Papers
Topics
Authors
Recent
Gemini 2.5 Flash
Gemini 2.5 Flash
169 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

Genie: Smart ROS-based Caching for Connected Autonomous Robots (2402.19410v2)

Published 29 Feb 2024 in cs.RO, cs.SY, and eess.SY

Abstract: Despite the promising future of autonomous robots, several key issues currently remain that can lead to compromised performance and safety. One such issue is latency, where we find that even the latest embedded platforms from NVIDIA fail to execute intelligence tasks (e.g., object detection) of autonomous vehicles in a real-time fashion. One remedy to this problem is the promising paradigm of edge computing. Through collaboration with our industry partner, we identify key prohibitive limitations of the current edge mindset: (1) servers are not distributed enough and thus, are not close enough to vehicles, (2) current proposed edge solutions do not provide substantially better performance and extra information specific to autonomous vehicles to warrant their cost to the user, and (3) the state-of-the-art solutions are not compatible with popular frameworks used in autonomous systems, particularly the Robot Operating System (ROS). To remedy these issues, we provide Genie, an encapsulation technique that can enable transparent caching in ROS in a non-intrusive way (i.e., without modifying the source code), can build the cache in a distributed manner (in contrast to traditional central caching methods), and can construct a collective three-dimensional object map to provide substantially better latency (even on low-power edge servers) and higher quality data to all vehicles in a certain locality. We fully implement our design on state-of-the-art industry-adopted embedded and edge platforms, using the prominent autonomous driving software Autoware, and find that Genie can enhance the latency of Autoware Vision Detector by 82% on average, enable object reusability 31% of the time on average and as much as 67% for the incoming requests, and boost the confidence in its object map considerably over time.

