TY - CHAP A1 - Rollwagen, Alice A1 - Alvarez, Ignacio A1 - Riener, Andreas T1 - Reality Check: Real-World Observations of State-of-the-Art Driver Assistance Systems T2 - Adjunct Conference Proceedings: 17th International ACM Conference on Automotive User Interfaces and Interactive Vehicular Applications, September 22-25, 2025, Brisbane, Australia UR - https://doi.org/10.1145/3744335.3758487 Y1 - 2025 UR - https://doi.org/10.1145/3744335.3758487 SN - 979-8-4007-2014-7 SP - 88 EP - 93 PB - ACM CY - New York ER - TY - CHAP A1 - Raghvani, Vidhi A1 - Gerber, Michael A. A1 - Riener, Andreas T1 - Inclusive Vehicle Dashboard Design: Supporting Neuro diverse ADHD Drivers Through Visual Simplicity T2 - Adjunct Conference Proceedings: 17th International ACM Conference on Automotive User Interfaces and Interactive Vehicular Applications, September 22-25, 2025, Brisbane, Australia UR - https://doi.org/10.1145/3744335.3756860 Y1 - 2025 UR - https://doi.org/10.1145/3744335.3756860 SN - 979-8-4007-2014-7 SP - 359 EP - 361 PB - ACM CY - New York ER - TY - CHAP A1 - Karur, Krishna Sahithi A1 - Riener, Andreas A1 - Alvarez, Ignacio A1 - Wintersberger, Philipp A1 - Park, Jeongeun A1 - Lee, Seulchan T1 - 1st Workshop on Exploring the Potential of XAI and HMI to Alleviate Ethical, Legal, and Social Conflicts in Automated Vehicles T2 - Adjunct Conference Proceedings: 17th International ACM Conference on Automotive User Interfaces and Interactive Vehicular Applications, September 22-25, 2025, Brisbane, Australia UR - https://doi.org/10.1145/3744335.3749143 Y1 - 2025 UR - https://doi.org/10.1145/3744335.3749143 SN - 979-8-4007-2014-7 SP - 295 EP - 297 PB - ACM CY - New York ER - TY - CHAP A1 - Berger, Melanie A1 - Ebel, Patrick A1 - Riener, Andreas A1 - Alvarez, Ignacio A1 - Wintersberger, Philipp A1 - Sadeghian, Shadan T1 - Sustainable by Design: A Workshop on Life-Cycle-Aware Future Mobility T2 - Adjunct Conference Proceedings: 17th International ACM Conference on Automotive User Interfaces and Interactive Vehicular Applications, September 22-25, 2025, Brisbane, Australia UR - https://doi.org/10.1145/3744335.3749137 Y1 - 2025 UR - https://doi.org/10.1145/3744335.3749137 SN - 979-8-4007-2014-7 SP - 306 EP - 309 PB - ACM CY - New York ER - TY - CHAP A1 - Mohamed, Nourhan A1 - Gerber, Michael A. A1 - Riener, Andreas T1 - Investigation of Habituation Effects of Visual Variations of Cues to the Fallback-Driver for Automated Level 3 Vehicles T2 - Adjunct Conference Proceedings: 17th International ACM Conference on Automotive User Interfaces and Interactive Vehicular Applications, September 22-25, 2025, Brisbane, Australia UR - https://doi.org/10.1145/3744335.3756862 Y1 - 2025 UR - https://doi.org/10.1145/3744335.3756862 SN - 979-8-4007-2014-7 SP - 365 EP - 368 PB - ACM CY - New York ER - TY - CHAP A1 - Solombrino, Laetitia Pina-Lydia A1 - Gerber, Michael A. A1 - Riener, Andreas T1 - Dynamic Head-Up Display Design: Cognitive Load as a Parametric Driver T2 - Adjunct Conference Proceedings: 17th International ACM Conference on Automotive User Interfaces and Interactive Vehicular Applications, September 22-25, 2025, Brisbane, Australia UR - https://doi.org/10.1145/3744335.3756861 Y1 - 2025 UR - https://doi.org/10.1145/3744335.3756861 SN - 979-8-4007-2014-7 SP - 362 EP - 364 PB - ACM CY - New York ER - TY - CHAP A1 - Önbaş, Tuğcan A1 - Gerber, Michael A. A1 - Riener, Andreas T1 - Context-Aware Take-Over Requests for Promoting Emergency Corridor Formation in Level 3 Automated Vehicles T2 - Adjunct Conference Proceedings: 17th International ACM Conference on Automotive User Interfaces and Interactive Vehicular Applications, September 22-25, 2025, Brisbane, Australia UR - https://doi.org/10.1145/3744335.3756855 Y1 - 2025 UR - https://doi.org/10.1145/3744335.3756855 SN - 979-8-4007-2014-7 SP - 