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Connected and Automated Vehicles: Where Are We Going and What Happens When We Get There?

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Presentation by Chandra Bhat and James Kuhr at 2017 CTR Symposium http://ctr.utexas.edu/ctr-symp/

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Connected and Automated Vehicles: Where Are We Going and What Happens When We Get There?

  1. 1. COLLABORATE. INNOVATE. EDUCATE. Connected and Autonomous Vehicles: Where Are We Going and What Happens When We Get There? Dr. Chandra Bhat & James Kuhr, Esq. Acknowledgements: TxDOT, D-STOP NCTCOG, Humboldt Award, Dr. Ram Pendyala, Dr. Kostas Goulias, all of Dr. Bhat’s graduate/undergraduate students
  2. 2. COLLABORATE. INNOVATE. EDUCATE. CTR Research • Overview • Autonomous Technology • Connected Technology • CARSTOP project • Adoption • Societal Effects • Planning
  3. 3. COLLABORATE. INNOVATE. EDUCATE. Definitions • NHTSA – National Highway Transportation Safety Administration, they are the federal regulating body for autonomous vehicles • Autonomous – A vehicle that can operate, in some manner, without constant direction from the driver • Connected – A vehicle that can communicate with other vehicles and infrastructure • Ridesourcing – the new name for Ridesharing
  4. 4. COLLABORATE. INNOVATE. EDUCATE. Levels of Autonomy Level 1 Level 2 Level 3 Level 4 Adaptive Cruise Control Adaptive Cruise Control + Lane Assist Open Road Automated Vehicle Generally Hands Off Driving IncreaseinRoadwaySafety IncreaseinNetwork Effects Level 5
  5. 5. COLLABORATE. INNOVATE. EDUCATE. Did you know? • All New 2018 Vehicles Must Have A Back-Up Camera • Roughly 200 people are killed each year and another 14,000 are injured in so-called backover accidents, when drivers reverse over another person without noticing him or her. • 20 Automakers (99% of the US market) have agreed to make automatic braking standard by 2022 • IIHS estimates that automated braking at full penetration would have prevented 700,000 crashes in 2013 (13% of all crashes) • “The data show that the Tesla vehicles crash rate dropped by almost 40 percent after Autosteer installation.” – NHTSA Report • Tesla expects a 90% drop with Autopilot 2 • Tesla is selling insurance in Australia and Hong Kong
  6. 6. COLLABORATE. INNOVATE. EDUCATE. AUTOMATED VEHICLE TECHNOLOGY
  7. 7. COLLABORATE. INNOVATE. EDUCATE. LIDAR Produces a 360 degree 3d model of the surroundings Video Camera Monitors frontward, lane departure and reads traffic signals Radar Monitors surroundings Odometry Sensors Monitors vehicle distance travel and speed GPS Tracks the car location geospatially Ultrasonic Senses at low speeds Internal CPU V2V, V2I Communication Connects with other cars and supporting infrastructure Autonomous Vehicle Technology
  8. 8. COLLABORATE. INNOVATE. EDUCATE. LIDAR
  9. 9. COLLABORATE. INNOVATE. EDUCATE. Cameras Radar
  10. 10. COLLABORATE. INNOVATE. EDUCATE. GPS GPS position (white box) vs. Google Car Ultrasonic and Odometry CPU
  11. 11. COLLABORATE. INNOVATE. EDUCATE. LIDAR vs Camera
  12. 12. COLLABORATE. INNOVATE. EDUCATE. The car will identify the vehicle in front of it and match speeds to maintain a safe following distance (set by the user) while not exceeding a certain speed (also set by the user) Adaptive Cruise Control Automatically adjust speeds in a traffic jam, including braking to a full stop, and handles the steering. Driver must stay alert, but does not have to touch the wheel or pedals. Traffic Jam Assist Alerts the driver when the system detects that the vehicle is about to leave its lane and can automatically correct the steering and keep the car on course Lane Keep Assist The car will detect panicked breaking and apply more pressure to the brakes to stop the car faster. Emergency Brake Assist Automatically parallel parks a car, as long as the gap is 1.2 times the size of the car. Parking Assist Automatically applies the brakes for obstacle avoidance. Auto Braking Semi-autonomous features are safety based – and their incorporation in current models will begin to reduce accidents in the next 5 to 10 years. Conclusion Semi Autonomous Features
