Vision-Based Work Zone Safety Alert System in a Connected Vehicle Environment

• Published in: Transportation research record Vol. 2677; no. 11; pp. 499 - 520
• Main Authors: Cui, Haibo, Hou, Kaizhe, Zhang, Jiarui, Yan, Siqi, Seraj, Mudasser, Wang, Yingke, Tavakoli, Mahdi, Qiu, Tony
• Format: Journal Article
• Language: English
• Published: Los Angeles, CA SAGE Publications 01.11.2023
• Subjects: roadside safety; deep learning; infrastructure; connected vehicles; work zones; computer vision
• ISSN: 0361-1981; 2169-4052
• DOI: 10.1177/03611981231165997

Work zones, being a critical component of roadway transportation systems, can benefit greatly from computer vision-enabled roadway infrastructures, specifically in connected vehicle (CV) environments. Connected infrastructures, such as roadside units (RSU) and on-board units (OBU), can greatly improve the environmental awareness and safety of CVs driving through a work zone. The contribution of this paper lies in developing a vision-based approach to generate work zone safety messages in real time, utilizing video streams from roadside monocular traffic cameras that can be used by CV work zone safety apps on mobile devices to reliably navigate through a work zone. A monocular traffic camera calibration method is proposed to establish the accurate mapping between the image plane and Global Position System (GPS) space. Real test scenarios show that our algorithm can precisely and effectively locate work zone boundaries from a monocular traffic camera in real time. We demonstrate the capabilities and features of our system through real-world experiments where the driver cannot see the work zone. End-to-end latency analysis reveals that the vision-based work zone safety warning system satisfies the active safety latency requirements. This vision-based work zone safety alert system ensures the safety of both the worker and the driver in a CV environment.