Effects of Cognitive Load on Driving Performance: The Cognitive Control Hypothesis

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.

Utilising raw psycho-physiological data and functional data analysis for estimating mental workload in human drivers

Recent studies have focused on accurately estimating mental workload using machine learning algorithms and extracting features from physiological measures. However, feature extraction leads to the loss of valuable information and often results in binary classifications that lack specificity in the identification of optimum mental workload. This study investigates the feasibility of using raw physiological data (EEG, facial EMG, ECG, EDA, pupillometry) combined with Functional Data Analysis (FDA) to estimate the mental workload of human drivers. A driving scenario with five tasks was employed, and subjective ratings were collected. Results demonstrate that the FDA applied nine different combinations of raw physiological signals achieving a maximum 90% accuracy, outperforming extracted features by 73%. This study shows that the mental workload of human drivers can be accurately estimated without utilising burdensome feature extraction. The approach proposed in this study offers promise for mental workload assessment in real-world applications.

Gaze entropy metrics for mental workload estimation are heterogenous during hands-off level 2 automation

As the level of vehicle automation increases, drivers are more likely to engage in non-driving related tasks which take their hands, eyes, and/or mind away from the driving task. Consequently, there has been increased interest in creating Driver Monitoring Systems (DMS) that are valid and reliable for detecting elements of driver state. Workload is one element of driver state that has remained elusive within the literature. Whilst there has been promising work in estimating mental workload using gaze-based metrics, the literature has placed too much emphasis on point estimate differences. Whilst these are useful for establishing whether effects exist, they ignore the inherent variability within individuals and between different drivers. The current work builds on this by using a Bayesian distributional modelling approach to quantify the within and between participants variability in Information Theoretical gaze metrics. Drivers (N = 38) undertook two experimental drives in hands-off Level 2 automation with their hands and feet away from operational controls. During both drives, their priority was to monitor the road before a critical takeover. During one drive participants had to complete a secondary cognitive task (2-back) during the hands-off Level 2 automation. Changes in Stationary Gaze Entropy and Gaze Transition Entropy were assessed for conditions with and without the 2-back to investigate whether consistent differences between workload conditions could be found across the sample. Stationary Gaze Entropy proved a reliable indicator of mental workload; 92 % of the population were predicted to show a decrease when completing 2-back during hands-off Level 2 automated driving. Conversely, Gaze Transition Entropy showed substantial heterogeneity; only 66 % of the population were predicted to have similar decreases. Furthermore, age was a strong predictor of the heterogeneity of the average causal effect that high mental workload had on eye movements. These results indicate that, whilst certain elements of Information Theoretic metrics can be used to estimate mental workload by DMS, future research needs to focus on the heterogeneity of these processes. Understanding this heterogeneity has important implications toward the design of future DMS and thus the safety of drivers using automated vehicle functions. It must be ensured that metrics used to detect mental workload are valid (accurately detecting a particular driver state) as well as reliable (consistently detecting this driver state across a population).

Understanding The Role of Cognitive Load In Paramedical Contexts: A Systematic Review

Objectives: Cognitive load refers to the working memory resources required during a task. When the load is too high or too low this has implications for an individual's task performance. In the context of paramedicine and emergency medical services (EMS) broadly, high cognitive load could potentially put patient and personnel safety at risk. This systematic review aimed to determine the current understanding of the role of cognitive load in paramedical contexts.Methods: To do this, five databases were searched (Elsevier Embase, ProQuest Psychology, CINAHL, Ovid Medline, and Ovid PsychINFO) using synonyms of cognitive load and paramedical contexts. Included articles were full text, peer reviewed empirical research, with a focus on cognitive load and EMS work. Two reviewers screened titles, abstracts, and full text using a traffic light system against the inclusion and exclusion criteria. The quality of evidence was assessed using the GRADE framework. This study was registered on PROSPERO (CRD42022384246). No funding was received for this research.Results: The searches identified 73 unique articles and after title/abstract and full text screening, 25 articles were included in the final review. Synthesis of the research revealed 10 categories of findings in the area. These are clinical performance, cognitive processes, emotional responses, physical expenditure, physiological responses, equipment and ergonomics, expertise and experience, multiple loads, cognitive load measures, and task complexity.Conclusions: From these findings it was determined that there is agreement in terms of what factors influence cognitive load in paramedical contexts, such as cognitive processes, task complexity, physical expenditure, level of experience, multiple types of loads, and the use of equipment. Cognitive load influences clinical task performance and has a bi-directional relationship with emotion. However, the literature is mixed regarding physiological responses to cognitive load, and how they are best measured. These findings highlight potential intervention points where cognitive load can be managed or reduced to improve working conditions for EMS clinicians and safety for their patients.

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.

Evaluation of eye-catching effect in highway tunnel entrance area based on saccade behavior

OBJECTIVE

This study aimed to investigate the effects of visual attraction in the entrance areas of highway tunnels on drivers' saccade behavior and driving safety, with the objective of providing insights for tunnel entrance design and driver education.

METHODS

Fifty participants were recruited for the naturalistic driving experiment. Four different visually attractive driving scenarios (baseline, landscape-style architecture, tip slogan, and billboard) were selected. Eye-tracking technology was utilized to record and analyze the scanning behavior of participating drivers. Various metrics, including saccade duration, frequency, amplitude, velocity, and the peak-to-average ratio of saccade velocity (PARSV), were measured and compared across different scenarios.

RESULTS

The results revealed different patterns of visual scanning dynamics among the four experimental scenarios, reflecting the different levels of visual-cognitive demands and situation awareness of drivers. The visual attraction in the tunnel entrance area resulted in prolonged saccade duration, increased saccade amplitude, reduced saccade frequency, and slower, less stable saccade velocity. Among these, drivers exhibited the most intricate and unstable scanning behavior, accompanied by the lowest level of situational awareness, particularly under the influence of visual attraction of tip slogan.

CONCLUSIONS

The findings highlight the importance of considering visual attraction in tunnel entrance design. Minimizing unnecessary visual attraction can help reduce distractions and maintain driver attention, ultimately enhancing driving safety. Driver education programs should emphasize the significance of adapting to visual attraction at tunnel entrances to promote safer driving practices. Overall, this study contributes valuable insights into the effects of visual attraction on driver scanning behavior, facilitating the development of strategies to improve tunnel design and driver education.

Modeling road user response timing in naturalistic traffic conflicts: A surprise-based framework

There is currently no established method for evaluating human response timing across a range of naturalistic traffic conflict types. Traditional notions derived from controlled experiments, such as perception-response time, fail to account for the situation-dependency of human responses and offer no clear way to define the stimulus in many common traffic conflict scenarios. As a result, they are not well suited for application in naturalistic settings. We present a novel framework for measuring and modeling response times in naturalistic traffic conflicts applicable to automated driving systems as well as other traffic safety domains. The framework suggests that response timing must be understood relative to the subject's current (prior) belief and is always embedded in, and dependent on, the dynamically evolving situation. The response process is modeled as a belief update process driven by perceived violations to this prior belief, that is, by surprising stimuli. The framework resolves two key limitations with traditional notions of response time when applied in naturalistic scenarios: (1) The strong situation dependence of response timing and (2) how to unambiguously define the stimulus. Resolving these issues is a challenge that must be addressed by any response timing model intended to be applied in naturalistic traffic conflicts. We show how the framework can be implemented by means of a relatively simple heuristic model fit to naturalistic human response data from real crashes and near crashes from the SHRP2 dataset and discuss how it is, in principle, generalizable to any traffic conflict scenario. We also discuss how the response timing framework can be implemented computationally based on evidence accumulation enhanced by machine learning-based generative models and the information-theoretic concept of surprise.

