skip to main content
research-article
Public Access

The Architectural Implications of Autonomous Driving: Constraints and Acceleration

Authors Info & Claims
Published:19 March 2018Publication History
Skip Abstract Section

Abstract

Autonomous driving systems have attracted a significant amount of interest recently, and many industry leaders, such as Google, Uber, Tesla, and Mobileye, have invested a large amount of capital and engineering power on developing such systems. Building autonomous driving systems is particularly challenging due to stringent performance requirements in terms of both making the safe operational decisions and finishing processing at real-time. Despite the recent advancements in technology, such systems are still largely under experimentation and architecting end-to-end autonomous driving systems remains an open research question. To investigate this question, we first present and formalize the design constraints for building an autonomous driving system in terms of performance, predictability, storage, thermal and power. We then build an end-to-end autonomous driving system using state-of-the-art award-winning algorithms to understand the design trade-offs for building such systems. In our real-system characterization, we identify three computational bottlenecks, which conventional multicore CPUs are incapable of processing under the identified design constraints. To meet these constraints, we accelerate these algorithms using three accelerator platforms including GPUs, FPGAs, and ASICs, which can reduce the tail latency of the system by 169x, 10x, and 93x respectively. With accelerator-based designs, we are able to build an end-to-end autonomous driving system that meets all the design constraints, and explore the trade-offs among performance, power and the higher accuracy enabled by higher resolution cameras.

