  A major challenge for autonomous vehicles is interacting with other traffic
participants safely and smoothly. A promising approach to handle such traffic
interactions is equipping autonomous vehicles with interaction-aware
controllers (IACs). These controllers predict how surrounding human drivers
will respond to the autonomous vehicle's actions, based on a driver model.
However, the predictive validity of driver models used in IACs is rarely
validated, which can limit the interactive capabilities of IACs outside the
simple simulated environments in which they are demonstrated. In this paper, we
argue that besides evaluating the interactive capabilities of IACs, their
underlying driver models should be validated on natural human driving behavior.
We propose a workflow for this validation that includes scenario-based data
extraction and a two-stage (tactical/operational) evaluation procedure based on
human factors literature. We demonstrate this workflow in a case study on an
inverse-reinforcement-learning-based driver model replicated from an existing
IAC. This model only showed the correct tactical behavior in 40% of the
predictions. The model's operational behavior was inconsistent with observed
human behavior. The case study illustrates that a principled evaluation
workflow is useful and needed. We believe that our workflow will support the
development of appropriate driver models for future automated vehicles.
