Designing User Interfaces for Semi-Autonomous Tram Systems: Human–Machine Interaction, Future Scenarios, and the Transition Toward Automated Mobility

Abstract: The integration of semi-autonomous systems in public transport introduces new design challenges related to user experience, human–machine interaction, and situational awareness. This research, conducted within the Master’s program in Advanced Sustainable Design at the University of Florence in collaboration with an industry leader in autonomous tram technology, explores how user interfaces (UIs) can effectively mediate between automation and human control in complex operational contexts. Using the semi-autonomous tram line of Florence as a case study, the project investigates present issues and future scenarios for interface evolution in public transportation.Students were divided into project groups and engaged in a comprehensive research process combining design thinking and human-centered design approaches. The methodology included a review of the scientific literature, benchmarking of international case studies, and extensive on-field investigation. Empirical data were collected through direct observation, interviews with drivers, and anonymous questionnaires aimed at identifying recurring challenges, user needs, and perceptions of trust and safety in interactions with semi-autonomous systems. This multi-layered analysis enabled a critical comparison between theoretical frameworks and operational realities, revealing gaps and opportunities for UI innovation. The collected evidence was organized into categories of disruptions frequently observed in semi-autonomous scenarios, including issues related to attention management, information overload, and the adaptation of human skills to automated contexts.A parallel line of research explored the evolving role of the driver, focusing on ergonomic factors, cognitive load, stress management, and the need for continuous monitoring of attention and physical condition. Findings demonstrate that the effectiveness of semi-autonomous mobility systems depends largely on the quality of human–machine interaction and the system’s ability to support the operator during high-complexity events. Multimodal feedback—visual, auditory, and tactile—proved to be essential for maintaining situational awareness and reducing reaction time.Building on these insights, the project proposed several future-oriented interface solutions. These included adaptive dashboards capable of dynamically reorganizing information, multimodal alert systems designed to mitigate cognitive overload, and AI-driven virtual assistants to support drivers in diagnosing anomalies, prioritizing warnings, and managing emergency procedures. These speculative scenarios position UIs as cooperative partners that can enhance transparency, reliability, and trust while ensuring meaningful human oversight.Looking ahead, the study highlights the potential of emerging technologies such as Augmented Reality (AR), Virtual Reality (VR), and Mixed Reality (MR) to improve information delivery and skill development. AR overlays within the driver’s field of view could present contextual information—track status, obstacle alerts, route variations—without increasing distraction, while immersive technologies could support training and simulation.Ultimately, this research demonstrates the value of design-driven methodologies in shaping next-generation UIs for public transport systems. By integrating human factors, emerging technologies, and speculative design approaches, the project outlines how adaptive, multimodal, and intelligent interfaces can enhance safety, efficiency, and comfort while guiding the cultural and professional transition toward autonomous mobility.

Keywords: Semi-autonomous tram systems, Future mobility scenarios, Human–machine interaction (HMI)

DOI: 10.54941/ahfe1007114

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