Driver drowsiness evaluation by means of thermal infrared imaging: preliminary results

Cardone, Daniela; Filippini, Chiara; Mancini, Lorenza; Pomante, A.; Tritto, Michele; Nocco, Sergio; Perpetuini, David

doi:10.1117/12.2594504

Cited by 7 publications

(2 citation statements)

References 17 publications

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“…Given the advantages of the use of IR imaging in psycho-physiological state monitoring, a relevant number of scientific works on the automotive research field are available. Most of these publications concern driver drowsiness/fatigue monitoring and emotional state detection [ 25 , 26 , 27 , 28 , 29 , 30 ]. Relative to drivers’ MW monitoring using thermal IR imaging, the literature is instead scarce.…”

Section: Related Workmentioning

confidence: 99%

Classification of Drivers’ Mental Workload Levels: Comparison of Machine Learning Methods Based on ECG and Infrared Thermal Signals

Cardone

Perpetuini

Filippini

et al. 2022

Sensors

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Mental workload (MW) represents the amount of brain resources required to perform concurrent tasks. The evaluation of MW is of paramount importance for Advanced Driver-Assistance Systems, given its correlation with traffic accidents risk. In the present research, two cognitive tests (Digit Span Test—DST and Ray Auditory Verbal Learning Test—RAVLT) were administered to participants while driving in a simulated environment. The tests were chosen to investigate the drivers’ response to predefined levels of cognitive load to categorize the classes of MW. Infrared (IR) thermal imaging concurrently with heart rate variability (HRV) were used to obtain features related to the psychophysiology of the subjects, in order to feed machine learning (ML) classifiers. Six categories of models have been compared basing on unimodal IR/unimodal HRV/multimodal IR + HRV features. The best classifier performances were reached by the multimodal IR + HRV features-based classifiers (DST: accuracy = 73.1%, sensitivity = 0.71, specificity = 0.69; RAVLT: accuracy = 75.0%, average sensitivity = 0.75, average specificity = 0.87). The unimodal IR features based classifiers revealed high performances as well (DST: accuracy = 73.1%, sensitivity = 0.73, specificity = 0.73; RAVLT: accuracy = 71.1%, average sensitivity = 0.71, average specificity = 0.85). These results demonstrated the possibility to assess drivers’ MW levels with high accuracy, also using a completely non-contact and non-invasive technique alone, representing a key advancement with respect to the state of the art in traffic accident prevention.

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Section: Related Workmentioning

confidence: 99%

Classification of Drivers’ Mental Workload Levels: Comparison of Machine Learning Methods Based on ECG and Infrared Thermal Signals

Cardone

Perpetuini

Filippini

et al. 2022

Sensors

Self Cite

View full text Add to dashboard Cite

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“…A longer time window was deemed necessary for eye-tracking measures given that, for example, the average blink frequency was recorded to be around 10 times/minute in De Padova et al ( 29 ) and 24 times/minute in our dataset. Thus, a 10-s window size was chosen to provide a long enough period for reliable eye-tracking data extraction, but not too long compared with the entire data extraction period for each level of cognitive load (100 s), although earlier research used a longer time window for PERCLOS (30 s in Cardone et al [ 30 ] and 1 min in Rodríguez-Ibáñez et al [ 31 ]).…”

Section: Data Processing and Model Trainingmentioning

confidence: 99%

Classification of Driver Cognitive Load: Exploring the Benefits of Fusing Eye-Tracking and Physiological Measures

Wang

Khalil

et al. 2022

Transportation Research Record: Journal of the Transportation R

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In-vehicle infotainment systems can increase cognitive load and impair driving performance. These effects can be alleviated through interfaces that can assess cognitive load and adapt accordingly. Eye-tracking and physiological measures that are sensitive to cognitive load, such as pupil diameter, gaze dispersion, heart rate (HR), and galvanic skin response (GSR), can enable cognitive load estimation. The advancement in cost-effective and nonintrusive sensors in wearable devices provides an opportunity to enhance driver state detection by fusing eye-tracking and physiological measures. As a preliminary investigation of the added benefits of utilizing physiological data along with eye-tracking data in driver cognitive load detection, this paper explores the performance of several machine learning models in classifying three levels of cognitive load imposed on 33 drivers in a driving simulator study: no external load, lower difficulty 1-back task, and higher difficulty 2-back task. We built five machine learning models, including k-nearest neighbor, support vector machine, feedforward neural network, recurrent neural network, and random forest (RF) on (1) eye-tracking data only, (2) HR and GSR, (3) eye-tracking and HR, (4) eye-tracking and GSR, and (5) eye-tracking, HR, and GSR. Although physiological data provided 1%–15% lower classification accuracies compared with eye-tracking data, adding physiological data to eye-tracking data increased model accuracies, with an RF classifier achieving 97.8% accuracy. GSR led to a larger boost in accuracy (29.3%) over HR (17.9%), with the combination of the two factors boosting accuracy by 34.5%. Overall, utilizing both physiological and eye-tracking measures shows promise for driver state detection applications.

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A systematic review of physiological signals based driver drowsiness detection systems

et al. 2022

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Driver drowsiness evaluation by means of thermal infrared imaging: preliminary results

Cited by 7 publications

References 17 publications

Classification of Drivers’ Mental Workload Levels: Comparison of Machine Learning Methods Based on ECG and Infrared Thermal Signals

Classification of Drivers’ Mental Workload Levels: Comparison of Machine Learning Methods Based on ECG and Infrared Thermal Signals

Classification of Driver Cognitive Load: Exploring the Benefits of Fusing Eye-Tracking and Physiological Measures

A systematic review of physiological signals based driver drowsiness detection systems

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