MPC
Project Details

Title:	Connected Vehicle Winter Safety Improvement with Infrared Thermography Technology
Principal Investigators:	Xuan Zhu
University:	University of Utah
Status:	Active
Year:	2022
Grant #:	69A3551747108 (FAST Act)
Project #:	MPC-698
RiP #:	01863380
RH Display ID:	159662
Keywords:	connected vehicles, data mining, detection and identification systems, highway safety, road weather information systems, slipperiness, thermal imagery, warning systems, winter

Abstract

Safety is the principal concern of highway transportation. Slippery roads can pose high risks of traffic collisions in snowy regions, which cover about 70 percent of road networks and population in the U.S. Icy/snowy roads can significantly reduce tire frictions, lengthen vehicle braking distance, and thereby induce high risks of car crashing. Hence, if early warning could be provided to drivers before they enter hazardous locations, potential crash risks could be significantly reduced.

Conventionally, state DOTs use warning signage to alert drivers of hazardous road segments. However, slippery spots are usually hard to predict, and their locations could be changing overtime. Placing warning devices would then have limitations in addressing such dynamic situations. Recent advancements of connected vehicle or CV technology offers a new and effective solution to tackle this issue. For example, in the work with Panasonic, UDOT has already developed a Spot Weather Impact Warning application, which uses data from RWIS stations and CV information to determine the existence of potentially slippery road surfaces and then send a message to oncoming CVs. The current Spot Weather Impact Warning application relies on ITS roadway equipment (RWIS) and partially on information from Connected Vehicles to detect the road surface temperature, moisture, icing, and other metrics to detect road slippery conditions, where the adopted sensors typically provide single-spot measurements. This project could fill the gap in augmenting or complementing the road slippery detection algorithm.

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