Resolving uncertainty on the fly: modeling adaptive driving behavior as active inference

Understanding adaptive human driving behavior, in particular how drivers manage uncertainty, is of key importance for developing simulated human driver models that can be used in the evaluation and development of autonomous vehicles. However, existing traffic psychology models of adaptive driving behavior either lack computational rigor or only address specific scenarios and/or behavioral phenomena. While models developed in the fields of machine learning and robotics can effectively learn adaptive driving behavior from data, due to their black box nature, they offer little or no explanation of the mechanisms underlying the adaptive behavior. Thus, generalizable, interpretable, computational models of adaptive human driving behavior are still rare. This paper proposes such a model based on active inference, a behavioral modeling framework originating in computational neuroscience. The model offers a principled solution to how humans trade progress against caution through policy selection based on the single mandate to minimize expected free energy. This casts goal-seeking and information-seeking (uncertainty-resolving) behavior under a single objective function, allowing the model to seamlessly resolve uncertainty as a means to obtain its goals. We apply the model in two apparently disparate driving scenarios that require managing uncertainty, (1) driving past an occluding object and (2) visual time-sharing between driving and a secondary task, and show how human-like adaptive driving behavior emerges from the single principle of expected free energy minimization.

Driver Vigilance Decrement is More Severe During Automated Driving than Manual Driving

OBJECTIVE

The present study compared the performance, workload, and stress associated with driver vigilance in two types of vehicle: a traditional, manually operated vehicle, and a partially automated vehicle.

BACKGROUND

Drivers of partially automated vehicles must monitor for hazards that constitute automation failures and the need for human intervention, but recent research indicates that a driver's ability to do so declines as a function of time. That research lacked a comparison measure of driving without vehicle automation, so it is unknown to what degree these effects are specific symptoms of monitoring the roadway during an automated drive. Drivers in manual control of their vehicle must similarly monitor for hazards and may suffer similar vigilance decrements.

METHOD

Participants completed a simulated 40-minute drive while monitoring for hazards. Half of participants completed the drive with an automated driving system that maintained speed and lane position; the remaining half manually controlled the vehicle's speed and lane position.

RESULTS

Driver sensitivity to hazards decreased and tendency to make false alarms increased over time in the automated control condition, but not in the manual control condition. Drivers in both conditions detected fewer hazards as the drive progressed. Ratings of workload and task-induced stress were elevated similarly in both conditions.

CONCLUSION

Partially automated driving appears to uniquely impair driver vigilance by reducing the ability to discriminate between benign and dangerous events in the driving environment as the drive progresses.

APPLICATION

Applied interventions should target improvements in driver sensitivity to hazardous situations that signal potential automation failures.

Exploring the Timing of Disengagement From Nondriving Related Tasks in Scheduled Takeovers With Pre-Alerts: An Analysis of Takeover-Related Measures

OBJECTIVES

This study aimed to investigate drivers' disengagement from nondriving related tasks (NDRT) during scheduled takeovers and to evaluate its impact on takeover performance.

BACKGROUND

During scheduled takeovers, drivers typically have sufficient time to prepare. However, inadequate disengagement from NDRTs can introduce safety risks.

METHOD

Participants experienced scheduled takeovers using a driving simulator, undergoing two conditions, with and without an NDRT. We assessed their takeover performance and monitored their NDRT disengagement from visual, cognitive, and physical perspectives.

RESULTS

The study examined three NDRT disengagement timings (DTs): DT1 (disengaged before the takeover request), DT2 (disengaged after the request but before taking over), and DT3 (not disengaged). The impact of NDRT on takeover performance varied depending on DTs. Specifically, DT1 demonstrated no adverse effects; DT2 impaired takeover time, while DT3 impaired both takeover time and quality. Additionally, participants who displayed DT1 exhibited longer eye-off-NDRT duration and a higher eye-off-NDRT count during the prewarning stage compared to those with DT2 and DT3.

CONCLUSION

Drivers can benefit from earlier disengagement from NDRTs, demonstrating resilience to the adverse effects of NDRTs on takeover performance. The disengagement of cognition is often delayed compared to that of eyes and hands, potentially leading to DT3. Moreover, visual disengagement from NDRTs during the prewarning stage could distinguish DT1 from the other two.

APPLICATION

Our study emphasizes considering NDRT disengagement in designing systems for scheduled takeovers. Measures should be taken to promote early disengagement, facilitate cognitive disengagement, and employ visual disengagement during the prewarning period as predictive indicators of DTs.

The evaluation of eye-catching effect in freeway tunnel entrance zone based on fixation behavior characteristics

OBJECTIVE

The visual attraction of the freeway tunnel entrance zone will affect the driver's visual performance and visual workload, and then affect the driving safety. The purpose of this study is to explore the influence of different visual attraction conditions in the entrance zone of the freeway tunnel on driver's fixation behavior, and then evaluate the impact of eye-catching effect on driving safety.

METHODS

Thirty-five participants were recruited for the naturalistic driving experiment. Four different visually attractive driving scenarios (baseline, landscape-style architecture, tip slogan, and billboard) were selected. Participants' fixation behavior data were collected using an eye-tracking device. The effects of the visual attraction conditions in the freeway tunnel entrance zones were analyzed on driver fixation duration, number of fixations, fixation deviation, and pupil diameter.

RESULTS

The results demonstrated significant effects of different visual attractions in the entrance zone of the freeway tunnel on drivers' fixation behavior. Under the visual attraction of the billboard, drivers exhibited the most number of fixations. And under the visual attraction of tip slogan, drivers had the longest average fixation duration, the smallest horizontal fixation deviation, the largest vertical fixation deviation, and the highest pupil diameter and its coefficient of variation.

CONCLUSIONS

It can be concluded that different visual attractions in the freeway tunnel entrance zone have different effects on the eye-catching effect of drivers, by influencing the fixation behavior characteristics of drivers. Billboard is most effective in capturing drivers' visual attention, and tip slogan requires greater attention allocation and visual cognitive effort from drivers, resulting in the highest visual cognitive workload. This study highlights the complex relationship between visual attraction and its performance on driver fixation behavior. The insights from this study can provide valuable references for the design and optimization of the visual environment in the entrance zone of freeway tunnels.

Visual and steering behaviours during lane departures: a longitudinal study of interactions between lane departure warning system, driving task and driving experience

Lane Departure Warning Systems (LDWS) generate a warning in case of imminent lane departure. LDWS have proven to be effective and associated human-machine cooperation modelled. In this study, LDWS acceptance and its impact on visual and steering behaviour have been investigated over 6 weeks for novice and experienced drivers. Unprovoked lane departures were analysed along three driving tasks gradually more demanding. These observations were compared to a baseline condition without automation. The number of lane departures and their duration were dramatically reduced by LDWS, and a narrower visual spread of search during lane departure events was recorded. The findings confirmed LDWS effectiveness and suggested that these benefits are supported by visuo-attentional guidance. No specific influence of driving experience on LDWS was found, suggesting that similar cognitive processes are engaged with or without driving experience. Drivers' acceptance of LDWS lowered after automation use, but LDWS effectiveness remained stable during prolonged use.Practitioner summary: Lane Departure Warning Systems (LDWS) have been designed to prevent lane departure crashes. Here, LDWS assessment over a 6-week period showed a major drop in the number of lane departure events increasing over time. LDWS effectiveness is supported by the guidance of drivers' visual attention during lane departure events.

The impact of co-pilot displays use on driver workload and driving performance exploring the impact of co-pilot display on drivers' workload and driving performance

New production vehicles equipped with multiple screens have various functions and technologies at the user's fingertips that create informative and immersive cockpits not only for the driver but also for the passenger. Despite the growing popularity of the co-pilot display, surprisingly little is known about the impact of its engagement on drivers. Engagement, in the context of this paper, is defined as visual, auditory, and mental involvement but no manual involvement. The present study sought to investigate the effects of co-pilot display engagement on drivers' visual workload, cognitive workload, and driving performance and offered a deeper insight into the design factors and task involvement of the co-pilot display. The study involved a driving simulator testing of 25 participants in five automobiles with a co-pilot display. Our analysis found increased driver visual workload and cognitive workload in the conditions of co-pilot display engagement. Participants exhibited more erratic driving behavior (standard deviation of speed and standard deviation of lane position) while the front passenger was watching a video. Furthermore, five linear mixed models were built to further analyze the effects of task type, screen size, screen layout, and task involvement on dependent measures. Potential applications of this research include refinements to the co-pilot display and workload estimation of co-pilot display engagement.

The effect of punishment and feedback on correcting erroneous behavior

INTRODUCTION

Understanding the consequences of non-punitive sanctions and feedback for nonintentional deviations (i.e., errors) is important to effective safety policy. This study aims to address a lack of research on the effects of punishment and feedback on correcting erroneous behavior in the context of multitasking.

