Simulation plays a critical role in the research and development of
autonomous driving and intelligent transportation systems. However, the current
simulation platforms exhibit limitations in the realism and diversity of agent
behaviors, which impede the transfer of simulation outcomes to the real world.
In this paper, we propose a generative driver agent simulation framework based
on large language models (LLMs), capable of perceiving complex traffic
scenarios and providing realistic driving maneuvers. Notably, we conducted
interviews with 24 drivers and used their detailed descriptions of driving
behavior as chain-of-thought prompts to develop a `coach agent' module, which
can evaluate and assist driver agents in accumulating driving experience and
developing human-like driving styles. Through practical simulation experiments
and user experiments, we validate the feasibility of this framework in
generating reliable driver agents and analyze the roles of each module. The
results show that the framework with full architect decreased the collision
rate by 81.04% and increased the human-likeness by 50%. Our research proposes
the first urban context driver agent simulation framework based on LLMs and
provides valuable insights into the future of agent simulation for complex
tasks.Comment: 12 pages, 8 figure