Human-level driving is an ultimate goal of autonomous driving. Conventional
approaches formulate autonomous driving as a perception-prediction-planning
framework, yet their systems do not capitalize on the inherent reasoning
ability and experiential knowledge of humans. In this paper, we propose a
fundamental paradigm shift from current pipelines, exploiting Large Language
Models (LLMs) as a cognitive agent to integrate human-like intelligence into
autonomous driving systems. Our approach, termed Agent-Driver, transforms the
traditional autonomous driving pipeline by introducing a versatile tool library
accessible via function calls, a cognitive memory of common sense and
experiential knowledge for decision-making, and a reasoning engine capable of
chain-of-thought reasoning, task planning, motion planning, and
self-reflection. Powered by LLMs, our Agent-Driver is endowed with intuitive
common sense and robust reasoning capabilities, thus enabling a more nuanced,
human-like approach to autonomous driving. We evaluate our approach on the
large-scale nuScenes benchmark, and extensive experiments substantiate that our
Agent-Driver significantly outperforms the state-of-the-art driving methods by
a large margin. Our approach also demonstrates superior interpretability and
few-shot learning ability to these methods. Project page:
\href{this https URL}{here}.

我们提出了一种从普通流程转变的方法，即通过引入大型语言模型作为认知代理，将人类智能融入自动驾驶系统，实现更加人性化的自动驾驶。我们的方法通过函数调用访问一个多功能工具库、具有常识和经验知识的认知记忆以进行决策制定，并且具备由思维推理、任务规划、动作规划和自我反思构成的推理引擎。在大规模 nuScenes 基准测试中，我们的 Agent-Driver 显著优于现有的自动驾驶方法，并且还表现出较强的可解释性和少样本学习能力。