Dependable integration concepts for human-centric AI-based systems

The rising demand for adaptive, cloud-based and AI-based systems is calling for an upgrade of the associated dependability con- cepts. 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 engi- neering 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 work- ing 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.