Quantitative Analysis of Eye-Tracking Data Based on Information-Theoretic Tools for Measuring Driver Drowsiness

Yueming Zhu, Qing Xu, Kai Zhen, Runlin Zhang, Shunbo Wang

Abstract: Assessing the drowsy state of driver is important for driving safety. In this paper, three kinds of information-theoretic tools are utilized to objectively and quantitatively measure the driver's detailed degrees of drowsiness. That is, fixation cross entropy (FCE) defined as the cross entropy between fixation count and duration, stationary gaze entropies based on fixation count (SGEc) and duration (SGEd) respectively, gaze transition entropy (GTE) based on fixation count are exploited to successfully obtain the drowsiness measurements. These measurements are based on eye-tracking data focusing on a novel division approach to area of interest (AOI), particularly emphasizing the significance of gaze deviation from the center point of screen. Psychophysical results showed that all the proposed measures are strongly correlated with Karolinska Sleepiness Scale (KSS), a widely used subjective measure of drowsiness. Particularly, compared with classical indicators for measuring drowsiness, FCE shows a stronger robustness under different drowsy conditions.