Screening Naturalistic Driving Study Data for Safety-Critical Events

Abstract

This study responds to the need to screen events observed during naturalistic driving studies to derive a set of crashes and near crashes with common etiologies; these crashes are referred to as “well-defined surrogate events.” Two factors are critical to the identification of these well-defined surrogate events: selection of screening criteria and the designation of a time window to be used for event search. Testing conducted by using an algorithm developed in a previous study is described. The algorithm allows for the use of a range of search criteria to identify events with common etiology from unrefined naturalistic driving data. A range of kinematic search criteria was used to screen events, including lateral and longitudinal accelerations averaged over different time windows and characterized by average as well as maximum values during a time window. The testing was conducted with data from road departure events collected during a concluded 100-car naturalistic driving study. Fifty-one nonintersection and 12 intersection-related run-off-road events were included in the testing. Different sets of events were identified with different search criteria and different time windows. Diagnostic tools borrowed from medicine identified the best screening criteria and time windows. The methods allowed for enhanced identification of well-defined surrogates by using covariates such as driver attribute context and driver fatigue. The research illustrates a flexible procedure that uses a variety of statistical methods shown to effectively screen crashes and near crashes.