  13. 13. COLLABORATE. INNOVATE. EDUCATE. US Market Share Level 2 Level 3 Level 4 20% 2016 2021 16% 2016 2021 13% 2016 2020 12% 9% 2016 2020 7% 2016 2018 2020 7% 2016 2020 2030 3% 2016 2020 2% 2016 2017 2021 Level 2 Level 3 Level 4 2016 2025 2016 2021 2016 2030 2016 2017 2018 2016 2016 2018 2020 Others: Market Shares 2018 (ride sourcing)
  14. 14. COLLABORATE. INNOVATE. EDUCATE. CONNECTED VEHICLE TECHNOLOGY
  15. 15. COLLABORATE. INNOVATE. EDUCATE. Dedicated Short Range Communications (DSRC)
  16. 16. COLLABORATE. INNOVATE. EDUCATE. Why Worry About Communication? • The best autonomous vehicles are connected vehicles! • Commercially scorned by the Private Sector • Value is found in a saturated market, first mover gains no competitive edge • Pieces are available off the shelf, now • Retrofit possibilities are much larger than autonomy • Infrastructure is a necessary piece
  17. 17. COLLABORATE. INNOVATE. EDUCATE. • Through use of just V2V BSM to warn drivers, with a mature system, NHTSA studies indicate that up to 79% of unimpaired crashes could be avoided. • Using just a V2I communication system, NHTSA estimates that 26% of unimpaired crashes could be avoided. Benefits V2X Safety • By 2029, seven years after the projected phase-in of the light vehicle V2V rule, 60% of all vehicles, or a cumulative 146 million cars, will have DSRC/V2X equipment. • Adoption of aftermarket/consumer electronics DSRC devices will outpace factory installed DSRC within five to six years after a NHTSA Light Vehicle V2V rule requiring 100% of all new vehicles to be equipped with V2V. FHWA ITS JPO Prediction https://ntl.bts.gov/lib/60000/60500/60535/FHWA-JPO-17-487_Final_.pdf • Through use of just V2V BSM to warn drivers, with a mature system, NHTSA studies indicate that up to 79% of unimpaired crashes could be avoided. • Using just a V2I communication system, NHTSA estimates that 26% of unimpaired crashes could be avoided. Benefits • Together, NHTSA studies indicate that 81% of all unimpaired crashes could be avoided with a fully mature V2V and V2I system.
  18. 18. COLLABORATE. INNOVATE. EDUCATE. • MLK Speed Limit: 50mph The Case of Bundyhill • 19 recorded crashes since 2010 • 41 Units, 77 Persons
  19. 19. COLLABORATE. INNOVATE. EDUCATE. • Safety • Shorter headways/spacing between vehicles • Platooning • Smaller startup lost times at signalized intersection • A smoother stop-and-go movement through intersections without traffic signals • String stability in Mixed Traffic • Data support optimized traffic management operations V2X System Performance
  20. 20. COLLABORATE. INNOVATE. EDUCATE. Connected Vehicle Adoption Curve it is worth noting that in case of no regulations, even at 10% annual drop in technology prices and no-zero, but constant [Willingness To Pay]…83.5% [of vehicles] would have connectivity in 2045 – Bansal & Kockleman 0 20 40 60 80 100 120 2021 2025 2030 2035 2040 2045 2050 2055 2060 % Veh w/ DSRC % w/ Safety Apps
  21. 21. COLLABORATE. INNOVATE. EDUCATE. CAR STOP COMMUNICATIONS AND RADAR-SUPPORTED TRANSPORTATION OPERATIONS AND PLANNING
  22. 22. COLLABORATE. INNOVATE. EDUCATE. Car Stop – Come see us during the break! Develop prototypes of Crash Warning & Crash Avoidance (CW/CA) systems that use joint sensing and communication technologies 22 So that: • We can determine the sensor and communication equipment and configuration needed in-vehicle and road-side to maximize roadway safety. • We can test next generation level communication technology (mmWave) that may enable an even wider range of safety based communications.
