This paper proposes a novel pipeline that leverages large language models (LLMs) to derive legal driving requirements for autonomous vehicles (AVs) by grounding LLM reasoning in a traffic scenario taxonomy. The method significantly improves law-scenario matching and the accuracy of derived mandatory and prohibitive requirements, providing a solid foundation for lawful AV development and deployment.