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    <title language="eng">UrbanIng-V2X: A Large-Scale Multi-Vehicle, Multi-Infrastructure Dataset Across Multiple&#13;
Intersections for Cooperative Perception</title>
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    <author>
      <first_name>Torsten</first_name>
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    <title language="eng">CSCS '25 - Cyber Security in CarS Workshop</title>
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    <title language="eng">GMP3: Learning-Driven, Bellman-Guided Trajectory Planning for UAVs in Real-Time on SE(3)</title>
    <abstract language="eng">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.</abstract>
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    <author>
      <first_name>Babak</first_name>
      <last_name>Salamat</last_name>
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      <first_name>Dominik</first_name>
      <last_name>Mattern</last_name>
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      <first_name>Sebastian-Sven</first_name>
      <last_name>Olzem</last_name>
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      <last_name>Elsbacher</last_name>
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      <first_name>Christian</first_name>
      <last_name>Seidel</last_name>
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    <author>
      <first_name>Andrea M.</first_name>
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    <language>eng</language>
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    <publisherPlace>Dagstuhl</publisherPlace>
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    <completedDate>2025-12-04</completedDate>
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    <title language="eng">Trust Calibration for RPA</title>
    <abstract language="eng">This working group focused on foundational issues of trust in RPA: How we can conceptualize trust between users and software robots and specify it in a way that, eventually, we can measure that trust empirically.</abstract>
    <parentTitle language="eng">Dagstuhl Reports</parentTitle>
    <identifier type="issn">2192-5283</identifier>
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    <author>
      <first_name>Simone</first_name>
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    <editor>
      <first_name>Adela</first_name>
      <last_name>del-Río-Ortega</last_name>
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      <first_name>Aleksandre</first_name>
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      <last_name>Axmann</last_name>
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      <first_name>Hajo A.</first_name>
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      <first_name>Glenda</first_name>
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    <pageNumber>16</pageNumber>
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    <completedDate>2025-12-01</completedDate>
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    <title language="eng">Subphase-Labeled Mitotic Dataset for AI-powered Cell Division Analysis</title>
    <abstract language="eng">Mitosis detection represents a critical task in the field of digital pathology, as determination of the mitotic index (MI) plays an important role in the tumor grading and prognostic assessment of patients. Manual determination of MI is a labor-intensive and time-consuming task for practitioners with rather high interobserver variability, thus, automation has become a priority. There has been substantial progress towards creating robust mitosis detection algorithms in recent years, primarily driven by the Mitosis Domain Generalization (MIDOG) challenges. In parallel, there has been growing interest in the molecular characterization of mitosis with the goal of achieving a more comprehensive understanding of its underlying mechanisms in a subphase-specific manner. Here, we introduce a new mitotic figure dataset annotated with subphase information based on the MIDOG++ dataset as well as a previously unrepresented tumor domain to enhance the diversity and applicability of the dataset. We envision a new perspective for domain generalization by improving the performance of models with subtyping mitotic cells into the 5 main stages of normal mitosis, complemented with an atypical mitotic class. We believe that our work broadens the horizon in digital pathology: subtyping information could provide useful help for mitosis detection, while also providing promising new directions in answering biological questions, such as molecular analysis of the subphases on a single cell level.</abstract>
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    <author>
      <first_name>Zsanett Zsofia</first_name>
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      <first_name>Dominik</first_name>
      <last_name>Hirling</last_name>
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      <first_name>Jonas</first_name>
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      <first_name>Levente</first_name>
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    <author>
      <first_name>Farkas</first_name>
      <last_name>Sukosd</last_name>
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    <author>
      <first_name>Marc</first_name>
      <last_name>Aubreville</last_name>
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      <first_name>Vivien</first_name>
      <last_name>Miczan</last_name>
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    <id>6455</id>
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    <publishedYear>2025</publishedYear>
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    <language>eng</language>
    <pageFirst/>
    <pageLast/>
    <pageNumber>130</pageNumber>
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    <publisherName>Research Square</publisherName>
    <publisherPlace>Durham</publisherPlace>
