Modeling Naturalistic Driving Environment with High-Resolution Trajectory Data

Details

• Creators:
  Feng, Shuo ; Yan, Xintao ; Liu, Henry
• Corporate Creators:
  Center for Connected and Automated Transportation (CCAT) ; University of Michigan. Transportation Research Institute
• Corporate Contributors:
  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 vehicles
  • Simulation
  • Trajectory
  • Data
  • Automatic data collection systems
  • Connected vehicles
  • Macroscopic traffic flow
  • Vehicle safety
  • Vehicle tests
• Publication/ Report Number:
  69A3551747105
• DOI:
  https://doi.org/10.7302/22417
• Resource Type:
  Tech Report
• Geographical Coverage:
  United States
• Edition:
  Final Report, April 2022 - March 2023
• Corporate Publisher:
  University of Michigan. Center for Connected and Automated Transportation
• Abstract:
  For simulation to be an effective tool for the development and testing of autonomous vehicles, the simulator must be able to produce realistic safety-critical scenarios with distribution-level accuracy. However, due to the high dimensionality of real-world driving environments and the rarity of long-tail safety-critical events, how to achieve statistical realism in simulation is a long-standing problem. In this project, we develop Neural DE, a deep learning-based framework to learn multi-agent interaction behavior from high-resolution vehicle trajectory data and propose a conflict critic model and a safety mapping network to refine the generation process of safety-critical events, following real-world occurring frequencies and patterns. The results show that Neural NDE can achieve both accurate safety-critical driving statistics (e.g., crash rate/type/severity and near-miss statistics, etc.) and normal driving statistics (e.g., vehicle speed/distance/yielding behavior distributions, etc.), as demonstrated in the simulation of urban driving environments.
• Format:
  PDF
• Funding:
  UMTRI-2024-2
• Download URL:
  https://rosap.ntl.bts.gov/view/dot/73556/dot_73556_DS1.pdf
• File Type:
  [PDF - 3.93 MB]
• Collection(s):
  Artificial Intelligence Deployment Center of Excellence (AI COE) ; Autonomous Vehicles ; University Transportation Centers
• Main Document Checksum:
  urn:sha-512:9cd1182a0fdff7b147b6438e0e2d713079b37bb810d0e1fd8362501809cab6b15c78d906ed12de7650dbe8a6a7c42d7bfa69025c45ad37bb8e565f22bc6c82e2