TY - INPR A1 - Rosbach, Emely A1 - Ganz, Jonathan A1 - Ammeling, Jonas A1 - Riener, Andreas A1 - Aubreville, Marc T1 - Automation Bias in AI-Assisted Medical Decision-Making under Time Pressure in Computational Pathology UR - https://doi.org/10.48550/arXiv.2411.00998 Y1 - 2024 UR - https://doi.org/10.48550/arXiv.2411.00998 ER - TY - CHAP A1 - Ehsan, Upol A1 - Wintersberger, Philipp A1 - Liao, Q. Vera A1 - Mara, Martina A1 - Streit, Marc A1 - Wachter, Sandra A1 - Riener, Andreas A1 - Riedl, Mark O. T1 - Operationalizing Human-Centered Perspectives in Explainable AI T2 - CHI’21: Extended Abstracts of the 2021 CHI Conference on Human Factors in Computing Systems UR - https://doi.org/10.1145/3411763.3441342 KW - Artificial Intelligence KW - Explainable AI KW - human-centered Y1 - 2021 UR - https://doi.org/10.1145/3411763.3441342 SN - 978-1-4503-8095-9 PB - ACM CY - New York ER - TY - CHAP A1 - Ehsan, Upol A1 - Watkins, Elizabeth Anne A1 - Wintersberger, Philipp A1 - Manger, Carina A1 - Hubig, Nina A1 - Savage, Saiph A1 - Weisz, Justin A1 - Riener, Andreas ED - Yamashita, Naomi ED - Evers, Vanessa ED - Yatani, Koji ED - Ding, Xianghua T1 - New Frontiers of Human-centered Explainable AI (HCXAI): Participatory Civic AI, Benchmarking LLMs, XAI Hallucinations, and Responsible AI Audits T2 - CHI’25: Extended Abstracts of the 2025 CHI Conference on Human Factors in Computing Systems UR - https://doi.org/10.1145/3706599.3706713 Y1 - 2025 UR - https://doi.org/10.1145/3706599.3706713 SN - 979-8-4007-1395-8 PB - ACM CY - New York ER - TY - CHAP A1 - Ehsan, Upol A1 - Watkins, Elizabeth Anne A1 - Wintersberger, Philipp A1 - Manger, Carina A1 - Kim, Sunnie S. Y. A1 - Van Berkel, Niels A1 - Riener, Andreas A1 - Riedl, Mark O ED - Mueller, Florian 'Floyd' ED - Kyburz, Penny ED - Williamson, Julie R. ED - Sas, Corina T1 - Human-Centered Explainable AI (HCXAI): Reloading Explainability in the Era of Large Language Models (LLMs) T2 - CHI'24: Extended Abstracts of the 2024 CHI Conference on Human Factors in Computing Systems UR - https://doi.org/10.1145/3613905.3636311 Y1 - 2024 UR - https://doi.org/10.1145/3613905.3636311 SN - 979-8-4007-0331-7 PB - ACM CY - New York ER - TY - JOUR A1 - Janssen, Christian P. A1 - Boyle, Linda Ng A1 - Ju, Wendy A1 - Riener, Andreas A1 - Alvarez, Ignacio T1 - Agents, environments, scenarios: A framework for examining models and simulations of human-vehicle interaction JF - Transportation Research Interdisciplinary Perspectives N2 - This paper provides a framework for examining human-vehicle interactions with respect to three dimensions that can involve models or simulations: the agents, the environments, and the scenarios. Agents are considered on a spectrum from human to artificial actors. Environments are considered on a spectrum from simulated to real. Scenarios are considered on a spectrum from constrained to unconstrained. It is argued that these three dimensions capture key differences in research approaches within the field of human-vehicle interaction, and that explicitly situating research and discussions within this framework will allow researchers to better compare and contrast research outcomes and contributions. The framework is used to locate different disciplines in the community with respect to one another, and to identify areas which are as-yet unexplored. UR - https://doi.org/10.1016/j.trip.2020.100214 KW - theory KW - models KW - simulation KW - human-vehicle interaction KW - environment KW - scenario KW - agent KW - fidelity KW - control Y1 - 2020 UR - https://doi.org/10.1016/j.trip.2020.100214 UR - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:573-9259 SN - 2590-1982 VL - 2020 IS - 8 PB - Elsevier CY - Amsterdam ER - TY - JOUR A1 - Kundinger, Thomas A1 - Sofra, Nikoletta A1 - Riener, Andreas T1 - Assessment of the Potential of Wrist-Worn Wearable Sensors for Driver Drowsiness Detection JF - Sensors N2 - Drowsy driving imposes a high safety