Connected Vehicle Winter Safety Improvement With Infrared Thermography Technology

Details

• Creators:
  Zhu, Xuan ; Yang, Xianfeng (Terry) ; Kami, Moein Ramezanpour
• Corporate Creators:
  University of Utah. Department of Civil and Environmental Engineering ; University of Maryland (College Park, Md.). Department of Civil and Environmental Engineering
• Corporate Contributors:
  Utah. Department of Transportation. Research and Innovation Division ; United States. Department of Transportation. Federal Highway Administration
• Subject/TRT Terms:
  • Winter maintenance
  • Highway safety
  • Snow
  • Ice
  • Infrared detectors
  • Computer vision
  • Machine learning
  • Weather conditions
  • Warning devices
  • Cameras
  More +
• Publication/ Report Number:
  UT-26.17
• Resource Type:
  Tech Report
• Geographical Coverage:
  United States ; Utah
• Edition:
  Final Report: Oct 2022 to December 2025
• Corporate Publisher:
  Utah Department of Transportation
• Abstract:
  The primary objective of this research is to utilize dual-spectrum cameras, optical, and infrared thermography (IRT) cameras, to detect slippery road spots and high-crash-risk locations in winter seasons, using field evaluations and machine learning to improve detection algorithms. This capability can be used to detect road surface snow/ice patterns to augment or complement the Spot Weather Impact Warning (SWIW) application that UDOT is in the process of implementing. SWIW uses RWIS and Connected Vehicle (CV) data as inputs, whereas the proposed technology uses wayside IRT camaras and machine learning algorithms for direct measurements, which can be potentially extended for snow/ice detection at non-RWIS locations. The outcome of this research is expected to examine the feasibility of integrating IRT sensor data in future CV control systems.
• Format:
  PDF
• Funding:
  23-8194
• Download URL:
  https://rosap.ntl.bts.gov/view/dot/92775/dot_92775_DS1.pdf
• File Type:
  [PDF - 1.82 MB]
• Collection(s):
  US Transportation Collection
• Main Document Checksum:
  urn:sha-512:db0c136847e8114448b317a3431d9685f05bd194ef7ee09888c48a97d5a66b3653090a1dc81d553dfae5023c73f852fa425079969df4c4dc1ff0b2be5a398be0