Ensuring the Safe Operation of Automated or Partially Automated Locomotives: The Role of Driver State Monitoring Systems

Details

• Creators:
  Dunn, Naomi J. ; Miller, Daniel M. ; Greatbatch, Richard ; Neurauter, Luke
• Corporate Creators:
  Virginia Tech Transportation Institute
• Corporate Contributors:
  United States. Department of Transportation. Federal Railroad Administration. Office of Research and Development
• Subject/TRT Terms:
  Alertness Distraction Driver Monitoring Driver State Monitoring Fatigue Human Factors Inattention Locomotive Engineers Locomotive Operation Physiological Fatigue Railroad Safety
• Publication/ Report Number:
  DOT/FRA/ORD-26/06
• Resource Type:
  Tech Report
• Geographical Coverage:
  United States
• Edition:
  Final Report: 6/30/2022 to 12/31/2024
• Contracting Officer:
  Mike Jones
• Corporate Publisher:
  United States. Department of Transportation. Federal Railroad Administration. Office of Research, Development, and Technology
• Abstract:
  States of reduced operator alertness, such as fatigue and inattention, are well-known factors contributing to railway accidents and safety incidents. However, more effective solutions are needed to mitigate their impact. Driver state monitoring (DSM) systems can potentially play a mitigatory role, as these systems use cameras and sensors to continuously observe operator behavior to detect likely signs of fatigue, distraction, or inattentiveness, and provide visual or audible warnings to the operator if a change in operator state is detected. While various DSM systems are available and in use by the automotive industry (e.g., GMC Super Cruise, Ford Blue Cruise), these systems must be evaluated for their applicability in the rail environment and potentially modified to suit the control tasks of locomotive engineers. The Federal Railroad Administration (FRA) sponsored the Virginia Tech Transportation Institute to identify DSM systems for further testing in an in-cab rail setting to address challenges and find solutions to enhance their effectiveness. The team evaluated and demonstrated the potential of DSM technologies so that, with further refinement, DSM systems could be implemented to reduce human error and improve locomotive engineer performance. This research was conducted from 2022 to 2024 using FRA's Cab Technology Integration Laboratory (CTIL) high-fidelity, full-sized locomotive simulator.
• Format:
  PDF
• Funding:
  693JJ622C000004
• Collection(s):
  Federal Railroad Administration
• Main Document Checksum:
  urn:sha-512:e81b5981c1cecbe54f9ba5e6bbdf3d7ebb61c6e785ec879f7ca74e57b41151246fa00cd989f278dabeb72ff0f3ea37e41db8253ca8cb49928e2d9173e984c4b8
• Download URL:
  https://rosap.ntl.bts.gov/view/dot/92715/dot_92715_DS1.pdf
• File Type:
  [PDF - 1.68 MB ]