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（乡村环境）和2007年城市挑战赛以来，自动驾驶一直是人工智能应用中最活跃的领域。近年来，在大型语言模型（LLM）的驱动下，诸如chatGPT和PaLM等对话系统相继涌现，并迅速成为自然语言处理（NLP）领域实现通用人工智能（AGI）的重要方向。由此自然产生一个思路：我们能否利用这些能力来重塑自动驾驶？通过将LLM与基础模型相结合，有可能借助人类知识、常识和推理能力，从当前长尾人工智能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，涵盖仿真、世界模型、数据标注以及规划或端到端解决方案等方向。