An analysis of the suitability of a low-cost eye tracker for assessing the cognitive load of drivers

Čegovnik, Tomaž; Stojmenova, Kristina; Jakus, Grega; Sodnik, Jaka

doi:10.1016/j.apergo.2017.10.011

Cited by 72 publications

(29 citation statements)

References 13 publications

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“…Eye-tracking has been used extensively in studying visual perception and attention in driving contexts, however the unique use of pupillometry as an index of real-time physiological indicator of cognitive workload is only lately growing in popularity (Schwalm et al, 2008). For example, Cegovnik et al (2018) recently validated a low-cost eye-tracker and showed that pupil dilation increases with increments in cognitive load due to a secondary memory task (n-back) (see also Recarte and Nunes, 2000 for similar results). Pupillometry has also been adopted in driving research while motorists drove in a simulated driving context.…”

Section: Psychophysiological Measures To Assess Cognitive Statesmentioning

confidence: 92%

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

Lohani

Payne

Strayer

2019

Front. Hum. Neurosci.

224

181

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

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Section: Psychophysiological Measures To Assess Cognitive Statesmentioning

confidence: 92%

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

Lohani

Payne

Strayer

2019

Front. Hum. Neurosci.

224

181

View full text Add to dashboard Cite

show abstract

“…Pupil size, for example, increases when humans are exposed to increased cognitive demand [ 57 , 58 ]. Researchers have reported studies in which the effects of cognitively demanding tasks while driving can be detected with both low-cost and high-end eye trackers [ 24 , 59 , 60 ]. Instead of only observing the physical changes in the driver’s eyes, it has been found that cognitive distraction can also be understood from visual behaviour, such as following the driver’s gaze (eye tracking) and effects (visual tunnelling and eye fixations), as it affects the driver’s visual field horizontally and vertically, and glances at the rear mirrors or speed signs are less frequent [ 6 , 61 ].…”

Section: Alternative Methods For the Assessment Of Driver’ Cognitimentioning

confidence: 99%

Detection-Response Task—Uses and Limitations

Stojmenova

Sodnik

2018

Sensors

Self Cite

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The Detection-Response Task is a method for assessing the attentional effects of cognitive load in a driving environment. Drivers are presented with a sensory stimulus every 3–5 s, and are asked to respond to it by pressing a button attached to their finger. Response times and hit rates are interpreted as indicators of the attentional effect of cognitive load. The stimuli can be visual, tactile and auditory, and are chosen based on the type of in-vehicle system or device that is being evaluated. Its biggest disadvantage is that the method itself also affects the driver’s performance and secondary task completion times. Nevertheless, this is an easy to use and implement method, which allows relevant assessment and evaluation of in-vehicle systems. By following the recommendations and taking into account its limitations, researchers can obtain reliable and valuable results on the attentional effects of cognitive load on drivers.

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“…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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An analysis of the suitability of a low-cost eye tracker for assessing the cognitive load of drivers

Cited by 72 publications

References 13 publications

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

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

Detection-Response Task—Uses and Limitations

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

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