Foundation Models in Autonomous Driving: A Survey on Scenario Generation and Scenario Analysis

Ensuring the safety of autonomous vehicles in real-world environments requires handling a wide spectrum of diverse and rare driving scenarios. Scenario-based testing addresses this need by offering a scalable and controlled approach to develop and validate autonomous driving systems. However, traditional scenario generation methods relying on rule-based logic, knowledge-driven models, or data-driven synthesis often yield limited diversity and unrealistic cases. With the emergence of foundation models, which represent a new generation of pre-trained, general-purpose Artificial Intelligence (AI) models, developers can process heterogeneous inputs (e.g., natural language, sensor data, maps, and control actions),…