mlsanityData to 13 Oct 2026

JACoP: Joint Alignment for Compliant Multi-Agent Prediction

arXiv 2605.11385 ↗First posted 12 May 2026cs.CV cs.ROcited by 0

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Abstract (arXiv)

Stochastic Human Trajectory Prediction (HTP) using generative modeling has emerged as a significant area of research. Although state-of-the-art models excel in optimizing the accuracy of individual agents, they often struggle to generate predictions that are collectively compliant, leading to output trajectories marred by social collisions and environmental violations, thus rendering them impractical for real-world applications. To bridge this gap, we present JACoP: Joint Alignment for Compliant Multi-Agent Prediction, an innovative multi-stage framework that ensures scene-level plausibility. JACoP incorporates an Anchor-Based Agent-Centric Profiler for effective initial compliance filtering and employs a Markov Random Field (MRF) based aligner to formalize the joint selection for scene predictions. By representing inter-agent spatial and social costs as MRF energy potentials, we successfully infer and sample from the joint trajectory distribution, achieving prediction with optimal scene compliance. Comprehensive experiments show that JACoP not only achieves competitive accuracy, but also sets a new standard in reducing both environmental violations and social collisions, thereby confirming its ability to produce collectively feasible and practically applicable trajectory predictions.

Key facts. No citing papers in the map yet (data through 13 Oct 2026). Builds on 31 of its 47 references that are in the map. First posted on arXiv 12 May 2026.

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References · 31

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19 more
  1. 2023MTR++: Multi-Agent Motion Prediction with Symmetric Scene Modeling and Guided Intention Queryingcited by 140
  2. 2023Leapfrog Diffusion Model for Stochastic Trajectory Predictioncited by 130
  3. 2023EqMotion: Equivariant Multi-agent Motion Prediction with Invariant Interaction Reasoningcited by 97
  4. 2022Remember Intentions: Retrospective-Memory-based Trajectory Predictioncited by 86
  5. 2020Recursive Social Behavior Graph for Trajectory Predictioncited by 66
  6. 2022SocialVAE: Human Trajectory Prediction using Timewise Latentscited by 66
  7. 2022MUSE-VAE: Multi-Scale VAE for Environment-Aware Long Term Trajectory Predictioncited by 59
  8. 2022Learning Pedestrian Group Representations for Multi-modal Trajectory Predictioncited by 44
  9. 2022FJMP: Factorized Joint Multi-Agent Motion Prediction over Learned Directed Acyclic Interaction Graphscited by 41
  10. 2022JFP: Joint Future Prediction with Interactive Multi-Agent Modeling for Autonomous Drivingcited by 40
  11. 2019Analyzing the Variety Loss in the Context of Probabilistic Trajectory Predictioncited by 31
  12. 2023FEND: A Future Enhanced Distribution-Aware Contrastive Learning Framework for Long-tail Trajectory Predictioncited by 31
  13. 2025MoFlow: One-Step Flow Matching for Human Trajectory Forecasting via Implicit Maximum Likelihood Estimation based Distillationcited by 31
  14. 2024MART: MultiscAle Relational Transformer Networks for Multi-agent Trajectory Predictioncited by 21
  15. 2023Joint Metrics Matter: A Better Standard for Trajectory Forecastingcited by 17
  16. 2025Unified Uncertainty-Aware Diffusion for Multi-Agent Trajectory Modelingcited by 15
  17. 2025SocialMOIF: Multi-Order Intention Fusion for Pedestrian Trajectory Predictioncited by 7
  18. 2024TrajDiffuse: A Conditional Diffusion Model for Environment-Aware Trajectory Predictioncited by 4
  19. 2025AMD: Adaptive Momentum and Decoupled Contrastive Learning Framework for Robust Long-Tail Trajectory Predictioncited by 4

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