This article presents research in developing two new performance measures for vehicle driving and cognitive workload inspired by the mathematical function of cross-correlation: one which evaluates the cumulative effect and the other which evaluates the effects of individual instances of in-vehicle interactions on driving and cognitive load.
Driving is a cognitively loading task which requires drivers' full attention and coordination of both mind and body. However, drivers often engage in side-activities which can negatively impact safety. A typical approach for analyzing the influences of side activities on driving is to conduct experiments in which various driving performance measures are collected, such as steering wheel angle and lane position. Those measures are then transformed, typically using means and variances, before being analyzed statistically; however, the problem is that hose transformations perform averaging of the acquired data, which can result in missing short, but important events (such as glances directed off-road). Consequently, statistically significant differences may not be observed between the tested conditions. Nevertheless, just because the influences of in-vehicle interactions do not show in the averages, it does not mean that they do not exist or should be neglected, especially if the nature of the interactions is such that they can be performed frequently (for example, with an infotainment system). This can create a false conclusion about the lack of influence of the tested side activity on driving. (Publisher abstract provided)
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(2012), Doctoral Dissertations. 700.