Abstract
Human trajectory forecasting requires capturing the multimodal nature of pedestrian behavior. However, existing approaches suffer from prior misalignment. Their learned or fixed priors often fail to capture the full distribution of plausible futures, limiting both prediction accuracy and diversity. We theoretically establish that prediction error is lower-bounded by prior quality, making prior modeling a key performance bottleneck. Guided by this insight, we propose AGMA (Adaptive Gaussian Mixture Anchors), which constructs expressive priors through two stages: extracting diverse behavioral patterns from training data and distilling them into a scene-adaptive global prior for inference. Extensive experiments on ETH-UCY, Stanford Drone, and JRDB datasets demonstrate that AGMA achieves state-of-the-art performance, confirming the critical role of high-quality priors in trajectory forecasting.
Abstract (translated)
人类轨迹预测需要捕捉行人行为的多模态特性。然而，现有的方法存在先验偏差的问题。它们学习到或固定的先验通常无法完全捕捉到所有可能未来的分布，从而限制了预测的准确性和多样性。理论上讲，我们建立了预测误差由先验质量下限决定的观点，表明先验建模是性能瓶颈的关键所在。基于这一见解，我们提出了AGMA（自适应高斯混合锚点）方法，通过两个阶段构建表达性的先验：从训练数据中提取多样化的行为模式，并将其提炼成用于推理的场景适应性全局先验。在ETH-UCY、斯坦福无人机和JRDB数据集上的大量实验表明，AGMA实现了最先进的性能，证实了高质量先验在轨迹预测中的关键作用。
URL
https://arxiv.org/abs/2602.04204
