Cooperative Edge Intelligence and Network Slicing for Autonomous Vehicle Communication Systems
Keywords:
cooperative edge intelligence; network slicing; autonomous vehicles; 5G networks; deep reinforcement learning; quality of service; system architectureAbstract
The proliferation of autonomous vehicles demands communication infrastructures capable of supporting extreme requirements for latency, reliability, and data throughput. Cooperative edge intelligence, realized through multi-access edge computing and distributed learning paradigms, and network slicing, as a softwarized 5G and beyond capability, together form a promising coalition to meet these demands. This paper presents a system-level investigation into the architectural integration of cooperative edge intelligence and network slicing for autonomous vehicle communication systems. It examines the structural trade-offs inherent in orchestrating computation and communication resources across multi-tier edge clouds while maintaining stringent service-level agreements through logical network partitions. The analysis extends to governance models for slice lifecycle management, resource isolation policies, and fairness mechanisms that address diverse stakeholder interests. Further, deployment challenges such as mobility-induced session continuity, energy efficiency, resilience under infrastructure degradation, and security vulnerabilities are critically evaluated. Policy and regulatory implications concerning spectrum management, data sovereignty, and inter-operator coordination are discussed in the context of ongoing standardization and real-world deployments. By synthesizing advances from distributed intelligence, softwarized networking, and autonomous driving ecosystems, the paper provides a forward-looking perspective on constructing robust, sustainable, and equitable vehicular communication systems. The discussion is firmly grounded in the relevant literature and draws cross-domain comparisons to highlight the unique constraints and opportunities of this converged infrastructure.
References
1. Khan, M. A., & Salah, K. (2018). Autonomous vehicles: A study on communication requirements and enabling technologies. IEEE Access, 6, 13708–13728. https://doi.org/10.1109/ACCESS.2018.2812883
2. Mao, Y., You, C., Zhang, J., Huang, K., & Letaief, K. B. (2017). A survey on mobile edge computing: The communication perspective. IEEE Communications Surveys & Tutorials, 19(4), 2322–2358. https://doi.org/10.1109/COMST.2017.2745201
3. Foukas, X., Patounas, G., Elmokashfi, A., & Marina, M. K. (2017). Network slicing in 5G: Survey and challenges. IEEE Communications Magazine, 55(5), 94–100. https://doi.org/10.1109/MCOM.2017.1600951
4. Bennis, M., Debbah, M., & Poor, H. V. (2018). Ultrareliable and low-latency wireless communication: Tail, risk, and scale. Proceedings of the IEEE, 106(10), 1834–1853. https://doi.org/10.1109/JPROC.2018.2867029
5. Liu, L., Chen, C., Pei, Q., & Maharjan, S. (2021). Cooperative edge intelligence for autonomous driving: A joint perception and planning perspective. IEEE Network, 35(2), 26–32. https://doi.org/10.1109/MNET.011.2000486
6. Naik, G., Choudhury, A., & Park, J. M. (2019). IEEE 802.11bd & 5G NR-V2X: Evolution of radio access technologies for V2X communications. IEEE Access, 7, 70169–70184. https://doi.org/10.1109/ACCESS.2019.2919489
7. Giang, N. K., Leung, V. C. M., & Lea, R. (2018). On integrating mobile edge computing with autonomous vehicles: A hierarchical architecture and use cases. IEEE Communications Magazine, 56(11), 32–38. https://doi.org/10.1109/MCOM.2018.1800227
8. Vilalta, R., Mayoral, A., Pubill, D., Casellas, R., Martinez, R., Serra, J., & Munoz, R. (2016). Network slicing using SDN/NFV: Survey and taxonomy. IEEE Communications Magazine, 54(7), 70–77. https://doi.org/10.1109/MCOM.2016.7514163
9. He, Y., Zhao, N., & Yin, H. (2020). Integrated networking, caching, and computing for connected vehicles: A deep reinforcement learning approach. IEEE Transactions on Vehicular Technology, 69(5), 5454–5465. https://doi.org/10.1109/TVT.2020.2982391
10. Chen, Q., Ma, X., Tang, S., & Wu, T. (2021). Cooperative perception for 3D object detection in driving scenarios using infrastructure sensors. IEEE Transactions on Intelligent Transportation Systems, 22(9), 5845–5856. https://doi.org/10.1109/TITS.2020.3028424
11. Li, Q. (2026). QoS Assurance Mechanism for 5G Network Slicing Based on the Deep Reinforcement Learning PPO Algorithm. arXiv preprint arXiv:2605.03345.
