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.

翻译：人类轨迹预测是机器人学和自动驾驶等领域中的一项关键挑战。由于真实场景中人类行为和意图的内在不确定性，各种意外情况可能发生。为揭示人类行为中的潜在运动模式，我们提出了一种新颖的基于记忆的方法，称为运动模式先验记忆网络。我们的方法涉及构建一个从训练集轨迹中观察到的运动模式聚类先验知识导出的记忆库。我们引入一种寻址机制，从记忆库中为每次预测检索匹配的模式和潜在目标分布，从而能够识别和检索智能体表现出的自然运动模式，随后利用目标先验记忆令牌引导扩散模型生成预测。大量实验验证了我们方法的有效性，实现了最先进的轨迹预测精度。代码将公开发布。