Disentangling Interaction and Intention for Long-Tail Pedestrian Trajectory Prediction

Pedestrian trajectory prediction remains a challenging task, particularly in long-tail scenarios where goal distributions are sparse and inter-agent behaviors are uncertain. In this work, we propose to disentangle the trajectory prediction task into two complementary components: interaction modeling and intention modeling. For interaction modeling, we introduce an adaptive meta-strategy that proactively extracts latent and rare-yet-critical interaction patterns often overlooked by conventional trajectory-only approaches. For intention modeling, we propose Continuous Waypoint Slot-Driven Prototypical Contrastive Learning (PCL). It adapts prototype learning to the multi-modal reality where conventional PCL fails to model diverse and continuous goal distributions. Capitalizing on the complementary strengths of both components, we orchestrate a unified frequency-based fusion module that seamlessly integrates interaction and intention modeling, yielding enhanced overall prediction accuracy. In particular, our method is model-agnostic and can be seamlessly incorporated into a wide range of existing prediction frameworks. Extensive experiments on several datasets demonstrate that our approach not only achieves consistent performance gains in standard settings, but also significantly alleviates degradation on hard or long-tail trajectory samples.