, Director, Mechanical Engineering
This project proposes a novel safety system that enables real-time prediction for safety risks near highway work zones, through recent advances in Artificial Intelligence (AI). The proposed safety system provides real-time notification to highway workers through smart glasses when a work zone intrusion is about to happen. In particular, this project focuses on short-duration highway work zones which cause higher safety risks due to lack of proper safety mechanisms. This project enhances the health and prosperity of the nation by making highways safer places for workers and preventing potential fatalities or injuries caused by highway work zones.
This project departs from existing reactive safety systems to a true proactive safety system. It makes fundamental contributions in real-time deep learning algorithm design and processing, edge computing, and assisted reality systems to enable real-time prediction of work zone intrusions and notification of highway workers. The proposed worker-in-the-loop safety system will be co-designed and co-created with the direct help of highway work zone workers, leading industries, and human factors experts to identify the best feedback mechanisms for alarming workers regarding upcoming safety risks. This project will play a key role in the development of the next generation cyber-physical systems with powerful edge computing for many emerging safety and security-related applications.
- Project number: 2020054
- Start date: 09/2019
- Project status: Active
- Research area: Transportation Safety and Traffic Flow