Definition Search Book Streamline Icon: https://streamlinehq.com
References (40)
  1. A. J. Hawkins, “Uber’s self-driving cars return to public roads for the first time since fatal crash,” Dec 2018. [Online]. Available: https://www.theverge.com/2018/12/20/18148946/uber-self-driving-car-return-public-road-pittsburgh-crash
  2. R. GUPTA, “Alphabet’s waymo is planning massive expansion of taxi service,” Jun 2019. [Online]. Available: https://articles2.marketrealist.com/2019/06/alphabets-waymo-is-planning-massive-expansion-of-taxi-service/
  3. Y. Mao, C. You, J. Zhang, K. Huang, and K. B. Letaief, “A survey on mobile edge computing: The communication perspective,” IEEE Communications Surveys & Tutorials, vol. 19, no. 4, 2017.
  4. M. Quigley, J. Faust, T. Foote, and J. Leibs, “Ros: an open-source robot operating system.”
  5. S. Kato, S. Tokunaga, Y. Maruyama, S. Maeda, M. Hirabayashi, Y. Kitsukawa, A. Monrroy, T. Ando, Y. Fujii, and T. Azumi, “Autoware on board: Enabling autonomous vehicles with embedded systems,” in 2018 ACM/IEEE 9th International Conference on Cyber-Physical Systems (ICCPS).   IEEE, 2018, pp. 287–296.
  6. I. Gog, S. Kalra, P. Schafhalter, M. A. Wright, J. E. Gonzalez, and I. Stoica, “Pylot: A modular platform for exploring latency-accuracy tradeoffs in autonomous vehicles,” in 2021 IEEE International Conference on Robotics and Automation (ICRA).   IEEE, 2021.
  7. J. Redmon, S. Divvala, R. Girshick, and A. Farhadi, “You only look once: Unified, real-time object detection,” in Proceedings of the IEEE conference on computer vision and pattern recognition, 2016.
  8. N. Carion, F. Massa, G. Synnaeve, N. Usunier, A. Kirillov, and S. Zagoruyko, “End-to-end object detection with transformers,” in European conference on computer vision.   Springer, 2020.
  9. P. Schafhalter, S. Kalra, L. Xu, J. E. Gonzalez, and I. Stoica, “Leveraging cloud computing to make autonomous vehicles safer,” in 2023 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS).   IEEE, 2023, pp. 5559–5566.
  10. F. Nazari, M. Noruzoliaee, and A. K. Mohammadian, “Shared versus private mobility: Modeling public interest in autonomous vehicles accounting for latent attitudes,” Transportation Research Part C: Emerging Technologies, vol. 97, pp. 456–477, 2018.
  11. M. Fu, W. Song, Y. Yi, and M. Wang, “Path planning and decision making for autonomous vehicle in urban environment,” in 2015 IEEE 18th International Conference on Intelligent Transportation Systems.   IEEE, 2015, pp. 686–692.
  12. K. Wiggers, “Tier iv raises over $100 million to develop open source software for driverless cars,” Jul 2019. [Online]. Available: https://venturebeat.com/2019/07/05/tier-iv-raises-100-million-to-develop-open-source-software-for-driverless-cars/
  13. M. Aeberhard, “Automated driving with ros at bmw.” [Online]. Available: https://roscon.ros.org/2015/presentations/ROSCon-Automated-Driving.pdf
  14. L. Zhang, R. Merrifield, A. Deguet, and G.-Z. Yang, “Powering the world’s robots-10 years of ros.”   American Association for the Advancement of Science, 2017.
  15. A. Geiger, P. Lenz, C. Stiller, and R. Urtasun, “The kitti vision benchmark suite,” URL http://www. cvlibs. net/datasets/kitti, 2015.
  16. M. Larabel, “Nvidia’s jetson agx xavier carmel performance vs. low-power x86 processors.” [Online]. Available: https://www.phoronix.com/scan.php?page=article&item=nvidia-xavier-carmel&num=4
  17. M. Y. Lachachi, M. Ouslim, S. Niar, and A. Taleb-Ahmed, “Trueview: A lidar only perception system for autonomous vehicle (interactive presentation),” in Workshop on Autonomous Systems Design (ASD 2019).   Schloss Dagstuhl-Leibniz-Zentrum fuer Informatik, 2019.
  18. P. Guo, B. Hu, R. Li, and W. Hu, “Foggycache: Cross-device approximate computation reuse,” in Proceedings of the 24th Annual International Conference on Mobile Computing and Networking.   ACM, 2018, pp. 19–34.
  19. U. Drolia, K. Guo, J. Tan, R. Gandhi, and P. Narasimhan, “Cachier: Edge-caching for recognition applications,” in 2017 IEEE 37th International Conference on Distributed Computing Systems (ICDCS), June 2017, pp. 276–286.
  20. K. Bonawitz, H. Eichner, W. Grieskamp, D. Huba, A. Ingerman, V. Ivanov, C. Kiddon, J. Konecny, S. Mazzocchi, H. B. McMahan et al., “Towards federated learning at scale: System design,” arXiv preprint arXiv:1902.01046, 2019.
  21. D. Crankshaw, X. Wang, G. Zhou, M. J. Franklin, J. E. Gonzalez, and I. Stoica, “Clipper: A low-latency online prediction serving system,” in 14th {normal-{\{{USENIX}normal-}\}} Symposium on Networked Systems Design and Implementation ({normal-{\{{NSDI}normal-}\}} 17), 2017, pp. 613–627.