METHOD

A Multi-Attribute Task Battery (MATB-II) was employed to simulate the demands of aviating, an important area of applied safety. Sixty participants were randomly assigned to one of four experimental groups (no intervention, punishment, feedback, punishment + feedback) and asked to perform the MATB-II. Punishment, feedback, and punishment + feedback decreased error and increased performance, with punishment alone having the greatest effect.

RESULTS

The results highlight the need for behavioral consequences or feedback to reduce erroneous behavior.

PRACTICAL APPLICATIONS

From an applied perspective, these results have implications for policy and training.

Associations Between Hospitalist Shift Busyness, Diagnostic Confidence, and Resource Utilization: A Pilot Study

OBJECTIVES

Hospitalized patients are at risk for diagnostic errors. Hospitalists caring for these patients are often multitasking when overseeing patient care. We aimed to measure hospitalist workload and understand its influences on diagnostic performance in a real-world clinical setting.

METHODS

We conducted a single-center, prospective, pilot observational study of hospitalists admitting new patients to the hospital. Hospitalists completed an abridged Mindful Attention Awareness Tool and a survey about diagnostic confidence at shift completion. Data on differential diagnoses and resource utilization (e.g., laboratory, imaging tests ordered, and consultations) were collected from the medical record. The number of admissions and paging volume per shift were used as separate proxies for shift busyness. Data were analyzed using linear mixed effects models (continuous outcomes) or mixed effects logistic regression (dichotomous outcomes).

RESULTS

Of the 53 hospitalists approached, 47 (89%) agreed to participate; complete data were available for 37 unique hospitalists who admitted 160 unique patients. Increases in admissions (odds ratio, 1.99; 95% confidence interval [CI], 1.04 to 3.82; P = 0.04) and pages (odds ratio, 1.11; 95% CI, 1.02 to 1.21; P = 0.01) were associated with increased odds of hospitalists finding it "difficult to focus on what is happening in the present." Increased pages was associated with a decrease in the number of listed differential diagnoses (coefficient, -0.02; 95% CI, -0.04 to -0.003; P = 0.02).

CONCLUSIONS

Evaluation of hospitalist busyness and its associations with factors that may influence diagnosis in a real-world environment was feasible and demonstrated important implications on physician focus and differential diagnosis.

On the importance of working memory in the driving safety field: A systematic review

In recent years, many studies have used poor cognitive functions to explain risk safety differences among drivers. Working memory is a cognitive function with information storage and attentional control that plays a crucial role in driver information processing. Furthermore, it is inextricably linked to parameters such as driving performance, driving eye movements and driving neurophysiology, which have a significant impact on drivers' risky behavior and crash risk. In particular, crash risk is a serious risk to social safety and economic development. For this reason, it is necessary to understand how risk-related working memory affects driving so that pre-driving safety pre-training programs and in-vehicle safety assistance systems for driving can be developed accordingly, contributing to the development of semi-autonomous vehicles and even autonomous vehicles. In this paper, a systematic search of the literature over the past 23 years resulted in 78 articles that met the eligibility criteria and quality assessment. The results show that higher working memory capacity, as measured neuropsychologically, is associated with more consistent and safer driving-related parameters for drivers (e.g., lane keeping) and may be related to pupil dilation during risk perception while driving, which is associated with driving outcomes (tickets, pull-overs, penalty points and fines,and driving accidents) is closely related to the perceived usefulness of the human-machine interface, reaction time, standard deviation of steering wheel corners, etc. when the autonomous driving takes over. In addition, higher working memory load interference was associated with more inconsistent and unsafe driving-related parameters (including but not limited to eye movements, electrophysiology, etc.), with higher working memory load being associated with easier driver concentration on the road, faster heart rate, lower heart rate variability, and lower oxyhemoglobin (OxyHb) and deoxyhemoglobin (DeoxyHb). Only a limited number of studies have simultaneously investigated the relationship between working memory capacity, working memory load and driving, showing an interaction between working memory capacity and working memory load on lane change initiation and lane change correctness, with working memory capacity acting as a covariate that mediated the effect of working memory load on braking reaction time. In addition, working memory-related cognitive training had a transfer effect on improving driving ability. Overall, working memory capacity determines the upper limit of the number of working memory attention resources, while working memory load occupies part of the working memory attention resources, thus influencing information perception, decision judgment, operational response, and collision avoidance in driving. Future effective interventions for safe driving can be combined with capacity training and load alerting. These findings contribute to our understanding of the role of working memory in driving and provide new insights into the design of driver safety training programs and automated driving personalized in-vehicle safety systems and roadside devices such as signage.

Effects of verbal tasks with varying difficulty on real-time respiratory airflow during speech generation in healthy young adults

Objective

Respiratory function is linked to sensory, affective, and cognitive processes and it is affected by environmental constraints such as cognitive demands. It is suggested that specific cognitive processes, such as working memory or executive functioning, may impact breathing. In turn, various lines of research have suggested a link between peak expiratory airflow (PEF) and cognitive function. However, there is scarce experimental support to the above assertions, especially regarding spoken language. Therefore, the present investigation aims to evaluate whether breathing varies as a function of performing verbal naming tasks with different difficulty levels.

Methods

Thirty healthy young adults, (age M = 25.37 years), participated in the study. Participants were required to perform aloud five verbal tasks ranged in order of difficulty: Reading single words, reading a text passage, object naming, semantic and phonemic fluency. A pneumotachograph mask was employed to acquire simultaneously the verbal responses, and three airflow parameters: Duration, peak, and volume at both stages of the respiratory cycle (i.e., inspiration/expiration). Data were analyzed with one-way repeated measures MANOVA.

Results

No significant differences were found between reading single words and object naming. In comparison, distinctive airflow requirements were found for reading a text passage, which were proportionally related to number of pronounced words. Though, the main finding of the study concerns the data on verbal fluency tasks, which not only entailed higher inhaled airflow resources but also a significant PEF.

Conclusion

Our data demonstrated that the most difficult tasks, namely semantic and phonemic verbal fluencies, relying on semantic search, executive function, and fast lexical retrieval of words were those requiring important amount of inhaled airflow and displaying a high peak expiratory airflow. The present findings demonstrated for the first time a direct association between complex verbal tasks and PEF. Inconclusive data related to object naming and reading single words are discussed in light of the methodological challenges inherent to the assessment of speech breathing and cognition in this line of investigation.

Young Novice Drivers' Cognitive Distraction Detection: Comparing Support Vector Machines and Random Forest Model of Vehicle Control Behavior

The use of mobile phones has become one of the major threats to road safety, especially in young novice drivers. To avoid crashes induced by distraction, adaptive distraction mitigation systems have been developed that can determine how to detect a driver's distraction state. A driving simulator experiment was conducted in this paper to better explore the relationship between drivers' cognitive distractions and traffic safety, and to better analyze the mechanism of distracting effects on young drivers during the driving process. A total of 36 participants were recruited and asked to complete an n-back memory task while following the lead vehicle. Drivers' vehicle control behavior was collected, and an ANOVA was conducted on both lateral driving performance and longitudinal driving performance. Indicators from three aspects, i.e., lateral indicators only, longitudinal indicators only, and combined lateral and longitudinal indicators, were inputted into both SVM and random forest models, respectively. Results demonstrated that the SVM model with parameter optimization outperformed the random forest model in all aspects, among which the genetic algorithm had the best parameter optimization effect. For both lateral and longitudinal indicators, the identification effect of lateral indicators was better than that of longitudinal indicators, probably because drivers are more inclined to control the vehicle in lateral operation when they were cognitively distracted. Overall, the comprehensive model built in this paper can effectively identify the distracted state of drivers and provide theoretical support for control strategies of driving distraction.

