@inproceedings{fecdbd5e7fb6428b8108b9833f6bc0c1,
title = "Physics Constrained Motion Prediction with Uncertainty Quantification",
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.",
keywords = "autonomous-driving, conformal-prediction, machine-learning, motion-prediction, physics-constrained",
author = "Renukanandan Tumu and Lars Lindemann and Truong Nghiem and Rahul Mangharam",
note = "Publisher Copyright: {\textcopyright} 2023 IEEE.; 34th IEEE Intelligent Vehicles Symposium, IV 2023 ; Conference date: 04-06-2023 Through 07-06-2023",
year = "2023",
doi = "10.1109/IV55152.2023.10186812",
language = "English (US)",
series = "IEEE Intelligent Vehicles Symposium, Proceedings",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
booktitle = "IV 2023 - IEEE Intelligent Vehicles Symposium, Proceedings",
}