動きの中の不確実性：情報理論に基づく軌道予測アプローチ
Stochasticity in Motion: An Information-Theoretic Approach to Trajectory Prediction
自動運転の軌道予測において、情報理論を用いて不確実性を偶然的不確実性と認識的不確実性に分解し、既存の予測モデルに適用可能な手法を提案した論文。
著者: Aron Distelzweig, Andreas Look, Eitan Kosman, Faris Janjoš, Jörg Wagner, Abhinav Valada
分類: cs.RO, cs.AI
原文アブストラクト
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.
