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 …

View Video Presentation: https://doi. org/10.2514/6.2022-1133. vid Machine learning (ML)
has proven to be the tool of choice for achieving human-like or even super-human …

Integration of machine learning (ML) components in critical applications introduces novel
challenges for software certification and verification. New safety standards and technical …

To enable highly automated vehicles where the driver is no longer a safety backup, the
vehicle must deal with various Functional Insufficiencies (FIs). Thus-far, there is no widely …

Artificial Intelligence (AI), particularly current Machine Learning approaches, promises new
and innovative solutions also for realizing safety-critical functions. Assurance cases can …

Approximating while compressing lookup tables (LUT) with a set of neural networks (NN) is
an emerging trend in safety critical systems, such as control/command or navigation …

Safety assurance is a central concern for the development and societal acceptance of
automated driving (AD) systems. Perception is a key aspect of AD that relies heavily on …

Label noise is a primary point of interest for safety concerns in previous works as it affects
the robustness of a machine learning system by a considerable amount. This paper studies …

Justifying the correct implementation of the non-functional requirements (eg, safety, security)
of mission-critical systems is crucial to prevent system failure. The later could have severe …

This paper proposes a practical list of safety concerns and mitigation methods for visual
deep learning algorithms. The growing success of deep learning algorithms in solving non …