Grade Crossing Monitoring Using Deep Learning

Details

• Creators:
  Ali, Gasser ; Tarawneh, Constantine ; Chavez, Felix ; Espinoza, Darren
• Corporate Creators:
  University Transportation Center for Railway Safety (UTCRS) [UTC] ; University of Texas Rio Grande Valley
• Corporate Publisher:
  University Transportation Center for Railway Safety (UTCRS) Tier-1 University Transportation Center (UTC)
• 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
  • Computer vision
  • Crash avoidance systems
  • Neural networks
  • Railroad crashes
  • Railroad grade crossings
  • Railroad safety
• Publication/ Report Number:
  UTCRS-UTRGV-O6CY23
• Resource Type:
  Tech Report
• Geographical Coverage:
  United States
• Edition:
  June 1, 2023 - August 31, 2024
• Description:
  Railway crossings are critical elements of railway safety due to the heightened risk of collisions. Transportation agencies and researchers are continuously working to enhance safety at railway crossings with better operating procedures and equipment to avoid accidents. Many innovative methods have been proposed to detect hazards at crossings and rail tracks using technologies such as sensors, computer vision, depth cameras, and many others. However, there is still a need to develop a holistic approach that is robust and generalizable to the many conditions and hazards related to grade crossing accidents. This project investigates Artificial Intelligence (AI) and Deep Learning (DL) models to monitor grade crossings and detect various hazardous conditions such as vehicles, pedestrians, cyclists, animals, warning lights, and others. To achieve that, the methodology consists of (1) collecting visual data of railway crossings; (2) labeling the data for training; and (3) developing a computer vision model using deep learning that can detect hazardous conditions at railway crossings. Ultimately, the outcomes of this research support modernizing and improving safety at crossings.
• Format:
  PDF
• Funding:
  69A3552348340
• Download URL:
  https://rosap.ntl.bts.gov/view/dot/79174/dot_79174_DS1.pdf
• File Type:
  [PDF - 1.48 MB]
• Collection(s):
  Artificial Intelligence Deployment Center of Excellence (AI COE) ; University Transportation Centers
• Main Document Checksum:
  urn:sha-512:0d2bce6a6bba198739e248ae3f388275b87540cd988a2525980f34c4d73203d7f68949884d2dab16482323e3064724998fa552ae60c1f3e477e859a8b6b54ad5