TY - INPR 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 T2 - SSRN UR - https://dx.doi.org/10.2139/ssrn.4588275 Y1 - 2023 UR - https://dx.doi.org/10.2139/ssrn.4588275 PB - Elsevier CY - Amsterdam 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 - 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 - INPR 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 T2 - SSRN UR - https://dx.doi.org/10.2139/ssrn.5027721 Y1 - 2024 UR - https://dx.doi.org/10.2139/ssrn.5027721 PB - Elsevier CY - Amsterdam ER - TY - INPR A1 - Rößle, Dominik A1 - Xie, Xujun A1 - Mohan, Adithya A1 - Thirugnana Sambandham, Venkatesh A1 - Cremers, Daniel A1 - Schön, Torsten T1 - DrivIng: A Large-Scale Multimodal Driving Dataset with Full Digital Twin Integration UR - https://doi.org/10.48550/arXiv.2601.15260 Y1 - 2026 UR - https://doi.org/10.48550/arXiv.2601.15260 PB - arXiv CY - Ithaca ER - TY - CHAP A1 - Göbel, Jan-Philipp A1 - Mertens, Jan Cedric A1 - Riener, Andreas T1 - Evaluating Alcohol-Induced Impairment: A Comprehensive Study on Objective Tests and Subjective Self-Perception T2 - 2025 IEEE International Conference on Vehicular Electronics and Safety (ICVES) UR - https://doi.org/10.1109/ICVES65691.2025.11376306 Y1 - 2026 UR - https://doi.org/10.1109/ICVES65691.2025.11376306 SN - 978-1-6654-7778-9 SP - 73 EP - 80 PB - IEEE CY - Piscataway ER - TY - JOUR A1 - Salamat, Babak A1 - Mattern, Dominik A1 - Olzem, Sebastian-Sven A1 - Elsbacher, Gerhard A1 - Seidel, Christian A1 - Tonello, Andrea M. T1 - GMP3: Learning-Driven, Bellman-Guided Trajectory Planning for AAVs in Real-Time on SE(3) JF - IEEE Access N2 - We propose GMP3, a multiphase global path planning framework that generates dynamically feasible three-dimensional trajectories for unmanned aerial vehicles (UAVs) operating in cluttered environments. The framework extends traditional path planning from Euclidean position spaces to the Lie group SE(3), allowing joint learning of translational motion and rotational dynamics. A modified Bellman-based operator is introduced to support reinforcement learning (RL) policy updates while leveraging prior trajectory information for improved convergence. GMP3 is designed as a distributed framework in which agents influence each other and share policy information along the trajectory: each agent refines its assigned segment and shares with its neighbors via a consensus-based scheme, enabling cooperative policy updates and convergence toward a path shaped globally even under kinematic constraints.We also propose DroneManager, a modular ground control software that interfaces the planner with real UAVs platforms via the Micro Air Vehicle Link (MAVLink) protocol, supporting real-time deployment and feedback. Simulation studies and indoor flight experiments validate the effectiveness of the proposed method in constrained 3D environments, demonstrating reliable obstacle avoidance and smooth, feasible trajectories across both position and orientation. UR - https://doi.org/10.1109/ACCESS.2026.3661193 Y1 - 2026 UR - https://doi.org/10.1109/ACCESS.2026.3661193 UR - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:573-66826 SN - 2169-3536 VL - 14 SP - 22720 EP - 22733 PB - IEEE CY - New York ER - TY - CHAP A1 - Jimenez, Victor A1 - Reyes de los Mozos, Mario A1 - Perea Paños, Pau A1 - Fritzsche, Paula Cecilia A1 - Mayer, Kevin A1 - Volkersdorfer, Tina A1 - Hof, Hans-Joachim A1 - Couturier, Christophe A1 - Ernst, Thierry A1 - Zhang, Miao A1 - Saied Mohamed, Mohamed A1 - Rodríguez-Arozamena, Mario A1 - Aranguren-Mendieta, Iñigo A1 - Pérez, Joshué A1 - Jousse, Adrien A1 - Murguia, Carlos A1 - van de Wouw, Nathan A1 - Bellessort, Romain A1 - Salami, Behzad A1 - Jevtić, Aleksandar A1 - Bannour, Boutheina A1 - Rodríguez Recasens, Manel A1 - Ropero, Isaac A1 - Ozbay, Burcu A1 - Eren, Ali A1 - Bektas, Mustafa A1 - Tezcan, Deryanur A1 - Pilz, Christoph A1 - Haas, Sarah A1 - Lenz, Gernot ED - McNally, Ciaran ED - Carroll, Páraic ED - Martinez-Pastor, Beatriz ED - Ghosh, Bidisha ED - Efthymiou, Marina ED - Valantasis-Kanellos, Nikolaos T1 - SELFY - Self Assessment, Protection and Healing Tools for a Trustworthy and Resilient CCAM T2 - Transport Transitions: Advancing Sustainable and Inclusive Mobility, Proceedings of the 10th TRA Conference, 2024 Dublin, Ireland - Volume 6: Connected Mobility Ecosy N2 - SELFY envisions an agnostic toolbox for the self-management of security and resilience of the CCAM (Connected, Cooperative and Automated Mobility) ecosystem, which can be easily deployed to extend the current Operational Design Domain (ODD), providing self-awareness, self-resilience and self-healing mechanisms and enhancing trust between stakeholders. SELFY is based on four pillars: Situational awareness, Resilience, Secure Data Sharing and Trust and provides three groups of tools. SACP (Situational Awareness and Collaborative Perception) tools aim at providing all CCAM actors with a comprehensive understanding of their environment, i.e., the perception of objects, such as other traffic participants and stationary objects. CRHS (Cooperative Resilience and Healing System) tools enable self-protection actions whenever a compromising situation is detected in relation to assets, vehicles, operations, or the system itself. TDMS (Trust and Data Management System) tools establish a secure and trusted environment for data in a collaborative and cooperative context, both for infrastructure and assets, as well as for citizen’s data, such as drivers or pedestrians with special attention to privacy considerations. By defining a collaborative environment between the different tools to respond to new threats, risks and attacks SELFY facilitates the comprehension of new challenges in the cybersecurity aspect of CCAMs. UR - https://doi.org/10.1007/978-3-032-06763-0_106 Y1 - 2025 UR - https://doi.org/10.1007/978-3-032-06763-0_106 UR - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:573-66819 SN - 978-3-032-06763-0 SP - 745 EP - 751 PB - Springer CY - Cham ER - TY - INPR A1 - Löw, Jakob A1 - Vasu Ashoka, Vishwa Vimukthi A1 - Hutzelmann, Thomas A1 - Hof, Hans-Joachim T1 - Security Aspects of ISO 15118 Plug and Charge Payment UR - https://doi.org/10.48550/arXiv.2512.15966 Y1 - 2025 UR - https://doi.org/10.48550/arXiv.2512.15966 PB - arXiv CY - Ithaca ER - TY - CHAP A1 - Krüger, Max ED - Voigt, Guido ED - Fliedner, Malte ED - Haase, Knut ED - Brüggemann, Wolfgang ED - Hoberg, Kai ED - Meissner, Joern T1 - Cross-Validation of Random-Forests’ Classification Performance in Maritime Scenarios with Aggregated AIS Messages T2 - Operations Research Proceedings 2023: Selected Papers of the Annual International Conference of the German Operations Research Society (GOR), Germany, August 29–September 1, 2023 UR - https://doi.org/10.1007/978-3-031-58405-3_78 Y1 - 2025 UR - https://doi.org/10.1007/978-3-031-58405-3_78 SN - 978-3-031-58405-3 SP - 609 EP - 616 PB - Springer CY - Cham ER - TY - JOUR A1 - Mohan, Adithya A1 - Schön, Torsten T1 - Toward Robust Agents: A Survey of Adversarial Attacks and Defenses in Deep Reinforcement Learning JF - IEEE Access N2 - Deep Reinforcement Learning (DRL) has demonstrated remarkable success in autonomous decision-making across diverse domains, including robotics, autonomous driving, and game playing. However, recent studies have uncovered a critical vulnerability: DRL agents are highly susceptible to adversarial attacks that can significantly degrade their performance or lead to catastrophic failure. These attacks exploit different components of the learning pipeline observations, actions, rewards, and policies exposing new challenges unique to DRL compared to supervised learning. This survey provides a comprehensive examination of adversarial threats and corresponding defense mechanisms within the DRL paradigm. This also aims to serve as a foundational reference for researchers and practitioners seeking to understand and mitigate adversarial vulnerabilities in DRL. UR - https://doi.org/10.1109/ACCESS.2026.3657855 Y1 - 2026 UR - https://doi.org/10.1109/ACCESS.2026.3657855 UR - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:573-66232 SN - 2169-3536 VL - 14 SP - 14481 EP - 14497 PB - IEEE CY - New York ER - TY - JOUR A1 - Ammeling, Jonas A1 - Ganz, Jonathan A1 - Rosbach, Emely A1 - Lausser, Ludwig A1 - Bertram, Christof A1 - Breininger, Katharina A1 - Aubreville, Marc T1 - Benchmarking Foundation Models for Mitotic Figure Classification JF - Machine Learning for Biomedical Imaging N2 - The performance of deep learning models is known to scale with data quantity and diversity. In pathology, as in many other medical imaging domains, the availability of labeled images for a specific task is often limited. Self-supervised learning techniques have enabled the use of vast amounts of unlabeled data to train large-scale neural networks, i.e., foundation models, that can address the limited data problem by providing semantically rich feature vectors that can generalize well to new tasks with minimal training effort increasing model performance and robustness. In this work, we investigate the use of foundation models for mitotic figure classification. The mitotic count, which can be derived from this classification task, is an independent prognostic marker for specific tumors and part of certain tumor grading systems. In particular, we investigate the data scaling laws on multiple current foundation models and evaluate their robustness to unseen tumor domains. Next to the commonly used linear probing paradigm, we also adapt the models using low-rank adaptation (LoRA) of their attention mechanisms. We compare all models against end-to-end-trained baselines, both CNNs and Vision Transformers. Our results demonstrate that LoRA-adapted foundation models provide superior performance to those adapted with standard linear probing, reaching performance levels close to 100 % data availability with only 10 % of training data. Furthermore, LoRA-adaptation of the most recent foundation models almost closes the out-of-domain performance gap when evaluated on unseen tumor domains. However, full fine-tuning of traditional architectures still yields competitive performance. UR - https://doi.org/10.59275/j.melba.2026-a3eb Y1 - 2026 UR - https://doi.org/10.59275/j.melba.2026-a3eb UR - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:573-66220 SN - 2766-905X VL - 3 IS - MELBA–BVM 2025 Special