In engineering design, one often wishes to calculate the probability that the performance of a system is satisfactory under uncertainty. State of the art algorithms exist to solve this problem using active learning with Gaussian process models. However, these algorithms cannot be applied to problems which often occur in the autonomous vehicle domain where the performance of a system may be undefined under certain circumstances. To solve this problem, we introduce a hierarchical model for the system performance, where undefined performance is classified before the performance is regressed. This enables active learning Gaussian process methods to be applied to problems where the performance of the system is sometimes undefined, and we demonstrate the effectiveness of our approach by testing our methodology on synthetic numerical examples for the autonomous driving domain.

翻译：在工程设计中,人们往往希望计算一个系统的性能在不确定情况下是否令人满意的可能性。艺术算法的现状存在,以便通过与高斯进程模型的积极学习来解决这个问题。然而,这些算法不能适用于通常发生在自动车辆领域的问题,因为在某些情况下,系统性能可能没有界定。为了解决这个问题,我们为系统性能引入了等级模式,在性能倒退之前对未界定性能进行分类。这样,就可以对系统性能有时不确定的问题应用积极学习高斯进程方法,我们通过测试自主驾驶领域合成数字实例的方法来证明我们的方法的有效性。