We present LaMPilot, a novel framework for planning in the field of
autonomous driving, rethinking the task as a code-generation process that
leverages established behavioral primitives. This approach aims to address the
challenge of interpreting and executing spontaneous user instructions such as
"overtake the car ahead," which have typically posed difficulties for existing
frameworks. We introduce the LaMPilot benchmark specifically designed to
quantitatively evaluate the efficacy of Large Language Models (LLMs) in
translating human directives into actionable driving policies. We then evaluate
a wide range of state-of-the-art code generation language models on tasks from
the LaMPilot Benchmark. The results of the experiments showed that GPT-4, with
human feedback, achieved an impressive task completion rate of 92.7% and a
minimal collision rate of 0.9%. To encourage further investigation in this
area, our code and dataset will be made available.

我们提出了 LaMPilot，一个新颖的自主驾驶规划框架，将任务重新定义为一个利用已建立的行为基元的代码生成过程，旨在解决解释和执行 spontanous 用户指令（如 “超过前面的车”）的挑战，这通常对现有框架构成困难。我们介绍了 LaMPilot 基准，专门设计用于量化大型语言模型（LLMs）在将人类指令转化为可执行驾驶策略方面的效能。我们还在 LaMPilot 基准的任务上评估了一系列最先进的代码生成语言模型。实验结果显示，带有人类反馈的 GPT-4​​的任务完成率达到了 92.7％，最小碰撞率为 0.9％。为了鼓励进一步的研究，我们将提供代码和数据集。