# A Unified Framework for Trajectory Prediction with Explicit Planning and Reaction Decomposition

arXiv: https://arxiv.org/abs/2608.05673 · HTML: https://mlsanity.com/paper/2608.05673/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 25 of its 44 references that are in the map.
- First posted on arXiv 6 Aug 2026.

## Builds on (most cited first, 25 of 25)

- [VectorNet: Encoding HD Maps and Agent Dynamics from Vectorized Representation](https://mlsanity.com/paper/2005.04259/) (2020)
- [AgentFormer: Agent-Aware Transformers for Socio-Temporal Multi-Agent Forecasting](https://mlsanity.com/paper/2103.14023/) (2021)
- [It Is Not the Journey but the Destination: Endpoint Conditioned Trajectory Prediction](https://mlsanity.com/paper/2004.02025/) (2020)
- [Human Motion Trajectory Prediction: A Survey](https://mlsanity.com/paper/1905.06113/) (2019)
- [From Goals, Waypoints & Paths To Long Term Human Trajectory Forecasting](https://mlsanity.com/paper/2012.01526/) (2020)
- [THOMAS: Trajectory Heatmap Output with learned Multi-Agent Sampling](https://mlsanity.com/paper/2110.06607/) (2021)
- [Forecast-MAE: Self-supervised Pre-training for Motion Forecasting with Masked Autoencoders](https://mlsanity.com/paper/2308.09882/) (2023)
- [GoRela: Go Relative for Viewpoint-Invariant Motion Forecasting](https://mlsanity.com/paper/2211.02545/) (2022)
- [MUSE-VAE: Multi-Scale VAE for Environment-Aware Long Term Trajectory Prediction](https://mlsanity.com/paper/2201.07189/) (2022)
- [GANet: Goal Area Network for Motion Forecasting](https://mlsanity.com/paper/2209.09723/) (2022)
- [SmartRefine: A Scenario-Adaptive Refinement Framework for Efficient Motion Prediction](https://mlsanity.com/paper/2403.11492/) (2024)
- [MoFlow: One-Step Flow Matching for Human Trajectory Forecasting via Implicit Maximum Likelihood Estimation based Distillation](https://mlsanity.com/paper/2503.09950/) (2025)
- [Dynamic Scenario Representation Learning for Motion Forecasting with Heterogeneous Graph Convolutional Recurrent Networks](https://mlsanity.com/paper/2303.04364/) (2023)
- [Fast Inference and Update of Probabilistic Density Estimation on Trajectory Prediction](https://mlsanity.com/paper/2308.08824/) (2023)
- [SIMPL: A Simple and Efficient Multi-agent Motion Prediction Baseline for Autonomous Driving](https://mlsanity.com/paper/2402.02519/) (2024)
- [SMEMO: Social Memory for Trajectory Forecasting](https://mlsanity.com/paper/2203.12446/) (2022)
- [Another Vertical View: A Hierarchical Network for Heterogeneous Trajectory Prediction via Spectrums](https://mlsanity.com/paper/2304.05106/) (2023)
- [Uncertainty-Aware Pedestrian Trajectory Prediction via Distributional Diffusion](https://mlsanity.com/paper/2303.08367/) (2023)
- [Evaluating Pedestrian Trajectory Prediction Methods with Respect to Autonomous Driving](https://mlsanity.com/paper/2308.05194/) (2023)
- [BiFF: Bi-level Future Fusion with Polyline-based Coordinate for Interactive Trajectory Prediction](https://mlsanity.com/paper/2306.14161/) (2023)
- [DONUT: A Decoder-Only Model for Trajectory Prediction](https://mlsanity.com/paper/2506.06854/) (2025)
- [Foresight in Motion: Reinforcing Trajectory Prediction with Reward Heuristics](https://mlsanity.com/paper/2507.12083/) (2025)
- [Resonance: Learning to Predict Social-Aware Pedestrian Trajectories as Co-Vibrations](https://mlsanity.com/paper/2412.02447/) (2024)
- [Future-Aware Interaction Network For Motion Forecasting](https://mlsanity.com/paper/2503.06565/) (2025)
- [Snapshot: Towards Application-centered Models for Pedestrian Trajectory Prediction in Urban Traffic Environments](https://mlsanity.com/paper/2409.01971/) (2024)

## Abstract (arXiv)

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.