| Popis: |
Neural networks are increasingly being used for dealing with complex real-world applications. Despite their success, there are still important open issues such as their limited application in safety and security- critical contexts, wherein assurance about networks’ behavior must be provided. The development of reliable neural networks for safety-critical contexts is one of the topics investigated in the AIDOaRt Project, a 3 years long H2020-ECSEL European project focusing on Artificial Intelligence augmented automation supporting modeling, coding, testing, monitoring, and continuous development of Cyber- Physical Systems. In this paper, we present an interesting safety-critical use case – related to the automotive domain – from the AIDOaRt project. In addition, we outline the challenges we are facing in bridging the gap between the scalability of state-of-the-art verification methodologies and the complexity of the neural networks best suited for the task of interest. |
