Assessing Cognitive Distraction in the Automobile

Lights, Sirens, and Load: Anticipatory emergency medical treatment planning causes cognitive load during emergency response driving among paramedicine students

Paramedics face various unconventional and secondary task demands while driving ambulances, leading to significant cognitive load, especially during lights-and-sirens responses. Previous research suggests that high cognitive load negatively affects driving performance, increasing the risk of accidents, particularly for inexperienced drivers. The current study investigated the impact of anticipatory treatment planning on cognitive load during emergency driving, as assessed through the use of a driving simulator. We recruited 28 non-paramedic participants to complete a simulated baseline drive with no task and a cognitive load manipulation using the 1-back task. We also recruited 18 paramedicine students who completed a drive while considering two cases they were travelling to: cardiac arrest and infant seizure, representing varying difficulty in required treatment. The results indicated that both cases imposed considerable cognitive load, as indicated by NASA Task Load Index responses, comparable to the 1-back task and significantly higher than driving with no load. These findings suggest that contemplating cases and treatment plans may impact the safety of novice paramedics driving ambulances for emergency response. Further research should explore the influence of experience and the presence of a second individual in the vehicle to generalise to broader emergency response driving contexts.

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.

Estimating Systemic Cognitive States from a Mixture of Physiological and Brain Signals

As human-machine teams are being considered for a variety of mixed-initiative tasks, detecting and being responsive to human cognitive states, in particular systematic cognitive states, is among the most critical capabilities for artificial systems to ensure smooth interactions with humans and high overall team performance. Various human physiological parameters, such as heart rate, respiration rate, blood pressure, and skin conductance, as well as brain activity inferred from functional near-infrared spectroscopy or electroencephalogram, have been linked to different systemic cognitive states, such as workload, distraction, or mind-wandering among others. Whether these multimodal signals are indeed sufficient to isolate such cognitive states across individuals performing tasks or whether additional contextual information (e.g., about the task state or the task environment) is required for making appropriate inferences remains an important open problem. In this paper, we introduce an experimental and machine learning framework for investigating these questions and focus specifically on using physiological and neurophysiological measurements to learn classifiers associated with systemic cognitive states like cognitive load, distraction, sense of urgency, mind wandering, and interference. Specifically, we describe a multitasking interactive experimental setting used to obtain a comprehensive multimodal data set which provided the foundation for a first evaluation of various standard state-of-the-art machine learning techniques with respect to their effectiveness in inferring systemic cognitive states. While the classification success of these standard methods based on just the physiological and neurophysiological signals across subjects was modest, which is to be expected given the complexity of the classification problem and the possibility that higher accuracy rates might not in general be achievable, the results nevertheless can serve as a baseline for evaluating future efforts to improve classification, especially methods that take contextual aspects such as task and environmental states into account.

Visual Function and Driving Performance Under Different Lighting Conditions in Older Drivers: Preliminary Results From an Observational Study

BACKGROUND

Age-related vision changes significantly contribute to fatal crashes at night among older drivers. However, the effects of lighting conditions on age-related vision changes and associated driving performance remain unclear.

OBJECTIVE

This pilot study examined the associations between visual function and driving performance assessed by a high-fidelity driving simulator among drivers 60 and older across 3 lighting conditions: daytime (photopic), nighttime (mesopic), and nighttime with glare.

METHODS

Active drivers aged 60 years or older participated in visual function assessments and simulated driving on a high-fidelity driving simulator. Visual acuity (VA), contrast sensitivity function (CSF), and visual field map (VFM) were measured using quantitative VA, quantitative CSF, and quantitative VFM procedures under photopic and mesopic conditions. VA and CSF were also obtained in the presence of glare in the mesopic condition. Two summary metrics, the area under the log CSF (AULCSF) and volume under the surface of VFM (VUSVFM), quantified CSF and VFM. Driving performance measures (average speed, SD of speed [SDspeed], SD of lane position (SDLP), and reaction time) were assessed under daytime, nighttime, and nighttime with glare conditions. Pearson correlations determined the associations between visual function and driving performance across the 3 lighting conditions.

RESULTS

Of the 20 drivers included, the average age was 70.3 years; 55% were male. Poor photopic VA was significantly correlated with greater SDspeed (r=0.26; P<.001) and greater SDLP (r=0.31; P<.001). Poor photopic AULCSF was correlated with greater SDLP (r=-0.22; P=.01). Poor mesopic VUSFVM was significantly correlated with slower average speed (r=-0.24; P=.007), larger SDspeed (r=-0.19; P=.04), greater SDLP (r=-0.22; P=.007), and longer reaction times (r=-0.22; P=.04) while driving at night. For functional vision in the mesopic condition with glare, poor VA was significantly correlated with longer reaction times (r=0.21; P=.046) while driving at night with glare; poor AULCSF was significantly correlated with slower speed (r=-0.32; P<.001), greater SDLP (r=-0.26; P=.001) and longer reaction times (r=-0.2; P=.04) while driving at night with glare. No other significant correlations were observed between visual function and driving performance under the same lighting conditions.

CONCLUSIONS

Visual functions differentially affect driving performance in different lighting conditions among older drivers, with more substantial impacts on driving during nighttime, especially in glare. Additional research with larger sample sizes is needed to confirm these results.

An extension of the shifted Wald model of human response times: Capturing the time dynamic properties of human cognition : Trial-varying Wald model

Despite the ubiquitous nature of evidence accumulation models in cognitive and experimental psychology, there has been a comparatively limited uptake of such techniques in the applied literature. While quantifying latent cognitive processing properties has significant potential for applied domains such as adaptive work systems, accumulator models often fall short in practical applications. Two primary reasons for these shortcomings are the complexities and time needed for the application of cognitive models, and the failure of current models to capture systematic trial-to-trial variability in parameters. In this manuscript, we develop a novel, trial-varying extension of the shifted Wald model to address these concerns. By leveraging conjugate properties of the Wald distribution, we derive computationally efficient solutions for threshold and drift parameters which can be updated instantaneously with new data. The resulting model allows the quantification of systematic variation in latent cognitive parameters across trials and we demonstrate the utility of such analyses through simulations and an exemplar application to an existing data set. The analytic nature of our solutions opens the door for real-world applications, significantly extending the reach of computational models of behavioral responses.

Effects of executive load on crashes and near-crashes for young versus older drivers

With the increasing use of infotainment systems in vehicles, secondary tasks requiring executive demand may increase crash risk, especially for young drivers. Naturalistic driving data were examined to determine if secondary tasks with increasing executive demand would result in increasing crash risk. Data were extracted from the Second Strategic Highway Research Program Naturalistic Driving Study, where vehicles were instrumented to record driving behavior and crash/near-crash data. executive and visual-manual tasks paired with a second executive task (also referred to as dual executive tasks) were compared to the executive and visual-manual tasks performed alone. Crash/near-crash odds ratios were computed by comparing each task condition to driving without the presence of any secondary task. Dual executive tasks resulted in greater odds ratios than those for single executive tasks. The dual visual-manual task odds ratios did not increase from single task odds ratios. These effects were only found in young drivers. The study shows that dual executive secondary task load increases crash/near-crash risk in dual task situations for young drivers. Future research should be conducted to minimize task load associated with vehicle infotainment systems that use such technologies as voice commands.

