Small Talk, Big Impact? LLM-based Conversational Agents to Mitigate Passive Fatigue in Conditional Automated Driving
This work addresses driver safety in automated vehicles, though it is incremental as it applies existing LLM technology to a new context.
The study tackled passive fatigue in conditional automated driving by testing an LLM-based conversational agent with 40 participants, finding it helpful for supporting vigilance and identifying three user preference profiles for future design.
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.