  In this work, we propose a method for learning driver models that account for
variables that cannot be observed directly. When trained on a synthetic
dataset, our models are able to learn encodings for vehicle trajectories that
distinguish between four distinct classes of driver behavior. Such encodings
are learned without any knowledge of the number of driver classes or any
objective that directly requires the models to learn encodings for each class.
We show that driving policies trained with knowledge of latent variables are
more effective than baseline methods at imitating the driver behavior that they
are trained to replicate. Furthermore, we demonstrate that the actions chosen
by our policy are heavily influenced by the latent variable settings that are
provided to them.
