no. 1, pp. 40–46, 2021.

[27]

Y. Hui, X. Ma, Z. Su, N. Cheng, Z. Yin, T. H. Luan, and Y. Chen, Collaboration as a service: Digital-twin-enabled collaborative and distributed autonomous driving, IEEE Internet Things J., vol. 9, no. 19, pp. 18607–18619, 2022.

[28]
H. Zhang, S. Leng, F. Wu, and H. Chai, A DAG blockchain-enhanced user-autonomy spectrum sharing framework for 6G-enabled IoT, IEEE Internet Things J., vol. 9, no. 11, pp. 8012–8023, 2022.
[29]

Y. Chen, Y. Wang, J. Zhang, and M. Di Renzo, QoS-driven spectrum sharing for reconfigurable intelligent surfaces (RISs) aided vehicular networks, IEEE Trans. Wirel. Commun., vol. 20, no. 9, pp. 5969–5985, 2021.

[30]

A. Liu, V. K. N. Lau, and B. Kananian, Stochastic successive convex approximation for non-convex constrained stochastic optimization, IEEE Trans. Signal Process., vol. 67, no. 16, pp. 4189–4203, 2019.

[31]
S. Boyd, L. Xiao, and A. Mutapcic, Subgradient methods, Lecture Notes of EE392o, Stanford University, 2003.
[32]
International traffic safety data and analysis group, IRTAD road safety database, Available at https://www.itfoecd.org/IRTAD.
Tsinghua Science and Technology
Pages 1096-1111
Cite this article:
Cao J, Leng S, Xiong K, et al. A Digital Twin and Consensus Empowered Cooperative Control Framework for Platoon-Based Autonomous Driving. Tsinghua Science and Technology, 2025, 30(3): 1096-1111. https://doi.org/10.26599/TST.2024.9010076

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Received: 15 December 2023
Revised: 08 March 2024
Accepted: 11 April 2024
Published: 30 December 2024
© The Author(s) 2025.

The articles published in this open access journal are distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/).

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