IPCC-TP：利用增量皮尔逊相关系数实现多智能体联合轨迹预测
IPCC-TP: Utilizing Incremental Pearson Correlation Coefficient for Joint Multi-Agent Trajectory Prediction
AI总结：
针对多智能体轨迹预测中交互建模次优的问题，提出基于增量皮尔逊相关系数的IPCC-TP模块，通过紧耦合估计学习成对联合高斯分布，可嵌入现有方法并在nuScenes、Argoverse 2数据集上大幅提升基线性能。
摘要：
可靠的多智能体轨迹预测对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核心挑战在于建模由多样驾驶意图和道路状况引发的复杂社会交互。现有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类交互，但这本质上并非最优方案。本文提出IPCC-TP，这是一种基于增量皮尔逊相关系数的新型相关性感知模块，用于改进多智能体交互建模。IPCC-TP根据交互性增量运动，通过对均值和协方差的紧耦合估计，学习成对联合高斯分布。该模块可便捷嵌入现有多智能体预测方法中，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能大幅提升基线模型的性能。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
