Papers
Topics
Authors
Recent
Gemini 2.5 Flash
Gemini 2.5 Flash
144 tokens/sec
GPT-4o
7 tokens/sec
Gemini 2.5 Pro Pro
45 tokens/sec
o3 Pro
4 tokens/sec
GPT-4.1 Pro
38 tokens/sec
DeepSeek R1 via Azure Pro
28 tokens/sec
2000 character limit reached

Accuracy-Aware Cooperative Sensing and Computing for Connected Autonomous Vehicles (2403.16408v1)

Published 25 Mar 2024 in cs.NI and eess.SP

Abstract: To maintain high perception performance among connected and autonomous vehicles (CAVs), in this paper, we propose an accuracy-aware and resource-efficient raw-level cooperative sensing and computing scheme among CAVs and road-side infrastructure. The scheme enables fined-grained partial raw sensing data selection, transmission, fusion, and processing in per-object granularity, by exploiting the parallelism among object classification subtasks associated with each object. A supervised learning model is trained to capture the relationship between the object classification accuracy and the data quality of selected object sensing data, facilitating accuracy-aware sensing data selection. We formulate an optimization problem for joint sensing data selection, subtask placement and resource allocation among multiple object classification subtasks, to minimize the total resource cost while satisfying the delay and accuracy requirements. A genetic algorithm based iterative solution is proposed for the optimization problem. Simulation results demonstrate the accuracy awareness and resource efficiency achieved by the proposed cooperative sensing and computing scheme, in comparison with benchmark solutions.

