Safety Monitoring for Learning-Enabled Cyber-Physical Systems in Out-of-Distribution Scenarios
Existing out-of-distribution (OOD) detection methods for learning-based cyber-physical systems lack direct interpretability with respect to safety property violations, limiting their reliability for safety-critical monitoring under OOD conditions. Method: We propose a robust, STL-based safety monitoring framework that directly predicts violations of signal temporal logic (STL) safety specifications—bypassing OOD detection entirely. Our approach integrates adaptive conformal prediction with incremental learning within a neural trajectory prediction architecture, yielding theoretically grounded confidence guarantees for future safety assessments. Contribution/Results: The method achieves high recall, real-time performance, and prediction accuracy while providing strict statistical validity. Evaluated on two benchmarks—F1Tenth static obstacle avoidance and multi-dynamic obstacle collision prediction—it significantly outperforms state-of-the-art baselines. Crucially, it maintains both high timeliness and rigorous confidence guarantees even under OOD inputs.