Research demonstrates that the proactivity of in-vehicle conversational assistants (IVCAs) can help to reduce distractions and enhance driving safety, better meeting users' cognitive needs. However, existing IVCAs struggle with user intent recognition and context awareness, which leads to suboptimal proactive interactions. Large language models (LLMs) have shown potential for generalizing to various tasks with prompts, but their application in IVCAs and exploration of proactive interaction remain under-explored. These raise questions about how LLMs improve proactive interactions for IVCAs and influence user perception. To investigate these questions systematically, we establish a framework with five proactivity levels across two dimensions-assumption and autonomy-for IVCAs. According to the framework, we propose a "Rewrite + ReAct + Reflect" strategy, aiming to empower LLMs to fulfill the specific demands of each proactivity level when interacting with users. Both feasibility and subjective experiments are conducted. The LLM outperforms the state-of-the-art model in success rate and achieves satisfactory results for each proactivity level. Subjective experiments with 40 participants validate the effectiveness of our framework and show the proactive level with strong assumptions and user confirmation is most appropriate.

翻译：研究表明，车载对话助手的主动性有助于减少驾驶员分心、提升驾驶安全，并更好地满足用户的认知需求。然而，现有车载助手在用户意图识别和上下文感知方面存在不足，导致主动交互效果欠佳。大语言模型展现出通过提示泛化至多种任务的潜力，但其在车载助手中的应用及对主动交互的探索仍显不足。这引出了大语言模型如何改进车载主动交互、影响用户感知的问题。为系统探究这些问题，我们建立了一个涵盖五个主动性层级（基于假设性与自主性两维度）的车载助手框架。依据该框架，我们提出"重写+推理-行动-反思"策略，旨在赋能大语言模型在与用户交互时满足各主动性层级的特定需求。我们开展了可行性实验与主观实验。结果表明，大语言模型在成功率上优于现有最优模型，并在各主动性层级上均取得满意效果。基于40名被试的主观实验验证了框架有效性，并指出强假设性与用户确认的主动层级最为适宜。