Artificial Intelligence for Pedestrian and Bicyclist Safety: Using AI To Detect Near-Miss Collisions [Research Brief]

Details

• Creators:
  Pourhomayoun, Mohammad
• Corporate Creators:
  San Jose State University. College of Business. Mineta Transportation Institute ; California State University, Los Angeles
• Corporate Contributors:
  State of California SB1 2017/2018, Trustees of the California State University Sponsored Programs Administration ; 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:
  • Artificial intelligence
  • Machine learning
  • Pedestrian safety
  • Traffic safety
  • Near crashes
  • Cyclists
• Publication/ Report Number:
  2350
• Resource Type:
  Brief
• Geographical Coverage:
  California ; United States
• Edition:
  Research Brief
• Corporate Publisher:
  San Jose State University. College of Business. Mineta Transportation Institute
• Abstract:
  For this project, we developed an end-to-end system based on advanced artificial intelligence (AI) models and effective computer vision algorithms to detect and report near-miss collisions as an important indicator to identify and measure safety risks, especially in specific circumstances such as right turn on a red light. The main objective is to improve the safety of pedestrians and bicyclists by applying automated AI-powered systems to detect accident risks for pedestrians and cyclists.
• Format:
  PDF
• Download URL:
  https://rosap.ntl.bts.gov/view/dot/78241/dot_78241_DS1.pdf
• File Type:
  [PDF - 677.06 KB]
• Collection(s):
  Artificial Intelligence Deployment Center of Excellence (AI COE) ; University Transportation Centers
• Main Document Checksum:
  urn:sha-512:171f49da87b6edc034bf65733139b2a3b12babcde6184ea438a77bd1dbc50375240634877b89ad357b6f287c771ea98ee326567e5f33125e7ba407aca91e8a9d