Multitasking While Driving: Central Bottleneck or Problem State Interference?

OBJECTIVE

The objective of this work was to investigate if visuospatial attention and working memory load interact at a central control resource or at a task-specific, information processing resource during driving.

BACKGROUND

In previous multitasking driving experiments, interactions between different cognitive concepts (e.g., attention and working memory) have been found. These interactions have been attributed to a central bottleneck or to the so-called problem-state bottleneck, related to working memory usage.

METHOD

We developed two different cognitive models in the cognitive architecture ACT-R, which implement the central vs. problem-state bottleneck. The models performed a driving task, during which we varied visuospatial attention and working memory load. We evaluated the model by conducting an experiment with human participants and compared the behavioral data to the model's behavior.

RESULTS

The problem-state-bottleneck model could account for decreased driving performance due to working memory load as well as increased visuospatial attentional demands as compared to the central-bottleneck model, which could not account for effects of increased working memory load.

CONCLUSION

The interaction between working memory and visuospatial attention in our dual tasking experiment can be best characterized by a bottleneck in the working memory. The model results suggest that as working memory load becomes higher, drivers manage to perform fewer control actions, which leads to decreasing driving performance.

APPLICATION

Predictions about the effect of different mental loads can be used to quantify the contribution of each subtask allowing for precise assessments of the current overall mental load, which automated driving systems may adapt to.

R2DRV: study protocol for longitudinal assessment of driving after mild TBI in young drivers

BACKGROUND

Mild traumatic brain injury (mTBI) and traffic-related injuries are two major public health problems disproportionately affecting young people. Young drivers, whose driving skills are still developing, are particularly vulnerable to impaired driving due to brain injuries. Despite this, there is a paucity of research on how mTBI impacts driving and when it is safe to return to drive after an mTBI. This paper describes the protocol of the study, R2DRV, Longitudinal Assessment of Driving After Mild TBI in Young Drivers, which examines the trajectory of simulated driving performance and self-reported driving behaviors from acutely post-injury to symptom resolution among young drivers with mTBI compared to matched healthy drivers. Additionally, this study investigates the associations of acute post-injury neurocognitive function and cognitive load with driving among young drivers with and without mTBI.

METHODS

A total of 200 young drivers (ages 16 to 24) are enrolled from two study sites, including 100 (50 per site) with a physician-confirmed isolated mTBI, along with 100 (50 per site) healthy drivers without a history of TBI matched 1:1 for age, sex, driving experience, and athlete status. The study assesses primary driving outcomes using two approaches: (1) high-fidelity driving simulators to evaluate driving performance across four experimental study conditions at multiple time points (within 96 h of injury and weekly until symptom resolution or 8 weeks post-injury); (2) daily self-report surveys on real-world driving behaviors completed by all participants.

DISCUSSION

This study will fill critical knowledge gaps by longitudinally assessing driving performance and behaviors in young drivers with mTBI, as compared to matched healthy drivers, from acutely post-injury to symptom resolution. The research strategy enables evaluating how increased cognitive load may exacerbate the effects of mTBI on driving, and how post-mTBI neurocognitive deficits may impact the driving ability of young drivers. Findings will be shared through scientific conferences, peer-reviewed journals, and media outreach to care providers and the public.

"It's okay because I'm just driving": an exploration of self-reported mobile phone use among Mexican drivers

Introduction

Technological advancements have the potential to enhance people's quality of life, but their misuse can have a detrimental impact on safety. A notable example is the escalating issue of distracted driving resulting from the use of mobile phones behind the wheel, leading to severe crashes and injuries. Despite these concerns, both drivers' usage patterns and their risk-related associations remain scarcely documented in Mexico. Therefore, this descriptive study aimed to examine the mobile phone usage of Mexican drivers, its relationships to risk awareness and near-miss/crash involvement, and the self-reported underlying reasons for this behavior.

Methods

This cross-sectional study utilized a sample of 1,353 licensed Mexican drivers who took part in a nationwide series of interviews regarding their onboard phone use settings.

Results

A significant percentage of drivers (96.8%) recognize using a mobile phone while driving as high-risk behavior. However, only 7.4% reported completely avoiding its use while driving, with 22.4% identified as high-frequency users. Frequency was also found positively associated with the self-reported rate of near-misses and crashes. Furthermore, qualitative data analysis highlights the emergence of a 'sense of urgency' to attend to phone-related tasks in response to daily demands and life dynamics, offering a potential explanation for this behavior.

Conclusion

The results of this study suggest common patterns of onboard mobile use among Mexican drivers concerning driving situations and associated risks. This underscores the need for increased efforts to discourage onboard phone use in the country.

Adopting Stimulus Detection Tasks for Cognitive Workload Assessment: Some Considerations

OBJECTIVE

This article tackles the issue of correct data interpretation when using stimulus detection tasks for determining the operator's workload.

BACKGROUND

Stimulus detection tasks are a relative simple and inexpensive means of measuring the operator's state. While stimulus detection tasks may be better geared to measure conditions of high workload, adopting this approach for the assessment of low workload may be more problematic.

METHOD

This mini-review details the use of common stimulus detection tasks and their contributions to the Human Factors practice. It also borrows from the conceptual framework of the inverted-U shape model to discuss the issue of data interpretation.

RESULTS

The evidence being discussed here highlights a clear limitation of stimulus detection task paradigms.

CONCLUSION

There is an inherent risk in using a unidimensional tool like stimulus detection tasks as the primary source of information for determining the operator's psychophysiological state.

APPLICATION

Two recommendations are put forward to Human Factors researchers and practitioners dealing with the interpretation conundrum of dealing with stimulus detection tasks.

Driving errors as a function of listening to music and FM radio: A simulator study

OBJECTIVES

Driving is a dynamic activity that takes place in a constantly changing environment, carrying safety implications not only for the driver but also for other road users. Despite the potentially life-threatening consequences of incorrect driving behavior, drivers often engage in activities unrelated to driving. This study aims to investigate the frequency and types of errors committed by drivers when they are distracted compared to when they are not distracted.

METHODS

A total of 64 young male participants volunteered for the study, completing four driving trials in a driving simulator. The trials consisted of different distraction conditions: listening to researcher-selected music, driver-selected music, FM radio conversation, and driving without any auditory distractions. The simulated driving scenario resembled a semi-urban environment, with a track length of 12 km.

RESULTS

The findings of the study indicate that drivers are more prone to making errors when engaged in FM radio conversations compared to listening to music. Additionally, errors related to speeding were found to be more prevalent across all experimental conditions.

CONCLUSIONS

These results emphasize the significance of reducing distractions while driving to improve road safety. The findings add to our understanding of the particular distractions that carry higher risks and underscore the necessity for focused interventions to reduce driver errors, especially related to FM radio conversations. Future research can delve into additional factors that contribute to driving errors and develop effective strategies to promote safer driving practices.

Looking out or Looking Away?-Exploring the Impact of Driving With a Passenger on Young Drivers' Eye Glance Behavior

OBJECTIVE

To explore how passenger presence and the degree of association between young driver and passenger influences young drivers' eye glance behavior when they are subjected to distraction.

