提出自适应关系变换器，精准预测行人轨迹并高效处理动态交互。
ART: Adaptive Relational Transformer for Pedestrian Trajectory Prediction with Temporal-Aware Relations
- 构建时序感知关系图，动态捕捉行人间交互变化
- 引入自适应剪枝机制，计算量降低30%以上且精度提升
- 适用于机器人导航等实时场景，兼顾精度与效率
准确预测真实世界中行人的运动轨迹对众多机器人应用至关重要。现有方法多采用基于图或变压器的框架建模交互，但往往带来不必要的计算开销，或难以表征人类交互的多样性和时变特性。本文提出自适应关系变压器（ART），引入时序感知关系图（TARG）显式捕捉成对交互的演化过程，并设计自适应交互剪枝（AIP）机制，高效减少冗余计算。在ETH/UCY和NBA数据集上的大量实验表明，ART在保持高计算效率的同时实现了最先进的预测精度。
原文摘要 · Abstract (English)
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.
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