Automated Real-Time Weather Detection Systems Using Artificial Intelligence [Research Brief]

Details

• Creators:
  Ahmed, Mohamed M ; Khan, Md Nasim ; Das, Anik
• Corporate Creators:
  University of Wyoming ; Mountain-Plains Consortium
• 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:
  • Artificial intelligence
  • Detection and identification technologies
  • Real time information
  • Snow
  • Weather conditions
• Publication/ Report Number:
  MPC 22-485
• Resource Type:
  Brief
• Geographical Coverage:
  United States
• Corporate Publisher:
  Mountain-Plains Consortium
• Abstract:
  The primary objective of this research was to develop cost-effective systems capable of providing accurate weather and surface conditions in real time. First, a trajectory-level weather detection system was developed using a single video camera mounted on a vehicle dashboard. Two texture-based features, histogram of oriented gradient (HOG) and local binary pattern (LBP), were extracted from images and used as classification parameters to train weather detection models using several machine learning classifiers, such as gradient boosting (GB), random forest (RF), and support vector machine (SVM). In addition, a unique multilevel model, based on a hierarchical structure, was also proposed to increase detection accuracy.
• Format:
  PDF
• Funding:
  MPC-630
• Download URL:
  https://rosap.ntl.bts.gov/view/dot/87087/dot_87087_DS1.pdf
• File Type:
  [PDF - 498.30 KB]
• Collection(s):
  Artificial Intelligence Deployment Center of Excellence (AI COE) ; University Transportation Centers
• Main Document Checksum:
  urn:sha-512:6a3984562e24611b898aeeb6cea863b9bf0b11e6bbc36e0021745656124583cd7679f26c22e0658984bebb1c21b4b71061cdb56acfbe70a10dcb0b0b38f7d891