TY - JOUR A1 - Sánchez Morales, Eduardo A1 - Dauth, Julian A1 - Huber, Bertold A1 - García Higuera, Andrés A1 - Botsch, Michael T1 - High precision outdoor and indoor reference state estimation for testing autonomous vehicles JF - Sensors N2 - A current trend in automotive research is autonomous driving. For the proper testing and validation of automated driving functions a reference vehicle state is required. Global Navigation Satellite Systems (GNSS) are useful in the automation of the vehicles because of their practicality and accuracy. However, there are situations where the satellite signal is absent or unusable. This research work presents a methodology that addresses those situations, thus largely reducing the dependency of Inertial Navigation Systems (INSs) on the SatNav. The proposed methodology includes (1) a standstill recognition based on machine learning, (2) a detailed mathematical description of the horizontation of inertial measurements, (3) sensor fusion by means of statistical filtering, (4) an outlier detection for correction data, (5) a drift detector, and (6) a novel LiDAR-based Positioning Method (LbPM) for indoor navigation. The robustness and accuracy of the methodology are validated with a state-of-the-art INS with Real-Time Kinematic (RTK) correction data. The results obtained show a great improvement in the accuracy of vehicle state estimation under adverse driving conditions, such as when the correction data is corrupted, when there are extended periods with no correction data and in the case of drifting. The proposed LbPM method achieves an accuracy closely resembling that of a system with RTK. UR - https://doi.org/10.3390/s21041131 KW - machine learning KW - autonomous vehicles KW - Inertial Navigation System KW - Satellite Navigation KW - Real-Time Kinematic KW - indoor navigation KW - reference state Y1 - 2021 UR - https://doi.org/10.3390/s21041131 UR - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:573-10932 SN - 1424-8220 N1 - "This paper is an extended version of our paper published in : Sánchez Morales, E.; Botsch, M.; Huber, B.; García Higuera, A. High precision indoor positioning by means of LiDAR. In Proceedings of the 2019 DGON Inertial Sensors and Systems (ISS), Braunschweig, Germany, 10–11 September 2019." VL - 21 IS - 4 PB - MDPI CY - Basel ER - TY - JOUR A1 - Schartmüller, Clemens A1 - Weigl, Klemens A1 - Löcken, Andreas A1 - Wintersberger, Philipp A1 - Steinhauser, Marco A1 - Riener, Andreas T1 - Displays for Productive Non-Driving Related Tasks: Visual Behavior and Its Impact in Conditionally Automated Driving JF - Multimodal Technologies and Interaction N2 - (1) Background: Primary driving tasks are increasingly being handled by vehicle automation so that support for non-driving related tasks (NDRTs) is becoming more and more important. In SAE L3 automation, vehicles can require the driver-passenger to take over driving controls, though. Interfaces for NDRTs must therefore guarantee safe operation and should also support productive work. (2) Method: We conducted a within-subjects driving simulator study (N=53) comparing Heads-Up Displays (HUDs) and Auditory Speech Displays (ASDs) for productive NDRT engagement. In this article, we assess the NDRT displays’ effectiveness by evaluating eye-tracking measures and setting them into relation to workload measures, self-ratings, and NDRT/take-over performance. (3) Results: Our data highlights substantially higher gaze dispersion but more extensive glances on the road center in the auditory condition than the HUD condition during automated driving. We further observed potentially safety-critical glance deviations from the road during take-overs after a HUD was used. These differences are reflected in self-ratings, workload indicators and take-over reaction times, but not in driving performance. (4) Conclusion: NDRT interfaces can influence visual attention even beyond their usage during automated driving. In particular, the HUD has resulted in safety-critical glances during manual driving after take-overs. We found this impacted workload and productivity but not driving performance UR - https://doi.org/10.3390/mti5040021 KW - automated driving KW - take-over requests KW - conditional automation KW - non-driving related tasks KW - eye-tracking KW - performance KW - behavior KW - displays KW - productivity KW - visual attention Y1 - 2021 UR - https://doi.org/10.3390/mti5040021 UR - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:573-10965 SN - 2414-4088 VL - 5 IS - 4 PB - MDPI CY - Basel ER - TY - JOUR A1 - Riegler, Andreas A1 - Riener, Andreas A1 - Holzmann, Clemens T1 - A systematic review of virtual reality applications for automated driving: 2009–2020 JF - Frontiers in human dynamics N2 - While virtual reality (VR) interfaces have been researched extensively over the last decades, studies on their application in vehicles have only recently advanced. In this paper, we systematically review 12 years of VR research in the context of automated driving (AD), from 2009 to 2020. Due to the multitude of possibilities for studies with regard to VR technology, at present, the pool of findings is heterogeneous and non-transparent. We investigated N = 176 scientific papers of relevant journals and conferences with the goal to analyze the status quo of existing VR studies in AD, and to classify the related literature into application areas. We provide insights into the utilization of VR technology which is applicable at specific level of vehicle automation and for different users (drivers, passengers, pedestrians) and tasks. Results show that most studies focused on designing automotive experiences in VR, safety aspects, and vulnerable road users. Trust, simulator and motion sickness, and external human-machine interfaces (eHMIs) also marked a significant portion of the published papers, however a wide range of different parameters was investigated by researchers. Finally, we discuss a set of open challenges, and give recommendation for future research in automated driving at the VR side of the reality-virtuality continuum. UR - https://doi.org/10.3389/fhumd.2021.689856 KW - virtual reality KW - automated driving KW - systematic review KW - user studies KW - augmented reality KW - user experience KW - usability KW - classifications Y1 - 2021 UR - https://doi.org/10.3389/fhumd.2021.689856 UR - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:573-11067 SN - 2673-2726 VL - 3 PB - Frontiers Media CY - Lausanne ER - TY - JOUR A1 - Schuß, Martina A1 - Wintersberger, Philipp A1 - Riener, Andreas T1 - Security Issues in Shared Automated Mobility Systems: A Feminist HCI Perspective JF - Multimodal Technologies and Interaction N2 - The spread of automated vehicles (AVs) is expected to disrupt our mobility behavior. Currently, a male bias is prevalent in the technology industry