A Model-Based Approach to Dynamic Self-assessment for Automated Performance and Safety Awareness of Cyber-Physical Systems

Modern automotive vehicles represent one category of CPS (Cyber-Physical Systems) that are inherently timeand safety-critical. To justify the actions for quality-of-service adaptation and safety assurance, it is fundamental to perceive the uncertainties of system components in operation, which are caused by emergent properties, design or operation anomalies. From an industrial point of view, a further challenge is related to the usages of generic purpose COTS (Commercial-Off-The-Shelf) components, which are separately developed and evolved, often not sufficiently verified and validated for specific automotive contexts. While introducing additional uncertainties in regard to the overall system performance and safety, the adoption of COTS components constitutes a necessary means for effective product evolution and innovation. Accordingly, we propose in this paper a novel approach that aims to enable advanced operation monitoring and self-assessment in regard to operational uncertainties and thereby automated performance and safety awareness. The emphasis is on the integration of several modeling technologies, including the domain-specific modeling framework EAST-ADL, the A-G contract theory and Hidden Markov Model (HMM). In particular, we also present some initial concepts in regard to the usage performance and safety awareness for quality-of-service adaptation and dynamic risk mitigation.