TY  - GEN
AB  - 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.
AD  - School of Engineering and Architecture (HEIA-FR), HES-SO University of Applied Sciences Western Switzerland
AD  - School of Engineering – HE-Arc Ingénierie, HES-SO University of Applied Sciences Western Switzerland
AD  - School of Engineering and Architecture (HEIA-FR), HES-SO University of Applied Sciences Western Switzerland
AD  - University of Fribourg, Fribourg, Switzerland
AD  - School of Engineering and Architecture (HEIA-FR), HES-SO University of Applied Sciences Western Switzerland
AD  - School of Engineering and Architecture (HEIA-FR), HES-SO University of Applied Sciences Western Switzerland
AD  - Bern University of Applied Sciences, Bern, Switzerland
AD  - School of Engineering and Architecture (HEIA-FR), HES-SO University of Applied Sciences Western Switzerland
AU  - Meteier, Quentin
AU  - de Salis, Emmanuel
AU  - Capallera, Marine
AU  - Widmer, Marino
AU  - Angelini, Leonardo
AU  - Khaled, Omar Abou
AU  - Sonderegger, Andreas
AU  - Mugellini, Elena
DA  - 2022-01
DO  - 10.3389/fcomp.2021.775282
DO  - DOI
ID  - 11319
JF  - Frontiers in Computer Science
KW  - automated driving
KW  - classification
KW  - driver
KW  - indicators
KW  - physiology
KW  - regression
KW  - workload
KW  - non-driving related task
L1  - https://arodes.hes-so.ch/record/11319/files/Meteier_2022_relevant_physiological_indicators_assessing_workload_conditionally_automated_driving.pdf
L2  - https://arodes.hes-so.ch/record/11319/files/Meteier_2022_relevant_physiological_indicators_assessing_workload_conditionally_automated_driving.pdf
L4  - https://arodes.hes-so.ch/record/11319/files/Meteier_2022_relevant_physiological_indicators_assessing_workload_conditionally_automated_driving.pdf
LA  - eng
LK  - https://arodes.hes-so.ch/record/11319/files/Meteier_2022_relevant_physiological_indicators_assessing_workload_conditionally_automated_driving.pdf
N2  - 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.
PY  - 2022-01
SN  - 2624-9898
T1  - Relevant physiological indicators for assessing workload in conditionally automated driving, through three-class classification and regression
TI  - Relevant physiological indicators for assessing workload in conditionally automated driving, through three-class classification and regression
UR  - https://arodes.hes-so.ch/record/11319/files/Meteier_2022_relevant_physiological_indicators_assessing_workload_conditionally_automated_driving.pdf
VL  - 2022, vol. 3, article no. 775282
Y1  - 2022-01
ER  -