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About this paper
Qiujing Lu, Xuanhan Wang, Yiwei Jiang, Guangming Zhao, Mingyue Ma, Shuo Feng
Tsinghua University and Research Institute for Road Safety of the Ministry of Public Security researchers developed OmniTester, an LLM-driven framework for autonomous vehicle scenario generation that creates complete road structures and vehicle movements from text requests. The system achieved high accuracy in generating diverse and challenging scenarios, including reconstructing crash reports, thereby enhancing AV testing efficiency and coverage.
Key insights
- Multimodal LLMs can effectively generate complex, domain-specific structured outputs like entire road networks and vehicle routes from abstract text, demonstrating their capability for "from-scratch" content creation beyond simple text generation.
- Integrating external simulation tools and providing their error messages as direct feedback to the LLM is crucial for ensuring the validity and accuracy of generated scenarios, highlighting a powerful tool-augmented LLM approach.
- Contextual grounding through RAG (using VLM-summarized real-world road data) and CoT prompting significantly enhances the LLM's ability to produce realistic, diverse, and syntactically correct scenarios, effectively mitigating LLM hallucinations in structured tasks.
Summary by alphaXiv.
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