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动驾驶系统安全性的关键任务。其中一项特殊挑战在于运动预测算法需遵循动力学约束，并以不确定性量化作为置信度度量。本文提出一种物理约束的运动预测方法，该方法采用代理动力学模型确保预测轨迹满足动力学可行性。我们设计了一个由意图预测与轨迹预测组成的二阶段集成框架，两者均受动力学约束。同时，通过运用流行的统计工具——共形预测，我们构建了专为自动驾驶定制的不确定性量化预测区域。基于自动驾驶赛车数据集的实验表明，物理约束运动预测方法相较于基线模型，在平均位移误差（ADE）、最终位移误差（FDE）和交并比（IoU）上分别提升41%、56%和19%。