Entropy-Based Uncertainty Modeling for Trajectory Prediction in Autonomous Driving

• URL: http://arxiv.org/abs/2410.01628v2
• Date: Mon, 7 Oct 2024 11:57:37 GMT
• Title: Entropy-Based Uncertainty Modeling for Trajectory Prediction in Autonomous Driving
• Authors: Aron Distelzweig, Andreas Look, Eitan Kosman, Faris Janjoš, Jörg Wagner, Abhinav Valada,
• Abstract summary: 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. We conduct extensive experiments on the nuScenes dataset to assess how different model architectures and configurations affect uncertainty quantification and model robustness.
• Score: 9.365269316773219
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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