TY - JOUR A1 - Göbel, Jan-Philipp A1 - Mertens, Jan Cedric A1 - Riener, Andreas T1 - Thermal imaging for the detection of driver impairment: evidence from a high-fidelity driving simulator study JF - Traffic Injury Prevention N2 - Objectives Driving under the influence of alcohol (DUI) remains a major contributor to fatal traffic crashes worldwide. With increasing regulatory pressure, such as requirements by Euro NCAP for in-vehicle impairment detection, there is a growing need for reliable, real-time monitoring solutions. While traditional DUI detection approaches focus on driving behavior or eye movement analysis, this study explores the potential of thermal imaging as a noninvasive alternative for detecting alcohol impairment. Methods We conducted a large-scale experimental study with 120 participants in a high-fidelity driving simulator, capturing thermal facial data under both non-impaired and alcohol-impaired conditions. A novel temperature extraction method was developed based on facial landmarks, incorporating multiple frames to reduce noise and improve temporal stability. Ambient cabin temperature was also recorded to normalize facial temperature readings and control for environmental influences. Several machine learning classifiers, including Logistic Regression, Random Forest, Support Vector Machine (SVM), and Gradient-Boosting Models, were trained using five facial temperature features (cheek, temple, ear, forehead, and nasal tip) and evaluated via five-fold subject-wise cross-validation. Results Significant temperature changes were observed in specific facial regions (particularly the cheek, ear, temple, and nasal tip) under alcohol influence. Among the evaluated models, Logistic Regression achieved the highest average classification accuracy (62%), while SVM demonstrated the most stable performance across folds. The model showed a slight conservative bias toward predicting the baseline (non-impaired) class, thereby reducing the risk of false positive classifications. Environmental conditions, including cabin temperature, were verified to be stable across both driving sessions, ensuring the validity of the physiological measurements. Conclusions This study demonstrates the feasibility of using thermal imaging for in-vehicle DUI detection under realistic conditions. Our contributions include the development of a robust facial temperature processing pipeline, the creation of a unique dataset collected under ecologically valid conditions, and a comprehensive comparison of seven state-of-the-art classification models. Thermal imaging represents a promising complementary modality for future driver monitoring systems focused on safety and impairment detection. UR - https://doi.org/10.1080/15389588.2026.2624006 Y1 - 2026 UR - https://doi.org/10.1080/15389588.2026.2624006 SN - 1538-9588 PB - Taylor & Francis CY - Philadelphia 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 - TY - CHAP A1 - Göbel, Jan-Philipp A1 - Mertens, Jan Cedric A1 - Riener, Andreas T1 - Evaluating Alcohol-Induced Impairment: A Comprehensive Study on Objective Tests and Subjective Self-Perception T2 - 2025 IEEE International Conference on Vehicular Electronics and Safety (ICVES) UR - https://doi.org/10.1109/ICVES65691.2025.11376306 Y1 - 2026 UR - https://doi.org/10.1109/ICVES65691.2025.11376306 SN - 978-1-6654-7778-9 SP - 73 EP - 80 PB - IEEE CY - Piscataway ER - TY - JOUR A1 - Riegler, Andreas A1 - Riener, Andreas A1 - Holzmann, Clemens T1 - A Systematic Review of Augmented Reality Applications for Automated Driving: 2009–2020 JF - Presence: Teleoperators and Virtual Environments UR - https://doi.org/10.1162/pres_a_00343 Y1 - 2019 UR - https://doi.org/10.1162/pres_a_00343 SN - 1054-7460 SN - 1531-3263 VL - 28 SP - 87 EP - 126 PB - MIT Press CY - Cambridge 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 - CHAP A1 - Göbel, Jan-Philipp A1 - Mertens, Jan Cedric A1 - Riener, Andreas ED - Muštra, Mario ED - Vuković, Josip ED - Božek, Jelena T1 - Benchmarking the MQ-3 Gas Sensor for Alcohol Impairment Detection: Insights from a Driving Simulator Study T2 - Proceedings of ELMAR-2025: 67th International Symposium ELMAR-2025, 15-17 September 2025, Zadar, Croatia UR - https://doi.org/10.1109/ELMAR66948.2025.11194001 Y1 - 2025 UR - https://doi.org/10.1109/ELMAR66948.2025.11194001 SN - 979-8-3315-9679-8 SP - 77 EP - 84 PB - IEEE CY - Piscataway 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 - 