  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.
