Search

Semi-automated vehicles (Level-2) provide driving assistance, but they still require driver supervision to maintain safe driving. However, little is known about potential differences in drivers’ cognitive states during manual vs. Level-2 automated driving. The current study systematically examined the effects of manual and Level-2 driving on drivers’ arousal and workload during on-road driving. No differences between the two driving modes were found for the five outcomes that assessed cognitive arousal and workload (i.e., heart rate, root mean square of successive heart period differences, EEG alpha power, and hit rate and reaction time on a secondary task). A Bayes Factor analysis suggested that there is strong evidence that cognitive arousal and workload during Level-2 driving did not differ from manual driving. These novel and theoretically meaningful findings provide strong evidence of similar cognitive arousal and workload states in Level-2 automation and manual driving.

Many challenges associated with aging faced by older drivers may be attenuated by advancements of in-vehicle technologies. One class in particular, In Vehicle Information Systems (IVIS), refers to a collection of features and functions that allow motorists to complete tasks in addition to operating the vehicle. Little is known about how interactions with IVIS may impact older drivers. This study explored comments provided by drivers while completing goal-oriented IVIS tasks in six vehicles; collected via survey, and in an open-ended prompt. Two age cohorts were evaluated: younger drivers between 21-36 years of age, and older drivers between 55-75 years of age. Analyses of comments investigated differences in the thematic content and emotional valence present in comments. Results indicated that older and younger drivers were surprisingly symmetrical in their responses pertaining to valence and themes, and older drivers were more willing to take time to become familiar with IVIS.

IntroductionPartial driving automation is not always reliable and requires that drivers maintain readiness to take over control and manually operate the vehicle. Little is known about differences in drivers’ arousal and cognitive demands under partial automation and how it may make it difficult for drivers to transition from automated to manual modes. This research examined whether there are differences in drivers’ arousal and cognitive demands during manual versus partial automation driving.MethodWe compared arousal (using heart rate) and cognitive demands (using the root mean square of successive differences in normal heartbeats; RMSSD, and Detection Response Task; DRT) while 39 younger (M= 28.82 years) and 32 late-middle-aged (M= 52.72 years) participants drove four partially automated vehicles (Cadillac, Nissan Rogue, Tesla, and Volvo) on interstate highways. If compared to manual driving, drivers’ arousal and cognitive demands were different under partial automation, then corresponding differences in heart rate, RMSSD, and DRT would be expected. Alternatively, if drivers’ arousal and cognitive demands were similar in manual and partially automated driving, no difference in the two driving modes would be expected.ResultsResults suggest no significant differences in heart rate, RMSSD, or DRT reaction time performance between manual and partially automated modes of driving for either younger or late-middle-aged adults across the four test vehicles. A Bayes Factor analysis suggested that heart rate, RMSSD, and DRT data showed extreme evidence in favor of the null hypothesis.ConclusionThis novel study conducted on real roads with a representative sample provides important evidence of no difference in arousal and cognitive demands. Younger and late-middle-aged motorists who are new to partial automation are able to maintain arousal and cognitive demands comparable to manual driving while using the partially automated technology. Drivers who are more experienced with partially automated technology may respond differently than those with limited prior experience.

In-vehicle information systems (IVIS) refer to a collection of features in vehicles that allow motorists to complete tasks (often unrelated to driving) while operating the vehicle. These systems may interfere, to a greater extent, with older drivers' ability to attend to the visual and cognitive demands of the driving environment. The current study sought to examine age-related differences in the visual, cognitive and temporal demands associated with IVIS interactions. Older and younger drivers completed a set of common tasks using the IVIS of a representative sample of six different vehicles while they drove along a low-density residential street. Evaluation measures included a Detection Response Task (DRT), to assess both cognitive and visual attention, and subjective measures following each condition using the NASA Task Load Index (TLX). Two age cohorts were evaluated: younger drivers between 21 and 36 years of age, and older drivers between 55 and 75 years of age. Participants completed experimental tasks involving interactions with the IVIS to achieve a specific goal (i.e., using the touch screen to tune the radio to a station; using voice commands to find a specified navigation destination, etc.). Performance of tasks varied according to different modes of interaction available in the vehicles. Older drivers took longer to complete tasks, were slower to react to stimuli, and reported higher task demand when interacting with IVIS. Older drivers stand to benefit the most from advancements in-vehicle technology, but ironically may struggle the most to use them. The results document significant age-related costs in the potential for distraction from IVIS interactions on the road.

ObjectiveThis research explores the effect of partial vehicle automation on neural indices of mental workload and visual engagement during on-road driving.BackgroundThere is concern that the introduction of automated technology in vehicles may lead to low driver stimulation and subsequent disengagement from the driving environment. Simulator-based studies have examined the effect of automation on a driver’s cognitive state, but it is unknown how the conclusions translate to on-road driving. Electroencephalographic (EEG) measures of frontal theta and parietal alpha can provide insight into a driver’s mental workload and visual engagement while driving under various conditions.MethodEEG was recorded from 71 participants while driving on the roadway. We examined two age cohorts, on two different highway configurations, in four different vehicles, with partial vehicle automation both engaged and disengaged.ResultsAnalysis of frontal theta and parietal alpha power revealed that there was no change in mental workload or visual engagement when driving manually compared with driving under partial vehicle automation.ConclusionDrivers new to the technology remained engaged with the driving environment when operating under partial vehicle automation. These findings suggest that the concern surrounding driver disengagement under vehicle automation may need to be tempered, at least for drivers new to the experience.ApplicationThese findings expand our understanding of the effects of partial vehicle automation on drivers’ cognitive states.