342 EP - 344 PB - ACM CY - New York ER - TY - CHAP A1 - Preiwisch, Anna A1 - Riener, Andreas T1 - Exploring Human Abuse of Automated Vehicles: A Review Framed by Robot Abuse Research T2 - Adjunct Conference Proceedings: 17th International ACM Conference on Automotive User Interfaces and Interactive Vehicular Applications, September 22-25, 2025, Brisbane, Australia UR - https://doi.org/10.1145/3744335.3758491 Y1 - 2025 UR - https://doi.org/10.1145/3744335.3758491 SN - 979-8-4007-2014-7 SP - 114 EP - 119 PB - ACM CY - New York ER - TY - CHAP A1 - Manger, Carina A1 - Albrecht, Kathrin A1 - Riener, Andreas ED - Maedche, Alexander ED - Beigl, Michael ED - Gerling, Kathrin ED - Mayer, Sven T1 - Explanation Needs in Automated Driving: Insights from German Driving Education and Vehicle Acquisition T2 - MuC'24: Proceedings of the 2024 Conference on Mensch und Computer N2 - As driving assistance driving systems become increasingly advanced, a correct understanding of the functionality of these systems is crucial for safe use. In this work we explored drivers’ explanation needs and current explanation methods from an important but underlooked perspective: driver training and vehicle acquisition. In a two-step approach, we conducted expert interviews with n = 7 driving instructors and vehicle salespeople in Germany and validated these results with an online survey of n = 105. Our results show that Driver Assistance Systems (DASs) and Advanced Driver Assistance Systems (ADASs), are currently covered in both driver training and vehicle acquisition but to a varying extent and in a very application-oriented manner. A drivers’ tendency for preferring comparative explanations that build upon knowledge about similar systems was found. Based on the combined results, we emphasize the need for mandatory and standardized explanation methods to ensure a safe transition to automated driving. UR - https://doi.org/10.1145/3670653.3670679 Y1 - 2024 UR - https://doi.org/10.1145/3670653.3670679 UR - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:573-50497 SN - 979-8-4007-0998-2 SP - 288 EP - 298 PB - ACM CY - New York ER - TY - CHAP A1 - Schuß, Martina A1 - Röhr, Tatjana A1 - Riener, Andreas ED - Maedche, Alexander ED - Beigl, Michael ED - Gerling, Kathrin ED - Mayer, Sven T1 - ‘Baymax’ or ‘RoboCop’? Exploring Different Feminine Avatar Personalities for Shared Automated Vehicles T2 - MuC'24: Proceedings of the 2024 Conference on Mensch und Computer UR - https://doi.org/10.1145/3670653.3670682 Y1 - 2024 UR - https://doi.org/10.1145/3670653.3670682 SN - 979-8-4007-0998-2 SP - 256 EP - 268 PB - ACM CY - New York ER - TY - CHAP A1 - Escher, Bengt A1 - Peintner, Jakob A1 - Riener, Andreas T1 - The Significance of the Bystander Effect on Personal Responsibility in Critical Situations in Shared Automated Vehicles T2 - 16th International Conference on Automotive User Interfaces and Interactive Vehicular Applications, September 22–25, 2024, Stanford, USA, Main Conference Proceedings N2 - Shared Automated Vehicles (SAVs) present a promising solution for future urban mobility. However, SAVs will reach the limits of their capabilities in some edge cases. Similar to personal AVs, passengers in SAVs might be utilized for this purpose, thus, methods for interaction between passengers and the automation or a teleoperator need to be explored. This study investigates whether the presence of other passengers leads to more passive behavior in critical situations (bystander effect). The results did not show significant differences in the participants’ behavior depending on whether the ride was experienced alone or with other passengers. However, the qualitative data indicate that the presence of other passengers can trigger psychological processes that promote a bystander effect. The findings emphasize