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    <completedDate>2025-11-28</completedDate>
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    <title language="eng">A Practical Approach to Multivariate Time Series Anomaly Detection in Automotive Bus Systems Testing</title>
    <abstract language="eng">The increasing complexity of modern vehicles and their testing procedures generates vast amounts of multivariate time series data, making manual anomaly detection during automotive testing increasingly challenging. This article investigates the application of deep learning algorithms for automated anomaly detection in automotive bus data collected during dynamic driving scenarios. Three distinct architectures are implemented and compared: a CNN-based forecasting approach (DeepAnT), an LSTM-based model (LSTM-AD), and a Convolutional Autoencoder (CAE).&#13;
&#13;
Real-world driving data collected across various scenarios, ranging from normal operation to extreme maneuvers, is employed. Through evaluation across seven distinct test scenarios, findings reveal that while each architecture demonstrates specific strengths, their effectiveness varies significantly based on anomaly type and driving context. DeepAnT shows the most consistent performance across different scenarios, while LSTM-AD achieves superior detection capability for complex temporal patterns, particularly in scenarios involving coordinated changes across multiple features. The CAE excels at identifying pronounced deviations but shows limitations in detecting subtle anomalies.&#13;
&#13;
This study demonstrates that while deep learning models effectively detect anomalies in automotive time series data, their practical implementation requires careful consideration of specific use cases, emphasizing the critical role of data preprocessing and threshold calculation in ensuring reliable anomaly detection.</abstract>
    <parentTitle language="deu">Research Square</parentTitle>
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    <author>
      <first_name>Tobias</first_name>
      <last_name>Suciu</last_name>
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      <first_name>Jürgen</first_name>
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    <id>6416</id>
    <completedYear/>
    <publishedYear>2025</publishedYear>
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    <language>eng</language>
    <pageFirst>342</pageFirst>
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    <completedDate>2025-11-21</completedDate>
    <publishedDate>--</publishedDate>
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    <title language="eng">Context-Aware Take-Over Requests for Promoting Emergency Corridor Formation in Level 3 Automated Vehicles</title>
    <parentTitle language="eng">Adjunct Conference Proceedings: 17th International ACM Conference on Automotive User Interfaces and Interactive Vehicular Applications, September 22-25, 2025, Brisbane, Australia</parentTitle>
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      <first_name>Michael A.</first_name>
      <last_name>Gerber</last_name>
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      <first_name>Andreas</first_name>
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    <parentTitle language="eng">Adjunct Conference Proceedings: 17th International ACM Conference on Automotive User Interfaces and Interactive Vehicular Applications, September 22-25, 2025, Brisbane, Australia</parentTitle>
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    <title language="eng">Accelerated Real-Life (ARL) Testing and Characterization of Automotive LiDAR Sensors to facilitate the Development and Validation of Enhanced Sensor Models</title>
    <abstract language="eng">In the realm of automated driving simulation and sensor modeling, the need for highly accurate sensor models is paramount for ensuring the reliability and safety of advanced driving assistance systems (ADAS). Hence, numerous works focus on the development of high-fidelity models of ADAS sensors, such as camera, Radar as well as modern LiDAR systems to simulate the sensor behavior in different driving scenarios, even under varying environmental conditions, considering for example adverse weather effects. However, aging effects of sensors, leading to suboptimal system performance, are mostly overlooked by current simulation techniques. This paper introduces a cutting-edge Hardware-in-the-Loop (HiL) test bench designed for the automated, accelerated aging and characterization of Automotive LiDAR sensors. The primary objective of this research is to address the aging effects of LiDAR sensors over the product life cycle, specifically focusing on aspects such as laser beam profile deterioration, output power reduction and intrinsic parameter drift, which are mostly neglected in current sensor models. By that, this proceeding research is intended to path the way, not only towards identifying and modeling respective degradation effects, but also to suggest quantitative model validation metrics.</abstract>
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      <first_name>Tjorven</first_name>
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    <author>
      <first_name>Hüseyin</first_name>
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    <author>
      <first_name>Bernhard</first_name>
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      <first_name>Gordon</first_name>
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    <title language="eng">Modeling Spot Market Pricing with the Residual Load</title>
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    <author>
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    <author>
      <first_name>Matthias</first_name>
      <last_name>Huber</last_name>
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    <publishedYear>2016</publishedYear>
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    <publisherName>Hochschule Offenburg</publisherName>
