Arlington, VA

A submission by CNA's Center for Data Management and Analytics was selected earlier this week to advance to the next round of the U.S. Department of Transportation (DOT) Intersection Safety Challenge. The competition encourages the development of new and emerging technologies that identify and address unsafe conditions involving vehicles and vulnerable road users at intersections. CNA's entry was developed in partnership with RIIS, a small business that specializes in mobile and machine learning applications.

CNA's proposal is called the Safe Warnings for Intersections Forecasting Tool, or SWIFT. The tool builds upon the award-winning CNA-RIIS First Responder Awareness Monitoring during Emergencies (FRAME) prototype, a situational awareness application for the first responder community. In real time, SWIFT will detect and identify intersection users and predict unsafe conditions. To do this, it will ingest, process, and effectively display Internet of Things data from sources such as existing intersection infrastructure, using AI, and machine learning classification techniques. SWIFT will support the DOT's vision of zero fatalities across the nation's transportation system.

"We are honored to have been chosen as one of 15 prize winners from a pool of 120 innovative plans submitted by researchers and practitioners from universities, state and local agencies, private sector developers, and other organizations," said Shaelynn Hales, director of CNA's Center for Data Management and Analytics. "We are looking forward to the next phase of this challenge."

The concept development team under her leadership included CNA scientists and engineers Rebekah Yang, Addam Jordan, Jeff Daily, Matt Prebble, and Trent Berger, as well as RIIS President Godfrey Nolan.

Each of the 15 winning teams in Stage 1A will receive a prize of $100,000 and an invitation to participate in the system assessment and virtual testing phase. The teams are now expected to develop, train, and improve algorithms for the detection, localization, and classification of vulnerable road users and vehicles using DOT-supplied sensor data collected at a controlled test roadway intersection.