Fatigue is a major risk factor in commercial motor vehicle operations, identified in naturalistic driving studies as a contributing factor in approximately 20 percent of safety-critical incidents. Understanding the nature of fatigued driving requires attention to several elements of the driving situation, including driver characteristics. The purpose of the present report is to explore driver body mass index (BMI) as a characteristic which may put one at increased risk for driving while fatigued.

VTTI researchers have developed a method for rating driver drowsiness based on the evaluation of naturalistic video footage of the driver's face and upper torso. This measure, referred to as the Observer Rating of Drowsiness (ORD) is based on subjective assessments of the driver's facial tone, behavior, and mannerisms, and is set to a 100-point continuous scale. ORD is assessed based on the 60 seconds of video prior to a trigger event (or baseline epoch). Therefore, ORD is a relatively quick/efficient method for assessing one's drowsiness level, which can then be used to describe a driver's state and investigate whether drowsiness was a contributing factor to a safety-critical event.

Under the sponsorship of the National Surface Transportation Safety Center for Excellence (NSTSCE), an effort was undertaken to develop a system of image capture to analyze luminance data gathered in naturalistic driving research.

Under the sponsorship of the National Surface Transportation Safety Center for Excellence, an existing naturalistic data set from the Drowsy Driver Warning System Field Operational Test (DDWS FOT) was expanded and analyzed to gain a greater understanding of the conditions which are associated with fatigue in commercial motor vehicle (CMV) driving.

By employing the functionality of GIS, code was written which allows for an automated process to compare the GPS data recorded in the naturalistic driving data with geographic map data from the U.S. Census Bureau and road data from various sources, such as state departments of transportation or other providers. Points recorded in the naturalistic driving data which fall outside the boundaries of the Census Bureau's urbanized Areas or urban Clusters are determined to be rural. The points are further evaluated to determine whether or not the vehicle was being driven on an interstate highway. Points that are determined to be rural and not on interstate highways are segments of interest in addressing the rural road crash problem.

This study developed an integrated framework for modeling the safety outcomes of naturalistic driving studies and addressed several critical methodological issues. The results indicate a certain level of discrepancy between the model-based approaches and the crude odds ratios.