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This paper discusses the perspective of the H2020 TEACHING project on the next generation of autonomous applications running in a distributed and highly heterogeneous environment comprising both virtual and physical resources spanning the edge-cloud continuum. TEACHING puts forward a human-centred vision leveraging the physiological, emotional, and cognitive state of the users as a driver for the adaptation and optimization of the autonomous applications. It does so by building a distributed, embedded and federated learning system complemented by methods and tools to enforce its dependability, security and privacy preservation. The paper discusses the main concepts of the TEACHING approach and singles out the main AI-related research challenges associated with it. Further, we provide a discussion of the design choices for the TEACHING system to tackle the aforementioned challenges Index Terms-distributed neural networks, human-centred artificial intelligence, cyber-physical systems, ubiquitous and pervasive computing, edge artificial intelligence

The exciting new features, such as advanced driver assistance systems, fleet management systems, and autonomous driving, drive the need for built-in security solutions and architectural designs to mitigate emerging security threats. Thus, cybersecurity joins reliability and safety as a cornerstone for success in the automotive industry. As vehicle providers gear up for cybersecurity challenges, they can capitalize on experiences from many other domains, but nevertheless must face several unique challenges. Therefore, this article focuses on the enhancement of state-of-the-art development lifecycle for automotive cyber-physical systems toward the integration of security, safety and reliability engineering methods. Especially, four engineering approaches (HARA at concept level, FMEA and FTA at design level and HSI at implementation level) are extended to integrate security considerations into the development lifecycle.