  23. 23. COLLABORATE. INNOVATE. EDUCATE. Automated Vehicles and Transportation Technology Infrastructure Traveler Behavior
  24. 24. COLLABORATE. INNOVATE. EDUCATE. Self-Driving Vehicle (e.g., Google) Connected Vehicle AI located within the vehicle AI wirelessly connected to an external communications network “Outward-facing” in that sensors blast outward from the vehicle to collect information without receiving data inward from other sources “Inward-facing” with the vehicle receiving external environment information through wireless connectivity, and operational commands from an external entity AI used to make autonomous decisions on what is best for the individual driver Used in cooperation with other pieces of information to make decisions on what is “best” from a system optimal standpoint AI not shared with other entities beyond the vehicle AI shared across multiple vehicles A more “Capitalistic” set-up A more “Socialistic” set-up Two Types of Technology
  25. 25. COLLABORATE. INNOVATE. EDUCATE. Regular Traffic Conditions PRESENT DAY
  26. 26. COLLABORATE. INNOVATE. EDUCATE. Icy Patch PRESENT DAY
  27. 27. COLLABORATE. INNOVATE. EDUCATE. Incident PRESENT DAY
  28. 28. COLLABORATE. INNOVATE. EDUCATE. Lane blocking, traffic slow down PRESENT DAY
  29. 29. COLLABORATE. INNOVATE. EDUCATE. Congestion buildup, late lane changes PRESENT DAY
  30. 30. COLLABORATE. INNOVATE. EDUCATE. Congestion propagation to frontage, ramp backed up PRESENT DAY
  31. 31. COLLABORATE. INNOVATE. EDUCATE. Regular Traffic Conditions V2V
  32. 32. COLLABORATE. INNOVATE. EDUCATE. Icy Patch V2V
  33. 33. COLLABORATE. INNOVATE. EDUCATE. Incident: Information propagation V2V
  34. 34. COLLABORATE. INNOVATE. EDUCATE. Preemptive lane changing, freeway exit V2V
  35. 35. COLLABORATE. INNOVATE. EDUCATE. Re-optimization of signal timing, upstream detours INCIDENT AHEAD TAKE DETOUR V2I
  36. 36. COLLABORATE. INNOVATE. EDUCATE. Regular Traffic Conditions AUTONOMOUS
  37. 37. COLLABORATE. INNOVATE. EDUCATE. Icy Patch AUTONOMOUS
  38. 38. COLLABORATE. INNOVATE. EDUCATE. Avoidance of icy patch, no incident AUTONOMOUS
  39. 39. COLLABORATE. INNOVATE. EDUCATE. Traffic slowdown, late lane changing, congestion AUTONOMOUS
  40. 40. COLLABORATE. INNOVATE. EDUCATE. Icy Patch AUTONOMOUS + V2X
  41. 41. COLLABORATE. INNOVATE. EDUCATE. Avoidance of icy patch, no incident AUTONOMOUS + V2X
  42. 42. COLLABORATE. INNOVATE. EDUCATE. Information propagation, preemptive lane changing, freeway exit AUTONOMOUS + V2V
  43. 43. COLLABORATE. INNOVATE. EDUCATE. Re-optimization of signal timing, upstream detours INCIDENT AHEAD TAKE DETOUR AUTONOMOUS + V2I
  44. 44. COLLABORATE. INNOVATE. EDUCATE. Infrastructure Needs/Planning Driven By…  Complex activity-travel patterns  Growth in long distance travel demand  Limited availability of land to dedicate to infrastructure  Budget/fiscal constraints  Energy and environmental concerns  Information/ communication technologies (ICT) and mobile platform advances Autonomous vehicles leverage technology to increase flow without the need to expand capacity
  45. 45. COLLABORATE. INNOVATE. EDUCATE. Technology and Infrastructure Combination Leads To…  Safety enhancement  Virtual elimination of driver error – factor in 80% of crashes  Enhanced vehicle control, positioning, spacing, speed, harmonization  No drowsy, impaired, stressed, or aggressive drivers  Reduced incidents and network disruptions  Offsetting behavior on part of driver
