@article{HimmelsBuchnerSchmitzetal.2025, author = {Himmels, Chantal and Buchner, Claudia and Schmitz, Jan and Parduzi, Arben and Riener, Andreas}, title = {Validity of Driver Assistance Systems in Driving Simulators: A Comparative Study of Real-World Driving and Two Simulator Environments}, volume = {41}, journal = {International Journal of Human-Computer Interaction}, number = {23}, publisher = {Taylor \& Francis}, address = {London}, issn = {1044-7318}, doi = {https://doi.org/10.1080/10447318.2025.2495120}, pages = {15197 -- 15214}, year = {2025}, language = {en} } @article{RieglerRienerHolzmann2019, author = {Riegler, Andreas and Riener, Andreas and Holzmann, Clemens}, title = {A Systematic Review of Augmented Reality Applications for Automated Driving: 2009-2020}, volume = {28}, journal = {Presence: Teleoperators and Virtual Environments}, publisher = {MIT Press}, address = {Cambridge}, issn = {1054-7460}, doi = {https://doi.org/10.1162/pres_a_00343}, pages = {87 -- 126}, year = {2019}, language = {en} } @article{WintersbergerFrisonRieneretal.2018, author = {Wintersberger, Philipp and Frison, Anna-Katharina and Riener, Andreas and von Sawitzky, Tamara}, title = {Fostering User Acceptance and Trust in Fully Automated Vehicles: Evaluating the Potential of Augmented Reality}, volume = {27}, journal = {Presence: Virtual and Augmented Reality}, publisher = {MIT Press}, address = {Cambridge}, issn = {1531-3263}, doi = {https://doi.org/10.1162/pres_a_00320}, pages = {46 -- 62}, year = {2018}, language = {en} } @article{RienerGabbardTrivedi2018, author = {Riener, Andreas and Gabbard, Joseph and Trivedi, Mohan}, title = {Special Issue of "Presence: Virtual and Augmented Reality" Virtual and Augmented Reality for Autonomous Driving and Intelligent Vehicles: Guest Editors' Introduction}, volume = {27}, journal = {Presence: Virtual and Augmented Reality}, publisher = {MIT Press}, address = {Cambridge}, issn = {1531-3263}, doi = {https://doi.org/10.1162/pres_e_00323}, pages = {i -- iv}, year = {2018}, language = {en} } @article{HaimerlColleyRiener2022, author = {Haimerl, Mathias and Colley, Mark and Riener, Andreas}, title = {Evaluation of Common External Communication Concepts of Automated Vehicles for People With Intellectual Disabilities}, volume = {6}, pages = {182}, journal = {Proceedings of the ACM on Human-Computer Interaction}, number = {MHCI}, publisher = {ACM}, address = {New York}, issn = {2573-0142}, doi = {https://doi.org/10.1145/3546717}, year = {2022}, language = {en} } @article{PeintnerMangerBergeretal.2025, author = {Peintner, Jakob and Manger, Carina and Berger, Melanie and Riener, Andreas}, title = {Balancing transparency and control: The impact of AI explanation detail on user perception in automated vehicles}, volume = {2025}, pages = {101742}, journal = {Transportation Research Interdisciplinary Perspectives}, number = {34}, publisher = {Elsevier}, address = {Amsterdam}, issn = {2590-1982}, doi = {https://doi.org/10.1016/j.trip.2025.101742}, year = {2025}, abstract = {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.}, language = {en} } @article{RosbachAmmelingGanzetal.2026, author = {Rosbach, Emely and Ammeling, Jonas and Ganz, Jonathan and Bertram, Christof and Conrad, Thomas and Riener, Andreas and Aubreville, Marc}, title = {Stuck on Suggestions: Automation Bias, the Anchoring Effect, and the Factors That Shape Them in Computational Pathology}, volume = {3}, pages = {2026:007}, journal = {Machine Learning for Biomedical Imaging}, number = {MELBA-BVM 2025 Special Issue}, publisher = {Melba editors}, address = {[s. l.]}, issn = {2766-905X}, doi = {https://doi.org/10.59275/j.melba.2026-87b1}, pages = {126 -- 147}, year = {2026}, abstract = {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.}, language = {en} } @article{GoebelMertensRiener2026, author = {G{\"o}bel, Jan-Philipp and Mertens, Jan Cedric and Riener, Andreas}, title = {Thermal imaging for the detection of driver impairment: evidence from a high-fidelity driving simulator study}, journal = {Traffic Injury Prevention}, publisher = {Taylor \& Francis}, address = {Philadelphia}, issn = {1538-9588}, doi = {https://doi.org/10.1080/15389588.2026.2624006}, year = {2026}, abstract = {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.}, language = {en} } @article{BauderPaulaPfeilschifteretal.2024, author = {Bauder, Maximilian and Paula, Daniel and Pfeilschifter, Claus and Petermeier, Franziska and Kubjatko, Tibor and Riener, Andreas and Schweiger, Hans-Georg}, title = {Influences of Vehicle Communication on Human Driving Reactions: A Simulator Study on Reaction Times and Behavior for Forensic Accident Analysis}, volume = {24}, pages = {4481}, journal = {Sensors}, number = {14}, publisher = {MDPI}, address = {Basel}, issn = {1424-8220}, doi = {https://doi.org/10.3390/s24144481}, year = {2024}, abstract = {Cooperative intelligent transport systems (C-ITSs) are mass-produced and sold in Europe, promising enhanced safety and comfort. Direct vehicle communication, known as vehicle-to-everything (V2X) communication, is crucial in this context. Drivers receive warnings about potential hazards by exchanging vehicle status and environmental data with other communication-enabled vehicles. However, the impact of these warnings on drivers and their inclusion in accident reconstruction remains uncertain. Unlike sensor-based warnings, V2X warnings may not provide a visible reason for the alert, potentially affecting reaction times and behavior. In this work, a simulator study on V2X warnings was conducted with 32 participants to generate findings on reaction times and behavior for accident reconstruction in connection with these systems. Two scenarios from the Car-2-Car Communication Consortium were implemented: "Stationary Vehicle Warning—Broken-Down Vehicle" and "Dangerous Situation—Electronic Emergency Brake Lights". Volkswagen's warning concept was utilized, as they are the sole provider of cooperative vehicles in Europe. Results show that V2X warnings without visible reasons did not negatively impact reaction times or behavior, with average reaction times between 0.58 s (steering) and 0.69 s (braking). No significant distraction or search for warning reasons was observed. However, additional information in the warnings caused confusion and was seldom noticed by subjects. In this study, participants responded correctly and appropriately to the shown false-positive warnings. A wrong reaction triggering an accident is possible but unlikely. Overall, V2X warnings showed no negative impacts compared with sensor-based systems. This means that there are no differences in accident reconstruction regarding the source of the warning (sensors or communication). However, it is important that it is known that there was a warning, which is why the occurrence of V2X warnings should also be saved in the EDR in the future.}, language = {en} }