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Professor Michael Milford's (Queensland University of Technology) presentation at Mumbrella's Automotive Marketing Summit

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Professor Michael Milford from Queensland University of Technology presented on Hand on with the Self-Driving Car at Mumbrella's Automotive Marketing Summit.

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Professor Michael Milford's (Queensland University of Technology) presentation at Mumbrella's Automotive Marketing Summit

  1. 1. www.roboticvision.orgroboticvision.orgARC CENTRE OF EXCELLENCE FOR ROBOTIC VISION Hands On With The Self-Driving Car Past and Present Funding SupportPast and Present Collaborators Professor Michael Milford Australian Research Council Future Fellow | Microsoft Research Faculty Fellow | Chief Investigator, Australian Centre for Robotic Vision
  2. 2. www.roboticvision.orgroboticvision.orgARC CENTRE OF EXCELLENCE FOR ROBOTIC VISION Overview • Introductions • Key Motivations – Why Self-driving Cars? • Self-driving Cars: A Technology Holy Grail with History • Current State of Play – Major Tech Players • The Technology Behind Self-driving Cars • Perception and Sensing • To Map or Not to Map: That is the Question • Infrastructure Reliance • The AI Technology Driving Self-driving Cars and What It Can Do • Flying Cars… • Unsolved Challenges in Self-driving Cars – The Last 1% • Disruption Highlights • Key Takeaways
  3. 3. www.roboticvision.orgroboticvision.orgARC CENTRE OF EXCELLENCE FOR ROBOTIC VISION
  4. 4. www.roboticvision.orgroboticvision.orgARC CENTRE OF EXCELLENCE FOR ROBOTIC VISION Publication references appear in red For More Details…
  5. 5. www.roboticvision.orgroboticvision.orgARC CENTRE OF EXCELLENCE FOR ROBOTIC VISION Where this Information Comes From
  6. 6. www.roboticvision.orgroboticvision.orgARC CENTRE OF EXCELLENCE FOR ROBOTIC VISION
  7. 7. www.roboticvision.orgroboticvision.orgARC CENTRE OF EXCELLENCE FOR ROBOTIC VISION Why?
  8. 8. www.roboticvision.orgroboticvision.orgARC CENTRE OF EXCELLENCE FOR ROBOTIC VISION Why? 1.3 million deaths / year http://asirt.org/initiatives/informing-road- users/road-safety-facts/road-crash-statistics
  9. 9. www.roboticvision.orgroboticvision.orgARC CENTRE OF EXCELLENCE FOR ROBOTIC VISION History
  10. 10. www.roboticvision.orgroboticvision.orgARC CENTRE OF EXCELLENCE FOR ROBOTIC VISION NavLab 1, CMU (1986) https://www.youtube.com/watch?v=ntIczNQKfjQ
  11. 11. www.roboticvision.orgroboticvision.orgARC CENTRE OF EXCELLENCE FOR ROBOTIC VISION NavLab 5, CMU (1996) https://www.youtube.com/watch?v=bdQ5rsVgPuk
  12. 12. www.roboticvision.orgroboticvision.orgARC CENTRE OF EXCELLENCE FOR ROBOTIC VISION DARPA Grand Challenge, 2004-2005 https://www.youtube.com/watch?v=M2AcMnfzpN
  13. 13. www.roboticvision.orgroboticvision.orgARC CENTRE OF EXCELLENCE FOR ROBOTIC VISION DARPA Urban Grand Challenge, 2007 https://www.youtube.com/watch?v=M2AcMnfzpN
