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.

基于记忆的Motion Pattern Priors Memory Network方法通过构建从训练集轨迹中观察到的运动模式的聚类先验知识构成的记忆库，并引入地址机制来检索匹配的模式和每个预测的潜在目标分布，从而使得在真实世界场景中的不确定性下，准确地预测人类行为轨迹成为可能。

揭示人类动作模式：基于模式记忆的扩散模型的轨迹预测