Abstract
The rising demand for adaptive, cloud-based and AI-based systems is calling for an upgrade of the associated dependability concepts. That demands instantiation of dependability-orientated processes and methods to cover the whole life cycle. However, a common solution is not in sight yet That is especially evident for continuously learning AI and/or dynamic runtime-based approaches. This work focuses on engineering methods and design patterns that support the development of dependable AI-based autonomous systems. The emphasis on the human-centric aspect leverages users’ physiological, emotional, and cognitive state for the adaptation and optimisation of autonomous applications. We present the related body of knowledge of the TEACHING project and several automotive domain regulation activities and industrial working groups. We also consider the dependable architectural concepts and their applicability to different scenarios to ensure the dependability of evolving AI-based Cyber-Physical Systems of Systems (CPSoS) in the automotive domain. The paper shines the light on potential paths for dependable integration of AI-based systems into the automotive domain through identified analysis methods and targets.
Supported by the H2020 project TEACHING (n. 871385) - www.teaching-h2020.eu.
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Acknowledgment
The presented work is partially supported by TEACHING, a project funded by the EU Horizon 2020 research and innovation programme under GA n. 871385.
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Macher, G. et al. (2021). Dependable Integration Concepts for Human-Centric AI-Based Systems. In: Habli, I., Sujan, M., Gerasimou, S., Schoitsch, E., Bitsch, F. (eds) Computer Safety, Reliability, and Security. SAFECOMP 2021 Workshops. SAFECOMP 2021. Lecture Notes in Computer Science(), vol 12853. Springer, Cham. https://doi.org/10.1007/978-3-030-83906-2_1
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