Safety Critical System

Safety-critical systems, those whose failure could lead to catastrophic consequences, demand rigorous verification and validation. Current research emphasizes formal methods for verifying system properties, including the use of control barrier functions, inductive invariants for neural network verification, and assurance cases enhanced by large language models for identifying potential weaknesses. This focus reflects the growing integration of AI and machine learning into these systems, necessitating new approaches to ensure safety and reliability across diverse applications like autonomous vehicles and industrial robotics. The ultimate goal is to develop robust methodologies and tools that guarantee the safety of increasingly complex systems.