TY - JOUR A1 - Paula, Daniel A1 - Bauder, Maximilian A1 - Pfeilschifter, Claus A1 - Petermeier, Franziska A1 - Kubjatko, Tibor A1 - Böhm, Klaus A1 - Riener, Andreas A1 - Schweiger, Hans-Georg T1 - Impact of Partially Automated Driving Functions on Forensic Accident Reconstruction: A Simulator Study on Driver Reaction Behavior in the Event of a Malfunctioning System Behavior JF - Sensors N2 - Partially automated driving functions (SAE Level 2) can control a vehicle’s longitudinal and lateral movements. However, taking over the driving task involves automation risks that the driver must manage. In severe accidents, the driver’s ability to avoid a collision must be assessed, considering their expected reaction behavior. The primary goal of this study is to generate essential data on driver reaction behavior in case of malfunctions in partially automated driving functions for use in legal affairs. A simulator study with two scenarios involving 32 subjects was conducted for this purpose. The first scenario investigated driver reactions to system limitations during cornering. The results show that none of the subjects could avoid leaving their lane and moving into the oncoming lane and, therefore, could not control the situation safely. Due to partial automation, we could also identify a new part of the reaction time, the hands-on time, which leads to increased steering reaction times of 1.18 to 1.74 s. The second scenario examined driver responses to phantom braking caused by AEBS. We found that 25 of the 32 subjects could not override the phantom braking by pressing the accelerator pedal, although 16 subjects were informed about the system analog to the actual vehicle manuals. Overall, the study suggests that the current legal perspective on vehicle control and the expected driver reaction behavior for accident avoidance should be reconsidered. UR - https://doi.org/10.3390/s23249785 Y1 - 2023 UR - https://doi.org/10.3390/s23249785 UR - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:573-42766 SN - 1424-8220 VL - 23 IS - 24 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 - JOUR A1 - Himmels, Chantal A1 - Parduzi, Arben A1 - Löcken, Andreas A1 - Protschky, Valentin A1 - Venrooij, Joost A1 - Riener, Andreas T1 - Validating risk behavior in driving simulation using naturalistic driving data JF - Transportation Research Part F: Traffic Psychology and Behaviour UR - https://doi.org/10.1016/j.trf.2024.09.026 Y1 - 2024 UR - https://doi.org/10.1016/j.trf.2024.09.026 SN - 1369-8478 SN - 1873-5517 VL - 2024 IS - 107 SP - 710 EP - 725 PB - Elsevier CY - Amsterdam ER - TY - JOUR A1 - Frison, Anna-Katharina A1 - Wintersberger, Philipp A1 - Riener, Andreas T1 - Resurrecting the ghost in the shell: a need-centered development approach for optimizing user experience in highly automated vehicles JF - Transportation Research Part F: Traffic Psychology and Behaviour UR - https://doi.org/10.1016/j.trf.2019.08.001 KW - automated driving KW - user experience KW - user acceptance KW - SAE J3016 KW - psychological needs KW - ISO 9241-210 Y1 - 2019 UR - https://doi.org/10.1016/j.trf.2019.08.001 SN - 1873-5517 VL - 2019 IS - 65 SP - 439 EP - 456 PB - Elsevier CY - Amsterdam ER - TY - JOUR A1 - Weigl, Klemens A1 - Schartmüller, Clemens A1 - Riener, Andreas A1 - Steinhauser, Marco T1 - Development of the Questionnaire on the Acceptance of Automated Driving (QAAD): Data-driven models for Level 3 and Level 5 automated driving JF - Transportation Research Part F: Traffic Psychology and Behaviour UR - https://doi.org/10.1016/j.trf.2021.09.011 KW - automated driving KW - level 3 KW - level 5 KW - questionnaire development KW - data-driven models KW - sustainability Y1 - 2021 UR - https://doi.org/10.1016/j.trf.2021.09.011 SN - 1369-8478 SN - 1873-5517 VL - 2021 IS - 83 SP - 42 EP - 59 PB - Elsevier CY - Amsterdam 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 - Himmels, Chantal A1 - Buchner, Claudia A1 - Schmitz, Jan A1 - Parduzi, Arben A1 - Riener, Andreas T1 - Validity of Driver Assistance Systems in Driving Simulators: A Comparative Study of Real-World Driving and Two Simulator Environments JF - International Journal of Human–Computer Interaction UR - https://doi.org/10.1080/10447318.2025.2495120 Y1 - 2025 UR - https://doi.org/10.1080/10447318.2025.2495120 SN - 1044-7318 VL - 41 IS - 23 SP - 15197 EP - 15214 PB - Taylor & Francis CY - London 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 - JOUR A1 - Wintersberger, Philipp A1 - Frison, Anna-Katharina A1 - Riener, Andreas A1 - von Sawitzky, Tamara T1 - Fostering User Acceptance and Trust in Fully Automated Vehicles: Evaluating the Potential of Augmented Reality JF - Presence: Virtual and Augmented Reality UR - https://doi.org/10.1162/pres_a_00320 Y1 - 2018 UR - https://doi.org/10.1162/pres_a_00320 SN - 1531-3263 VL - 27 SP - 46 EP - 62 PB - MIT Press CY - Cambridge ER - TY - JOUR A1 - Riener, Andreas A1 - Gabbard, Joseph A1 - Trivedi, Mohan T1 - Special Issue of "Presence: Virtual and Augmented Reality" Virtual and Augmented Reality for Autonomous Driving and Intelligent Vehicles: Guest Editors' Introduction JF - Presence: Virtual and Augmented Reality UR - https://doi.org/10.1162/pres_e_00323 Y1 - 2018 UR - https://doi.org/10.1162/pres_e_00323 SN - 1531-3263 VL - 27 SP - i EP - iv PB - MIT Press CY - Cambridge ER - TY - JOUR A1 - Haimerl, Mathias A1 - Colley, Mark A1 - Riener, Andreas T1 - Evaluation of Common External Communication Concepts of Automated Vehicles for People With Intellectual Disabilities JF - Proceedings of the ACM on Human-Computer Interaction UR - https://doi.org/10.1145/3546717 KW - accessibility KW - learning disorder KW - eHMI KW - automated vehicles KW - vulnerable road-users Y1 - 2022 UR - https://doi.org/10.1145/3546717 SN - 2573-0142 VL - 6 IS - MHCI 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 - 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 - 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 - Bauder, Maximilian A1 - Paula, Daniel A1 - Pfeilschifter, Claus A1 - Petermeier, Franziska A1 - Kubjatko, Tibor A1 - Riener, Andreas A1 - Schweiger, Hans-Georg T1 - Influences of Vehicle Communication on Human Driving Reactions: A Simulator Study on Reaction Times and Behavior for Forensic Accident Analysis JF - Sensors N2 - 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. UR - https://doi.org/10.3390/s24144481 Y1 - 2024 UR - https://doi.org/10.3390/s24144481 UR - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:573-48982 SN - 1424-8220 VL - 24 IS - 14 PB - MDPI CY - Basel ER -