A scalable method for characterizing visual search strategies: A novel application of time-series clustering to raw eye-tracking data.

Situational awareness-the ability to comprehend the surrounding environment and anticipate future events-is necessary for safely avoiding hazards while performing a task (e.g., driving a car without crashing). Good situational awareness relies on sufficient visual search of the environment, known as visual search strategy. Insufficient visual search can result in a failure to detect hazards, such as a pedestrian entering the roadway from an obstructed crosswalk. To date, the study of visual search strategy remains limited to small cohorts and short experimental time periods due to the conventional labor-intensive approach of characterizing visual search strategy: video coding. To address this limitation, this study presents an efficient, scalable, machine learning approach to characterize visual search strategy: time series clustering. To demonstrate the feasibility of this novel approach, time series clustering was applied to eye-tracking during driving in a simulated environment. Eye-tracking data were collected from a cohort of young (16-24 years) drivers (n = 36) during a virtual driving assessment of performance. Raw time-series eye-tracking data were compared using dynamic localized coordinate aligned warping (DCLAW), an extension of dynamic time warping. Visual search strategy clusters were identified using k-medoids unsupervised clustering. Characteristics of the visual search strategy clusters were defined by review of the cluster medoids. Time series clustering successfully identified generalizable visual search strategies during a curved roadway driving scenario. This methodology can reduce the need for manual preprocessing of raw eye-tracking data, allowing for analysis of larger, more generalizable datasets to study visual search strategy.