Issue SP - 38 EP - 55 PB - Melba editors CY - [s. l.] ER - TY - INPR A1 - Mosaferchi, Saeedeh A1 - Riener, Andreas A1 - Mortezapoursoufiani, Alireza A1 - Naddeo, Alessandro T1 - Enhancing UX in Automated Vehicles through Biophilic Interfaces: Insights from Prospective End Users T2 - SSRN UR - https://dx.doi.org/10.2139/ssrn.5359199 Y1 - 2025 UR - https://dx.doi.org/10.2139/ssrn.5359199 PB - Elsevier CY - Amsterdam ER - TY - CHAP A1 - Maksimovski, Daniel A1 - Lobo, Silas A1 - Facchi, Christian T1 - Efficient Channel Load Reduction for V2X Maneuver Coordination Service T2 - 2025 IEEE 102nd Vehicular Technology Conference (VTC2025-Fall): Proceedings UR - https://doi.org/10.1109/VTC2025-Fall65116.2025.11310480 Y1 - 2026 UR - https://doi.org/10.1109/VTC2025-Fall65116.2025.11310480 SN - 979-8-3315-0320-8 PB - IEEE CY - Piscataway ER - TY - JOUR A1 - Upadhyay, Navneet A1 - Georges, Munir T1 - Transform-based nonlinear speech enhancement for monaural scenarios JF - International Journal of Speech Technology UR - https://doi.org/10.1007/s10772-025-10239-4 Y1 - 2025 UR - https://doi.org/10.1007/s10772-025-10239-4 SN - 1572-8110 VL - 29 IS - 1 PB - Springer Science+Business Media CY - Dordrecht ER - TY - JOUR A1 - Maksimovski, Daniel A1 - Festag, Andreas A1 - Facchi, Christian T1 - Adaptive Message Generation Rules for V2X Maneuver Coordination Service JF - IEEE Access N2 - Maneuver coordination enables connected and automated vehicles (CAVs) to collaboratively plan, negotiate, and execute driving maneuvers, aiming to enhance safety, traffic flow, and energy efficiency. As part of the Vehicle-to-Everything (V2X) communication system, it operates as a dedicated service that relies on detailed, bidirectional interactions between vehicles, unlike traditional broadcast-based V2X services. While maneuver coordination is the focus of ongoing research and standardization, the rules that govern when and how vehicles generate Maneuver Coordination Messages (MCMs) within the Maneuver Coordination Service (MCS) are still not fully defined. These rules are essential for ensuring timely and reliable coordination. This paper proposes three adaptive MCM generation strategies based on the operation mode of the MCS, maneuver priority, and current channel load. The first approach defines MCM rules for intent sharing, maneuver negotiation, and execution phases, factoring in maneuver importance. The second adjusts the frequency of intent MCMs when a negotiation message is received from another vehicle. The third adapts frequency based on real-time channel load during intent sharing. For comparison, two baseline strategies are also evaluated. The proposed rules are tested for highway merging and lane change use cases in traffic scenarios with increasing vehicle density and high channel loads. A comprehensive evaluation is conducted using metrics related to the network, message generation frequency, congestion control, and maneuver coordination. The results demonstrate significantly enhanced channel efficiency and communication reliability across all metrics. For example, under the final approach, maneuver negotiation time is reduced by a factor of five in congested V2X environments. UR - https://doi.org/10.1109/ACCESS.2026.3652364 Y1 - 2026 UR - https://doi.org/10.1109/ACCESS.2026.3652364 UR - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:573-66051 SN - 2169-3536 VL - 14 SP - 6417 EP - 6437 PB - IEEE CY - New York ER - TY - CHAP A1 - José Souza, Bruno A1 - Szejka, Anderson A1 - Zanetti Freire, Roberto A1 - Schön, Torsten ED - Srihari, Krishnaswami ED - Khasawneh, Mohammad T. ED - Yoon, Sangwon ED - Won, Daehan T1 - A Computer Vision Approach for Enhancing Precision in Manufacturing Assembly Under the Industry 5.0 Concept T2 - Flexible Automation and Intelligent Manufacturing: The Future of Automation and Manufacturing: Intelligence, Agility, and Sustainability, Proceedings of FAIM 2025, June 21–24, 2025, New York City, NY, USA, Volume 2 UR - https://doi.org/10.1007/978-3-032-05610-8_52 Y1 - 2025 UR - https://doi.org/10.1007/978-3-032-05610-8_52 SN - 978-3-032-05610-8 SP - 531 EP - 539 PB - Springer CY - Cham ER - TY - CHAP A1 - Rodríguez-Arozamena, Mario A1 - Matute, Jose A1 - Pérez, Joshué A1 - Ozbay, Burcu A1 - Tezcan, Deryanur A1 - Begecarslan, Enes A1 - Mutlukaya, Irem A1 - Mayer, Kevin A1 - Volkersdorfer, Tina A1 - Hof, Hans-Joachim ED - McNally, Ciaran ED - Carroll, Páraic ED - Martinez-Pastor, Beatriz ED - Ghosh, Bidisha ED - Efthymiou, Marina ED - Valantasis-Kanellos, Nikolaos T1 - A Fail-Safe Decision Architecture for CCAM Applications T2 - Transport Transitions: Advancing Sustainable and Inclusive Mobility, Proceedings of the 10th TRA Conference, 2024 Dublin, Ireland - Volume 6: Connected Mobility Ecosystems N2 - In the context of Connected, Cooperative, and Automated Mobility (CCAM), precise ego-vehicle positioning and environmental status assessment are crucial. However, these tasks can be susceptible to sensor failures, misuse, and cyberattacks. Automation disengagements and system redundancy are common strategies to achieve Minimum Risk Conditions when failures occur. This paper presents a Fail-Safe decision architecture formulated within the framework of the SELFY project (https://selfy-project.eu/). The main aim is to reduce inaccuracies in GNSS-derived positioning through the incorporation of sensor fusion, AI-guided situational assessment, trajectory planning, and mode decision components. Additionally, the architecture has been designed to enable real-time updates and communication with external entities, including the Vehicle Security Operations Centre. UR - https://doi.org/10.1007/978-3-032-06763-0_104 Y1 - 2025 UR - https://doi.org/10.1007/978-3-032-06763-0_104 UR - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:573-65868 SN - 978-3-032-06763-0 SP - 731 EP - 737 PB - Springer CY - Cham ER - TY - JOUR A1 - Gleissner, Christian A1 - Kotonski, Julia T1 - Crystallographic groups and Calabi-Yau 3-folds of type II0 JF - The Asian Journal of Mathematics UR - https://dx.doi.org/10.4310/AJM.251216034253 Y1 - 2025 UR - https://dx.doi.org/10.4310/AJM.251216034253 SN - 1945-0036 VL - 29 IS - 4 SP - 517 EP - 534 PB - International Press of Boston CY - Somerville ER - TY - THES A1 - Salaj, Ina T1 - Integrating cross-modality fusion for joint audio-visual quality assessment N2 - Assessing the quality of audio-visual (AV) content is essential for understanding how users perceive the overall experience of combined audio and video in the modern multimedia applications. Even though there has been a lot of work in assessing the quality of audio and video separately, the integration of these channels has not been well explored especially using advanced deep learning techniques. Current literature tends to use non-industry-standard datasets and simple fusion strategies, thus hindering the real-world relevance and advancement of the field. To address these gaps, this work conducts a series of experiments starting from simple approaches and baseline models, progressively advancing to more sophisticated methods such as integrating cross-modality fusion via cross and self-attention. Our method incorporates a deep neural network based audio model along with a dedicated video quality model where the features of both modalities are fused using attention mechanisms. We extend the feature set of one modality when needed in order to preserve temporal correspondence and relevance. One of the novelties of the proposed approach is the usage of the Concordance Correlation Coefficient (CCC) loss, which is a measure that has been employed in emotion recognition but not in AV quality modeling before. This loss function increases the stability of our quality estimations since it forces the model to output quality scores that are closer to the actual human ratings. We also make a unique contribution by using an AV dataset that is closer to the industry practices, which contains high quality audio and video content with realistic distortions. To the best of our knowledge, this is the first work that employs cross-attention for AV feature fusion in an intrusive AV quality assessment setting. The experimental results show that this approach can enhance the prediction performance and yield high Pearson and Spearman correlations as well as low RMSE. This work sets a new baseline for AV quality assessment and demonstrates how cross-modal fusion can be useful in real-life multimedia applications. Y1 - 2025 UR - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:573-65575 CY - Ingolstadt ER - TY - CHAP A1 - Kumar, Ankit A1 - Georges, Munir T1 - DRI-GAN: A Novel Dual Real Input GAN with Triplet Loss for Cross-Lingual and Noisy SLU T2 - Interspeech 2025 UR - https://doi.org/10.21437/Interspeech.2025-1220 Y1 - 2025 UR - https://doi.org/10.21437/Interspeech.2025-1220 SP - 4118 EP - 4122 PB - ISCA CY - Grenoble ER - TY - CHAP A1 - Semmler, Gregor A1 - Thomas, Julian A1 - Frey, Andreas T1 - Architecture of Drone Swarm with Autonomous Decision Making based on Reinforcement Learning T2 - 2025 AIAA DATC/IEEE 44th Digital Avionics Systems Conference (DASC) UR - https://doi.org/10.1109/DASC66011.2025.11257425 Y1 - 2025 UR - https://doi.org/10.1109/DASC66011.2025.11257425 SN - 979-8-3315-2519-4 PB - IEEE CY - Piscataway ER - TY - CHAP A1 - Irawati, Diah Ayu A1 - Bolukbasi, Elif A1 - Riener, Andreas ED - Sorce, Salvatore ED - Elagroudy, Passant ED - Khamis, Mohamed T1 - Advancing Generative AI Collaboration in Design-to-Code Workflows: Insights from Two Empirical Studies T2 - Proceedings of MUM 2025: The 24th International Conference on Mobile and Ubiquitous Multimedia N2 - For expert users to accept Generative AI (GenAI) as a true collaborative partner, it must move beyond simple task-awareness to an understanding of their workflow’s underlying structural rules. This paper introduces a paradigm for AI collaborators that moves beyond simple task awareness to an understanding of the semantic and hierarchical relationships within a component-based system. We investigate this concept within the context of the design-to-code workflow, where inefficiencies arise from the modification of components within design systems. Through two empirical studies with designers and developers, we found that GenAI output was often rejected because it violated the component hierarchy. Designers required granular