Physics Constrained Motion Prediction with Uncertainty Quantification
This addresses safety-critical motion prediction for autonomous driving by integrating physics constraints and uncertainty quantification, though it is incremental as it builds on existing methods like conformal prediction.
The paper tackles the problem of predicting motion for autonomous systems by ensuring trajectories are dynamically feasible and quantifying uncertainty, achieving a 41% better ADE, 56% better FDE, and 19% better IoU over a baseline in experiments.
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.