用大模型主动劝导司机专注路况，降低接管时的认知负担。
Driver Assistant: Persuading Drivers to Adjust Secondary Tasks Using Large Language Models
- 用大模型根据路况自动发出人性化劝导，通过视听双通道干预。
- 实验证明能有效维持注意力，同时减轻认知负荷。
- 适合研究人机交互与自动驾驶辅助系统的学者参考。
Level 3自动驾驶系统允许司机从事次要任务，但会降低其风险感知。紧急情况下系统需在有限时间内唤醒司机，造成显著认知负担。本研究利用大语言模型（LLM）通过“人性化”说服性建议协助司机保持对道路状况的关注。该工具以Level 3系统遇到的路况为触发条件，主动通过视觉和听觉双路径引导司机行为。实证研究表明，该方法能有效维持司机注意力，降低认知负荷，并协调次要任务与接管行为。本工作揭示了大语言模型在多任务自动驾驶中支持司机的潜力。
原文摘要 · Abstract (English)
Level 3 automated driving systems allows drivers to engage in secondary tasks while diminishing their perception of risk. In the event of an emergency necessitating driver intervention, the system will alert the driver with a limited window for reaction and imposing a substantial cognitive burden. To address this challenge, this study employs a Large Language Model (LLM) to assist drivers in maintaining an appropriate attention on road conditions through a "humanized" persuasive advice. Our tool leverages the road conditions encountered by Level 3 systems as triggers, proactively steering driver behavior via both visual and auditory routes. Empirical study indicates that our tool is effective in sustaining driver attention with reduced cognitive load and coordinating secondary tasks with takeover behavior. Our work provides insights into the potential of using LLMs to support drivers during multi-task automated driving.
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