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 - Peintner, Jakob A1 - Manger, Carina A1 - Alvarez, Ignacio A1 - Riener, Andreas T1 - Enhancing Pedestrian Realism in Adverse-Weather Driving Simulations Using Motion Capture Data 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/10.1145/3744335.3758507 Y1 - 2025 UR - https://doi.org/10.1145/3744335.3758507 SN - 979-8-4007-2014-7 SP - 214 EP - 219 PB - ACM CY - New York ER - TY - CHAP A1 - Manger, Carina A1 - Preiwisch, Anna A1 - Riener, Andreas T1 - Invisible Barriers: Understanding and Supporting the Mobility Needs and Challenges of Individuals with Mental Health Conditions 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/10.1145/3744335.3758489 Y1 - 2025 UR - https://doi.org/10.1145/3744335.3758489 SN - 979-8-4007-2014-7 SP - 100 EP - 105 PB - ACM CY - New York ER - TY - JOUR A1 - Manger, Carina A1 - Vogl, Annalena A1 - Riener, Andreas T1 - Cognitive Biases in User Interaction with Automated Vehicles: The Influence of Explainability and Mental Models JF - Applied Sciences N2 - To develop truly human-centered automated systems, it is essential to acknowledge that human reasoning is prone to systematic deviations from rational judgment, known as Cognitive Biases. The present study investigated such flawed reasoning in the context of automated driving. In a multi-step study with N = 34 participants, the occurrence of four Cognitive Biases was examined: Truthiness Effect, Automation Bias, Action Bias, and Illusory Control. Additionally, the study explored how the Explainability of the automation’s behavior and the driver’s Mental Model influenced the manifestation of these biases. The findings indicate a notable susceptibility to the Truthiness Effect and Illusory Control, although all biases appeared highly dependent on the specific driving context. Moreover, Explainability strongly impacted the perceived credibility of information and participants’ agreement with the system’s behavior. Given the exploratory nature of the study, this work aims to initiate a discussion on how Cognitive Biases shape human reasoning and decision-making in interactions with automated vehicles. Based on the results, several directions for future research are proposed: (1) investigation of additional cognitive biases, (2) analysis of biases across different levels of automation, (3) exploration of mitigation strategies versus deliberate use of biases, (4) examination of dynamic and context-dependent manifestations, and (5) validation in high-fidelity simulations or real-world settings. UR - https://doi.org/10.3390/app152011030 Y1 - 2025 UR - https://doi.org/10.3390/app152011030 UR - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:573-63831 SN - 2076-3417 VL - 15 IS - 20 PB - MDPI CY - Basel ER - TY - JOUR A1 - Kolff, Maurice A1 - Himmels, Chantal A1 - Venrooij, Joost A1 - Parduzi, Arben A1 - Pool, Daan M. A1 - Riener, Andreas A1 - Mulder, Max T1 - Effect of motion mismatches on ratings of motion incongruence and simulator sickness in urban driving simulations JF - Transportation Research Part F: Traffic Psychology and Behaviour N2 - This paper investigates the effects of motion mismatches on simulator sickness and subjective ratings of the motion. In an open-loop driving simulator experiment, participants were driven through a recorded urban drive twelve times, in which mismatches were induced by manipulating the following three aspects in motion cueing: (i) mismatches in specific vehicle axes, (ii) mismatch types (scaling, missing, and false cues), and (iii) inconsistent scaling between different motion axes. Subjects (N=52) reported simulator sickness post-hoc (after each drive), as well as continuously during each drive, a first in simulator sickness research. Furthermore, subjective post-hoc motion incongruence ratings on the quality of the motion were extracted. Results show that longitudinal motion mismatches lead to the most simulator sickness and the highest ratings, followed by mismatches in lateral motion, then yaw rate. False cues induce the most sickness, followed by missing and then scaled motion. Inconsistent scaling between the axes has no significant effect. The continuous sickness ratings support that the occurrence and severity of simulator sickness are indeed related to mismatches in simulator motion of specific maneuvers. This paper contributes to an improved understanding of the relationship between simulator motion and sickness, allowing for more targeted