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    <completedDate>2025-10-21</completedDate>
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    <title language="eng">Improving gradient-based LSTM training for offine handwriting recognition by careful selection of the optimization method</title>
    <parentTitle language="eng">3rd Baden-Württemberg Center of Applied Research Symposium on Information and Communication Systems, SInCom 2016</parentTitle>
    <identifier type="isbn">978-3-943301-21-2</identifier>
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    <enrichment key="THI_conferenceName">3rd BW-CAR Symposium on Information and Communication Systems (SInCom 2016), Karlsruhe (Germany), 02.12.2016</enrichment>
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    <author>
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    <editor>
      <first_name>Franz</first_name>
      <last_name>Quint</last_name>
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    <author>
      <first_name>Marc-Peter</first_name>
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    <editor>
      <first_name>Dirk</first_name>
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    <language>deu</language>
    <pageFirst>94</pageFirst>
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    <publisherName>KTBL</publisherName>
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    <belongsToBibliography>1</belongsToBibliography>
    <completedDate>2025-10-14</completedDate>
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    <title language="deu">Hochflexible Biomethan-BHKW-Anlagen und der Einfluss eines Biomethanspeichers auf den möglichen Biogaspreis für Post-EEG-Biogasanlagen</title>
    <parentTitle language="deu">Biogas in der Landwirtschaft – Stand und Perspektiven: FNR/KTBL-Kongress am 8. und 9. September 2025</parentTitle>
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    <author>
      <first_name>Johannes</first_name>
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      <first_name>Volker</first_name>
      <last_name>Selleneit</last_name>
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    <author>
      <first_name>Uwe</first_name>
      <last_name>Holzhammer</last_name>
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    <collection role="institutes" number="19321">Institut für neue Energie-Systeme (InES)</collection>
    <collection role="persons" number="26479">Holzhammer, Uwe</collection>
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    <pageFirst>46</pageFirst>
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    <issue>Mai/Juni</issue>
    <volume>35</volume>
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    <publisherName>LID</publisherName>
    <publisherPlace>Lampertheim</publisherPlace>
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    <completedDate>2025-10-14</completedDate>
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    <title language="deu">Integration von Flexibilität in die Energieversorgung</title>
    <parentTitle language="deu">Ernährung aktuell - der Lebensmittelbrief</parentTitle>
    <subTitle language="deu">Ein Aspekt zur Erreichung der Klimaziele und zur Stabilisierung der Energiekosten - Fallbeispiel Kühlhaus</subTitle>
    <identifier type="issn">1866-6787</identifier>
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    <author>
      <first_name>Martin</first_name>
      <last_name>Stöckl</last_name>
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    <author>
      <first_name>Uwe</first_name>
      <last_name>Holzhammer</last_name>
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    <collection role="institutes" number="19321">Institut für neue Energie-Systeme (InES)</collection>
    <collection role="persons" number="26479">Holzhammer, Uwe</collection>
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    <pageFirst>57</pageFirst>
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    <pageNumber/>
    <edition/>
    <issue>9</issue>
    <volume>63</volume>
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    <publisherName>VDE Verlag</publisherName>
    <publisherPlace>Berlin</publisherPlace>
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    <completedDate>2025-10-04</completedDate>
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    <title language="deu">Das „Post-EEG“-Potenzial von Photovoltaik im privaten Strom- und Wärmesektor</title>
    <parentTitle language="deu">Energiewirtschaftliche Tagesfragen: et</parentTitle>
    <identifier type="issn">0013-743X</identifier>
    <identifier type="issn">0720-6240</identifier>
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    <author>
      <first_name>Florian</first_name>
      <last_name>Sänger</last_name>
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    <author>
      <first_name>Thomas</first_name>
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    <id>6251</id>
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    <thesisYearAccepted/>
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    <pageFirst>50</pageFirst>
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    <issue>07</issue>
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    <publisherName>Ifo Institut</publisherName>
    <publisherPlace>München</publisherPlace>
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    <completedDate>2025-10-06</completedDate>
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    <title language="deu">Maschinelles Lernen in der ökonomischen Forschung</title>
    <parentTitle language="deu">Ifo Schnelldienst</parentTitle>
    <identifier type="url">https://www.ifo.de/publikationen/2018/aufsatz-zeitschrift/maschinelles-lernen-der-oekonomischen-forschung</identifier>
    <identifier type="issn">2199-4455</identifier>
    <identifier type="issn">0018-974X</identifier>
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    <author>
      <first_name>Huber</first_name>
      <last_name>Matthias</last_name>
    </author>
