Entropy-Based Uncertainty Modeling for Trajectory Prediction in Autonomous Driving
Keywords: Autonomous Driving, Trajectory Prediction, Uncertainty Quantification
Abstract: In autonomous driving, accurate motion prediction is essential for safe and efficient motion planning. To ensure safety, planners must rely on reliable uncertainties in the future behavior of surrounding agents, yet this aspect has received limited attention. This paper addresses the 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. Our analysis thoroughly explores the uncertainty quantification capabilities of several state-of-the-art prediction models, examining the relationship between uncertainty and prediction error in both in- and out-of-distribution scenarios, as well as robustness in out-of-distribution.
Primary Area: applications to robotics, autonomy, planning
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Submission Number: 6820
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