  Accurately predicting the future motion of surrounding vehicles requires
reasoning about the inherent uncertainty in driving behavior. This uncertainty
can be loosely decoupled into lateral (e.g., keeping lane, turning) and
longitudinal (e.g., accelerating, braking). We present a novel method that
combines learned discrete policy rollouts with a focused decoder on subsets of
the lane graph. The policy rollouts explore different goals given current
observations, ensuring that the model captures lateral variability.
Longitudinal variability is captured by our latent variable model decoder that
is conditioned on various subsets of the lane graph. Our model achieves
state-of-the-art performance on the nuScenes motion prediction dataset, and
qualitatively demonstrates excellent scene compliance. Detailed ablations
highlight the importance of the policy rollouts and the decoder architecture.