in general, and in the automotive industry in particular, mainly focusing on white men. This leads to an under-representation of groups of people with other social, physiological, and psychological characteristics. The advent of automated driving (AD) should be taken as an opportunity to mitigate this bias and consider a diverse variety of people within the development process. We conducted a qualitative, exploratory study to investigate how shared automated vehicles (SAVs) should be designed from a pluralistic perspective considering a holistic viewpoint on the whole passenger journey by including booking, pick-up, and drop-off points. Both, men and women, emphasized the importance of SAVs being flexible and clean, whereas security issues were mentioned exclusively by our female participants. While proposing different potential solutions to mitigate security matters, we discuss them through the lens of the feminist HCI framework. UR - https://doi.org/10.3390/mti5080043 KW - shared automated vehicles KW - feminist HCI KW - gender-bias KW - automated systems Y1 - 2021 UR - https://doi.org/10.3390/mti5080043 UR - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:573-9191 SN - 2414-4088 VL - 5 IS - 8 PB - MDPI CY - Basel ER - TY - JOUR A1 - Riener, Andreas A1 - Schlackl, Dominik A1 - Malsam, Julia A1 - Huber, Josef A1 - Homm, Benjamin A1 - Kaczmar, Marion A1 - Kleitsch, Iris A1 - Megos, Alina A1 - Park, Eunji A1 - Sanverdi, Gülsüm A1 - Schmidt, Sabrina A1 - Bracaci, Daniel A1 - Anees, Esha T1 - Improving the UX for Users of Automated Shuttle Buses in Public Transport: Investigating Aspects of Exterior Communication and Interior Design JF - Multimodal Technologies and Interaction N2 - As a precursor to future public transportation, automated shuttle buses can already be experienced in some test regions, but the general public still has reservations and may not yet be ready for this change. For example, the fact that such vehicles might operate independently (without a human driver) creates a barrier of uncertainty and mistrust among people. In this work, we aim to identify and classify the prevailing reservations and propose solutions. We followed the User Centered Design (UCD) process to design concepts that are specifically tailored to the needs of future public transport users. After related work analysis, on-site research, and pre-studies, two main studies were conducted specifically to address communication in the exterior (n = 24) and interior/service design (n = 21). For both studies, we applied a mixed-methods approach combining quantitative and qualitative measures. Our results indicate that, in general, existing ways of communication in the exterior are insufficient to meet future needs. The two visualization concepts for external communication developed in this work were rated (significantly) better in most dimensions of the User Experience Questionnaire (UEQ), when compared to the baseline condition with no additional visualization. Furthermore, preferences among the study participants towards simple, highly visible, and well-known lighting concepts could be observed. As for the interior, the results show that participants rated attractiveness highly for the two design concepts (closer, further in the future) as compared to current, state-of-the-art solutions (automated buses currently in operation). For the “near future” concept, the pragmatic quality dominated, while in the other (the “far future”) concept the hedonic quality was in the foreground. From the results, design recommendations in different categories were derived, which reflect the general openness of the public towards new technologies and interior approaches, but also point out the importance for privacy and designated personal spaces inside an (automated) shuttle bus. Some of the results do not strictly apply to automated shuttle buses, and can serve as valuable suggestions for improving conventional shuttle buses. UR - https://doi.org/10.3390/mti5100061 KW - automated shuttle buses KW - public transport KW - user experience KW - user centered design KW - exterior communication KW - interior design KW - future mobility services Y1 - 2021 UR - https://doi.org/10.3390/mti5100061 UR - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:573-9220 SN - 2414-4088 VL - 5 IS - 10 PB - MDPI CY - Basel ER - TY - JOUR A1 - Dey, Debargha A1 - Habibovic, Azra A1 - Löcken, Andreas A1 - Wintersberger, Philipp A1 - Pfleging, Bastian A1 - Riener, Andreas A1 - Martens, Marieke A1 - Terken, Jacques T1 - Taming the eHMI jungle: A classification taxonomy to guide, compare, and assess the design principles of automated vehicles' external human-machine interfaces JF - Transportation Research Interdisciplinary Perspectives N2 - There is a growing body of research in the field of interaction between automated vehicles and other road users in their vicinity. To facilitate such interactions, researchers and designers have explored designs, and this line of work has yielded several concepts of external Human-Machine Interfaces (eHMI) for vehicles. Literature and media review reveals that the description of interfaces is often lacking in fidelity or details of their functionalities in specific situations, which makes it challenging to understand the originating concepts. There is also a lack of a universal understanding of the various dimensions of a communication interface, which has impeded a consistent and coherent addressal of the different aspects of the functionalities of such interface concepts. In this paper, we present a unified taxonomy that allowsa systematic comparison of the eHMI across 18 dimensions, covering their physical characteristics and communication aspects from the perspective of human factors and human-machine interaction. We analyzed and coded 70eHMI concepts according to this taxonomy to portray the state of the art and highlight the relative maturity of different contributions. The results point to a number of unexplored research areas that could inspire future work. Additionally, we believe that our proposed taxonomy can serve as a checklist for user interface designers and researchers when developing their interfaces. UR - https://doi.org/10.1016/j.trip.2020.100174 KW - Automated vehicle KW - Vulnerable road user KW - Pedestrian KW - External human-machine interface KW - Taxonomy KW - Human factors KW - Human-computer interaction Y1 - 2020 UR - https://doi.org/10.1016/j.trip.2020.100174 UR - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:573-9241 SN - 2590-1982 VL - 2020 IS - 7 PB - Elsevier CY - Amsterdam ER - TY - JOUR A1 - Bano, Tayyaba A1 - Hegner, Franziska A1 - Heinrich, Martin A1 - Schwarze, Rüdiger T1 - Investigation of Fluid-Structure Interaction Induced Bending for Elastic Flaps in a Cross Flow JF - Applied Sciences N2 - With the recent increase in the design