This paper explores the emerging knowledge-driven autonomous driving technologies. Our investigation highlights the limitations of current autonomous driving systems, in particular their sensitivity to data bias, difficulty in handling long-tail scenarios, and lack of interpretability. Conversely, knowledge-driven methods with the abilities of cognition, generalization and life-long learning emerge as a promising way to overcome these challenges. This paper delves into the essence of knowledge-driven autonomous driving and examines its core components: dataset \& benchmark, environment, and driver agent. By leveraging large language models, world models, neural rendering, and other advanced artificial intelligence techniques, these components collectively contribute to a more holistic, adaptive, and intelligent autonomous driving system. The paper systematically organizes and reviews previous research efforts in this area, and provides insights and guidance for future research and practical applications of autonomous driving. We will continually share the latest updates on cutting-edge developments in knowledge-driven autonomous driving along with the relevant valuable open-source resources at: \url{https://github.com/PJLab-ADG/awesome-knowledge-driven-AD}.

翻译：本文探讨了新兴的知识驱动自动驾驶技术。我们的研究揭示了当前自动驾驶系统的局限性，特别是对数据偏差的敏感性、处理长尾场景的困难以及缺乏可解释性。相比之下，具备认知、泛化和终身学习能力的知识驱动方法成为克服这些挑战的有前景途径。本文深入探讨了知识驱动自动驾驶的本质，并考察了其核心组成部分：数据集与基准、环境以及驾驶员代理。通过利用大语言模型、世界模型、神经渲染及其他先进人工智能技术，这些组件共同构建了一个更全面、自适应且智能的自动驾驶系统。本文系统性地整理并综述了该领域的先前研究成果，为自动驾驶的未来研究和实际应用提供了见解与指导。我们将持续在以下网址分享知识驱动自动驾驶前沿进展的最新动态及相关有价值的开源资源：\url{https://github.com/PJLab-ADG/awesome-knowledge-driven-AD}。