and visual control for refinements, whereas developers valued automated setup but required transparent validation of the generated code’s logic. Based on these findings, we contribute design guidelines for achieving Component-Structure Awareness (CSA), with two core principles: the Atomic Recommender, which provides assistance that respects the component hierarchy, and Communication Archetypes, which allow GenAI to adapt its interaction style to the user’s role and the atomic nature of their task. This work provides a new, higher-level concept for designing the next generation of truly collaborative GenAI agents. UR - https://doi.org/10.1145/3771882.3771913 Y1 - 2025 UR - https://doi.org/10.1145/3771882.3771913 UR - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:573-64848 SN - 979-8-4007-2015-4 SP - 33 EP - 46 PB - ACM CY - New York ER - TY - INPR A1 - Chandra Sekaran, Karthikeyan A1 - Geisler, Markus A1 - Rößle, Dominik A1 - Mohan, Adithya A1 - Cremers, Daniel A1 - Utschick, Wolfgang A1 - Botsch, Michael A1 - Huber, Werner A1 - Schön, Torsten T1 - UrbanIng-V2X: A Large-Scale Multi-Vehicle, Multi-Infrastructure Dataset Across Multiple Intersections for Cooperative Perception UR - https://doi.org/10.48550/arXiv.2510.23478 Y1 - 2025 UR - https://doi.org/10.48550/arXiv.2510.23478 PB - arXiv CY - Ithaca ER - TY - CHAP A1 - Hof, Hans-Joachim A1 - Fritz, Mario A1 - Krauß, Christoph T1 - CSCS '25 - Cyber Security in CarS Workshop T2 - CCS ’25: Proceedings of the 2025 ACM SIGSAC Conference on Computer and Communications Security UR - https://doi.org/10.1145/3719027.3769666 Y1 - 2025 UR - https://doi.org/10.1145/3719027.3769666 SN - 979-8-4007-1525-9 SP - 4930 EP - 4931 PB - ACM CY - New York ER - TY - INPR A1 - Salamat, Babak A1 - Mattern, Dominik A1 - Olzem, Sebastian-Sven A1 - Elsbacher, Gerhard A1 - Seidel, Christian A1 - Tonello, Andrea M. T1 - GMP3: Learning-Driven, Bellman-Guided Trajectory Planning for UAVs in Real-Time on SE(3) N2 - We propose GMP3, a multiphase global path planning framework that generates dynamically feasible three-dimensional trajectories for unmanned aerial vehicles (UAVs) operating in cluttered environments. The framework extends traditional path planning from Euclidean position spaces to the Lie group SE(3), allowing joint learning of translational motion and rotational dynamics. A modified Bellman-based operator is introduced to support reinforcement learning (RL) policy updates while leveraging prior trajectory information for improved convergence. GMP3 is designed as a distributed framework in which agents influence each other and share policy information along the trajectory: each agent refines its assigned segment and shares with its neighbors via a consensus-based scheme, enabling cooperative policy updates and convergence toward a path shaped globally even under kinematic constraints. We also propose DroneManager, a modular ground control software that interfaces the planner with real UAV platforms via the MAVLink protocol, supporting real-time deployment and feedback. Simulation studies and indoor flight experiments validate the effectiveness of the proposed method in constrained 3D environments, demonstrating reliable obstacle avoidance and smooth, feasible trajectories across both position and orientation. The open-source implementation is available at https://github.com/Domattee/DroneManager. UR - https://doi.org/10.48550/arXiv.2509.21264 Y1 - 2025 UR - https://doi.org/10.48550/arXiv.2509.21264 UR - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:573-64770 PB - arXiv CY - Ithaca ER - TY - JOUR A1 - Peintner, Jakob A1 - Manger, Carina A1 - Berger, Melanie A1 - Riener, Andreas T1 - Balancing transparency and control: The impact of AI explanation detail on user perception in automated vehicles JF - Transportation Research Interdisciplinary Perspectives N2 - As automated vehicle technology advances, explainable AI has emerged as a critical tool to enable users to understand and predict the behavior of AI systems, particularly in safety-critical applications such as automated driving. However, increased transparency in AI explanations may inadvertently contribute to an “illusion of control”, a cognitive bias in which drivers overestimate their influence or understanding of the AI’s actions. We aim to better understand how the level of detail in AI explanations affects users of automated vehicles. In a virtual reality study, N = 44 participants experienced different explanation levels (low, medium, high) in an automated ride (SAE L4) compared to a baseline condition with no explanations. The results show a significant improvement in participants’ user experience, acceptance, and explanation satisfaction, with more detailed explanations. Our findings also indicate that as AI explanations become more detailed, users’ perceived level of control increases significantly, although this perception does not correlate with actual control capabilities. At the same time, it decreased their desire to take control, indicating users’ susceptibility to the ’illusion of control’ bias in the context of automated driving. Overall, this suggests that the design of explanation interfaces should strive for a balanced level of detail that promotes AI transparency without causing cognitive overload. At the same time, explainable AI can be utilized to decrease users’ desire to intervene in the AI’s actions. UR - https://doi.org/10.1016/j.trip.2025.101742 Y1 - 2025 UR - https://doi.org/10.1016/j.trip.2025.101742 UR - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:573-64740 SN - 2590-1982 VL - 2025 IS - 34 PB - Elsevier CY - Amsterdam ER - TY - CHAP A1 - Michl, Marco A1 - Hof, Hans-Joachim A1 - Katzenbeisser, Stefan T1 - From Access to Trust: Policy-Based Automotive Trust Management With XACML T2 - Proceedings of the 2nd Cyber Security in CarS Workshop (CSCS’25) N2 - Trust is a fundamental requirement in vehicular systems, particularly in the context of autonomous and cooperative driving, where decisions often depend on data received from other vehicles or infrastructure. This paper presents a novel approach to integrating trust-based reasoning—specifically, decision-making based on subjective logic—into Attribute-Based Access Control (ABAC) systems, using the XACML policy language. We draw a parallel between trust management and access control, showing that trust assessments can be modeled as access decisions and expressed through standard access control policies. This integration allows for the reuse of mature ABAC infrastructure to represent and enforce trust-based decisions. Furthermore, we demonstrate how trust discounting mechanisms, which combine node-based and data-based trust, can be implemented within XACML policies. To evaluate this approach, we introduce a Rust-based XACML engine capable of interpreting trust-enhanced policies. Its integration in both inter- and intra-vehicle scenarios is discussed, assuming a modern zone-based architecture. Our results suggest that policy-based trust management can be efficiently implemented, enabling flexible and standardized trust reasoning in automotive environments. UR - https://doi.org/10.1145/3736130.3764511 Y1 - 2025 UR - https://doi.org/10.1145/3736130.3764511 UR - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:573-64731 SN - 979-8-4007-1928-8 PB - ACM CY - New York ER - TY - CHAP A1 - Laves, Claudius A1 - Hutzelmann, Thomas A1 - Mayer, Kevin A1 - Hof, Hans-Joachim T1 - Driving Down Premiums: A Security and Forensic Analysis of the DriveWell Insurance System T2 - Proceedings of the 2nd Cyber Security in CarS Workshop (CSCS’25) N2 - Modern car insurance companies can dynamically adjust premiums with mobile sensor units that constantly record, analyze, and grade driving behavior. However, attackers can tamper with these systems to cheat the insurance company and reduce their premiums. We developed an attacker model for this threat in conjunction with a risk assessment using the ISO/SAE 21434 Risk Rating Methodology, a widely accepted standard for evaluating and managing risks in automotive systems. To support and showcase our model, we examined the static IT security of the mobile sensor unit DriveWell and its companion Android application HUK Mein Auto. In particular, we utilized digital forensic tools to analyze the stored data in the sensor’s internal storage and the app’s smartphone storage, with a focus on encryption mechanisms for locally stored data and internet communication. During this analysis, we spotted a critical vulnerability that enables attackers to manipulate their insurance scores and potentially reduce premiums by up to 30%. Our model and real-world analysis provide a solid foundation for reducing the risk of such manipulation of insurance premiums. UR - https://doi.org/10.1145/3736130.3762688 Y1 - 2025 UR - https://doi.org/10.1145/3736130.3762688 UR - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:573-64728 SN - 979-8-4007-1928-8 PB - ACM CY - New York ER - TY - CHAP A1 - Ullrich, Henning A1 - Bayerl, Dominik A1 - Hutzelmann, Thomas A1 - Hof, Hans-Joachim T1 - Application of Quantum Key Distribution in Intelligent Transport Systems T2 - Proceedings of the 2nd Cyber Security in CarS Workshop (CSCS’25) N2 - Quantum Key Distribution (QKD) enables two parties to generate a secret key, based on quantum physical properties. The secrecy of the exchanged key is guaranteed by these quantum physical properties, even against an attacker using a quantum computer. QKD therefore offers an Quantum Information Theoretically Secure (Q-ITS) alternative to Post Quantum Cryptography (PQC) schemes. This paper discusses advantages and challenges of deploying a QKD system in an Intelligent Transport System (ITS), by examining two exemplary use cases. Approaches to effectively implement QKD in these use cases are proposed, with a short discussion of advantages over the use of classical and PQC schemes. UR - https://doi.org/10.1145/3736130.3762692 Y1 - 2025 UR - https://doi.org/10.1145/3736130.3762692 UR - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:573-63186 SN - 979-8-4007-1928-8 PB - ACM CY - New York ER - TY - CHAP A1 - Chouta, Gnanambica A1 - Ulas, Basak A1 - Facchi, Christian T1 - Enhancing V2X Simulation Framework for Motorcycles T2 - 2025 IEEE International Automated Vehicle Validation Conference (IAVVC) Proceedings UR - https://doi.org/10.1109/IAVVC61942.2025.11219479 Y1 - 2025 UR - https://doi.org/10.1109/IAVVC61942.2025.11219479 SN - 979-8-3315-2526-2 PB - IEEE CY - Piscataway ER - TY - JOUR A1 - Gleißner, Christian A1 - Kotonski, Julia T1 - The classification of rigid torus quotients with canonical singularities in dimension three JF - Rendiconti Lincei: Matematica e Applicazioni N2 - We provide a fine classification of rigid 3-dimensional torus quotients with isolated canonical singularities, up to biholomorphism and diffeomorphism. This complements the classification of Calabi–Yau 3-folds of type III0​, which are those quotients with Gorenstein singularities. UR - https://doi.org/10.4171/RLM/1070 Y1 - 2025 UR - https://doi.org/10.4171/RLM/1070 UR - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:573-64657 SN - 1720-0768 VL - 36 IS - 2 SP - 315 EP - 352 PB - EMS Press CY - Berlin ER - TY - JOUR A1 - Kaiser, Carolin A1 - Jungbluth, Michael A1 - Ulrichshofer, Anna A1 - Kames, Lucas A1 - Cato, Patrick T1 - How sponsored content affects consumer decisions in AI-powered retail JF - NIM Insights Y1 - 2025 UR - https://www.nim.org/forschung/uebersicht-forschungsprojekte/forschungsprojekt/how-sponsored-sontent-affects-consumer-decisions-in-ai-powered-retail SN - 2944-5981 VL - 7 SP - 26 EP - 29 PB - NIM CY - Nürnberg ER - TY - CHAP A1 - Mohamed, Nourhan ED - Lazem, Shaimaa ED - Anya, Obinna ED - Saleh, Mennatallah ED - Nkwo, Makuochi S. ED - Gamundani, Attlee M. ED - Isafiade, Omowunmi E. ED - Ogunyemi, Abiodun A. T1 - Khepri: The Beetle Experience: A Culturally-Grounded Mobility System for Luxor’s Heritage Tourism T2 - Proceedings of the Fifth Biennial African Human-Computer Interaction Conference, AfriCHI’25 UR - https://doi.org/10.1145/3757232.3757342 Y1 - 2025 UR - https://doi.org/10.1145/3757232.3757342 SN - 979-8-4007-1849-6 SP - 464 EP - 467 PB - ACM CY - New York ER - TY - CHAP A1 - Göbel, Jan-Philipp A1 - Mertens, Jan Cedric A1 - Riener, Andreas ED - Muštra, Mario ED - Vuković, Josip ED - Božek, Jelena T1 - Benchmarking the MQ-3 Gas Sensor for Alcohol Impairment Detection: Insights from a Driving Simulator Study T2 - Proceedings of ELMAR-2025: 67th International Symposium ELMAR-2025, 15-17 September 2025, Zadar, Croatia UR - https://doi.org/10.1109/ELMAR66948.2025.11194001 Y1 - 2025 UR - https://doi.org/10.1109/ELMAR66948.2025.11194001 SN - 979-8-3315-9679-8 SP - 77 EP - 84 PB - IEEE CY - Piscataway ER - TY - CHAP A1 - Menden, Christian A1 - Müller-Steinfahrt, Ulrich A1 - Gölzer, Philipp A1 - Cato, Patrick ED - Wehner, Rainer ED - Akçaoğlu, Emin T1 - Forecasting the Demand for Recycled Plastics using a Digital Twin for Regional Plastic Cycles T2 - Conference Proceedings: Würzburg International Business Forum, 8th International Business Conference 2025, Balancing Innovation and Resilience in International Business, Special Focus: Tourism & Hospitality Y1 - 2025 UR - https://wibf.thws.de/publications/conference-proceedings-books/ SN - 978-3-949864-10-0 SP - 65 EP - 68 PB - Würzburg International Business Press CY - Würzburg ER - TY - CHAP A1 - Mondal, Rahul A1 - Ignatova, Evelina A1 - Heinzmann, Jonas A1 - Do, Minh Dung A1 - Murali, Abhivanth A1 - Walke, Daniel A1 - Cato, Patrick A1 - Becker, Robert A. A1 - Bleistein, Thomas A1 - Saake, Gunter A1 - Broneske, David A1 - Heyer, Robert T1 - SimKit: Similarity Graphs, Eigendecomposition and Spectral Clustering in Neo4j T2 - Proceedings: 2025 IEEE International Conference on High Performance Computing and Communications, HPCC/DSS/SmartCity/DependSys/ICESS 2025 UR - https://doi.org/10.1109/HPCC67675.2025.00145 Y1 - 2025 UR - https://doi.org/10.1109/HPCC67675.2025.00145 SN - 979-8-3315-6874-0 SP - 985 EP - 991 PB - IEEE CY - Piscataway ER - TY - CHAP A1 - Preiwisch, Anna A1 - Riener, Andreas T1 - Exploring Human Abuse of Automated Vehicles: A Review Framed by Robot Abuse Research T2 - Adjunct Conference Proceedings: 17th International ACM Conference on Automotive User Interfaces and Interactive Vehicular Applications, September 22-25, 2025, Brisbane, Australia UR - https://doi.org/10.1145/3744335.3758491 Y1 - 2025 UR - https://doi.org/10.1145/3744335.3758491 SN - 979-8-4007-2014-7 SP - 114 EP - 119 PB - ACM CY - New York ER - TY - CHAP A1 - Önbaş, Tuğcan A1 - Gerber, Michael A. A1 - Riener, Andreas T1 - Context-Aware Take-Over Requests for Promoting Emergency Corridor Formation in Level 3 Automated Vehicles T2 - Adjunct Conference Proceedings: 17th International ACM Conference on Automotive User Interfaces and Interactive Vehicular Applications, September 22-25, 2025, Brisbane, Australia UR - https://doi.org/10.1145/3744335.3756855 Y1 - 2025 UR - https://doi.org/10.1145/3744335.3756855 SN - 979-8-4007-2014-7 SP - 342 EP - 344 PB - ACM CY - New York ER - TY - CHAP A1 - Kaehne, Esther Carolina A1 - Patzer, Amelie A1 - Lachmann, Julia A1 - Alvarez, Ignacio T1 - Designing Multimodal In-Car Conversational Agents for Parents: A Simulator Study T2 - Adjunct Conference Proceedings: 17th International ACM Conference on Automotive User Interfaces and Interactive Vehicular Applications, September 22-25, 2025, Brisbane, Australia UR - https://doi.org/10.1145/3744335.3758503 Y1 - 2025 UR - https://doi.org/10.1145/3744335.3758503 SN - 979-8-4007-2014-7 SP - 190 EP - 195 PB - ACM CY - New York ER - TY - CHAP A1 - Solombrino, Laetitia Pina-Lydia A1 - Gerber, Michael A. A1 - Riener, Andreas T1 - Dynamic Head-Up Display Design: Cognitive Load as a Parametric Driver T2 - Adjunct Conference Proceedings: 17th International ACM Conference on Automotive User Interfaces and Interactive Vehicular Applications, September 22-25, 2025, Brisbane, Australia UR - https://doi.org/10.1145/3744335.3756861 Y1 - 2025 UR - https://doi.org/10.1145/3744335.3756861 SN - 979-8-4007-2014-7 SP - 362 EP - 364 PB - ACM CY - New York ER - TY - CHAP A1 - Berger, Melanie A1 - Ebel, Patrick A1 - Riener, Andreas A1 - Alvarez, Ignacio A1 - Wintersberger, Philipp A1 - Sadeghian, Shadan T1 - Sustainable by Design: A Workshop on Life-Cycle-Aware Future Mobility T2 - Adjunct Conference Proceedings: 17th International ACM Conference on Automotive User Interfaces and Interactive Vehicular Applications, September 22-25, 2025, Brisbane, Australia UR - https://doi.org/10.1145/3744335.3749137 Y1 - 2025 UR - https://doi.org/10.1145/3744335.3749137 SN - 979-8-4007-2014-7 SP - 306 EP - 309 PB - ACM CY - New York ER - TY - CHAP A1 - Mohamed, Nourhan A1 - Gerber, Michael A. A1 - Riener, Andreas T1 - Investigation of Habituation Effects of Visual Variations of Cues to the Fallback-Driver for Automated Level 3 Vehicles T2 - Adjunct Conference Proceedings: 17th International ACM Conference on Automotive User Interfaces and Interactive Vehicular Applications, September 22-25, 2025, Brisbane, Australia UR - https://doi.org/10.1145/3744335.3756862 Y1 - 2025 UR - https://doi.org/10.1145/3744335.3756862 SN - 979-8-4007-2014-7 SP - 365 EP - 368 PB - ACM CY - New York ER - TY - CHAP A1 - Karur, Krishna Sahithi A1 - Riener, Andreas A1 - Alvarez, Ignacio A1 - Wintersberger, Philipp A1 - Park, Jeongeun A1 - Lee, Seulchan T1 - 1st Workshop on Exploring the Potential of XAI and HMI to Alleviate Ethical, Legal, and Social Conflicts in Automated Vehicles T2 - Adjunct Conference Proceedings: 17th International ACM Conference on Automotive User Interfaces and Interactive Vehicular Applications, September 22-25, 2025, Brisbane, Australia UR - https://doi.org/10.1145/3744335.3749143 Y1 - 2025 UR - https://doi.org/10.1145/3744335.3749143 SN - 979-8-4007-2014-7 SP - 295 EP - 297 PB - ACM CY - New York ER - TY - CHAP A1 - Rollwagen, Alice A1 - Alvarez, Ignacio A1 - Riener, Andreas T1 - Reality Check: Real-World Observations of State-of-the-Art Driver Assistance Systems T2 - Adjunct Conference Proceedings: 17th International ACM Conference on Automotive User Interfaces and Interactive Vehicular Applications, September 22-25, 2025, Brisbane, Australia UR - https://doi.org/10.1145/3744335.3758487 Y1 - 2025 UR - https://doi.org/10.1145/3744335.3758487 SN - 979-8-4007-2014-7 SP - 88 EP - 93 PB - ACM CY - New York ER - TY - CHAP A1 - Peintner, Jakob A1 - Manger, Carina A1 - Alvarez, Ignacio A1 - Riener, Andreas T1 - Enhancing Pedestrian Realism in Adverse-Weather Driving Simulations Using Motion Capture Data T2 - Adjunct Conference Proceedings: 17th International ACM Conference on Automotive User Interfaces and Interactive Vehicular Applications, September 22-25, 2025, Brisbane, Australia UR - https://doi.org/10.1145/3744335.3758507 Y1 - 2025 UR - https://doi.org/10.1145/3744335.3758507 SN - 979-8-4007-2014-7 SP - 214 EP - 219 PB - ACM CY - New York ER - TY - CHAP A1 - Raghvani, Vidhi A1 - Gerber, Michael A. A1 - Riener, Andreas T1 - Inclusive Vehicle Dashboard Design: Supporting Neuro diverse ADHD Drivers Through Visual Simplicity T2 - Adjunct Conference Proceedings: 17th International ACM Conference on Automotive User Interfaces and Interactive Vehicular Applications, September 22-25, 2025, Brisbane, Australia UR - https://doi.org/10.1145/3744335.3756860 Y1 - 2025 UR - https://doi.org/10.1145/3744335.3756860 SN - 979-8-4007-2014-7 SP - 359 EP - 361 PB - ACM CY - New York ER - TY - CHAP A1 - Manger, Carina A1 - Preiwisch, Anna A1 - Riener, Andreas T1 - Invisible Barriers: Understanding and Supporting the Mobility Needs and Challenges of Individuals with Mental Health Conditions T2 - Adjunct Conference Proceedings: 17th International ACM Conference on Automotive User Interfaces and Interactive Vehicular Applications, September 22-25, 2025, Brisbane, Australia UR - https://doi.org/10.1145/3744335.3758489 Y1 - 2025 UR - https://doi.org/10.1145/3744335.3758489 SN - 979-8-4007-2014-7 SP - 100 EP - 105 PB - ACM CY - New York ER - TY - CHAP A1 - Scheiter, Andrea Isabell A1 - Fahmüller, Tobias A1 - Herbst, Uwe A1 - Bengler, Klaus ED - Buchanan, George ED - Davis, Hilary ED - Al Mahmud, Abdullah ED - Sarsenbayeva, Zhanna ED - Soro, Alessandro ED - Muñoz, Diego ED - Potter, Leigh Ellen ED - Taylor, Jennyfer Lawrence ED - Tsimeris, Jessica T1 - Development and Initial Evaluation of an Assisting Display Concept for Driving Wizards T2 - Proceedings of the 33rd Australian Computer-Human Interaction Conference (OzCHI 2021) UR - https://doi.org/10.1145/3520495.3520508 Y1 - 2021 UR - https://doi.org/10.1145/3520495.3520508 SN - 978-1-4503-9598-4 SP - 257 EP - 264 PB - ACM CY - New York ER - TY - CHAP A1 - Verma, Tarun A1 - Valsangkar, Natalie Sachin A1 - Szuban, Monika Marta A1 - Müller, Hannah Maria ED - Yamashita, Naomi ED - Evers, Vanessa ED - Yatani, Koji ED - Ding, Xianghua T1 - Grannify – Sustainable Living through Generational Knowledge T2 - CHI’25: Extended Abstracts of the 2025 CHI Conference on Human Factors in Computing Systems UR - https://doi.org/10.1145/3706599.3720306 Y1 - 2025 UR - https://doi.org/10.1145/3706599.3720306 SN - 979-8-4007-1395-8 PB - ACM CY - New York ER - TY - JOUR A1 - Manger, Carina A1 - Vogl, Annalena A1 - Riener, Andreas T1 - Cognitive Biases in User Interaction with Automated Vehicles: The Influence of Explainability and Mental Models JF - Applied Sciences N2 - To develop truly human-centered automated systems, it is essential to acknowledge that human reasoning is prone to systematic deviations from rational judgment, known as Cognitive Biases. The present study investigated such flawed reasoning in the context of automated driving. In a multi-step study with N = 34 participants, the occurrence of four Cognitive Biases was examined: Truthiness Effect, Automation Bias, Action Bias, and Illusory Control. Additionally, the study explored how the Explainability of the automation’s behavior and the driver’s Mental Model influenced the manifestation of these biases. The findings indicate a notable susceptibility to the Truthiness Effect and Illusory Control, although all biases appeared highly dependent on the specific driving context. Moreover, Explainability strongly impacted the perceived credibility of information and participants’ agreement with the system’s behavior. Given the exploratory nature of the study, this work aims to initiate a discussion on how Cognitive Biases shape human reasoning and decision-making in interactions with automated vehicles. Based on the results, several directions for future research are proposed: (1) investigation of additional cognitive biases, (2) analysis of biases across different levels of automation, (3) exploration of mitigation strategies versus deliberate use of biases, (4) examination of dynamic and context-dependent manifestations, and (5) validation in high-fidelity simulations or real-world settings. UR - https://doi.org/10.3390/app152011030 Y1 - 2025 UR - https://doi.org/10.3390/app152011030 UR - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:573-63831 SN - 2076-3417 VL - 15 IS - 20 PB - MDPI CY - Basel ER - TY - CHAP A1 - Saleh, Mennatallah A1 - Macdonald, Shaun A1 - Sturm, Christian ED - Ardito, Carmelo ED - Diniz Junqueira Barbosa, Simone ED - Conte, Tayana ED - Freire, André ED - Gasparini, Isabela ED - Palanque, Philippe ED - Prates, Raquel T1 - Dancing with Data: Privacy Compromises of Egyptian Women Reconciling Westernized Social Media Sharing with Local Cultural Values T2 - Human-Computer Interaction – INTERACT 2025: 20th IFIP TC 13 International Conference, Belo Horizonte, Brazil, September 8–12, 2025, Proceedings, Part II UR - https://doi.org/10.1007/978-3-032-05002-1_11 Y1 - 2025 UR - https://doi.org/10.1007/978-3-032-05002-1_11 SN - 978-3-032-05002-1 SP - 203 EP - 225 PB - Springer CY - Cham 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 - INPR A1 - Ammeling, Jonas A1 - Ganz, Jonathan A1 - Rosbach, Emely A1 - Lausser, Ludwig A1 - Bertram, Christof A1 - Breininger, Katharina A1 - Aubreville, Marc T1 - Benchmarking Foundation Models for Mitotic Figure Classification UR - https://doi.org/10.48550/arXiv.2508.04441 Y1 - 2025 UR - https://doi.org/10.48550/arXiv.2508.04441 PB - arXiv CY - Ithaca ER - TY - GEN A1 - Siddig, Ibrahim A1 - Tugeev, Sviatoslav A1 - Georges, Munir T1 - THI Lehrvideo Community Handbook Version 1.0 N2 - This guidebook is offered as part of the THI Lehrvideocommunity Project. It is designed to assist both students and professors in producing high-quality videos for various purposes - from work and project presentation to peer education. It offers a useful starting point if you are facing such task for the first time, as well as potential avenues for improvement for those already more experienced. Y1 - 2025 UR - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:573-63271 CY - Ingolstadt ER - TY - CHAP A1 - Huber, Vinzenz Baptist A1 - Ertl, Finn Luca A1 - Schlaak, Anja A1 - Simson, Hannah A1 - Alvarez, Ignacio T1 - Can an AI Voice Assistant reduce Stress for Drivers confronted with warning Signals? T2 - Adjunct Conference Proceedings: 17th International ACM Conference on Automotive User Interfaces and Interactive Vehicular Applications, September 22-25, 2025, Brisbane, Australia UR - https://doi.org/10.1145/3744335.3758490 Y1 - 2025 UR - https://doi.org/10.1145/3744335.3758490 SN - 979-8-4007-2014-7 SP - 106 EP - 113 PB - ACM CY - New York ER - TY - CHAP A1 - Löw, Jakob A1 - Bayerl, Dominik A1 - Mayer, Kevin A1 - Hof, Hans-Joachim T1 - DrainDead: Emptying Batteries of Parked Electric Vehicles T2 - Proceedings of the 3rd USENIX Symposium on Vehicle Security and Privacy (VehicleSec ’25) Y1 - 2025 UR - https://www.usenix.org/conference/vehiclesec25/presentation/low SN - 978-1-939133-49-6 SP - 233 EP - 241 PB - USENIX Association CY - Berkeley ER - TY - CHAP A1 - Müller, Hannah Maria A1 - Valsangkar, Natalie Sachin A1 - Rashid, Warda A1 - Bhanderi, Smit A1 - Berghegger, Lukas Peter A1 - Kolhe, Ritvik Rajiv A1 - Malik, Maham A1 - Sukani, Meetkumar A1 - Volkan, Berde A1 - Riener, Andreas T1 - Ride Recall: An Aftermarket Item-Reminder System for Shared & Rented Cars T2 - Adjunct Conference Proceedings: 17th International ACM Conference on Automotive User Interfaces and Interactive Vehicular Applications, September 22-25, 2025, Brisbane, Australia UR - https://doi.org/10.1145/3744335.3756858 Y1 - 2025 UR - https://doi.org/10.1145/3744335.3756858 SN - 979-8-4007-2014-7 SP - 352 EP - 355 PB - ACM CY - New York ER - TY - CHAP A1 - Yu, Hang A1 - Shedge, Krishnakant A1 - Mohamed, Nourhan A1 - Samak, Nada A1 - Binsad, Waleed A1 - Bhosale, Simantini A1 - Kim, Eunji A1 - Kondvilkar, Shreya A1 - Riener, Andreas T1 - AwareDoor: Enhancing Vehicle-Exit Safety via Multimodal Risk Communication T2 - Adjunct Conference Proceedings: 17th International ACM Conference on Automotive User Interfaces and Interactive Vehicular Applications, September 22-25, 2025, Brisbane, Australia UR - https://doi.org/10.1145/3744335.3756856 Y1 - 2025 UR - https://doi.org/10.1145/3744335.3756856 SN - 979-8-4007-2014-7 SP - 345 EP - 348 PB - ACM CY - New York ER - TY - CHAP A1 - Blümke, Julian A1 - Laves, Claudius A1 - Hof, Hans-Joachim ED - Leonowicz, Zbigniew ED - Stracqualursi, Erika ED - Jasinski, Michal T1 - Identifying Building Blocks for Second-Life-Enabled Battery Management Systems T2 - Conference Proceedings: 2025 IEEE International Conference on Environment and Electrical Engineering and 2025 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I&CPS Europe), 15-18 July 2025, Chania, Crete - Greece UR - https://doi.org/10.1109/EEEIC/ICPSEurope64998.2025.11169196 Y1 - 2025 UR - https://doi.org/10.1109/EEEIC/ICPSEurope64998.2025.11169196 SN - 979-8-3315-9515-9 PB - IEEE CY - Piscataway ER - TY - INPR A1 - Mohan, Adithya A1 - Rößle, Dominik A1 - Cremers, Daniel A1 - Schön, Torsten T1 - Advancing Robustness in Deep Reinforcement Learning with an Ensemble Defense Approach UR - https://doi.org/10.48550/arXiv.2507.17070 Y1 - 2025 UR - https://doi.org/10.48550/arXiv.2507.17070 PB - arXiv CY - Ithaca ER - TY - CHAP A1 - Geier, Johannes A1 - Heinl, Patrizia ED - Skopik, Florian ED - Naessens, Vincent ED - De Sutter, Bjorn T1 - Towards a Metric to Assess Neural Network Resilience Against Adversarial Samples T2 - Availability, Reliability and Security: ARES 2025 EU Projects Symposium Workshops, Ghent, Belgium, August 11–14, 2025, Proceedings, Part I N2 - Neural networks are vulnerable to adversarial attacks. Existing robustness evaluation methods have notable limitations, which makes robustness assessment challenging. This work explores robustness evaluation techniques and identifies key factors, including distance metrics, loss functions, attack generation algorithms, attacker models, specificity, and computational resources. Building on those factors, a novel robustness metric for classification tasks is proposed. Our metric accounts for both, targeted and untargeted attacks across three attacker models, while incorporating accuracy and loss into a weighted aggregation. The scoring includes robustness-versus-perturbation and loss-versus-perturbation curves. Our robustness metric offers a more reliable evaluation and deeper insights into model vulnerability compared to previous approaches. UR - https://doi.org/10.1007/978-3-032-00642-4_16 Y1 - 2025 UR - https://doi.org/10.1007/978-3-032-00642-4_16 UR - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:573-62443 SN - 978-3-032-00642-4 SP - 272 EP - 290 PB - Springer CY - Cham ER - TY - CHAP A1 - Krüger, Max T1 - ChatGPT as a Subject Matter Expert in the Parameterization of Bayesian Network Classifiers T2 - Proceedings of the 2025 28th International Conference on Information Fusion (FUSION 2025) UR - https://doi.org/10.23919/FUSION65864.2025.11124000 Y1 - 2025 UR - https://doi.org/10.23919/FUSION65864.2025.11124000 SN - 978-1-0370-5623-9 PB - IEEE CY - Piscataway ER - TY - JOUR A1 - Kaiser, Carolin A1 - Schallner, René A1 - Jungbluth, Michael A1 - Cato, Patrick A1 - Ulrichshofer, Anna T1 - AI-powered Digital Sales Assistants BT - The Future of Shopper Engagement JF - NIM Insights Y1 - 2024 UR - https://www.nim.org/publikationen/detail/ai-powered-digital-sales-assistants SN - 2944-5981 VL - 6 SP - 10 EP - 13 PB - NIM CY - Nürnberg ER - TY - CHAP A1 - Ulrichshofer, Anna A1 - Jungbluth, Michael A1 - Cato, Patrick A1 - Kaiser, Carolin T1 - Digital Sales Assistants in ‘Bricks & Clicks’ – Deciphering Relative Feature Importance for Shopper Engagement T2 - Proceedings of the European Marketing Academy: EMAC 2024 Annual Y1 - 2024 UR - https://proceedings.emac-online.org/index.cfm?eventid=48&EMAC%202024%20Annual PB - EMAC CY - Brüssel ER - TY - JOUR A1 - Choe, Mungyeong A1 - Dong, Jiayuan A1 - Bosch, Esther A1 - Alvarez, Ignacio A1 - Oehl, Michael A1 - Jallais, Christophe A1 - Alsaid, Areen A1 - Jeon, Myounghoon T1 - Driving with Empathy: Workshop Report on AI-driven In-vehicle Empathic Agent Design for Automated Vehicles JF - Proceedings of the Human Factors and Ergonomics Society Annual Meeting UR - https://doi.org/10.1177/10711813251369800 