  14. 14. www.roboticvision.orgroboticvision.orgARC CENTRE OF EXCELLENCE FOR ROBOTIC VISION Current State of Play
  15. 15. www.roboticvision.orgroboticvision.orgARC CENTRE OF EXCELLENCE FOR ROBOTIC VISION Autonomy Levels https://www.theverge.com/2016/9/28/13076948/self-driving-car-poll-autonomy-kelley-blue-book Where the technology is currently
  16. 16. www.roboticvision.orgroboticvision.orgARC CENTRE OF EXCELLENCE FOR ROBOTIC VISION Conventional Startup Example: NuTonomy https://www.youtube.com/watch?v=iP_lAjIfZwU • Partnerships for vehicles, ride sharing market, government • Controlled ride sharing fleet • Formal rigorous approach • People don’t own cars
  17. 17. www.roboticvision.orgroboticvision.orgARC CENTRE OF EXCELLENCE FOR ROBOTIC VISION Conventional Startup Example: Cruise Automation • Rapidly scaling • Software and testing-intensive approach
  18. 18. www.roboticvision.orgroboticvision.orgARC CENTRE OF EXCELLENCE FOR ROBOTIC VISION AI-based Startup Example: DriveAI https://www.youtube.com/watch?v=GMvgtPN2IBU • Machine learning and deep learning as the answer • Small fleets • AI-background
  19. 19. www.roboticvision.orgroboticvision.orgARC CENTRE OF EXCELLENCE FOR ROBOTIC VISION Car Maker Corporate Example: Toyota & Toyota Research Institute https://www.youtube.com/watch?v=5s6HbrOYads • Long term outlook, cultural context • Vehicle making legacy • Dual assistive (Guardian) and fully autonomous projects (Chauffeur) • Private car ownership still feasible
  20. 20. www.roboticvision.orgroboticvision.orgARC CENTRE OF EXCELLENCE FOR ROBOTIC VISION Tech Accessibility Implications • Toyota’s Guardian program (and other equivalents) have marginal if any benefits in terms of accessibility and mobility • Safety vigilance issue is still not resolved
  21. 21. www.roboticvision.orgroboticvision.orgARC CENTRE OF EXCELLENCE FOR ROBOTIC VISION Tech Company Corporate Example: Waymo/Google https://www.youtube.com/watch?v=uHbMt6WDhQ8 • Long history • New builder of vehicles • Existing tech and consumer infrastructure Watch for Nathaniel later in this presentation…
  22. 22. www.roboticvision.orgroboticvision.orgARC CENTRE OF EXCELLENCE FOR ROBOTIC VISION A Note About Reliability Company X in 2016: 0.0002 driver takeovers per mile Our car is more reliable than a human driver in a limited range of circumstances, using our own interpretation of “takeover” BUT If forced to be autonomous without intervention, it will fail repeatedly and sometimes catastrophically in a small percentage of corner cases Translation
  23. 23. www.roboticvision.orgroboticvision.orgARC CENTRE OF EXCELLENCE FOR ROBOTIC VISION DARPA Grand Challenge 2005 https://www.youtube.com/watch?v=M2AcMnfzpN 26-year-old hacker George Hotz https://www.youtube.com/watch?v=YuKAmsMg2ZE Google hits a bus (slowly) https://www.youtube.com/watch?v=I9T6LkNm-5w Graphics Card Company NVIDIA https://www.youtube.com/watch?v=qhUvQiKec2U
