The use of machine learning (ML) is on the rise in many sectors of software development,
and automotive software development is no different. In particular, Advanced Driver …

Machine learning (ML) plays an ever-increasing role in advanced automotive functionality
for driver assistance and autonomous operation; however, its adequacy from the perspective …

Machine learning (ML) applications generate a continuous stream of success stories from
various domains. ML enables many novel applications, also in safety-critical contexts …

Enabled by recent advances in the field of machine learning, the automotive industry pushes
towards automated driving. The development of traditional safety-critical automotive …

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

Autonomous vehicles rely on machine learning to solve challenging tasks in perception and
motion planning. However, automotive software safety standards have not fully evolved to …

The open-world deployment of Machine Learning (ML) algorithms in safety-critical
applications such as autonomous vehicles needs to address a variety of ML vulnerabilities …

Abstract Context Machine Learning (ML) has been at the heart of many innovations over the
past years. However, including it in so-called “safety-critical” systems such as automotive or …

In an industrial context, high software quality is mandatory in order to avoid costly patching.
We present a state of the art analysis of approaches to ensure that a specific Artificial …

Machine Learning (ML) is now used in a range of systems with results that are reported to
exceed, under certain conditions, human performance. Many of these systems, in domains …