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| Main Authors: | , , , , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2510.25421 |
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| _version_ | 1866908890551025664 |
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| author | Cockram, Lewis Yu, Yueteng Pardo, Jorge Li, Xiaomeng Rakotonirainy, Andry Kuo, Jonny Demmel, Sebastien Lenné, Mike Schroeter, Ronald |
| author_facet | Cockram, Lewis Yu, Yueteng Pardo, Jorge Li, Xiaomeng Rakotonirainy, Andry Kuo, Jonny Demmel, Sebastien Lenné, Mike Schroeter, Ronald |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2510_25421 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Small Talk, Big Impact? LLM-based Conversational Agents to Mitigate Passive Fatigue in Conditional Automated Driving Cockram, Lewis Yu, Yueteng Pardo, Jorge Li, Xiaomeng Rakotonirainy, Andry Kuo, Jonny Demmel, Sebastien Lenné, Mike Schroeter, Ronald Human-Computer Interaction 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. |
| title | Small Talk, Big Impact? LLM-based Conversational Agents to Mitigate Passive Fatigue in Conditional Automated Driving |
| topic | Human-Computer Interaction |
| url | https://arxiv.org/abs/2510.25421 |