Thomas Kundinger, Andreas Riener, Nikoletta Sofra, Klemens Weigl,
"Drowsiness Detection and Warning in Manual and Automated Driving: Results from Subjective Evaluation"
: AutomotiveUI '18 Proceedings of the 10th International Conference on Automotive User Interfaces and Interactive Vehicular Applications, ACM DL, Seite(n) 229-236, 9-2018
Original Titel:
Drowsiness Detection and Warning in Manual and Automated Driving: Results from Subjective Evaluation
Sprache des Titels:
Englisch
Original Buchtitel:
AutomotiveUI '18 Proceedings of the 10th International Conference on Automotive User Interfaces and Interactive Vehicular Applications
Original Kurzfassung:
Drowsiness is a main cause of serious traffic accidents, and problematic within the ongoing automation of the driving task. Several approaches for drowsiness detection have been published and are in operation in production cars for manual driving. To assess differences in the development of drowsiness between manual and automated driving, and to further investigate the potential of subjective ratings, we conducted a driving simulator study (N=30). The self-assessment was based on the Karolinska Sleepiness Scale (KSS), during and after driving. Furthermore, we examined the impact of travel time and driver age (20-25, 65-70 years). Results confirm that driving mode and travel time have a significant effect on the development of drowsiness. In both age groups, self-ratings were higher for automated driving and particularly by younger subjects. All subjects estimated themselves drowsier during driving. The gained knowledge can be helpful for the development of future driver-vehicle interfaces in driver drowsiness detection.
Forschungs-