  In this paper, we present Goal-GAN, an interpretable and end-to-end trainable
model for human trajectory prediction. Inspired by human navigation, we model
the task of trajectory prediction as an intuitive two-stage process: (i) goal
estimation, which predicts the most likely target positions of the agent,
followed by a (ii) routing module which estimates a set of plausible
trajectories that route towards the estimated goal. We leverage information
about the past trajectory and visual context of the scene to estimate a
multi-modal probability distribution over the possible goal positions, which is
used to sample a potential goal during the inference. The routing is governed
by a recurrent neural network that reacts to physical constraints in the nearby
surroundings and generates feasible paths that route towards the sampled goal.
Our extensive experimental evaluation shows that our method establishes a new
state-of-the-art on several benchmarks while being able to generate a realistic
and diverse set of trajectories that conform to physical constraints.
