Detecting short periods of elevated workload. A compari­son of nine workload assessment techniques

The present experiment tested the merits of 9 common workload assessment techniques with relatively short periods of workload in a car-driving task. Twelve participants drove an instrumented car and performed a visually loading task and a mentally loading task for 10, 30, and 60 s. The results show that 10-s periods of visual and mental workload can be measured successfully with subjective ratings and secondary task performance. With respect to longer loading periods (30 and 60 s), steering frequency was found to be sensitive to visual workload, and skin conductance response (SCR) was sensitive to mental workload. The results lead to preliminary guidelines that will help applied researchers to determine which techniques are best suited for assessing visual and mental workload

A proposed psychological model of driving automation

This paper considers psychological variables pertinent to driver automation. It is anticipated that driving with automated systems is likely to have a major impact on the drivers and a multiplicity of factors needs to be taken into account. A systems analysis of the driver, vehicle and automation served as the basis for eliciting psychological factors. The main variables to be considered were: feed-back, locus of control, mental workload, driver stress, situational awareness and mental representations. It is expected that anticipating the effects on the driver brought about by vehicle automation could lead to improved design strategies. Based on research evidence in the literature, the psychological factors were assembled into a model for further investigation

Timesharing performance as an indicator of pilot mental workload

Attentional deficits (workloads) were evaluated in a timesharing task. The results from this and other experiments were incorporated into an expert system designed to provide workload metric selection advice to non-experts in the field interested in operator workload

The evaluation of pilots performance and mental workload by eye movement

Pilots make important decisions often using ambiguous information, while under stresses and with very little time. During flight operations detecting the warning light of system failure is a task with real-world application relates to measurement of pilot's performance and eye movement. The demand for a pilot’s visual and situational awareness in multiple tasks can be detrimental during pilots’ mental overload conditions. The purpose of this research is to evaluate the relationship between pilot’s mental workload and operational performance by eye tracking. Collecting eye movement data during flight operations in a virtual reality of flight simulator provided useful information to analysis participants’ cognitive processes. There were 36 pilots participated in this research, the experience of flight hours between 320 and 2,920, the range of age between 26 and 51 years old. The apparatus included Applied Science Laboratories (ASL) eye tracking, IDF flight simulator and NASA_TLX for data collection. The results show that pilots with high SA detecting hydraulic malfunction have shorter total fixation duration on Air Speed Indicator and longer total fixation duration on Altitude Indicator, Vertical Speed Indicator, Right multi-display and Left multi-display compared with pilots without detecting the signal of hydraulic malfunction. Pilots’ total fixation time on Integration Control Panel, Altitude Indicator, Attitude Indicator and Right Multi-display, and pilots’ subjective rating on NASA-TLX effort dimension for the mission of close pattern have significant relationship with pilots’ performance on the operational time for completing the tactic mission. Experienced pilots operate aircraft familiar with monitoring Airspeed Indicator and kinetic maneuvering result in less fuel consumption. This study could provide guidelines for future training design to reduce pilots mental workload and improve situational awareness for enhancing flight safety

The dissociation of subjective measures of mental workload and performance

Dissociation between performance and subjective workload measures was investigated in the theoretical framework of the multiple resources model. Subjective measures do not preserve the vector characteristics in the multidimensional space described by the model. A theory of dissociation was proposed to locate the sources that may produce dissociation between the two workload measures. According to the theory, performance is affected by every aspect of processing whereas subjective workload is sensitive to the amount of aggregate resource investment and is dominated by the demands on the perceptual/central resources. The proposed theory was tested in three experiments. Results showed that performance improved but subjective workload was elevated with an increasing amount of resource investment. Furthermore, subjective workload was not as sensitive as was performance to differences in the amount of resource competition between two tasks. The demand on perceptual/central resources was found to be the most salient component of subjective workload. Dissociation occurred when the demand on this component was increased by the number of concurrent tasks or by the number of display elements. However, demands on response resources were weighted in subjective introspection as much as demands on perceptual/central resources. The implications of these results for workload practitioners are described

Pilot workload and fatigue: A critical survey of concepts and assessment techniques

The principal unresolved issues in conceptualizing and measuring pilot workload and fatigue are discussed. These issues are seen as limiting the development of more useful working concepts and techniques and their application to systems engineering and management activities. A conceptual analysis of pilot workload and fatigue, an overview and critique of approaches to the assessment of these phenomena, and a discussion of current trends in the management of unwanted workload and fatigue effects are presented. Refinements and innovations in assessment methods are recommended for enhancing the practical significance of workload and fatigue studies

Integrating driving and traffic simulators for the study of railway level crossing safety interventions: a methodology

