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Inadequate situation awareness and response are increasingly recognized as prevalent critical errors that lead to young driver crashes. To identify and assess key indicators of young driver performance (including situation awareness), we previously developed and validated a Simulated Driving Assessment (SDA) in which drivers are safely and reproducibly exposed to a set of common and potentially serious crash scenarios. Many of the standardized safety measures can be calculated in near real-time from simulator variables. Assessment of situation awareness, however, largely relies on time-consuming data reduction and video coding. Therefore, the objective of this research was to develop a near real-time automated method for analyzing general direction and location of driver's gaze in order to assess situation awareness.

Driving simulators provide a safe, highly reproducible environment in which to assess driver behavior. Nevertheless, data reduction to standardized metrics can be time-consuming and cumbersome. Further, the validity of the results is challenged by inconsistent definitions of metrics, precluding comparison across studies and integration of data. No established tool has yet been made available and kept current for the systematic reduction of literature-derived safety metrics. The long term goal of this work is to develop DriveLab, a set of widely applicable routines for reducing simulator data to expert-approved metrics. Since Matlab™ is so widely used in the research community, it was chosen as a suitable environment. This paper aims to serve as a case study of data reduction techniques and programming choices that were made for simulator analysis of a specific research project, the Simulated Driving Assessment.