Y1 - 2025 UR - https://doi.org/10.1177/10711813251369800 SN - 1071-1813 SN - 2169-5067 VL - 69 IS - 1 SP - 1770 EP - 1775 PB - Sage CY - London ER - TY - CHAP A1 - Peintner, Jakob A1 - Malve, Bhavana A1 - Sadeghian, Shadan A1 - Riener, Andreas T1 - Driving Together: An Analysis of Passengers' Needs and Desire for Cooperative Control in Automated Vehicles T2 - MuC´25: Proceedings of the 2025 Conference on Mensch und Computer N2 - Driving automation aims to enhance comfort, safety, and traffic flow by removing the human driver from the control loop. However, the human experience of commuting involves more than just reaching a destination or assuming the role of a driver. Factors like personal driving style and courtesy towards fellow road users are integral to the driving experience but often overlooked in the development of driving algorithms for automated vehicles. In this study, we explored the needs of passengers in highly automated vehicles. A qualitative use case analysis was conducted (N=16). In a second study, N=15 participants experienced the resulting use cases in an automated vehicle. In these scenarios, they were able to interact with the automation through a cooperation HMI. Results indicate that most participants expressed a desire for cooperative driving, albeit varying with the driving situation. Moreover, allowing cooperation improves passengers’ overall experience by satisfying psychological needs for autonomy, security, competence, and relatedness. UR - https://doi.org/10.1145/3743049.3743061 Y1 - 2025 UR - https://doi.org/10.1145/3743049.3743061 UR - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:573-62112 SN - 979-8-4007-1582-2 SP - 333 EP - 344 PB - ACM CY - New York ER - TY - CHAP A1 - Dietl, Laura A1 - Facchi, Christian T1 - A Glimpse into the Future: An Inverse Soft Q-Learning's Soft Actor-Critic Approach for Pedestrian Path Prediction T2 - 2025 IEEE Intelligent Vehicles Symposium (IV) UR - https://doi.org/10.1109/IV64158.2025.11097431 Y1 - 2025 UR - https://doi.org/10.1109/IV64158.2025.11097431 SN - 979-8-3315-3803-3 SP - 111 EP - 118 PB - IEEE CY - Piscataway ER - TY - CHAP A1 - Dietl, Laura A1 - Facchi, Christian T1 - Really, Pedestrian Trajectories: How Realistic are the Datasets? T2 - 2025 IEEE Intelligent Vehicles Symposium (IV) UR - https://doi.org/10.1109/IV64158.2025.11097441 Y1 - 2025 UR - https://doi.org/10.1109/IV64158.2025.11097441 SN - 979-8-3315-3803-3 SP - 301 EP - 308 PB - IEEE CY - Piscataway ER - TY - CHAP A1 - Mosaferchi, Saeedeh A1 - Riener, Andreas A1 - Najafi-Ghobadi, Khadijeh A1 - Li, Tingnan A1 - Naddeo, Alessandro T1 - Shaping Affective Trust in Automated Vehicles: The Interplay of Initial Trust, Gender, and Biophilic Design T2 - 2025 IEEE Intelligent Vehicles Symposium (IV) UR - https://doi.org/10.1109/IV64158.2025.11097605 Y1 - 2025 UR - https://doi.org/10.1109/IV64158.2025.11097605 SN - 979-8-3315-3803-3 SP - 1293 EP - 1298 PB - IEEE CY - Piscataway ER - TY - CHAP A1 - Escher, Bengt A1 - Herde, Jonas A1 - Nikolai, Florian A1 - Riener, Andreas T1 - Regulating Teleoperation on Public Roads: Key Takeaways From an Expert Workshop T2 - 2025 IEEE Intelligent Vehicles Symposium (IV) UR - https://doi.org/10.1109/IV64158.2025.11097504 Y1 - 2025 UR - https://doi.org/10.1109/IV64158.2025.11097504 SN - 979-8-3315-3803-3 SP - 2601 EP - 2606 PB - IEEE CY - Piscataway ER - TY - CHAP A1 - Heinl, Patrizia A1 - Reichl-Streich, Andrea-Julia A1 - Hof, Hans-Joachim T1 - Through the Looking-Glass: The Current AI Landscape in German Hospitals from a Cybersecurity Perspective T2 - Proceedings: 2025 IEEE 13th International Conference on Healthcare Informatics, ICHI 2025 UR - https://doi.org/10.1109/ICHI64645.2025.00078 Y1 - 2025 UR - https://doi.org/10.1109/ICHI64645.2025.00078 SN - 979-8-3315-2094-6 SP - 636 EP - 647 PB - IEEE CY - Piscataway ER - TY - CHAP A1 - Okumus, Fazli Faruk A1 - Zacchi, João-Vitor A1 - Salfeld, Maike A1 - Schweizer, Markus A1 - Mata, Núria A1 - Kugele, Stefan ED - Andrikopoulos, Vasilios ED - Pautasso, Cesare ED - Ali, Nour ED - Soldani, Jacopo ED - Xu, Xiwei T1 - Runtime Monitor Synthesis for Automotive Software Architectures T2 - Software Architecture: 19th European Conference, ECSA 2025, Limassol, Cyprus, September 15–19, 2025, Proceedings UR - https://doi.org/10.1007/978-3-032-02138-0_11 Y1 - 2025 UR - https://doi.org/10.1007/978-3-032-02138-0_11 SN - 978-3-032-02138-0 SP - 161 EP - 177 PB - Springer CY - Cham ER - TY - CHAP A1 - Lobo, Silas A1 - Barbosa da Silva, Leonardo A1 - Facchi, Christian ED - Aguiar, Ana ED - Higuchi, Takamasa ED - Sargento, Susana ED - Vinel, Alexey ED - Memedi, Agon T1 - VRU Basic Service: A V2X Comprehensive Analysis in a VRU Large-Scale Scenario T2 - 2025 IEEE Vehicular Networking Conference (VNC) UR - https://doi.org/10.1109/VNC64509.2025.11054143 Y1 - 2025 UR - https://doi.org/10.1109/VNC64509.2025.11054143 SN - 979-8-3315-2437-1 SP - 116 EP - 123 PB - IEEE CY - Piscataway ER - TY - INPR A1 - Sümer, Ömer A1 - Huber, Tobias A1 - Duong, Dat A1 - Ledgister Hanchard, Suzanna E. A1 - Conati, Cristina A1 - André, Elisabeth A1 - Solomon, Benjamin D. A1 - Waikel, Rebekah L. T1 - Evaluation of a Deep Learning and XAI based Facial Phenotyping Tool for Genetic Syndromes: A Clinical User Study N2 - Artificial intelligence (AI) tools are increasingly employed in clinical genetics to assist in diagnosing genetic conditions by assessing photographs of patients. For medical uses of AI, explainable AI (XAI) methods offer a promising approach by providing interpretable outputs, such as saliency maps and region relevance visualizations. XAI has been discussed as important for regulatory purposes and to enable clinicians to better understand how AI tools work in practice. However, the real-world effects of XAI on clinician performance, confidence, and trust remain underexplored. This study involved a web-based user experiment with 31 medical geneticists to assess the impact of AI-only diagnostic assistance compared to XAI-supported diagnostics. Participants were randomly assigned to either group and completed diagnostic tasks with 18 facial images of individuals with known genetic syndromes and unaffected individuals, before and after experiencing the AI outputs. The results show that both AI-only and XAI approaches improved diagnostic accuracy and clinician confidence. The effects varied according to the accuracy of AI predictions and the clarity of syndromic features (sample difficulty). While AI support was viewed positively, users approached XAI with skepticism. Interestingly, we found a positive correlation between diagnostic improvement and XAI intervention. Although XAI support did not significantly enhance overall performance relative to AI alone, it prompted users to critically evaluate images with false predictions and influenced their confidence levels. These findings highlight the complexities of trust, perceived usefulness, and interpretability in AI-assisted diagnostics, with important implications for developing and implementing clinical decision-support tools in facial phenotyping for rare genetic diseases. UR - https://doi.org/10.1101/2025.06.08.25328588 Y1 - 2025 UR - https://doi.org/10.1101/2025.06.08.25328588 UR - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:573-61696 PB - medRxiv CY - Cold Spring Harbor ER - TY - CHAP A1 - Haimerl, Mathias A1 - Radetzky, Laura A1 - Brenner, Evi Stefanie A1 - Strapak, Viktoria A1 - Riener, Andreas T1 - Leveling the Playing Field BT - Neurodiversity in Research using the Potential of Gamified Questionnaires T2 - MuC´25: Proceedings of the 2025 Conference on Mensch und Computer N2 - Participating in studies with lengthy questionnaires can be exhausting, especially for individuals with disabilities or neurodivergent individuals. Gamification can potentially enhance user experience (UX) and, thus, create a more inclusive engagement with studies. This study investigates gamified studies’ UX for neurodivergent persons. A gamified version of the Quality of Life Enjoyment and Satisfaction Questionnaire – Short Form (Q-LES-Q-SF) was created using motivational theory and the gamification frameworks Marczewski’s Periodic Table of Gamification Elements and Gamification User Types Hexad Scale. First results of a preliminary comparative survey with N = 72 (gamified n = 31) suggest that gamification increases the UX for neurodivergent participants. UR - https://doi.org/10.1145/3743049.3748578 Y1 - 2025 UR - https://doi.org/10.1145/3743049.3748578 UR - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:573-61794 SN - 979-8-4007-1582-2 SP - 659 EP - 667 PB - ACM CY - New York ER - TY - CHAP A1 - Rollwagen, Alice A1 - Horn, Alexander A1 - Schmidtner, Stefanie A1 - Riener, Andreas T1 - Communicating Uncertainty in Arrival Time Predictions for Public Transport: A Comparison of Point and Interval Forecasts T2 - MuC´25: Proceedings of the 2025 Conference on Mensch und Computer N2 - In public transport, arrival times are typically communicated as point forecasts, aiming to present precise estimates. However, current prediction models are unable to provide such precise and reliable estimates due to unpredictable events. This results in arrival times on passenger information systems appearing inaccurate due to the lack of communicated uncertainty. We therefore investigated interval forecasts as an alternative in an online study, aiming to better communicate uncertainty in arrival times. Our findings indicate that interval forecasts improve the communication of uncertainty. Further, user satisfaction was driven primarily by waiting time, and this relationship was moderated by the forecast concept. Point forecasts were only well received when the bus arrived as predicted, otherwise users preferred the broader interval forecasts. Participants valued accuracy over precision when judging arrival times. UR - https://doi.org/10.1145/3743049.3748542 Y1 - 2025 UR - https://doi.org/10.1145/3743049.3748542 UR - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:573-61783 SN - 979-8-4007-1582-2 SP - 515 EP - 519 PB - ACM CY - New York ER - TY - CHAP A1 - Neufeld, Lisa A1 - Heim, Elena A1 - Klinger, Madita Joy A1 - Himmels, Chantal A1 - Böck, Henrike A1 - Riener, Andreas T1 - Beats vs. Talks: Alleviating Virtual Reality Sickness with Music and Podcasts T2 - MuC´25: Proceedings of the 2025 Conference on Mensch und Computer N2 - As virtual