Safety at Railway Level Crossings (RLXs) is an important issue within the Australian transport system. Crashes at RLXs involving road vehicles in Australia are estimated to cost $10 million each year. Such crashes are mainly due to human factors; unintentional errors contribute to 46% of all fatal collisions and are far more common than deliberate violations. This suggests that innovative intervention targeting drivers are particularly promising to improve RLX safety. In recent years there has been a rapid development of a variety of affordable technologies which can be used to increase driver’s risk awareness around crossings. To date, no research has evaluated the potential effects of such technologies at RLXs in terms of safety, traffic and acceptance of the technology. Integrating driving and traffic simulations is a safe and affordable approach for evaluating these effects. This methodology will be implemented in a driving simulator, where we recreated realistic driving scenario with typical road environments and realistic traffic. This paper presents a methodology for evaluating comprehensively potential benefits and negative effects of such interventions: this methodology evaluates driver awareness at RLXs , driver distraction and workload when using the technology . Subjective assessment on perceived usefulness and ease of use of the technology is obtained from standard questionnaires. Driving simulation will provide a model of driving behaviour at RLXs which will be used to estimate the effects of such new technology on a road network featuring RLX for different market penetrations using a traffic simulation. This methodology can assist in evaluating future safety interventions at RLXs

Selecting Metrics to Evaluate Human Supervisory Control Applications

The goal of this research is to develop a methodology to select supervisory control metrics. This methodology is based on cost-benefit analyses and generic metric classes. In the context of this research, a metric class is defined as the set of metrics that quantify a certain aspect or component of a system. Generic metric classes are developed because metrics are mission-specific, but metric classes are generalizable across different missions. Cost-benefit analyses are utilized because each metric set has advantages, limitations, and costs, thus the added value of different sets for a given context can be calculated to select the set that maximizes value and minimizes costs. This report summarizes the findings of the first part of this research effort that has focused on developing a supervisory control metric taxonomy that defines generic metric classes and categorizes existing metrics. Future research will focus on applying cost benefit analysis methodologies to metric selection. Five main metric classes have been identified that apply to supervisory control teams composed of humans and autonomous platforms: mission effectiveness, autonomous platform behavior efficiency, human behavior efficiency, human behavior precursors, and collaborative metrics. Mission effectiveness measures how well the mission goals are achieved. Autonomous platform and human behavior efficiency measure the actions and decisions made by the humans and the automation that compose the team. Human behavior precursors measure human initial state, including certain attitudes and cognitive constructs that can be the cause of and drive a given behavior. Collaborative metrics address three different aspects of collaboration: collaboration between the human and the autonomous platform he is controlling, collaboration among humans that compose the team, and autonomous collaboration among platforms. These five metric classes have been populated with metrics and measuring techniques from the existing literature. Which specific metrics should be used to evaluate a system will depend on many factors, but as a rule-of-thumb, we propose that at a minimum, one metric from each class should be used to provide a multi-dimensional assessment of the human-automation team. To determine what the impact on our research has been by not following such a principled approach, we evaluated recent large-scale supervisory control experiments conducted in the MIT Humans and Automation Laboratory. The results show that prior to adapting this metric classification approach, we were fairly consistent in measuring mission effectiveness and human behavior through such metrics as reaction times and decision accuracies. However, despite our supervisory control focus, we were remiss in gathering attention allocation metrics and collaboration metrics, and we often gathered too many correlated metrics that were redundant and wasteful. This meta-analysis of our experimental shortcomings reflect those in the general research population in that we tended to gravitate to popular metrics that are relatively easy to gather, without a clear understanding of exactly what aspect of the systems we were measuring and how the various metrics informed an overall research question

Improvement of workload estimation techniques in piloting tasks

The Modified Cooper-Harper (MCH) scale has been shown to be a sensitive indicator of workload in several different types of aircrew tasks. The MCH scale, which is a 10 point scale, and five newly devised scales were examined in two different aircraft simulator experiments in which pilot loading was treated as an independent variable. The five scales included a 15 point scale, computerized versions of the MCH and 15 point scales, a scale in which the decision tree was removed, and one in which a 15 point left-to-right format was used. The results indicate that while one of the new scales may be more sensitive in a given experiment, task dependency is a problem. The MCH scale on the other hand exhibits consistent sensitivity and remains the scale recommended for general use. The MCH scale results are consistent with earlier experiments also. The results of the rating scale experiments are presented and the questionnaire results which were directed at obtaining a better understanding of the reasons for the relative sensitivity of the MCH scale and its variations are described

Effects of combining vertical and horizontal information into a primary flight display

A ground-based aircraft simulation study was conducted to determine the effects of combining vertical and horizontal flight information into a single display. Two display configurations were used in this study. The first configuration consisted of a Primary Flight Display (PFD) format and a Horizontal Situation Display (HSD) with the PFD displayed conventionally above the HSD. For the second display configuration, the HSD format was combined with the PFD format. Four subjects participated in this study. Data were collected on performance parameters, pilot-control inputs, auditory evoked response parameters (AEP), oculometer measurements (eye-scan), and heart rate. Subjective pilot opinion was gathered through questionnaire data and scorings for both the Subjective Workload Assessment Technique (SWAT) and the NASA Task Load Index (NASA-TLX). The results of this study showed that, from a performance and subjective standpoint, the combined configuration was better than the separate configuration. Additionally, both the eye-transition and eye-dwell times for the separate HSD were notably higher than expected, with a 46% increase in available visual time when going from double to single display configuration