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和自动驾驶等领域中的一个关键挑战。由于现实场景中人类行为和意图固有的不确定性，可能会出现各种意外情况。为了揭示人类行为中潜在的运动模式，我们引入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。我们的方法包括构建一个记忆库，该记忆库源自对训练集轨迹中观察到的运动模式进行聚类得到的先验知识。我们引入了一种寻址机制，从记忆库中为每个预测检索匹配的模式以及潜在的目标分布，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，达到了最先进的轨迹预测精度。相关代码将开源。