This paper presents a new conformal method for generating simultaneous forecasting bands guaranteed to cover the entire path of a new random trajectory with sufficiently high probability. Prompted by the need for dependable uncertainty estimates in motion planning applications where the behavior of diverse objects may be more or less unpredictable, we blend different techniques from online conformal prediction of single and multiple time series, as well as ideas for addressing heteroscedasticity in regression. This solution is both principled, providing precise finite-sample guarantees, and effective, often leading to more informative predictions than prior methods.

翻译：本文提出了一种新的共形方法，用于生成同时预测带，保证以足够高的概率覆盖新随机轨迹的完整路径。针对运动规划应用中各类对象行为可能具有不同程度不可预测性时对可靠不确定性估计的需求，我们融合了单变量与多变量时间序列在线共形预测的不同技术，以及处理回归中异方差性的思路。该解决方案既具有理论原则性，能提供精确的有限样本保证，又具有实际有效性，通常能生成比先前方法更具信息量的预测。