显式规划与反应分解的轨迹预测统一框架
A Unified Framework for Trajectory Prediction with Explicit Planning and Reaction Decomposition
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中文总结 AI 辅助
针对现有轨迹预测方法未充分区分社会影响功能角色的问题，提出INTraJ框架，将社会影响分解为规划与反应两阶段，在四个基准上实现性能提升，验证了阶段性社会建模的重要性。
中文摘要 AI 辅助
轨迹预测已转向采用显式社会建模的结构化形式，但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调整前，会通过预判他人未来行为形成运动计划，我们确定社会交互发挥阶段性作用，即规划先于反应。我们提出INTraJ，一个将社会影响分解为两个阶段的统一框架：规划阶段利用未来社会信息构建参考轨迹，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支持多目标和单目标范式。在Argoverse 2、Argoverse 2-ped、ETH/UCY和SDD四个标准基准上的大量实验表明，其取得了一致改进，尤其在最终位移误差（FDE）和长时一致性方面，在多个设置中达到了当前最优性能。INTraJ将轨迹预测重新表述为规划驱动的两阶段过程，验证了阶段性社会建模对稳定预测至关重要。代码可在指定URL公开获取。
英文摘要
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.
发表机构
- Software College, Northeastern University（东北大学软件学院）
机构由 AI 辅助整理，请以论文原文为准。
