  Human trajectory forecasting is a critical challenge in fields such as
robotics and autonomous driving. Due to the inherent uncertainty of human
actions and intentions in real-world scenarios, various unexpected occurrences
may arise. To uncover latent motion patterns in human behavior, we introduce a
novel memory-based method, named Motion Pattern Priors Memory Network. Our
method involves constructing a memory bank derived from clustered prior
knowledge of motion patterns observed in the training set trajectories. We
introduce an addressing mechanism to retrieve the matched pattern and the
potential target distributions for each prediction from the memory bank, which
enables the identification and retrieval of natural motion patterns exhibited
by agents, subsequently using the target priors memory token to guide the
diffusion model to generate predictions. Extensive experiments validate the
effectiveness of our approach, achieving state-of-the-art trajectory prediction
accuracy. The code will be made publicly available.
