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.

翻译：可靠的多智能体轨迹预测对于自主系统的安全规划与控制至关重要。与单智能体场景相比，同时处理多个智能体的主要挑战在于建模由不同驾驶意图和道路条件引起的复杂社交交互。先前的方法通常利用基于图的消息传播或注意力机制，以边缘概率分布的形式封装这些交互。然而，这种方法本质上并非最优。本文提出IPCC-TP，一种基于增量皮尔逊相关系数的新型相关性感知模块，用于提升多智能体交互建模能力。IPCC-TP通过根据交互式增量运动对均值和协方差进行紧耦合估计，学习成对联合高斯分布。该模块可便捷地嵌入现有的大多智能体预测方法中，以扩展原有的运动分布解码器。在nuScenes和Argoverse 2数据集上的大量实验表明，IPCC-TP显著提升了基线方法的性能。