Advanced systems that require shared control are becoming increasingly pervasive. One advantage of a shared control approach is that the human and machine work together to accomplish safe operations. However, data about the human is needed to implement successful strategies. The goal of this study was to quantify naturalistic driving by collecting performance and physiological data during manual, open-loop driving. Sixteen participants performed a single drive that included four sudden obstacles of increasing difficulty (road debris, construction, inclement weather, and an animal). Participants were asked to traverse each obstacle using self-employed judgement and strategies. Action selection, lane deviation, speed, and heart rate data were recorded. Results showed two distinct driving strategies for avoiding the moving obstacle/animal (left vs. right lane navigation). Also, maximum speed was affected by obstacle type, but heart rate variability was not. Results can be used to inform shared control algorithms designed to combat poor driving performance.
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