Statistical Methods for Artificial Intelligence Reliability with Applications in Autonomous Vehicles

Abstract

Recurrent event data provide an important source of information for assessing the reliability of artificial intelligence (AI) systems. As AI technologies continue to be adopted in safety-critical applications, there is a growing need for statistical methodologies that can effectively model, test, and assure their reliability. This dissertation develops statistical methods for AI reliability analysis using recurrent event data, with a particular focus on autonomous vehicles (AVs). The proposed research addresses reliability evaluation across multiple stages of the AI system life cycle, including reliability modeling, accelerated testing, and reliability assurance. First, a statistical recurrent-event modeling framework with multivariate random effects is developed to analyze multiple types of recurrent events simultaneously. The proposed approach accounts for dependence among recurrent-event processes while accommodating unit-level heterogeneity. Second, a screening-based accelerated life testing methodology incorporating regularization techniques is proposed for AI systems with recurrent-event responses. The proposed framework utilizes a regularized semiparametric recurrent-event model with random effects to identify influential driving factors, interaction effects, and potential nonlinear relationships, providing an efficient strategy for reliability evaluation during the development stage. Third, a statistical framework for reliability assurance test planning based on recurrent-event processes is developed to support deployment decisions. The proposed methods establish testing requirements and decision criteria for demonstrating reliability before field operation. The proposed methodologies are evaluated through simulation studies and illustrated using publicly available AV data from the California Department of Motor Vehicles testing program, as well as recurrent-event data generated from physics-based simulation environments such as CARLA. Overall, this dissertation provides a comprehensive statistical framework for AI reliability analysis that supports the safe development, evaluation, and deployment of AVs and other AI-enabled systems.