Towards a transactional ergonomics for driver stress and fatigue

This article reviews a transactional model of driver stress and fatigue, and its ergonomic application to designing vehicle systems for 'stress-tolerance'. Disturbances of subjective state are controlled by cognitive stress processes of appraisal and coping. Both personality factors and situational stressors may elicit maladaptive patterns of cognition that generate subjective stress symptoms, elicit potentially dangerous coping strategies, and interfere with information-processing and attention to the task at hand. Studies using a driving simulator have explored the behavioural consequences of several qualitatively different forms of 'stress', that can be loosely labelled as anxiety, anger and fatigue. Implications of the model for design are reviewed, focusing on road engineering, in-car systems, and automation of vehicle functions. A transactional analysis focuses on evaluation of the cognitions produced by vehicle systems, problems of distraction and overload, and maintaining active task involvement. The article concludes with guidelines for design to minimize safety problems associated with stress and fatigue.     

Thematic issue: driving automation and autonomy

ABSTRACT

Automated driving has the potential to support drivers, freeing them up to do other things, such as work, rest or play. The problem is that in the present instantiation, automated driving requires the driver to perform a monitoring function and be ready to intervene if required. This is the worst of all worlds of automation. The monitoring task can be (if performed properly) more demanding than manual driving and the driver is not freed up to do other things. Worse still, is that the monitoring task cannot be sustained for long and, on occasions, led to a vehicle collisions because the driver cannot intervene in a timely manner. One of the first studies conducted over twenty years shows this to be the case and there really has not been any improvements since. This special issue reports on the latest development in vehicle automation and points to future directions that research should be directed.     

How to keep drivers engaged while supervising driving automation? A literature survey and categorisation of six solution areas

Abstract

This work aimed to organise recommendations for keeping people engaged during human supervision of driving automation, encouraging a safe and acceptable introduction of automated driving systems. First, heuristic knowledge of human factors, ergonomics, and psychological theory was used to propose solution areas to human supervisory control problems of sustained attention. Driving and non-driving research examples were drawn to substantiate the solution areas. Automotive manufacturers might (1) avoid this supervisory role altogether, (2) reduce it in objective ways or (3) alter its subjective experiences, (4) utilize conditioning learning principles such as with gamification and/or selection/training techniques, (5) support internal driver cognitive processes and mental models and/or (6) leverage externally situated information regarding relations between the driver, the driving task, and the driving environment. Second, a cross-domain literature survey of influential human-automation interaction research was conducted for how to keep engagement/attention in supervisory control. The solution areas (via numeric theme codes) were found to be reliably applied from independent rater categorisations of research recommendations. Areas (5) and (6) were addressed by around 70% or more of the studies, areas (2) and (4) in around 50% of the studies, and areas (3) and (1) in less than around 20% and 5%, respectively. The present contribution offers a guiding organisational framework towards improving human attention while supervising driving automation.     

Attention and automation: New perspectives on mental underload and performance

There is considerable evidence in the ergonomics literature that automation can significantly reduce operator mental workload. Furthermore, reducing mental workload is not necessarily a good thing, particularly in cases where the level is already manageable. This raises the issue of mental underload, which can be at least as detrimental to performance as overload. However, although it is widely recognized that mental underload is detrimental to performance, there are very few attempts to explain why this may be the case. It is argued in this paper that, until the need for a human operator is completely eliminated, automation has psychological implications relevant in both theoretical and applied domains. The present paper reviews theories of attention, as well as the literature on mental workload and automation, to synthesize a new explanation for the effects of mental underload on performance. Malleable attentional resources theory proposes that attentional capacity shrinks to accommodate reductions in mental workload, and that this shrinkage is responsible for the underload effect. The theory is discussed with respect to the applied implications for ergonomics research.     

Psychological constructs in driving automation: a consensus model and critical comment on construct proliferation

As automation in vehicles becomes more prevalent, the call for understanding the behaviour of the driver while driving an automated vehicle becomes more salient. Although a variety of driver behaviour models exist, and various psychological constructs have been said to be influenced by automation, an empirically testable psychological model of automated driving has yet to be developed. Building upon Stanton and Young's model of driving automation, this article presents an updated model of interrelated psychological constructs. The proposed model was created based upon a systematic literature search of driving automation papers and a subsequent quantification of the number of reported links between a selected set of psychological constructs. A secondary aim of this article is to reach consensus in the use of psychological constructs regarding driving automation. Henceforth special attention is paid to resolving the issue of construct proliferation.     

