Abstract
With the rapid advancement of intelligent transportation systems, cooperative vehicle infrastructure systems emerge as a vital frontier for development. Real-time mutual fusion of perception data is a crucial technology for ensuring the security of ITS systems. However, the computation-intensive nature of perception data fusion poses a significant challenge in terms of computing resource allocation and scheduling. In this paper, we propose a vehicle-road cooperative network that facilitates computation offloading during the real-time perception data fusion process. We present an architecture that enables users to generate tasks and offload computations, and we formulate an integer nonlinear programming problem within this framework. Considering the dynamic, random, and time-varying characteristics of cooperative vehicle infrastructure systems, we introduced the Deep Deterministic Policy Gradient (DDPG) algorithm for perception fusion computing offloading (DDPG-PFCO). Through extensive experiments conducted on a real map, experimental results show that the proposed algorithm outperforms other comparison algorithms, exhibiting significant improvements in performance.
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References
Storck CR, Duarte-Figueiredo F (2020) A survey of 5g technology evolution, standards, and infrastructure associated with vehicle-to-everything communications by internet of vehicles. IEEE Access 8:117593–117614
Li B, Ruizhi W (2023) Joint perception data caching and computation offloading in MEC-enabled vehicular networks. Comput Commun 199:139–152
Dai B, Fanglin X, Cao Y, Yang X (2021) Hybrid sensing data fusion of cooperative perception for autonomous driving with augmented vehicular reality. IEEE Syst J 15(1):1413–1422
Mahbub M, Shubair RM (2023) Contemporary advances in multi-access edge computing: a survey of fundamentals, architecture, technologies, deployment cases, security, challenges, and directions. J Netw Comput Appl 219:103726
Lv P, Wenbiao X, Nie J, Yuan Y, Cai C, Chen Z, Jia X (2023) Edge computing task offloading for environmental perception of autonomous vehicles in 6g networks. IEEE Trans Netw Sci Eng 10(3):1228–1245
Shahian Jahromi B, Tulabandhula T, Cetin S (2019) Real-time hybrid multi-sensor fusion framework for perception in autonomous vehicles. Sensors 19(20):4357
Yu R, Yang D, Zhang H (2021) Edge-assisted collaborative perception in autonomous driving: a reflection on communication design. In: 2021 IEEE/ACM Symposium on Edge Computing (SEC), pp 371–375
Saeik F, Avgeris M, Spatharakis D, Santi N, Dechouniotis D, Violos J, Leivadeas A, Athanasopoulos N, Mitton N, Papavassiliou S (2021) Task offloading in edge and cloud computing: a survey on mathematical, artificial intelligence and control theory solutions. Comput Netw 195:108177
Liu Q, Liang H, Luo R, Liu Q (2022) Energy-efficiency computation offloading strategy in uav aided v2x network with integrated sensing and communication. IEEE Open J Commun Soc 3:1337–1346
Liu D, Sun F, Wang W, Dev K (2023) Distributed computation offloading with low latency for artificial intelligence in vehicular networking. IEEE Commun Standards Mag 7(1):74–80
Xiao Z, Shu J, Jiang H, Min G, Chen H, Han Z (2023) Perception task offloading with collaborative computation for autonomous driving. IEEE J Sel Areas Commun 41(2):457–473
Qi Y, Zhou Y, Liu Y-F, Liu L, Pan Z (2021) Traffic-aware task offloading based on convergence of communication and sensing in vehicular edge computing. IEEE Internet Things J 8(24):17762–17777
Xu D, Anguelov D, Jain A (2018) Pointfusion: Deep sensor fusion for 3d bounding box estimation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp 244–253
Huynh Lam, Nguyen Phong, Matas Jiří, Rahtu Esa, Heikkilä Janne (2021) Boosting monocular depth estimation with lightweight 3d point fusion. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp 12767–12776
Liu M, Li D, Wu H, Lyu F, Shen XS (2021) Cooperative edge-cloud caching for real-time sensing big data search in vehicular networks. In: IEEE International Conference on Communications (ICC 2021), IEEE, pp 1–6
Shi S, Cui J, Jiang Z, Yan Z, Xing G, Niu J, Ouyang Z (2022) Vips: real-time perception fusion for infrastructure-assisted autonomous driving. In: Proceedings of the 28th annual international conference on mobile computing and networking. pp 133–146
Liu Z, Cai Y, Wang H, Chen L, Gao H, Jia Y, Li Y (2021) Robust target recognition and tracking of self-driving cars with radar and camera information fusion under severe weather conditions. IEEE Trans Intell Transp Syst 23(7):6640–6653
Yao J, Han T, Ansari N (2019) On mobile edge caching. IEEE Commun Surv Tutor 21(3):2525–2553
Zhu H, Cao Y, Wei X, Wang W, Jiang T, Jin S (2018) Caching transient data for internet of things: a deep reinforcement learning approach. IEEE Internet Things J 6(2):2074–2083
Suto K, Nishiyama H, Kato N (2017) Postdisaster user location maneuvering method for improving the qoe guaranteed service time in energy harvesting small cell networks. IEEE Trans Veh Technol 66(10):9410–9420
Abdel-Aziz MK, Samarakoon S, Perfecto C, Bennis M (2020) Cooperative perception in vehicular networks using multi-agent reinforcement learning. In: 54th Asilomar Conference on Signals, Systems, and Computers, pages 408–412. IEEE
Chen J, Wu H, Lyu F, Yang P, Shen XS (2021) Multi-dimensional resource allocation for diverse safety message transmissions in vehicular networks. In: IEEE International Conference on Communications (ICC 2021), pages 1–6. IEEE
Lv P, Huang J, Liu H (2023) Cooperative environmental perception task offloading for connected and autonomous vehicles. Electronics 12(17):3714
Xiao Z, Shu J, Jiang H, Min G, Chen H, Han Z (2023) Overcoming occlusions: perception task-oriented information sharing in connected and autonomous vehicles. IEEE Network 37:224–229
He Y, Ma L, Jiang Z, Tang Y, Xing G (2021) Vi-eye: semantic-based 3d point cloud registration for infrastructure-assisted autonomous driving. In: Proceedings of the 27th Annual International Conference on Mobile Computing and Networking, pages 573–586
He Y, Ma L, Jiang Z, Tang Y, Xing G (2019) Bridging the edge-cloud barrier for real-time advanced vision analytics. In: USENIX Workshop on Hot Topics in Cloud Computing, USENIX Association
Haklay M, Weber P (2008) Openstreetmap: user-generated street maps. IEEE Pervasive Comput 7(4):12–18
Behrisch M, Bieker L, Erdmann J, Krajzewicz D (2011) Sumo–simulation of urban mobility: an overview. In: The Third International Conference on Advances in System Simulation. ThinkMind
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This work was supported by the National Natural Science Foundation of China (Grant number 61562092).
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RW, PH, and FH were involved in conceptualization, methodology, formal analysis and writing—reviewing and editing. BL was responsible for conceptualization, methodology, funding acquisition, investigation, formal analysis, writing—reviewing and editing, and supervision.
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Wu, R., Li, B., Hou, P. et al. Perception data fusion-based computation offloading in cooperative vehicle infrastructure systems. J Supercomput 80, 17688–17710 (2024). https://doi.org/10.1007/s11227-024-06145-2
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DOI: https://doi.org/10.1007/s11227-024-06145-2