Journal of Automotive Software Engineering
Volume 1, Issue 1, 2020
1. Safely Entering the Deep: A Review of Verification and Validation for Machine Learning and a Challenge Elicitation in the Automotive Industry
Markus Borg, Cristofer Englund, Krzysztof Wnuk, Boris Duran, Christoffer Levandowski, Shenjian Gao, Yanwen Tan, Henrik Kaijser, Henrik Lönn, Jonas Törnqvist
Pages: 1 - 19
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...
Alex Serban, Erik Poll, Joost Visser
Pages: 20 - 33
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...
Alessio Di Sandro, Sahar Kokaly, Rick Salay, Marsha Chechik
Pages: 34 - 50
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...
Betty H. C. Cheng, Bradley Doherty, Nicholas Polanco, Matthew Pasco
Pages: 51 - 77
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...