A number of researchers have attempted to model human driving or flying skills (e.g., acceleration, steering, vehicle following, etc) in an effort to develop robotic or simulated driver models. In these applications, validation consists of comparing the source data to the model output; thus, it is assumed that model fidelity is correlated with similarity to true human performance. This paper reviews some of the validation metrics found in the literature and discusses the limitations of these metrics. It also presents an alternative metric designed to mitigate these limitations and the test-bed designed to derive this metric. Finally, it illustrates the application of this metric to a number of different trajectory models built through a variety of modeling techniques (e.g., Kalman filters, neural networks, and Newtonian equations).