New developments in UTMOST: Application to electronic stability control

The Unified Tool for Mapping Opportunities for Safety Technology (UTMOST) is a model of crash data that incorporates the complex relationships among different vehicle and driver variables. It is designed to visualize the effect of multiple safety countermeasures on elements of the driver, vehicle, or crash population. We have recently updated UTMOST to model the effects of the time-course of fleet penetration of vehicle-based safety measures, as well as changes in the populations of drivers and vehicle types in the fleet. This report illustrates some of the capabilities of UTMOST with examples of predicted effects for one reasonably well understood countermeasure (electronic stability control, ESC) and three countermeasures just entering the vehicle fleet (forward collision warning, FCW; road departure warning, RDW; and lane change warning, LCW). Results include the relative effects of the countermeasures on the overall number of crashes and on drivers of different ages. The report also illustrates the time-course capability of UTMOST by showing year-to-year savings in serious injuries and fatalities for a driver-based countermeasure (increased belt use), which would have an immediate effect throughout the vehicle fleet, compared to ESC, which as a vehicle-based countermeasure would affect new vehicles as they enter the fleet.The University of Michigan Sustainable Worldwide Transportationhttp://deepblue.lib.umich.edu/bitstream/2027.42/64278/1/102395.pd

UTMOST: a tool for comprehensive assessment of safety benefits

This report describes a software tool that is being developed at UMTRI to represent the effects of nonindependent safety measures (the Unified Tool for Mapping Opportunities for Safety Technology, UTMOST). The tool has as its core a model representing crashes in terms of precrash conditions, occupant characteristics, crash type, and outcome. Overlaid on this is a model of the effect of implementing each of a number of safety measures, including public policy and technological measures. This portion of the model allows for visualization of the potential benefits of various approaches and combinations of approaches to safety. UTMOST is being developed and validated using existing U.S. crash databases for the purpose of understanding future safety trends in the U.S., as well as current differences between the U.S. and selected other countries, and future trends in those countries. Our goal is to be able to use this model to: 1) predict the benefit of specific changes in policy or technology in the context of other safety measures; 2) describe the largest remaining problems after a policy or technology has been implemented; and 3) assess the overall safety performance of individual vehicles, both in general and with respect to particular demographic groups.The University of Michigan Strategic Worldwide Transportation 2020http://deepblue.lib.umich.edu/bitstream/2027.42/61189/1/99833.pd

Driver distraction from cell phone use and potential for self-limiting behavior

This project consists of three parts. The first is a review of the literature on driver distraction that primarily focuses on cell phone use. The second two parts involve analysis of an existing field operational test (FOT) database to examine: 1) self-limiting behavior on the part of drivers who use cell phones, and 2) eye glance patterns for drivers involved in cell phone conversations and visual-manual tasks (e.g., texting) as compared to no-task baseline driving. The literature review discusses the apparent contradiction between results of case-crossover and simulator studies that show increases in instantaneous risk due to talking on a cell phone and results of crash-data analyses that show no substantial increase in crashes associated with increases in cell phone use in vehicles. The first data analysis shows some evidence of self-limiting behavior in cell phone conversations. Drivers initiate calls when on slower roads and at slower speeds, often when stopped. However, they call more at night, which is a higher-risk time to drive. The second analysis showed that eye glances when talking on the phone are fixated on the road for longer periods of time than in baseline driving. In contrast, on-road eye glances when engaged in a visual-manual (VM) task are short and numerous. Eye glances on and off the road are about equal in length, and the average total off-road gaze time for a five-second interval is about 2.8 secs, or 57% of the time. Average off-road gaze time out of five seconds in baseline driving is about 0.8 sec, or 16% of the time. Results show the differences in distraction mechanism between cell-phone conversations and texting. Ramifications for potential interventions are discussed.State Farm Insurancehttp://deepblue.lib.umich.edu/bitstream/2027.42/108381/1/103022.pd

