In autonomous driving, accurate motion prediction is essential for safe and efficient motion planning. To ensure safety, planners must rely on reliable uncertainty information about the predicted future behavior of surrounding agents, yet this aspect has received limited attention. This paper addresses the so-far neglected problem of uncertainty modeling in trajectory prediction. We adopt a holistic approach that focuses on uncertainty quantification, decomposition, and the influence of model composition. Our method is based on a theoretically grounded information-theoretic approach to measure uncertainty, allowing us to decompose total uncertainty into its aleatoric and epistemic components. We conduct extensive experiments on the nuScenes dataset to assess how different model architectures and configurations affect uncertainty quantification and model robustness.

翻译：在自动驾驶中，精确的运动预测对于安全高效的运动规划至关重要。为确保安全性，规划器必须依赖关于周围智能体未来预测行为的可靠不确定性信息，然而这一方面迄今未获得充分关注。本文针对轨迹预测中迄今被忽视的不确定性建模问题展开研究。我们采用一种整体性方法，重点关注不确定性的量化、分解以及模型构成的影响。我们的方法基于理论完备的信息论框架来度量不确定性，使我们能够将总不确定性分解为偶然不确定性和认知不确定性两个组成部分。我们在nuScenes数据集上进行了大量实验，以评估不同模型架构与配置如何影响不确定性量化及模型鲁棒性。