A User-driven Design Framework for Robotaxi
Yue Deng, Changyang He
TL;DR
This paper investigates real-world user experiences with robotaxis to address gaps in design-focused research. It uses a mixed qualitative approach (18 interviews and 22 autoethnographic rides) to reveal motivations, benefits, and tensions around privacy, safety, and trust, then translates these insights into a practical end-to-end design framework. The framework covers pre-ride configuration, context-aware pickup, in-ride explainability and enrichment, and accountable post-ride feedback, aiming to align robotaxi behavior with human expectations and social norms. The work highlights the potential of robotaxis as autonomous, semi-private transition spaces and informs design directions to improve transparency, robustness, and user engagement for broader, safer deployment.
Abstract
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.