arXiv 2508.081012025-08-11cs.HCcs.AIcs.SE

路上的ChatGPT：利用大语言模型驱动的车载对话代理实现更安全、更愉悦的驾驶体验

ChatGPT on the Road: Leveraging Large Language Model-Powered In-vehicle Conversational Agents for Safer and More Enjoyable Driving Experience

Computer Science, Virginia Tech, Blacksburg, Virginia, USA（计算机科学，弗吉尼亚理工学院，弗吉尼亚州黑斯堡，美国；弗吉尼亚理工学院黑斯堡弗吉尼亚美国）；Virginia Tech Blacksburg Virginia USA

Yeana Lee Bond, Mungyeong Choe, Baker Kasim Hasan, Arsh Siddiqui, Myounghoon Jeon

备注：: Submitted to International Journal of Human-Computer Studies. Bond and Choe: Drafting, Review, Editing, Validation, Software, Methodology, Investigation, Data Analysis, Conceptualization, Experiment training. Hasan and Siddiqui: Experimental and Data Analysis Support. Jeon: Supervision, Review, Resources, Project Admin, Methodology, Conceptualization. Total 34 pages

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AI总结：

本研究探索基于ChatGPT的车载对话代理，通过40名驾驶员的模拟器实验发现，相比无代理和预设脚本代理，该代理能显著提升驾驶稳定性、主观评价及交互多样性，从而增强驾驶安全与用户体验。

摘要：

关于车载对话代理的研究传统上依赖于预设脚本提示或有限的语音指令，限制了驾驶员与代理之间的自然交互。为解决此问题，本研究探索了基于ChatGPT的车载代理的潜力，该代理能够进行连续的多轮对话。40名驾驶员参与了我们的实验，使用基于运动的驾驶模拟器，比较了三种条件（无代理、预设脚本代理和基于ChatGPT的代理）作为被试内变量。结果显示，基于ChatGPT的代理条件在多项指标上带来了更稳定的驾驶表现。与其他两种条件相比，参与者在纵向加速度、横向加速度和车道偏离上的变异性更低。在主观评价中，与预设脚本代理相比，基于ChatGPT的代理在能力、生动性、情感信任和偏好方面也获得了显著更高的评分。我们对驾驶员与代理对话的主题分析揭示了话题上的多样化交互模式，包括驾驶辅助/提问、娱乐请求和拟人化交互。我们的结果突显了由LLM驱动的车载对话代理通过自然、上下文丰富的交互来提升驾驶安全与用户体验的潜力。

英文摘要：

Studies on in-vehicle conversational agents have traditionally relied on pre-scripted prompts or limited voice commands, constraining natural driver-agent interaction. To resolve this issue, the present study explored the potential of a ChatGPT-based in-vehicle agent capable of carrying continuous, multi-turn dialogues. Forty drivers participated in our experiment using a motion-based driving simulator, comparing three conditions (No agent, Pre-scripted agent, and ChatGPT-based agent) as a within-subjects variable. Results showed that the ChatGPT-based agent condition led to more stable driving performance across multiple metrics. Participants demonstrated lower variability in longitudinal acceleration, lateral acceleration, and lane deviation compared to the other two conditions. In subjective evaluations, the ChatGPT-based agent also received significantly higher ratings in competence, animacy, affective trust, and preference compared to the Pre-scripted agent. Our thematic analysis of driver-agent conversations revealed diverse interaction patterns in topics, including driving assistance/questions, entertainment requests, and anthropomorphic interactions. Our results highlight the potential of LLM-powered in-vehicle conversational agents to enhance driving safety and user experience through natural, context-rich interactions.

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