000011319 001__ 11319
000011319 005__ 20230905152721.0
000011319 022__ $$a2624-9898
000011319 0247_ $$2DOI$$a10.3389/fcomp.2021.775282
000011319 037__ $$aARTICLE
000011319 039_9 $$a2023-09-05 15:27:21$$b1$$c2023-06-06 10:13:40$$d1001252$$c2022-11-23 15:17:17$$d0$$c2022-11-23 08:53:20$$d1000062$$c2022-11-22 14:57:16$$d1000099$$y2022-11-22 13:46:22$$z1000099
000011319 041__ $$aeng
000011319 245__ $$aRelevant physiological indicators for assessing workload in conditionally automated driving, through three-class classification and regression
000011319 269__ $$a2022-01
000011319 300__ $$a23 p.
000011319 506__ $$avisible
000011319 520__ $$9eng$$aIn future conditionally automated driving, drivers may be asked to take over control of the car while it is driving autonomously. Performing a non-driving-related task could degrade their takeover performance, which could be detected by continuous assessment of drivers' mental load. In this regard, three physiological signals from 80 subjects were collected during 1 h of conditionally automated driving in a simulator. Participants were asked to perform a non-driving cognitive task (N-back) for 90 s, 15 times during driving. The modality and difficulty of the task were experimentally manipulated. The experiment yielded a dataset of drivers' physiological indicators during the task sequences, which was used to predict drivers' workload. This was done by classifying task difficulty (three classes) and regressing participants' reported level of subjective workload after each task (on a 0–20 scale). Classification of task modality was also studied. For each task, the effect of sensor fusion and task performance were studied. The implemented pipeline consisted of a repeated cross validation approach with grid search applied to three machine learning algorithms. The results showed that three different levels of mental load could be classified with a f1-score of 0.713 using the skin conductance and respiration signals as inputs of a random forest classifier. The best regression model predicted the subjective level of workload with a mean absolute error of 3.195 using the three signals. The accuracy of the model increased with participants' task performance. However, classification of task modality (visual or auditory) was not successful. Some physiological indicators such as estimates of respiratory sinus arrhythmia, respiratory amplitude, and temporal indices of heart rate variability were found to be relevant measures of mental workload. Their use should be preferred for ongoing assessment of driver workload in automated driving.
000011319 540__ $$acorrect
000011319 592__ $$aHEIA-FR
000011319 592__ $$aHE-Arc Ingénierie
000011319 592__ $$bHumanTech - Technology for Human Wellbeing Institute
000011319 592__ $$cIngénierie et Architecture
000011319 6531_ $$9eng$$aautomated driving
000011319 6531_ $$9eng$$aclassification
000011319 6531_ $$9eng$$adriver
000011319 6531_ $$9eng$$aindicators
000011319 6531_ $$9eng$$aphysiology
000011319 6531_ $$9eng$$aregression
000011319 6531_ $$9eng$$aworkload
000011319 6531_ $$9eng$$anon-driving related task
000011319 655__ $$ascientifique
000011319 700__ $$aMeteier, Quentin$$uSchool of Engineering and Architecture (HEIA-FR), HES-SO University of Applied Sciences Western Switzerland
000011319 700__ $$ade Salis, Emmanuel$$uSchool of Engineering – HE-Arc Ingénierie, HES-SO University of Applied Sciences Western Switzerland
000011319 700__ $$aCapallera, Marine$$uSchool of Engineering and Architecture (HEIA-FR), HES-SO University of Applied Sciences Western Switzerland
000011319 700__ $$aWidmer, Marino$$uUniversity of Fribourg, Fribourg, Switzerland
000011319 700__ $$aAngelini, Leonardo$$uSchool of Engineering and Architecture (HEIA-FR), HES-SO University of Applied Sciences Western Switzerland
000011319 700__ $$aKhaled, Omar Abou$$uSchool of Engineering and Architecture (HEIA-FR), HES-SO University of Applied Sciences Western Switzerland
000011319 700__ $$aSonderegger, Andreas$$uBern University of Applied Sciences, Bern, Switzerland
000011319 700__ $$aMugellini, Elena$$uSchool of Engineering and Architecture (HEIA-FR), HES-SO University of Applied Sciences Western Switzerland
000011319 773__ $$j2022, vol. 3, article no. 775282$$tFrontiers in Computer Science
000011319 8564_ $$yPublished version$$9b130d1c7-34a7-41e3-9d91-f63202290c0f$$s3167261$$uhttps://arodes.hes-so.ch/record/11319/files/Meteier_2022_relevant_physiological_indicators_assessing_workload_conditionally_automated_driving.pdf
000011319 906__ $$aGOLD
000011319 909CO $$ooai:hesso.tind.io:11319$$pGLOBAL_SET
000011319 950__ $$aaucun
000011319 980__ $$ascientifique
000011319 981__ $$ascientifique