Deep Neural Networks (DNN) will emerge as a cornerstone in automotive software
engineering. However, developing systems with DNNs introduces novel challenges for …

While the increased use of AI in the manufacturing sector has been widely noted, there is
little understanding on the risks that it may raise in a manufacturing organisation. Although …

Software-intensive systems that rely on machine learning (ML) and artificial intelligence (AI)
are increasingly becoming part of our daily life, eg, in recommendation systems or semi …

Autonomous driving systems (ADS) in recent years have been the subject of focus, evolving
as one of the major mobility disruptors and being a potential candidate for deployment in …

The importance of software based on machine learning is growing rapidly, but the potential
of prototypes may not be realized in operation. This study identified six categories of …

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

This paper focuses on the problem of hidden faults, which is seen like a growth one inherent
in modern safety-related systems. The special feature of these is the designing for operation …

Deep Neural Networks (DNN) have improved the quality of several non-safety related
products in the past years. However, before DNNs should be deployed to safety-critical …

Driving automation systems, including autonomous driving and advanced driver assistance,
are an important safety-critical domain. Such systems often incorporate perception systems …

The use of neural networks and reinforcement learning has become increasingly popular in
autonomous vehicle control. However, the opaqueness of the resulting control policies …