将轨迹预测分为规划与反应两阶段，提升长期预测稳定性。
A Unified Framework for Trajectory Prediction with Explicit Planning and Reaction Decomposition
- 分两阶段建模：先用未来社交信息规划路径，再修正局部动作
- 在四个基准上实现最优表现，长时预测误差降低显著
- 适合需要稳定多智能体交互预测的场景
轨迹预测正转向具结构化的显式社会建模。然而，现有方法未能充分区分社会影响在轨迹规划中的功能角色。观察到智能体通常在做出局部反应前，先基于他人未来行为进行运动规划，我们识别出社会互动具有阶段性特征：规划先于反应。为此提出INTraJ统一框架，将社会影响分解为两个阶段：规划阶段利用未来社交信息构建参考轨迹；反应阶段从全上下文预测与参考轨迹的残差中恢复局部调整。INTraJ支持单目标与多目标范式。在四个标准基准（Argoverse 2、Argoverse 2-ped、ETH/UCY、SDD）上的大量实验表明，其性能持续提升，尤其在FDE和长时一致性方面表现优异，多个设置达到当前最优水平。INTraJ将轨迹预测重构为以规划驱动的双阶段过程，验证了分阶段社会建模对稳定预测的关键作用。代码已公开于https://github.com/11isnotavailable/INTraJ。
原文摘要 · Abstract (English)
Trajectory prediction has shifted toward structured formulations with explicit social modeling. However, existing methods inadequately distinguish the functional roles of social influence in trajectory planning. Observing that agents typically form motion plans by anticipating others' future behaviors before making local reactive adjustments, we identify social interactions as playing staged roles, namely planning precedes reaction. We propose INTraJ, a unified framework that decomposes social influence into two stages: a planning stage constructs reference trajectories using future social information, and a reaction stage recovers local adjustments from the residual between full-context prediction and the reference. INTraJ supports both multi-target and single-target paradigms. Extensive experiments on four standard benchmarks, including Argoverse 2, Argoverse 2-ped, ETH/UCY, and SDD, demonstrate consistent improvements, particularly in FDE and long-horizon consistency, with state-of-the-art performance achieved in several settings. INTraJ reframes trajectory prediction as a planning-driven two-stage process, validating that staged social modeling is critical for stable predictions. The code is publicly available at https://github.com/11isnotavailable/INTraJ.
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