The paper introduces Text-to-Drive (T2D), a method using LLMs to generate diverse driving behaviors through language descriptions. By leveraging LLM reasoning capabilities, T2D constructs a state chart for behavior synthesis. This approach facilitates policy alignment, low-level observation summarization, and more without human supervision. Check the paper for detailed insights.

• T2D utilizes LLMs for diverse behavior generation
• State chart construction aids in behavior synthesis
• Enhances policy alignment and observation summarization

This paper is important as it demonstrates the potential of LLMs in creating diverse driving scenarios, offering an interactive approach for behavior synthesis.