Journal of Automotive Software Engineering

Deep neural networks (DNNs) will emerge as a cornerstone in automotive software engineering. However, developing systems with DNNs introduces novel challenges for safety assessments. This paper reviews the state-of-the-art in verification and validation of safety-critical systems that rely on machine...

The development of self-driving vehicles is often regarded as adding a layer of intelligence on top of classic vehicle platforms. However, the amount of software needed to reach autonomy will exceed the software deployed for operation of normal vehicles. As complexity increases, the demand for proper...

The automotive domain has recently increased its reliance on model-based software development. Automotive models are often heterogeneous, large and interconnected through traceability links. When introducing safety-related artifacts, such as Hazard Analysis, fault tree analysis (FTA), failure modes and...

As automotive systems become increasingly sophisticated with numerous onboard features that support extensive inward and outward-facing communication, cybersecurity vulnerabilities are exposed. The relatively recent acknowledgement of automotive cybersecurity challenges has prompted numerous research...