reality (VR) technology advances, its potential for widespread application increases. However, VR sickness (VRS) remains a major obstacle to broader adoption. This study investigates the effect of auditory stimuli on VRS during VR experiences. It contrasts the effects of music and podcasts, with a no-sound environment serving as reference. Twenty-three participants experienced three different auditory conditions during a VR roller coaster ride in a randomized, balanced order. VRS was quantified using the Simulator Sickness Questionnaire (SSQ) and physiological data. The SSQ results indicate that music significantly reduced oculomotor disturbances and disorientation, while podcasts had no positive effects. The physiological data demonstrated no significant effects. The majority of participants preferred the music scenario, describing it as relaxing and pleasant. This highlights the potential of music, especially when self-selected and perceived as pleasant, to improve VR experience by significantly reducing VRS. This effect appears to be independent of physiological data. UR - https://doi.org/10.1145/3743049.3748539 Y1 - 2025 UR - https://doi.org/10.1145/3743049.3748539 UR - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:573-61776 SN - 979-8-4007-1582-2 SP - 488 EP - 493 PB - ACM CY - New York ER - TY - CHAP A1 - Appel, Patricia B. A1 - Böck, Henrike A1 - Hompf, Filippa Viktoria Franziska A1 - Plümer, Kolja Sam A1 - Russ, Sarah A1 - Riener, Andreas T1 - Understanding Bystander Preferences for Medical Emergency Support Measures in VR-Simulated eVTOL Flights T2 - MuC´25: Proceedings of the 2025 Conference on Mensch und Computer N2 - With the emergence of Urban Air Mobility (UAM), eVTOLs are set to revolutionize the way of traveling. However, there will be situations where things do not go as planned, such as medical emergencies during a flight. Due to the lack of cabin crew, tailored support measures must be identified to ensure passenger safety and well-being. To explore how potential passengers perceive medical emergencies on board as bystanders and what kind of assistance they expect, we conducted a VR study. The results show significant increases in negative emotions during a medical in-flight emergency. Moreover, support measures involving human interaction were rated significantly higher than those relying solely on information displayed on onboard screens. Particularly well-received were pilot announcements. For UAM to enter the market successfully, it is essential to address passenger needs in critical situations. Our findings provide valuable insights into shaping effective and user-centered emergency support strategies for eVTOL operations. UR - https://doi.org/10.1145/3743049.3748549 Y1 - 2025 UR - https://doi.org/10.1145/3743049.3748549 UR - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:573-61761 SN - 979-8-4007-1582-2 SP - 755 EP - 760 PB - ACM CY - New York ER - TY - CHAP A1 - Decaro, Gianluca A1 - Bogarin Heurich, Sofia A1 - Appel, Patricia B. A1 - Kul, Sergen A1 - Riener, Andreas T1 - Too Close for Comfort? The Impact of eVTOL-Overflights in Residential Areas on Non-Users' Acceptance T2 - MuC´25: Proceedings of the 2025 Conference on Mensch und Computer N2 - Urban Air Mobility (UAM) has the potential to revolutionize commuting by allowing passengers to travel quickly and efficiently within and between cities and airports. However, this innovation also raises concerns for residents on the ground, who are expected to tolerate frequent eVTOL overflights above their homes - an issue that this paper seeks to address. To investigate acceptance of eVTOLs from the perspective of residents on the ground being overflown at 1000 ft, 1500 ft, and 2000 ft, a virtual reality study was conducted. Results showed significant differences in emotions, the feeling of being disturbed by the noise, the spatial proximity, and the presence of the eVTOL in lower altitudes. Additionally, privacy concerns were expressed. The findings help the scientific community and regulators in developing guidelines for operating eVTOLs in residential areas in an acceptable manner for non-passengers. UR - https://doi.org/10.1145/3743049.3748548 Y1 - 2025 UR - https://doi.org/10.1145/3743049.3748548 UR - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:573-61755 SN - 979-8-4007-1582-2 SP - 744 EP - 748 PB - ACM CY - New York ER - TY - CHAP A1 - Bayerl, Dominik A1 - Hutzelmann, Thomas A1 - Hof, Hans-Joachim T1 - Efficient Cross-Architecture Binary Function Embeddings through Knowledge Distillation T2 - Proceedings of the 11th ACM Cyber-Physical System Security Workshop N2 - Deep learning has recently been shown to be effective in various tasks related to static binary analysis. One important analysis task is the binary function similarity problem: Given the binary code of two functions compiled with different compilers, different settings, and different processor architectures, the goal is to decide whether the functions are semantically equivalent (i.e. "similar") or not. This problem has numerous applications for embedded systems, for example plagiarism detection, validation of compliance restrictions with usable software licenses, more efficient reverse engineering of existing binary codebases, or vulnerability scanning by detecting known vulnerable functions. In this paper, we propose a novel training scheme for the popular transformer neural network architecture to learn function embeddings directly from instruction listings. Unlike existing approaches, our solution explicitly considers the cross-architecture scenario: we propose a training method to adapt the model to different instruction set architectures (ISA) without having to train a new model from scratch, which allows the model to also be used efficiently for embedded systems, where there are a variety of different processor architectures. We show that our solution achieves a similarity classification accuracy of 89.6% on a dataset consisting of several real-world open source software projects. Finally, we conduct extensive experiments to demonstrate the effectiveness of knowledge distillation in increasing the computational efficiency of the embedding model. We demonstrate a reduction in the number of parameters from 87M to 23M, while still maintaining a classification accuracy of 87.8%. Our code and artifacts are available as open source. UR - https://doi.org/10.1145/3709017.3737709 Y1 - 2025 UR - https://doi.org/10.1145/3709017.3737709 UR - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:573-61584 SN - 979-8-4007-1413-9 SP - 43 EP - 51 PB - ACM CY - New York ER - TY - INPR A1 - Petrovska, Ana A1 - Erjiage, Guan A1 - Kugele, Stefan T1 - Defining Self-adaptive Systems: A Systematic Literature Review N2 - In the last two decades, the popularity of self-adaptive systems in the field of software and systems engineering has drastically increased. However, despite the extensive work on self-adaptive systems, the literature still lacks a common agreement on the definition of these systems. To this day, the notion of self-adaptive systems is mainly used intuitively without a precise understanding of the terminology. Using terminology only by intuition does not suffice, especially in engineering and science, where a more rigorous definition is necessary. In this paper, we investigate the existing formal definitions of self-adaptive systems and how these systems are characterised across the literature. Additionally, we analyse and summarise the limitations of the existing formal definitions in order to understand why none of the existing formal definitions is used more broadly by the community. To achieve this, we have conducted a systematic literature review in which we have analysed over 1400 papers related to self-adaptive systems. Concretely, from an initial pool of 1493 papers, we have selected 314 relevant papers, which resulted in nine primary studies whose primary objective was to define self-adaptive systems formally. Our systematic review reveals that although there has been an increasing interest in self-adaptive systems over the years, there is a scarcity of efforts to define these systems formally. Finally, as part of this paper, based on the analysed primary studies, we also elicit requirements and set a foundation for a potential (formal) definition in the future that is accepted by the community on a broader range. UR - https://doi.org/10.48550/arXiv.2505.17798 Y1 - 2025 UR - https://doi.org/10.48550/arXiv.2505.17798 UR - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:573-61576 PB - arXiv CY - Ithaca ER - TY - INPR A1 - Ramic, Amra A1 - Kugele, Stefan T1 - A Systematic Mapping Study on Software Architecture for AI-based Mobility Systems N2 - Background: Due to their diversity, complexity, and above all importance, safety-critical and dependable systems must be developed with special diligence. Criticality increases as these systems likely contain artificial intelligence (AI) components known for their uncertainty. As software and reference architectures form the backbone of any successful system, including safety-critical dependable systems with learning-enabled components, choosing the suitable architecture that guarantees safety despite uncertainties is of great eminence. Aim: We aim to provide the missing overview of all existing architectures, their contribution to safety, and their level of maturity in AI-based safety-critical systems. Method: To achieve this aim, we report a systematic mapping study. From a set of 1,639 primary studies, we selected 38 relevant studies dealing with safety assurance through software architecture in AI-based safety-critical systems. The selected studies were then examined using various criteria to answer the research questions and identify gaps in this area of research. Results: Our findings showed which architectures have been proposed and to what extent they have been implemented. Furthermore, we identified gaps in different application areas of those systems and explained these gaps with various arguments. Conclusion: As the AI trend continues to grow, the system complexity will inevitably increase, too. To ensure the lasting safety of the systems, we provide an overview of the state of the art, intending to identify best practices and research gaps and direct future research more focused. UR - https://doi.org/10.48550/arXiv.2506.01595 