用信息论方法分解轨迹预测中的不确定性，提升自动驾驶安全性。
Stochasticity in Motion: An Information-Theoretic Approach to Trajectory Prediction
- 基于信息论构建不确定性量化框架，分离环境随机性与模型不确定
- 在nuScenes数据集上验证不同模型配置对不确定性估计的影响
- 兼容主流预测模型，适合需可靠风险评估的自动驾驶系统
在自动驾驶中，准确的运动预测对安全高效的路径规划至关重要。为确保安全，规划器需要对周围车辆行为预测的不确定性进行可靠估计，但该问题尚未得到充分关注。尤其重要的是将不确定性分解为固有随机性（aleatoric）和认知不确定性（epistemic），以区分环境固有的随机性与模型自身的不确定性，从而支持更鲁棒、更明智的决策。本文提出一种整体性方法，强调不确定性量化、分解及模型结构的影响。所提方法基于信息论，提供理论严谨的不确定性度量方式，并可将不确定性分解为两类成分。与以往工作不同，该方法兼容当前最先进的运动预测模型，具有更广适用性。通过在nuScenes数据集上进行大量实验，验证了不同网络架构与配置对不确定性估计及模型鲁棒性的影响。
原文摘要 · Abstract (English)
In autonomous driving, accurate motion prediction is crucial for safe and efficient motion planning. To ensure safety, planners require reliable uncertainty estimates of the predicted behavior of surrounding agents, yet this aspect has received limited attention. In particular, decomposing uncertainty into its aleatoric and epistemic components is essential for distinguishing between inherent environmental randomness and model uncertainty, thereby enabling more robust and informed decision-making. This paper addresses the challenge of uncertainty modeling in trajectory prediction with a holistic approach that emphasizes uncertainty quantification, decomposition, and the impact of model composition. Our method, grounded in information theory, provides a theoretically principled way to measure uncertainty and decompose it into aleatoric and epistemic components. Unlike prior work, our approach is compatible with state-of-the-art motion predictors, allowing for broader applicability. We demonstrate its utility by conducting extensive experiments on the nuScenes dataset, which shows how different architectures and configurations influence uncertainty quantification and model robustness.
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