[2604.03649] ART: Adaptive Relational Transformer for Pedestrian Trajectory Prediction with Temporal-Aware Relations

Computer Science > Computer Vision and Pattern Recognition arXiv:2604.03649 (cs) [Submitted on 4 Apr 2026] Title:ART: Adaptive Relational Transformer for Pedestrian Trajectory Prediction with Temporal-Aware Relations Authors:Ruochen Li, Ziyi Chang, Junyan Hu, Jiannan Li, Amir Atapour-Abarghouei, Hubert P. H. Shum View a PDF of the paper titled ART: Adaptive Relational Transformer for Pedestrian Trajectory Prediction with Temporal-Aware Relations, by Ruochen Li and 5 other authors View PDF HTML (experimental) 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 Relational Transformer (ART), which introduces a Temporal-Aware Relation Graph (TARG) to explicitly capture the evolution of pairwise interactions and an Adaptive Interaction Pruning (AIP) mechanism to reduce redundant computations efficiently. Extensive evaluations on ETH/UCY and NBA benchmarks show that ART delivers state-of-the-art accuracy with high computational efficiency. Subjects: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI) Cite as: arXiv:2604.03649 [cs.CV]   (or arXiv:2604.03649v1...