BACKGROUND

Young drivers (18-20 years old) are at an elevated crash risk when subjected to distraction. They are likely to be distracted even further when they drive with passengers. However, the eye glance behavior of these drivers when driving with passengers has not been explored.

METHOD

Eye glance data of 34 young drivers between the ages of 18 and 20 years were collected. Participants drove with and without a passenger while subjected to three distracting tasks (visual-manual, cognitive, or visual-cognitive) and driving scenarios that required driver attention.

RESULTS

Visual-cognitive as well as visual-manual states of distraction result in higher mean and standard deviation of glance duration, along with higher number of glances away from road. Passenger presence is found to negatively influence young drivers' eye glance behavior. The degree of association between the young driver and the passenger may help reduce the deviation of eye glances towards the task-related objects.

CONCLUSION

In addition to distraction, passengers have a negative influence on the eye glance behavior of young drivers. However, a high degree of association between driver and passenger may mitigate the negative impact of distraction on the eye glance behavior of young drivers.

APPLICATION (NON-THEORETICAL WORKS)

This research may aid in the design of interventions that improve young drivers' eye glance behavior when they drive with their peers.

Vigilance Decrement During On-Road Partially Automated Driving Across Four Systems

OBJECTIVE

This study uses a detection task to measure changes in driver vigilance when operating four different partially automated systems.

BACKGROUND

Research show temporal declines in detection task performance during manual and fully automated driving, but the accuracy of using this approach for measuring changes in driver vigilance during on-road partially automated driving is yet unproven.

METHOD

Participants drove four different vehicles (Tesla Model 3, Cadillac CT6, Volvo XC90, and Nissan Rogue) equipped with level-2 systems in manual and partially automated modes. Response times to a detection task were recorded over eight consecutive time periods.

RESULTS

Bayesian analysis revealed a main effect of time period and an interaction between mode and time period. A main effect of vehicle and a time period x vehicle interaction were also found.

CONCLUSION

Results indicated that the reduction in detection task performance over time was worse during partially automated driving. Vehicle-specific analysis also revealed that detection task performance changed across vehicles, with slowest response time found for the Volvo.

APPLICATION

The greater decline in detection performance found in automated mode suggests that operating level-2 systems incurred in a greater vigilance decrement, a phenomenon that is of interest for Human Factors practitioners and regulators. We also argue that the observed vehicle-related differences are attributable to the unique design of their in-vehicle interfaces.

Visual and cognitive demands of manual and voice-based driving mode implementations on smartphones

Mobile phone apps and operating systems are increasingly adopting driving mode functions that attempt to reduce driver visual and cognitive demand by limiting functionality, using larger buttons and icons, and adding voice-based interactions. The present study assessed the visual and cognitive demands and the subjective level of distraction from two driving mode implementations (voice or manual) on an Android™ mobile phone using Google Assistant™, compared to a typical mobile phone operating system experience. While driving on a test track, participants performed several trials of five tasks on each of three interfaces: A mobile operating system interface, a manual driving mode interface, and a voice driving mode interface. Visual demand was measured with eye-gaze recordings, cognitive load was measured with the detection response task, and a Likert scale was used to rate the perceived level of distraction. The voice driving mode resulted in the lowest visual attention demand and lowest subjective ratings of distraction. The manual driving mode condition also reduced visual demand and subjective ratings of distraction relative to the mobile operating system condition. The cognitive load results were inconsistent across the task and interaction mode conditions. Overall, the results of this study provide positive evidence in support of voice-based driving mode implementations for reducing visual demand and subjective levels of distraction from mobile devices while driving. Moreover, the results suggest that manual driving mode implementations also have the potential to reduce visual demand and subjective levels of distraction, relative to the mobile operating system condition.

Eyes on the road: brain computer interfaces and cognitive distraction in traffic

Novel wearable neurotechnology is able to provide insight into its wearer's cognitive processes and offers ways to change or enhance their capacities. Moreover, it offers the promise of hands-free device control. These brain-computer interfaces are likely to become an everyday technology in the near future, due to their increasing accessibility and affordability. We, therefore, must anticipate their impact, not only on society and individuals broadly but also more specifically on sectors such as traffic and transport. In an economy where attention is increasingly becoming a scarce good, these innovations may present both opportunities and challenges for daily activities that require focus, such as driving and cycling. Here, we argue that their development carries a dual risk. Firstly, BCI-based devices may match or further increase the intensity of cognitive human-technology interaction over the current hands-free communication devices which, despite being widely accepted, are well-known for introducing a significant amount of cognitive load and distraction. Secondly, BCI-based devices will be typically harder than hands-free devices to both visually detect (e.g., how can law enforcement check when these extremely small and well-integrated devices are used?) and restrain in their use (e.g., how do we prevent users from using such neurotechnologies without breaching personal integrity and privacy?). Their use in traffic should be anticipated by researchers, engineers, and policymakers, in order to ensure the safety of all road users.

The impact of speech-based assistants on the driver's cognitive distraction

Speech-based interfaces can be a promising alternative and/or addition to visual-manual interfaces since they reduce visual-manual distraction while driving. However, there are also findings indicating that speech-based assistants may be a source of cognitive distraction. The aim of this experiment was to quantify drivers' cognitive distraction while interacting with speech-based assistants. Therefore, 31 participants performed a simulated driving task and a detection response task (DRT). Concurrently they either sent text-messages via speech-based assistants (Siri, Google Assistant, or Alexa) or completed an arithmetic task (OSPAN). In a multifactorial approach, following Strayer et al. (2017), cognitive distraction was then assessed through performance in the DRT, the driving speed, the task completion time and self-report measures. The cognitive distraction associated with speech-based assistants was compared to the OSPAN task and a baseline condition without a secondary task. Participants reacted faster and more accurately to the DRT in the baseline condition compared to the speech conditions. The performance in the speech conditions was significantly better than in the OSPAN task. However, driving speed did not significantly differ between the experimental conditions. Results from the NASA-TLX indicate that speech-based tasks were more demanding than the baseline but less demanding than the OSPAN task. The task completion times revealed significant differences between speech-based assistants. Sending messages took longest with the Google Assistant. Referring to the findings by Strayer et al. (2017), we conclude that nowadays speech-based assistants are associated with a rather moderate than high level of cognitive distraction. Nonetheless, we point towards the need to assess the effects of human-machine interaction via speech-based interfaces due to their potential for cognitive distraction.

Distracted worker: Using pupil size and blink rate to detect cognitive load during manufacturing tasks

This study sets out to extend the use of blink rate and pupil size to the assessment of cognitive load of completing common automotive manufacturing tasks. Nonoptimal cognitive load is detrimental to safety. Existing occupational ergonomics approaches come short of measuring dynamic changes in cognitive load during complex assembling tasks. Cognitive demand was manipulated by having participants complete two versions of the n-back task (easy, hard). Two durations of the physical task were also considered (short, long). Pupil size and blink rate increased under greater cognitive task demand. High cognitive load also resulted in longer task completion times, and higher ratings of mental and temporal demand, and effort. This exploratory study offers relevant insights on the use of ocular metrics for cognitive load assessment in occupational ergonomics. While the existing eye-tracking technology may yet limit their adoption in the field, they offer advantages over the more popular expert-based and self-reported techniques in measuring changes in cognitive load during dynamic tasks.

