Future autonomous systems will employ sophisticated machine learning techniques for the
sensing and perception of the surroundings and the making corresponding decisions for …

Learning enabled autonomous systems provide increased capabilities compared to
traditional systems. However, the complexity of and probabilistic nature in the underlying …

Since deep learning systems do not generalize well when training data is incomplete and
missing coverage of corner cases, it is difficult to ensure the robustness of safety-critical self …

There is no question that machine learning is a hot topic with potential to change the way
humans interact with the world around them. Digital assistants have become common, and …

Increasingly, safety-critical systems include artificial intelligence and machine learning
components (ie, Learning-Enabled Components (LECs)). However, when behavior is …

There have been major developments in Automated Driving (AD) and Driving Assist
Systems (ADAS) in recent years. However, their safety assurance, thus methodologies for …

Explainability remains the holy grail in designing the next-generation pervasive artificial
intelligence (AI) systems. Current neural network based AI design methods do not naturally …

Artificial Intelligence (AI) technologies could be broadly categorised into Analytics and
Autonomy. Analytics focuses on algorithms offering perception, comprehension, and …

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 …