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In engineering design, one often wishes to calculate the probability that theperformance of a system is satisfactory under uncertainty. State of the artalgorithms exist to solve this problem using active learning with Gaussianprocess models. However, these algorithms cannot be applied to problems whichoften occur in the autonomous vehicle domain where the performance of a systemmay be undefined under certain circumstances. To solve this problem, weintroduce a hierarchical model for the system performance, where undefinedperformance is classified before the performance is regressed. This enablesactive learning Gaussian process methods to be applied to problems where theperformance of the system is sometimes undefined, and we demonstrate theeffectiveness of our approach by testing our methodology on synthetic numericalexamples for the autonomous driving domain.

An Active Learning Reliability Method for Systems with Partially Defined Performance Functions