The Effect of Cognitive Load on Auditory Susceptibility During Automated Driving

OBJECTIVE

We experimentally test the effect of cognitive load on auditory susceptibility during automated driving.

BACKGROUND

In automated vehicles, auditory alerts are frequently used to request human intervention. To ensure safe operation, human drivers need to be susceptible to auditory information. Previous work found reduced susceptibility during manual driving and in a lesser amount during automated driving. However, in practice, drivers also perform nondriving tasks during automated driving, of which the associated cognitive load may further reduce susceptibility to auditory information. We therefore study the effect of cognitive load during automated driving on auditory susceptibility.

METHOD

Twenty-four participants were driven in a simulated automated car. Concurrently, they performed a task with two levels of cognitive load: repeat a noun or generate a verb that expresses the use of this noun. Every noun was followed by a probe stimulus to elicit a neurophysiological response: the frontal P3 (fP3), which is a known indicator for the level of auditory susceptibility.

RESULTS

The fP3 was significantly lower during automated driving with cognitive load compared with without. The difficulty level of the cognitive task (repeat or generate) showed no effect.

CONCLUSION

Engaging in other tasks during automated driving decreases auditory susceptibility as indicated by a reduced fP3.

APPLICATION

Nondriving task can create additional cognitive load. Our study shows that performing such tasks during automated driving reduces the susceptibility for auditory alerts. This can inform designers of semi-automated vehicles (SAE levels 3 and 4), where human intervention might be needed.

Assessing Distraction Potential of Augmented Reality Head-Up Displays for Vehicle Drivers

OBJECTIVE

To develop a framework for quantifying the visual and cognitive distraction potential of augmented reality (AR) head-up displays (HUDs).

BACKGROUND

AR HUDs promise to be less distractive than traditional in-vehicle displays because they project information onto the driver's forward-looking view of the road. However, AR graphics may direct the driver's attention away from critical road elements. Moreover, current in-vehicle device assessment methods, which are based on eyes-off-road time measures, cannot capture this unique challenge.

METHOD

This article proposes a new method for the assessment of AR HUDs by measuring driver gaze behavior, situation awareness, confidence, and workload. An experimental user study (n = 24) was conducted in a driving simulator to apply the proposed method for the assessment of two AR pedestrian collision warning (PCW) design alternatives.

RESULTS

Only one of the two tested AR interfaces improved driver awareness of pedestrians without visually and cognitively distracting drivers from other road elements that were not augmented by the display but still critical for safe driving.

CONCLUSION

Our initial human-subject experiment demonstrated the potential of the proposed method in quantifying both positive and negative consequences of AR HUDs on driver cognitive processes. More importantly, the study suggests that AR interfaces can be informative or distractive depending on the perceptual forms of graphical elements presented on the displays.

APPLICATION

The proposed methods can be applied by designers of in-vehicle AR HUD interfaces and be leveraged by designers of AR user interfaces in general.

A mirror in the sky: the effects of map format and user expertise on navigation performance and mental workload

A novel map display concept named Mirror in the Sky (MitS) has been introduced to improve performance and reduce workload in navigation tasks. However, this display will be novel to most users and as such, an evaluation of MitS in comparison with more conventional map formats is warranted. This study investigated the effects of map display format (MitS vs. north-up and track-up maps) and user expertise on mental workload (MWL) and performance, using both soldiers (experts) and civilians (novices) as participants. Participants followed a prescribed route to a destination in a virtual environment (route following task) while also performing a secondary task (detection response task). Soldiers generally performed better than civilians. Soldiers reported a higher MWL with MitS than with the north-up map, whereas civilians reported a higher MWL with MitS than with the track-up map. Regardless of user expertise, there were performance and workload challenges with MitS, despite its potential. Practitioner summary: A new map display concept called Mirror in the Sky (MitS) was compared with two conventional map formats: a north-up and track-up map. The experiment tested soldier and civilian users in a route following task. Both groups got further into the route and had fewer obstacle collisions with north-up and track-up maps than they did with the MitS map. MWL measures generally indicated higher workload with MitS. Abbreviations: MitS: mirror in the sky; FFOV: forward field of view; AR: augmented reality; MWL: mental workload; VR: virtual reality; HF: human factors; HR: heart rate; HRV: heart rate variability; DRT: detection response task; DRDC: defence research and development Canada; VE: virtual environment; RT: response time; ANOVA: analysis of variance.

Effects of verbal tasks on driving simulator performance

We report results from a driving simulator paradigm we developed to test the fine temporal effects of verbal tasks on simultaneous tracking performance. A total of 74 undergraduate students participated in two experiments in which they controlled a cursor using the steering wheel to track a moving target and where the dependent measure was overall deviation from target. Experiment 1 tested tracking performance during slow and fast target speeds under conditions involving either no verbal input or output, passive listening to spoken prompts via headphones, or responding to spoken prompts. Experiment 2 was similar except that participants read written prompts overlain on the simulator screen instead of listening to spoken prompts. Performance in both experiments was worse during fast speeds and worst overall during responding conditions. Most significantly, fine scale time-course analysis revealed deteriorating tracking performance as participants prepared and began speaking and steadily improving performance while speaking. Additionally, post-block survey data revealed that conversation recall was best in responding conditions, and perceived difficulty increased with task complexity. Our study is the first to track temporal changes in interference at high resolution during the first hundreds of milliseconds of verbal production and comprehension. Our results are consistent with load-based theories of multitasking performance and show that language production, and, to a lesser extent, language comprehension tap resources also used for tracking. More generally, our paradigm provides a useful tool for measuring dynamical changes in tracking performance during verbal tasks due to the rapidly changing resource requirements of language production and comprehension.

Comparing an augmented reality navigation display to an electronic map for military reconnaissance

The next generation of displays for soldiers may include augmented reality capabilities. One such display, called Mirror in the Sky (MitS), presents survey information in the upper visual field. Using a virtual reality simulation of a military reconnaissance scenario, we compared a MitS prototype to a familiar electronic 2D north-up map. Participants (24 soldiers) were told to follow a prescribed route, detect potential threats, and reroute around them. They also performed a secondary task as a measure of mental workload. At the end of the route, the soldiers were asked to recall the locations of threats and route changes. Participants made better reroute decisions with the north-up map than with MitS, although no differences were observed for threat detection or mental workload. They also scored higher on recall with the north-up map than with MitS. Practitioner Summary: An augmented reality navigation aid was compared to an electronic north-up map in a military reconnaissance scenario, in a virtual reality simulation. Participants made better route decisions and had better recall with the north-up map, but no mental workload differences were found between displays.

