Critical Scenario Generation

Critical scenario generation focuses on creating realistic yet challenging situations—especially rare, high-risk events—to rigorously test the safety and robustness of autonomous systems, particularly autonomous vehicles. Current research emphasizes methods leveraging both data-driven approaches (e.g., modifying real-world driving data) and knowledge-driven approaches (e.g., incorporating expert rules and large language models), often integrating reinforcement learning to efficiently explore the vast space of possible scenarios. This research is crucial for accelerating the development and deployment of safe and reliable autonomous systems, providing a more efficient and comprehensive alternative to extensive real-world testing.