Joel M. Cooper

Text Messaging During Simulated Driving

Objective: This research aims to identify the impact of text messaging on simulated driving performance. Background: In the past decade, a number of on-road, epidemiological, and simulator-based studies reported the negative impact of talking on a cell phone on driving behavior. However, the impact of text messaging on simulated driving performance is still not fully understood. Method: Forty participants engaged in both a single task (driving) and a dual task (driving and text messaging) in a high-fidelity driving simulator. Results: Analysis of driving performance revealed that participants in the dual-task condition responded more slowly to the onset of braking lights and showed impairments in forward and lateral control compared with a driving-only condition. Moreover, text-messaging drivers were involved in more crashes than drivers not engaged in text messaging. Conclusion: Text messaging while driving has a negative impact on simulated driving performance. This negative impact appears to exceed the impact of conversing on a cell phone while driving. Application: The results increase our understanding of driver distraction and have potential implications for public safety and device development.

Effects of Simulator Practice and Real-World Experience on Cell-Phone—Related Driver Distraction

Objective: Our research examined the effects of practice on cell-phone—related driver distraction. Background: The driving literature is ambiguous as to whether practice can reduce driver distraction from concurrent cell phone conversation. Methods: Drivers reporting either high or low real-world cell phone usage were selected to participate in four 90-min simulated driving sessions on successive days. The research consisted of two phases: a practice phase and a novel transfer phase. Results: Dual-task performance deficits persisted through practice and transfer driving conditions. Moreover, groups reporting high and low real-world experience exhibited similar driving impairments when conversing on a hands-free cell phone. Conclusions: These data indicate that practice is unlikely to eliminate the disruptive effects of concurrent cell phone use on driving. Application: Multiple regulatory agencies have considered, or are currently considering, legislation to restrict in-vehicle cell phone use. Findings reported herein may be useful to inform these public policy decisions.

An Investigation of Driver Distraction Near the Tipping Point of Traffic Flow Stability

Objective: The purpose of this study was to explore the interrelationship between driver distraction and characteristics of driver behavior associated with reduced highway traffic efficiency. Background: Research on the three-phase traffic theory and on behavioral driving suggests that a number of characteristics associated with efficient traffic flow may be affected by driver distraction. Previous studies have been limited, however, by the fact that researchers typically do not allow participants to change lanes, nor do they account for the impact of varying traffic states on driving performance. Methods: Participants drove in three simulated environments with differing traffic congestion while both using and not using a cell phone. Instructed only to obey the speed limit, participants were allowed to vary driving behaviors, such as those involving forward following distance, speed, and lane-changing frequency. Results: Both driver distraction and traffic congestion were found to significantly affect lane change frequency, mean speed, and the likelihood of remaining behind a slower-moving lead vehicle. Conclusions: This research suggests that the behavioral profile of “cell phone drivers,” which is often described as compensatory, may have far-reaching and unexpected consequences for traffic efficiency. Application: By considering the dynamic interplay between characteristics of traffic flow and driver behavior, this research may inform both public policy regarding in-vehicle cell phone use and future investigations of driving behavior.

What do Drivers Fail to See When Conversing on a Cell Phone

Our research examined the effects of hands-free cell phone conversations on simulated driving. We found that even when participants looked directly at objects in the driving environment, they were less likely to create a durable memory of those objects if they were conversing on a cell phone. Moreover, this pattern was obtained for objects of both high- and low-relevance, suggesting that very little semantic analysis of the objects occurs outside the restricted focus of attention. These data support the inattention-blindness interpretation in which the disruptive effects of cell phone conversations on driving are due in large part to the diversion of attention from driving to the phone conversation. We suggest that even when participants are directing their gaze at objects in the driving environment that they may fail to “see” them when they are on the phone because attention is directed elsewhere.

Assessing cognitive distraction in the automobile

Objective: The objective was to establish a systematic framework for measuring and understanding cognitive distraction in the automobile. Background: Driver distraction from secondary in-vehicle activities is increasingly recognized as a significant source of injuries and fatalities on the roadway. Method: Across three studies, participants completed eight in-vehicle tasks commonly performed by the driver of an automobile. Primary, secondary, subjective, and physiological measures were collected and integrated into a cognitive distraction scale. Results: In-vehicle activities, such as listening to the radio or an audio book, were associated with a low level of cognitive workload; the conversation activities of talking to a passenger in the vehicle or conversing with a friend on a handheld or hands-free cell phone were associated with a moderate level of cognitive workload; and using a speech-to-text interfaced e-mail system involved a high level of cognitive workload. Conclusion: The research established that there are significant impairments to driving that stem from the diversion of attention from the task of operating a motor vehicle and that the impairments to driving are directly related to the cognitive workload of these in-vehicle activities. Moreover, the adoption of voice-based systems in the vehicle may have unintended consequences that adversely affect traffic safety. Application: These findings can be used to help inform scientifically based policies on driver distraction, particularly as they relate to cognitive distraction stemming from the diversion of attention to other concurrent activities in the vehicle.

Driven to Distraction.

We address several themes that emerged in the commentaries related to our target article. First, we consider the relationship between cognitive distraction and crash risk. Second, we discuss the development of our cognitive distraction scale. Third, we weigh issues of self-regulation, appropriate baselines, and satisficing. Finally, we identify several areas where additional research is needed to refine our understanding of driver distraction and crash risk.

Hierarchical control and driving.