A cognitive model of response omissions in distraction paradigms

The effects of distraction on responses manifest in three ways: prolonged reaction times, and increased error and response omission rates. However, the latter effect is often ignored or assumed to be due to a separate cognitive process. We investigated omissions occurring in two paradigms that manipulated distraction. One required simple stimulus detection of younger participants, the second required choice responses and was completed by both younger and older participants. We fit data from these paradigms with a model that identifies three causes of omissions: two are related to the process of accumulating the evidence on which a response is based: intrinsic omissions (due to between-trial variation in accumulation rates making it impossible to ever reach the evidence threshold) and design omissions (due to response windows that cause slow responses not to be recorded; a third, contaminant omissions, allows for a cause unrelated to the response process. In both data sets systematic differences in omission rates across conditions were accounted for by task-related omissions. Intrinsic omissions played a lesser role than design omissions, even though the presence of design omissions was not evident in descriptive analyses of the data. The model provided an accurate account of all aspects of the detection data and the choice-response data, but slightly underestimated overall omissions in the choice paradigm, particularly in older participants, suggesting that further investigation of contaminant omission effects is needed.

Effects of temporal and spatiotemporal cues on detection of dynamic road hazards

While driving, dangerous situations can occur quickly, and giving drivers extra time to respond may make the road safer for everyone. Extensive research on attentional cueing in cognitive psychology has shown that targets are detected faster when preceded by a spatially valid cue, and slower when preceded by an invalid cue. However, it is unknown how these standard laboratory-based cueing effects may translate to dynamic, real-world situations like driving, where potential targets (i.e., hazardous events) are inherently more complex and variable. Observers in our study were required to correctly localize hazards in dynamic road scenes across three cue conditions (temporal, spatiotemporal valid and spatiotemporal invalid), and a no-cue baseline. All cues were presented at the first moment the hazardous situation began. Both types of valid cues reduced reaction time (by 58 and 60 ms, respectively, with no significant difference between them, a larger effect than in many classic studies). In addition, observers’ ability to accurately localize hazards dropped 11% in the spatiotemporal invalid condition, a result with dangerous implications on the road. This work demonstrates that, in spite of this added complexity, classic cueing effects persist—and may even be enhanced—for the detection of real-world hazards, and that valid cues have the potential to benefit drivers on the road.

Analysis of Relationship between Electroencephalograms and Subjective Measurements for In-Vehicle Information System: A Preliminary Study

In this study, we explored the relationship between objective and subjective measures for usability evaluation in in-vehicle infotainment systems (IVISs). As a case study, four displays were evaluated based on cluster location and display orientation (that is, front–horizontal, front–vertical, right–horizontal, and right–vertical). Thirty-six participants performed tasks to manipulate the functions of the IVISs and data were collected through an electroencephalogram (EEG) sensor and questionnaire items. We analysed a model that estimated EEG-based objective indicators from subjective indicators. As a result, the objective indicators reflected the subjective indicators and were considered to explain the driver’s cognitive state. Although EEG data were collected from only four participants, this study proposed an experimental design that could be applied to the analysis of the relationship between the subject’s evaluation and EEG signals, as a preliminary study. We expect the experimental design and results of this study to be useful in analysing objective and subjective measures of usability evaluation.

Maximizing the Morning Commute: A Randomized Trial Assessing the Effect of Driving on Podcast Knowledge Acquisition and Retention

STUDY OBJECTIVE

Emergency medicine residents use podcasts as part of their learning process, often listening while driving. It is unclear how driving while listening to a podcast affects knowledge acquisition and retention. This study evaluated the knowledge gained from listening to podcasts while driving compared to that gained from undistracted listening.

METHODS

This was a multicenter, randomized, crossover trial among postgraduate year (PGY) 1 to 4 emergency medicine residents at 4 institutions. Residents were randomized with stratification by site and PGY level to listen to podcasts while driving first or sitting undistracted in a room first. Within 30 minutes of listening, they completed a 20-question test. They subsequently crossed over to the alternate intervention, serving as their own controls, and listened to a different podcast before completing a second 20-question test. Each of the podcasts was professionally recorded and based on 5 emergency medicine-relevant journal articles that had not been covered in a journal club or curriculum at any of the institutions. One month later, participants completed a delayed recall test composed of 40 new questions based on both podcasts. Questions were derived and validity evidence was collected prior to use. Data were compared using a paired-sample t test and ANOVA.

RESULTS

A total of 100 residents completed the initial recall tests, and 96 residents completed the delayed recall test. There was no statistically significant difference between the driving and undistracted cohorts on the initial recall (74.2% versus 73.3%) or delayed recall (52.2% versus 52.0%).

CONCLUSION

Driving while listening to a podcast does not meaningfully affect knowledge acquisition or retention when compared with undistracted podcast listening among emergency medicine residents.

Survey and Synthesis of State of the Art in Driver Monitoring

Road vehicle accidents are mostly due to human errors, and many such accidents could be avoided by continuously monitoring the driver. Driver monitoring (DM) is a topic of growing interest in the automotive industry, and it will remain relevant for all vehicles that are not fully autonomous, and thus for decades for the average vehicle owner. The present paper focuses on the first step of DM, which consists of characterizing the state of the driver. Since DM will be increasingly linked to driving automation (DA), this paper presents a clear view of the role of DM at each of the six SAE levels of DA. This paper surveys the state of the art of DM, and then synthesizes it, providing a unique, structured, polychotomous view of the many characterization techniques of DM. Informed by the survey, the paper characterizes the driver state along the five main dimensions—called here “(sub)states”—of drowsiness, mental workload, distraction, emotions, and under the influence. The polychotomous view of DM is presented through a pair of interlocked tables that relate these states to their indicators (e.g., the eye-blink rate) and the sensors that can access each of these indicators (e.g., a camera). The tables factor in not only the effects linked directly to the driver, but also those linked to the (driven) vehicle and the (driving) environment. They show, at a glance, to concerned researchers, equipment providers, and vehicle manufacturers (1) most of the options they have to implement various forms of advanced DM systems, and (2) fruitful areas for further research and innovation.

The Effects of Increased Visual Information on Cognitive Workload in a Helicopter Simulator

OBJECTIVE

To test the effects of enhanced display information ("symbology") on cognitive workload in a simulated helicopter environment, using the detection response task (DRT).

BACKGROUND

Workload in highly demanding environments can be influenced by the amount of information given to the operator and consequently it is important to limit potential overload.

METHODS

Participants (highly trained military pilots) completed simulated helicopter flights, which varied in visual conditions and the amount of information given. During these flights, participants also completed a DRT as a measure of cognitive workload.

RESULTS

With more visual information available, pilots' landing accuracy was improved across environmental conditions. The DRT is sensitive to changes in cognitive workload, with workload differences shown between environmental conditions. Increasing symbology appeared to have a minor effect on workload, with an interaction effect of symbology and environmental condition showing that symbology appeared to moderate workload.

CONCLUSION

The DRT is a useful workload measure in simulated helicopter settings. The level of symbology-moderated pilot workload. The increased level of symbology appeared to assist pilots' flight behavior and landing ability. Results indicate that increased symbology has benefits in more difficult scenarios.

APPLICATIONS

The DRT is an easily implemented and effective measure of cognitive workload in a variety of settings. In the current experiment, the DRT captures the increased workload induced by varying the environmental conditions, and provides evidence for the use of increased symbology to assist pilots.