of light and flexible structures, numerical investigations of fluid and structure together play a significant role in most engineering applications. Therefore, the current study presents an examination of fluid-structure interaction involving flexible structures. The problem is numerically solved by a commercial software ANSYS-Workbench. Two-way coupled three-dimensional transient simulations are carried out for the flexible flaps of different thicknesses in glycerin for a laminar flow and Reynolds number ranging from 3 < Re < 12. The bending line of the flaps is compared with experimental data for different alignments of the flaps relative to the fluid flow. The study reports the computation of the maximum tip-deflection and deformation of flaps fixed at the bottom and mounted normal to the flow. Additionally, drag coefficients for flexible flaps are computed and flow regimes in the wake of the flaps are presented. As well, the study gives an understanding on how the fluid response changes as the structure deforms and the model is appropriate to predict the behavior of thick and comparatively thinner flaps. The results are sufficiently encouraging to consider the present model for analyzing turbulent flow processes against flexible objects. UR - https://doi.org/10.3390/app10186177 KW - fluid-structure interaction KW - flexible flaps KW - bending line KW - deformation Y1 - 2020 UR - https://doi.org/10.3390/app10186177 UR - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:573-9299 SN - 2076-3417 VL - 10 IS - 18 PB - MDPI CY - Basel ER - TY - JOUR A1 - Frison, Anna-Katharina A1 - Forster, Yannick A1 - Wintersberger, Philipp A1 - Geisel, Viktoria A1 - Riener, Andreas T1 - Where We Come from and Where We Are Going: A Systematic Review of Human Factors Research in Driving Automation JF - Applied Sciences N2 - During the last decade, research has brought forth a large amount of studies that investigated driving automation from a human factor perspective. Due to the multitude of possibilities for the study design with regard to the investigated constructs, data collection methods, and evaluated parameters, at present, the pool of findings is heterogeneous and nontransparent. This literature review applied a structured approach, where five reviewers investigated n = 161 scientific papers of relevant journals and conferences focusing on driving automation between 2010 and 2018. The aim was to present an overview of the status quo of existing methodological approaches and investigated constructs to help scientists in conducting research with established methods and advanced study setups. Results show that most studies focused on safety aspects, followed by trust and acceptance, which were mainly collected through self-report measures. Driving/Take-Over performance also marked a significant portion of the published papers; however, a wide range of different parameters were investigated by researchers. Based on our insights, we propose a set of recommendations for future studies. Amongst others, this includes validation of existing results on real roads, studying long-term effects on trust and acceptance (and of course other constructs), or triangulation of self-reported and behavioral data. We furthermore emphasize the need to establish a standardized set of parameters for recurring use cases to increase comparability. To assure a holistic contemplation of automated driving, we moreover encourage researchers to investigate other constructs that go beyond safety. UR - https://doi.org/10.3390/app10248914 KW - automated driving KW - literature review KW - human–automation interaction KW - method development Y1 - 2020 UR - https://doi.org/10.3390/app10248914 UR - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:573-9369 SN - 2076-3417 VL - 10 IS - 24 PB - MDPI CY - Basel ER - TY - JOUR A1 - Kundinger, Thomas A1 - Yalavarthi, Phani Krishna A1 - Riener, Andreas A1 - Wintersberger, Philipp A1 - Schartmüller, Clemens T1 - Feasibility of smart wearables for driver drowsiness detection and its potential among different age groups JF - International Journal of Pervasive Computing and Communications UR - https://doi.org/10.1108/IJPCC-03-2019-0017 KW - wearable devices KW - physiological measures KW - driver drowsiness detection KW - advanced driver assistance systems (ADAS) KW - simulator study KW - active safety KW - driver monitoring KW - heart rate variability (HRV) KW - machine learning Y1 - 2020 UR - https://doi.org/10.1108/IJPCC-03-2019-0017 SN - 1742-7371 VL - 16 IS - 1 SP - 1 EP - 23 PB - Emerald CY - Bingley ER - TY - JOUR A1 - von Sawitzky, Tamara A1 - Grauschopf, Thomas A1 - Riener, Andreas T1 - “Attention! A Door Could Open.”—Introducing Awareness Messages for Cyclists to Safely Evade Potential Hazards JF - Multimodal Technologies and Interaction N2 - Numerous statistics show that cyclists are often involved in road traffic accidents, often with serious outcomes. One potential hazard of cycling, especially in cities, is “dooring”—passing parked vehicles that still have occupants inside. These occupants could open the vehicle door unexpectedly in the cyclist’s path—requiring a quick evasive response by the cyclist to avoid a collision. Dooring can be very poorly anticipated; as a possible solution, we propose in this work a system that notifies the cyclist of opening doors based on a networked intelligent transportation infrastructure. In a user study with a bicycle simulator (N = 24), we examined the effects of three user interface designs compared to a baseline (no notifications) on cycling behavior (speed and lateral position), perceived safety, and ease of use. Awareness messages (either visual message, visual message + auditory icon, or visual + voice message) were displayed on a smart bicycle helmet at different times before passing a parked, still-occupied vehicle. Our participants found the notifications of potential hazards very easy to understand and appealing and felt that the alerts could help them navigate traffic more safely. Those concepts that (additionally) used auditory icons or voice messages were preferred. In addition, the lateral distance increased significantly when a potentially opening door was indicated. In these situations, cyclists were able to safely pass the parked vehicle without braking. In summary, we are convinced that notification systems, such as the one presented here, are an important component for increasing road safety, especially for vulnerable road users. UR - https://doi.org/10.3390/mti6010003 KW - accident prevention KW - awareness message KW - connected traffic KW - cycling KW - head-mounted display KW - lateral position KW - simulation KW - vulnerable road users Y1 - 2021 UR - https://doi.org/10.3390/mti6010003 UR - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:573-13079 SN - 2414-4088 VL - 6 IS - 1 PB - MDPI CY - Basel ER - TY - INPR A1 - Flohr, Lukas A. A1 - Schuß, Martina A1 - Wallach, Dieter P. A1 - Krüger, Antonio A1 - Riener, Andreas T1 - Designing for Passengers’ Information Needs on Fellow Travelers: A Comparison of Day and Night Rides in Shared Automated Vehicles N2 - Shared automated mobility-on-demand promises efficient, sustainable, and flexible transportation. Nevertheless, security concerns, resilience, and their mutual influence - especially at night - will likely be the most critical barriers to public adoption since passengers have to share rides with strangers without a human driver on board. As related work points out that information about fellow travelers might mitigate passengers' concerns, we designed two user interface variants to investigate the role of this information in an exploratory within-subjects user study (N = 24). Participants experienced four automated day and night rides with varying personal information about co-passengers in a simulated environment. The results of the mixed-method study indicate that having information about other passengers (e.g., photo, gender, and name) positively affects user experience at night. In contrast, it is less necessary during the day. Considering participants' simultaneously raised privacy demands poses a substantial challenge for resilient system design. UR - https://doi.org/10.48550/arXiv.2308.02616 Y1 - 2023 UR - https://doi.org/10.48550/arXiv.2308.02616 UR - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:573-59892 PB - arXiv CY - Ithaca ER - TY - INPR A1 - Novicki Neto, Leonardo A1 - Reway, Fabio A1 - Poledna, Yuri A1 - Funk Drechsler, Maikol A1 - Parente Ribeiro, Eduardo A1 - Huber, Werner A1 - Icking, Christian T1 - TWICE Dataset: Digital Twin of Test Scenarios in a Controlled Environment N2 - Ensuring the safe and reliable operation of autonomous vehicles under adverse weather remains a significant challenge. To address this, we have developed a comprehensive dataset composed of sensor data acquired in a real test track and reproduced in the laboratory for the same test scenarios. The provided dataset includes camera, radar, LiDAR, inertial measurement unit (IMU), and GPS data recorded under adverse weather conditions (rainy, night-time, and snowy conditions). We recorded test scenarios using objects of interest such as car, cyclist, truck and pedestrian -- some of which are inspired by EURONCAP (European New Car Assessment Programme). The sensor data generated in the laboratory is acquired by the execution of simulation-based tests in hardware-in-the-loop environment with the digital twin of each real test scenario. The dataset contains more than 2 hours of recording, which totals more than 280GB of data. Therefore, it is a valuable resource for researchers in the field of autonomous vehicles to test and improve their algorithms in adverse weather conditions, as well as explore the simulation-to-reality gap. UR - https://doi.org/10.48550/arXiv.2310.03895 Y1 - 2023 UR - https://doi.org/10.48550/arXiv.2310.03895 UR - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:573-59887 PB - arXiv CY - Ithaca ER - TY - INPR A1 - Fidelis, Eduardo A1 - Reway, Fabio A1 - Ribeiro, Herick Y. S. A1 - Campos, Pietro A1 - Huber, Werner A1 - Icking, Christian A1 - Faria, Lester A1 - Schön, Torsten T1 - Generation of Realistic Synthetic Raw Radar Data for Automated Driving Applications using Generative Adversarial Networks N2 - The main approaches for simulating FMCW radar are based on ray tracing, which is usually computationally intensive and do not account for background noise. This work proposes a faster method for FMCW radar simulation capable of generating synthetic raw radar data using generative adversarial networks (GAN). The code and pre-trained weights are open-source and available on GitHub. This method generates 16 simultaneous chirps, which allows the generated data to be used for the further development of algorithms for processing radar data (filtering and clustering). This can increase the potential for data augmentation, e.g., by generating data in non-existent or safety-critical scenarios that are not reproducible in real life. In this work, the GAN was trained with radar measurements of a motorcycle and used to generate synthetic raw radar data of a motorcycle traveling in a straight line. For generating this data, the distance of the motorcycle and Gaussian noise are used as input to the neural network. The synthetic generated radar chirps were evaluated using the Frechet Inception Distance (FID). Then, the Range-Azimuth (RA) map is calculated twice: first, based on synthetic data using this GAN and, second, based on real data. Based on these RA maps, an algorithm with adaptive threshold and edge detection is used for object detection. The results have shown that the data is realistic in terms of coherent radar reflections of the motorcycle and background noise based on the comparison of chirps, the RA maps and the object detection results. Thus, the proposed method in this work has shown to minimize the simulation-to-reality gap for the generation of radar data. UR - https://doi.org/10.48550/arXiv.2308.02632 Y1 - 2023 UR - https://doi.org/10.48550/arXiv.2308.02632 UR - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:573-59872 PB - arXiv CY - Ithaca ER - TY - CHAP A1 - Rosbach, Emely A1 - Ammeling, Jonas A1 - Krügel, Sebastian A1 - Kießig, Angelika A1 - Fritz, Alexis A1 - Ganz, Jonathan A1 - Puget, Chloé A1 - Donovan, Taryn A1 - Klang, Andrea A1 - Köller, Maximilian C. A1 - Bolfa, Pompei A1 - Tecilla, Marco A1 - Denk, Daniela A1 - Kiupel, Matti A1 - Paraschou, Georgios A1 - Kok, Mun Keong A1 - Haake, Alexander F. H. A1 - de Krijger, Ronald R. A1 - Sonnen, Andreas F.-P. A1 - Kasantikul, Tanit A1 - Dorrestein, Gerry M. A1 - Smedley, Rebecca C. A1 - Stathonikos, Nikolas A1 - Uhl, Matthias A1 - Bertram, Christof A1 - Riener, Andreas A1 - Aubreville, Marc ED - Yamashita, Naomi ED - Evers, Vanessa ED - Yatani, Koji ED - Ding, Xianghua ED - Lee, Bongshin ED - Chetty, Marshini ED - Toups-Dugas, Phoebe T1 - "When Two Wrongs Don't Make a Right" - Examining Confirmation Bias and the Role of Time Pressure During Human-AI Collaboration in Computational Pathology T2 - CHI'25: Proceedings of the 2025 CHI Conference on Human Factors in Computing Systems N2 - Artificial intelligence (AI)-based decision support systems hold promise for enhancing diagnostic accuracy and efficiency in computational pathology. However, human-AI collaboration can introduce and amplify cognitive biases, like confirmation bias caused by false confirmation when erroneous human opinions are reinforced by inaccurate AI output. This bias may increase under time pressure, a ubiquitous factor in routine pathology, as it strains practitioners’ cognitive resources. We quantified confirmation bias triggered by AI-induced