risk. Current systems often use driving behavior parameters for driver drowsiness detection. The continuous driving automation reduces the availability of these parameters, therefore reducing the scope of such methods. Especially, techniques that include physiological measurements seem to be a promising alternative. However, in a dynamic environment such as driving, only non- or minimal intrusive methods are accepted, and vibrations from the roadbed could lead to degraded sensor technology. This work contributes to driver drowsiness detection with a machine learning approach applied solely to physiological data collected from a non-intrusive retrofittable system in the form of a wrist-worn wearable sensor. To check accuracy and feasibility, results are compared with reference data from a medical-grade ECG device. A user study with 30 participants in a high-fidelity driving simulator was conducted. Several machine learning algorithms for binary classification were applied in user-dependent and independent tests. Results provide evidence that the non-intrusive setting achieves a similar accuracy as compared to the medical-grade device, and high accuracies (>92%) could be achieved, especially in a user-dependent scenario. The proposed approach offers new possibilities for human–machine interaction in a car and especially for driver state monitoring in the field of automated driving. UR - https://doi.org/10.3390/s20041029 KW - drowsiness detection KW - driver state KW - simulator study KW - physiological measures KW - machine learning KW - wearable sensors KW - automated driving Y1 - 2020 UR - https://doi.org/10.3390/s20041029 UR - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:573-9506 SN - 1424-8220 VL - 20 IS - 4 PB - MDPI CY - Basel ER - TY - CHAP A1 - Kundinger, Thomas A1 - Riener, Andreas A1 - Sofra, Nikoletta A1 - Weigl, Klemens T1 - Drowsiness Detection and Warning in Manual and Automated Driving: Results from Subjective Evaluation T2 - Proceedings: 10th International ACM Conference on Automotive User Interfaces and Interactive Vehicular Applications UR - https://doi.org/10.1145/3239060.3239073 KW - Automated Driving KW - driver drowsiness detection KW - driver state KW - simulator study KW - Subjective methods Y1 - 2018 UR - https://doi.org/10.1145/3239060.3239073 SN - 978-1-4503-5946-7 SP - 229 EP - 236 PB - ACM CY - New York ER - TY - CHAP A1 - Kundinger, Thomas A1 - Riener, Andreas A1 - Sofra, Nikoletta A1 - Weigl, Klemens T1 - Driver drowsiness in automated and manual driving: insights from a test track study T2 - IUI 2020: Proceedings of the 25th International Conference on Intelligent User Interfaces UR - https://doi.org/10.1145/3377325.3377506 KW - automated driving KW - driver drowsiness detection KW - driver state KW - field study KW - subjective methods KW - wearables Y1 - 2020 UR - https://doi.org/10.1145/3377325.3377506 SN - 978-1-4503-7118-6 SP - 369 EP - 379 PB - ACM CY - New York ER - TY - CHAP A1 - Riener, Andreas A1 - Geisler, Stefan A1 - Pfleging, Bastian A1 - von Sawitzky, Tamara A1 - Detjen, Henrik T1 - 8th Workshop Automotive HMIs: UI Research in the Age of New Digital Realities T2 - Mensch und Computer 2019 - Workshopband UR - https://doi.org/10.18420/muc2019-ws-282 KW - user interfaces KW - automotive HMIs KW - automated driving KW - interaction concepts Y1 - 2019 UR - https://doi.org/10.18420/muc2019-ws-282 SP - 397 EP - 399 PB - Gesellschaft für Informatik CY - Bonn ER - TY - CHAP A1 - Kundinger, Thomas A1 - Riener, Andreas A1 - Sofra, Nikoletta ED - Burghardt, Manuel ED - Wimmer, Raphael ED - Wolff, Christian ED - Womser-Hacker, Christa T1 - A Robust Drowsiness Detection Method based on Vehicle and Driver Vital Data T2 - Mensch und Computer 2017 - Workshopband UR - https://doi.org/10.18420/muc2017-ws09-0307 Y1 - 2017 UR - https://doi.org/10.18420/muc2017-ws09-0307 SP - 433 EP - 440 PB - Gesellschaft für Informatik CY - Regensburg ER -