Driver Impairment Detection & Safety Enhancement Through Comprehensive Volatility Analysis [Slides]

Details

• Alternative Title:
  Driver Impairment Detection and Safety Enhancement Through Comprehensive Volatility Analysis
• Creators:
  Khattak, Asad J. ; Arvin, Ramin ; Chakraborty, Subhadeep ; Melton, Chad ; Clamann, Michael
• Corporate Creators:
  University of Tennessee, Knoxville ; University of North Carolina at Chapel Hill ; Collaborative Sciences Center for Road Safety
• 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 ; United States. Department of Transportation. Federal Highway Administration
• Subject/TRT Terms:
  • Algorithms
  • Artificial intelligence
  • Automatic data collection systems
  • Behavior
  • Biometrics
  • Data analysis
  • Distracted driving
  • Drivers
  • Driving simulators
  • Impaired drivers
  More +
• Publication/ Report Number:
  CSCRS-R23
• Resource Type:
  Presentations
• Geographical Coverage:
  United States
• Corporate Publisher:
  Collaborative Sciences Center for Road Safety
• Abstract:
  Distraction and impairment can substantially lower driver performance. Distracted and impaired driving contribute to approximately 35% of all transportation deaths. This project focuses on creating new knowledge to advance safety through a systems-based approach.
• Format:
  PDF
• Funding:
  69A3551747113
• Download URL:
  https://rosap.ntl.bts.gov/view/dot/82613/dot_82613_DS1.pdf
• File Type:
  [PDF - 836.86 KB]
• Collection(s):
  University Transportation Centers
• Main Document Checksum:
  urn:sha-512:b284afde2c1ba1c4186a3feb3178e887587df60d49696967d8b65defd434e6228536ca1d7f9ed998689f817c95abe5c049e73b547ef57b4809a21a82a2dfb243