Abstract
The main objective of this study was to examine driver demographic, anthropometric, and functional characteristics that may influence the ability of drivers in identifying critical targets (e.g., emergency vehicles) in the rear quarter blind spots while changing lane. This research employed a cost-effective approach using a consumer grade virtual reality apparatus to create driving scenarios. The experimental task was vehicle identification in various blind spots during a driving simulation. Logistic regression models were constructed to identify characteristics that were associated with critical target identification failures. Results from this experimental task indicated that factors contributed to vehicle identification error were age (p < 0.01) and degree of functional rotation (p < 0.1). The effect of participants’ baseline neck range of motion was statistically insignificant in the logistic regression models, which suggests that age and degree of functional rotation played a larger role in vehicle identification for this task. Findings may contribute to the design of related training and education programs for drivers in the future.
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