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Objective: In this study, the authors used algorithms to estimate driver distraction and predict crash and near-crash risk on the basis of driver glance behavior using the data set of the 100-Car Naturalistic Driving Study. Background: Driver distraction has been a leading cause of motor vehicle crashes, but the relationship between distractions and crash risk lacks detailed quantification. Method: The authors compared 24 algorithms that varied according to how they incorporated three potential contributors to distraction—glance duration, glance history, and glance location—on how well the algorithms predicted crash risk. Results: Distraction estimated from driver eye-glance patterns was positively associated with crash risk. The algorithms incorporating ongoing off-road glance duration predicted crash risk better than did the algorithms incorporating glance history. Augmenting glance duration with other elements of glance behavior—1.5th power of duration and duration weighted by glance location—produced similar prediction performance as glance duration alone. Conclusions: The distraction level estimated by the algorithms that include current glance duration provides the most sensitive indicator of crash risk. Application: The results inform the design of algorithms to monitor driver state that support real-time distraction mitigation systems.

Objective In this study, we investigated how drivers adapt secondary-task initiation and time-sharing behavior when faced with fluctuating driving demands. Background Reading text while driving is particularly detrimental; however, in real-world driving, drivers actively decide when to perform the task. Method In a test track experiment, participants were free to decide when to read messages while driving along a straight road consisting of an area with increased driving demands (demand zone) followed by an area with low demands. A message was made available shortly before the vehicle entered the demand zone. We manipulated the type of driving demands (baseline, narrow lane, pace clock, combined), message format (no message, paragraph, parsed), and the distance from the demand zone when the message was available (near, far). Results In all conditions, drivers started reading messages (drivers' first glance to the display) before entering or before leaving the demand zone but tended to wait longer when faced with increased driving demands. While reading messages, drivers looked more or less off road, depending on types of driving demands. Conclusions For task initiation, drivers avoid transitions from low to high demands; however, they are not discouraged when driving demands are already elevated. Drivers adjust time-sharing behavior according to driving demands while performing secondary tasks. Nonetheless, such adjustment may be less effective when total demands are high. Application This study helps us to understand a driver's role as an active controller in the context of distracted driving and provides insights for developing distraction interventions.

Objective: The aim of this study was to illustrate how a consideration of glance sequences to in-vehicle tasks and their associated distributions can be informative. Background: The rapid growth in the number of nomadic technologies and in-vehicle devices has the potential to create complex, visually intensive tasks for drivers that may incur long in-vehicle glances. Such glances place drivers at increased risk of a motor vehicle crash. Method: We used eye-glance data from a study of distraction training programs to examine the change in glance duration distributions across consecutive glances during the performance of various in-vehicle tasks. Results: The sequential analysis across trained and untrained drivers showed that the proportion of late-sequence glances longer than a 2-s threshold among untrained drivers was almost double the number of such glances for the trained drivers, that the third and later glances were particularly problematic, and that training reduced the proportion of early- and later-sequence glances. Conclusion: Examining how the duration of off-road glances varies as a function of their order in a sequence of glances and the visual demands of the task can offer important insights into the change in the distracting potential of in-vehicle tasks across glances and the effects of training. Application: The sequential analysis of in-vehicle glance data can be useful for researchers and practitioners and has implications for the development and evaluation of training programs as well as for task and interface design.

Objective: This study tested the effects of two fundamental forms of distraction, including visual-manual and cognitive-audio distraction, with comparison under both operational and tactical driving. Strategic control remains for future study. Background: Driving is a complex control task involving operational, tactical, and strategic control. Although operational control, such as lead-car following, has been studied, the influence of in-vehicle distractions on higher levels of control, including tactical and strategic, remains unclear. Method: Two secondary tasks were designed to independently represent visual-manual and cognitive-audio distractions, based on multiple resource theory. Drivers performed operational vehicle control maneuvers (lead-car following) or tactical control maneuvers (passing) along with the distraction tasks in a driving simulator. Response measures included driving performance and visual behavior. Results: Results revealed drivers' ability to accommodate either visual or cognitive distractions in following tasks but not in passing. The simultaneous distraction condition led to the greatest decrement in performance. Conclusion: Findings support the need to assess the impacts of in-vehicle distraction on different levels of driving control. Future study should investigate driver distraction under strategic control.