Safety Certificate

Safety certification research focuses on rigorously verifying the safety of autonomous systems, particularly in high-stakes applications, by quantifying and bounding potential errors. Current efforts concentrate on developing robust safety certificates using methods like control barrier functions and neural networks (including Bayesian neural networks and Deep Kernel Learning), often incorporating techniques from reinforcement learning and Bayesian optimization to handle uncertainty and adapt to unknown dynamics. These advancements are crucial for enabling the wider adoption of autonomous systems in safety-critical domains such as robotics, healthcare, and transportation, by providing provable guarantees of safe operation.