SurrealDriver: Designing LLM-Powered Generative Driver Agent Framework Based on Human Drivers' Driving-Thinking Data

Leveraging advanced reasoning capabilities and extensive world knowledge of large language models (LLMs) to construct generative agents for solving complex real-world problems is a major trend. However, LLMs inherently lack embodiment as humans, resulting in suboptimal performance in many embodied decision-making tasks. In this paper, we introduce a framework for building human-like generative driving agents using post-driving self-report driving-thinking data from human drivers as both demonstration and feed- back. To capture high-quality, natural language data from drivers, we conducted urban driving experiments, recording drivers’ verbalized thoughts under various conditions to serve as chain-of-thought prompts and demonstration examples for the LLM-Agent. The framework’s effectiveness was evalu- ated through simulations and human assessments. Results indicate that incorporating expert demonstration data sig- nificantly reduced collision rates by 81.04% and increased human likeness by 50% compared to a baseline LLM-based agent. Our study provides insights into using natural language- based human demonstration data for embodied tasks. The driving-thinking dataset is available at https://github. com/AIR-DISCOVER/Driving-Thinking-Dataset.