DeepScenario: City Scale Scenario Generation for Automated Driving System Testing & Evaluation

Details

• Creators:
  Liu, Henry ; Feng, Yiheng
• Corporate Creators:
  University of Michigan. Transportation Research Institute ; University of Michigan, Ann Arbor
• Corporate Contributors:
  Center for Connected and Automated Transportation (CCAT) ; United States. Department of Transportation. University Transportation Centers (UTC) Program ; United States. Department of Transportation. Office of the Assistant Secretary for Research and Technology
• Subject/TRT Terms:
  • Autonomous land vehicles
  • Autonomous vehicles
  • Connected vehicles
  • Environment
  • Routes
  • Simulation
  • Testing equipment
  • Virtual reality
  • Vulnerable road users
• Publication/ Report Number:
  UMTRI-2023-3
• DOI:
  https://doi.org/10.7302/7018
• Resource Type:
  Tech Report
• Geographical Coverage:
  United States
• Edition:
  Final Report March 2020 – February 2022
• Corporate Publisher:
  University of Michigan. Center for Connected and Automated Transportation
• Abstract:
  Driving intelligence test is critical to the development and deployment of autonomous vehicles. The prevailing approach tests autonomous vehicles in life-like simulations of the naturalistic driving environment. However, due to the high dimensionality of the environment and the rareness of safety-critical events, hundreds of millions of miles would be required to demonstrate the safety performance of autonomous vehicles, which is severely inefficient. We discover that sparse but adversarial adjustments to the naturalistic driving environment, resulting in the naturalistic and adversarial driving environment, can significantly reduce the required test miles without loss of evaluation unbiasedness. By training the background vehicles to learn when to execute what adversarial maneuver, the proposed environment becomes an intelligent environment for driving intelligence testing. We highway-driving simulation. Comparing with the naturalistic driving environment, the proposed environment can accelerate the evaluation process by multiple orders of magnitude.
• Format:
  PDF
• Funding:
  69A3551747105
• Download URL:
  https://rosap.ntl.bts.gov/view/dot/73488/dot_73488_DS1.pdf
• File Type:
  [PDF - 7.46 MB]
• Collection(s):
  Autonomous Vehicles ; University Transportation Centers
• Main Document Checksum:
  urn:sha-512:2b0ae095754f1afa03b87357aab9b11792a15e6ce6c4478ed3892d7531d681187a257339f3b67449133db2bbb2550c0bbcd81d78a7d923d604edcd7b1b27c049