Getting the driver back into the loop: the quality of manual vehicle control following long and short non-critical transfer-of-control requests: TI:NS

ABSTRACT

Specific vehicle automation use-cases such as traffic jams will be the first level 3 functions on the market. When the ‘traffic jam pilot’ nears its limits in non-critical situations, control needs to be handed back to the driver, enabling appropriate situation awareness (SA) and vehicle handling. According to previous research, operational vehicle stabilisation can be achieved within a transfer-of-control (TOC) of a few seconds in simple traffic environments, but tactical level decisions benefit from longer hand-over times. To date, the effects of non-critical TOCs have not been studied using set time frames. To investigate the impact of short (unplanned, 5 seconds) and long (planned, 50 seconds) TOC requests, while playing/not playing an engaging tablet game, a simulator experiment was conducted with 16 participants. Comparisons of the 60-second-period of manual driving following automation suggest better longitudinal vehicle control as well as more appropriate SA following the long TOC request, and automation periods without the game. However, following no engaging game, lateral performance was worse during the first 10 seconds of manual driving. Control-level visual search patterns did not change with TOC time or the game. Future research needs to consider support for drivers’ SA maintenance and readiness to drive following high automation.     

Analysis of driver roles: modelling the changing role of the driver in automated driving systems using EAST

ABSTRACT

The purpose of this paper is to analyse the role of the driver within automated driving systems using the Event Analysis of Systemic Teamwork (EAST) method. We already know that as the level of automation increases within the driving task, the role of the driver shifts from that of an active operator (i.e. a driver driving) to more of a passive monitor (i.e. a driver monitoring). Task, social and information networks were constructed using the Hierarchical Task Analysis of Driving and evidence from driver verbalisations collected during a previous study to further explore the changing role of the driver using network analysis. A ‘broken links’ approach was conducted to show that momentary engagement in non-driving-related secondary tasks within an automated driving system can dramatically change the structure of driving system.     

Usability criteria for intelligent driver assistance systems

Increasing numbers of intelligent driver assistance systems are now being installed in motor vehicles to support drivers. In order to ensure that the stress reduction benefits obtained from these systems are not nullified or even outweighed by new stresses at the vehicle's man-machine interfaces, the systems' control concepts must be designed to high ergonomic standards. This review seeks to identify design weaknesses in assistance systems by presenting criteria that must, on the one hand, be observed when designing the control concept of a new assistance system and, on the other hand, be applied when assessing the man-machine interfaces of assistance systems already installed in a vehicle. Currently available and future driver assistance systems are evaluated against recognized usability criteria, and their weak points are discussed.     

Traumatic brain injury,driver aggression and motor vehicle collisions in Canadian adults

ObjectiveThis study examines the associations between lifetime traumatic brain injury (TBI), driver aggression, and motor vehicle collisions among a population sample of adults who reside in the province of Ontario, Canada.MethodA cross-sectional sample of 3993 Ontario adults, aged 18–97 were surveyed by telephone in 2011 and 2012 as part of Center for Addiction and Mental Health’s ongoing representative survey of adult mental health and substance use in Canada. TBI was defined as trauma to the head that resulted in loss of consciousness for at least five minutes or overnight hospitalization.ResultsAn estimated 91% (95% CI: 90.0, 91.9) of individuals in this sample held a valid Ontario driver’s license at the time of testing. Among those, 16.7% reported a history of lifetime TBI and 83.3% reported no TBI. The prevalence of TBI was higher among men than women. Relative to licensed adults without TBI, adults with a history of TBI had significantly higher odds of engaging in serious driver aggression in the past 12 months, such as making threats to hurt another driver, passenger or their vehicle (AOR = 4.39). These individuals also reported significantly higher odds (AOR = 1.74) of being involved in a motor vehicle collision that resulted in hurting themselves, their passenger(s) or their vehicle.ConclusionThis is the first population-based study to demonstrate a relationship between a history of TBI and higher rates of serious driver aggression and collision involvement. Given the large proportion of adult drivers with a history of TBI, these individuals may account for a disproportion burden of all traffic safety problems. Whether the increased road safety risk of adults with a history of TBI is reflective of neurocognitive deficits or is merely evidence of a cluster of unsafe activities produced by a higher risk lifestyles requires further research attention.     

