Platoon intelligence: edge learning in vehicle platooning networks

Abstract

With the increasing computing capacity of intelligent connected vehicles (ICVs), there will be a considerable amount of spare resources available in the vehicle cluster. It is necessary to make full use of these spare resources. In this paper, we investigate a platoon intelligence-based edge learning (PIEL) framework for the deep integration of terminal and network edge. Specifically, this paper discusses the characteristics and applications of platooning in edge learning and introduces the general architecture of PIEL. Subsequently, we conduct a discussion on the learning scheduling based on PIEL, including co-scheduling in the Intra-Platoon, Inter-Platoon, and Platoon-edge cases. An intelligent decision-making methodology framework for PIEL is also presented. Finally, we discuss future research opportunities on PIEL to achieve platoon intelligence. We believe that this investigation will elicit escalating attentions, stimulate fruitful discussions, and inspire further research ideas on edge intelligence.

References

[1] Zhou J, Tian D, Sheng Z, Duan X, Qu G, Cao D, Shen X (2022) Decentralized robust control for vehicle platooning subject to uncertain disturbances via super-twisting second-order sliding-mode observer technique. IEEE Trans Veh Technol 1–1. https://doi.org/10.1109/TVT.2022.3170572

[2] Zhao J, Sun X, Li Q, Ma X. Edge caching and computation management for real-time internet of vehicles: an online and distributed approach IEEE Trans Intell Trans Syst, 2020, 22(4): 2183-2197.

[3] Zhang D, Wang W, Zhang J, Zhang T, Du J, Yang C. Novel edge caching approach based on multi-agent deep reinforcement learning for internet of vehicles IEEE Trans Intell Transp Syst, 2023, 24(8): 8324-8338.

[4] Zhao J, Ni S, Yang L, Zhang Z, Gong Y, You X. Multiband cooperation for 5G HetNets: A promising network paradigm IEEE Veh Technol Mag, 2019, 14(4): 85-93.

[5] Cao B, Sun Z, Zhang J, Gu Y. Resource allocation in 5g iov architecture based on sdn and fog-cloud computing IEEE Trans Intell Transp Syst, 2021, 22(6): 3832-3840.

[6] Zhao J, Li Q, Gong Y, Zhang K. Computation offloading and resource allocation for cloud assisted mobile edge computing in vehicular networks IEEE Trans Veh Technol, 2019, 68(8): 7944-7956.

[7] Zhu L, Yu FR, Wang Y, Ning B, Tang T. Big data analytics in intelligent transportation systems: A survey IEEE Intell Trans Sys, 2019, 20(1): 383-398.

[8] Zhou Z, Chen X, Li E, Zeng L, Luo K, Zhang J. Edge intelligence: Paving the last mile of artificial intelligence with edge computing Proc IEEE, 2019, 107(8): 1738-1762.

[9] Yang K, Shi Y, Zhou Y, Yang Z, Fu L, Chen W. Federated machine learning for intelligent IoT via reconfigurable intelligent surface IEEE Netw, 2020, 34(5): 16-22.

[10] Zeng L, Li E, Zhou Z, Chen X. Boomerang: On-demand cooperative deep neural network inference for edge intelligence on the industrial internet of things IEEE Netw, 2019, 33(5): 96-103.

[11] Deng S, Zhao H, Fang W, Yin J, Dustdar S, Zomaya AY. Edge intelligence: The confluence of edge computing and artificial intelligence IEEE Internet Things J, 2020, 7(8): 7457-7469.

[12] Shi W, Zhou S, Niu Z, Jiang M, Geng L. Joint device scheduling and resource allocation for latency constrained wireless federated learning IEEE Trans Wireless Commun, 2021, 20(1): 453-467.

[13] Zhao C, Duan X, Cai L, Cheng P. Vehicle platooning with non-ideal communication networks IEEE Trans Veh Technol, 2020, 70(1): 18-32.

[14] Wang P, Di B, Zhang H, Bian K, Song L. Platoon cooperation in cellular V2X networks for 5G and beyond IEEE Trans Wireless Commun, 2019, 18(8): 3919-3932.

[15] Li Y, Bailey J, Kulik L. Efficient mining of platoon patterns in trajectory databases Data Knowl Eng, 2015, 100: 167-187.

[16] Ma X, Zhao J, Gong Y, Sun X. Carrier sense multiple access with collision avoidance-aware connectivity quality of downlink broadcast in vehicular relay networks IET Microwaves Antennas Propag, 2019, 13(8): 1096-1103.

[17] Ma X, Zhao J, Gong Y. Joint scheduling and resource allocation for efficiency-oriented distributed learning over vehicle platooning networks IEEE Trans Veh Technol, 2021, 70(10): 10894-10908.

[18] Li W, Su Z, Li R, Zhang K, Wang Y. Blockchain-based data security for artificial intelligence applications in 6G networks IEEE Netw, 2020, 34(6): 31-37.

[19] Zhao J, He L, Zhang D, Gao X. A tp-ddpg algorithm based on cache assistance for task offloading in urban rail transit IEEE Trans Veh Technol, 2023, 72(8): 10671-10681.

[20] Belogaev A, Elokhin A, Krasilov A, Khorov E, Akyildiz IF. Cost-effective v2x task offloading in mec-assisted intelligent transportation systems IEEE Access, 2020, 8: 169010-169023.

Funding

National Natural Science Foundation of China(U2001213)

National Key Research and Development Project(2020YFB1807204)

Jiangxi Key Laboratory of Artificial Intelligence Transportation Information Transmission and Processing(20202BCD42010)

RIGHTS & PERMISSIONS

The Author(s)