Relevant Physiological Indicators for Assessing Workload in Conditionally Automated Driving, Through Three-Class Classification and Regression

Meteier, Quentin; De Salis, Emmanuel; Capallera, Marine; Widmer, Marino; Angelini, Leonardo; Abou Khaled, Omar; Sonderegger, Andreas; Mugellini, Elena (2022). Relevant Physiological Indicators for Assessing Workload in Conditionally Automated Driving, Through Three-Class Classification and Regression Frontiers in Computer Science, 3 Frontiers 10.3389/fcomp.2021.775282

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In future conditionally automated driving, drivers may be asked to take over control of the car while it is driving autonomously. Performing a non-driving-related task could degrade their takeover performance, which could be detected by continuous assessment of drivers' mental load. In this regard, three physiological signals from 80 subjects were collected during 1 h of conditionally automated driving in a simulator. Participants were asked to perform a non-driving cognitive task (N-back) for 90 s, 15 times during driving. The modality and difficulty of the task were experimentally manipulated. The experiment yielded a dataset of drivers' physiological indicators during the task sequences, which was used to predict drivers' workload. This was done by classifying task difficulty (three classes) and regressing participants' reported level of subjective workload after each task (on a 0–20 scale). Classification of task modality was also studied. For each task, the effect of sensor fusion and task performance were studied. The implemented pipeline consisted of a repeated cross validation approach with grid search applied to three machine learning algorithms. The results showed that three different levels of mental load could be classified with a f1-score of 0.713 using the skin conductance and respiration signals as inputs of a random forest classifier. The best regression model predicted the subjective level of workload with a mean absolute error of 3.195 using the three signals. The accuracy of the model increased with participants' task performance. However, classification of task modality (visual or auditory) was not successful. Some physiological indicators such as estimates of respiratory sinus arrhythmia, respiratory amplitude, and temporal indices of heart rate variability were found to be relevant measures of mental workload. Their use should be preferred for ongoing assessment of driver workload in automated driving.

Item Type:	Journal Article (Original Article)
Division/Institute:	Business School > Institute for New Work Business School > Institute for New Work > New Forms of Work and Organisation Business School
Name:	Meteier, Quentin; De Salis, Emmanuel; Capallera, Marine; Widmer, Marino; Angelini, Leonardo; Abou Khaled, Omar; Sonderegger, Andreas0000-0003-0054-0544 and Mugellini, Elena
Subjects:	B Philosophy. Psychology. Religion > BF Psychology Q Science > QA Mathematics > QA75 Electronic computers. Computer science
ISSN:	2624-9898
Publisher:	Frontiers
Funders:	[UNSPECIFIED] Hasler Stiftung
Projects:	[UNSPECIFIED] AdVitam
Language:	English
Submitter:	Andreas Sonderegger
Date Deposited:	23 Feb 2022 09:05
Last Modified:	23 Feb 2022 09:05
Publisher DOI:	10.3389/fcomp.2021.775282
Uncontrolled Keywords:	automated driving, classification, driver, indicators, physiology, regression, workload, non-driving related task
ARBOR DOI:	10.24451/arbor.16505
URI:	https://arbor.bfh.ch/id/eprint/16505