We manipulated primary task predictability and secondary task workload in the context of driving an automobile. As the driving task became less predictable (by adding wind gusts), more attention was required to maintain lane position. When drivers concurrently engaged in a secondary cognitive task in the windy driving condition, attention was diverted from driving and the ability to maintain lane position was degraded. By contrast, when the driving task was predictable (no wind), lane maintenance actually improved when a secondary cognitive task diverted attention from driving. These data provide evidence for a hierarchical control network that coordinates an interaction between automatic, encapsulated routines and limited capacity attention.

The Impact of Eye Movements and Cognitive Workload on Lateral Position Variability in Driving

Objective: The objective of this work was to understand the relationship between eye movements and cognitive workload in maintaining lane position while driving. Background: Recent findings in driving research have found that, paradoxically, increases in cognitive workload decrease lateral position variability. If people drive where they look and drivers look more centrally with increased cognitive workload, then one could explain the decreases in lateral position variability as a result of changes in lateral eye movements. In contrast, it is also possible that cognitive workload brings about these patterns regardless of changes in eye movements. Method: We conducted three experiments involving a fixed-base driving simulator to independently manipulate eye movements and cognitive workload. Results: Results indicated that eye movements played a modest role in lateral position variability, whereas cognitive workload played a much more substantial role. Conclusions: Increases in cognitive workload decrease lane position variability independently from eye movements. These findings are discussed in terms of hierarchical control theory. Applications: These findings could potentially be used to identify periods of high cognitive workload during driving.

The Effects of Reading and Writing Text-Based Messages While Driving

Previous research, using driving simulation, crash data, and naturalistic methods, has begun to shed light on the dangers of texting while driving. Perhaps because of the dangers, no published work has experimentally investigated the dangers of texting while driving using an actual vehicle. Additionally, previous research does not clearly differentiate the dangers associated with reading and writing text-based messages. To address these issues, 42 participants drove an instrumented research vehicle on a closed driving course. Participants drove under control, text reading, and text writing conditions using a QWERTY keyboard mobile phone. Baseline text reading and writing data were also collected outside of the research vehicle. When reading or writing text-based messages, drivers exhibited reductions in reaction time that were nearly twice as great as previously thought. Drivers also exhibited nearly identical impairment in the reading and writing conditions, suggesting that both reading and writing text-based messages may be equally dangerous. These results have immediate implications for improving our understanding of the dangers of texting while driving and may be useful for future public policy discussions.

The smartphone and the driver’s cognitive workload: A comparison of Apple, Google, and Microsoft’s intelligent personal assistants.

The goal of this research was to examine the impact of voice-based interactions using 3 different intelligent personal assistants (Apple’s Siri, Google’s Google Now for Android phones, and Microsoft’s Cortana) on the cognitive workload of the driver. In 2 experiments using an instrumented vehicle on suburban roadways, we measured the cognitive workload of drivers when they used the voice-based features of each smartphone to place a call, select music, or send text messages. Cognitive workload was derived from primary task performance through video analysis, secondary-task performance using the Detection Response Task (DRT), and subjective mental workload. We found that workload was significantly higher than that measured in the single-task drive. There were also systematic differences between the smartphones: The Google system placed lower cognitive demands on the driver than the Apple and Microsoft systems, which did not differ. Video analysis revealed that the difference in mental workload between the smartphones was associated with the number of system errors, the time to complete an action, and the complexity and intuitiveness of the devices. Finally, surprisingly high levels of cognitive workload were observed when drivers were interacting with the devices: “on-task” workload measures did not systematically differ from that associated with a mentally demanding Operation Span (OSPAN) task. The analysis also found residual costs associated using each of the smartphones that took a significant time to dissipate. The data suggest that caution is warranted in the use of smartphone voice-based technology in the vehicle because of the high levels of cognitive workload associated with these interactions. Le but de cette recherche consistait à examiner, au moyen de trois différents assistants personnels (Siri de Apple, Google Now de Google pour téléphone Androïde et Cortana de Microsoft), l’impact d’interactions vocales sur la charge de travail cognitive du conducteur. À l’aide de deux expériences employant un véhicule instrumenté sur des routes de banlieue, nous avons mesuré la charge de travail cognitive de conducteurs alors qu’ils utilisaient les fonctionnalités vocales de chacun des téléphones intelligents pour effectuer un appel, sélectionner de la musique ou envoyer un message texte. La charge de travail cognitive a pu être déterminée après évaluation de la performance de la tâche principale par analyse-vidéo, de la performance de la tâche secondaire par tâche de détection-réponse (DRT) puis, de la charge de travail mentale subjective. Nous avons constaté que la charge de travail y était nettement plus élevée que celle associée à la tâche simple de conduire. Il y avait aussi des différences systématiques entre les téléphones intelligents. Le système Google était moins demandant cognitivement sur le conducteur que les systèmes Apple et Microsoft, lesquels avaient le même effet. L’analyse vidéo a montré que la différence au niveau de la charge de travail mentale entre téléphones intelligents était associée au nombre d’erreurs de système, à la durée de temps requise pour mener à bien une action et à la complexité et à l’intuitivité des appareils. Finalement, des niveaux étonnamment élevés de charge de travail cognitive ont été observés lorsque les conducteurs étaient en interaction avec leurs appareils : Les mesures de la charge de travail associée à la concentration sur une tâche ne différaient pas systématiquement de celles associées à une tâche (OSPAN) exigeante sur le plan mental. L’analyse a aussi révélé la présence de coûts résiduels associée à l’utilisation de chacun des téléphones intelligents, lesquels ont pris un temps considérable pour se dissiper. Les données suggèrent que la prudence est de mise en ce qui a trait à l’utilisation de technologie vocale sur téléphone intelligent dans un véhicule étant donné les niveaux élevés de la charge de travail cognitive associée à ces interactions.