Both ethnographers and engineers deal with contextual and situational complexity in human behavior. For engineers, this complexity creates challenges in designing systems to support humans that are useful and predictable across a wide range of contexts because of the difficulty to analyze and model the contextual impact on observable behavior. The rich descriptive understanding found in the annotated case studies of ethnography could be of substantial use here, however, the ethnographer’s language is considerably different from that of the causal systems and input/output models typical of engineering. Through a unique collaboration between automotive engineers and UCSD ethnographers, we employed a common graphical state-space based modeling language called Petri nets that enables annotated case studies to be represented in a computational framework that can be used in standard engineering practices. The ethnographer’s goal was to explain and quantify the importance of context on driver behavior such that the engineers would be better able to design useful human-centered support systems and to assess whether those necessarily practically constrained support systems will most likely function as expected across a wide range of situations. This paper offers a discussion of the issues that arise in bridging the gap between engineering and ethnographic practice in the context of lane change behavior analysis.