A workload model to evaluate distracters and driver's aids

Kandemir, Cansu; Handley, Holly A. H.; Thompson, Dana

doi:10.1016/j.ergon.2016.09.004

Cited by 12 publications

(10 citation statements)

References 12 publications

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“…The model assigns workload values to the driver depending on the task type, the interface used, and the level of different human mental resources required. The simulation results provide insights into the workload levels of the driver under these conditions and the likelihood for driver overload (Kandemir et al, 2018). To cross compare data across experimental settings, a framework is required to ensure that the resulting data are comparable.…”

Section: Discussionmentioning

confidence: 99%

Blink Rate as a Measure of Driver Workload during Simulated Driving

Yahoodik

Tahami

Unverricht

et al. 2020

Proceedings of the Human Factors and Ergonomics Society Annual

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Cognitive distraction has been identified as a critical factor for automobile crashes in the United States. Previous research indicates that cognitive tasks such as phone conversations may take attention away from the driving task, degrading driving performance. One continuing issue with cognitive distraction research is establishing a reliable, continuous, online measure of driver workload. Blink rate is a potential candidate for such a measure. The present driving simulator experiment examines whether blink rate is sensitive to variations of driver workload while controlling the vehicle. Participants will navigate simulated environments while performing the auditory 0-and 2-back task with their blink and driving performance data continuously recorded during the drive. Blink and performance data will be compared not only between the different workload conditions, but also to predictions of a human driver performance workload model based on the Improved Performance Research Integration Tool (IMPRINT), developed by the US Army Research Laboratory.

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Section: Discussionmentioning

confidence: 99%

Blink Rate as a Measure of Driver Workload during Simulated Driving

Yahoodik

Tahami

Unverricht

et al. 2020

Proceedings of the Human Factors and Ergonomics Society Annual

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“…These are all indicators that the driver's mental workload has been taxed and may impact the ability to safely operate the vehicle. Kandemir, Handley & Thompson (2018) created a computational model to provide commercial vehicle interface designers a tool to explore the impact of the different driving tasks on driver performance. As vehicles increase the number of technologies embedded in the primary and secondary displays in vehicle dashboards, model simulations can be used to confirm that a driver's workload remains below a set threshold.…”

Section: Mental Workload As a Surrogate For Driver Distractionmentioning

confidence: 99%

“…The computational model assigned workload values to the driver depending on the task type, the interface used, and the level of different human mental resources required. The simulation results provided insights into the workload levels of the driver under different task combinations and the likelihood of driver overload (Kandemir et al, 2018).…”

Section: Mental Workload As a Surrogate For Driver Distractionmentioning

confidence: 99%

“…These models can then be used to evaluate increases in workload caused by different driving distractors. For example, a computational model was developed as part of a driving study to identify combinations of tasks that contributed to the peak workload of drivers (Kandemir, Handley, & Thompson, 2018). The model was used to evaluate in-cab HMI designs by identifying greater workload levels at specific time points during driving sequences; modifications could then be made to the HMI design to reduce workload and improve driver performance.…”

Section: Introductionmentioning

confidence: 99%

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Computational Models for Workload Analysis of Driving Tasks

Handley

Thompson

2021

Proceedings of the Human Factors and Ergonomics Society Annual

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This paper describes a methodology to design computational models to evaluate the workload for driving tasks. A computational model was configured for a driving scenario used in a pilot study that included a secondary task at varying levels of difficulty to increase the driver’s workload. The computational model results provided a workload analysis of the concurrent driving tasks. This analysis can be used to explain the experimental findings from subject experiments and to evaluate the workload trade-offs between primary and secondary driving tasks.

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“…For example, eye movements are faster and heart rate (HR) is higher in stressful conditions ( Healey and Picard, 2005 ). Even if the driver is not stressed, the increased workload can increase the probability of other HDS such as distraction ( Hwang et al, 2014 ; Kandemir et al, 2016 ). Thus, many studies have used workload as an indirect indicator of driver impairment ( Čegovnik et al, 2018 ; Rusnock and Borghetti, 2018 ).…”

Section: Introductionmentioning

confidence: 99%

Identifying the Causes of Drivers’ Hazardous States Using Driver Characteristics, Vehicle Kinematics, and Physiological Measurements

et al. 2018

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Drivers’ hazardous physical and mental states (e.g., distraction, fatigue, stress, and high workload) have a major effect on driving performance and strongly contribute to 25–50% of all traffic accidents. They are caused by numerous factors, such as cell phone use or lack of sleep. However, while significant research has been done on detecting hazardous states, most studies have not tried to identify the causes of the hazardous states. Such information would be very useful, as it would allow intelligent vehicles to better respond to a detected hazardous state. Thus, this study examined whether the cause of a driver’s hazardous state can be automatically identified using a combination of driver characteristics, vehicle kinematics, and physiological measures. Twenty-one healthy participants took part in four 45-min sessions of simulated driving, of which they were mildly sleep-deprived for two sessions. Within each session, there were eight different scenarios with different weather (sunny or snowy), traffic density and cell phone usage (with or without cell phone). During each scenario, four physiological (respiration, electrocardiogram, skin conductance, and body temperature) and eight vehicle kinematics measures were monitored. Additionally, three self-reported driver characteristics were obtained: personality, stress level, and mood. Three feature sets were formed based on driver characteristics, vehicle kinematics, and physiological signals. All possible combinations of the three feature sets were used to classify sleep deprivation (drowsy vs. alert), traffic density (low vs. high), cell phone use, and weather conditions (foggy/snowy vs. sunny) with highest accuracies of 98.8%, 91.4%, 82.3%, and 71.5%, respectively. Vehicle kinematics were most useful for classification of weather and traffic density while physiology and driver characteristics were useful for classification of sleep deprivation and cell phone use. Furthermore, a second classification scheme was tested that also incorporates information about whether or not other causes of hazardous states are present, though this did not result in higher classification accuracy. In the future, these classifiers could be used to identify both the presence and cause of a driver’s hazardous state, which could serve as the basis for more intelligent intervention systems.

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A workload model to evaluate distracters and driver's aids

Cited by 12 publications

References 12 publications

Blink Rate as a Measure of Driver Workload during Simulated Driving

Blink Rate as a Measure of Driver Workload during Simulated Driving

Computational Models for Workload Analysis of Driving Tasks

Identifying the Causes of Drivers’ Hazardous States Using Driver Characteristics, Vehicle Kinematics, and Physiological Measurements

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