JACoP: Joint Alignment for Compliant Multi-Agent Prediction

Qingze (Tony) Liu1 Alen Mrdovic1 Danrui Li1 Mathew Schwartz1 Sejong Yoon2 Mubbasir Kapadia1
1Rutgers University, New Brunswick 2The College of New Jersey
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JACoP motivation showing that prior methods can achieve best-case accuracy while still causing environmental violations and social collisions, while JACoP produces accurate and compliant predictions.
JACoP aligns multi-agent predictions for accuracy, environmental compliance, and social compliance.
Qualitative comparison of trajectory prediction methods showing JACoP with fewer social collisions across dense scenes.
Qualitative comparisons show fewer social collisions in dense multi-agent scenes.

Abstract

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.

BibTeX

@inproceedings{liu2026jacop,
  title={JACoP: Joint Alignment for Compliant Multi-Agent Prediction},
  author={Liu, Qingze Tony and Mrdovic, Alen and Li, Danrui and Schwartz, Mathew and Yoon, Sejong and Kapadia, Mubbasir},
  booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
  pages={910--919},
  year={2026}
}