  46. 46. COLLABORATE. INNOVATE. EDUCATE.  Capacity enhancement  Platooning reduces headways and improves flow at transitions  Vehicle positioning (lateral control) allows reduced lane widths and utilization of shoulders; accurate mapping critical  Optimized route choice  Energy and environmental benefits  Increased fuel efficiency and reduced pollutant emissions  Clean fuel vehicles  Car-sharing
  47. 47. COLLABORATE. INNOVATE. EDUCATE. • NHTSA Crash Report: 40% reduction in Tesla crashes attributable to autopilot Already Happening: Safety • IIHS: 2010-14 study on FCW systems on the road: – For 383,868 cars, there were only 1277 actual rear end crashes vs 1872 projected – Extrapolating: that would be ~390,000 less crashes with full fleet penetration
  48. 48. COLLABORATE. INNOVATE. EDUCATE. • Tesla surpassed Ford and GM as most valuable auto-maker • Ridesourcing entities are changing mobility • Parking Revenue is being affected at airports and parking meters • Transit is taking advantage of Ridesourcing opportunities Already Happening
  49. 49. COLLABORATE. INNOVATE. EDUCATE. BUT LET’S NOT FORGET TRAVELER BEHAVIOR ISSUES!
  50. 50. COLLABORATE. INNOVATE. EDUCATE. Adoption
  51. 51. COLLABORATE. INNOVATE. EDUCATE. 0% 20% 40% 60% 80% 100% 2020 2021 2022 2023 2024 2025 2026 2027 2028 2029 2030 2031 2032 % of Vehicles Replaced Globally In 2015, just over 90 million light passenger and commercial vehicles were produced in the world. Currently, there are over 1.2 billion vehicles in use in the world. On the roughest possible level: at current production rates, if every vehicle going forward was autonomous, it would still be 13 years before all the existing vehicles on the road could be replaced. Assumption: Current Production capabilities provide a most-aggressive bound Consider
  52. 52. COLLABORATE. INNOVATE. EDUCATE. Concerns about Autonomous Cars Survey with 1800 individuals in the Puget sound Region: Type of concern Not concerned Somewhat unconcerned Neutral/doesn’t know Somewhat concerned Very concerned Equipment and system safety 6.9% 4.4% 22.2% 26.9% 39.6% System and vehicle security 8.4% 5.0% 26.2% 26.8% 33.7% Capability to react to the environment 6.2% 3.2% 18.9% 22.8% 48.9% Performance in poor weather or other unexpected conditions 6.3% 4.3% 21.5% 26.5% 41.4% Legal liability for drivers or owners 6.4% 4.2% 24.3% 27.4% 37.7%
  53. 53. COLLABORATE. INNOVATE. EDUCATE. Adoption Timelines based on: • Previous Adoption of New Technology • Consultant Reports • Behavior studies …all indicate an extended period of mixed traffic Victoria Transport Policy Institute (VTPI)
  54. 54. COLLABORATE. INNOVATE. EDUCATE. Planning for the Future
  55. 55. COLLABORATE. INNOVATE. EDUCATE. Going to Happen? Land Use Patterns • Live and work farther away – Use travel time productively – Access more desirable and higher paying jobs – Attend better school/college • Visit destinations farther away – Access more desirable destinations for various activities – Reduced impact of distances and time on activity participation • Influence on developers – Sprawled cities? – Impacts on community/regional planning and urban design Impacts on Household Vehicle Fleet • Potential to redefine vehicle ownership – No longer own personal vehicles; move toward car sourcing enterprise where rental vehicles come to traveler • More efficient vehicle ownership and sharing scheme may reduce the need for additional infrastructure – Reduced demand for parking • Desire to work and be productive in vehicle – More use of personal vehicle for long distance travel – Purchase large multi-purpose vehicle with amenities to work and play in vehicle
  56. 56. COLLABORATE. INNOVATE. EDUCATE.
