Automatic, AI-enabled, Panic Light and Siren System for Paint Crew Follow Vehicle

Work zones introduce roadway complexities, significantly heightening the likelihood of traffic incidents. These areas often necessitate sudden changes in traffic patterns, such as lane closures and shifts, which can catch drivers off guard and reduce reaction times. Implementing advanced safety systems like ROADZ Armor is an efficient approach to enhance roadway safety. These technologies play a crucial role in preempting potential hazards by integrating preemptive alerts and improving situational awareness for drivers and construction personnel. The ROADZ Armor system is designed to enhance safety in mobile work zones. It employs advanced technology, including optical sensors and 3D scanning radar, integrated with a deep neural network for real-time threat detection and boundary definition. This system can alert oncoming motorists and roadway workers to potential dangers using visual and acoustic signals, aiming to reduce crashes and improve safety in work zones. This research is committed to conducting a thorough analysis of the ROADZ Armor system's effectiveness in reducing incidents within work zones, enhancing overall road safety, and minimizing the adverse consequences associated with work zone crashes, both in terms of human life and economic impact.

Language

• English

Project

• Status: Active
• Funding: $55000
• Contract Numbers:

  25-8203

• Sponsor Organizations:

  Utah Department of Transportation

  Research and Innovation Division
  Salt Lake City, UT  United States  84114

  University of Utah, Salt Lake City

  College of Engineering, Department of Civil Engineering
  Salt Lake City, UT  United States  84112-0561
• Managing Organizations:

  Utah Department of Transportation

  Research and Innovation Division
  Salt Lake City, UT  United States  84114
• Project Managers:

  Jensen, Travis

• Performing Organizations:

  University of Utah, Salt Lake City

  College of Engineering, Department of Civil Engineering
  Salt Lake City, UT  United States  84112-0561
• Principal Investigators:

  Rashidi, Abbas

• Start Date: 20240820
• Expected Completion Date: 20250930
• Actual Completion Date: 0

Subject/Index Terms

• TRT Terms: Artificial intelligence; Warning systems; Work zone safety
• Subject Areas: Highways; Safety and Human Factors;

Filing Info

• Accession Number: 01927699
• Record Type: Research project
• Source Agency: Utah Department of Transportation
• Contract Numbers: 25-8203
• Files: RIP, STATEDOT
• Created Date: Aug 20 2024 10:37PM