References

  1. J. Albericio, P. Judd, T. Hetherington, T. Aamodt, N. E. Jerger, and A. Moshovos. 2016. Cnvlutin: Ineffectual-Neuron-Free Deep Neural Network Computing 2016 ACM/IEEE 43rd Annual International Symposium on Computer Architecture (ISCA). 1--13. Google ScholarGoogle ScholarDigital LibraryDigital Library
  2. Jesse Levinson, Michael Montemerlo, and Sebastian Thrun. 2007. Map-Based Precision Vehicle Localization in Urban Environments. Robotics: Science and Systems (RSS), 2007.Google ScholarGoogle ScholarCross RefCross Ref
  3. Daofu Liu, Tianshi Chen, Shaoli Liu, Jinhong Zhou, Shengyuan Zhou, Olivier Teman, Xiaobing Feng, Xuehai Zhou, and Yunji Chen. 2015. PuDianNao: A Polyvalent Machine Learning Accelerator Proceedings of the Twentieth International Conference on Architectural Support for Programming Languages and Operating Systems (ASPLOS '15). ACM, New York, NY, USA, 369--381. Google ScholarGoogle ScholarDigital LibraryDigital Library
  4. D. Mahajan, J. Park, E. Amaro, H. Sharma, A. Yazdanbakhsh, J. K. Kim, and H. Esmaeilzadeh. 2016. TABLA: A unified template-based framework for accelerating statistical machine learning 2016 IEEE International Symposium on High Performance Computer Architecture (HPCA). 14--26.Google ScholarGoogle Scholar
  5. Daniel V McGehee, Elizabeth N Mazzae, and GH Scott Baldwin. 2000. Driver reaction time in crash avoidance research: Validation of a driving simulator study on a test track. In Proceedings of the human factors and ergonomics society annual meeting, Vol. Vol. 44. SAGE Publications, 3--320.Google ScholarGoogle ScholarCross RefCross Ref
  6. Colin McManus, Winston Churchill, Ashley Napier, Ben Davis, and Paul Newman. 2013. Distraction suppression for vision-based pose estimation at city scales Robotics and Automation (ICRA), 2013 IEEE International Conference on. IEEE, 3762--3769.Google ScholarGoogle Scholar
  7. Matthew McNaughton, Chris Urmson, John M Dolan, and Jin-Woo Lee. 2011. Motion planning for autonomous driving with a conformal spatiotemporal lattice Robotics and Automation (ICRA), 2011 IEEE International Conference on. IEEE, 4889--4895.Google ScholarGoogle Scholar
  8. JM Miller, A Emadi, AV Rajarathnam, and M Ehsani. 1999. Current status and future trends in more electric car power systems Vehicular Technology Conference, 1999 IEEE 49th, Vol. Vol. 2. IEEE, 1380--1384.Google ScholarGoogle Scholar
  9. Mobileye. 2017. Autonomous Driving. https://www.mobileye.com/. (2017).Google ScholarGoogle Scholar
  10. Mobileye. 2017. Enabling Autonomous. http://www.mobileye.com/future-of-mobility/mobileye-enabling-autonomous/. (2017).Google ScholarGoogle Scholar
  11. Mobileye. 2017. Mobileye C2--270 Essentials. (2017).Google ScholarGoogle Scholar
  12. Mobileye. 2017. Mobileye CES 2017 Press Conference. (2017).Google ScholarGoogle Scholar
  13. Mihir Mody. 2016. ADAS Front Camera: Demystifying Resolution and Frame-Rate. EETimes. (2016).Google ScholarGoogle Scholar
  14. Raul Mur-Artal, Jose Maria Martinez Montiel, and Juan D Tardos. 2015. ORB-SLAM: a versatile and accurate monocular SLAM system. IEEE Transactions on Robotics Vol. 31, 5 (2015), 1147--1163.Google ScholarGoogle ScholarDigital LibraryDigital Library
  15. Ashley Napier and Paul Newman. 2012. Generation and exploitation of local orthographic imagery for road vehicle localisation. In Intelligent Vehicles Symposium (IV), 2012 IEEE. IEEE, 590--596.Google ScholarGoogle ScholarCross RefCross Ref
  16. Allen Newell and Stuart K Card. 1985. The prospects for psychological science in human-computer interaction. Human-computer interaction Vol. 1, 3 (1985), 209--242. Google ScholarGoogle ScholarDigital LibraryDigital Library
  17. David J Perreault and Vahe Caliskan. 2004. Automotive power generation and control. IEEE Transactions on Power Electronics Vol. 19, 3 (2004), 618--630.Google ScholarGoogle ScholarCross RefCross Ref
  18. Mihail Pivtoraiko, Ross A Knepper, and Alonzo Kelly. 2009. Differentially constrained mobile robot motion planning in state lattices. Journal of Field Robotics Vol. 26, 3 (2009), 308--333. Google ScholarGoogle ScholarDigital LibraryDigital Library
  19. Joseph Redmon and Ali Farhadi. 2016. YOLO9000: Better, Faster, Stronger. arXiv preprint arXiv:1612.08242 (2016).Google ScholarGoogle Scholar
  20. M. Rhu, N. Gimelshein, J. Clemons, A. Zulfiqar, and S. W. Keckler. 2016. vDNN: Virtualized deep neural networks for scalable, memory-efficient neural network design. In 2016 49th Annual IEEE/ACM International Symposium on Microarchitecture (MICRO). 1--13. Google ScholarGoogle ScholarDigital LibraryDigital Library
  21. SAE International. 2014. AUTOMATED DRIVING, Levels of driving automation are defined in new SAE International standard J3016. http://www.sae.org/misc/pdfs/automated_driving.pdf. (2014).Google ScholarGoogle Scholar
  22. Erwin M Schau, Marzia Traverso, and Matthias Finkbeiner. 2012. Life cycle approach to sustainability assessment: a case study of remanufactured alternators. Journal of Remanufacturing Vol. 2, 1 (2012), 1--14.Google ScholarGoogle ScholarCross RefCross Ref
  23. Seagate Technology LLC. 2017. Seagate Desktop HDD Specification. http://www.seagate.com/consumer/upgrade/desktop-hdd/#specs. (2017).Google ScholarGoogle Scholar
  24. Shai Shalev-Shwartz, Shaked Shammah, and Amnon Shashua. 2016. Safe, Multi-Agent, Reinforcement Learning for Autonomous Driving. NIPS Workshop on Learning, Inference and Control of Multi-Agent Systems (2016).Google ScholarGoogle Scholar
  25. H. Sharma, J. Park, D. Mahajan, E. Amaro, J. K. Kim, C. Shao, A. Mishra, and H. Esmaeilzadeh. 2016. From high-level deep neural models to FPGAs. In 2016 49th Annual IEEE/ACM International Symposium on Microarchitecture (MICRO). 1--12. Google ScholarGoogle ScholarDigital LibraryDigital Library
