Enhanced Datasets and AI Models for Monitoring of Grade Crossings

Details

• Creators:
  Ali, Gasser ; Tarawneh, Constantine ; Flores-Osuna, Silvia ; Chavez, Felix ; Espinoza, Darren
• Corporate Creators:
  University of Texas Rio Grande Valley. University Transportation Center for Railway 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
• Subject/TRT Terms:
  Artificial Intelligence Computer Vision Hazards Railroad Grade Crossings
• Publication/ Report Number:
  UTCRS-UTRGV-O1CY24
• Resource Type:
  Tech Report
• Geographical Coverage:
  United States
• Edition:
  June 1, 2024 – August 31, 2025
• Corporate Publisher:
  University Transportation Center for Railway Safety (UTCRS) Tier-1 University Transportation Center (UTC)
• Abstract:
  Safety at grade crossings is a major concern due to the number of accidents every year. Many innovative technologies have been proposed to automate the monitoring of crossings. The goal of this research is to investigate the use of Artificial Intelligence (AI) and Deep Learning (DL) to monitor grade crossings and detect various hazardous conditions such as vehicles, pedestrians, cyclists, animals, warning lights, and others. Limitations of previous work show the need to improve the size and balance of the data. The work in this proposal aims to address limitations in the current model and to make new advances by (1) increasing the number of photos in the dataset using real video streams; (2) using captures from a train simulator videogame environment; (3) addressing the issue of imbalanced dataset for training and validation; and (4) hyper-optimizing the model for accuracy and real-time performance. This research relates directly to the strategic research goal of UTCRS of reducing fatalities and injuries at highway-rail grade crossings (HRGCs); and relates to the railway operation systems research area of autonomous systems for grade crossing safety. The outcomes of this research should advance knowledge in automated monitoring of hazards at grade crossings, and result in a model that can be implemented in cameras for automated hazard monitoring at grade crossings.
• Format:
  PDF
• Funding:
  69A3552348340
• Collection(s):
  University Transportation Centers
• Main Document Checksum:
  urn:sha-512:9231544238fdd152dcabc06f9e9f8c312d1c966d278dd4f9bb688aef205343def037051a9c42409743129dda2f8184c94932687543be17172c97f973116559e3
• Download URL:
  https://rosap.ntl.bts.gov/view/dot/88089/dot_88089_DS1.pdf
• File Type:
  [PDF - 2.09 MB ]