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- W4385300825 abstract "Predicting the motion of dynamic agents is a critical task for guaranteeing the safety of autonomous systems. A particular challenge is that motion prediction algorithms should obey dynamics constraints and quantify prediction uncertainty as a measure of confidence. We present a physics-constrained approach for motion prediction which uses a surrogate dynamical model to ensure that predicted trajectories are dynamically feasible. We propose a two-step integration consisting of intent and trajectory prediction subject to dynamics constraints. We also construct prediction regions that quantify uncertainty and are tailored for autonomous driving by using conformal prediction, a popular statistical tool. Physics Constrained Motion Prediction achieves a 41% better ADE, 56% better FDE, and 19% better IoU over a baseline in experiments using an autonomous racing dataset." @default.
- W4385300825 created "2023-07-28" @default.
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- W4385300825 date "2023-06-04" @default.
- W4385300825 modified "2023-10-16" @default.
- W4385300825 title "Physics Constrained Motion Prediction with Uncertainty Quantification" @default.
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- W4385300825 doi "https://doi.org/10.1109/iv55152.2023.10186812" @default.
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