What determines the take-over time? An integrated model approach of driver take-over after automated driving

In recent years the automation level of driver assistance systems has increased continuously. One of the major challenges for highly automated driving is to ensure a safe driver take-over of the vehicle guidance. This must be ensured especially when the driver is engaged in non-driving related secondary tasks. For this purpose it is essential to find indicators of the driver’s readiness to take over and to gain more knowledge about the take-over process in general.     

Modernization of the Driver Behaviour Questionnaire

IntroductionThe current study builds on previous versions of the Driver Behaviour Questionnaire (DBQ) by incorporating a larger sample of driving behaviors targeting inattention, distraction, aggressive driving, and health related to aging. The goals of this study were to determine if the resulting factor structure was consistent with a more contemporary view of unsafe driving behaviors, and to determine whether scores on the factors could predict self-reported collisions and police citations.MethodsThe instrument was given to a sample of 3295 drivers ranging in age from 19 to 80+ years old. It was divided in two sections, the first to provide demographic information and driving history data and the second containing 105 driver behavior questions.ResultsAn exploratory factor analysis resulted in a 65-item scale organized in four factors. The factors were labeled tentatively as Inattention Errors, Age-Related Problems, Distraction and Hurry, and Aggressive Violations. Regression analyses showed that the factors were predictors of self-reported, at-fault collisions and police citations.Practical implicationsThe factor scores found in this research are consistent with a useful theoretical framework for understanding unsafe driver actions, and demonstrate some potential to identify several individual difference variables that predict self-reported collisions and citations.     

Does gender moderate the relationship between driver aggression and its risk factors?

Aim

The current study assessed gender as a potential moderator of the relationship between self-reported driver aggression and various demographic variables, general and driving-related risk factors.

Methods

Using data from a general-population telephone survey conducted from July 2002 through June 2005, two approaches to binary logistic regression were adopted. Based on the full dataset (n = 6259), the initial analysis was a hierarchical-entry regression examining self-reported driver aggression in the last 12 months. All demographic variables (i.e., gender, age, income, education, marital status), general risk factors (i.e., psychological distress, binge drinking, cannabis use), and driving-related risk factors (i.e., driving exposure, stressful driving, exposure to busy roads, driving after drinking, driving after cannabis use) were entered in the first block, and all two-way interactions with gender were entered stepwise in the second block. The subsequent analysis involved dividing the sample by gender and conducting logistic regressions with main effects only for males (n = 2921) and females (n = 3338) separately.

Results

Although the prevalence of driver aggression in the current sample was slightly higher among males (38.5%) than females (32.9%), the difference was small, and gender did not enter as a significant predictor of driver aggression in the overall logistic regression. In that analysis, difficulty with social functioning and being older were associated with a reduced risk of driver aggression. Marital status and education were unrelated to aggression, and all other variables were associated with an increased risk of aggression. Gender was found to moderate the relationships between driver aggression and only three variables: income, psychological distress, and driving exposure. Separate analyses on the male and female sub-samples also found differences in the predictive value of income and driving exposure; however, the difference for psychological distress could not be detected using this separate regression approach. The secondary analysis also identified slight differences in the predictive value of four of the risk factors, where the odds ratios for both males and females were in the same direction but only one of the two was statistically significant.

Conclusions

The results demonstrate the importance of conducting the gender analysis using both regression approaches. With few exceptions, factors that were predictive of driver aggression were generally the same for both male and female drivers.     