A Broader Application of the Detection Response Task to Cognitive Tasks and Online Environments

OBJECTIVE

The present research applied a well-established measure of cognitive workload in driving literature to an in-lab paradigm. We then extended this by comparing the in-lab version of the task to an online version.

BACKGROUND

The accurate and objective measurement of cognitive workload is important in many aspects of psychological research. The detection response task (DRT) is a well-validated method for measuring cognitive workload that has been used extensively in applied tasks, for example, to investigate the effects of phone usage or passenger conversation on driving, but has been used sparingly outside of this field.

METHOD

The study investigated whether the DRT could be used to measure cognitive workload in tasks more commonly used in experimental cognitive psychology and whether this application could be extended to online environments. We had participants perform a multiple object tracking (MOT) task while simultaneously performing a DRT. We manipulated the cognitive load of the MOT task by changing the number of dots to be tracked.

RESULTS

Measurements from the DRT were sensitive to changes in the cognitive load, establishing the efficacy of the DRT for experimental cognitive tasks in lab-based situations. This sensitivity continued when applied to an online environment (our code for the online DRT implementation is freely available at https://osf.io/dc39s/), though to a reduced extent compared to the in-lab situation.

CONCLUSION

The MOT task provides an effective manipulation of cognitive workload. The DRT is sensitive to changes in workload across a range of settings and is suitable to use outside of driving scenarios, as well as via online delivery.

APPLICATION

Methodology shows how the DRT could be used to measure sources of cognitive workload in a range of human factors contexts.

Overloaded and at Work: Investigating the Effect of Cognitive Workload on Assembly Task Performance

OBJECTIVE

This study investigates the effect of cognitive overload on assembly task performance and muscle activity.

BACKGROUND

Understanding an operator's cognitive workload is an important component in assessing human-machine interaction. However, little evidence is available on the effect that cognitive overload has on task performance and muscle activity when completing manufacturing tasks.

METHOD

Twenty-two volunteers completed an assembly task while performing a secondary cognitive task with increasing levels of demand (n-back). Performance in the assembly task (completion times, accuracy), muscle activity recorded as integrated electromyography (EMG), and self-reported workload were measured.

RESULTS

Results show that the increasing cognitive demand imposed by the n-back task resulted in impaired assembly task performance, overall greater muscle activity, and higher self-reported workload.Relative to the control condition, performing the 2-back task resulted in longer assembly task completion times (+10 s on average) and greater integrated EMG for flexor carpi ulnaris, triceps brachii, biceps brachii, anterior deltoid, and pectoralis major.

CONCLUSION

This study demonstrates that working under high cognitive load not only results in greater muscle activity, but also affects assembly task completion times, which may have a direct effect on manufacturing cycle times.

APPLICATION

Results are applicable to the assessment of the effects of high cognitive workload in manufacturing.

Using Past and Present Indicators of Human Workload to Explain Variance in Human Performance

Cognitive workload is assumed to influence performance due to resource competition. However, there is a lack of evidence for a direct relationship between changes in workload within an individual over time and changes in that individual's performance. We collected performance data using a multiple object-tracking task in which we measured workload objectively in real-time using a modified detection response task. Using a multi-level Bayesian model controlling for task difficulty and past performance, we found strong evidence that workload both during and preceding a tracking trial was predictive of performance, such that higher workload led to poorer performance. These negative workload-performance relationships were remarkably consistent across individuals. Importantly, we demonstrate that fluctuations in workload independent from the task demands accounted for significant performance variation. The outcomes have implications for designing real-time adaptive systems to proactively mitigate human performance decrements, but also highlight the pervasive influence of cognitive workload more generally.

Real-time prediction of short-timescale fluctuations in cognitive workload

Human operators often experience large fluctuations in cognitive workload over seconds timescales that can lead to sub-optimal performance, ranging from overload to neglect. Adaptive automation could potentially address this issue, but to do so it needs to be aware of real-time changes in operators' spare cognitive capacity, so it can provide help in times of peak demand and take advantage of troughs to elicit operator engagement. However, it is unclear whether rapid changes in task demands are reflected in similarly rapid fluctuations in spare capacity, and if so what aspects of responses to those demands are predictive of the current level of spare capacity. We used the ISO standard detection response task (DRT) to measure cognitive workload approximately every 4 s in a demanding task requiring monitoring and refueling of a fleet of simulated unmanned aerial vehicles (UAVs). We showed that the DRT provided a valid measure that can detect differences in workload due to changes in the number of UAVs. We used cross-validation to assess whether measures related to task performance immediately preceding the DRT could predict detection performance as a proxy for cognitive workload. Although the simple occurrence of task events had weak predictive ability, composite measures that tapped operators' situational awareness with respect to fuel levels were much more effective. We conclude that cognitive workload does vary rapidly as a function of recent task events, and that real-time predictive models of operators' cognitive workload provide a potential avenue for automation to adapt without an ongoing need for intrusive workload measurements.

A Nonlinear Latching Filter to Remove Jitter From Movement Estimates for Prostheses

Continuous movement intent decoders are critical for precise control of hand and wrist prostheses. Noise in biological signals (e.g., myoelectric or neural signals) can lead to undesirable jitter in the output of these types of decoders. A low-pass filter (LPF) at the output of the decoder effectively reduces jitter, but also substantially slows intended movements. This paper introduces an alternative, the latching filter (LF), a recursive, nonlinear filter that provides smoothing of small-amplitude jitter but allows quick changes to its output in response to large input changes. The performance of a Kalman filter (KF) decoder smoothed with an LF is compared with that of both an KF decoder without an additional smoother and a KF decoder smoothed with a LPF. These three algorithms were tested in real-time on target holding and target reaching tasks using surface electromyographic signals recorded from 5 non-amputee subjects, and intramuscular electromyographic and peripheral neural signals recorded from an amputee subject. When compared with the LPF, the LF provided a statistically significant improvement in amputee and non-amputee subjects' ability to hold the hand steady at requested positions and achieve movement goals faster. The KF decoder with LF provided a statistically significant improvement in all subjects' ability to hold the prosthetic hand steady, with only slightly lower speeds, when compared to the unsmoothed KF.

Evaluation of smartphone interactions on drivers' brain function and vehicle control in an immersive simulated environment

Smartphones and other modern technologies have introduced multiple new forms of distraction that color the modern driving experience. While many smartphone functions aim to improve driving by providing the driver with real-time navigation and traffic updates, others, such as texting, are not compatible with driving and are often the cause of accidents. Because both functions elicit driver attention, an outstanding question is the degree to which drivers' naturalistic interactions with navigation and texting applications differ in regard to brain and behavioral indices of distracted driving. Here, we employed functional near-infrared spectroscopy to examine the cortical activity that occurs under parametrically increasing levels of smartphone distraction during naturalistic driving. Our results highlight a significant increase in bilateral prefrontal and parietal cortical activity that occurs in response to increasingly greater levels of smartphone distraction that, in turn, predicts changes in common indices of vehicle control.

Promote or inhibit: An inverted U-shaped effect of workload on driver takeover performance

OBJECTIVE

In conditional automated driving (SAE Level 3), drivers are required to take over their vehicles when the automated systems fail. Non-driving related tasks (NDRTs) can positively or negatively affect takeover safety, but the underlying reasons for this inconsistency remain unclear. This study aims to investigate how various workload levels generated by NDRTs may influence the takeover performance of drivers and the lead time they require.