  57. 57. COLLABORATE. INNOVATE. EDUCATE. Impacts on Mode Choice Automated vehicles combine the advantages of public transportation with that of traditional private vehicles What will happen to public transportation? Also automated vehicles may result in lesser walking and bicycling shares • Driving personal vehicle more convenient and safe • Traditional transit captive market segments now able to use auto (e.g., elderly, disabled) • Reduced reliance/usage of public transit? • However, autonomous vehicles may present an opportunity for public transit and car sharing – Lower cost of operation (driverless) and can cut out low volume routes – More personalized and reliable service - smaller vehicles providing demand-responsive transit service – No parking needed – kiss-and-ride; no vehicles “sitting” around – 20-80% of urban land area can be reclaimed – Chaining may not discourage transit use
  58. 58. COLLABORATE. INNOVATE. EDUCATE. More Impacts: Long Term Facilities Long Distance Travel • Less incentive to use public transportation? – Should we even be investing in high capital high-speed rail systems? – Individuals may travel mostly in the night – Speed difference? Infrastructure Investment in Tolled Facilities • How does this affect Traffic and Revenue? Parking • Do we need it? Highway Design • Smaller lanes or bigger?
  59. 59. COLLABORATE. INNOVATE. EDUCATE. Can We Guide the Future? • Results show:  Individuals with green lifestyle preferences and who are tech-savvy are more likely to adopt car-sharing services, use ride-sourcing services, and embrace autonomous vehicle-sharing in the future.  Younger and more educated urban residents are more likely to be early adopters of autonomous vehicle technologies, favoring a sharing-based service model.  Individuals who currently eschew vehicle ownership, and have already experienced car-sharing or ride-sourcing services, are especially likely to be early adopters of AV sharing services.  Most effective way to move AV adoption toward a sharing model (rather than an ownership model) is to enhance neighborhood densification.  Will new mobility options reduce bicycling, walking, and the use of public transportation (PT) services?
  60. 60. COLLABORATE. INNOVATE. EDUCATE. Mixed Vehicle Operations Uncertainty in penetration rates of driverless cars Considerable amount of time of both driverless and traditional car operation When will we see full adoption of autonomous? Depends on regulatory policies Need infrastructure planning to support both, with intelligent/dedicated infrastructure for driverless
  61. 61. COLLABORATE. INNOVATE. EDUCATE. TEXAS AUTOMATED VEHICLE PROVING GROUNDS PARTNERSHIP Chandra Bhat, Ph.D., P.E. Director, Center for Transportation Research 1
  62. 62. COLLABORATE. INNOVATE. EDUCATE. USDOT AV Proving Grounds • USDOT seeking to: – Create national network of proving grounds – Encourage new levels of public safety – Establish Community of Practice on testing and demonstration of best practices – Accelerate the pace of safe deployment • January 2017, USDOT designated 10 sites 62
  63. 63. COLLABORATE. INNOVATE. EDUCATE. Texas AV Testing Needs • Automated vehicles are here – and more are coming! • How do the public agencies plan for: – AVs to help with safety and mobility needs – What is needed to safely accommodate AVs – Safe introduction of AVs into mixed traffic
  64. 64. COLLABORATE. INNOVATE. EDUCATE. National AV Proving Grounds USDOT selected 10 sites out of 60+ proposals 1. City of Pittsburgh and the Thomas D. Larson Pennsylvania Transportation Institute 2. Texas AV Proving Grounds Partnership 3. U.S. Army Aberdeen Test Center 4. American Center for Mobility (ACM) at Willow Run 5. Contra Costa Transportation Authority (CCTA) & GoMentum Station 6. San Diego Association of Governments 7. Iowa City Area Development Group 8. University of Wisconsin-Madison 9. Central Florida Automated Vehicle Partners 10. North Carolina Turnpike Authority 64
  65. 65. COLLABORATE. INNOVATE. EDUCATE. Texas Proving Ground Partnership 65
  66. 66. COLLABORATE. INNOVATE. EDUCATE. Proving Ground Partners • Texas A&M, University of Texas, and Southwest Research Institute • All are conducting AV research • All have controlled proving grounds on their campuses 66