false confirmation and examined the role of time constraints in a web-based experiment, where trained pathology experts (n=28) estimated tumor cell percentages. Our results suggest that AI integration fuels confirmation bias, evidenced by a statistically significant positive linear-mixed-effects model coefficient linking AI recommendations mirroring flawed human judgment and alignment with system advice. Conversely, time pressure appeared to weaken this relationship. These findings highlight potential risks of AI in healthcare and aim to support the safe integration of clinical decision support systems. UR - https://doi.org/10.1145/3706598.3713319 Y1 - 2025 UR - https://doi.org/10.1145/3706598.3713319 UR - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:573-58797 SN - 979-8-4007-1394-1 PB - ACM CY - New York ER - TY - INPR A1 - Kalyanasundaram, Abinav A1 - Chandra Sekaran, Karthikeyan A1 - Stäuber, Philipp A1 - Lange, Michael A1 - Utschick, Wolfgang A1 - Botsch, Michael T1 - Uncertainty-Aware Hybrid Machine Learning in Virtual Sensors for Vehicle Sideslip Angle Estimation N2 - Precise vehicle state estimation is crucial for safe and reliable autonomous driving. The number of measurable states and their precision offered by the onboard vehicle sensor system are often constrained by cost. For instance, measuring critical quantities such as the Vehicle Sideslip Angle (VSA) poses significant commercial challenges using current optical sensors. This paper addresses these limitations by focusing on the development of high-performance virtual sensors to enhance vehicle state estimation for active safety. The proposed Uncertainty-Aware Hybrid Learning (UAHL) architecture integrates a machine learning model with vehicle motion models to estimate VSA directly from onboard sensor data. A key aspect of the UAHL architecture is its focus on uncertainty quantification for individual model estimates and hybrid fusion. These mechanisms enable the dynamic weighting of uncertainty-aware predictions from machine learning and vehicle motion models to produce accurate and reliable hybrid VSA estimates. This work also presents a novel dataset named Real-world Vehicle State Estimation Dataset (ReV-StED), comprising synchronized measurements from advanced vehicle dynamic sensors. The experimental results demonstrate the superior performance of the proposed method for VSA estimation, highlighting UAHL as a promising architecture for advancing virtual sensors and enhancing active safety in autonomous vehicles. UR - https://doi.org/10.48550/arXiv.2504.06105 Y1 - 2025 UR - https://doi.org/10.48550/arXiv.2504.06105 UR - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:573-59578 PB - arXiv CY - Ithaca ER - TY - CHAP A1 - Michahelles, Florian A1 - Riener, Andreas A1 - Grundel, Ida A1 - Boztepe, Suzan A1 - Trygg, Kristina A1 - Israel, Habakuk A1 - Pfleging, Bastian A1 - Veenstra, Mettina A1 - Wintersberger, Philipp T1 - Urban-Engage: Pioneering Urban Planning with Citizen-Driven 15-Minute City Solutions T2 - Mensch und Computer 2024 – Workshopband N2 - We propose an innovative approach to empower urban planners by integrating comprehensive and qualified citizen input into the planning of the 15-minute city (15mC) through immersive digital technologies. Our methodology includes (1) enabling citizens to annotate their real environment using augmented reality, (2) generating urban space alternatives based on generative AI and citizen annotations, (3) allowing modifications to AI-generated alternatives, (4) providing immersive 3D simulation environments for experiencing these alternatives, and (5) facilitating better negotiation between citizens and political players to identify the optimal solution. This process aims to account for diverse stakeholder interests, ensuring inclusive contributions and an experiential understanding of potential urban adaptation. UR - https://doi.org/10.18420/muc2024-mci-ws16-393 Y1 - 2024 UR - https://doi.org/10.18420/muc2024-mci-ws16-393 UR - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:573-52073 PB - Gesellschaft für Informatik CY - Bonn ER - TY - INPR 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 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 second scenario examined driver responses to phantom braking caused by the AEBS. As a result, the first scenario shows that none of the subjects could 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 seconds. In the second scenario, 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/10.20944/preprints202311.0947.v1 Y1 - 2023 UR - https://doi.org/10.20944/preprints202311.0947.v1 UR - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:573-42173 PB - Preprints CY - Basel ER - TY - CHAP A1 - Cristófoli Duarte Silva, Letícia A1 - Funk Drechsler, Maikol A1 - Poledna, Yuri A1 - Huber, Werner A1 - Antonio Fiorentin, Thiago ED - Ariwa, Ezendu ED - Fong, Simon T1 - Synthetic Extreme Weather for AI Training: Concept and Validation T2 - Proceedings: 2023 Third International Conference on Digital Data Processing, DDP 2023 UR - https://doi.org/10.1109/DDP60485.2023.00044 Y1 - 2023 UR - https://doi.org/10.1109/DDP60485.2023.00044 SN - 979-8-3503-2901-8 SP - 188 EP - 194 PB - IEEE CY - Piscataway ER - TY - CHAP A1 - Poledna, Yuri A1 - Reway, Fabio A1 - Funk Drechsler, Maikol A1 - Huber, Werner A1 - Icking, Christian A1 - Parente Ribeiro, Eduardo ED - Juárez-Ramírez, Reyes ED - Fernández y Fernández, Carlos ED - Perez Gonzalez, Hector G. ED - Ramírez-Noriega, Alan ED - Jiménez, Samantha ED - Guerra-García, César ED - Licea Sandoval, Guillermo T1 - An Open-Source High-Level Fusion Algorithm in ROS for Automated Driving Applications T2 - Proceedings: 2022 10th International Conference in Software Engineering Research and Innovation (CONISOFT 2022) UR - https://doi.org/10.1109/CONISOFT55708.2022.00031 Y1 - 2023 UR - https://doi.org/10.1109/CONISOFT55708.2022.00031 SN - 978-1-6654-6126-9 SP - 174 EP - 181 PB - IEEE CY - Los Alamitos ER - TY - CHAP A1 - Wurst, Jonas A1 - Balasubramanian, Lakshman A1 - Botsch, Michael A1 - Utschick, Wolfgang T1 - Expert-LaSTS: Expert-Knowledge Guided Latent Space for Traffic Scenarios T2 - 2022 IEEE Intelligent Vehicles Symposium (IV) UR - https://doi.org/10.1109/IV51971.2022.9827187 KW - clustering KW - novelty detection KW - scenario-based testing KW - deep learning Y1 - 2022 UR - https://doi.org/10.1109/IV51971.2022.9827187 SN - 978-1-6654-8821-1 SP - 484 EP - 491 PB - IEEE CY - Piscataway ER - TY - CHAP A1 - Manger, Carina A1 - Preiwisch, Anna A1 - Gambirasio, Chiara A1 - Golks, Simon A1 - Schuß, Martina A1 - Riener, Andreas ED - Michahelles, Florian ED - Knierim, Pascal ED - Häkkilä, Jonna T1 - We're in This Together: Exploring Explanation Needs and Methods in Shared Automated Shuttle Buses T2 - Proceedings of MUM 2023: the 22nd International Conference on