Incorporating Failure of Machine Learning in Dynamic Probabilistic Safety Assurance

Overview

The framework combines SafeML distribution-shift detection with Bayesian networks to represent learning failures in causal safety analysis. A simulated vehicle-platooning case with traffic-sign recognition illustrates dynamic assessment and adaptation under uncertainty.