Safety Analysis

Safety analysis is evolving rapidly to address the challenges posed by increasingly complex systems, particularly those incorporating artificial intelligence. Current research focuses on developing and validating automated methods for hazard identification and risk assessment, leveraging large language models (LLMs), deep neural networks (DNNs), and multi-agent optimization techniques to analyze diverse data modalities (e.g., video, sensor data, textual reports). These advancements aim to improve the accuracy, efficiency, and transparency of safety evaluations across various domains, from autonomous vehicles and aviation to industrial automation and weather forecasting, ultimately enhancing system reliability and mitigating risks.