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
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 instill knowledge-driven capability into
autonomous driving systems from the perspective of how humans drive.

我们提出了 DiLu 框架，结合推理和反思模块，使自主驾驶系统能够根据常识知识做决策，并能不断进化，从而在泛化能力上展现了显著优势，并能直接从实际数据中获取经验，突显其在实用自主驾驶系统中的潜力。