  24. 24. www.roboticvision.orgroboticvision.orgARC CENTRE OF EXCELLENCE FOR ROBOTIC VISION An Incredible Race Dr Will Maddern Automotive Flagship Lead, Oxford University (formerly QUT) Dr Brett Browning VP Robotics (Formerly of UQ)
  25. 25. www.roboticvision.orgroboticvision.orgARC CENTRE OF EXCELLENCE FOR ROBOTIC VISION An Irrational Environment Fosters Irrational Behaviour
  26. 26. www.roboticvision.orgroboticvision.orgARC CENTRE OF EXCELLENCE FOR ROBOTIC VISION http://www.prweb.com/releases/VSI/SegmentsAutonomousVehicle/prweb13472308.htm
  27. 27. www.roboticvision.orgroboticvision.orgARC CENTRE OF EXCELLENCE FOR ROBOTIC VISION http://medicalfuturist.com/wp-content/uploads/2016/08/artificial-intelligence-map6.jpg
  28. 28. www.roboticvision.orgroboticvision.orgARC CENTRE OF EXCELLENCE FOR ROBOTIC VISION The Technology Behind Self-driving Cars
  29. 29. www.roboticvision.orgroboticvision.orgARC CENTRE OF EXCELLENCE FOR ROBOTIC VISION Sensing & Perception
  30. 30. www.roboticvision.orgroboticvision.orgARC CENTRE OF EXCELLENCE FOR ROBOTIC VISION
  31. 31. www.roboticvision.orgroboticvision.orgARC CENTRE OF EXCELLENCE FOR ROBOTIC VISIONhttps://tctechcrunch2011.files.wordpress.com/2016/12/uber-atg-volvo.jpg?w=2234&h=1694
  32. 32. www.roboticvision.orgroboticvision.orgARC CENTRE OF EXCELLENCE FOR ROBOTIC VISION GPS https://www.youtube.com/watch?v=C5s1ucmiACU • Provides position, good for journey planning • Satellite-based, not 100% reliable (tunnels, urban canyons) • “Autonomy-enabling” still not universally available (drop outs, accuracy, latency) • Almost universal assumption that it can’t be used as primary source of positioning information
  33. 33. www.roboticvision.orgroboticvision.orgARC CENTRE OF EXCELLENCE FOR ROBOTIC VISION RADAR http://www.dailymail.co.uk/sciencetech/article-2288472/Volvo-unveils-scanner-automatically-slam-brakes-detects-cyclist-wobbling-cars-path.html • Can see through fog, smoke, some rain • Coarse resolution, no fine detail • Primarily used for collision safety with people, vehicles and the environment • Moderate to expensive price
  34. 34. www.roboticvision.orgroboticvision.orgARC CENTRE OF EXCELLENCE FOR ROBOTIC VISION LIDAR • 3D scan of range to all line of sight objects in all directions • Medium resolution, high range accuracy • Disrupted by heavy rain, particulates • Good for detecting vehicles, pedestrians, mapping and positioning • Expensive In-car photo by Michael Milford. Google Self-driving Car, Velodyne Here’s Nathaniel back at Google
  35. 35. www.roboticvision.orgroboticvision.orgARC CENTRE OF EXCELLENCE FOR ROBOTIC VISION VISION (CAMERAS) • Richest source of information • High resolution • Ranging to objects difficult • Affected by environmental conditions (day-night, weather, atmospheric conditions) • Relatively cheap
  36. 36. www.roboticvision.orgroboticvision.orgARC CENTRE OF EXCELLENCE FOR ROBOTIC VISION Does Sensing Solve Everything?
