We set out to compare the utility of different representations of driving simulator time series data in the context of both supervised and unsupervised learning algorithms. Given the task of identifying similar time series; it is important to understand how a dataset of time series samples might be distributed and how effectively different methods capture the groupings of distinct behaviors. First we engineer three representations of the driving simulator data: converting them to feature vectors, using the raw time series, and rendering them as images. At which point, we introduce a novel method for comparing time series using temporal and spatial alignments. Then, we employ a battery of clustering algorithms to isolate groups of samples with similar traits and evaluate the quality of clusters produced. We also explore the performance of k-NN classifiers using the different dissimilarity measures resulting from these representations.