  67. 67. COLLABORATE. INNOVATE. EDUCATE. Areas of Research Connected & Autonomous Vehicles Emerging Technologies Policy Impact Analysis Pavements & Infrastructure
  68. 68. COLLABORATE. INNOVATE. EDUCATE. Connected & Autonomous Vehicles Data-Supported Transportation Operations & Planning Consumer Preferences and Willingness to Pay for Advanced Vehicle Technology Options and Fuel Types  Objective – Analyze consumer preferences for advanced vehicular technologies  Outcomes – Heterogeneity in preferences for wireless internet, vehicle connectivity, voice command features – Less heterogeneity in preferences for real-time traveler information Other Projects  Transit Demand and Routing after Autonomous Vehicle Availability  Semi-Autonomous Parking for Enhanced Safety and Efficiency  Learning Approach to Beam Alignment for mmWave Vehicular Communications
  69. 69. COLLABORATE. INNOVATE. EDUCATE. Connected & Autonomous Vehicles Ensuring Benefits of CAV in Texas  Objective – Smart transport technologies and practices  Outcomes – Benefit cost assessment, e.g. AV managed lanes – Proactive policy making • Vehicle licensing • Liability • Privacy standards Other Projects  Assessing Impact on Traffic and Infrastructure Needs  Implications of AVs on Safety, Design, and Operation of Texas Highways  Semi Autonomous Operations, such as parking and/or transit facilities
  70. 70. COLLABORATE. INNOVATE. EDUCATE. Areas of Research Connected & Autonomous Vehicles Emerging Technologies Policy Impact Analysis Pavements & Infrastructure
  71. 71. COLLABORATE. INNOVATE. EDUCATE. Emerging Technologies Active Traffic Management Strategies  Objective – Analyze effectiveness: Varying congestion levels and data availability  Outcomes – Tools for evaluating where each strategy works best – Network level Impacts using DTA – Corridor level impacts using microsimulation Other Projects  Integrating Activity Based Modeling with Dynamic Traffic Assignment  Smart Cities  High Precision GPS Tracking for safer Operations
  72. 72. COLLABORATE. INNOVATE. EDUCATE. Areas of Research Connected & Autonomous Vehicles Emerging Technologies Policy Impact Analysis Pavements & Infrastructure
  73. 73. COLLABORATE. INNOVATE. EDUCATE. Policy Impact Analysis Texas Technology Task Force  Objective – Develop a vision for the future of the Texas transportation system  Outcomes – Network of thought leaders – Emerging Technology Portfolio – Strategic Technology Business Plan Interconnected Applications Next Generation Vehicles & Energy Information & Communications Service-Based Platforms Other Technologies Materials & Additive Manufacturing Infrastructure & Construction
  74. 74. COLLABORATE. INNOVATE. EDUCATE. Policy Impact Analysis Network Impacts post WMD Attacks  Objective – Modeling outages in critical infrastructure  Outcomes – Propagation of degradation due to contaminants – Insight on vulnerability of interdependent networks Panama Canal Expansion Effects  Objective – Predict its impacts on Texas freight infrastructure  Outcomes – Project traffic growth – Suggest Improvements to freight infrastructure- roads and rails
  75. 75. COLLABORATE. INNOVATE. EDUCATE. Areas of Research Connected & Autonomous Vehicles Emerging Technologies Policy Impact Analysis Pavements & Infrastructure
  76. 76. COLLABORATE. INNOVATE. EDUCATE. Pavements & Infrastructure Designing Quieter Pavements  Objective – Optimal pore size in asphalt mixes to reduce noise  Outcomes – Developing new asphalt mix and test against noises – Benefit cost analysis for real field implementation Other Projects  Continuum Approaches to Quantify Healing in Asphalt Composites  Asphalt Genome Investigation: Impact of Different Chemical Fractions  Evaluation of Pavement Surface Micro and Macro-Texture  Optimal Resource Allocations for Highway Infrastructure Maintenance under Budget Fluctuations
  77. 77. COLLABORATE. INNOVATE. EDUCATE. Summary Broad Areas of Research  Enhancing safety  Relieving congestion  Multi-modal transportation  Strategic planning  Improving current infrastructure management practices
  78. 78. COLLABORATE. INNOVATE. EDUCATE. Our Sincerest Thanks! We love what we do, and we can do it because of the generosity of TxDOT among others. Thank you!

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