A distraction index for quantification of driver eye glance behavior: A study using SHRP2 NEST database

•A renewal cycle method is developed to analyze how drivers allocate their attention while driving.•Two distraction measures are developed using the Naturalistic Engagement in Secondary Task data.•The two distraction measures revealed that they increase significantly for safety critical event.•Findings can help design in-vehicle driving assistance systems to alert distracted drivers. Distracted driving behavior and driving inattention are two leading causes of roadway crashes. The state-of-the-art safety research made several attempts to understand and quantify distracted driving and driver inattention. While each attempt had its limitation, there was a consensus on the relevance of eye glance behavior as a promising parameter in understanding distracted driving. In this study, a renewal cycle approach is implemented to provide deeper insights into how drivers allocate their attention while driving. This approach is then applied to the Naturalistic Engagement in Secondary Tasks (NEST) dataset to analyze drivers’ eye glance patterns and determine the relationship between their visual behavior and engagement in different types of secondary tasks (activities performed while driving). The analysis revealed that distracted driving behavior could be well characterized by two new measures: the number of renewal cycles per event (NRC) and a distraction level index (DI). Consequently, mixed-effects modeling is implemented to test the effectiveness of the two measures to differentiate crash/near-crash events from non-crash events. The analysis showed that the two measures increase significantly for crash/near-crash events compared to non-crash driving events with p-values less than 0.0001. The findings of this paper are promising to the quantification of the risk associated with distraction related visual behavior. The finding can also help build reliable algorithms for in-vehicle driving assistance systems to alert drivers before crash/near-crash events.