Predicting mirror adjustment range for driver accommodation

Although the question of how large a driver's outside rearview mirror must be in order to see a specified target has been addressed in other publications, the related problem of required adjustment range has not. In this paper, we present a series of equations that predict, for a given vehicle, the size and location of the mirror adjustment range needed in order to accommodate some percentage of the driver population (e.g., 96%). To complete the calculations for 96% accommodations, eye locations in the vehicle are represented by the 99% SAE J941 eyellipse. Because the transformation from eye location to target location in the mirror will not preserve the tangent properties of the eyellipse, we propose a method in which the side and plan views of the eyellipse are treated separately. Eye location in plan view affects only horizontal adjustment of the mirror, and eye location in side view affects only vertical adjustment of the mirror. In each view, there are two points that lie on lines that are tangent to the eyellipse and pass through the mirror center. These two points are used to represent two extremes of mirror adjustment. Thus, we exclude the 2% of driver eye locations that lie outside either of the tangent lines (no cases lie outside both, so each tangent excludes 1%). In plan view, eye locations must first be adjusted for head turn. We also present equations to calculate mirror adjustment, referenced to an arbitrary line, for each of the four tangent points, given a specified target. We discuss various choices of target location and type, including centered point targets, centered extended targets, and targets that are located at the edge of the field of view. For the latter target type, the calculation of head turn is somewhat different than for centered targets, but the rest of the calculations are the same. The end result of these equations is a rectangle in two-dimensional mirror-adjustment space such that 96% of drivers can find a suitable mirror position within those bounds. An example is carried out using dimensions from a specific vehicle and a target located at the inner edge of the field of view, in order to illustrate the procedure.Michigan University, Ann Arbor, Industry Affiliation Program for Human Factors in Transportation Safetyhttp://deepblue.lib.umich.edu/bitstream/2027.42/49385/1/UMTRI-98-45.pd

Just noticeable differences for low-beam headlamp intensities

A recent study by Huey, Dekker, and Lyons (1994) concluded that a difference between two signal lamp intensities of less than 25% cannot be detected reliably by most drivers. Consequently, Huey et al. recommended that an intensity difference of 25% be used as a criterion for inconsequential noncompliance with federal regulations for signal lamps. The present study was designed to evaluate just noticeable differences for glare intensities of oncoming low-beam headlamps. The results of this study indicate that, under controlled conditions, just noticeable differences in the low-beam headlighting context are between 11% and 19%. In real-world conditions, just noticeable differences would probably be somewhat larger. Therefore, the recommendation by Huey et al. of using 25% as a criterion for inconsequential noncompliance of signal lamps is also about right for low-beam headlamps, at least with respect to how headlamps themselves are perceived by other drivers (such as discomfort glare). The 25% value may also apply with respect to how headlamps affect the ability of drivers to see illuminated objects, but further research on that issue would be desirable.Michigan University, Ann Arbor, Industry Affiliation Program for Human Factors in Transportation Safetyhttp://deepblue.lib.umich.edu/bitstream/2027.42/49359/1/UMTRI-97-4.pd

Do changes in voltage result in proportional changes throughout headlamp beam pattern?

This study evaluated the effects of voltage changes on beam patterns of low-beam headlamps. Seven different types of filament lamps were tested. The voltages used were 12.0, 12.8 and 13.5V. The photometry was performed from 20° left to 20° right, and from 5° down to 5° up, all in steps of 0.5°. The main finding of this study is that, for all seven lamps tested, voltage changes between 12.0V and 13.5V caused light output to change by the same proportion throughout the beam pattern. Therefore, for filament lamps, it is reasonable to use a single constant for all values in a beam pattern when converting a headlighting specification at one voltage to a specification at a different voltage, at least if the voltages in question are between 12.0 V and 13.5 V. The constants obtained across the seven lamps tested were similar to each other. Furthermore, these constants were in general agreement with the constants derived using the standard IES formula relating light-output changes to voltage changes.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/68799/2/10.1177_096032719903100101.pd