@article{ref1,
title="Comparing eye-tracking metrics of mental workload caused by NDRTs in semi-autonomous driving",
journal="Transportation research part F: traffic psychology and behaviour",
year="2022",
author="Chen, Weiya and Sawaragi, Tetsuo and Hiraoka, Toshihiro",
volume="89",
number="",
pages="109-128",
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.  Keywords: Driver distraction <p /> <p>Language: en</p>",
language="en",
issn="1369-8478",
doi="10.1016/j.trf.2022.05.004",
url="http://dx.doi.org/10.1016/j.trf.2022.05.004"
}