Comparing eye-tracking metrics of mental workload caused by NDRTs in semi-autonomous driving. (August 2022)

• Record Type: Journal Article
• Authors: Chen, Weiya; Sawaragi, Tetsuo; Hiraoka, Toshihiro
• Is Part Of: Transportation research. Volume 89(2022)
• Page Start: 109
• Page End: 128
• Publication Date: 2022-08
• Subjects: Eye-tracking -- Autonomous driving -- Mental workload -- Multitasking; Automobile drivers -- Psychology -- Periodicals; Automobile driving -- Psychological aspects -- Periodicals; Transportation -- Psychological aspects -- Periodicals; 629.283019
• DOI: 10.1016/j.trf.2022.05.004
• Languages: English
• ISSNs: 1369-8478

Abstract

Highlights: In a driving simulator experiment, 36 participants experienced SAE Levels 0, 1, and 2 automated driving while engaging in either visual-verbal, auditory-spatial, or auditory-verbal tasks. The driver's mental workload was measured through the subjective rating of secondary-task performance and eye-tracking metrics. Different semi- autonomous levels were found to place different mental workload on the driver in both visual and auditory multi-tasking situations. Eye-tracking metrics (pupil diameter change, number of saccades, saccade duration, fixation duration, and 3D gaze entropy) were proven through correlation-matrix calculation and principal-component extraction to be effective indicators for mental-workload level prediction in both visual and auditory multi-tasking situations. The accuracy of predicting the mental-workload level using the KNN was 88.9% with bootstrapped experimental data. Abstract: The objective of this study was to verify the effectiveness of eye-tacking metrics in indicating driver's mental workload in semi-autonomous driving when the driver is engaged in different non-driving related tasks (NDRTs). A driving simulator was developed for three scenarios (high-, medium-, and low-mental workload presented by SAE (Society of Automotive Engineers) Levels 0, 1, and 2) and three uni-modality secondary tasks. Thirty-six individuals participated in the driving simulation experiment. NASA-TLX (Task Load Index), secondary task performance, and eye-tracking metrics were used as indicators of mental workload. The subjective rating using the NASA-TLX showed a main effect of autonomous level on mental workload in both visual and auditory tasks. Correlation-matrix calculation and principal-component extraction indicated that pupil diameter change, number of saccades, saccade duration, fixation duration, and 3D gaze entropy were effective indicators of a driver's mental workload in the visual and auditory multi-tasking situations of semi-autonomous driving. The accuracy of predicting the mental-workload level using the K-Nearest Neighbor (KNN) classifier was 88.9% with bootstrapped data. These results can be used to develop an adaptive multi-modal interface that issues efficient and safe takeover requests.