  37. 37. www.roboticvision.orgroboticvision.orgARC CENTRE OF EXCELLENCE FOR ROBOTIC VISION EXAMPLE – TESLA CRASH “Neither Autopilot nor the driver noticed the white side of the tractor trailer against a brightly lit sky, so the brake was not applied” https://electrek.co/2016/07/01/understanding-fatal-tesla-accident-autopilot-nhtsa-probe/
  38. 38. www.roboticvision.orgroboticvision.orgARC CENTRE OF EXCELLENCE FOR ROBOTIC VISION Visual Sensing Challenge
  39. 39. www.roboticvision.orgroboticvision.orgARC CENTRE OF EXCELLENCE FOR ROBOTIC VISION The Last 1% is Incredibly Difficult to Solve “This wouldn’t have happened with a long range sensor” https://electrek.co/2016/07/01/understanding-fatal-tesla-accident-autopilot-nhtsa-probe/
  40. 40. www.roboticvision.orgroboticvision.orgARC CENTRE OF EXCELLENCE FOR ROBOTIC VISION The tanker truck has to reliably predict, well ahead of time, that the yellow truck coming around the bend is not a threat “False Positives” are just as, if not more dangerous than “missed” detections when the system is already highly reliable
  41. 41. www.roboticvision.orgroboticvision.orgARC CENTRE OF EXCELLENCE FOR ROBOTIC VISION To Map or Not to Map: That is the Question https://www.gizmodo.com.au/2015/05/how-to-teach-an-autonomous-car-to-drive/
  42. 42. www.roboticvision.orgroboticvision.orgARC CENTRE OF EXCELLENCE FOR ROBOTIC VISION Most Companies Assume & Exploit Existing 3D Maps Advantages • Facilitates positioning and navigation technology • Prior knowledge of road rules, turning lanes, intersection behaviour • Prior knowledge about obscured obstacles, signs • Place-based learning Disadvantages • Expensive to acquire and maintain • May enable 99% performance quickly but not 100% - a “crutch” • We have a proof of concept in humans that they’re not needed – some companies are pursuing this approach
  43. 43. www.roboticvision.orgroboticvision.orgARC CENTRE OF EXCELLENCE FOR ROBOTIC VISION Sensible “Cheating” - Assistive Infrastructure
  44. 44. www.roboticvision.orgroboticvision.orgARC CENTRE OF EXCELLENCE FOR ROBOTIC VISION
  45. 45. www.roboticvision.orgroboticvision.orgARC CENTRE OF EXCELLENCE FOR ROBOTIC VISION Flying Autonomous Cars Power Price Fail-safe Regulation Infrastructure Laws of Physics suck If physics is solved, easier in many ways than ground-based autonomous vehicles
  46. 46. www.roboticvision.orgroboticvision.orgARC CENTRE OF EXCELLENCE FOR ROBOTIC VISION The AI Technology Driving Self-driving Cars
  47. 47. www.roboticvision.orgroboticvision.orgARC CENTRE OF EXCELLENCE FOR ROBOTIC VISION https://raw.githubusercontent.com/qingkaikong/blog/master/38_ANN_part1/figures/figure1_ANN_history.jpg
  48. 48. www.roboticvision.orgroboticvision.orgARC CENTRE OF EXCELLENCE FOR ROBOTIC VISION The “Comeback” What Changed in the Past Decade? 1. Scale of data available 2. Compute available https://lh3.googleusercontent.com/4fMZAd6hp-Zp2olrW3szbpkm8-zPZ3hPOFHIjnrlvA5wBLgwHaAiz6Wm5m2bfxBucQ=h900 https://gigaom.com/wp-content/uploads/sites/1/2010/11/tesla1.jpg
  49. 49. www.roboticvision.orgroboticvision.orgARC CENTRE OF EXCELLENCE FOR ROBOTIC VISION http://www.extremetech.com/wp-content/uploads/2015/07/NeuralNetwork.png Datain Recognition/description /understandingout
  50. 50. www.roboticvision.orgroboticvision.orgARC CENTRE OF EXCELLENCE FOR ROBOTIC VISION What Can the AI Tech Do?
  51. 51. www.roboticvision.orgroboticvision.orgARC CENTRE OF EXCELLENCE FOR ROBOTIC VISION What Deep Learning Can Do With enough attention, current learning-based technologies match or surpass many constrained tasks that humans currently do, if sufficient quantities of representative training data can be obtained and ideal performance can be defined.
