Passive fatigue during conditional automated driving can compromise driver readiness and safety. This paper presents findings from a test-track study with 40 participants in a real-world automated driving scenario. In this scenario, a Large Language Model (LLM) based conversational agent (CA) was designed to check in with drivers and re-engage them with their surroundings. Drawing on in-car video recordings, sleepiness ratings and interviews, we analysed how drivers interacted with the agent and how these interactions shaped alertness. Results show the CA is helpful for supporting vigilance during passive fatigue. Thematic analysis of acceptability further revealed three user preference profiles that implicate future intention to use CAs. Positioning empirically observed profiles within existing CA archetype frameworks highlights the need for adaptive design sensitive to diverse user groups. This work underscores the potential of CAs as proactive Human-Machine Interface (HMI) interventions, demonstrating how natural language can support context-aware interaction during automated driving.

翻译：条件自动驾驶中的被动疲劳会损害驾驶员的准备状态与安全性。本文呈现了一项在真实自动驾驶场景下开展的测试道研究结果，共40名参与者参与。在该场景中，我们设计了一个基于大型语言模型的对话代理，用于与驾驶员进行状态确认并重新引导其关注周围环境。通过车载视频记录、困倦程度评分及访谈，我们分析了驾驶员与代理的互动方式以及这些互动如何影响警觉性。结果表明，该对话代理能有效支持驾驶员在被动疲劳状态下的警觉维持。对接受度的主题分析进一步揭示了三种用户偏好类型，这些类型关联着未来使用对话代理的意愿。将实证观察到的用户类型置于现有对话代理原型框架中进行定位，凸显了针对不同用户群体进行自适应设计的必要性。本研究强调了对话代理作为主动式人机界面干预措施的潜力，展示了自然语言如何在自动驾驶过程中支持情境感知的交互。