Abstract
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.
| Original language | English |
|---|---|
| Title of host publication | AutomotiveUI '18 Proceedings of the 10th International Conference on Automotive User Interfaces and Interactive Vehicular Applications |
| Publisher | ACM DL |
| Pages | 229-236 |
| Number of pages | 8 |
| DOIs | |
| Publication status | Published - Sept 2018 |
Fields of science
- 102 Computer Sciences
- 102009 Computer simulation
- 102013 Human-computer interaction
- 102019 Machine learning
- 102021 Pervasive computing
- 102022 Software development
- 102025 Distributed systems
JKU Focus areas
- Computation in Informatics and Mathematics
- Engineering and Natural Sciences (in general)
