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Towards Autonomous Vehicles

Towards Autonomous Vehicles. Chris Schwarz National Advanced Driving Simulator. Acknowledgements. Mid-America Transportation Center 1 year project to survey literature and report on state of the art in autonomous vehicles Co-PI: Prof. Geb Thomas Undergraduate students Kory Nelson

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Towards Autonomous Vehicles

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  1. Towards Autonomous Vehicles Chris Schwarz National Advanced Driving Simulator

  2. Acknowledgements • Mid-America Transportation Center • 1 year project to survey literature and report on state of the art in autonomous vehicles • Co-PI: Prof. Geb Thomas • Undergraduate students • Kory Nelson • Michael McCrary • Mathew Powell • Nicholas Schlarmann • http://matc.unl.edu/research/research_projects.php?researchID=405 • https://www.zotero.org/groups/autonomous_vehicles/items

  3. Why Autonomous Vehicles? • Safety • 32,000 people killed each year, 93% due to driver error, billions in property damage • Autonomous vision is ‘crashless’ • Mobility • Safely increase traffic density (x2)-(x3) • Greater access for elderly, disabled, etc. • Sustainability • Fuel savings due to platooning (20%), eliminating traffic jams, reducing trip times, reducing ownership, reducing parking spaces

  4. Cycles of Innovation

  5. Vehicle Automation Partner Matrix

  6. An early experiment on automatic highways was conducted by RCA and the state of Nebraska on a 400 foot strip of public highway just outside Lincoln (“Electronic Highway of the Future - Science Digest (Apr, 1958)” 2013)

  7. CMU NAVLAB • RALPH, ALVINN, YARF • In 1995, RALPH drove NAVLAB 5 over 3000 miles from Pittsburgh to Washington, DC. • Steered autonomously 96% of the way from Pittsburgh, PA to Washington DC • Pomerleau, 1995, RALPH: Rapidly Adapting Lateral Position Handler, IEEE Symposium on Intelligent Vehicles, September, 1995

  8. National Automated Highway System 1994-1997 A demonstration of the automated highway system in San Diego (1997). University of California PATH Program

  9. Intelligent Vehicle Initiative 1997-2005 • Prevent driver distraction • Facilitate accelerated deployment of crash avoidance systems • Normal conditions • IVIS • Degraded condition • Visibility, drowsiness • Imminent crash • Rear end, lane depart, intersection, ESC Multiple ADAS system. Image from IVBSS materials, courtesy of UMTRI

  10. DARPA Grand Challenge Grand Challenge: 2004 – no winner 2005 – Stanley (Stanford) Urban Grand Challenge 2007 – Boss (CMU)

  11. Connected Vehicles 2004-present • DSRC (5.9 GHz) • Allocated in 2004 • Goals • Safety • Forward collision, intersection movement assist, lane change, blind spot, do not pass, control loss warning, emergency brake light warning • Mobility • Sustainability • AERIS VII -> IntelliDrive -> Connected Vehicles Regulatory decision from NHTSA recently announced. V2V will eventually be required in new cars.

  12. Google Self-Driving Car 2010

  13. NHTSA Automation Program 2012-present • Licensing • Testing • Regulations • Cybersecurity • Currently recommends states only allow testing NHTSA Levels of Automation

  14. Future Societal Impacts Light Cars: A Virtuous Cycle Autonomous Car Sharing MIT’s Stackable City Car

  15. A Bottom-up approach

  16. Advanced Driver Assistance Systems A 2011 review of commercial ADAS systems compares manufacturers, model year, and sensor type for three types of systems (Shaout, Colella, and Awad 2011)

  17. ADAS Automation

  18. A Top-down Approach

  19. Personal Rapid Transit (PRT) • Fully autonomous • No operator, no controls • Low speed • May use a guideway • Morgantown PRT entered operation in 1975 in West Virginia

  20. PRTs (cont.) • Morgantown, WV • Masdar City (on hold) • London Heathrow Airport • City Mobil 2 • Suncheon, South Korea • Punjab, India • Early criticisms of PRTs on guideways concern the scalability of the system • But new concepts are leaving guideways behind, alleviating some of these concerns

  21. Elements of Automation

  22. Automation Sensors High grade LIDAR Inconspicuous LIDAR GPS / IMU Cameras RADAR Digital Maps DSRC

  23. Localization & Object Detection

  24. Probabilistic Methods • The world is messy with uneven edges, bad lighting, poorly marked roads, and unpredictable people • Applications of probabilistic reasoning • Histogram filters (lane line tracking) • Particle filters, Kalman filters (object tracking) • Bayesian Networks (decision making) • Hidden Markov Models (state estimation)

  25. Some Online Courses • Udacity online courses

  26. Digital Maps & Mapping • Digital maps negate the need to dynamically map the environment • Simultaneous Localization & Mapping (SLAM) used to create environments in unmapped areas • Many modern path planning algorithms are based on A* algorithm • Must find the proper correspondence between the digital map and other sensor inputs

  27. Challenges of Automation

  28. Weather Challenges Bob Donaldson / Post-Gazette

  29. Testing & Certification Path Planning Decision Making Digital Maps All speeds Parking Lots Many more tests Histogram Filters Particle Filters Data Fusion More data (images & video) More test cases Logic Sensor Failures Kalman Filters False Positives

  30. Transfer of Control Example: Transfer of Control to a Platoon

  31. Legality • “Automated vehicles are probably legal in the United States” – Bryant Walker Smith • 1949 Geneva Convention on Road Traffic requires that the driver of a vehicle shall be at all times able to control it • Who is liable: the driver or the manufacturer? • California, Nevada, and Florida have paved the way with state laws for automated vehicles

  32. Hacking Entry Points

  33. Vehicle Networks to Secure

  34. Privacy • Electronic Data Recorders (Black Box) • Identified network traffic • De-identified data • The myth of anonymity • “Google’s self-driving car gathers almost 1 Gb per second” – Bill Gross, Idealab

  35. Privacy By Design • Proactive not reactive • Privacy by default • Privacy embedded into the design • Full functionality (positive sum, not zero sum) • End-to-end security (full lifecycle protection) • Visibility and transparency • Respect for user privacy

  36. Discussion

  37. Case Study: Autonomous Intersectionsand Time to Collision Perception • Time to Collision (TTC) • range / range rate • Autonomous Intersection Management • U Texas at Austin • Reservation system Autonomous Intersection (Top down) Autonomous Intersection (Driver's View) Van der Horst, 1991

  38. The Trouble With Levels • Levels are not a roadmap • Levels are not design guidelines • Levels discouragepotentially helpful ideas like adaptive automation strategies The evolution of vehicle automation and associated challenges

  39. 5 – 30 years until autonomous vehicles hit the road

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