Core Insights - The article presents a comprehensive review of the latest advancements in autonomous driving, focusing on the application of foundation models (FMs) such as LLMs, VLMs, MLLMs, diffusion models, and world models in scene generation and analysis [2][20][29] - It emphasizes the importance of simulating diverse and rare driving scenarios for the safety and performance validation of autonomous driving systems, highlighting the limitations of traditional scene generation methods [2][8][9] - The review identifies open research challenges and future directions for enhancing the adaptability, robustness, and evaluation capabilities of foundation model-driven approaches in autonomous driving [29][30] Group 1: Foundation Models in Autonomous Driving - Foundation models represent a new generation of pre-trained AI models capable of processing heterogeneous inputs, enabling the synthesis and interpretation of complex driving scenarios [2][9][10] - The emergence of foundation models has provided new opportunities to enhance the realism, diversity, and scalability of scene testing in autonomous driving [9][10] - The review categorizes the applications of LLMs, VLMs, MLLMs, diffusion models, and world models in scene generation and analysis, providing a structured classification system [29] Group 2: Scene Generation and Analysis - Scene generation in autonomous driving encompasses various formats, including annotated sensor data, multi-camera video streams, and simulated urban environments [21] - The article discusses the limitations of existing literature on scene generation, noting that many reviews focus on classical methods without adequately addressing the role of foundation models [23][24][25] - Scene analysis involves systematic evaluation tasks such as risk assessment and anomaly detection, which are crucial for ensuring the safety and robustness of autonomous systems [25][28] Group 3: Research Contributions and Future Directions - The review provides a structured classification of existing methods, datasets, simulation platforms, and benchmark competitions related to scene generation and analysis in autonomous driving [29] - It identifies key open research challenges, including the need for better integration of foundation models in scene generation and analysis tasks, and proposes future research directions to address these challenges [29][30] - The article highlights the necessity for efficient prompting techniques and lightweight model architectures to reduce inference latency and resource consumption in real-world applications [36][37]