Monotonic Safety for Scalable and Data-Efficient Probabilistic Safety Analysis

Autonomous systems with machine learning-based perception can exhibit unpredictable behaviors that are difficult to quantify, let alone verify. Such behaviors are convenient to capture in proba-bilistic models, but probabilistic model checking of such models is difficult to scale - largely due to the non-determinism added to models as a prerequisite for provable conservatism. Statistical model checking (SMC) has been proposed to address the scalabil-ity issue. However it requires large amounts of data to account for the aforementioned non-determinism, which in turn limits its scalability. This work introduces a general technique for reduction of non-determinism based on assumptions of “monotonic safety”, which define a partial order between system states in terms of their probabilities of being safe. We exploit these assumptions to remove non-determinism from controller/plant models to drasti-cally speed up probabilistic model checking and statistical model checking while providing provably conservative estimates as long as the safety is indeed monotonic. Our experiments demonstrate model-checking speed-ups of an order of magnitude while main-taining acceptable accuracy and require much less data for accurate estimates when running SMC - even when monotonic safety does not perfectly hold and provable conservatism is not achieved.