Robotaxis are emerging as a promising form of urban mobility, yet research has largely emphasized technical driving performance while leaving open how passengers experience and evaluate rides without a human driver. To address the limitations of prior work that often relies on simulated or hypothetical settings, we investigate real-world robotaxi use through 18 semi-structured interviews and autoethnographic ride experiences. We found that users were drawn to robotaxis by low cost, social recommendation, and curiosity. They valued a distinctive set of benefits, such as an increased sense of agency, and consistent driving behavioral consistency and standardized ride experiences. However, they encountered persistent challenges around limited flexibility, insufficient transparency, management difficulty, robustness concerns in edge cases, and emergency handling concerns. Robotaxi experiences were shaped by privacy, safety, ethics, and trust. Users were often privacy-indifferent yet sensitive to opaque access and leakage risks; safety perceptions were polarized; and ethical considerations surfaced round issues such as accountability, feedback responsibility and absence of human-like social norms. Based on these findings, we propose a user-driven design framework spanning the end-to-end journey, such as pre-ride configuration (hailing), context-aware pickup facilitation (pick-up) in-ride explainability (traveling), and accountable post-ride feedback (drop-off) to guide robotaxi interaction and service design.

翻译：Robotaxi正逐渐成为城市交通的一种有前景的形式，但现有研究主要强调技术驾驶性能，而对于乘客在无人驾驶情况下的乘坐体验和评价方式仍缺乏探讨。为弥补先前研究多依赖模拟或假设场景的局限，我们通过18次半结构化访谈和自传式乘坐体验，调查了真实世界中的Robotaxi使用情况。研究发现，用户被Robotaxi的低成本、社交推荐和好奇心所吸引。他们重视一系列独特的优势，例如更强的自主感、一致的驾驶行为以及标准化的乘坐体验。然而，他们也面临持续的挑战，包括灵活性有限、透明度不足、管理困难、边缘场景下的鲁棒性问题以及紧急情况处理担忧。Robotaxi的体验受到隐私、安全、伦理和信任的影响。用户通常对隐私持漠视态度，但对不透明的访问和数据泄露风险敏感；安全感知呈现两极分化；伦理考量则围绕责任归属、反馈责任以及缺乏类人社会规范等问题浮现。基于这些发现，我们提出了一个覆盖端到端行程的用户驱动设计框架，包括乘车前配置（叫车）、情境感知的接驳辅助（上车）、行程中的可解释性（行驶）以及可追责的行程后反馈（下车），以指导Robotaxi的交互与服务设计。