  Human trajectory prediction is critical for autonomous platforms like
self-driving cars or social robots. We present a latent belief energy-based
model (LB-EBM) for diverse human trajectory forecast. LB-EBM is a probabilistic
model with cost function defined in the latent space to account for the
movement history and social context. The low-dimensionality of the latent space
and the high expressivity of the EBM make it easy for the model to capture the
multimodality of pedestrian trajectory distributions. LB-EBM is learned from
expert demonstrations (i.e., human trajectories) projected into the latent
space. Sampling from or optimizing the learned LB-EBM yields a belief vector
which is used to make a path plan, which then in turn helps to predict a
long-range trajectory. The effectiveness of LB-EBM and the two-step approach
are supported by strong empirical results. Our model is able to make accurate,
multi-modal, and social compliant trajectory predictions and improves over
prior state-of-the-arts performance on the Stanford Drone trajectory prediction
benchmark by 10.9% and on the ETH-UCY benchmark by 27.6%.
