An Active Learning Reliability Method for Systems with Partially Defined Performance Functions
This work addresses a specific issue in autonomous vehicle engineering by extending existing reliability methods to handle partially defined performance functions, representing an incremental improvement.
The paper tackled the problem of calculating system reliability when performance functions are partially undefined, common in autonomous vehicles, by introducing a hierarchical model that classifies undefined performance before regression, enabling active learning Gaussian process methods to be applied effectively.
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.