LLMScenario: Large Language Model Driven Scenario Generation
提出LLMScenario框架，利用大语言模型生成自动驾驶中的罕见场景，通过提示工程、生成和评估反馈调优，提升场景的可靠性和可用性。
Scenario engineering plays a vital role in various Industry 5.0 applications. In the field of autonomous driving systems, driving scenario data are important for the training and testing of critical modules. However, the corner scenario cases are usually rare and necessary to be extended. Existing methods cannot handle the interpretation and reasoning of the generation process well, which reduces the reliability and usability of the generated scenarios. With the rapid development of Foundation Models, especially the large language model (LLM), we can conduct scenario generation via more powerful tools. In this article, we propose LLMScenario, a novel LLM-driven scenario generation framework, which is composed of scenario prompt engineering, LLM scenario generation, and evaluation feedback tuning. The minimum scenario description specific to LLM is given by scenario analysis and ablation studies. We also appropriately design the score functions in terms of reality and rarity to evaluate the generated scenarios. The model performance is further enhanced through chain-of-thoughts and experiences. Different LLMs are also compared with our framework. Experimental results on naturalistic datasets demonstrate the effectiveness of LLMScenario, which can provide solid support for scenario engineering in Industry 5.0.