  26. Alexander D Stewart and Paul Newman. 2012. Laps-localisation using appearance of prior structure: 6-dof monocular camera localisation using prior pointclouds. In Robotics and Automation (ICRA), 2012 IEEE International Conference on. IEEE, 2625--2632.Google ScholarGoogle ScholarCross RefCross Ref
  27. Jürgen Sturm, Nikolas Engelhard, Felix Endres, Wolfram Burgard, and Daniel Cremers. 2012. A benchmark for the evaluation of RGB-D SLAM systems Intelligent Robots and Systems (IROS), 2012 IEEE/RSJ International Conference on. IEEE, 573--580.Google ScholarGoogle Scholar
  28. TechCrunch. 2017. Intel buys Mobileye in $15.3B deal, moves its automotive unit to Israel. (2017).Google ScholarGoogle Scholar
  29. TechCrunch. 2017 b. Nvidia is powering the world's first Level 3 self-driving production car. (2017).Google ScholarGoogle Scholar
  30. TechCrunch. 2017 c. Waymo reveals completely homegrown sensor suite for Pacifica autonomous test car. (2017).Google ScholarGoogle Scholar
  31. Tesla. 2017. Full Self-Driving Hardware on All Cars. https://www.tesla.com/autopilot. (2017).Google ScholarGoogle Scholar
  32. Tesla Inc.. 2017. Tesla Autopilot: Full Self-Driving Hardware on All Cars. https://www.tesla.com/autopilot. (2017).Google ScholarGoogle Scholar
  33. Simon Thorpe, Denis Fize, Catherine Marlot, et almbox.. 1996. Speed of processing in the human visual system. nature Vol. 381, 6582 (1996), 520--522.Google ScholarGoogle Scholar
  34. Udacity. 2017. An Open Source Self-Driving Car. https://www.udacity.com/self-driving-car. (2017).Google ScholarGoogle Scholar
  35. Chris Urmson, Joshua Anhalt, Drew Bagnell, Christopher Baker, Robert Bittner, MN Clark, John Dolan, Dave Duggins, Tugrul Galatali, Chris Geyer, et almbox.. 2008. Autonomous driving in urban environments: Boss and the urban challenge. Journal of Field Robotics Vol. 25, 8 (2008), 425--466. Google ScholarGoogle ScholarDigital LibraryDigital Library
  36. U.S. Department of Transportation -- Federal Highway Administration. 2015. Highway Statistics 2015. https://www.fhwa.dot.gov/policyinformation/statistics.cfm. (2015).Google ScholarGoogle Scholar
  37. U.S. Department of Transportation -- National Highway Traffic Safety Administration. 2017. Federal Automated Vehicles Policy: Accelerating the Next Revolution in Roadway Safety. https://www.transportation.gov/AV. (2017).Google ScholarGoogle Scholar
  38. Velodyne. 2017. Velodyne HDL-64E LiDAR. http://velodynelidar.com/hdl-64e.html. (2017).Google ScholarGoogle Scholar
  39. Waymo. 2017. Introducing Waymo's suite of custom-built, self-driving hardware. (2017).Google ScholarGoogle Scholar
  40. Ryan W Wolcott and Ryan M Eustice. 2014. Visual localization within LIDAR maps for automated urban driving Intelligent Robots and Systems (IROS 2014), 2014 IEEE/RSJ International Conference on. IEEE, 176--183.Google ScholarGoogle Scholar
  41. Ryan W Wolcott and Ryan M Eustice. 2015. Fast LIDAR localization using multiresolution Gaussian mixture maps Robotics and Automation (ICRA), 2015 IEEE International Conference on. IEEE, 2814--2821.Google ScholarGoogle Scholar
  42. R. Yazdani, A. Segura, J. M. Arnau, and A. Gonzalez. 2016. An ultra low-power hardware accelerator for automatic speech recognition 2016 49th Annual IEEE/ACM International Symposium on Microarchitecture (MICRO). 1--12. Google ScholarGoogle ScholarDigital LibraryDigital Library
  43. Ying C Yeh. 1996. Triple-triple redundant 777 primary flight computer Aerospace Applications Conference, 1996. Proceedings., 1996 IEEE, Vol. Vol. 1. IEEE, 293--307.Google ScholarGoogle Scholar
  44. Chen Zhang, Zhenman Fang, Peipei Zhou, Peichen Pan, and Jason Cong. 2016. Caffeine: Towards uniformed representation and acceleration for deep convolutional neural networks. In Computer-Aided Design (ICCAD), 2016 IEEE/ACM International Conference on. IEEE, 1--8. Google ScholarGoogle ScholarDigital LibraryDigital Library
  45. Ji Zhang and Sanjiv Singh. 2015. Visual-lidar odometry and mapping: Low-drift, robust, and fast Robotics and Automation (ICRA), 2015 IEEE International Conference on. IEEE, 2174--2181.Google ScholarGoogle Scholar

Index Terms

  1. The Architectural Implications of Autonomous Driving: Constraints and Acceleration

      Recommendations

      Comments

      Login options

      Check if you have access through your login credentials or your institution to get full access on this article.

      Sign in

      Full Access

      • Published in

        cover image ACM SIGPLAN Notices
        ACM SIGPLAN Notices  Volume 53, Issue 2
        ASPLOS '18
        February 2018
        809 pages
        ISSN:0362-1340
        EISSN:1558-1160
        DOI:10.1145/3296957
        Issue’s Table of Contents
        • cover image ACM Conferences
          ASPLOS '18: Proceedings of the Twenty-Third International Conference on Architectural Support for Programming Languages and Operating Systems
          March 2018
          827 pages
          ISBN:9781450349116
          DOI:10.1145/3173162

        Copyright © 2018 ACM

        Publisher

        Association for Computing Machinery

        New York, NY, United States

        Publication History

        • Published: 19 March 2018

        Check for updates

        Qualifiers

        • research-article

      PDF Format

      View or Download as a PDF file.

      PDF

      eReader

      View online with eReader.

      eReader
      About Cookies On This Site

      We use cookies to ensure that we give you the best experience on our website.

      Learn more

      Got it!