ART: Adaptive Relational Transformer for Pedestrian Trajectory Prediction with Temporal-Aware Relations

arXiv:2604.03649v1 Announce Type: cross Abstract: Accurate prediction of real-world pedestrian trajectories is crucial for a wide range of robot-related applications. Recent approaches typically adopt graph-based or transformer-based frameworks to model interactions. Despite their effectiveness, these methods either introduce unnecessary computational overhead or struggle to represent the diverse and time-varying characteristics of human interactions. In this work, we present an Adaptive Relatio