Factors affecting drivers' off-road glance behavior while interacting with in-vehicle voice interfaces

In-vehicle voice-based interfaces have been massively embedded in modern vehicles as a countermeasure to visual-manual distractions. However, limited data are available regarding the actual visual demands imposed on the driver when interacting with such an interface. How those factors that are associated with the drivers themselves affect their visual behavior when interacting with an in-vehicle voice-based interface remains understudied. This study focused on investigating factors affecting drivers' off-road visual behavior while interacting with a voice-based interface. A secondary data analysis leveraging structural equation modeling was performed based on data collected from a large-scale field experiment wherein participants drove a vehicle on the highway and performed a series of tasks using an in-vehicle voice interface. The results indicate that while voice-based interfaces are designed to help reduce drivers' visual demands, drivers can still direct their eyes off the road for a prolonged period of time during the interaction and exhibit potentially risky visual behavior. In addition, individual-level factors can exert influence on drivers' off-road visual behavior in such a way that older drivers and drivers with higher trust in technologies are associated with more long eyes-off-road glances when interacting with a voice-based interface. To promote the general public's adoption of in-vehicle voice interfaces and support safe interactions, we recommend relevant research and system design consider drivers' trust in, perceptions, and previous usage of similar technologies, as well as their individual characteristics such as age.

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.

ADABase: A Multimodal Dataset for Cognitive Load Estimation

Driver monitoring systems play an important role in lower to mid-level autonomous vehicles. Our work focuses on the detection of cognitive load as a component of driver-state estimation to improve traffic safety. By inducing single and dual-task workloads of increasing intensity on 51 subjects, while continuously measuring signals from multiple modalities, based on physiological measurements such as ECG, EDA, EMG, PPG, respiration rate, skin temperature and eye tracker data, as well as behavioral measurements such as action units extracted from facial videos, performance metrics like reaction time and subjective feedback using questionnaires, we create ADABase (Autonomous Driving Cognitive Load Assessment Database) As a reference method to induce cognitive load onto subjects, we use the well-established n-back test, in addition to our novel simulator-based k-drive test, motivated by real-world semi-autonomously vehicles. We extract expert features of all measurements and find significant changes in multiple modalities. Ultimately we train and evaluate machine learning algorithms using single and multimodal inputs to distinguish cognitive load levels. We carefully evaluate model behavior and study feature importance. In summary, we introduce a novel cognitive load test, create a cognitive load database, validate changes using statistical tests, introduce novel classification and regression tasks for machine learning and train and evaluate machine learning models.

Negative mood mind wandering and unsafe driving in young male drivers

OBJECTIVE

Road traffic crash fatalities disproportionately affect young male drivers. Driver distraction is a leading contributor to crashes. Mind wandering (MW) is a prevalent form of driver distraction that is linked to certain unsafe driving behaviours that are associated with increased crash risk (e.g., faster driving). Negative mood can lead to MW, and thus may represent a causal pathway to MW-related unsafe driving. This preliminary pre-post (T1, T2), randomized, controlled, single-blinded experiment tested whether negative mood, compared to neutral mood, increases MW while driving as well as unsafe driving and emotional arousal during MW. It also tested the moderating contribution of trait rumination and inhibitory control to this proposed causal pathway.

METHODS

Forty healthy male drivers aged 20 to 24 were randomly allocated to a negative or neutral mood manipulation involving deception. Individual differences in trait rumination and inhibitory control were measured at T1. At T1 and T2, participants drove in a driving simulator measuring driving speed, headway distance, steering behaviour, and overtaking. Heart rate and thought probes during simulation measured emotional arousal and MW, respectively.

RESULTS

Negative mood exposure led to more MW while driving (Odds Ratio = 1.79, p = .022). Trait rumination positively moderated the relationship between negative mood and MW (Odds Ratio = 2.11, p = .002). Negative versus neutral mood exposure led to increases in headway variability (Cohen's d = 1.46, p = .026) and steering reversals (Rate Ratio = 1.33, p = .032) during MW relative to focused driving. Between-group differences in emotional arousal were not significant.

CONCLUSION

Results support a causal pathway from negative mood to unsafe driving via MW, including the moderating contribution of trait rumination. If replicated, these preliminary findings may inform the development of interventions targeting this potential crash-risk pathway in vulnerable young driver subgroups.

Impacts of visual and cognitive distractions and time pressure on pedestrian crossing behaviour: A simulator study

Distractions have been recognised as one important factor associated with pedestrian injuries, as the increasing use of cell phones and personal devices. However, the situation is less clear regarding the differences in the effects of visual-manual and auditory-cognitive distractions. Here, we investigated distracted pedestrians in a one-lane road with continuous traffic using an immersive CAVE-based simulator. Sixty participants were recruited to complete a crossing task and perform one of two distractions, a visual-manual task and an auditory-cognitive task. Moreover, normal and time pressure crossing conditions were included as a baseline and comparison. For the first time, this study directly compared the impacts of visual-manual, auditory-cognitive distractions, and time pressure on pedestrian crossing behaviour and safety in a controlled environment. The results indicated that although pedestrian safety was compromised under both types of distraction, the effects of the applied distractions were different. When engaged in the visual-manual distraction, participants crossed the road slowly, but there was no significant difference in gap acceptance or initiation time compared to baseline. In contrast, participants walked slowly, crossed earlier, and accepted smaller gaps when performing the auditory-cognitive distraction. This has interesting parallels to existing findings on how these two types of distractions affect driver performance. Moreover, the effects of the visual-manual distraction were found to be dynamic, as these effects were affected by the gap size. Finally, compared to baseline, time pressure resulted in participants accepting smaller time gaps with shorter initiation times and crossing durations, leading to an increase in unsafe decisions and a decrease in near-collisions. These results provide new evidence that two types of distraction and time pressure impair pedestrian safety, but in different ways. Our findings may provide insights for further studies involving pedestrians with different distraction components.

Multimodal driver state modeling through unsupervised learning

Naturalistic driving data (NDD) can help understand drivers' reactions to each driving scenario and provide personalized context to driving behavior. However, NDD requires a high amount of manual labor to label certain driver's state and behavioral patterns. Unsupervised analysis of NDD can be used to automatically detect different patterns from the driver and vehicle data. In this paper, we propose a methodology to understand changes in driver's physiological responses within different driving patterns. Our methodology first decomposes a driving scenario by using a Bayesian Change Point detection model. We then apply the Latent Dirichlet Allocation method on both driver state and behavior data to detect patterns. We present two case studies in which vehicles were equipped to collect exterior, interior, and driver behavioral data. Four patterns of driving behaviors (i.e., harsh brake, normal brake, curved driving, and highway driving), as well as two patterns of driver's heart rate (HR) (i.e., normal vs. abnormal high HR), and gaze entropy (i.e., low versus high), were detected in these two case studies. The findings of these case studies indicated that among our participants, the drivers' HR had a higher fraction of abnormal patterns during harsh brakes, accelerating and curved driving. Additionally, free-flow driving with close to zero accelerations on the highway was accompanied by more fraction of normal HR as well as a lower gaze entropy pattern. With the proposed methodology we can better understand variations in driver's psychophysiological states within different driving scenarios. The findings of this work, has the potential to guide future autonomous vehicles to take actions that are fit to each specific driver.

Evaluating Driver Features for Cognitive Distraction Detection and Validation in Manual and Level 2 Automated Driving

OBJECTIVE

This study aimed to investigate the impacts of feature selection on driver cognitive distraction (CD) detection and validation in real-world nonautomated and Level 2 automated driving scenarios.

BACKGROUND

Real-time driver state monitoring is critical to promote road user safety.

METHOD

Twenty-four participants were recruited to drive a Tesla Model S in manual and Autopilot modes on the highway while engaging in the N-back task. In each driving mode, CD was classified by the random forest algorithm built on three "hand-crafted" glance features (i.e., percent road center [PRC], the standard deviation of gaze pitch, and yaw angles), or through a large number of features that were transformed from the output of a driver monitoring system (DMS) and other sensing systems.

RESULTS

In manual driving, the small set of glance features was as effective as the large set of machine-generated features in terms of classification accuracy. Whereas in Level 2 automated driving, both glance and vehicle features were less sensitive to CD. The glance features also revealed that the misclassified driver state was the result of the dynamic fluctuations and individual differences of cognitive loads under CD.

CONCLUSION

Glance metrics are critical for the detection and validation of CD in on-road driving.

APPLICATIONS

The paper suggests the practical value of human factors domain knowledge in feature selection and ground truth validation for the development of driver monitoring technologies.