Mobile and Ubiquitous Multimedia UR - https://doi.org/10.1145/3626705.3627798 Y1 - 2023 UR - https://doi.org/10.1145/3626705.3627798 SN - 979-8-4007-0921-0 SP - 145 EP - 151 PB - ACM CY - New York ER - TY - CHAP A1 - Manger, Carina A1 - Pusch, Florian A1 - Thöne, Manuel A1 - Wenger, Marco A1 - Löcken, Andreas A1 - Riener, Andreas ED - Michahelles, Florian ED - Knierim, Pascal ED - Häkkilä, Jonna T1 - Explainability in Automated Parking: The Effect of Augmented Reality Visualizations on User Experience and Situation Awareness T2 - Proceedings of MUM 2023: the 22nd International Conference on Mobile and Ubiquitous Multimedia UR - https://doi.org/10.1145/3626705.3627796 Y1 - 2023 UR - https://doi.org/10.1145/3626705.3627796 SN - 979-8-4007-0921-0 SP - 146 EP - 152 PB - ACM CY - New York ER - TY - JOUR A1 - Himmels, Chantal A1 - Weigl, Klemens A1 - Riener, Andreas A1 - Venrooij, Joost T1 - Establishing driving simulator validity: drawbacks of null-hypothesis significance testing when compared to equivalence tests and Bayes factors JF - Theoretical Issues in Ergonomics Science UR - https://doi.org/10.1080/1463922X.2023.2286478 Y1 - 2023 UR - https://doi.org/10.1080/1463922X.2023.2286478 SN - 1464-536X VL - 25 IS - 5 SP - 546 EP - 566 PB - Taylor & Francis CY - London ER - TY - CHAP A1 - Manger, Carina A1 - Peintner, Jakob A1 - Devaraju, Manoj A1 - Riener, Andreas ED - Michahelles, Florian ED - Knierim, Pascal ED - Häkkilä, Jonna T1 - Good Vibes Only: A Comparative Study of Seat Vibration Concepts for Redirecting Driver Attention During Visual Distraction T2 - Proceedings of MUM 2023: the 22nd International Conference on Mobile and Ubiquitous Multimedia UR - https://doi.org/10.1145/3626705.3631786 Y1 - 2023 UR - https://doi.org/10.1145/3626705.3631786 SN - 979-8-4007-0921-0 SP - 492 EP - 494 PB - ACM CY - New York ER - TY - CHAP A1 - Escher, Bengt A1 - Manger, Carina A1 - Daou, Makram A1 - Dünninger, Theresa A1 - Riener, Andreas ED - Michahelles, Florian ED - Knierim, Pascal ED - Häkkilä, Jonna T1 - A Comparative Analysis of Preferred Communication Options of Passengers in Networked Shared Automated Vehicles T2 - Proceedings of MUM 2023: the 22nd International Conference on Mobile and Ubiquitous Multimedia UR - https://doi.org/10.1145/3626705.3631801 Y1 - 2023 UR - https://doi.org/10.1145/3626705.3631801 SN - 979-8-4007-0921-0 SP - 541 EP - 543 PB - ACM CY - New York ER - TY - CHAP A1 - Ulreich, Fabian A1 - Kaup, André A1 - Ebert, Martin T1 - Novel Test Bench for End-to-End Validation of Monocular Depth Estimation Under the Influence of Glaring Situations T2 - 2024 IEEE 27th International Conference on Intelligent Transportation Systems (ITSC) UR - https://doi.org/10.1109/ITSC58415.2024.10920009 Y1 - 2025 UR - https://doi.org/10.1109/ITSC58415.2024.10920009 SN - 979-8-3315-0592-9 SP - 2426 EP - 2432 PB - IEEE CY - Piscataway ER - TY - JOUR A1 - Göbel, Jan-Philipp A1 - Peuckmann, Niklas A1 - Kundinger, Thomas A1 - Riener, Andreas T1 - Fusion of Driving Behavior and Monitoring System in Scenarios of Driving Under the Influence: An Experimental Approach JF - Applied Sciences N2 - Driving under the influence of alcohol (DUI) remains a leading cause of accidents globally, with accident risk rising exponentially with blood alcohol concentration (BAC). This study aims to distinguish between sober and intoxicated drivers using driving behavior analysis and driver monitoring system (DMS), technologies that align with emerging EU regulations. In a driving simulator, twenty-three participants (average age: 32) completed five drives (one practice and two each while sober and intoxicated) on separate days across city, rural, and highway settings. Each 30-minute drive was analyzed using eye-tracking and driving behavior data. We applied significance testing and classification models to assess the data. Our study goes beyond the state of the art by a) combining data from various sensors and b) not only examining the effects of alcohol on driving behavior but also using these data to classify driver impairment. Fusing gaze and driving behavior data improved classification accuracy, with models achieving over 70% accuracy in city and rural conditions and a Long Short-Term Memory (LSTM) network reaching up to 80% on rural roads. Although the detection rate is, of course, still far too low for a productive system, the results nevertheless provide valuable insights for improving DUI detection technologies and enhancing road safety. UR - https://doi.org/10.3390/app15105302 Y1 - 2025 UR - https://doi.org/10.3390/app15105302 UR - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:573-59467 SN - 2076-3417 VL - 15 IS - 10 PB - MDPI CY - Basel ER - TY - JOUR A1 - Flores Fernández, Alberto A1 - Sánchez Morales, Eduardo A1 - Botsch, Michael A1 - Facchi, Christian A1 - García Higuera, Andrés T1 - Generation of Correction Data for Autonomous Driving by Means of Machine Learning and On-Board Diagnostics JF - Sensors N2 - A highly accurate reference vehicle state is a requisite for the evaluation and validation of Autonomous Driving (AD) and Advanced Driver Assistance Systems (ADASs). This highly accurate vehicle state is usually obtained by means of Inertial Navigation Systems (INSs) that obtain position, velocity, and Course Over Ground (COG) correction data from Satellite Navigation (SatNav). However, SatNav is not always available, as is the case of roofed places, such as parking structures, tunnels, or urban canyons. This leads to a degradation over time of the estimated vehicle state. In the present paper, a methodology is proposed that consists on the use of a Machine Learning (ML)-method (Transformer Neural Network—TNN) with the objective of generating highly accurate velocity correction data from On-Board Diagnostics (OBD) data. The TNN obtains OBD data as input and measurements from state-of-the-art reference sensors as a learning target. The results show that the TNN is able to infer the velocity over ground with a Mean Absolute Error (MAE) of 0.167 kmh (0.046 ms) when a database of 3,428,099 OBD measurements is considered. The accuracy decreases to 0.863 kmh (0.24 ms) when only 5000 OBD measurements are used. Given that the obtained accuracy closely resembles that of state-of-the-art reference sensors, it allows INSs to be provided with accurate velocity correction data. An inference time of less than 40 ms for the generation of new correction data is achieved, which suggests the possibility of online implementation. This supports a highly accurate estimation of the vehicle state for the evaluation and validation of AD and ADAS, even in SatNav-deprived environments. UR - https://doi.org/10.3390/s23010159 KW - On-Board Diagnostics KW - Machine Learning KW - Transformer Neural Network KW - Autonomous Driving KW - ADAS KW - Inertial Navigation Systems Y1 - 2022 UR - https://doi.org/10.3390/s23010159 UR - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:573-30464 SN - 1424-8220 VL - 23 IS - 1 PB - MDPI CY - Basel ER - TY - JOUR A1 - Funk Drechsler, Maikol A1 - Sharma, Varun A1 - Reway, Fabio A1 - Schütz, Christoph A1 - Huber, Werner T1 - Dynamic Vehicle-in-the-Loop: A Novel Method for Testing Automated Driving Functions JF - SAE International Journal of Connected and Automated Vehicles N2 - In automated driving functions (ADF) testing, novel methods have been developed to allow the combination of hardware and simulation to ensure safety in usage even at an early stage of development. This article proposes an architecture to integrate an entire test vehicle—denominated Dynamic Vehicle-in-the-Loop (DynViL)—in a virtual environment. This approach enables the interaction of a real vehicle with virtual traffic participants. The vehicle is physically tested on an empty track, but connected to the CARLA simulator, in which virtual driving scenarios are created. The simulated environment is transmitted to the vehicle driving function which controls the real vehicle in reaction to the virtual objects perceived in simulation. Furthermore, the performance of the DynViL in different test scenarios is evaluated. The results show that the reproducibility of the tests with the DynViL is satisfactory. Furthermore, the results indicate that the deviation between simulation and DynViL variates according to the vehicle speed and the complexity of the scenario. Based on the performance of the DynViL in comparison to simulation, the DynViL can be implemented as a complementary test method to be added to the transition between hardware in the loop (HiL) and proving ground. In this test method, erratic or unexpected behavior generated by the driving function and controllers can be detected in the real vehicle dynamics in a risk-free manner. UR - https://doi.org/10.4271/12-05-04-0029 KW - automated driving KW - safety KW - test KW - track KW - vehicle dynamics KW - virtual KW - driving functions KW - automated emergency braking Y1 - 2022 UR - https://doi.org/10.4271/12-05-04-0029 UR - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:573-24952 SN - 2574-0741 SN - 2574-075X VL - 5 IS - 4 PB - SAE International CY - Warrendale ER - TY - CHAP A1 - Rollwagen, Alice A1 - Riener, Andreas ED - Vaculin, Ondrej T1 - Enhancing User Acceptance: Investigating Effective Approaches to Communicating Information on Automated Shuttle Buses through eHMI Concepts T2 - Applied Research Conference 2023 Conference Proceedings: 3rd July 2023 Technische Hochschule Ingolstadt UR - https://doi.org/10.57825/repo_in-5244 Y1 - 2023 UR - https://doi.org/10.57825/repo_in-5244 SP - 265 EP - 270 PB - Technische Hochschule Ingolstadt CY - Ingolstadt ER - TY - INPR A1 - Rosbach, Emely A1 - Ammeling, Jonas A1 - Krügel, Sebastian A1 - Kießig, Angelika A1 - Fritz, Alexis A1 - Ganz, Jonathan A1 - Puget, Chloé A1 - Donovan, Taryn A1 - Klang, Andrea A1 - Köller, Maximilian C. A1 - Bolfa, Pompei A1 - Tecilla, Marco A1 - Denk, Daniela A1 - Kiupel, Matti A1 - Paraschou, Georgios A1 - Kok, Mun Keong A1 - Haake, Alexander F. H. A1 - de Krijger, Ronald R. A1 - Sonnen, Andreas F.-P. A1 - Kasantikul, Tanit A1 - Dorrestein, Gerry M. A1 - Smedley, Rebecca C. A1 - Stathonikos, Nikolas A1 - Uhl, Matthias A1 - Bertram, Christof A1 - Riener, Andreas A1 - Aubreville, Marc T1 - "When TwoWrongs Don’t Make a Right" - Examining Confirmation Bias and the Role of Time Pressure During Human-AI Collaboration in Computational Pathology UR - https://doi.org/10.48550/arXiv.2411.01007 Y1 - 2024 UR - https://doi.org/10.48550/arXiv.2411.01007 PB - arXiv CY - Ithaca ER - TY - JOUR A1 - Ju, Wendy A1 - Pfleging, Bastian A1 - Riener, Andreas ED - Ju, Wendy ED - Pfleging, Bastian ED - Riener, Andreas T1 - Radical Innovation and Design for Connected and Automated Vehicles JF - Dagstuhl Reports N2 - This report documents the program and the outcomes of Dagstuhl Seminar 22222 “Radical Innovation and Design for Connected and Automated Vehicles”. Automated driving will most likely be the next big change in individual mobility. While research is still primarily concerned with technical challenges of Automated Driving Systems (ADS), HCI researchers and designers already started to develop concepts on how to use privately owned ADS as a space for non-driving-related activities, going beyond what is possible today. There is, however, room to think about creative ways to use automated vehicles (AV) and connected technology towards the public interest beyond incremental changes, which is what we addressed in this seminar. We challenged the current generative/evaluative research approach for automated driving systems against a radical innovations attempt and questioned whether the current incremental research approach is appropriate for the development of future vehicles. As an integral part of the seminar we wanted participants to learn from each other and to disseminate each individual’s experience for boosting subsequent research by trying out different methods that support the “out of the box” thinking (e.g., brainwriting, bodystorming, focus groups, World Café, amongst others). UR - https://doi.org/10.4230/DagRep.12.5.188 KW - Racidal Innovation KW - Automated Driving KW - Future Mobility KW - Sustainability Y1 - 2022 UR - https://doi.org/10.4230/DagRep.12.5.188 UR - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:573-40255 SN - 2192-5283 VL - 12 IS - 5 SP - 188 EP - 230 PB - Schloss Dagstuhl CY - Dagstuhl ER - TY - JOUR A1 - Wintersberger, Philipp A1 - Schartmüller, Clemens A1 - Sadeghian, Shadan A1 - Frison, Anna-Katharina A1 - Riener, Andreas T1 - Evaluation of Imminent Take-Over Requests With Real Automation on a Test Track JF - Human Factors: The Journal of the Human Factors and Ergonomics Society UR - https://doi.org/10.1177/00187208211051435 Y1 - 2021 UR - https://doi.org/10.1177/00187208211051435 SN - 0018-7208 SN - 1547-8181 VL - 65 IS - 8 SP - 1776 EP - 1792 PB - SAGE CY - London ER - TY - CHAP A1 - Riegl, Peter A1 - Gaull, Andreas A1 - Beitelschmidt, Michael T1 - Parameterization of automated driving functions in virtual environments based on characteristic test scenarios T2 - 2022 IEEE Intelligent Vehicles Symposium (IV) UR - https://doi.org/10.1109/IV51971.2022.9827407 Y1 - 2022 UR - https://doi.org/10.1109/IV51971.2022.9827407 SN - 978-1-6654-8821-1 SP - 511 EP - 518 PB - IEEE CY - Piscataway ER - TY - CHAP A1 - Capdevila, Marc Gonzàlez A1 - Vitorino, Natanael A1 - Poledna, Yuri A1 - Malena, Bruno A1 - Funk Drechsler, Maikol A1 - Albuquerque, Gustavo G. A1 - Furlan, Tales A1 - Netto, Roberto S. ED - Mattos, Diogo Menezes Ferrazani ED - Moraes, Igor Monteiro ED - Nguyen, Thi Mai Trang ED - de Souza Couto, Rodrigo ED - Rubinstein, Marcelo Goncalves T1 - Smart&Safe Mobility Lab: Mixed Reality Environment with HIL, CV2X