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This paper introduces INTraJ, a novel framework for trajectory prediction that explicitly separates the planning and reaction stages of social influence in agent behavior. By modeling social interactions as a two-stage process鈥攚here planning anticipates future behaviors and reaction adjusts for local discrepancies鈥攖he authors achieve significant improvements in prediction accuracy across multiple benchmarks. The results indicate that this structured approach enhances long-horizon consistency and overall performance, establishing a new standard in trajectory prediction methodologies.
Staging social interactions in trajectory prediction leads to remarkable gains in accuracy and consistency, reshaping how we approach agent behavior modeling.
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.