  22. S. Han, H. Shen, M. Philipose, S. Agarwal, A. Wolman, and A. Krishnamurthy, “Mcdnn: An approximation-based execution framework for deep stream processing under resource constraints,” in Proceedings of the 14th Annual International Conference on Mobile Systems, Applications, and Services, ser. MobiSys ’16.   ACM, 2016.
  23. M. Abadi, A. Agarwal, P. Barham, E. Brevdo, Z. Chen, C. Citro, G. S. Corrado, A. Davis, J. Dean, M. Devin et al., “Tensorflow: Large-scale machine learning on heterogeneous distributed systems,” arXiv preprint arXiv:1603.04467, 2016.
  24. H. Cui, H. Zhang, G. R. Ganger, P. B. Gibbons, and E. P. Xing, “Geeps: Scalable deep learning on distributed gpus with a gpu-specialized parameter server,” in Proceedings of the Eleventh European Conference on Computer Systems.   ACM, 2016, p. 4.
  25. C. Slocum, Y. Zhang, N. Abu-Ghazaleh, and J. Chen, “Going through the motions:{{\{{AR/VR}}\}} keylogging from user head motions,” in 32nd USENIX Security Symposium (USENIX Security 23), 2023.
  26. D. Arteaga, J. Cabrera, J. Xu, S. Sundararaman, and M. Zhao, “Cloudcache: On-demand flash cache management for cloud computing,” in Proceedings of the 14th Usenix Conference on File and Storage Technologies, ser. FAST’16.   Berkeley, CA, USA: USENIX Association, 2016, pp. 355–369.
  27. P. Mach and Z. Becvar, “Mobile edge computing: A survey on architecture and computation offloading,” IEEE Communications Surveys & Tutorials, vol. 19, no. 3, pp. 1628–1656, 2017.
  28. U. Drolia, K. Guo, and P. Narasimhan, “Precog: prefetching for image recognition applications at the edge,” in SEC, 2017.
  29. Z. Li, X. He, Y. Li, S. Nikkhoo, W. Yang, L. Thiele, and C. Liu, “Mimonet: Multi-input multi-output on-device deep learning,” arXiv preprint arXiv:2307.11962, 2023.
  30. S. Lu, Y. Yao, and W. Shi, “Collaborative learning on the edges: A case study on connected vehicles,” in 2nd {normal-{\{{USENIX}normal-}\}} Workshop on Hot Topics in Edge Computing (HotEdge 19), 2019.
  31. P. M. Grulich and F. Nawab, “Collaborative edge and cloud neural networks for real-time video processing,” Proceedings of the VLDB Endowment, vol. 11, no. 12, pp. 2046–2049, 2018.
  32. P. Guo and W. Hu, “Potluck: Cross-application approximate deduplication for computation-intensive mobile applications,” in Proceedings of the Twenty-Third International Conference on Architectural Support for Programming Languages and Operating Systems, ser. ASPLOS ’18.   ACM, 2018.
  33. E. Miluzzo, C. T. Cornelius, A. Ramaswamy, T. Choudhury, Z. Liu, and A. T. Campbell, “Darwin phones: The evolution of sensing and inference on mobile phones,” in Proceedings of the 8th International Conference on Mobile Systems, Applications, and Services, ser. MobiSys ’10.   New York, NY, USA: ACM, 2010, pp. 5–20.
  34. S. Bateni and C. Liu, “Apnet: Approximation-aware real-time neural network,” in 2018 IEEE Real-Time Systems Symposium (RTSS), Dec 2018, pp. 67–79.
  35. G. A. Elliott, B. C. Ward, and J. H. Anderson, “Gpusync: A framework for real-time gpu management,” in 2013 IEEE 34th Real-Time Systems Symposium, Dec 2013, pp. 33–44.
  36. Z. Li, T. Ren, X. He, and C. Liu, “Red: A systematic real-time scheduling approach for robotic environmental dynamics,” in 2023 IEEE Real-Time Systems Symposium (RTSS).   IEEE, 2023, pp. 210–223.
  37. Z. Li, Y. Zhang, A. Ding, H. Zhou, and C. Liu, “Efficient algorithms for task mapping on heterogeneous cpu/gpu platforms for fast completion time,” Journal of Systems Architecture, vol. 114, p. 101936, 2021.
  38. Y. Li, Z. Li, W. Yang, and C. Liu, “Rt-lm: Uncertainty-aware resource management for real-time inference of language models,” arXiv preprint arXiv:2309.06619, 2023.
  39. A. Agrawal, R. Mancuso, R. Pellizzoni, and G. Fohler, “Analysis of dynamic memory bandwidth regulation in multi-core real-time systems,” CoRR, vol. abs/1809.05921, 2018.
  40. Z. Li, A. Samanta, Y. Li, A. Soltoggio, H. Kim, and C. Liu, “$\mathrm{R}^{3}$: On-device real-time deep reinforcement learning for autonomous robotics,” in IEEE Real-Time Systems Symposium, RTSS 2023, Taipei, Taiwan, December 5-8, 2023.   IEEE, 2023, pp. 131–144. [Online]. Available: https://doi.org/10.1109/RTSS59052.2023.00021
Citations (1)
View on Semantic Scholar

Summary

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

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