Evaluation of a Driver Alerting System for a Truck Mounted Attenuator

The safety of workers and drivers is paramount in a work zone setting. One of the most important tools used in providing that safety is the truck mounted attenuator (TMA). The vehicle is designed to provide guidance and notification to drivers of a lane closure through vehicle mounted signage and lighting. The other aspect of the TMA is that it is designed to absorb the brunt of the energy in the case of a collision with an approaching vehicle. These collisions however remain very dangerous not only for the approaching driver but also the TMA operator. This TMA alert project considers the application of an alarm and additional warning lights that will be triggered based on the potential of a vehicle collision. Using a binocular camera, the system identifies not only approaching vehicles but also fixed objects typically associated with the work zone such as barrels, cones, and signage. The system then uses an artificial intelligence (AI) approach to identify the work zone layout and fixed objects as well as the potentially intruding vehicle. Following a learning period to train the AI, the AI interface allows for the system to be used in any configuration of work zone without further specific calibration of the roadway areas of interest for an encroaching vehicle. The purpose of this study is to test the effectiveness of the system though a comparison of the system performance to data collected live in the field.

Language

• English

Project

• Status: Active
• Funding: $357482
• Contract Numbers:

  123952

• Sponsor Organizations:

  Virginia Transportation Research Council

  530 Edgemont Road
  Charlottesville, VA  United States  22903
• Managing Organizations:

  Virginia Transportation Research Council

  530 Edgemont Road
  Charlottesville, VA  United States  22903
• Project Managers:

  Cottrell, Benjamin

• Performing Organizations:

  Virginia Tech Transportation Institute

  3500 Transportation Research Plaza
  Blacksburg, Virginia  United States  24061
• Principal Investigators:

  Gibbons, Ronald

• Start Date: 20230701
• Expected Completion Date: 20250430
• Actual Completion Date: 0

Subject/Index Terms

• TRT Terms: Alarm systems; Artificial intelligence; Cameras; Crash avoidance systems; Truck mounted attenuators; Work zone safety
• Subject Areas: Data and Information Technology; Highways; Safety and Human Factors; Vehicles and Equipment;

Filing Info

• Accession Number: 01885970
• Record Type: Research project
• Source Agency: Virginia Department of Transportation
• Contract Numbers: 123952
• Files: RIP, STATEDOT
• Created Date: Jun 24 2023 8:47AM