A User-driven Design Framework for Robotaxi

2026-02-22

AI Summary

This study addresses a critical gap in existing Robotaxi research, which has predominantly emphasized technical performance while overlooking passenger experience in driverless contexts. Drawing on 18 semi-structured interviews and autoethnographic ride observations, the work presents the first empirically grounded analysis of user concerns derived from real-world riding experiences. It identifies key user preferences regarding cost, experiential consistency, and perceived autonomy, alongside critical challenges related to system transparency, emergency handling, and ethical accountability. Building on these insights, the authors propose an end-to-end, user-driven interaction design framework that spans ride hailing, vehicle pickup, in-journey explanation, and feedback closure. This framework offers both empirical evidence and theoretical grounding for optimizing Robotaxi services to better align with human-centered design principles.

Technology Category

• Humans and AI: User Experience and Usability
• Intelligent Robots: Human-Robot Interaction
• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems

Application Category

• User Modeling, Personalization and Recommendation: Accountability, Transparency, and Ethics for personalization
• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Economics, Online Markets and Human Computation: The sharing economy

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.

Problem

• robotaxi
• user experience
• autonomous vehicles
• trust
• human-robot interaction

Innovation

• user-driven design
• robotaxi
• human-robot interaction
• explainability
• trust and ethics