This work compares the degradation in driving performance associated with secondary tasks performed with voice-based and visual/manual interfaces, including radio tuning, phone dialing, and more complex tasks involving a sequence of interactions with an in-vehicle computer system. Twenty-one participants drove an instrumented vehicle while performing a combination of car-following, peripheral target detection, and secondary tasks on a closed test track. Drivers compensated for increased task demands associated with secondary tasks by increasing their following distance. Performing secondary tasks also resulted in significant decrements to vehicle control, target detection, and car-following performance. The voice-based interface helped reduce the distracting effects of secondary task performance. Modest improvements were observed for measures of vehicle control and target detection but not for car following. The results indicated that performing in-vehicle tasks required diversion of both peripheral (visual and manual) and attentional (cognitive) resources from driving. The voice-based interface reduced the peripheral impairment but did not appreciably reduce the attentional impairment. Actual or potential applications of this research include improvements to the design of invehicle information systems and the development of evaluation protocols to assess their distraction potential.

In the distributed signal detection theoretic (DSDT) model, the human operator and the warning mechanism are independent decision makers who work together as a team. The DSDT demonstrates that the optimal warning threshold, in general, differs from the signal detection theoretic (SDT) threshold, which assumes a single decision maker. This prediction was tested in an experiment where drivers received monetary rewards for making safe passing decisions on a driving simulator. The experiment focused on evaluating the quality of the decision making of the drivers, and not on perceptual issues. A collision avoidance system provided a warning when the probability of an inadequate overtaking gap exceeded a threshold. Three thresholds were tested. The control threshold resulted in no detections or false alarms. The DSDT threshold resulted in some misses but no false alarms. The SDT threshold resulted in no misses but frequent false alarms. As predicted, (1) drivers performed the best when the warning system used the DSDT threshold, and (2) use of the SDT threshold improved performance over the control threshold, even though four of the 10 drivers occasionally ignored the warning and made risky passing attempts in the SDT conditions, possibly because of earlier false alarms. These findings support the conclusion that the DSDT model is a useful, quantitative tool that should be used by warning designers.

In this experiment 12 experienced truck drivers drove a fixed-base driving simulator for three 8-h sessions under simulated nighttime driving conditions. Sessions included (a) no glare, (b) intermittent glare presented in the exterior rearview mirrors to simulate following vehicles, and (c) intermittent glare with electrochromic glare reduction. The driving task combined vehicle control on straight and curved road segments with detection of pedestrians appearing alongside the road and targets appearing in the rearview mirrors. The presence of glare slowed detection of pedestrians and, to a lesser extent, slowed the detection of targets appearing in mirrors. Glare was also associated with increased lane position variability, reduced speed on curves, and, most consistently, increased steering variability. We found only meager evidence that electrochromic glare reduction improved target detection performance and no evidence that glare reduction improved vehicle control, despite the fact that participants consistently voiced positive preferences for glare reduction. The results will aid decision making that requires incorporation of the benefits of electrochromic glare-reducing mirrors.

Training takes place in complex environments. Typically, there are many different tasks which need to be learned; each task can be performed at one of several different levels of proficiency and each level of proficiency within a given task can be trained in one of various different ways. Much is known about what tasks need to be trained in order to achieve a particular objective, what methods are best for training a particular level of a particular task, and what measures should be used to evaluate training. Curiously, given that the tasks, methods, and measures have been selected, very little is known about how to determine which level of which task it is best to train in each session. In this article, a framework for pursuing the optimization of training schedules which are sensitive to (dynamic) and not sensitive to (static) the session by session (trial by trial) progress of the learner is developed. The framework takes as its starting point the early state models of learning first proposed within mathematical psychology in the 1950s. We show that the state models can be used to predict how performance will vary as a function of the scheduling of training trials. Practically, it is important to consider the effect of changes in the scheduling of training trials because such changes can substantially reduce the time it takes any given individual to learn a composite skill. Theoretically, it is important to consider the effect of changes in the scheduling of training trials because such changes can potentially provide the answers to a number of questions central to research in training. We conclude that the state models of learning provide both of the hoped for practical and theoretical benefits.

Motor vehicle travel through roadway construction workzones has been shown to increase the risk of a crash. The number of workzones has increased due to recent congressional funding in 1991 for expanded roadway maintenance and repair. In this paper, we describe the characteristics and costs of motor vehicle crashes in roadway construction workzones. As opposed to using standard accident codes to identify accident types, automobile insurance claims files from 1990–1993 were searched to identify records with the keyword “construction” in the accident narrative field. A total of 3,686 claims were used for the analysis of crashes. Keywords from the accident narrative field were used to identify five pre-crash vehicle activities and five crash types. We evaluated misclassification error by reading 560 randomly selected claims and found it to be only 5%. For each of four years, 1990–1993, there was a total of 648, 996, 977 and 1,065 crashes, respectively. There was a 70% increase in the crash rate per 10,000 personal insured vehicles from 1990–1993 (2.1–3.6). Most crashes (26%) involved a stopped or slowing vehicle in the workzone. The most common crash (31%) was a rear-end collision. The most costly pre-crash activity was a major judgment error on the part of a driver (n = 120, median cost = $2,628). An overturned vehicle was the most costly crash type (n = 16, edian cost = $4,745). In summary, keyword text analysis of accident narrative data used in this study demonstrated its utility and potential for enhancing injury epidemiology. The results suggest interventions are needed to respond to growing traffic hazards in construction workzones.

This article reports on a number of trends in roadway transportation including increasing traffic congestion, emerging technology, and the increasing age of the driving population. It also considers aging of the highway infrastructure, and increasing heavy truck operations. This review is followed by speculations concerning the future of these trends and their likely impact on future safety.

A study was conducted to investigate driver performance on curves. The between-trial factors were Blood Alcohol Content (BAC) level (0.00, 0.7, 0.12 %) and type of driving scenario (eventful versus uneventful). The within-session factors were edgeline width, type of curve-warning sign, and curve type. Twelve male drivers drove continuously for two hours on each of three nights. Each subject negotiated 150 curves during each two-hour drive. Curve radii ranged from 57.3 to 94.2 m (188 to 309.2 ft). Advisory speeds presented on curve-warning signs ranged from 32.2 to 72.4 km/h (20 to 45 mile/h). The driving simulator was a completely instrumented cab resting on a fixed base. The results showed that curve-entry speed increased as radius of curvature increased. Lateral position error was greater on the curve with the smallest radius and least on the curve with the shortest length. Heading error first increased then decreased as curve radius increased. Neither the amount of road used nor the mean computed lateral acceleration were related to curve radius, heading change or length. These results are attributed to the absence of lateral-acceleration cues in the driving simulator.

Accident studies have identified nighttime conditions on rural roads as particular problems for alcohol-impaired drivers. Uneventful driving is hypothesized to result in progressive degradation of tracking performance and a reduced ability to handle the demands of hazardous locations, such as curves. To address these problems, four spot treatments (i.e. herringbone road marking, flashing beacon, chevron, and post delineator) were evaluated in a driving simulator. Twelve subjects drove a simulator under two conditions of task demand (with and without obstacles) and three levels of blood alcohol concentration (BAC): .00%, .07%, and .12%. The purpose of the study was to determine whether providing enhanced visual information about hazardous areas would improve the performance of subjects when sober or alcohol-dosed. Driver performance measures included speed, lateral position, and lateral acceleration on the approach and negotiation of horizontal curves of varying length and curvature. The results indicate that spot treatment effects were primarily curve-specific rather than uniform across curves. The effectiveness of spot treatments as alcohol countermeasures is discussed.