There is a gap in risk assessment of trajectories between the trajectory information coming from a traffic motion prediction module and what is actually needed. Closing this gap necessitates advancements in prediction beyond current practices. Existing prediction models yield joint predictions of agents' future trajectories with uncertainty weights or marginal Gaussian probability density functions (PDFs) for single agents. Although, these methods achieve high accurate trajectory predictions, they only provide little or no information about the dependencies of interacting agents. Since traffic is a process of highly interdependent agents, whose actions directly influence their mutual behavior, the existing methods are not sufficient to reliably assess the risk of future trajectories. This paper addresses that gap by introducing a novel approach to motion prediction, focusing on predicting agent-pair covariance matrices in a ``scene-centric'' manner, which can then be used to model Gaussian joint PDFs for all agent-pairs in a scene. We propose a model capable of predicting those agent-pair covariance matrices, leveraging an enhanced awareness of interactions. Utilizing the prediction results of our model, this work forms the foundation for comprehensive risk assessment with statistically based methods for analyzing agents' relations by their joint PDFs.

翻译：现有交通运动预测模块输出的轨迹信息与实际风险评估需求之间存在差距。弥合这一差距需要突破现有预测方法。现有预测模型能生成多智能体未来轨迹的联合预测（含不确定性权重）或单智能体的边缘高斯概率密度函数。尽管这些方法实现了高精度轨迹预测，但鲜少（或完全未）提供交互智能体间依赖关系的信息。由于交通系统是高度交互的智能体动态过程，其行为直接影响彼此互动，现有方法难以可靠评估未来轨迹风险。本文针对该问题提出一种新型运动预测方法，聚焦以“场景中心”方式预测智能体对协方差矩阵，进而可构建场景中所有智能体对的高斯联合概率密度函数。我们提出的模型通过增强交互感知能力预测这些智能体对协方差矩阵。基于模型预测结果，本研究为基于统计方法的综合风险评估奠定基础，通过联合概率密度函数分析智能体间的关联关系。