for VRU Detection T2 - 2024 IEEE 13th International Conference on Cloud Networking (CloudNet) UR - https://doi.org/10.1109/CloudNet62863.2024.10815791 Y1 - 2024 UR - https://doi.org/10.1109/CloudNet62863.2024.10815791 SN - 979-8-3503-7656-2 PB - IEEE CY - Piscataway ER - TY - CHAP A1 - Wachtel Granado, Diogo A1 - Schüßler, Christian A1 - von dem Bussche-Hünnefeld, Tetmar A1 - Rothmeier, Thomas A1 - Vossiek, Martin A1 - Huber, Werner T1 - Evaluation of Automotive Radar Simulation Tools for Adverse Weather Conditions: A Comparative Analysis of Real Measurement, HFSS and Monte-Carlo GO Methods T2 - 2024 IEEE Radar Conference (RadarConf24) Proceedings UR - https://doi.org/10.1109/RadarConf2458775.2024.10549398 Y1 - 2024 UR - https://doi.org/10.1109/RadarConf2458775.2024.10549398 SN - 979-8-3503-2920-9 PB - IEEE CY - Piscataway ER - TY - CHAP A1 - Wachtel Granado, Diogo A1 - Rothmeier, Thomas A1 - von dem Bussche-Hünnefeld, Tetmar A1 - Huber, Werner A1 - Vossiek, Martin T1 - Radar in the Rain: Understanding and Simulating Environmental Effects on ADAS Radar Sensors T2 - 2024 IEEE Radar Conference (RadarConf24) Proceedings UR - https://doi.org/10.1109/RadarConf2458775.2024.10548570 Y1 - 2024 UR - https://doi.org/10.1109/RadarConf2458775.2024.10548570 SN - 979-8-3503-2920-9 PB - IEEE CY - Piscataway ER - TY - CHAP A1 - Roßberg, Niklas A1 - Neumeier, Marion A1 - Hasirlioglu, Sinan A1 - Bouzouraa, Mohamed Essayed A1 - Botsch, Michael T1 - Assessing the Completeness of Traffic Scenario Categories for Automated Highway Driving Functions via Cluster-Based Analysis T2 - 2025 IEEE Intelligent Vehicles Symposium (IV) UR - https://doi.org/10.1109/IV64158.2025.11097767 Y1 - 2025 UR - https://doi.org/10.1109/IV64158.2025.11097767 SN - 979-8-3315-3803-3 SP - 1787 EP - 1794 PB - IEEE CY - Piscataway ER - TY - CHAP A1 - Elter, Tim A1 - Dirndorfer, Tobias A1 - Botsch, Michael A1 - Utschick, Wolfgang T1 - Interaction-aware Prediction of Occupancy Regions based on a POMDP Framework T2 - 2022 IEEE 25th International Conference on Intelligent Transportation Systems (ITSC) UR - https://doi.org/10.1109/ITSC55140.2022.9922127 KW - Trajectory planning KW - Estimation KW - Collaboration KW - Markov process KW - Complexity theory KW - Reliability KW - Junctions Y1 - 2022 UR - https://doi.org/10.1109/ITSC55140.2022.9922127 SN - 978-1-6654-6880-0 SP - 980 EP - 987 PB - IEEE CY - Piscataway ER - TY - BOOK ED - Riener, Andreas ED - Jeon, Myounghoon ED - Alvarez, Ignacio T1 - User experience design in the era of automated driving T3 - Studies in Computational Intelligence UR - https://doi.org/10.1007/978-3-030-77726-5 KW - automated driving KW - trust and acceptance KW - user experience design KW - ethical issues for automated driving KW - potential of AR/VR for automated driving KW - simulation sickness problem KW - UX-design recommendations Y1 - 2022 UR - https://doi.org/10.1007/978-3-030-77726-5 SN - 978-3-030-77725-8 SN - 978-3-030-77728-9 SN - 978-3-030-77726-5 SN - 1860-949X SN - 1860-9503 PB - Springer CY - Cham ER - TY - CHAP A1 - Wachtel Granado, Diogo A1 - Tasabat, Sinan A1 - Rothmeier, Thomas A1 - Cristófoli Duarte Silva, Letícia A1 - Huber, Werner T1 - Exploring Synthetic Radar Data and Deep Learning for Road User Classification in Autonomous Vehicles T2 - 2024 IEEE 27th International Conference on Intelligent Transportation Systems (ITSC) UR - https://doi.org/10.1109/ITSC58415.2024.10919565 Y1 - 2025 UR - https://doi.org/10.1109/ITSC58415.2024.10919565 SN - 979-8-3315-0592-9 SP - 2021 EP - 2026 PB - IEEE CY - Piscataway ER - TY - CHAP A1 - Wachtel Granado, Diogo A1 - Trevisol, Heitor Derder A1 - Rothmeier, Thomas A1 - Nassu, Bogdan Tomoyuki A1 - Huber, Werner T1 - Navigating on Adverse Weather: Enhancing LiDAR-Based Detection with the DBSPRY Dataset T2 - 2024 IEEE 27th International Conference on Intelligent Transportation Systems (ITSC) UR - https://doi.org/10.1109/ITSC58415.2024.10920224 Y1 - 2025 UR - https://doi.org/10.1109/ITSC58415.2024.10920224 SN - 979-8-3315-0592-9 SP - 4034 EP - 4039 PB - IEEE CY - Piscataway ER - TY - CHAP A1 - Rothmeier, Thomas A1 - Dal Vesco Hoger, Mayara A1 - Nassu, Bogdan Tomoyuki A1 - Huber, Werner A1 - Knoll, Alois T1 - Out of the Box: Weather Augmentation for Enhanced Detection in Bad Visibility Conditions T2 - 2024 IEEE 27th International Conference on Intelligent Transportation Systems (ITSC) UR - https://doi.org/10.1109/ITSC58415.2024.10919529 Y1 - 2025 UR - https://doi.org/10.1109/ITSC58415.2024.10919529 SN - 979-8-3315-0592-9 SP - 1410 EP - 1417 PB - IEEE CY - Piscataway ER - TY - CHAP A1 - Wachtel Granado, Diogo A1 - Rothmeier, Thomas A1 - Cristófoli Duarte Silva, Letícia A1 - Vossiek, Martin A1 - Huber, Werner T1 - Simulation and Validation of Automotive Radar Performance with Water Spray Over Radome T2 - 2024 IEEE Sensors 2024: Conference Proceedings UR - https://doi.org/10.1109/SENSORS60989.2024.10784774 Y1 - 2024 UR - https://doi.org/10.1109/SENSORS60989.2024.10784774 SN - 979-8-3503-6351-7 PB - IEEE CY - Piscataway ER - TY - CHAP A1 - Riegl, Peter A1 - Chandra Sekaran, Karthikeyan A1 - Botsch, Michael T1 - Generation of realistic traffic scenarios for virtual and real test drives based on a hybrid machine learning framework T2 - 2024 IEEE International Conference on Vehicular Electronics and Safety (ICVES) UR - https://doi.org/10.1109/ICVES61986.2024.10927934 Y1 - 2025 UR - https://doi.org/10.1109/ICVES61986.2024.10927934 SN - 979-8-3503-8727-8 PB - IEEE CY - Piscataway ER - 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 - JOUR A1 - Riegl, Peter A1 - Gaull, Andreas A1 - Beitelschmidt, Michael T1 - Simulation-Based Testing and Performance Evaluation of Vehicle Safety Functions JF - SAE International Journal of Connected and Automated Vehicles UR - https://doi.org/10.4271/12-07-01-0006 Y1 - 2023 UR - https://doi.org/10.4271/12-07-01-0006 SN - 2574-0741 SN - 2574-075X VL - 7 IS - 1 SP - 69 EP - 93 PB - SAE International CY - Warrendale ER - TY - INPR A1 - Neumeier, Marion A1 - Tollkühn, Andreas A1 - Dorn, Sebastian A1 - Botsch, Michael A1 - Utschick, Wolfgang T1 - Gradient Derivation for Learnable Parameters in Graph Attention Networks UR - https://doi.org/10.48550/arXiv.2304.10939 Y1 - 2023 UR - https://doi.org/10.48550/arXiv.2304.10939 PB - arXiv CY - Ithaca 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 - Pereira, J. C. L. A1 - Poledna, Yuri A1 - Parente Ribeiro, Eduardo A1 - Dias, João S. A1 - Oliveira, Gustavo H. C. A1 - Kuiava, Roman A1 - Leandro, Gideon Villar A1 - Vilela, João A. A1 - Demonti, Rogers A1 - Pedretti, A. T1 - Monitoring micro phasor measurement units at university campus T2 - 2019 Workshop on Communication Networks and Power Systems (WCNPS) UR - https://doi.org/10.1109/WCNPS.2019.8896288 Y1 - 2019 UR - https://doi.org/10.1109/WCNPS.2019.8896288 SN - 978-1-7281-2920-4 PB - IEEE CY - Piscataway ER -