the importance of considering group effects in the context of SAVs. By understanding these dynamics, we aim to help design SAVs to promote safer and more inclusive future transport systems. UR - https://doi.org/10.1145/3640792.3675704 Y1 - 2024 UR - https://doi.org/10.1145/3640792.3675704 UR - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:573-51643 SN - 979-8-4007-0510-6 SP - 384 EP - 394 PB - ACM CY - New York ER - TY - CHAP A1 - Holzhammer, Uwe A1 - Lenz, Maximilian A1 - Riener, Andreas A1 - Schweizer, Manuel A1 - Tutunaru, Robin ED - Wienrich, Carolin ED - Wintersberger, Philipp ED - Weyers, Benjamin T1 - 1st Workshop on “User Experience for Sustainability in the Age of Automated Driving and Electromobility” T2 - Mensch und Computer 2021 - Workshopband UR - https://doi.org/10.18420/muc2021-mci-ws15-125 KW - contextual UIs KW - automotive HMIs KW - automated driving KW - natural interaction KW - adaptive interfaces Y1 - 2021 UR - https://doi.org/10.18420/muc2021-mci-ws15-125 PB - Gesellschaft für Informatik e.V. CY - Bonn ER - TY - CHAP A1 - Frison, Anna-Katharina A1 - Wintersberger, Philipp A1 - Riener, Andreas A1 - Schartmüller, Clemens A1 - Boyle, Linda Ng A1 - Miller, Erika A1 - Weigl, Klemens T1 - In UX We Trust: Investigation of Aesthetics and Usability of Driver-Vehicle Interfaces and Their Impact on the Perception of Automated Driving T2 - CHI 2019: Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems UR - https://doi.org/10.1145/3290605.3300374 KW - automated driving systems KW - user experience KW - UX KW - trust KW - distrust KW - SAE J3016 KW - aesthetic KW - reliability Y1 - 2019 UR - https://doi.org/10.1145/3290605.3300374 SN - 978-1-4503-5970-2 PB - ACM CY - New York ER - TY - INPR A1 - Mosaferchi, Saeedeh A1 - Riener, Andreas A1 - Mortezapoursoufiani, Alireza A1 - Naddeo, Alessandro T1 - Enhancing UX in Automated Vehicles through Biophilic Interfaces: Insights from Prospective End Users T2 - SSRN UR - https://dx.doi.org/10.2139/ssrn.5359199 Y1 - 2025 UR - https://dx.doi.org/10.2139/ssrn.5359199 PB - Elsevier CY - Amsterdam ER - TY - CHAP A1 - Rosbach, Emely A1 - Ganz, Jonathan A1 - Ammeling, Jonas A1 - Riener, Andreas A1 - Aubreville, Marc ED - Palm, Christoph ED - Breininger, Katharina ED - Deserno, Thomas Martin ED - Handels, Heinz ED - Maier, Andreas ED - Maier-Hein, Klaus H. ED - Tolxdorff, Thomas T1 - Automation Bias in AI-assisted Medical Decision-making under Time Pressure in Computational Pathology T2 - Bildverarbeitung für die Medizin 2025: Proceedings, German Conference on Medical Image Computing, Regensburg March 09–11, 2025 UR - https://doi.org/10.1007/978-3-658-47422-5_27 Y1 - 2025 UR - https://doi.org/10.1007/978-3-658-47422-5_27 SN - 978-3-658-47422-5 SP - 129 EP - 134 PB - Springer Vieweg CY - Wiesbaden ER - TY - CHAP A1 - Irawati, Diah Ayu A1 - Bolukbasi, Elif A1 - Gerber, Michael A. A1 - Riener, Andreas T1 - The role of Explainable AI in the Design of Visual Texts for Trust Calibration in Level 3 Automated Vehicles T2 - 16th International Conference on Automotive User Interfaces and Interactive Vehicular Applications: Adjunct Conference Proceedings UR - https://doi.org/10.1145/3641308.3680514 Y1 - 2024 UR - https://doi.org/10.1145/3641308.3680514 SN - 979-8-4007-0520-5 SP - 300 EP - 303 PB - ACM CY - New York ER - TY - CHAP A1 - Bolukbasi, Elif A1 - Irawati, Diah Ayu A1 - Gerber, Michael A. A1 - Riener, Andreas T1 - Impact of Infotainment System Complexity on Driver Situation Awareness in Manual Vehicles T2 - 16th International Conference on Automotive User Interfaces and Interactive Vehicular Applications: Adjunct Conference Proceedings UR - https://doi.org/10.1145/3641308.3680518 Y1 - 2024 UR - https://doi.org/10.1145/3641308.3680518 SN - 979-8-4007-0520-5 SP - 312 EP - 315 PB - ACM CY - New York ER - TY - CHAP