Recent advancements in Large Language Models (LLMs) offer new opportunities to create natural language interfaces for Autonomous Driving Systems (ADSs), moving beyond rigid inputs. This paper addresses the challenge of mapping the complexity of human language to the structured action space of modular ADS software. We propose a framework that integrates an LLM-based interaction layer with Autoware, a widely used open-source software. This system enables passengers to issue high-level commands, from querying status information to modifying driving behavior. Our methodology is grounded in three key components: a taxonomization of interaction categories, an application-centric Domain Specific Language (DSL) for command translation, and a safety-preserving validation layer. A two-stage LLM architecture ensures high transparency by providing feedback based on the definitive execution status. Evaluation confirms the system's timing efficiency and translation robustness. Simulation successfully validated command execution across all five interaction categories. This work provides a foundation for extensible, DSL-assisted interaction in modular and safety-conscious autonomy stacks.

翻译：大型语言模型（LLM）的最新进展为自动驾驶系统（ADS）创建自然语言界面提供了新机遇，超越了传统刚性输入方式。本文致力于解决将复杂的人类语言映射至模块化ADS软件结构化动作空间的挑战。我们提出了一种将基于LLM的交互层与广泛使用的开源软件Autoware相集成的框架。该系统允许乘客发出高级指令，涵盖从状态信息查询到驾驶行为修改等多种功能。我们的方法基于三个关键组成部分：交互类别的分类体系、面向应用的领域特定语言（DSL）指令转换机制，以及保障安全的验证层。采用双阶段LLM架构，通过基于确定执行状态的反馈机制确保高度透明性。评估结果证实了系统的时间效率和转换鲁棒性。仿真实验成功验证了全部五类交互场景下的指令执行能力。本研究为模块化且注重安全的自主系统中可扩展的DSL辅助交互奠定了理论基础。