  Comma.ai's approach to Artificial Intelligence for self-driving cars is based
on an agent that learns to clone driver behaviors and plans maneuvers by
simulating future events in the road. This paper illustrates one of our
research approaches for driving simulation. One where we learn to simulate.
Here we investigate variational autoencoders with classical and learned cost
functions using generative adversarial networks for embedding road frames.
Afterwards, we learn a transition model in the embedded space using action
conditioned Recurrent Neural Networks. We show that our approach can keep
predicting realistic looking video for several frames despite the transition
model being optimized without a cost function in the pixel space.
