ScenarioFuzz-LLM: Enhancing Diversity in Autonomous Driving Scenario Fuzzing with LLMs

As Autonomous Driving Systems (ADS) are increasingly deployed, ensuring their safety in edge cases becomes critical to preventing catastrophic failures. However, the limited ADS test scenario diversity often hinders the discovery of new defects, especially in complex and rare situations. This paper presents ScenarioFuzz- Llm,a novel method that leverages Large Language Models (LLMs) to enhance the diversity of ADS test scenarios. By incorporating LLMs into a genetic algorithm-based testing framework, ScenarioFuzz- Llmdirects the mutation to address diversity bottlenecks, thereby enabling the exploration of a broader range of edge cases. Our experiments demonstrate that ScenarioFuzz- Llmenhances the number of violation sce-narios by 10.51 % outperforming the state-of-the-art methods, and uncovers 24 unique defects in ADS, three of which are previously undiscovered. These results highlight the superiority of our approach in enhancing ADS testing through more diverse and comprehensive simulation scenarios, ultimately improving the safety of ADS.