CPS: Small: Worker-in-the-loop real time safety system for short-duration highway workzones

CPS：小型：适用于短期高速公路工作区的工人循环实时安全系统

• 批准号: 1932524
• 负责人: Hamed Tabkhi
• 金额: $ 50万
• 依托单位: University of North Carolina at Charlotte
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-10-01 至 2023-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1932524&HistoricalAwards=false
• 关键词: CPS; Small; Worker; loop; real

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.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

该项目提出了一种新颖的安全系统，通过人工智能(AI)的最新进展，能够对高速公路工区附近的安全风险进行实时预测。当工作区域即将发生入侵时，拟议的安全系统通过智能眼镜向高速公路工人提供实时通知。特别是，该项目侧重于由于缺乏适当的安全机制而导致较高安全风险的短工期公路工区。该项目通过使高速公路为工人提供更安全的场所，并防止高速公路工区造成的潜在伤亡，促进国家的健康和繁荣。该项目从现有的反应性安全系统转向真正的主动式安全系统。它在实时深度学习算法设计和处理、边缘计算和辅助现实系统方面做出了基础性贡献，使工作区域入侵的实时预测和公路工人的通知成为可能。拟议的工人在环路安全系统将在高速公路工区工人、领先行业和人为因素专家的直接帮助下共同设计和共同创建，以确定针对即将到来的安全风险向工人发出警报的最佳反馈机制。该项目将在为许多新兴的安全和安保相关应用开发具有强大边缘计算的下一代网络物理系统方面发挥关键作用。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。