Andreas Riener

As automated vehicles (AVs) are increasingly deployed in real-world environments, users often do not understand what decisions their vehicles are making, or why. Explainable AI (XAI) offers a promising path toward transparency, yet most existing approaches remain algorithm-centric and inaccessible to end users. This workshop explores how Human-Centered XAI (HCXAI) and the Question-Driven Design framework can be used to systematically discover user explainability needs for AVs. Through scenario-based, participatory activities, participants will adopt different stakeholder perspectives and generate the questions they would want an AV to answer in ambiguous driving situations. Building on the prior workshop at AutomotiveUI 2025, this workshop aims to produce an initial taxonomy of AV-specific explanation needs grounded in real user questions, creating a shared resource that can guide future research, UI design, and evaluation of explainable automated driving systems.

Passenger comfort is emerging as a key challenge for the successful deployment and public acceptance of highly automated vehicles. Existing research has examined individual comfort determinants, including vehicle dynamics, driving style, trust, perceived safety, and human-machine interaction (HMI), although these factors have been largely investigated in isolation. Consequently, a comprehensive understanding of how they jointly influence passenger comfort remains limited. This workshop aims to bring together researchers and practitioners from the AutomotiveUI community to develop a holistic perspective on comfort in automated driving. Through interactive brainstorming, small-group discussions, and collaborative framework building, participants will identify key comfort determinants, explore the relationships between vehicle behavior, user characteristics, contextual factors, and HMI designs, and discuss methodological challenges in measuring and improving passenger comfort. The workshop will culminate in a community-driven research agenda and an initial conceptual framework to guide future interdisciplinary research on adaptive, human-centered automated mobility systems.

In-car infotainment systems are growing in complexity, making traditional design and engineering approaches costly and difficult to scale. Recent advances in large language models (LLMs) offer context-aware, dynamic capabilities, enabling interfaces to adapt in real-time and reducing the need for manual design. We propose a scalable framework for Constrained AI-Assembled Graphical User Interfaces (GUIs) that dynamically assembles the interface at runtime from a pre-validated library of components using an LLM. We evaluated this approach in a comparative user study (N = 30) in a simulated driving environment, measuring usability, as well as driver’s cognitive load, distraction, and situational awareness. Quantitative results showed no significant differences between AI-assembled and conventional pre-designed interfaces regarding usability and driver’s mental state. Our qualitative feedback highlighted occasional challenges in information prioritization. We conclude that AI-assembled GUIs have the potential to achieve performance similar to conventional pre-defined interfaces and provide design recommendations to guide future work.

Professional truck drivers represent a critical yet under-researched user group of automotive human-machine interfaces (HMIs). Adaptive HMIs support decision-making in demanding driving contexts. However, research on adaptive truck HMIs remains limited, particularly regarding adequate user control for expert drivers. To address this gap, we developed an interactive truck HMI prototype providing personalized suggestions. We systematically investigated the level of assistance in a driving simulator study (N = 27), comparing a manual baseline (M), adaptive suggestions with manual choice (AM), and an autonomous adaptive condition (AA). Results show that the personalized suggestions were well accepted, while indicating that the concept with user choice (AM) was preferred. Furthermore, higher patronization was significantly associated with lower acceptance. These findings highlight the potential of personalized truck HMIs to improve drivers’ daily work. At the same time, they underscore the importance of considering perceptions of patronization in future research by designing for user-involved adaptivity.