    <author>
      <first_name>Simone</first_name>
      <last_name>Schüller</last_name>
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    <author>
      <first_name>Marc</first_name>
      <last_name>Stöckli</last_name>
    </author>
    <author>
      <first_name>Klaus</first_name>
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    <thesisYearAccepted/>
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    <pageFirst/>
    <pageLast/>
    <pageNumber>6</pageNumber>
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    <publisherPlace>Ithaca</publisherPlace>
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    <completedDate>2025-10-02</completedDate>
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    <title language="eng">Advancing Robustness in Deep Reinforcement Learning with an Ensemble Defense Approach</title>
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      <first_name>Dominik</first_name>
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    <author>
      <first_name>Daniel</first_name>
      <last_name>Cremers</last_name>
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    <author>
      <first_name>Torsten</first_name>
      <last_name>Schön</last_name>
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    <language>eng</language>
    <pageFirst/>
    <pageLast/>
    <pageNumber>15</pageNumber>
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    <publisherName>Research Square</publisherName>
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    <completedDate>2025-10-02</completedDate>
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    <title language="eng">Quantitative Kernel Estimation from Traffic Signs using Slanted Edge Spatial Frequency Response as a Sharpness Metric</title>
    <abstract language="eng">The sharpness is a critical optical property of automotive cameras, measured by the Spatial Frequency Response (SFR) within the end of line (EOL) test after manufacturing. This work presents a method to estimate the blurring kernel of automotive camera for state monitoring. To achieve this, Principal Component Analysis (PCA) is performed, using synthetic kernels generated by Zemax. The PCA model is built with approximately 1300 base kernels representing spatially variant point spread functions (PSFs). This model generates kernel samples during the estimation process. Synthetic images are created by convolving the synthetic kernels with reference traffic sign images and compared with real-life data captured by an automotive camera. These synthetic data are utilized for algorithm development, and later on validation is performed on real-life data. The algorithm extracts two 45 x 45 pixels regions of interest (ROIs) containing slanted edges from the blurred image and crops matching ROIs from a reference sharp image. Each candidate kernel blurs the reference ROIs, and the resulting Spatial Frequency Response (SFR) is compared with the blurred ROIs’ SFR. Differential evolution optimization minimizes the SFR difference, selecting the kernel that best matches the observed blur. The final kernel is evaluated against the true kernel for accuracy. Structural similarity index measure (SSIM) between the original and estimated blurred ROIs ranges from 0.808 to 0.945. For true vs. estimated kernels, SSIM varies from 0.92 to 0.98. Pearson correlation coefficients range from 0.84 to 0.99, Cosine similarity from 0.86 to 0.98, and mean squared error (MSE) from 1.1 x 10-5 to 8.3 x 10-5. Validation on real-life camera images shows that the SSIM between estimated ROI is 0.82 indicating a sufficient level of accuracy in kernel estimation to detect potential degradation of the camera.</abstract>
    <parentTitle language="eng">Research Square</parentTitle>
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    <title language="eng">Asymptotic behavior of the Arrow–Hurwicz differential system with Tikhonov regularization</title>
    <abstract language="eng">In a real Hilbert space setting, we investigate the asymptotic behavior of the solutions of the classical Arrow–Hurwicz differential system combined with Tikhonov regularizing terms. Under some newly proposed conditions on the Tikhonov terms involved, we show that the solutions of the regularized Arrow–Hurwicz differential system strongly converge toward the element of least norm within its set of zeros. Moreover, we provide fast asymptotic decay rate estimates for the so-called primal-dual gap function and the norm of the solutions' velocity. If, in addition, the Tikhonov regularizing terms are decreasing, we provide some refined estimates in the sense of an exponentially weighted moving average. Under the additional assumption that the governing operator of the Arrow–Hurwicz differential system satisfies a reverse Lipschitz condition, we further provide a fast rate of strong convergence of the solutions toward the unique zero. We conclude our study by deriving the corresponding decay rate estimates with respect to the so-called viscosity curve. Numerical experiments illustrate our theoretical findings.</abstract>
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    <title language="eng">A Critical Analysis of the Trust Human Agents Have in Computational and Embodied AI</title>
    <parentTitle language="eng">Culturally Sustainable Social Robotics: Proceedings of Robophilosophy 2020, August 18-21, 2020, Aarhus University and Online</parentTitle>
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    <title language="eng">Driving with Empathy: Workshop Report on AI-driven In-vehicle Empathic Agent Design for Automated Vehicles</title>
    <parentTitle language="eng">Proceedings of the Human Factors and Ergonomics Society Annual Meeting</parentTitle>
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    <title language="eng">Deep Segmentation of 3+1D Radar Point Cloud for Real-Time Roadside Traffic User Detection</title>
