This yields an exact inference method that models trajectories at different spatio-temporal resolutions in a hierarchical manner.
For prediction of interacting agents' trajectories, we propose an end-to-end trainable architecture that hybridizes neural nets with game-theoretic reasoning, has interpretable intermediate representations, and transfers to downstream decision making.
This leads to a set of coupled Bellman equations that describes the behavior of the agents.
Prediction of future states of the environment and interacting agents is a key competence required for autonomous agents to operate successfully in the real world.
Ranked #6 on Trajectory Prediction on Stanford Drone