  Temporal prediction is critical for making intelligent and robust decisions
in complex dynamic environments. Motion prediction needs to model the
inherently uncertain future which often contains multiple potential outcomes,
due to multi-agent interactions and the latent goals of others. Towards these
goals, we introduce a probabilistic framework that efficiently learns latent
variables to jointly model the multi-step future motions of agents in a scene.
Our framework is data-driven and learns semantically meaningful latent
variables to represent the multimodal future, without requiring explicit
labels. Using a dynamic attention-based state encoder, we learn to encode the
past as well as the future interactions among agents, efficiently scaling to
any number of agents. Finally, our model can be used for planning via computing
a conditional probability density over the trajectories of other agents given a
hypothetical rollout of the 'self' agent. We demonstrate our algorithms by
predicting vehicle trajectories of both simulated and real data, demonstrating
the state-of-the-art results on several vehicle trajectory datasets.
