Shared Autonomous Vehicles (SAVs) are likely to become an important part of the transportation system, making effective human-SAV interactions an important area of research. This paper introduces a dataset of 200 human-SAV interactions to further this area of study. We present an open-source human-SAV conversational dataset, comprising both textual data (e.g., 2,136 human-SAV exchanges) and empirical data (e.g., post-interaction survey results on a range of psychological factors). The dataset's utility is demonstrated through two benchmark case studies: First, using random forest modeling and chord diagrams, we identify key predictors of SAV acceptance and perceived service quality, highlighting the critical influence of response sentiment polarity (i.e., perceived positivity). Second, we benchmark the performance of an LLM-based sentiment analysis tool against the traditional lexicon-based TextBlob method. Results indicate that even simple zero-shot LLM prompts more closely align with user-reported sentiment, though limitations remain. This study provides novel insights for designing conversational SAV interfaces and establishes a foundation for further exploration into advanced sentiment modeling, adaptive user interactions, and multimodal conversational systems.

翻译：共享自动驾驶汽车（SAVs）有望成为交通系统的重要组成部分，这使得有效的人机交互成为一个重要的研究领域。本文引入了一个包含200次人机交互的数据集，以推动该领域的研究。我们提出了一个开源的人机对话数据集，包含文本数据（例如，2,136次人机对话交换）和实证数据（例如，一系列心理因素的交互后调查结果）。通过两项基准案例研究展示了该数据集的实用性：首先，利用随机森林建模和弦图，我们识别了影响SAV接受度和感知服务质量的关键预测因子，突显了响应情感极性（即感知到的积极性）的关键影响。其次，我们将基于LLM的情感分析工具的性能与传统的基于词典的TextBlob方法进行了基准测试。结果表明，即使是简单的零样本LLM提示，也能更贴近用户报告的情感，尽管仍存在局限性。本研究为设计对话式SAV界面提供了新的见解，并为进一步探索高级情感建模、自适应用户交互和多模态对话系统奠定了基础。