Driver behavior while using Level 2 vehicle automation: a hybrid naturalistic study.

Vehicle automation is becoming more prevalent. Understanding how drivers use this technology and its safety implications is crucial. In a 6-8 week naturalistic study, we leveraged a hybrid naturalistic driving research design to evaluate driver behavior with Level 2 vehicle automation, incorporating unique naturalistic and experimental control conditions. Our investigation covered four main areas: automation usage, system warnings, driving demand, and driver arousal, as well as secondary task engagement. While on the interstate, drivers were advised to engage Level 2 automation whenever they deemed it safe, and they complied by using it over 70% of the time. Interestingly, the frequency of system warnings increased with prolonged use, suggesting an evolving relationship between drivers and the automation features. Our data also revealed that drivers were discerning in their use of automation, opting for manual control under high driving demand conditions. Contrary to common safety concerns, our data indicated no significant rise in driver fatigue or fidgeting when using automation, compared to a control condition. Additionally, observed patterns of engagement in secondary tasks like radio listening and text messaging challenge existing assumptions about automation leading to dangerous driver distraction. Overall, our findings provide new insights into the conditions under which drivers opt to use automation and reveal a nuanced behavioral profile that emerges when automation is in use.

This Is Your Brain on Autopilot 2.0: The Influence of Practice on Driver Workload and Engagement During On-Road, Partially Automated Driving.

OBJECTIVE: This on-road study employed behavioral and neurophysiological measurement techniques to assess the influence of six weeks of practice driving a Level 2 partially automated vehicle on driver workload and engagement. BACKGROUND: Level 2 partial automation requires a driver to maintain supervisory control of the vehicle to detect "edge cases" that the automation is not equipped to handle. There is mixed evidence regarding whether drivers can do so effectively. There is also an open question regarding how practice and familiarity with automation influence driver cognitive states over time. METHOD: Behavioral and neurophysiological measures of driver workload and visual engagement were recorded from 30 participants at two testing sessions-with a six-week familiarization period in-between. At both testing sessions, participants drove a vehicle with partial automation engaged (Level 2) and not engaged (Level 0) on two interstate highways while reaction times to the detection response task (DRT) and neurophysiological (EEG) metrics of frontal theta and parietal alpha were recorded. RESULTS: DRT results demonstrated that partially automated driving placed more cognitive load on drivers than manual driving and six weeks of practice decreased driver workload-though only when the driving environment was relatively simple. EEG metrics of frontal theta and parietal alpha showed null effects of partial automation. CONCLUSION: Driver workload was influenced by level of automation, specific highway characteristics, and by practice over time, but only on a behavioral level and not on a neural level. APPLICATION: These findings expand our understanding of the influence of practice on driver cognitive states under Level 2 partial automation.

This Is Your Brain on Autopilot: Neural Indices of Driver Workload and Engagement During Partial Vehicle Automation.

OBJECTIVE: This research explores the effect of partial vehicle automation on neural indices of mental workload and visual engagement during on-road driving. BACKGROUND: There 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. METHOD: EEG 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. RESULTS: Analysis 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. CONCLUSION: Drivers 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. APPLICATION: These findings expand our understanding of the effects of partial vehicle automation on drivers' cognitive states.

Dynamic workload measurement and modeling: Driving and conversing.

Tillman et al. (2017) used evidence-accumulation modeling to ascertain the effects of a conversation (either with a passenger or on a hands-free cell phone) on a drivers' mental workload. They found that a concurrent conversation increased the response threshold but did not alter the rate of evidence accumulation. However, this earlier research collapsed across speaking and listening components of a natural conversation, potentially masking any dynamic fluctuations associated with this dual-task combination. In the present study, a unique implementation of the detection response task was used to simultaneously measure the demands on the driver and the nondriver when they were speaking or when they were listening. We found that the natural ebb and flow of a conversation altered both the rate of evidence accumulation and the response threshold for drivers and nondrivers alike. The dynamic fluctuations in cognitive workload observed with this novel method illustrate how quickly the parameters of cognition are altered by real-time task demands. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

The persistence of distraction: The hidden costs of intermittent multitasking.