motion cueing strategies to prevent and reduce sickness in driving simulators. These strategies may include the appropriate selection of the simulator, the motion cueing, and the sample of participants, following the presented results. UR - https://doi.org/10.1016/j.trf.2025.103370 Y1 - 2025 UR - https://doi.org/10.1016/j.trf.2025.103370 UR - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:573-63433 SN - 1873-5517 VL - 2025 IS - 115 PB - Elsevier CY - Amsterdam ER - TY - CHAP A1 - Peintner, Jakob A1 - Malve, Bhavana A1 - Sadeghian, Shadan A1 - Riener, Andreas T1 - Driving Together: An Analysis of Passengers' Needs and Desire for Cooperative Control in Automated Vehicles T2 - MuC´25: Proceedings of the 2025 Conference on Mensch und Computer N2 - Driving automation aims to enhance comfort, safety, and traffic flow by removing the human driver from the control loop. However, the human experience of commuting involves more than just reaching a destination or assuming the role of a driver. Factors like personal driving style and courtesy towards fellow road users are integral to the driving experience but often overlooked in the development of driving algorithms for automated vehicles. In this study, we explored the needs of passengers in highly automated vehicles. A qualitative use case analysis was conducted (N=16). In a second study, N=15 participants experienced the resulting use cases in an automated vehicle. In these scenarios, they were able to interact with the automation through a cooperation HMI. Results indicate that most participants expressed a desire for cooperative driving, albeit varying with the driving situation. Moreover, allowing cooperation improves passengers’ overall experience by satisfying psychological needs for autonomy, security, competence, and relatedness. UR - https://doi.org/10.1145/3743049.3743061 Y1 - 2025 UR - https://doi.org/10.1145/3743049.3743061 UR - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:573-62112 SN - 979-8-4007-1582-2 SP - 333 EP - 344 PB - ACM CY - New York ER - TY - CHAP A1 - Mosaferchi, Saeedeh A1 - Riener, Andreas A1 - Najafi-Ghobadi, Khadijeh A1 - Li, Tingnan A1 - Naddeo, Alessandro T1 - Shaping Affective Trust in Automated Vehicles: The Interplay of Initial Trust, Gender, and Biophilic Design T2 - 2025 IEEE Intelligent Vehicles Symposium (IV) UR - https://doi.org/10.1109/IV64158.2025.11097605 Y1 - 2025 UR - https://doi.org/10.1109/IV64158.2025.11097605 SN - 979-8-3315-3803-3 SP - 1293 EP - 1298 PB - IEEE CY - Piscataway ER - TY - CHAP A1 - Escher, Bengt A1 - Herde, Jonas A1 - Nikolai, Florian A1 - Riener, Andreas T1 - Regulating Teleoperation on Public Roads: Key Takeaways From an Expert Workshop T2 - 2025 IEEE Intelligent Vehicles Symposium (IV) UR - https://doi.org/10.1109/IV64158.2025.11097504 Y1 - 2025 UR - https://doi.org/10.1109/IV64158.2025.11097504 SN - 979-8-3315-3803-3 SP - 2601 EP - 2606 PB - IEEE CY - Piscataway ER - TY - CHAP A1 - Haimerl, Mathias A1 - Radetzky, Laura A1 - Brenner, Evi Stefanie A1 - Strapak, Viktoria A1 - Riener, Andreas T1 - Leveling the Playing Field BT - Neurodiversity in Research using the Potential of Gamified Questionnaires T2 - MuC´25: Proceedings of the 2025 Conference on Mensch und Computer N2 - Participating in studies with lengthy questionnaires can be exhausting, especially for individuals with disabilities or neurodivergent individuals. Gamification can potentially enhance user experience (UX) and, thus, create a more inclusive engagement with studies. This study investigates gamified studies’ UX for neurodivergent persons. A gamified version of the Quality of Life Enjoyment and Satisfaction Questionnaire – Short Form (Q-LES-Q-SF) was created using motivational theory and the gamification frameworks Marczewski’s Periodic Table of Gamification Elements and Gamification User Types Hexad Scale. First results of a preliminary comparative survey with N = 72 (gamified n = 31) suggest that gamification increases the UX for neurodivergent participants. UR - https://doi.org/10.1145/3743049.3748578 Y1 - 2025 UR - https://doi.org/10.1145/3743049.3748578 UR - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:573-61794 SN - 979-8-4007-1582-2 SP - 659 EP - 667 PB - ACM CY - New York ER - TY - CHAP A1 - Rollwagen, Alice A1 - Horn, Alexander A1 - Schmidtner, Stefanie A1 - Riener, Andreas T1 - Communicating Uncertainty in Arrival Time Predictions for Public Transport: A Comparison of Point and Interval Forecasts T2 - MuC´25: Proceedings of the 2025 Conference on Mensch und Computer N2 - In public transport, arrival times are typically communicated as point forecasts, aiming to present precise estimates. However, current prediction models are unable to provide such precise and reliable estimates due to unpredictable events. This results in arrival times on passenger information systems appearing inaccurate due to the lack of communicated uncertainty. We therefore investigated interval forecasts as an alternative in an online study, aiming to better communicate uncertainty in arrival times. Our findings indicate that interval forecasts improve the communication of uncertainty. Further, user satisfaction was driven primarily by waiting time, and this relationship was moderated by the forecast concept. Point forecasts were only well received when the bus arrived as predicted, otherwise users preferred the broader interval forecasts. Participants valued accuracy over precision when judging arrival times. UR - https://doi.org/10.1145/3743049.3748542 Y1 - 2025 UR - https://doi.org/10.1145/3743049.3748542 UR - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:573-61783 SN - 979-8-4007-1582-2 SP - 515 EP - 519 PB - ACM CY - New York ER - TY - CHAP A1 - Neufeld, Lisa A1 - Heim, Elena A1 - Klinger, Madita Joy A1 - Himmels, Chantal A1 - Böck, Henrike A1 - Riener, Andreas T1 - Beats vs. Talks: Alleviating Virtual Reality Sickness with Music and Podcasts T2 - MuC´25: Proceedings of the 2025 Conference on Mensch und Computer N2 - As virtual reality (VR) technology advances, its potential for widespread application increases. However, VR sickness (VRS) remains a major obstacle to broader adoption. This study investigates the effect of auditory stimuli on VRS during VR experiences. It contrasts the effects of music and podcasts, with a no-sound environment serving as reference. Twenty-three participants experienced three different auditory conditions during a VR roller coaster ride in a randomized, balanced order. VRS was quantified using the Simulator Sickness Questionnaire (SSQ) and physiological data. The SSQ results indicate that music significantly reduced oculomotor disturbances and disorientation, while podcasts had no positive effects. The physiological data demonstrated no significant effects. The majority of participants preferred the music scenario, describing it as relaxing and pleasant. This highlights the potential of music, especially when self-selected and perceived as pleasant, to improve VR experience by significantly reducing VRS. This effect appears to be independent of physiological data. UR - https://doi.org/10.1145/3743049.3748539 Y1 - 2025 UR - https://doi.org/10.1145/3743049.3748539 UR - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:573-61776 SN - 979-8-4007-1582-2 SP - 488 EP - 493 PB - ACM CY - New York ER - TY - CHAP A1 - Appel, Patricia B. A1 - Böck, Henrike A1 - Hompf, Filippa Viktoria Franziska A1 - Plümer, Kolja Sam A1 - Russ, Sarah A1 - Riener, Andreas T1 - Understanding Bystander Preferences for Medical Emergency Support Measures in VR-Simulated eVTOL Flights T2 - MuC´25: Proceedings of the 2025 Conference on Mensch und Computer N2 - With the emergence of Urban Air Mobility (UAM), eVTOLs are set to revolutionize the way of traveling. However, there will be situations where things do not go as planned, such as medical emergencies during a flight. Due to the lack of cabin crew, tailored support measures must be identified to ensure passenger safety and well-being. To explore how potential passengers perceive medical emergencies on board as bystanders and what kind of assistance they expect, we conducted a VR study. The results show significant increases in negative emotions during a medical in-flight emergency. Moreover, support measures involving human interaction were rated significantly higher than those relying solely on information displayed on onboard screens. Particularly well-received were pilot announcements. For UAM to enter the market successfully, it is essential to address passenger needs in critical situations. Our findings provide valuable insights into shaping effective and user-centered emergency support strategies for eVTOL operations. UR - https://doi.org/10.1145/3743049.3748549 Y1 - 2025 UR - https://doi.org/10.1145/3743049.3748549 UR - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:573-61761 SN - 979-8-4007-1582-2 SP - 755 EP - 760 PB - ACM CY - New York ER -