MPC
Project Details

Title:	Automated Real-Time Weather Detection System using Artificial Intelligence
Principal Investigators:	Mohamed Ahmed
University:	University of Wyoming
Status:	Active
Year:	2020
Grant #:	69A3551747108 (FAST Act)
Project #:	MPC-630
RiP #:	01732206
RH Display ID:	154767
Keywords:	artificial intelligence, detection and identification technologies, real time information, snow, weather conditions

Abstract

Adverse weather conditions, such as snow, rain, and fog, can directly impact roadway safety, by reducing the visibility and roadway surface friction, negatively affecting vehicles' and drivers' performance. In Wyoming, the number of snow-related crashes are particularly significant. Merely in winter 2018, there were 1,438 snow-related crashes, which resulted in fatalities, extended closures, and significant economic loss. Therefore, detection of real-time weather conditions and providing timely Traveler Information Messages to drivers are crucial for safe driving. The state-of-practice of broadcasting road weather information to travelers has been predominantly based on sporadic and expensive Road Weather Information Systems. With consideration of the limitations of the existing weather detection systems, and in view of the opportunity of the emerging video-image processing technologies, this research aims at developing an affordable weather detection system, which will use video images collected primarily by the WYDOT roadside fixed webcams and secondarily by examining the feasibility of extending the algorithms to snow plows trajectory-level cameras. The product of this research will assist WYDOT with providing road users with accurate and reliable road surface weather conditions, resulting in safer travel decisions and more conservative driving behaviors to mitigate the negative impacts of adverse weather on traffic safety.

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