Critical Autonomous System

Critical autonomous systems (CAS) research focuses on developing safe and reliable autonomous systems for high-stakes applications, prioritizing verifiable safety guarantees over purely performance-driven approaches. Current efforts concentrate on integrating physics-based models with machine learning techniques like deep reinforcement learning (DRL), employing formal methods for verification and synthesis, and developing robust testing methodologies to identify and mitigate potential failures. This field is crucial for advancing the safe deployment of autonomous systems in various domains, from robotics and aerospace to transportation, by providing rigorous frameworks for ensuring their trustworthiness and preventing catastrophic failures.