Peripheral vision in real-world tasks: A systematic review

Peripheral vision is fundamental for many real-world tasks, including walking, driving, and aviation. Nonetheless, there has been no effort to connect these applied literatures to research in peripheral vision in basic vision science or sports science. To close this gap, we analyzed 60 relevant papers, chosen according to objective criteria. Applied research, with its real-world time constraints, complex stimuli, and performance measures, reveals new functions of peripheral vision. Peripheral vision is used to monitor the environment (e.g., road edges, traffic signs, or malfunctioning lights), in ways that differ from basic research. Applied research uncovers new actions that one can perform solely with peripheral vision (e.g., steering a car, climbing stairs). An important use of peripheral vision is that it helps compare the position of one’s body/vehicle to objects in the world. In addition, many real-world tasks require multitasking, and the fact that peripheral vision provides degraded but useful information means that tradeoffs are common in deciding whether to use peripheral vision or move one’s eyes. These tradeoffs are strongly influenced by factors like expertise, age, distraction, emotional state, task importance, and what the observer already knows. These tradeoffs make it hard to infer from eye movements alone what information is gathered from peripheral vision and what tasks we can do without it. Finally, we recommend three ways in which basic, sport, and applied science can benefit each other’s methodology, furthering our understanding of peripheral vision more generally.

Personal space in human-robot interaction at work: Effect of room size and working memory load

A recent literature review on personal space in human-robot interaction identified a research gap for the influence of contextual factors. At the same time, psychological research on interpersonal distancing as well as theoretical considerations based on compensatory control models suggest the importance to consider these factors in robot path planning. To address this gap, we tested the effect of room size and working memory load on participants‘ comfort distance toward an approaching robot. In a preregistered 3x2 within-subject design, N = 72 participants were approached by a mobile manufacturing robot in a corridor with varying room size and with and without a cognitive secondary task. As dependent variables, comfort distance, arousal, and perceived control were measured. While room size and working memory load had no significant direct effect on comfort distance, participants felt higher arousal and lower control in smaller rooms and in conditions with high working memory load, which in turn caused larger comfort distances (indirect effect). With experience, comfort distances decreased. Based on the indirect effects, future studies should test the effect of more extreme manipulations on comfort distances. Robots should adapt their path planning by keeping larger distances toward human workers in stressful environments to avoid discomfort.

Injury by design: A thematic networks and system dynamics analysis of work-related musculoskeletal disorders in tram drivers

Tram driving is a safety critical task where work-related musculoskeletal disorders (WRMSDs) and injuries are associated with interacting occupational design factors over time. These interactions then carry implications for workforce retention, public safety, workplace relations and supports. To better understand such interactions, this study used thematic networks and system dynamics (causal loop diagrams) analysis with the aim to unearth a global theme underscoring occurrence of WRMSDs, and describe the factors influencing the system dynamics of WRMSD occurrence in tram drivers. Building on earlier work focused on occupational participation, secondary analysis of driver interviews (n = 13) and driving observations (n = 11) produced thematic network and causal loop models of risk factors that highlighted an Injury by Design problem structure as a global theme. Research targeting organisational culture, human factors, and design standards is needed to minimise WRMSDs risk in tram drivers.

Let Complexity Bring Clarity: A Multidimensional Assessment of Cognitive Load Using Physiological Measures

The effects of cognitive load on driver behavior and traffic safety are unclear and in need of further investigation. Reliable measures of cognitive load for use in research and, subsequently, in the development and implementation of driver monitoring systems are therefore sought. Physiological measures are of interest since they can provide continuous recordings of driver state. Currently, however, a few issues related to their use in this context are not usually taken into consideration, despite being well-known. First, cognitive load is a multidimensional construct consisting of many mental responses (cognitive load components) to added task demand. Yet, researchers treat it as unidimensional. Second, cognitive load does not occur in isolation; rather, it is part of a complex response to task demands in a specific operational setting. Third, physiological measures typically correlate with more than one mental state, limiting the inferences that can be made from them individually. We suggest that acknowledging these issues and studying multiple mental responses using multiple physiological measures and independent variables will lead to greatly improved measurability of cognitive load. To demonstrate the potential of this approach, we used data from a driving simulator study in which a number of physiological measures (heart rate, heart rate variability, breathing rate, skin conductance, pupil diameter, eye blink rate, eye blink duration, EEG alpha power, and EEG theta power) were analyzed. Participants performed a cognitively loading n-back task at two levels of difficulty while driving through three different traffic scenarios, each repeated four times. Cognitive load components and other coinciding mental responses were assessed by considering response patterns of multiple physiological measures in relation to multiple independent variables. With this approach, the construct validity of cognitive load is improved, which is important for interpreting results accurately. Also, the use of multiple measures and independent variables makes the measurements (when analyzed jointly) more diagnostic-that is, better able to distinguish between different cognitive load components. This in turn improves the overall external validity. With more detailed, diagnostic, and valid measures of cognitive load, the effects of cognitive load on traffic safety can be better understood, and hence possibly mitigated.

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.

Dynamics of cognitive action control in late-life depression during action selection

INTRODUCTION

Late-Life Depression (LLD) is characterized by deficits in cognitive control. We investigated the effect of LLD on a subset of cognitive control functions, the Cognitive Action Control (CAC), distinguishing on-line and adaptive control.

METHODS

We compared LLD subjects (n = 31) and Healthy Controls (HC, n = 31) on their performance in a Simon task. The online congruency effect and adaptive effect were compared for reaction times (RT) and accuracy rates between the groups using mixed models. We applied distributional analyses of RT to differentiate the strength of impulsive action selection and the proficiency of selective action suppression. Finally, we measured correlations between the performances on the task and clinical scores of the LLD group.

RESULTS

LLD had increased error rates in congruent trials compared to HC. Conversely, the adaptive CAC was equivalent between the groups. Distributional analyses showed that the fastest actions were less led by pertinent information in LLD. This phenomenon was found exclusively for congruent trials preceded by non-congruent trials. On the other hand, LLD patients, when they take time, were better than HC to suppress selectively non-relevant information. No difference was observed for adaptation to the preceding condition. No association between behavioral measurements and clinical scores were found.

CONCLUSION

Our results suggest that LLD participants have a specific cognitive disturbance of CAC, showing less facilitation than HC in congruent situations. We propose that this originates in a difficulty in LLD patients in disengaging their attention from conflict situations, which is consistent with a biased CAC to aversive stimuli in depression.

Effects of Neuro-Cognitive Load on Learning Transfer Using a Virtual Reality-Based Driving System

Understanding the ways different people perceive and apply acquired knowledge, especially when driving, is an important area of study. This study introduced a novel virtual reality (VR)-based driving system to determine the effects of neuro-cognitive load on learning transfer. In the experiment, easy and difficult routes were introduced to the participants, and the VR system is capable of recording eye-gaze, pupil dilation, heart rate, as well as driving performance data. So, the main purpose here is to apply multimodal data fusion, several machine learning algorithms, and strategic analytic methods to measure neurocognitive load for user classification. A total of ninety-eight (98) university students participated in the experiment, in which forty-nine (49) were male participants and forty-nine (49) were female participants. The results showed that data fusion methods achieved higher accuracy compared to other classification methods. These findings highlight the importance of physiological monitoring to measure mental workload during the process of learning transfer.

Effect of Apolipoprotein E4 on the Driving Behavior of Patients with Amnestic Mild Cognitive Impairment or Mild Alzheimer's Disease Dementia

BACKGROUND

The driving behavior of patients with mild Alzheimer's disease dementia (ADD) and patients with mild cognitive impairment (MCI) is frequently characterized by errors. A genetic factor affecting cognition is apolipoprotein E4 (APOE4), with carriers of APOE4 showing greater episodic memory impairment than non-carriers. However, differences in the driving performance of the two groups have not been investigated.

OBJECTIVE

To compare driving performance in APOE4 carriers and matched non-carriers.

METHODS

Fourteen APOE4 carriers and 14 non-carriers with amnestic MCI or mild ADD underwent detailed medical and neuropsychological assessment and participated in a driving simulation experiment, involving driving in moderate and high traffic volume in a rural environment. Driving measures were speed, lateral position, headway distance and their SDs, and reaction time. APOE was genotyped through plasma samples.

