AGMA: Adaptive Gaussian Mixture Anchors for Prior-Guided Multimodal Human Trajectory Forecasting

2026-02-04

AI Summary

This work addresses the challenge that existing trajectory prediction methods struggle to balance accuracy and diversity due to a mismatch between imposed prior distributions and real-world behavioral patterns. To overcome this, we propose an adaptive Gaussian mixture anchor mechanism that extracts diverse motion modes directly from data to construct a scene-adaptive global prior for guiding multimodal trajectory forecasting. We theoretically establish, for the first time, that prediction error is fundamentally bounded below by the quality of the prior. Building on this insight, we design a two-stage prior modeling framework that integrates behavior clustering, prior distillation, and scene-adaptive inference. Extensive experiments on ETH-UCY, Stanford Drone, and JRDB datasets demonstrate state-of-the-art performance, confirming the critical role of high-quality priors in enhancing prediction effectiveness.

Technology Category

• Humans and AI: Human-Aware Planning and Behavior Prediction
• Machine Learning: Calibration & Uncertainty Quantification
• Planning, Routing, and Scheduling: Model-Based Reasoning

Application Category

• User Modeling, Personalization and Recommendation: Metrics for user behavior and evaluating success
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions

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.

Problem

• trajectory forecasting
• multimodal behavior
• prior misalignment
• human trajectory prediction
• Gaussian mixture

Innovation

• Adaptive Gaussian Mixture Anchors
• Prior-Guided Forecasting
• Multimodal Trajectory Prediction
• Scene-Adaptive Prior
• Human Trajectory Forecasting