A1 - Irawati, Diah Ayu A1 - Bolukbasi, Elif A1 - Riener, Andreas ED - Sorce, Salvatore ED - Elagroudy, Passant ED - Khamis, Mohamed T1 - Advancing Generative AI Collaboration in Design-to-Code Workflows: Insights from Two Empirical Studies T2 - Proceedings of MUM 2025: The 24th International Conference on Mobile and Ubiquitous Multimedia N2 - For expert users to accept Generative AI (GenAI) as a true collaborative partner, it must move beyond simple task-awareness to an understanding of their workflow’s underlying structural rules. This paper introduces a paradigm for AI collaborators that moves beyond simple task awareness to an understanding of the semantic and hierarchical relationships within a component-based system. We investigate this concept within the context of the design-to-code workflow, where inefficiencies arise from the modification of components within design systems. Through two empirical studies with designers and developers, we found that GenAI output was often rejected because it violated the component hierarchy. Designers required granular and visual control for refinements, whereas developers valued automated setup but required transparent validation of the generated code’s logic. Based on these findings, we contribute design guidelines for achieving Component-Structure Awareness (CSA), with two core principles: the Atomic Recommender, which provides assistance that respects the component hierarchy, and Communication Archetypes, which allow GenAI to adapt its interaction style to the user’s role and the atomic nature of their task. This work provides a new, higher-level concept for designing the next generation of truly collaborative GenAI agents. UR - https://doi.org/10.1145/3771882.3771913 Y1 - 2025 UR - https://doi.org/10.1145/3771882.3771913 UR - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:573-64848 SN - 979-8-4007-2015-4 SP - 33 EP - 46 PB - ACM CY - New York ER - TY - JOUR A1 - Peintner, Jakob A1 - Manger, Carina A1 - Berger, Melanie A1 - Riener, Andreas T1 - Balancing transparency and control: The impact of AI explanation detail on user perception in automated vehicles JF - Transportation Research Interdisciplinary Perspectives N2 - As automated vehicle technology advances, explainable AI has emerged as a critical tool to enable users to understand and predict the behavior of AI systems, particularly in safety-critical applications such as automated driving. However, increased transparency in AI explanations may inadvertently contribute to an “illusion of control”, a cognitive bias in which drivers overestimate their influence or understanding of the AI’s actions. We aim to better understand how the level of detail in AI explanations affects users of automated vehicles. In a virtual reality study, N = 44 participants experienced different explanation levels (low, medium, high) in an automated ride (SAE L4) compared to a baseline condition with no explanations. The results show a significant improvement in participants’ user experience, acceptance, and explanation satisfaction, with more detailed explanations. Our findings also indicate that as AI explanations become more detailed, users’ perceived level of control increases significantly, although this perception does not correlate with actual control capabilities. At the same time, it decreased their desire to take control, indicating users’ susceptibility to the ’illusion of control’ bias in the context of automated driving. Overall, this suggests that the design of explanation interfaces should strive for a balanced level of detail that promotes AI transparency without causing cognitive overload. At the same time, explainable AI can be utilized to decrease users’ desire to intervene in the AI’s actions. UR - https://doi.org/10.1016/j.trip.2025.101742 Y1 - 2025 UR - https://doi.org/10.1016/j.trip.2025.101742 UR - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:573-64740 SN - 2590-1982 VL - 2025 IS - 34 PB - Elsevier CY - Amsterdam ER - TY - INPR A1 - Peintner, Jakob A1 - Manger, Carina A1 - Berger, Melanie A1 - Riener, Andreas T1 - Balancing Transparency and Control: The Impact of Ai Explanation Detail on User Perception in Automated Vehicles T2 - SSRN UR - https://dx.doi.org/10.2139/ssrn.5027721 Y1 - 2024 UR - https://dx.doi.org/10.2139/ssrn.5027721 PB - Elsevier CY - Amsterdam ER - TY - JOUR A1 - Rosbach, Emely A1 - Ammeling, Jonas A1 - Ganz, Jonathan A1 - Bertram, Christof A1 - Conrad, Thomas A1 - Riener, Andreas A1 - Aubreville, Marc T1 - Stuck on Suggestions: Automation Bias, the Anchoring Effect, and the Factors That Shape Them in Computational Pathology JF - Machine Learning for Biomedical Imaging N2 - Artificial intelligence (AI)-driven clinical decision support systems (CDSS) hold promise to improve diagnostic accuracy and efficiency in computational pathology. However, collaboration between human experts and AI may give rise to cognitive biases, such as automation and anchoring bias, wherein users may be inclined to blindly adopt system recommendations or be disproportionately influenced by the presence of AI predictions, even when they are inaccurate. These biases may be exacerbated under time pressure, pervasive in routine pathology diagnostics, or shaped by individual user characteristics. To investigate these effects, we conducted a web-based experiment in which trained pathology experts (n = 28) estimated tumor cell percentages twice: once independently and once with the aid of an AI. A subset of the estimates in each condition was performed under time constraints. Our findings indicate that AI integration generally enhances diagnostic performance. However, it also introduced a 7% automation bias rate, quantified as the number of accepted negative consultations, where a previously correct independent assessment gets overturned by inaccurate AI guidance. While time pressure did not increase the frequency of automation bias occurrence, it appeared to intensify its severity, as evidenced by a performance decline linked to increased automation reliance under cognitive load. A linear mixed-effects model (LMM) analysis, simulating weighted averaging, revealed a statistically significant positive coefficient for AI advice, indicating a moderate degree of anchoring on system output. This effect was further intensified under time pressure, suggesting that anchoring bias may become more pronounced when cognitive resources are limited. A secondary LMM evaluation assessing automation reliance, used as a proxy for both automation and anchoring bias, demonstrated that professional experience and self-efficacy were associated with reduced dependence on system support, whereas higher confidence during AI-assisted decision-making was linked to increased automation reliance. Together, these findings underscore the dual nature of AI integration in clinical workflows, offering performance benefits while also introducing risks of cognitive bias–driven diagnostic errors. As an initial investigation focused on a single medical specialty and diagnostic task, this study aims to lay the groundwork for future research to explore these phenomena across diverse clinical contexts, ultimately supporting the establishment of appropriate reliance on automated systems and the safe, effective integration of human–AI collaboration in medical decision-making. UR - https://doi.org/10.59275/j.melba.2026-87b1 Y1 - 2026 UR - https://doi.org/10.59275/j.melba.2026-87b1 UR - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:573-67787 SN - 2766-905X VL - 3 IS - MELBA–BVM 2025 Special Issue SP - 126 EP - 147 PB - Melba editors CY - [s. l.] ER -