  Long-term human trajectory prediction is a challenging yet critical task in
robotics and autonomous systems. Prior work that studied how to predict
accurate short-term human trajectories with only unimodal features often failed
in long-term prediction. Reinforcement learning provides a good solution for
learning human long-term behaviors but can suffer from challenges in data
efficiency and optimization. In this work, we propose a long-term human
trajectory forecasting framework that leverages a guided diffusion model to
generate diverse long-term human behaviors in a high-level latent action space,
obtained via a hierarchical action quantization scheme using a VQ-VAE to
discretize continuous trajectories and the available context. The latent
actions are predicted by our guided diffusion model, which uses
physics-inspired guidance at test time to constrain generated multimodal action
distributions. Specifically, we use reachability analysis during the reverse
denoising process to guide the diffusion steps toward physically feasible
latent actions. We evaluate our framework on two publicly available human
trajectory forecasting datasets: SFU-Store-Nav and JRDB, and extensive
experimental results show that our framework achieves superior performance in
long-term human trajectory forecasting.
