Reliable multi-agent trajectory prediction is crucial for the safe planning
and control of autonomous systems. Compared with single-agent cases, the major
challenge in simultaneously processing multiple agents lies in modeling complex
social interactions caused by various driving intentions and road conditions.
Previous methods typically leverage graph-based message propagation or
attention mechanism to encapsulate such interactions in the format of marginal
probabilistic distributions. However, it is inherently sub-optimal. In this
paper, we propose IPCC-TP, a novel relevance-aware module based on Incremental
Pearson Correlation Coefficient to improve multi-agent interaction modeling.
IPCC-TP learns pairwise joint Gaussian Distributions through the
tightly-coupled estimation of the means and covariances according to
interactive incremental movements. Our module can be conveniently embedded into
existing multi-agent prediction methods to extend original motion distribution
decoders. Extensive experiments on nuScenes and Argoverse 2 datasets
demonstrate that IPCC-TP improves the performance of baselines by a large
margin.

介绍了一种基于 incremental Pearson 相关系数的新型模块，用于提高多智能体交互建模的性能，它可以方便地嵌入到现有的多智能体预测方法中，并通过在多个数据集上的实验表明，在 baselines 上的表现都有很大程度的提升。