CPS: Small: Worker-in-the-Loop Real Time Safety System for Short-Duration Highway Workzones
Lead PI:
Hamed Tabkhi
Co-PI:
Abstract
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.
Hamed Tabkhi
Hamed Tabkhi is the Associate Professor of Computer Engineering. He will present his recent works on Real-World AI to create the next generation of Human-in-the-Loop Cyber-Physical Systems. His recent projects aim to address public safety, workers' safety, and equitable public transit through co-designing and co-creating real-world AI systems with local communities and stakeholders.
Performance Period: 10/01/2019 - 09/30/2023
Institution: University of North Carolina at Charlotte
Sponsor: National Science Foundation
Award Number: 1932524