Mining Specifications for Predictive Safety Monitoring

Abstract

Safety-critical autonomous systems must reliably predict unsafe behavior to take timely corrective actions. Safety properties are often defined over variables that are not directly observable at runtime, making prediction and detection of violations hard. We present a new approach for learning interpretable monitors characterized by concise Signal Temporal Logic (STL) formulas that can predict safety property violations from the observable sensor data. We train these monitors from synthetic, possibly highly unbalanced data generated in a simulation environment. Our specification mining procedure combines a grammar-based method and two novel ensemble techniques. Our approach outperforms the existing solutions by enhancing accuracy and explainability, as demonstrated in two autonomous driving case studies.