The objective of this research is to present the state of the art of the safety assurance of
Artificial Intelligence (AI)-based systems and guidelines on future correlated work. For this …

This survey presents an overview of verification techniques for autonomous systems, with a
focus on safety-critical autonomous cyber-physical systems (CPS) and subcomponents …

Fully formal verification of perception models is likely to remain challenging in the
foreseeable future, and yet these models are being integrated into safety-critical control …

The use of neural networks in embedded systems is becoming increasingly common, but
these systems often operate in safety–critical environments, where a failure or incorrect …

Abstract Context: Neural Network (NN) algorithms have been successfully adopted in a
number of Safety-Critical Cyber-Physical Systems (SCCPSs). Testing and Verification (T&V) …

Editor's notes: Neural network control systems are often at the heart of autonomous systems.
The authors classify existing verification methods for these systems and advocate the …

The availability of representative datasets is an essential prerequisite for many successful
artificial intelligence and machine learning models. However, in real life applications these …

Neural networks are powerful tools. Applying them in computer systems—operating
systems, databases, and networked systems—attracts much attention. However, neural …

Interest in machine learning and neural networks has increased significantly in recent years.
However, their applications are limited in safety-critical domains due to the lack of formal …