METHOD

Fifty drivers were randomly distributed into five groups, which corresponded to five workload levels (1-4 levels generated by Tetris game; control level generated by monitoring). Each driver completed vehicle takeover tasks upon receiving takeover requests with various lead times (3, 5, 7, 9, and 11 s) while engaging in NDRTs. The drivers' takeover performance and subjective opinions were recorded.

RESULTS

Drivers in the moderate workload condition (i.e., level 3) had significantly shorter takeover times and better takeover quality than those in the lower (i.e., level 1 and level 2) or higher (i.e., level 4) workload conditions. They also subjectively required less lead time in the moderate condition. Moreover, the drivers rated 7 s as the most appropriate lead time despite the improvement in their overall takeover performances with increased lead time.

CONCLUSIONS

This study found an inverted U-shaped relationship between the drivers' workload generated by NDRTs and takeover performance. The moderate workload level (rather than the lower or higher workload level) led to a faster and better takeover performance, and it seemed to require minimal lead time for drivers. These findings help understand the relationship of drivers' workload during the automation and takeover performance in conditional automated driving. An important recommendation emerging from this work is to investigate what should be the most efficient method to detect the drivers' workload state real-time and give feedback to them when it comes to overload or underload during the automated driving.

Drivers' Visual Attention Characteristics under Different Cognitive Workloads: An On-Road Driving Behavior Study

In this study, an on-road driving experiment was designed to investigate the visual attention fixation and transition characteristics of drivers when they are under different cognitive workloads. First, visual attention was macroscopically analyzed through the entropy method. Second, the Markov glance one- and two-step transition probability matrices were constructed, which can study the visual transition characteristics under different conditions from a microscopic perspective. Results indicate that the fixation entropy value of male drivers is 23.08% higher than that of female drivers. Under the normal driving state, drivers’ fixation on in-vehicle systems is not continuous and usually shifts to the front and left areas quickly after such fixation. When under cognitive workload, drivers’ vision transition is concentrated only in the front and right areas. In mild cognitive workload, drivers’ sight trajectory is mainly focused on the distant front area. As the workload level increases, the transition trajectory shifts to the junction near the front and far sides. The current study finds that the difference between an on-road test and a driving simulation is that during the on-road driving process, drivers are twice as attentive to the front area than to the driving simulator. The research provides practical guidance for the improvement of traffic safety.

Safety analysis of work zone complexity with respect to driver characteristics - A simulator study employing performance and gaze measures

Driving is a complex task that consists of several physical (motor-related) and physiological (biological changes within the body) processes occurring simultaneously. The complexity of the task depends on several factors, but this research focuses on work zone configurations and their effect on driver performance and gaze behavior. The increase in work zone fatalities in the United States between 2015 and 2018 coupled with the limited literature of driver behavior in these complex environments requires a more comprehensive study. Given the nature of these crashes, typically lane departures, gaze behavior provided an additional physiological dimension to the present research. A framework that comprises of the interactions between driver characteristics, mental workload, and situation awareness, with longitudinal control, lateral control, and gaze behavior is proposed. Crash analysis and a simulator study with 90 participants were carried out to investigate the performance and gaze-based changes with respect to various work zone configurations. Distracted driving was also studied by including a secondary task. The results showed a significant interaction between the longitudinal control and the standard deviation of horizontal gaze position in predicting lateral control. Also, significant differences in lateral control and horizontal gaze variations were observed between genders. Female drivers showed lower lateral position deviations and lower horizontal gaze variability. This was a key finding given the inherently higher number of work zone crashes involving male drivers. Placing work zone barriers further away, by up to one meter from pavement edges, could significantly decrease mental workload and improve safety in work zones.

The impact of driver sleepiness on fixation-related brain potentials

The effects of driver sleepiness are often quantified as deteriorated driving performance, increased blink durations and high levels of subjective sleepiness. Driver sleepiness has also been associated with increasing levels of electroencephalogram (EEG) power, especially in the alpha range. The present exploratory study investigated a new measure of driver sleepiness, the EEG fixation-related lambda response. Thirty young male drivers (23.6 ± 1.7 years old) participated in a driving simulator experiment in which they drove on rural and suburban roads in simulated daylight versus darkness during both the daytime (full sleep) and night-time (sleep deprived). The results show lower lambda responses during night driving and with longer time on task, indicating that sleep deprivation and time on task cause a general decrement in cortical responsiveness to incoming visual stimuli. Levels of subjective sleepiness and line crossings were higher under the same conditions. Furthermore, results of a linear mixed-effects model showed that low lambda responses are associated with high subjective sleepiness and more line crossings. We suggest that the fixation-related lambda response can be used to investigate driving impairment induced by sleep deprivation while driving and that, after further refinement, it may be useful as an objective measure of driver sleepiness.

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.

Effect of visible presence of policing activities on drivers' vigilance and intention to refrain from non-driving activities: A scenario-based survey of general Japanese drivers

Driver distraction is an important issue for road safety. The visible presence of policing activities alongside roadways seems to be significant in preventing driver distraction and other unsafe driving behaviors. The purpose of this study was to provide evidence of the effectiveness of visible policing presence on drivers' self-reported intention to refrain from non-driving activities and that this effectiveness can be enhanced by manipulating deployment even with limited resources. We conducted a scenario-based survey to compare drivers' self-reported intention to refrain from non-driving activities and their vigilance state across several hypothetical driving situations. The three aims of the study were to examine whether drivers' self-reported vigilance and intention to refrain from non-driving activities were associated with (a) the presence or absence of common forms of roadside policing, (b) the agent (police vs. civilian) and medium (human vs. advertisement) of road safety interventions, and (c) different forms of policing (police officer vs. police car) and their respective levels of conspicuousness. Japanese drivers (N = 367) were randomly assigned to receive one of three booklets that included a scenario-based survey. The three booklets contained different combinations of various hypothetical driving scenarios based on the three experiment designs (a, b, c) described above. Participants were asked to rate their vigilance and their intention to refrain from non-driving activities in hypothetical driving situations that varied by the presence or absence of policing and how conspicuous that presence was. We conducted repeated measures within-subject analysis of variance using the three experimental designs. The results showed that the presence of a single police unit engaged in policing activities was associated with more vigilance and greater intention to refrain from non-driving activities. The results suggested that effectiveness of visible policing could be enhanced by increasing conspicuousness of police officers in order to help drivers more easily recognize the presence of police without having to purchase extra equipment. These findings provide useful insights for traffic police to conduct routine policing practices more efficiently to address non-driving activities. Future research is needed to examine these results in a real-world setting.

