JACoP: Joint Alignment for Compliant Multi-Agent Prediction

TL;DR

JACoP is a multi-stage framework that improves multi-agent trajectory prediction by ensuring scene-level compliance, reducing social collisions and environmental violations.

Contribution

It introduces a novel joint alignment method using an MRF-based aligner for more feasible multi-agent trajectory predictions.

Findings

Reduces social collisions in predicted trajectories

Decreases environmental violations compared to prior models

Maintains competitive accuracy in trajectory prediction

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.