Re-conceptualising the reckless driving behaviour of young drivers

Reckless driving is a major contributing factor to road morbidity and mortality. While further research into the nature and impact of reckless driving, particularly among young people, is urgently needed, the measurement of reckless driving behaviour also requires increased attention. Three major shortcomings apparent in established measures of driver behaviour are that they do not target the full range of reckless driving behaviours, they measure characteristics other than driving behaviours, and/or they fail to categorise and label reckless driver behaviour based on characteristics of the behaviours themselves. To combat these shortcomings, this paper reports the development and preliminary validation of a new measure of reckless driving behaviour for young drivers. Exploratory factor analysis of self-reported driving data revealed four, conceptually distinct categories of reckless driving behaviour: those that increase crash-risk due to (a) distractions or deficits in perception, attention or reaction time (labelled “distracted”), (b) driving under the influence of drugs or alcohol (labelled “substance-use”), (c) placing the vehicle in an unsafe environment beyond its design expectations (labelled “extreme”), and (d) speed and positioning of the vehicle relative to other vehicles and objects (labelled “positioning”). Confirmatory factor analysis of data collected from a separate, community sample confirmed this four-factor structure. Multiple regression analyses found differences in the demographic and psychological variables related to these four factors, suggesting that interventions in one reckless driving domain may not be helpful in others.     

Analysis of traffic accident injury severity on Spanish rural highways using Bayesian networks

Several different factors contribute to injury severity in traffic accidents, such as driver characteristics, highway characteristics, vehicle characteristics, accidents characteristics, and atmospheric factors. This paper shows the possibility of using Bayesian Networks (BNs) to classify traffic accidents according to their injury severity. BNs are capable of making predictions without the need for pre assumptions and are used to make graphic representations of complex systems with interrelated components. This paper presents an analysis of 1536 accidents on rural highways in Spain, where 18 variables representing the aforementioned contributing factors were used to build 3 different BNs that classified the severity of accidents into slightly injured and killed or severely injured. The variables that best identify the factors that are associated with a killed or seriously injured accident (accident type, driver age, lighting and number of injuries) were identified by inference.     

The impact of mental health symptoms on heavy goods vehicle drivers’ performance

High levels of psychological distress in fulltime employees are prevalent (4.5% per month). Symptoms of impaired mental health include difficulties with attention, concentration, motivation, decision-making, visuo-motor control, and psychomotor reaction times. There is limited research on the impact these symptoms have on heavy goods vehicle (HGV) drivers’ performance. In this study 1324 HGV drivers were surveyed using the Depression, Anxiety, Stress Scale (DASS) and the Health and Performance at Work Questionnaire (HPQ). Depression, anxiety and stress had little effect on driver absenteeism rates or self-rated driving performance. However, severe (1.5% of drivers) and very severe (1.8% of drivers) depression was associated with an increased odds ratio (OR = 4.5 and 5.0, respectively) for being involved in an accident or near miss in the past 28 days. This odd ratio is akin to driving with a blood alcohol content of about 0.08%. Given the number of HGV vehicles and the prevalence of depression this equates to 10,950 HGV drivers with an increased statistical risk of an accident or near miss. As the impact of HGV accidents is potentially large, including loss of life, it would be sensible to extend the research findings here into an action plan.     

Automation cueing modulates cerebral blood flow and vigilance in a simulated air traffic control task

Automation cueing of impending hazards or critical signals can have both beneficial and deleterious effects on the human operator, depending on automation reliability. To assess these effects, transcranial Doppler sonography (TCD), a low-cost non-invasive procedure that allows continuous monitoring of blood flow in the left and right cerebral hemispheres, was used to examine the influence of automation cues of varying reliability on vigilance performance in a 40-min simulated air traffic control task. Four levels of automation cue reliability (100, 80 and 40% reliable cueing and a no-cue control) were combined factorially with two levels of critical signal salience (high and low). For both levels of signal salience, the detection rate of critical signals was very high and remained stable over time with 100% cue-reliability, but declined over time in the remaining cue conditions, so that, by the end of the task, performance efficiency was best in the 100% condition followed in order by the 80%, 40%, and no-cue conditions. These performance effects for cueing were very closely mirrored by changes in cerebral blood flow in the right (but not the left) hemisphere in conjunction with low salience signals. The results fit well with an attentional resource model of vigilance and show that cerebral blood flow provides a metabolic index of the utilization of information-processing resources during sustained attention. The demonstration of systematic modulation of cerebral blood flow with time on task, automation cueing and signal salience also provides strong evidence for a right hemispheric brain system that is involved in the functional control of vigilance performance over time. Measurement of the activation of this system, as a reflection of operator mental workload, can, therefore, inform the design of optimal automation cueing.     

