Trajectory Prediction with Latent Belief Energy-Based Model

TL;DR

This paper introduces a latent belief energy-based model for human trajectory prediction that captures multimodal and social aspects, demonstrating significant improvements over previous methods on standard benchmarks.

Contribution

The novel LB-EBM model effectively models multimodal human trajectories using a probabilistic latent space with high expressivity, trained on expert demonstrations.

Findings

Achieves 10.9% improvement on Stanford Drone benchmark

Achieves 27.6% improvement on ETH-UCY benchmark

Produces accurate, multi-modal, and socially compliant trajectories

Abstract

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%.