RESULTS

Mixed two-way ANOVAs examining traffic volume and APOE4 status showed a significant effect of traffic volume on all driving variables, but a significant effect of APOE4 on speed variability only. APOE4 carriers were less variable in their speed than non-carriers; this remained significant after a Bonferroni correction. To further examine variability in the driving performance, coefficients of variation (COV) were computed. Larger headway distance COV and smaller lateral position COV were observed in high compared to moderate traffic. APOE4 carriers had smaller speed COV compared to non-carriers.

CONCLUSION

The lower speed variability of APOE4 carriers in the absence of neuropsychological test differences indicates reduced speed adaptations, possibly as a compensatory strategy. Simulated driving may be a sensitive method for detecting performance differences in the absence of cognitive differences.

Measuring Correlates of Mental Workload During Simulated Driving Using cEEGrid Electrodes: A Test-Retest Reliability Analysis

The EEG reflects mental processes, especially modulations in the alpha and theta frequency bands are associated with attention and the allocation of mental resources. EEG has also been used to study mental processes while driving, both in real environments and in virtual reality. However, conventional EEG methods are of limited use outside of controlled laboratory settings. While modern EEG technologies offer hardly any restrictions for the user, they often still have limitations in measurement reliability. We recently showed that low-density EEG methods using film-based round the ear electrodes (cEEGrids) are well-suited to map mental processes while driving a car in a driving simulator. In the present follow-up study, we explored aspects of ecological and internal validity of the cEEGrid measurements. We analyzed longitudinal data of 127 adults, who drove the same driving course in a virtual environment twice at intervals of 12-15 months while the EEG was recorded. Modulations in the alpha and theta frequency bands as well as within behavioral parameters (driving speed and steering wheel angular velocity) which were highly consistent over the two measurement time points were found to reflect the complexity of the driving task. At the intraindividual level, small to moderate (albeit significant) correlations were observed in about 2/3 of the participants, while other participants showed significant deviations between the two measurements. Thus, the test-retest reliability at the intra-individual level was rather low and challenges the value of the application for diagnostic purposes. However, across all participants the reliability and ecological validity of cEEGrid electrodes were satisfactory in the context of driving-related parameters.

The box task - a method for assessing in-vehicle system demand

The use of advanced in-vehicle information systems (IVIS) and other complex devices such as smartphones while driving can lead to driver distraction, which, in turn, increases safety-critical event risk. Therefore, using methods for measuring driver distraction caused by IVIS is crucial when developing new in-vehicle systems. In this paper, we present the setup and implementation of the Box Task combined with a Detection Response Task (BT+DRT) as a tool to assess visual-manual and cognitive distraction effects. The BT+DRT represents a low-cost and easy-to-use method which can be easily implemented by researchers in laboratory settings and which was validated in previous research. Moreover, at the end of this paper we describe the experimental procedure, the data analysis and discuss potential modifications of the method.•

The setup and implementation of the Box Task combined with a Detection Response Task (BT+DRT) is described.

•

The method allows for measuring visual-manual and cognitive distraction of drivers.

•

The BT+DRT is a cost-effective and easy-to-use method that can be implemented in laboratory settings or driving simulators.

Impact of Safety Culture Implementation on Driving Performance among Oil and Gas Tanker Drivers: A Partial Least Squares Structural Equation Modelling (PLS-SEM) Approach

This research aims to develop a safety culture model by investigating the relationship between safety culture and driving performance. In previous studies, safety culture has been one of the factors that determine safety issues. These issues were then contextually transformed via a pilot study and organized in the form of a theoretical model. The data were collected from 307 oil and gas tanker drivers in Malaysia through questionnaire surveys. Consequently, structural equation models of partial least squares (PLS-SEM) were applied to statistically assess the final model of this study. The results showed that the implementation of safety culture contributes to driving performance at a substantial level; there is a strong association with an effect of 67.3%. The findings of this research would serve as a benchmark for decision-makers in the oil and gas transportation sector, as promoting an awareness of safety culture should boost the efficiency of drivers. This research fills a gap in knowledge by identifying that positive safety culture practices and mindset are direct antecedents for the improvement of driver performance and, thus, the avoidance of road accidents.

The Impact of an Educational Intervention on Distracted Driving Knowledge, Attitudes, and Behaviors Among College Students

Distracted driving indiscriminately kills nearly 3500 people each year with young adults having greater risks associated with this phenomenon. Prevention programs targeting the distracted driving habits of young adults are necessary to ameliorate the high costs, both in dollars and in lives, associated with this behavior. Few health education and prevention programs have been assessed for their effectiveness in changing knowledge, attitudes, and behaviors related to distracted driving. This study explores a distracted driving intervention among undergraduate students enrolled in an introductory public health course. A quasi-experimental study design was used to compare the pre- and post-data of the group receiving 5-weeks of a distracted driving intervention to a control group. Questionnaires were administered to both groups prior to and 2 weeks following the intervention to assess changes in knowledge, attitudes, and behaviors related to distracted driving. A Difference-in-Difference technique showed significant changes in knowledge (β = 0.40, p = 0.03) and total scores (β = 2.48, p = 0.04) in the intervention (n = 97) compared to the control (n = 131). T-tests examining pre- and post-scores for individual behaviors showed the intervention group displayed positive changes for some behaviors (talking, texting, cellphone use, grooming) compared to the control. The results support the impact that a classroom-based distracted driving intervention can have on undergraduate college students. Implications for this type of health education program may lead to improvements in distracted driving attitudes and behavior among this age-group.

Carga cognitiva y control atencional en puertorriqueños con trastorno obsesivocompulsivo

Trasfondo:El control ejecutivo de la atención media la resolución de problemas y la acción voluntaria y está implicado en la regulación de las emociones. Se ha reportado que el control atencional está afectado en personas con trastorno obsesivo-compulsivo (TOC). Sin embargo, no se ha considerado la influencia de la carga cognitiva y la distracción en la ejecución de tareas, lo que pudiera tener un impacto en el control atencional. En este estudio evaluamos si la carga cognitiva alta y baja influyen en el control atencional de personas con TOC, en comparación con un grupo control. Método:Quince adultos puertorriqueños con TOC (M=31.60, DE=10.70) y 26 saludables (M=28.42, DE=10.73) participaron en el estudio. Se administró el Attention Network Test y una tarea de carga cognitiva para evaluar el control atencional. Resultados:No se observaron diferencias significativas en las puntuaciones de alerta, orientación y control atencional entre los grupos en ambas condiciones de carga cognitiva (p>0.05). Solo se observó una diferencia significativa en el control atencional de los participantes con TOC (z=1.99, p=0.047) y del grupo control (z=-2.83, p=0.005) durante tareas con carga cognitiva baja y alta. Conclusiones:Ambos grupos experimentaron menor interferencia de distractores bajo alta carga cognitiva, lo que sugiere un mayor control de la atención durante esta condición. Es posible que el aumento de carga cognitiva pueda reducir la distracción en puertorriqueños saludables y aquellos con TOC. Tomar en cuenta la carga cognitiva puede facilitar el entrenamiento cognitivo para el control atencional y aumentar la flexibilidad cognitiva, mejorando la respuesta al tratamiento.  

