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Planning Reliability Assurance Tests for Autonomous Vehicles
This paper aims to fill in this gap by developing statistical methods for planning AV reliability assurance tests based on recurrent events data.
Deep Neural Network Identification of Limnonectes Species and New Class Detection Using Image Data
We demonstrate that deep neural networks can successfully automate the classification of an image into a known species group for which it has been trained.
Statistical Perspectives on Reliability of Artificial Intelligence Systems
We also describe recent developments in modeling and analysis of AI reliability and outline statistical research challenges in this area, including out-of-distribution detection, the effect of the training set, adversarial attacks, model accuracy, and uncertainty quantification, and discuss how those topics can be related to AI reliability, with illustrative examples.
Reliability Analysis of Artificial Intelligence Systems Using Recurrent Events Data from Autonomous Vehicles
In this paper, we use recurrent disengagement events as a representation of the reliability of the AI system in AV, and propose a statistical framework for modeling and analyzing the recurrent events data from AV driving tests.
Investigating the Robustness of Artificial Intelligent Algorithms with Mixture Experiments
Artificial intelligent (AI) algorithms, such as deep learning and XGboost, are used in numerous applications including computer vision, autonomous driving, and medical diagnostics.
