  Recent advancements in autonomous driving have relied on data-driven
approaches, which are widely adopted but face challenges including dataset
bias, overfitting, and uninterpretability. Drawing inspiration from the
knowledge-driven nature of human driving, we explore the question of how to
instill similar capabilities into autonomous driving systems and summarize a
paradigm that integrates an interactive environment, a driver agent, as well as
a memory component to address this question. Leveraging large language models
(LLMs) with emergent abilities, we propose the DiLu framework, which combines a
Reasoning and a Reflection module to enable the system to perform
decision-making based on common-sense knowledge and evolve continuously.
Extensive experiments prove DiLu's capability to accumulate experience and
demonstrate a significant advantage in generalization ability over
reinforcement learning-based methods. Moreover, DiLu is able to directly
acquire experiences from real-world datasets which highlights its potential to
be deployed on practical autonomous driving systems. To the best of our
knowledge, we are the first to leverage knowledge-driven capability in
decision-making for autonomous vehicles. Through the proposed DiLu framework,
LLM is strengthened to apply knowledge and to reason causally in the autonomous
driving domain. Project page: https://pjlab-adg.github.io/DiLu/
