The evolution of autonomous driving has made remarkable advancements in
recent years, evolving into a tangible reality. However, a human-centric
large-scale adoption hinges on meeting a variety of multifaceted requirements.
To ensure that the autonomous system meets the user's intent, it is essential
to accurately discern and interpret user commands, especially in complex or
emergency situations. To this end, we propose to leverage the reasoning
capabilities of Large Language Models (LLMs) to infer system requirements from
in-cabin users' commands. Through a series of experiments that include
different LLM models and prompt designs, we explore the few-shot multivariate
binary classification accuracy of system requirements from natural language
textual commands. We confirm the general ability of LLMs to understand and
reason about prompts but underline that their effectiveness is conditioned on
the quality of both the LLM model and the design of appropriate sequential
prompts. Code and models are public with the link
https://github.com/KTH-RPL/DriveCmd_LLM.

自动驾驶的进化取得了显著的进展，并成为了一个实际存在的现实。为了确保自动驾驶系统符合用户意图，准确辨别和解释用户指令尤其是在复杂或紧急情况下是至关重要的。为了实现这一目标，我们提出利用大型语言模型（LLMs）的推理能力，从车载用户的指令中推断系统需求。通过一系列实验，包括不同的 LLM 模型和提示设计，我们探索了通过自然语言文本指令从少量样本进行多元二值分类的精确度。我们确认 LLM 可以理解和推理提示，但强调其有效性取决于 LLM 模型的质量和适当的连续提示的设计。代码和模型可在以下链接找到：https://github.com/KTH-RPL/DriveCmd_LLM。