  In order to enable autonomous vehicles (AV) to navigate busy traffic
situations, in recent years there has been a focus on game-theoretic models for
strategic behavior planning in AVs. However, a lack of common taxonomy impedes
a broader understanding of the strategies the models generate as well as the
development of safety specification to identity what strategies are safe for an
AV to execute. Based on common patterns of interaction in traffic conflicts, we
develop a taxonomy for strategic interactions along the dimensions of agents'
initial response to right-of-way rules and subsequent response to other agents'
behavior. Furthermore, we demonstrate a process of automatic mapping of
strategies generated by a strategic planner to the categories in the taxonomy,
and based on vehicle-vehicle and vehicle-pedestrian interaction simulation, we
evaluate two popular solution concepts used in strategic planning in AVs, QLk
and Subgame perfect $\epsilon$-Nash Equilibrium, with respect to those
categories.
