迈向知识驱动的自动驾驶
Towards Knowledge-driven Autonomous Driving
AI总结：
本文综述知识驱动自动驾驶技术，分析现有系统局限，提出以数据集、环境和驾驶员智能体为核心，结合大模型等AI技术构建更全面、自适应和智能的自动驾驶系统，并展望未来研究方向。
摘要：
本文探讨了新兴的知识驱动自动驾驶技术。我们的研究凸显了当前自动驾驶系统的局限性，特别是其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。相反，具备认知、泛化和终身学习能力的方法成为克服这些挑战的有前景的途径。本文深入探讨了知识驱动自动驾驶的本质，并审视了其核心组成部分：数据集与基准、环境和驾驶员智能体。通过利用大型语言模型、世界模型、神经渲染和其他先进的人工智能技术，这些组成部分共同促成了一个更全面、自适应和智能的自动驾驶系统。本文系统性地组织和回顾了该领域以往的研究工作，并为自动驾驶的未来研究和实际应用提供了见解和指导。我们将持续分享知识驱动自动驾驶前沿发展的最新进展以及相关有价值的开源资源，网址为：https://github.com/PJLab-ADG/awesome-knowledge-driven-AD。
英文摘要：
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}.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