    <abstract language="eng">Smart cities rely on intelligent infrastructure to enhance road safety, optimize traffic flow, and enable vehicle-to-infrastructure (V2I) communication. A key component of such infrastructure is an efficient and real-time perception system that accurately detects diverse traffic participants. Among various sensing modalities, automotive radar is one of the best choices due to its robust performance in adverse weather and low-light conditions. However, due to low spatial resolution, traditional clustering-based approaches for radar object detection often struggle with vulnerable road user detection and nearby object separation. Hence, this paper proposes a deep learning-based 3+1D radar point cloud clustering methodology tailored for smart infrastructure-based perception applications. This approach first performs semantic segmentation of the radar point cloud, followed by instance segmentation to generate well-formed clusters with class labels using a deep neural network. It also detects single-point objects that conventional methods often miss. The described approach is developed and experimented using a smart infrastructure-based sensor setup and it performs segmentation of the point cloud in real-time. Experimental results demonstrate 95.35% F1-macro score for semantic segmentation and 91.03% mean average precision (mAP) at an intersection over union (IoU) threshold of 0.5 for instance segmentation. Further, the complete pipeline operates at 43.61 frames per second with a memory requirement of less than 0.7 MB on the edge device (Nvidia Jetson AGX Orin).</abstract>
    <parentTitle language="eng">Research Square</parentTitle>
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      <first_name>Savankumar</first_name>
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      <first_name>Shiva</first_name>
      <last_name>Agrawal</last_name>
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      <last_name>Elger</last_name>
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    <title language="eng">Evaluation of a Deep Learning and XAI based Facial Phenotyping Tool for Genetic Syndromes: A Clinical User Study</title>
    <abstract language="eng">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.</abstract>
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    <author>
      <first_name>Ömer</first_name>
      <last_name>Sümer</last_name>
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      <first_name>Tobias</first_name>
      <last_name>Huber</last_name>
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    <author>
      <first_name>Dat</first_name>
      <last_name>Duong</last_name>
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    <author>
      <first_name>Suzanna E.</first_name>
      <last_name>Ledgister Hanchard</last_name>
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      <first_name>Cristina</first_name>
      <last_name>Conati</last_name>
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      <first_name>Elisabeth</first_name>
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      <first_name>Benjamin D.</first_name>
      <last_name>Solomon</last_name>
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      <first_name>Rebekah L.</first_name>
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    <title language="eng">Collision Probability Estimation for Optimization-based Vehicular Motion Planning</title>
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      <first_name>Arturo</first_name>
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      <first_name>Christian</first_name>
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      <first_name>Nathan</first_name>
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    <title language="eng">Higher Utilization of Multi-Core Processors in Dynamic Real-Time Software Systems</title>
    <parentTitle language="eng">International Journal of Electrical Energy</parentTitle>
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    <title language="eng">Defining Self-adaptive Systems: A Systematic Literature Review</title>
    <abstract language="eng">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.</abstract>
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    <title language="eng">A Systematic Mapping Study on Software Architecture for AI-based Mobility Systems</title>
    <abstract language="eng">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.&#13;
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.&#13;
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.&#13;
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.&#13;
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.</abstract>
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    <title language="eng">A Systematic Mapping Study on Contract-based Software Design for Dependable Systems</title>
    <abstract language="eng">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.&#13;
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. &#13;
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.</abstract>
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    <title language="eng">YoFlow Method for Scenario Based Automatic Accident Detection</title>
    <abstract language="eng">Recent advances in sensor and computing technologies have enabled roadside units (RSUs) to not only monitor traffic flow but also process data in real time to improve road safety. However, leveraging RSUs for proactive accident detection remains a challenging and underexplored task, partly due to the lack of diverse accident data. To address this, this study proposes two key contributions: (i) a scenario based synthetic data generation framework, and (ii) YoFlow, a novel system for vehicle-to-vehicle accident detection from a simulated RSU camera perspective. The proposed framework leverages the PEGASUS methodology and the BeamNG.tech simulation platform to create the SB-SIF dataset, which includes five representative intersection crash scenarios derived from German accident data. SB-SIF dataset contains 914 crash videos, 123 near-miss events, and 924 normal traffic instances and is publicly available at: https://doi.org/10.5281/zenodo.15267252. The proposed YoFlow system identifies accidents by analyzing temporal variations in vehicle speed vectors, using YOLO for vehicle classification and CUDA-accelerated dense optical flow to capture abrupt motion changes. Extracted features are processed and classified using an XGBoost model, achieving 94% recall and 90% precision in accident detection.</abstract>
