Abstract
We investigate the pupil response to hazard perception during driving simulation. Comple-mentary to gaze movement and physiological stress indicators, pupil size changes can pro-vide valuable information on traffic hazard perception with a relatively low temporal delay. We tackle the challenge of identifying those pupil dilation events associated with hazardous events from a noisy signal by a combination of wavelet transformation and machine learn-ing. Therefore, we use features of the wavelet components as training data of a support vector machine. We further demonstrate how to utilize the method for the analysis of actual hazard perception and how it may differ from the behavioral driving response.
Original language | English |
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Article number | 3 |
Journal | Journal of Eye Movement Research |
Volume | 10 |
Issue number | 4 |
DOIs | |
State | Published - 2017 |
Externally published | Yes |
Keywords
- Attention
- Driving
- Pupil diameter
- Stress indicators
- Supervised classification
- Visual field defect
- Wavelets