Scenario Based

Scenario-based approaches are increasingly used to evaluate and improve the performance and safety of complex systems, particularly in autonomous driving and AI applications. Current research focuses on developing robust methods for generating representative scenarios, often leveraging large datasets and machine learning models like large language models (LLMs) and deep neural networks (DNNs), to analyze and predict system behavior under diverse conditions. This work is crucial for enhancing the reliability and trustworthiness of AI systems across various domains, from autonomous vehicles and smart homes to medical simulations and urban planning, by providing a structured framework for testing and validation.