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.

翻译：近期自动驾驶领域的进展主要依赖于数据驱动方法，这些方法虽被广泛采用，却面临数据集偏差、过拟合及不可解释性等挑战。受人类驾驶知识驱动特性的启发，我们探讨如何将类似能力赋予自动驾驶系统的问题，并归纳出一种整合交互环境、驾驶员智能体及记忆组件的范式。利用具备涌现能力的大型语言模型，我们提出DiLu框架，该框架结合推理与反思模块，使系统能够基于常识知识进行决策并持续演进。大量实验证明，DiLu具备积累经验的能力，且在泛化能力上显著优于基于强化学习的方法。此外，DiLu可直接从真实世界数据集中获取经验，彰显其部署于实际自动驾驶系统的潜力。据我们所知，我们首次从人类驾驶视角将知识驱动能力注入自动驾驶系统。