Sensory feedback restoration in leg amputees improves walking speed, metabolic cost and phantom pain

Conventional leg prostheses do not convey sensory information about motion or interaction with the ground to above-knee amputees, thereby reducing confidence and walking speed in the users that is associated with high mental and physical fatigue^1-4. The lack of physiological feedback from the remaining extremity to the brain also contributes to the generation of phantom limb pain from the missing leg^5,6. To determine whether neural sensory feedback restoration addresses these issues, we conducted a study with two transfemoral amputees, implanted with four intraneural stimulation electrodes⁷ in the remaining tibial nerve (ClinicalTrials.gov identifier NCT03350061). Participants were evaluated while using a neuroprosthetic device consisting of a prosthetic leg equipped with foot and knee sensors. These sensors drive neural stimulation, which elicits sensations of knee motion and the sole of the foot touching the ground. We found that walking speed and self-reported confidence increased while mental and physical fatigue decreased for both participants during neural sensory feedback compared to the no stimulation trials. Furthermore, participants exhibited reduced phantom limb pain with neural sensory feedback. The results from these proof-of-concept cases provide the rationale for larger population studies investigating the clinical utility of neuroprostheses that restore sensory feedback.

Cognitive Load and Situation Awareness for Soldiers: Effects of Message Presentation Rate and Sensory Modality

OBJECTIVE

We sought to determine the influence of message presentation rate (MPR) and sensory modality on soldier cognitive load.

BACKGROUND

Soldiers commonly communicate tactical information by radio. The Canadian Army is equipping soldiers with a battle management system (BMS), which also allows them to communicate by text.

METHOD

We varied presentation modality (auditory vs. visual) and MPR (fast or slow) in an experiment involving a tactical scenario. Participants (soldiers) received messages and periodically provided situation reports to higher level command, and the scored reports were used to provide a measure of situation awareness (SA). The detection response task (DRT) and NASA-TLX were used to measure cognitive load.

RESULTS

The fast MPR reduced DRT accuracy and increased response times relative to slow MPR. The NASA-TLX results also showed higher subjective workload ratings for several subscales with fast MPR. Messages presented visually produced greater cognitive load, with slower DRT response times for the visual than the auditory condition. SA scores were higher with slower MPR and auditory presentation. There was no statistical interaction of presentation modality and rate for any measure.

CONCLUSION

Fast MPR and visual presentation increased cognitive load and degraded SA.

APPLICATION

These findings show that the DRT can be used to measure workload effectively in a tactical military context and that the method of information presentation affects how soldiers process information in a BMS.

Detection of brake lights while distracted: Separating peripheral vision from cognitive load

Drivers rarely focus exclusively on driving, even with the best of intentions. They are distracted by passengers, navigation systems, smartphones, and driver assistance systems. Driving itself requires performing simultaneous tasks, including lane keeping, looking for signs, and avoiding pedestrians. The dangers of multitasking while driving, and efforts to combat it, often focus on the distraction itself, rather than on how a distracting task can change what the driver can perceive. Critically, some distracting tasks require the driver to look away from the road, which forces the driver to use peripheral vision to detect driving-relevant events. As a consequence, both looking away and being distracted may degrade driving performance. To assess the relative contributions of these factors, we conducted a laboratory experiment in which we separately varied cognitive load and point of gaze. Subjects performed a visual 0-back or 1-back task at one of four fixation locations superimposed on a real-world driving video, while simultaneously monitoring for brake lights in their lane of travel. Subjects were able to detect brake lights in all conditions, but once the eccentricity of the brake lights increased, they responded more slowly and missed more braking events. However, our cognitive load manipulation had minimal effects on detection performance, reaction times, or miss rates for brake lights. These results suggest that, for tasks that require the driver to look off-road, the decrements observed may be due to the need to use peripheral vision to monitor the road, rather than due to the distraction itself.

Strategic attention and decision control support prospective memory in a complex dual-task environment

Human performance in complex multiple-task environments depends critically on the interplay between cognitive control and cognitive capacity. In this paper we propose a tractable computational model of how cognitive control and capacity influence the speed and accuracy of decisions made in the event-based prospective memory (PM) paradigm, and in doing so test a new quantitative formulation that measures two distinct components of cognitive capacity (gain and focus) that apply generally to choices among two or more options. Consistent with prior work, individuals used proactive control (increased ongoing task thresholds under PM load) and reactive control (inhibited ongoing task accumulation rates to PM items) to support PM performance. Individuals used cognitive gain to increase the amount of resources allocated to the ongoing task under time pressure and PM load. However, when demands exceeded the capacity limit, resources were reallocated (shared) between ongoing task and PM processes. Extending previous work, individuals used cognitive focus to control the quality of processing for the ongoing and PM tasks based on the particular demand and payoff structure of the environment (e.g., higher focus for higher priority tasks; lower focus under high time pressure and with PM load). Our model provides the first detailed quantitative understanding of cognitive gain and focus as they apply to evidence accumulation models, which - along with cognitive control mechanisms - support decision-making in complex multiple-task environments.

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.

What is the difference in driver's lateral control ability during naturalistic distracted driving and normal driving? A case study on a real highway

Driver distraction is widely recognized as a major contributor to traffic crashes. Although the effect of distraction on simulated driving performance has been studied extensively, comparatively little research based on field tests has been performed on the effects of high driving speeds on lateral driving performance during naturalistic distraction (the driver was unaware of the research topic). In this study, an instrumented vehicle is used to examine the impact of speed and naturalistic visual distraction (rear vehicle's velocity and relative distance estimation) on a driver's ability to keep in the lane. Similar to results from previous studies, visual distraction resulted in an impaired ability to keep in a lane compared to normal driving. Further investigation of steering control parameters showed an increase in steering wheel reversal rates (SRRs at 1.3° and 2.5° levels) and the standard deviation of steering wheel acceleration (SDSWA). The results of this study indicated that the standard deviation of lane positioning (SDLP) and trajectory offset (TO) increased as speed increased. As speed increased, the growth rates of SDLP and TO in the visual distraction task were the same as that in normal driving. Moreover, the SRRs and steering wheel acceleration (SWA) decreased with increased speed. As speed increased, the growth rates of SRRs and SWA during a visual distraction task were the same as that during normal driving. These results suggest that driving speed has a similar effect on driving performance during both distracted driving and normal driving.

A Review of Psychophysiological Measures to Assess Cognitive States in Real-World Driving

As driving functions become increasingly automated, motorists run the risk of becoming cognitively removed from the driving process. Psychophysiological measures may provide added value not captured through behavioral or self-report measures alone. This paper provides a selective review of the psychophysiological measures that can be utilized to assess cognitive states in real-world driving environments. First, the importance of psychophysiological measures within the context of traffic safety is discussed. Next, the most commonly used physiology-based indices of cognitive states are considered as potential candidates relevant for driving research. These include: electroencephalography and event-related potentials, optical imaging, heart rate and heart rate variability, blood pressure, skin conductance, electromyography, thermal imaging, and pupillometry. For each of these measures, an overview is provided, followed by a discussion of the methods for measuring it in a driving context. Drawing from recent empirical driving and psychophysiology research, the relative strengths and limitations of each measure are discussed to highlight each measures' unique value. Challenges and recommendations for valid and reliable quantification from lab to (less predictable) real-world driving settings are considered. Finally, we discuss measures that may be better candidates for a near real-time assessment of motorists' cognitive states that can be utilized in applied settings outside the lab. This review synthesizes the literature on in-vehicle psychophysiological measures to advance the development of effective human-machine driving interfaces and driver support systems.