Since DARPA Grand Challenges (rural) in 2004/05 and Urban Challenges in 2007, autonomous driving has been the most active field of AI applications. Recently powered by large language models (LLMs), chat systems, such as chatGPT and PaLM, emerge and rapidly become a promising direction to achieve artificial general intelligence (AGI) in natural language processing (NLP). There comes a natural thinking that we could employ these abilities to reformulate autonomous driving. By combining LLM with foundation models, it is possible to utilize the human knowledge, commonsense and reasoning to rebuild autonomous driving systems from the current long-tailed AI dilemma. In this paper, we investigate the techniques of foundation models and LLMs applied for autonomous driving, categorized as simulation, world model, data annotation and planning or E2E solutions etc.

翻译：自2004/2005年DARPA大挑战赛（越野场景）和2007年城市挑战赛以来，自动驾驶一直是人工智能应用最活跃的领域。近期，受大型语言模型（LLMs）驱动，chatGPT和PaLM等聊天系统涌现，并迅速成为自然语言处理（NLP）领域实现通用人工智能（AGI）的有前景方向。由此自然产生一个思路：我们可以利用这些能力重构自动驾驶。通过将LLM与基础模型相结合，有可能利用人类知识、常识和推理，从当前的长尾AI困境中重建自动驾驶系统。本文研究了应用于自动驾驶的基础模型和LLM技术，分类涵盖仿真、世界模型、数据标注与规划或端到端解决方案等。