  In order to plan a safe maneuver an autonomous vehicle must accurately
perceive its environment, and understand the interactions among traffic
participants. In this paper, we aim to learn scene-consistent motion forecasts
of complex urban traffic directly from sensor data. In particular, we propose
to characterize the joint distribution over future trajectories via an implicit
latent variable model. We model the scene as an interaction graph and employ
powerful graph neural networks to learn a distributed latent representation of
the scene. Coupled with a deterministic decoder, we obtain trajectory samples
that are consistent across traffic participants, achieving state-of-the-art
results in motion forecasting and interaction understanding. Last but not
least, we demonstrate that our motion forecasts result in safer and more
comfortable motion planning.