Y1 - 2025 UR - https://doi.org/10.48550/arXiv.2506.01595 UR - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:573-61560 PB - arXiv CY - Ithaca ER - TY - INPR A1 - Okumus, Fazli Faruk A1 - Ramic, Amra A1 - Kugele, Stefan T1 - A Systematic Mapping Study on Contract-based Software Design for Dependable Systems N2 - Background: Contract-based Design (CbD) is a valuable methodology for software design that allows annotation of code and architectural components with contracts, thereby enhancing clarity and reliability in software development. It establishes rules that outline the behaviour of software components and their interfaces and interactions. This modular approach enables the design process to be segmented into smaller, independently developed, tested, and verified system components, ultimately leading to more robust and dependable software. Aim: Despite the significance and well-established theoretical background of CbD, there is a need for a comprehensive systematic mapping study for reliable software systems. Our study provides an evidence-based overview of a method and demonstrates its practical feasibility. Method: To conduct this study, we systematically searched three different databases using specially formulated queries, which initially yielded 1,221 primary studies. After voting, we focused on 288 primary studies for more detailed analysis. Finally, a collaborative review allowed us to gather relevant evidence and information to address our research questions. Results: Our findings suggest potential avenues for future research trajectories in CbD, emphasising its role in improving the dependability of software systems. We highlight maturity levels across different domains and identify areas that may benefit from further research. Conclusion: Although CbD is a well-established software design approach, a more comprehensive literature review is needed to clarify its theoretical state about dependable systems. Our study addresses this gap by providing a detailed overview of CbD from various perspectives, identifying key gaps, and suggesting future research directions. UR - https://doi.org/10.48550/arXiv.2505.07542 Y1 - 2025 UR - https://doi.org/10.48550/arXiv.2505.07542 UR - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:573-61552 PB - arXiv CY - Ithaca ER - TY - CHAP A1 - Epple, Nico A1 - Chopra, Harshit A1 - Riener, Andreas T1 - How Do Drivers Observe Surrounding Vehicles in Real-World Traffic? Estimating the Drivers Primary Observed Traffic Objects T2 - 2021 IEEE Intelligent Vehicles Symposium (IV) UR - https://doi.org/10.1109/IV48863.2021.9575202 Y1 - 2021 UR - https://doi.org/10.1109/IV48863.2021.9575202 SN - 978-1-7281-5394-0 SP - 594 EP - 601 PB - IEEE CY - Piscataway ER - TY - CHAP A1 - Eder, Lukas A1 - Löw, Jakob A1 - Hof, Hans-Joachim T1 - Charging Communication Sniffing and Man-in-the-Middle Attacks T2 - Proceedings of the 16th ACM International Conference on Future and Sustainable Energy Systems N2 - In recent years an increasing amount of electric vehicle fast charging stations have been built to meet the growing demand from rising electric vehicle numbers. The standard for fast charging communication in europe is ISO 15118. In theory the standard includes security controls for authentication and transport encryption. In reality difficulties with implementing those security controls as well as insecure design within the standard lead to multiple possible attack vectors compromising the confidentiality and authenticity of charging communication sessions. The goal of this research is to present different approaches towards performing sniffing and man-in-the-middle attacks against charging communication. We also provide a novel approach, which does not rely on race conditions and thus is more reliable than previous approaches. UR - ttps://doi.org/10.1145/3679240.3734648 Y1 - 2025 UR - ttps://doi.org/10.1145/3679240.3734648 UR - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:573-60183 SN - 979-8-4007-1125-1 SP - 799 EP - 804 PB - ACM CY - New York ER - TY - CHAP A1 - Michl, Marco A1 - Hof, Hans-Joachim ED - Hussain, Fatima ED - Fries, Steffen T1 - Towards a Stakeholder-Centric Trust Management Approach for the Automotive Ecosystem T2 - SECURWARE 2024: The Eighteenth International Conference on Emerging Security Information, Systems and Technologies Y1 - 2024 UR - https://www.thinkmind.org/library/SECURWARE/SECURWARE_2024/securware_2024_2_70_30036.html SN - 978-1-68558-206-7 SP - 64 EP - 70 PB - IARIA CY - [s. l.] ER - TY - CHAP A1 - Löw, Jakob A1 - Mayer, Kevin A1 - Hof, Hans-Joachim ED - Hussain, Fatima ED - Fries, Steffen T1 - Fast Charging Communication and Cybersecurity: A Technology Review T2 - SECURWARE 2024: The Eighteenth International Conference on Emerging Security Information, Systems and Technologies Y1 - 2024 UR - https://www.thinkmind.org/library/SECURWARE/SECURWARE_2024/securware_2024_2_50_30030.html SN - 978-1-68558-206-7 SP - 52 EP - 57 PB - IARIA CY - [s. l.] 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 - 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 - Lobo, Silas A1 - Festag, Andreas A1 - Facchi, Christian T1 - Adaptive Message Prioritization: How to Prioritize VRU Awareness Messages in a Congested V2X Network T2 - 2024 IEEE 27th International Conference on Intelligent Transportation Systems (ITSC) UR - https://doi.org/10.1109/ITSC58415.2024.10919704 Y1 - 2025 UR - https://doi.org/10.1109/ITSC58415.2024.10919704 SN - 979-8-3315-0592-9 SP - 423 EP - 430 PB - IEEE CY - Piscataway 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 - 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 - INPR A1 - Ganz, Jonathan A1 - Ammeling, Jonas A1 - Rosbach, Emely A1 - Lausser, Ludwig A1 - Bertram, Christof A1 - Breininger, Katharina A1 - Aubreville, Marc T1 - Is Self-Supervision Enough? Benchmarking Foundation Models Against End-to-End Training for Mitotic Figure Classification N2 - Foundation models (FMs), i.e., models trained on a vast amount of typically unlabeled data, have become popular and available recently for the domain of histopathology. The key idea is to extract semantically rich vectors from any input patch, allowing for the use of simple subsequent classification networks potentially reducing the required amounts of labeled data, and increasing domain robustness. In this work, we investigate to which degree this also holds for mitotic figure classification. Utilizing two popular public mitotic figure datasets, we compared linear probing of five publicly available FMs against models trained on ImageNet and a simple ResNet50 end-to-end-trained baseline. We found that the end-to-end-trained baseline outperformed all FM-based classifiers, regardless of the amount of data provided. Additionally, we did not observe the FM-based classifiers to be more robust against domain shifts, rendering both of the above assumptions incorrect. UR - https://doi.org/10.48550/arXiv.2412.06365 Y1 - 2024 UR - https://doi.org/10.48550/arXiv.2412.06365 UR - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:573-59672 PB - arXiv CY - Ithaca 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 - CHAP A1 - Maksimovski, Daniel A1 - Facchi, Christian T1 - Priority-Based Cooperative Driving Planner for V2X Maneuver Coordination Application T2 - 2025 IEEE 22nd Consumer Communications & Networking Conference (CCNC) UR - https://doi.org/10.1109/CCNC54725.2025.10975982 Y1 - 2025 UR - https://doi.org/10.1109/CCNC54725.2025.10975982 SN - 979-8-3315-0805-0 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 - CHAP A1 - Moin, Sara A1 - Cannanure, Vikram Kamath A1 - Mthoko, Hafeni A1 - Rivera-Loaiza, Cuauhtémoc A1 - Kano, Tsuyoshi A1 - Varghese, Delvin A1 - Till, Sarina A1 - Gautam, Aakash A1 - Wong-Villacres, Marisol A1 - Gaytán-Lugo, Laura Sanely A1 - Dray, Susan A1 - Sturm, Christian ED - Yamashita, Naomi ED - Evers, Vanessa ED - Yatani, Koji ED - Ding, Xianghua T1 - HCI Across Borders: Building a Collective Vision for the Future T2 - CHI’25: Extended Abstracts of the 2025 CHI Conference on Human Factors in Computing Systems UR - https://doi.org/10.1145/3706599.3706724 Y1 - 2025 UR - https://doi.org/10.1145/3706599.3706724 SN - 979-8-4007-1395-8 PB - ACM CY - New York ER - TY - INPR A1 - Kammerlander, Calvin A1 - Kolb, Viola A1 - Luegmair, Marinus A1 - Scheermann, Lou A1 - Schmailzl, Maximilian A1 - Seufert, Marco A1 - Zhang, Jiayun A1 - Dalic, Denis A1 - Schön, Torsten T1 - Machine Learning Models for Soil Parameter Prediction Based on Satellite, Weather, Clay and Yield Data N2 - Efficient nutrient management and precise fertilization are essential for advancing modern agriculture, particularly in regions striving to optimize crop yields sustainably. The AgroLens project endeavors to address this challenge by develop ing Machine Learning (ML)-based methodologies to predict soil nutrient levels without reliance on laboratory tests. By leveraging state of the art techniques, the project lays a foundation for acionable insights to improve agricultural productivity in resource-constrained areas, such as Africa. The approach begins with the development of a robust European model using the LUCAS Soil dataset and Sentinel-2 satellite imagery to estimate key soil properties, including phosphorus, potassium, nitrogen, and pH levels. This model is then enhanced by integrating supplementary features, such as weather data, harvest rates, and Clay AI-generated embeddings. This report details the methodological framework, data preprocessing strategies, and ML pipelines employed in this project. Advanced algorithms, including Random Forests, Extreme Gradient Boosting (XGBoost), and Fully Connected Neural Networks (FCNN), were implemented and finetuned for precise nutrient prediction. Results showcase robust model performance, with root mean square error values meeting stringent accuracy thresholds. By establishing a reproducible and scalable pipeline for soil nutrient prediction, this research paves the way for transformative agricultural applications, including precision fertilization and improved resource allocation in underresourced regions like Africa. UR - https://doi.org/10.48550/arXiv.2503.22276 Y1 - 2025 UR - https://doi.org/10.48550/arXiv.2503.22276 UR - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:573-59345 PB - arXiv CY - Ithaca 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 -