Driver injury severity outcome analysis in rural interstate highway crashes: a two-level Bayesian logistic regression interpretation

There is a high potential of severe injury outcomes in traffic crashes on rural interstate highways due to the significant amount of high speed traffic on these corridors. Hierarchical Bayesian models are capable of incorporating between-crash variance and within-crash correlations into traffic crash data analysis and are increasingly utilized in traffic crash severity analysis. This paper applies a hierarchical Bayesian logistic model to examine the significant factors at crash and vehicle/driver levels and their heterogeneous impacts on driver injury severity in rural interstate highway crashes. Analysis results indicate that the majority of the total variance is induced by the between-crash variance, showing the appropriateness of the utilized hierarchical modeling approach. Three crash-level variables and six vehicle/driver-level variables are found significant in predicting driver injury severities: road curve, maximum vehicle damage in a crash, number of vehicles in a crash, wet road surface, vehicle type, driver age, driver gender, driver seatbelt use and driver alcohol or drug involvement. Among these variables, road curve, functional and disabled vehicle damage in crash, single-vehicle crashes, female drivers, senior drivers, motorcycles and driver alcohol or drug involvement tend to increase the odds of drivers being incapably injured or killed in rural interstate crashes, while wet road surface, male drivers and driver seatbelt use are more likely to decrease the probability of severe driver injuries. The developed methodology and estimation results provide insightful understanding of the internal mechanism of rural interstate crashes and beneficial references for developing effective countermeasures for rural interstate crash prevention.     

Investigating driver injury severity patterns in rollover crashes using support vector machine models

Rollover crash is one of the major types of traffic crashes that induce fatal injuries. It is important to investigate the factors that affect rollover crashes and their influence on driver injury severity outcomes. This study employs support vector machine (SVM) models to investigate driver injury severity patterns in rollover crashes based on two-year crash data gathered in New Mexico. The impacts of various explanatory variables are examined in terms of crash and environmental information, vehicle features, and driver demographics and behavior characteristics. A classification and regression tree (CART) model is utilized to identify significant variables and SVM models with polynomial and Gaussian radius basis function (RBF) kernels are used for model performance evaluation. It is shown that the SVM models produce reasonable prediction performance and the polynomial kernel outperforms the Gaussian RBF kernel. Variable impact analysis reveals that factors including comfortable driving environment conditions, driver alcohol or drug involvement, seatbelt use, number of travel lanes, driver demographic features, maximum vehicle damages in crashes, crash time, and crash location are significantly associated with driver incapacitating injuries and fatalities. These findings provide insights for better understanding rollover crash causes and the impacts of various explanatory factors on driver injury severity patterns.     

Neuroergonomics: Opportunities and challenges of merging cognitive neuroscience with cognitive ergonomics

As with any merger of previously unrelated scientific disciplines, the usefulness of combining cognitive neuroscience and cognitive ergonomics to create the new field of 'neuroergonomics' depends on the significance of the research subjects that could not be addressed if the two fields remained separated. Situations characterized by a scarcity of overt operator behaviour and performance represent one driving force for combining the two fields. In those situations, neurobiological indices of cognitive processes and capacities may emerge as the main, or even exclusive, source of information about the operator's cognitive status and activities. This information is needed, for example, to support adaptive aiding and dynamic function allocation in the context of human-automation cooperation. The utilization of research approaches and measurements more typical of cognitive neuroscience will benefit the field of cognitive ergonomics to the extent that multidimensional psychological constructs (such as situation awareness and mental workload) are reduced to their underlying cognitive operations which are more isomorphic with activity changes in brain regions, circuits and distributed neuronal systems. The transformation of these constructs and paradigms will, in turn, enable cognitive ergonomics to contribute to progress in the field of cognitive neuroscience.