Safety Assurance System Utilizing Visual Attention for Advanced Driver-Assistance Systems

Details

• Creators:
  Jing, Haoming ; Wang, Zhuoyuan ; Nakahira, Yorie
• Corporate Creators:
  Mobility21, Carnegie Mellon University
• 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:
  • Adaptive control
  • Algorithms
  • Attention
  • Autonomous vehicles
  • Driver support systems
  • Visual perception
  • Risk assessment
• Resource Type:
  Tech Report
• Geographical Coverage:
  United States
• Edition:
  Final Report
• Contracting Officer:
  Kline, Robin
• Corporate Publisher:
  Mobility21, Carnegie Mellon University
• Abstract:
  The authors will apply their results from neuroscience and safe control to improve driver-assistance technology as follows. First, the authors will study the use of visual attention information to detect risks early, before the failure to detect risk-critical obstacles can be identified from the drivers' control action and vehicle states. Second, the authors will find safer control actions even when conventional methods, which are unnecessarily conservative, become infeasible in finding safe intervention strategies.
• Format:
  PDF
• Funding:
  69A3551747111
• Download URL:
  https://rosap.ntl.bts.gov/view/dot/68701/dot_68701_DS1.pdf
• File Type:
  [PDF - 6.21 MB]
• Collection(s):
  Autonomous Vehicles ; University Transportation Centers
• Main Document Checksum:
  urn:sha-512:3cd70d116557b65e147e69c6c252b3f9e3059c2cb3fa26880cbafeb52840b286695cfcbf31c891fcfad867fbfaf3bd1958720c5f32da460380c28656ef2c1af4