Core Insights - The article discusses the critical role of foundation models in generating and analyzing complex driving scenarios for autonomous vehicles, emphasizing their ability to synthesize diverse and realistic high-risk safety scenarios [2][4]. Group 1: Foundation Models in Autonomous Driving - Foundation models enable the processing of heterogeneous inputs such as natural language, sensor data, and high-definition maps, facilitating the generation and analysis of complex driving scenarios [2]. - A unified classification system is proposed, covering various model types including Large Language Models (LLMs), Vision-Language Models (VLMs), Multimodal Large Language Models (MLLMs), Diffusion Models (DMs), and World Models (WMs) [2][4]. Group 2: Methodologies and Tools - The article reviews methodologies, open-source datasets, simulation platforms, and benchmark testing challenges relevant to scenario generation and analysis [2]. - Specific evaluation metrics for assessing scenario generation and analysis are discussed, highlighting the need for dedicated assessment standards in this field [2]. Group 3: Current Challenges and Future Directions - The article identifies open challenges and research questions in the field of scenario generation and analysis, suggesting areas for future research and development [2].