  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.