Definition Search Book Streamline Icon: https://streamlinehq.com
References (36)
  1. J. Zhang and K. B. Letaief, “Mobile edge intelligence and computing for the Internet of vehicles,” Proc. IEEE, vol. 108, no. 2, pp. 246–261, 2019.
  2. F. Malandrino, C. F. Chiasserini, and G. M. Dell’Aera, “Edge-powered assisted driving for connected cars,” IEEE Trans. Mobile Comput., vol. 22, no. 2, pp. 874–889, 2023.
  3. W. Zhuang, Q. Ye, F. Lyu, N. Cheng, and J. Ren, “SDN/NFV-empowered future IoV with enhanced communication, computing, and caching,” Proc. IEEE, vol. 108, no. 2, pp. 274–291, 2019.
  4. X. Shen, J. Gao, W. Wu, M. Li, C. Zhou, and W. Zhuang, “Holistic network virtualization and pervasive network intelligence for 6G,” IEEE Commun. Surv. Tutor., vol. 24, no. 1, pp. 1–30, 2021.
  5. 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, 2018.
  6. M. Cui, S. Zhong, B. Li, X. Chen, and K. Huang, “Offloading autonomous driving services via edge computing,” IEEE Internet Things J., vol. 7, no. 10, pp. 10 535–10 547, 2020.
  7. Z. Li, T. Yu, R. Fukatsu, G. K. Tran, and K. Sakaguchi, “Towards safe automated driving: design of software-defined dynamic mmwave V2X networks and PoC implementation,” IEEE open J. Veh. Technol., vol. 2, pp. 78–93, 2021.
  8. E. Arnold, M. Dianati, R. de Temple, and S. Fallah, “Cooperative perception for 3D object detection in driving scenarios using infrastructure sensors,” IEEE Trans. Intell. Transp. Syst., vol. 23, no. 3, pp. 1852–1864, 2020.
  9. M. K. Abdel-Aziz, C. Perfecto, S. Samarakoon, M. Bennis, and W. Saad, “Vehicular cooperative perception through action branching and federated reinforcement learning,” IEEE Trans. Commun., vol. 70, no. 2, pp. 891–903, 2022.
  10. A. A. Abdellatif, C. F. Chiasserini, F. Malandrino, A. Mohamed, and A. Erbad, “Active learning with noisy labelers for improving classification accuracy of connected vehicles,” IEEE Trans. Veh. Technol., vol. 70, no. 4, pp. 3059–3070, 2021.
  11. Q. Chen, S. Tang, Q. Yang, and S. Fu, “Cooper: Cooperative perception for connected autonomous vehicles based on 3D point clouds,” in Proc. IEEE Int. Conf. Distrib. Comput. Syst. (ICDCS), 2019, pp. 514–524.
  12. Y. Jia, R. Mao, Y. Sun, S. Zhou, and Z. Niu, “MASS: Mobility-aware sensor scheduling of cooperative perception for connected automated driving,” IEEE Trans. Veh. Technol., 2023, to appear, doi: 10.1109/TVT.2023.3285308.
  13. ETSI, “Intelligent Transport Systems (ITS); Vehicular Communications; Basic Set of Applications; Analysis of the Collective Perception Service,” 3rd Generation Partnership Project (3GPP), ETSI TR 103 562, 2019, v2.1.1.
  14. T.-H. Wang, S. Manivasagam, M. Liang, B. Yang, W. Zeng, and R. Urtasun, “V2VNeT: Vehicle-to-vehicle communication for joint perception and prediction,” in Proc. Eur. Conf. Comput. Vision (ECCV), 2020, pp. 605–621.
  15. X. Zhang, A. Zhang, J. Sun, X. Zhu, Y. E. Guo, F. Qian, and Z. M. Mao, “EMP: Edge-assisted multi-vehicle perception,” in Proc. 27th Annual International Conf. Mobile Computing and Networking, 2021, pp. 545–558.
  16. H. Qiu, P. Huang, N. Asavisanu, X. Liu, K. Psounis, and R. Govindan, “Autocast: Scalable infrastructure-less cooperative perception for distributed collaborative driving,” in Proc. ACM Int. Conf. Mobile Syst., Appl. and Services (MobiSys), 2021, pp. 128–141.
  17. J. Zhang, S. Chen, X. Wang, and Y. Zhu, “Dynamic reservation of edge servers via deep reinforcement learning for connected vehicles,” IEEE Trans. Mobile Comput., vol. 22, no. 5, pp. 2661–2674, 2023.
  18. Z. Xiao, J. Shu, H. Jiang, G. Min, H. Chen, and Z. Han, “Perception task offloading with collaborative computation for autonomous driving,” IEEE J. Sel. Areas Commun., vol. 41, no. 2, pp. 457–473, 2023.
  19. L. Dong, Z. Yang, X. Cai, Y. Zhao, Q. Ma, and X. Miao, “WAVE: Edge-device cooperated real-time object detection for open-air applications,” IEEE Trans. Mobile Comput., vol. 22, no. 7, pp. 4347–4357, 2023.
  20. W. Zhang, Z. He, L. Liu, Z. Jia, Y. Liu, M. Gruteser, D. Raychaudhuri, and Y. Zhang, “Elf: accelerate high-resolution mobile deep vision with content-aware parallel offloading,” in Proc. ACM MobiCom, 2021, pp. 201–214.
  21. H. Wang, Q. Li, H. Sun, Z. Chen, Y. Hao, J. Peng, Z. Yuan, J. Fu, and Y. Jiang, “VaBUS: Edge-cloud real-time video analytics via background understanding and subtraction,” IEEE J. Select. Areas Commun., vol. 41, no. 1, pp. 90–106, 2023.
  22. J. Liu, M. Ahmed, M. A. Mirza, W. U. Khan, D. Xu, J. Li, A. Aziz, and Z. Han, “RL/DRL meets vehicular task offloading using edge and vehicular cloudlet: A survey,” IEEE Internet Things J., vol. 9, no. 11, pp. 8315–8338, 2022.
  23. F. Liu, J. Chen, Q. Zhang, and B. Li, “Online MEC offloading for V2V networks,” IEEE Trans. Mobile Comput., vol. 22, no. 10, pp. 6097–6109, 2023.
  24. P. Lv, W. Xu, J. Nie, Y. Yuan, C. Cai, Z. Chen, and J. Xu, “Edge computing task offloading for environmental perception of autonomous vehicles in 6G networks,” IEEE Trans. Netw. Sci. Eng., vol. 10, no. 3, pp. 1228–1245, 2023.
  25. J. A. Rahal, G. de Veciana, T. Shimizu, and H. Lu, “Optimizing timely coverage in communication constrained collaborative sensing systems,” IEEE Trans. Control Netw. Syst., 2022, to appear, doi: 10.1109/TCNS.2022.3203806.
  26. Y. Hu, S. Fang, Z. Lei, Y. Zhong, and S. Chen, “Where2comm: Communication-efficient collaborative perception via spatial confidence maps,” Proc. Advances in neural information processing systems (NeurIPS), vol. 35, pp. 4874–4886, 2022.
  27. K. Qu, W. Zhuang, W. Wu, M. Li, X. Shen, X. Li, and W. Shi, “Stochastic cumulative DNN inference with RL-aided adaptive IoT device-edge collaboration,” IEEE Internet Things J., vol. 10, no. 20, pp. 18 000–18 015, 2023.
  28. K.-W. Chiang, G.-J. Tsai, H.-J. Chu, and N. El-Sheimy, “Performance enhancement of INS/GNSS/Refreshed-SLAM integration for acceptable lane-level navigation accuracy,” IEEE Trans. Veh. Technol., vol. 69, no. 3, pp. 2463–2476, 2020.
  29. L. Jia, Z. Zhou, F. Xu, and H. Jin, “Cost-efficient continuous edge learning for artificial intelligence of things,” IEEE Internet Things J., vol. 9, no. 10, pp. 7325–7337, 2022.
  30. “Gurobi Optimizer Reference Manual,” http://www.gurobi.com, 2022, [Online; accessed 17-November-2023].
  31. M. S. Lobo, L. Vandenberghe, S. Boyd, and H. Lebret, “Applications of second-order cone programming,” Linear Algebra and its Applications, vol. 284, no. 1-3, pp. 193–228, 1998.
  32. F. Sun, F. Hou, N. Cheng, M. Wang, H. Zhou, L. Gui, and X. Shen, “Cooperative task scheduling for computation offloading in vehicular cloud,” IEEE Trans. Veh. Technol., vol. 67, no. 11, pp. 11 049–11 061, 2018.
  33. L. He, B. Liang, J. Li, and M. Sheng, “Joint observation and transmission scheduling in agile satellite networks,” IEEE Trans. Mobile Comput., vol. 21, no. 12, pp. 4381–4396, 2022.
  34. B. Lorenzo and S. Glisic, “Optimal routing and traffic scheduling for multihop cellular networks using genetic algorithm,” IEEE Trans. Mobile Comput., vol. 12, no. 11, pp. 2274–2288, 2013.
  35. S. Xu, R. Li, and Q. Yang, “Improved genetic algorithm based intelligent resource allocation in 5G ultra dense networks,” in Proc. IEEE WCNC, 2018, pp. 1–6.
  36. Y. Zhou and O. Tuzel, “Voxelnet: End-to-end learning for point cloud based 3D object detection,” in Proc. IEEE CVPR’18, 2018, pp. 4490–4499.
Citations (6)
View on Semantic Scholar

Summary

We haven't generated a summary for this paper yet.

Ai Sparkles Streamline Icon: https://streamlinehq.com Summarize Now