  Imitation learning is a promising approach to end-to-end training of
autonomous vehicle controllers. Typically the driving process with such
approaches is entirely automatic and black-box, although in practice it is
desirable to control the vehicle through high-level commands, such as telling
it which way to go at an intersection. In existing work this has been
accomplished by the application of a branched neural architecture, since
directly providing the command as an additional input to the controller often
results in the command being ignored. In this work we overcome this limitation
by learning a disentangled probabilistic latent variable model that generates
the steering commands. We achieve faithful command-conditional generation
without using a branched architecture and demonstrate improved stability of the
controller, applying only a variational objective without any domain-specific
adjustments. On top of that, we extend our model with an additional latent
variable and augment the dataset to train a controller that is robust to unsafe
commands, such as asking it to turn into a wall. The main contribution of this
work is a recipe for building controllable imitation driving agents that
improves upon multiple aspects of the current state of the art relating to
robustness and interpretability.