We examined the hidden costs of intermittent multitasking. Participants performed a pursuit-tracking task (Experiment 1) or drove in a high-fidelity driving simulator (Experiment 2) by itself or while concurrently performing an easy or difficult backwards counting task that periodically started and stopped, creating on-task and off-task multitasking epochs. A novel application of the Detection Response Task (DRT), a standardized protocol for measuring cognitive workload (ISO 17488, 2016), was used to measure performance in the on-task and off-task intervals. We found striking costs that persisted well after the counting task had stopped. In fact, the multitasking costs dissipated as a negatively accelerated function of time with the largest costs observed immediately after multitasking ceased. Performance in the off-task interval remained above baseline levels throughout the 30-s off-task interval. We suggest that loading new procedures into working memory occurs fairly quickly, whereas purging this information from working memory takes considerably longer. (PsycInfo Database Record (c) 2022 APA, all rights reserved).

No Difference in Arousal or Cognitive Demands Between Manual and Partially Automated Driving: A Multi-Method On-Road Study.

INTRODUCTION: Partial 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. METHOD: We 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. RESULTS: Results 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. CONCLUSION: This 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.

Age-Related Differences in the Cognitive, Visual, and Temporal Demands of In-Vehicle Information Systems.

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.

Assessing the visual and cognitive demands of in-vehicle information systems.

BACKGROUND: New automobiles provide a variety of features that allow motorists to perform a plethora of secondary tasks unrelated to the primary task of driving. Despite their ubiquity, surprisingly little is known about how these complex multimodal in-vehicle information systems (IVIS) interactions impact a driver's workload. RESULTS: The current research sought to address three interrelated questions concerning this knowledge gap: (1) Are some task types more impairing than others? (2) Are some modes of interaction more distracting than others? (3) Are IVIS interactions easier to perform in some vehicles than others? Depending on the availability of the IVIS features in each vehicle, our testing involved an assessment of up to four task types (audio entertainment, calling and dialing, text messaging, and navigation) and up to three modes of interaction (e.g., center stack, auditory vocal, and the center console). The data collected from each participant provided a measure of cognitive demand, a measure of visual/manual demand, a subjective workload measure, and a measure of the time it took to complete the different tasks. The research provides empirical evidence that the workload experienced by drivers systematically varied as a function of the different tasks, modes of interaction, and vehicles that we evaluated. CONCLUSIONS: This objective assessment suggests that many of these IVIS features are too distracting to be enabled while the vehicle is in motion. Greater consideration should be given to what interactions should be available to the driver when the vehicle is in motion rather than to what IVIS features and functions could be available to motorists.

Visual and Cognitive Demands of CarPlay, Android Auto, and Five Native Infotainment Systems.

OBJECTIVE: The present research compared and contrasted the workload associated with using in-vehicle information systems commonly available in five different automotive original equipment manufacturers (OEMs) with that of CarPlay and Android Auto when used in the same vehicles. BACKGROUND: A growing trend is to provide access to portable smartphone-based systems (e.g., CarPlay and Android Auto) that support an expansion of various in-vehicle infotainment system features and functions. METHOD/RESULTS: The study involved on-road testing of 24 participants in each configuration of five vehicles crossed with the three different infotainment systems: the embedded portion of the native OEM systems, CarPlay, and Android Auto. Our analysis found that workload was significantly greater for the embedded portion of the native OEM systems than for CarPlay and Android Auto. The strengths and weaknesses of each CarPlay and Android Auto traded off in such a way that the overall demand associated with using the two systems did not differ. CONCLUSION: CarPlay and Android Auto provided more functionality and resulted in lower levels of workload than the embedded portion of the native OEM infotainment systems. APPLICATION: Potential applications of this research include refinements to CarPlay and Android Auto to address variations in workload as a function of task type, the modality of interaction, and OEM implementation of the system.

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. (PsycINFO Database Record

Talking to your car can drive you to distraction.

This research examined the impact of in-vehicle information system (IVIS) interactions on the driver's cognitive workload; 257 subjects participated in a weeklong evaluation of the IVIS interaction in one of ten different model-year 2015 automobiles. After an initial assessment of the cognitive workload associated with using the IVIS, participants took the vehicle home for 5 days and practiced using the system. At the end of the 5 days of practice, participants returned and the workload of these IVIS interactions was reassessed. The cognitive workload was found to be moderate to high, averaging 3.34 on a 5-point scale and ranged from 2.37 to 4.57. The workload was associated with the intuitiveness and complexity of the system and the time it took participants to complete the interaction. The workload experienced by older drivers was significantly greater than that experienced by younger drivers performing the same operations. Practice did not eliminate the interference from IVIS interactions. In fact, IVIS interactions that were difficult on the first day were still relatively difficult to perform after a week of practice. Finally, there were long-lasting residual costs after the IVIS interactions had terminated. The higher levels of workload should serve as a caution that these voice-based interactions can be cognitively demanding and ought not to be used indiscriminately while operating a motor vehicle.

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.

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.

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.