  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 feedback. 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 evaluated through simulations and human
assessments. Results indicate that incorporating expert demonstration data
significantly 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
\url{https://github.com/AIR-DISCOVER/Driving-Thinking-Dataset}.
