Find in Library

We investigate the pupil response to hazard perception during driving simulation. Complementary to gaze movement and physiological stress indicators, pupil size changes can provide 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 learning. 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.