Safety Validation

Safety validation for autonomous systems, particularly in robotics and autonomous vehicles, focuses on rigorously assessing system safety before real-world deployment. Current research emphasizes efficient data selection and scenario generation methods, often leveraging machine learning techniques like reinforcement learning and Bayesian optimization to identify critical failure scenarios and accelerate testing in simulation. These advancements aim to improve the reliability and trustworthiness of autonomous systems, ultimately contributing to safer and more robust deployment in various applications.