Comparison of Two Eye-Gaze Based Real-Time Driver Distraction Detection Algorithms in a Small-Scale Field Operational Test

Abstract

Summary: Driver distraction is a field which has received increasing attention in the last years, especially after it became evident that distraction is a major factor contributing to road casualties. Monitoring, detecting and limiting driver distraction could contribute significantly to improve road traffic safety. With the introduction of novel unobtrusive gaze-tracking systems real-time algorithms based on the driver's gaze direction can be developed for driver distraction warning systems. The study describes and compares two different algorithms for gaze-based driver distraction detection based on the eye tracking data obtained in a field study. One algorithm relies on the metric "percent road centre" of gaze direction, the other on gaze zones in the vehicle. Results show that both algorithms have potential for detecting driver distraction, but that no effect of the distraction warnings on attention as defined by the algorithms could be observed.

Analysis of Temporal Relationships between Eye Gaze and Peripheral Vehicle Behavior for Detecting Driver Distraction

“…We consider that the participants frequently looked at the speedometer or navigation system while performing the neutral task. This behavior agrees with the prior findings of PRC [13]. In the temporal section without the overtaking event, PRC needs to be addressed to detect cognitive distraction.…”

“…As this is for testing our hypothesis (see Section 4.3), we limited the time interval for discrimination to from = 0 (= 0) to = 2(s). Here, we employed a baseline method based on the percentage road center (PRC) [13], that is, proportion of total gaze duration toward road scene ahead to total time of sample events, which detected C and C by thresholding PRC at ; that is, if PRC was larger than , the method determined the data as distraction state C ; otherwise, neutral state C was assigned. The thresholds for E and E were set to 79.0%, by searching for an equal rate between detection of C and C .…”

“…Miyaji et al reported that the standard deviations of eye movement and head movement could be suitable for detecting cognitive distraction that caused gaze concentration and slow saccades when drivers looked at the roadway [12]. Kircher et al indicated the percentage of time that the driver spent observing the road ahead, which is called the percentage road center (PRC) of gaze direction, was more than 92% under cognitive distraction in a field study [13]. Johansson et al have reviewed the existing gaze-based techniques and metrics for analyzing visual and cognitive distractions [8].…”

Driving context and visual-manual phone tasks influence glance behavior in naturalistic driving

“…Several studies, such as Kircher, Ahlström, andKircher (2009) andVictor (2010), have proposed distraction detection algorithms based on drivers' eye glance behavior. The present study shows that driving context variables such as curvature and the presence of other vehicles should be added to existing algorithms, to improve the detection and assessment of visual distraction.…”

“…Furthermore, the modest influence of driving speed on off-road glance duration may also have some implications for the development of future algorithms. Some of the existing algorithms only assess driver distraction above a certain speed, such as 40 or 50 km/h (Kircher et al, 2009;Victor, 2010). The results suggest that it might not be necessary to take a speed dependency into account, thus enabling algorithms to include a larger range of driving conditions.…”