论文标题
动态代理的运动计划的自适应共形预测
Adaptive Conformal Prediction for Motion Planning among Dynamic Agents
论文作者
论文摘要
本文提出了一种使用自适应共形预测在动态剂之间进行运动计划的算法。我们考虑确定性控制系统，并使用轨迹预测因子来预测动态代理的未来运动，该运动被认为遵循未知分布。然后，我们利用从自适应共形预测到从在线数据流中动态量化预测不确定性的想法。特别是，我们提供了一种在线算法使用延迟的代理观测值，以获取具有概率覆盖范围的多步骤预测的不确定性集。这些不确定性集用于模型预测控制器中，以安全地在动态代理之间导航。虽然大多数现有数据驱动的预测都以量化预测不确定性的启发性，但我们以无分配的自适应方式量化了真实的预测不确定性，甚至可以捕获预测质量和代理运动的变化。我们在模拟案例研究中对算法进行经验评估，在该案例研究中，无人机避免飞行飞盘。
This paper proposes an algorithm for motion planning among dynamic agents using adaptive conformal prediction. We consider a deterministic control system and use trajectory predictors to predict the dynamic agents' future motion, which is assumed to follow an unknown distribution. We then leverage ideas from adaptive conformal prediction to dynamically quantify prediction uncertainty from an online data stream. Particularly, we provide an online algorithm uses delayed agent observations to obtain uncertainty sets for multistep-ahead predictions with probabilistic coverage. These uncertainty sets are used within a model predictive controller to safely navigate among dynamic agents. While most existing data-driven prediction approached quantify prediction uncertainty heuristically, we quantify the true prediction uncertainty in a distribution-free, adaptive manner that even allows to capture changes in prediction quality and the agents' motion. We empirically evaluate of our algorithm on a simulation case studies where a drone avoids a flying frisbee.
