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：动态智能体运动预测是保障自主系统安全性的关键任务。其特殊挑战在于运动预测算法必须遵循动力学约束，并量化预测不确定性以作为置信度度量。我们提出一种物理约束的运动预测方法，该方法利用替代动力学模型确保预测轨迹的动力学可行性。我们设计了两阶段集成框架，分别进行受动力学约束的意图预测与轨迹预测。此外，通过采用流行的统计工具共形预测，我们构建了专为自动驾驶定制的预测区域以量化不确定性。基于自主赛车数据集的实验表明，与基准方法相比，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%。