Large language models (LLMs) have opened up new possibilities for intelligent agents, endowing them with human-like thinking and cognitive abilities. In this work, we delve into the potential of large language models (LLMs) in autonomous driving (AD). We introduce DriveMLM, an LLM-based AD framework that can perform close-loop autonomous driving in realistic simulators. To this end, (1) we bridge the gap between the language decisions and the vehicle control commands by standardizing the decision states according to the off-the-shelf motion planning module. (2) We employ a multi-modal LLM (MLLM) to model the behavior planning module of a module AD system, which uses driving rules, user commands, and inputs from various sensors (e.g., camera, lidar) as input and makes driving decisions and provide explanations; This model can plug-and-play in existing AD systems such as Apollo for close-loop driving. (3) We design an effective data engine to collect a dataset that includes decision state and corresponding explanation annotation for model training and evaluation. We conduct extensive experiments and show that our model achieves 76.1 driving score on the CARLA Town05 Long, and surpasses the Apollo baseline by 4.7 points under the same settings, demonstrating the effectiveness of our model. We hope this work can serve as a baseline for autonomous driving with LLMs. Code and models shall be released at https://github.com/OpenGVLab/DriveMLM.

翻译：大语言模型（LLMs）为智能体开辟了新的可能性，赋予其类人思维与认知能力。本研究深入探讨大语言模型在自动驾驶领域的潜力，提出基于LLM的自动驾驶框架DriveMLM，可在真实模拟器中实现闭环自动驾驶。为此：(1) 我们根据现有运动规划模块标准化决策状态，弥合语言决策与车辆控制指令之间的鸿沟；(2) 采用多模态大语言模型（MLLM）对模块化自动驾驶系统中的行为规划模块进行建模，该模型以驾驶规则、用户指令及多传感器（如摄像头、激光雷达）输入为驱动，生成驾驶决策并提供解释性输出，可即插即用于Apollo等现有自动驾驶系统实现闭环驾驶；(3) 设计高效数据引擎，构建包含决策状态及对应解释标注的数据集，用于模型训练与评估。通过广泛实验验证，本模型在CARLA Town05 Long场景中取得76.1驾驶评分，在相同设置下超过Apollo基线4.7分，充分证明了模型的有效性。期待本研究可作为大语言模型自动驾驶领域的基线工作。代码与模型将发布于https://github.com/OpenGVLab/DriveMLM。