Cyber-physical systems (CPSs) are now widely deployed in many industrial domains, e.g., manufacturing systems and autonomous vehicles. To further enhance the capability and applicability of CPSs, there comes a recent trend from both academia and industry to utilize learning-based AI controllers for the system control process, resulting in an emerging class of AI-enabled cyber-physical systems (AI-CPSs). Although such AI-CPSs could achieve obvious performance enhancement from the lens of some key industrial requirement indicators, due to the random exploration nature and lack of systematic explanations for their behavior, such AI-based techniques also bring uncertainties and safety risks to the controlled system, posing an urgent need for effective safety analysis techniques for AI-CPSs. Hence in this work, we propose Mosaic, a model-based safety analysis framework for AI-CPSs. Mosaic first constructs a Markov decision process (MDP) model as an abstract model of the AI-CPS, which tries to characterize the behaviors of the original AI-CPS. Then, based on the derived abstract model, safety analysis is designed in two aspects: online safety monitoring and offline model-guided falsification. The usefulness of Mosaic is evaluated on diverse and representative industry-level AI-CPSs, the results of which demonstrate that Mosaic is effective in providing safety monitoring to AI-CPSs and enables to outperform the state-of-the-art falsification techniques, providing the basis for advanced safety analysis of AI-CPSs.

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