Much of the driver distraction and inattention work to date has focused on concerns over drivers removing their eyes from the forward roadway to perform non-driving-related tasks, and its demonstrable link to safety consequences when these glances are timed at inopportune moments. This extensive literature has established, through the analyses of glance from naturalistic datasets, a clear relationship between eyes-off-road, lead vehicle closing kinematics, and near-crash/crash involvement. This paper looks at the role of driver expectation in influencing drivers' decisions about when and for how long to remove their eyes from the forward roadway in an analysis that consider the combined role of on- and off-road glances. Using glance data collected in the 100-Car Naturalistic Driving Study (NDS), near-crashes were examined separately from crashes to examine how momentary differences in glance allocation over the 25-s prior to a precipitating event can differentiate between these two distinct outcomes. Individual glance metrics of mean single glance duration (MSGD), total glance time (TGT), and glance count for off-road and on-road glance locations were analyzed. Output from the AttenD algorithm (Kircher and Ahlström, 2009) was also analyzed as a hybrid measure; in threading together on- and off-road glances over time, its output produces a pattern of glance behavior meaningful for examining attentional effects. Individual glance metrics calculated at the epoch-level and binned by 10-s units of time across the available epoch lengths revealed that drivers in near-crashes have significantly longer on-road glances, and look less frequently between on- and off- road locations in the moments preceding a precipitating event as compared to crashes. During on-road glances, drivers in near-crashes were found to more frequently sample peripheral regions of the roadway than drivers in crashes. Output from the AttenD algorithm affirmed the cumulative net benefit of longer on-road glances and of improved attention management between on- and off-road locations. The finding of longer on-road glances differentiating between safety-critical outcomes in the 100-Car NDS data underscores the importance of attention management in how drivers look both on and off the road. It is in the pattern of glances to and from the forward roadway that drivers obtained critical information necessary to inform their expectation of hazard potential to avoid a crash. This work may have important implications for attention management in the context of the increasing prevalence of in-vehicle demands as well as of vehicle automation.

The emergence of partial-automation in consumer vehicles is reshaping the driving task, the driver role, and subsequent driver behavior. When using partial-automation, drivers delegate the operational control of the dynamic driving task to the automation system, while remaining responsible for monitoring, object/event detection, response selection, and execution. Hence, driving has become a collaboration between driver and automation systems that is characterized by dynamic Transfers of Control (TOC). This study aimed to assess how drivers leverage automation in real-world driving, identify driver and system-initiated TOCs, and provide a taxonomy to capture the underlying driver behaviors associated with automation disengagement. Fourteen participants drove instrumented Cadillac CT6 vehicles for one-month each, yielding 1690 trips (22,108 miles), with a total of 5343 TOCs between manual driving, SAE Level 1 Adaptive Cruise Control (ACC), and SAE Level 2 Super Cruise (SC). The use of automation on limited access highways was prevalent (40 % of the miles driven were with SC and 10 % with ACC) yet not continuous. Drivers frequently initiated transitions between automation levels (mean = 9.98, SD = 8.32, transitions per trip), temporarily taking over the longitudinal and/or lateral vehicle control. These transitions were not necessarily related to immediate risk mitigation, but rather to the execution of functions beyond the automation system's capabilities or representing preferences in task execution. Driver-initiated TOCs from SC to manual driving followed the structure and temporal aspects of the hierarchical model of driver behavior. Strategic, Maneuver, and Control TOCs were associated with significantly different patterns of vehicle kinematics, automation disengagement modality, and TOC duration. System-initiated automation disengagements from SC to manual driving were rare (1%). Generalizing from objective, real-world driving data, this study provides an ecologically valid taxonomy for transfer of control building upon the hierarchical model of driver behavior. We show that driver-automation interactions can occur in each level of the hierarchical model and that TOCs are part of the driver's strategic, maneuver, and control levels of decision making. Thus, TOCs are not isolated or rare events, but rather an integral part of an ongoing, continuous and dynamic collaboration. This taxonomy contextualizes TOCs, paving the way for greater understanding of when and why drivers will takeover control, exposes the underlying motivations for TOCs, and characterizes how these are reflected in the driver's actions. The findings can inform the development of driver-centered automation systems as well as policies and guidelines for current and future automation levels.

Forward collision warning (FCW) systems typically employ forward sensing technologies to identify possible forward collisions and provide an alert to the driver in the event they have not recognized a threat. These systems have demonstrated safety benefits. However, because the base rate of collisions is low, sensitive FCW systems can provide a high rate of alarms in situations with no or low probability of collision, which may negatively impact driver responsiveness and satisfaction. We examined over 2000 naturally occurring FCWs in two modern vehicles as a part of a naturalistic driving study investigation into advanced vehicle technologies. Analysts used cabin and forward camera footage, as well as environmental characteristics, to judge the likelihood of a crash during each alert, which were used to model the likelihood of an alert representing a possible collision. Only nine FCWs were considered “crash possible and imminent”. Road-type, speed, traffic density, and deceleration profiles distinguished between alert severity. Modeling outcomes provide clues for reducing nuisance and false alerts, and the method of using subjective video annotation combined with vehicle kinematics shows promise for investigating FCW alerts in the real world.

Non-driving-related tasks (NDRTs) have the potential to affect safety in a number of ways, but the conditions under which drivers choose to engage in NDRTs has not been extensively studied. This analysis considers naturalistic driving data in which drivers were recorded driving and engaging in NDRTs at will for several weeks. Using human-annotated video captured from vehicle cabins, we examined the probabilities with which drivers engaged in NDRTs, and we examined the relationship between vehicle speed and NDRT probability, with the goal of modeling NDRT probability as a function of speed and type of NDRT observed. We found that tasks that contain significant visual and manual components, such as phone manipulation, show strong sensitivity to vehicle speed, while other tasks, such as phone conversation, show no effects of vehicle speed. These results suggest that there are systematic relationships between NDRT patterns and vehicle speed, and that the nature of these relationships is sensitive to the demands of the NDRT. The relationship between speed and NDRT probability has implications for understanding the effects of NDRTs on safety, but also for understanding how drivers may differ in terms of the strategies they employ to modulate their NDRT behaviors based upon driving demands.