Benefits of Higher Cardiovascular and Motor Coordinative Fitness on Driving Behavior Are Mediated by Cognitive Functioning: A Path Analysis

Driving is an important skill for older adults to maintain an independent lifestyle, and to preserve the quality of life. However, the ability to drive safely in older adults can be compromised by age-related cognitive decline. Performing an additional task during driving (e.g., adjusting the radio) increases cognitive demands and thus might additionally impair driving performance. Cognitive functioning has been shown to be positively related to physical activity/fitness such as cardiovascular and motor coordinative fitness. As such, a higher fitness level might be associated with higher cognitive resources and may therefore benefit driving performance under dual-task conditions. For the first time, the present study investigated whether this association of physical fitness and cognitive functioning causes an indirect relationship between physical fitness and dual-task driving performance through cognitive functions. Data from 120 healthy older adults (age: 69.56 ± 3.62, 53 female) were analyzed. Participants completed tests on cardiovascular fitness (cardiorespiratory capacity), motor coordinative fitness (composite score: static balance, psychomotor speed, bimanual dexterity), and cognitive functions (updating, inhibition, shifting, cognitive processing speed). Further, they performed a virtual car driving scenario where they additionally engaged in cognitively demanding tasks that were modeled after typical real-life activities during driving (typing or reasoning). Structural equation modeling (path analysis) was used to investigate whether cardiovascular and motor coordinative fitness were indirectly associated with lane keeping (i.e., variability in lateral position) and speed control (i.e., average velocity) while dual-task driving via cognitive functions. Both cardiovascular and motor coordinative fitness demonstrated the hypothesized indirect effects on dual-task driving. Motor coordinative fitness showed a significant indirect effect on lane keeping, while cardiovascular fitness demonstrated a trend-level indirect effect on speed control. Moreover, both fitness domains were positively related to different cognitive functions (processing speed and/or updating), and cognitive functions (updating or inhibition), in turn, were related to dual-task driving. These findings indicate that cognitive benefits associated with higher fitness may facilitate driving performance. Given that driving with lower cognitive capacity can result in serious consequences, this study emphasizes the importance for older adults to engage in a physically active lifestyle as it might serve as a preventive measure for driving safety.

Displays for Productive Non-Driving Related Tasks: Visual Behavior and Its Impact in Conditionally Automated Driving

(1) Background: Primary driving tasks are increasingly being handled by vehicle automation so that support for non-driving related tasks (NDRTs) is becoming more and more important. In SAE L3 automation, vehicles can require the driver-passenger to take over driving controls, though. Interfaces for NDRTs must therefore guarantee safe operation and should also support productive work. (2) Method: We conducted a within-subjects driving simulator study (N=53) comparing Heads-Up Displays (HUDs) and Auditory Speech Displays (ASDs) for productive NDRT engagement. In this article, we assess the NDRT displays’ effectiveness by evaluating eye-tracking measures and setting them into relation to workload measures, self-ratings, and NDRT/take-over performance. (3) Results: Our data highlights substantially higher gaze dispersion but more extensive glances on the road center in the auditory condition than the HUD condition during automated driving. We further observed potentially safety-critical glance deviations from the road during take-overs after a HUD was used. These differences are reflected in self-ratings, workload indicators and take-over reaction times, but not in driving performance. (4) Conclusion: NDRT interfaces can influence visual attention even beyond their usage during automated driving. In particular, the HUD has resulted in safety-critical glances during manual driving after take-overs. We found this impacted workload and productivity but not driving performance.

The effect of cellphone position on driving and gaze behaviour

Legislation frequently restricts the use of cellphones while driving. Despite this, many people continue to interact with cellphones covertly while driving, typically by concealing their device in their lap. This strategy leads to frequent diversion of the drivers' gaze from the road ahead, potentially reducing their driving performance. To evaluate the influence of cellphone use on driving, 30 participants took part in three randomly ordered 7-min virtual reality driving simulations. In each condition, drivers were presented with either (a) no cellphone, (b) a cellphone fixed to the windscreen, or (c) a cellphone positioned at lap level. Their task was to maintain road position and observe speed limits while answering maths problems (delivered intermittently via 'text message') and searching for external target objects. Outcome measures included speed, lane position standard deviation (LPSD), and fixation behaviour, which were compared between trials. In trials where a cellphone was present, participants shifted fixation more frequently, drove approximately 6 km/h faster, exhibited a lower LPSD and spent more time in the correct lane on the road (compared to the no-cellphone condition; all p < 0.001). Cellphone position influenced eye gaze behaviour, with drivers looking at the cellphone less frequently, and the speedometer more frequently. when the cellphone was in their lap compared to when the cellphone was positioned on the windscreen. Our results are consistent with participants driving more cautiously-checking speed and lane position more frequently-when they have a cellphone in the lap. Real-world driving data would be useful to determine whether this change in driving behaviour we observed is sufficient to offset the increased risk introduced by spending less time looking at the road ahead.

Cerebral haemodynamics during simulated driving: Changes in workload are detectable with functional near infrared spectroscopy

Motor vehicle operation is a complicated task and substantial cognitive resources are required for safe driving. Experimental paradigms examining cognitive workload using driving simulators often introduce secondary tasks, such as mathematical exercises, or utilise simulated in-vehicle information systems. The effects of manipulating the demands of the primary driving task have not been examined in detail using advanced neuroimaging techniques. This study used a manipulation of the simulated driving environment to test the impact of increased driving complexity on brain activity. Fifteen participants drove in two scenarios reflecting common driving environments differing in the amount of vehicular traffic, frequency of intersections, number of buildings, and speed limit restrictions. Functional near infrared spectroscopy was used to quantify changes in cortical activity; fifty-five optodes were placed over the prefrontal and occipital cortices, commonly assessed areas during driving. Compared to baseline, both scenarios increased oxyhaemoglobin in the bilateral prefrontal cortex and cerebral blood volume in the right prefrontal cortex (all p ≤ 0.05). Deoxyhaemoglobin decreased at the bilateral aspects of the prefrontal cortex but overall tended to increase in the medial aspect during both scenarios (both p ≤ 0.05). Cerebral oxygen exchange significantly declined at the lateral aspects of the prefrontal cortex, with a small but significant increase seen in the medial aspect (both p < 0.05). There were no significant differences for oxyhaemoglobin, deoxyhaemoglobin, or cerebral blood volume (all p > 0.05). This study demonstrates that functional near infrared spectroscopy is capable of detecting changes in cortical activity elicited by simulated driving tasks but may be less sensitive to variations in driving workload aggregated over a longer duration.

Exploring a sustainable building's impact on occupant mental health and cognitive function in a virtual environment

Even though people spend the majority of their time indoors, the role of buildings in shaping human experience is still not well understood. The objective of this experimental project is to develop, test, and validate a data-driven neuroscience approach to understand the built environment’s impact on occupant cognitive function and mental health. The present study utilized virtual environments and electroencephalogram (EEG) and event-related potential (ERP) approaches, to provide objective neurophysiological information about how sustainable buildings (SBs) impact people’s affective and cognitive functioning differently compared to conventional building (CBs). The long-term goal is to assess the validity of sustainable building design protocols in promoting and increasing mental health and well-being and the mechanism used to accomplish these increases. The findings showed test subjects demonstrated increased visual system engagement and modulated attentional focus and control processing in the SB compared to the CB environments. The findings can be explained by the cognitive load theory, which is consistent with the interpretation of greater focus on the present environment and reduced internal mental processing (cf. mindfulness), based on the observed increased theta/delta activities and greater engagement of visual systems and corresponding decreases in frontal activity in the SB environment. In addition, the combination of virtual environment (VE) and EEG/ERP has the potential to advance design methods by soliciting occupants’ responses prior to completion of the projects. Building design is more than aesthetics; expanding the horizon for neuroscience would eventually result in a new knowledge base for building design, particularly sustainable building design, since the sustainability of the building often needs to be quantified.

Messages beyond the phone: Processing variable message signs while attending hands-free phone calls

We examined the effects of different types of cognitive distraction coming from a hands-free phone conversation on the processing of information provided by variable message signs (VMS), on driving performance indicators, and on a physiological index of mental effort (heart rate). Participants drove a route in a driving simulator and had to respond to VMS messages under three conditions: no-distraction, visuospatial distraction (attending phone calls with questions inducing visuospatial processing), and conceptual distraction (attending phone calls with questions requiring semantic memory). Results showed more errors responding to VMS messages in the visuospatial distraction condition. In addition, both types of questions increased the intraindividual variability of response distances and the heart rate, as compared to the no-distraction condition. These results provide new evidence that talking on a hands-free phone entails costs in the processing of traffic information (in particular, text messages displayed on VMS) and it increases the driver's cognitive effort. Interestingly, the cognitive distraction had no effect on the driver's control of the vehicle speed or lateral position. Therefore, the effects of potential risk factors can critically vary among the different driving subtasks due to modulatory factors, such as the level of attentional task demands (relatively high in the processing of messages on VMS, but relatively low in controlling the speed and lateral position of the vehicle in quiet traffic conditions). In consequence, the current paper provides new evidence to discuss hands-free phone policies and highlights the importance of designing technological countermeasures to prevent drivers missing critical information displayed on VMS.