  52. 52. www.roboticvision.orgroboticvision.orgARC CENTRE OF EXCELLENCE FOR ROBOTIC VISION http://cs.stanford.edu/people/karpathy/deepimagesent/ Google Deepmind via Tech Insider Google Deepmind Michael Milford Guillaume Lample, Devendra Singh Chaplot A Neural Algorithm of Artistic Style: https://arxiv.org/abs/1508.06576
  53. 53. www.roboticvision.orgroboticvision.orgARC CENTRE OF EXCELLENCE FOR ROBOTIC VISION “I’m a luddite – give me some ammunition to throw back at the hype mongers” or “I don’t have budget to do any of that so I need a way out”
  54. 54. www.roboticvision.orgroboticvision.orgARC CENTRE OF EXCELLENCE FOR ROBOTIC VISIONSzegedy et al, “Intriguing properties of neural networks”, https://arxiv.org/abs/1312.6199 + = + = + = + = + = + = Current Limitations – Understanding Machine Learning Incredible Resources Being Brought to Bear on Solving These Problems
  55. 55. www.roboticvision.orgroboticvision.orgARC CENTRE OF EXCELLENCE FOR ROBOTIC VISION Unsolved Challenges in Self- driving Cars – The Last 1%
  56. 56. www.roboticvision.orgroboticvision.orgARC CENTRE OF EXCELLENCE FOR ROBOTIC VISION Adverse Conditions • Not an issue with centrally controlled ride sharing fleets that can be turned off
  57. 57. www.roboticvision.orgroboticvision.orgARC CENTRE OF EXCELLENCE FOR ROBOTIC VISION Extreme Corner Cases https://www.youtube.com/watch?v=GihuUYmoMXU • An issue for all technology versions
  58. 58. www.roboticvision.orgroboticvision.orgARC CENTRE OF EXCELLENCE FOR ROBOTIC VISION Human Interaction https://www.youtube.com/watch?v=VG68SKoG7vE • An issue for all deployments in mixed environments (excepting niches like airports, dedicated lanes or roads…)
  59. 59. www.roboticvision.orgroboticvision.orgARC CENTRE OF EXCELLENCE FOR ROBOTIC VISION Disruption Examples
  60. 60. www.roboticvision.orgroboticvision.orgARC CENTRE OF EXCELLENCE FOR ROBOTIC VISION Endless Disruption & Opportunity
  61. 61. www.roboticvision.orgroboticvision.orgARC CENTRE OF EXCELLENCE FOR ROBOTIC VISION Key Takeaways
  62. 62. www.roboticvision.orgroboticvision.orgARC CENTRE OF EXCELLENCE FOR ROBOTIC VISION Onboard intelligence 3 Primary Technological Scenarios Connected vehicles Infrastructure-reliant Requires occasional human intervention No human control ……
  63. 63. www.roboticvision.orgroboticvision.orgARC CENTRE OF EXCELLENCE FOR ROBOTIC VISION Inner-city Controlled Ride Sharing Fleets are Feasible Relatively Low Speeds Can Afford to “Cheat” by Modifying Infrastructure Ride-sharing & tech- saavy wealthy customer base High density High utilization Ease of transition, supported by human-driver fleets Can Be Turned off During Challenging Weather
  64. 64. www.roboticvision.orgroboticvision.orgARC CENTRE OF EXCELLENCE FOR ROBOTIC VISION Beyond That, A Range of Scenarios with Highly Varying Implications for all Stakeholders
  65. 65. www.roboticvision.orgroboticvision.orgARC CENTRE OF EXCELLENCE FOR ROBOTIC VISION My Recommendations Avoid the temptation to think in unchanging absolutes about the technology e.g. “never be like a human”, “solves everything” See through the hype but don’t dismiss everything out of hand Keep an informed, regularly updated awareness of the range of scenarios that could play out
  66. 66. www.roboticvision.orgroboticvision.orgARC CENTRE OF EXCELLENCE FOR ROBOTIC VISION We’re Always Here to Talk
  67. 67. www.roboticvision.orgroboticvision.orgARC CENTRE OF EXCELLENCE FOR ROBOTIC VISION Hands On With The Self-Driving Car Past and Present Funding SupportPast and Present Collaborators Professor Michael Milford Australian Research Council Future Fellow | Microsoft Research Faculty Fellow | Chief Investigator, Australian Centre for Robotic Vision

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