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.

翻译：预测动态智能体的运动是保障自主系统安全的关键任务。一个特殊挑战在于运动预测算法需遵循动力学约束，并量化预测不确定性以作为置信度指标。我们提出了一种物理约束的运动预测方法，通过代理动力学模型确保预测轨迹的动力学可行性。我们设计了一种两步集成方案，在动力学约束下分别进行意图预测与轨迹预测。此外，我们利用共形预测这一主流统计工具，构建了专为自动驾驶定制的预测区域以量化不确定性。在自主赛车数据集上的实验表明，相比基线方法，物理约束运动预测在ADE指标上提升41%、FDE指标上提升56%、IoU指标上提升19%。