Allocation of Driver Attention for Varying In-Vehicle System Modalities

OBJECTIVE

This paper examines drivers' allocation of attention using response time to a tactile detection response task (TDRT) while interacting with an in-vehicle information system (IVIS) over time.

BACKGROUND

Longer TDRT response time is associated with higher cognitive workload. However, it is not clear what role is assumed by the human and system in response to varying in-vehicle environments over time.

METHOD

A driving simulator study with 24 participants was conducted with a restaurant selection task of two difficulty levels (easy and hard) presented in three modalities (audio only, visual only, hybrid). A linear mixed-effects model was applied to identify factors that affect TDRT response time. A nonparametric time-series model was also used to explore the visual attention allocation under the hybrid mode over time.

RESULTS

The visual-only mode significantly increased participants' response time compared with the audio-only mode. Females took longer to respond to the TDRT when engaged with an IVIS. The study showed that participants tend to use the visual component more toward the end of the easy tasks, whereas the visual mode was used more at the beginning of the harder tasks.

CONCLUSION

The visual-only mode of the IVIS increased drivers' cognitive workload when compared with the auditory-only mode. Drivers showed different visual attention allocation during the easy and hard restaurant selection tasks in the hybrid mode.

APPLICATION

The findings can help guide the design of automotive user interfaces and help manage cognitive workload.

A Frontal Neuropsychological Profile in Fitness to Drive

Traffic accidents are a global concern due to the elevated mortality rates of both drivers and pedestrians. The World Health Organization declared 2011-2020 as the Decade of Action for Road Safety, endorsing initiatives to reduce traffic-related deaths. Yet, despite these incentives, fatal accidents still occur. Different studies have linked deficits in executive functions to risky driving attitudes and crashes. The present study focuses on demographic, cognitive and personality factors, related to the prefrontal cortex, that are characteristic of drivers prone to risky behavior behind the wheel. The penalty Points System was used to classify drivers as "safe", with no point loss over a two-year period, or "risky", with full point loss during the same interval. A neuropsychological assessment of prefrontal cognitive functions was carried out on each group to identify variables associated with safe and risky behavior. Neuropsychological indexes were obtained from a continuous performance task without cue (Simple Attention), a continuous performance task with cue (Conditioned Attention), the Tower of Hanoi test and the Neurologically-related Changes in Personality Inventory (NECHAPI). A Discriminant Analysis (DA) found that education level, reaction times in Simple and Conditioned Attention, learning errors in the Tower of Hanoi and vulnerability in the personality test, best predicted whether drivers were likely to be in the safe or risky group. Finally, a cross-validation analysis performed on the same sample correctly classified 87.5% of the drivers. These data suggest that prefrontal dysfunction contributes to risky behavior behind the wheel. The inclusion of cognitive programs to identify and train drivers with this propensity could reduce risky driving, and consequently, save lives on the road.

Predicting takeover response to silent automated vehicle failures

Current and foreseeable automated vehicles are not able to respond appropriately in all circumstances and require human monitoring. An experimental examination of steering automation failure shows that response latency, variability and corrective manoeuvring systematically depend on failure severity and the cognitive load of the driver. The results are formalised into a probabilistic predictive model of response latencies that accounts for failure severity, cognitive load and variability within and between drivers. The model predicts high rates of unsafe outcomes in plausible automation failure scenarios. These findings underline that understanding variability in failure responses is crucial for understanding outcomes in automation failures.

Neurophysiological correlates of age differences in driving behavior during concurrent subtask performance

Driving is a complex cognitive-motor task that requires the continuous integration of multisensory information, cognitive processes, and motor actions. With higher age, driving becomes increasingly challenging as a result of naturally declining neurophysiological resources. Performing additional subtasks, such as conversations with passengers or interactions with in-vehicle devices (e.g., adjusting the radio), may further challenge neurocognitive resources that are required to maintain driving performance. Based on declining brain physiological resources and inferior neurocognitive functioning, older adults (OA) may show higher brain activation and larger performance decrements than younger adults (YA) when engaging in additional subtasks during driving. Age differences, however, may further vary for different neurocognitive task demands, such that driving performance of OA might be particularly affected by certain subtasks. In this study, we hence investigated the brain functional correlates of age differences in driving behavior during concurrent subtask performance in YA and OA. Our final sample consisted of thirty younger (21.80 ± 1.73y, 15 female) and thirty older (69.43 ± 3.30y, 12 female) regular drivers that drove along a typical rural road (25 - 30 min) in a driving simulator and performed three different concurrent subtasks that were presented auditorily or visually: typing a 3-digit number (TYPE), comparing traffic news and gas station prices (working memory, WM), and stating arguments (ARG). We measured variability in lateral car position, velocity, and following distance to a frontal lead car as the standard deviation from 0 to 15 s after subtask onset. Brain activity was continuously recorded using functional near-infrared spectroscopy over the dorsolateral prefrontal cortex. Both YA and OA particularly varied in their lateral position during TYPE with a more pronounced effect in OA. For YA, in contrast, ARG led to higher variability in velocity compared to TYPE and WM, whereas OA showed no task-specific differences. Substantiating our behavioral findings, OA revealed the largest brain functional response to TYPE, while YA demonstrated a very distinct activation during ARG and smaller hemodynamic responses to TYPE and WM. Brain activity in the DLPFC was, overall, not significantly, but small to moderately related to certain behavioral performance parameters (mainly lateral position). We conclude that both OA and YA are vulnerable to distractive subtasks while driving. Age differences, however, seem to largely depend on neurocognitive task demands. OA may be at higher risk for accidents when performing visuo-motor subtasks (e.g., interacting with navigational systems) during driving while YA may be more (cognitively) distracted when talking to passengers.

Measuring driver distraction - Evaluation of the box task method as a tool for assessing in-vehicle system demand

Several tools have been developed over the past twenty years to assess the degree of driver distraction caused by secondary task engagement. A relatively new and promising method in this area is the box task combined with a detection response task (BT + DRT). However, no evaluation regarding the BT's sensitivity currently exists. Thus, the aim of the present study was to evaluate the BT + DRT by comparing its sensitivity to the sensitivity of already established methods. Twenty-nine participants engaged in several artificial and realistic secondary tasks while either performing the BT + DRT, the Lane Change Test (LCT), or driving through a simple course in a simulator. The results showed that the BT parameters (especially the standard deviation of box position and size) were sensitive to differences in demand across the visual-manual secondary tasks. This was comparable to what was found with the LCT. Surprisingly, the BT performance measures were more sensitive than those of the driving simulation task. The BT + DRT also captured cognitive distraction effects with the integration of the DRT. Hence, the BT + DRT could be a cost-effective method to assess in-vehicle system demand. However, further investigations are necessary to better understand the potential of the BT method.

Classification of Driver Distraction: A Comprehensive Analysis of Feature Generation, Machine Learning, and Input Measures

OBJECTIVE

The objective of this study was to analyze a set of driver performance and physiological data using advanced machine learning approaches, including feature generation, to determine the best-performing algorithms for detecting driver distraction and predicting the source of distraction.

BACKGROUND

Distracted driving is a causal factor in many vehicle crashes, often resulting in injuries and deaths. As mobile devices and in-vehicle information systems become more prevalent, the ability to detect and mitigate driver distraction becomes more important.

METHOD

This study trained 21 algorithms to identify when drivers were distracted by secondary cognitive and texting tasks. The algorithms included physiological and driving behavioral input processed with a comprehensive feature generation package, Time Series Feature Extraction based on Scalable Hypothesis tests.

RESULTS

Results showed that a Random Forest algorithm, trained using only driving behavior measures and excluding driver physiological data, was the highest-performing algorithm for accurately classifying driver distraction. The most important input measures identified were lane offset, speed, and steering, whereas the most important feature types were standard deviation, quantiles, and nonlinear transforms.

CONCLUSION

This work suggests that distraction detection algorithms may be improved by considering ensemble machine learning algorithms that are trained with driving behavior measures and nonstandard features. In addition, the study presents several new indicators of distraction derived from speed and steering measures.

APPLICATION

Future development of distraction mitigation systems should focus on driver behavior-based algorithms that use complex feature generation techniques.