12. Lim, W. Y. B., Luong, N. C., Hoang, D. T., Jiao, Y., Liang, Y. C., Yang, Q., Niyato, D., & Miao, C. (2020). Federated learning in mobile edge networks: A comprehensive survey. IEEE Communications Surveys & Tutorials, 22(3), 2031–2063. https://doi.org/10.1109/COMST.2020.2986024
13. Patel, M., Naughton, B., Chan, C., Sprecher, N., Abeta, S., & Neal, A. (2014). Mobile-edge computing—Introductory technical white paper. ETSI White Paper, 11(1), 1–36.
14. Zhang, S., Wang, Y., & Zhou, W. (2022). A cooperative edge intelligence framework for real-time autonomous driving: Architecture and evaluation. IEEE Transactions on Network and Service Management, 19(2), 1234–1246. https://doi.org/10.1109/TNSM.2021.3135774
15. Xu, W., Zhou, H., Cheng, N., Lyu, F., Shi, W., Chen, J., & Shen, X. (2018). Internet of vehicles in big data era. IEEE/CAA Journal of Automatica Sinica, 5(1), 19–35. https://doi.org/10.1109/JAS.2017.7510736
16. Ordonez-Lucena, J., Ameigeiras, P., Lopez, D., Ramos-Munoz, J. J., Lorca, J., & Folgueira, J. (2017). Network slicing for 5G with SDN/NFV: Concepts, architectures, and challenges. IEEE Communications Magazine, 55(5), 80–87. https://doi.org/10.1109/MCOM.2017.1600935
17. Wang, L., Chen, M., & Liu, H. (2021). QoS-aware network slicing framework for vehicular communication systems. IEEE Journal on Selected Areas in Communications, 39(9), 2856–2870. https://doi.org/10.1109/JSAC.2021.3088663
18. Taleb, T., Mada, B., Corici, M. I., Nakao, A., & Flinck, H. (2017). PERMIT: Network slicing for personalized 5G mobile networks. IEEE Communications Magazine, 55(5), 48–55. https://doi.org/10.1109/MCOM.2017.1600947
19. Rost, P., Banchs, A., Berberana, I., Breitbach, M., Doll, M., Droste, H., Mannweiler, C., Puente, M. A., Samdanis, K., & Samaan, N. (2016). Mobile network architecture evolution toward 5G. IEEE Communications Magazine, 54(5), 84–91. https://doi.org/10.1109/MCOM.2016.7470940
20. Mao, Y., Zhang, J., & Letaief, K. B. (2016). Dynamic computation offloading for mobile-edge computing with energy harvesting devices. IEEE Journal on Selected Areas in Communications, 34(12), 3590–3605. https://doi.org/10.1109/JSAC.2016.2611964
21. Roman, R., Lopez, J., & Mambo, M. (2018). Mobile edge computing, Fog et al.: A survey and analysis of security threats and challenges. Future Generation Computer Systems, 78, 680–698. https://doi.org/10.1016/j.future.2016.11.009
22. Xu, C., Wang, K., & Guo, S. (2021). Intelligent resource management for network slicing in 5G: A blockchain-assisted deep reinforcement learning approach. IEEE Transactions on Industrial Informatics, 17(7), 5095–5105. https://doi.org/10.1109/TII.2020.3034226
23. Bazzi, A., Masini, B. M., Zanella, A., & Thibault, I. (2017). On the performance of IEEE 802.11p and LTE-V2V for cooperative awareness messages in an urban scenario. IEEE Transactions on Vehicular Technology, 66(10), 9403–9415. https://doi.org/10.1109/TVT.2017.2711470
24. Ge, X., Yang, B., & Li, L. (2020). Green mobile edge computing for Internet of Things: Challenges and solutions. IEEE Network, 34(2), 78–83. https://doi.org/10.1109/MNET.001.1900410
25. Shirazi, S. N., Gouglidis, A., Farshad, A., & Hutchison, D. (2017). The extended cloud: Review and analysis of mobile edge computing and fog from a security and resilience perspective. IEEE Journal on Selected Areas in Communications, 35(11), 2586–2595. https://doi.org/10.1109/JSAC.2017.2760478
26. Ojanpera, T., & Kokkinen, H. (2020). Regulation and policy for connected and automated driving: The role of 5G and beyond. Telecommunications Policy, 44(9), 102005. https://doi.org/10.1016/j.telpol.2020.102005
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This article is published under the Creative Commons Attribution 4.0 International License (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.