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    <parentTitle language="eng">High Performance Scientific and Engineering Computing: Proceedings of the International FORTWIHR Conference on HPSEC, Munich, March 16–18, 1998</parentTitle>
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    <title language="eng">Engineering a Platform for Reinforcement Learning Workloads</title>
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    <abstract language="eng">In federated learning, all networked clients contribute to the model training cooperatively. However, with model sizes increasing, even sharing the trained partial models often leads to severe communication bottlenecks in underlying networks, especially when communicated iteratively. In this paper, we introduce a federated learning framework FedD3 requiring only one-shot communication by integrating dataset distillation instances. Instead of sharing model updates in other federated learning approaches, FedD3 allows the connected clients to distill the local datasets independently, and then aggregates those decentralized distilled datasets (e.g. a few unrecognizable images) from networks for model training. Our experimental results show that FedD3 significantly outperforms other federated learning frameworks in terms of needed communication volumes, while it provides the additional benefit to be able to balance the trade-off between accuracy and communication cost, depending on usage scenario or target dataset. For instance, for training an AlexNet model on CIFAR-10 with 10 clients under non-independent and identically distributed (Non-IID) setting, FedD3 can either increase the accuracy by over 71% with a similar communication volume, or save 98% of communication volume, while reaching the same accuracy, compared to other one-shot federated learning approaches.</abstract>
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    <title language="deu">Einfluss von Windenergieanlagen auf Erdbebenstationen: Kann ein Schutzradius&#13;
vorbelastete Stationen schützen?</title>
    <parentTitle language="deu">Berichte der Fachtagung Baustatik - Baupraxis 13 am 20. und 21. März 2017 an der Ruhr-Universität Bochum</parentTitle>
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    <title language="eng">Designing for Passengers’ Information Needs on Fellow Travelers: A Comparison of Day and Night Rides in Shared Automated Vehicles</title>
    <abstract language="eng">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.</abstract>
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    <abstract language="eng">Ensuring the safe and reliable operation of autonomous vehicles under adverse weather remains a significant challenge. To address this, we have developed a comprehensive dataset composed of sensor data acquired in a real test track and reproduced in the laboratory for the same test scenarios. The provided dataset includes camera, radar, LiDAR, inertial measurement unit (IMU), and GPS data recorded under adverse weather conditions (rainy, night-time, and snowy conditions). We recorded test scenarios using objects of interest such as car, cyclist, truck and pedestrian -- some of which are inspired by EURONCAP (European New Car Assessment Programme). The sensor data generated in the laboratory is acquired by the execution of simulation-based tests in hardware-in-the-loop environment with the digital twin of each real test scenario. The dataset contains more than 2 hours of recording, which totals more than 280GB of data. Therefore, it is a valuable resource for researchers in the field of autonomous vehicles to test and improve their algorithms in adverse weather conditions, as well as explore the simulation-to-reality gap.</abstract>
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    <id>5987</id>
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    <publishedYear>2023</publishedYear>
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    <pageNumber>5</pageNumber>
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    <publisherName>arXiv</publisherName>
    <publisherPlace>Ithaca</publisherPlace>
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    <completedDate>2025-06-13</completedDate>
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    <title language="eng">Generation of Realistic Synthetic Raw Radar Data for Automated Driving Applications using Generative Adversarial Networks</title>
    <abstract language="eng">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.</abstract>
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    <author>
      <first_name>Eduardo</first_name>
      <last_name>Fidelis</last_name>
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    <author>
      <first_name>Fabio</first_name>
      <last_name>Reway</last_name>
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    <author>
      <first_name>Herick Y. S.</first_name>
      <last_name>Ribeiro</last_name>
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    <author>
      <first_name>Pietro</first_name>
      <last_name>Campos</last_name>
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    <author>
      <first_name>Werner</first_name>
      <last_name>Huber</last_name>
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    <author>
      <first_name>Christian</first_name>
      <last_name>Icking</last_name>
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    <author>
      <first_name>Lester</first_name>
      <last_name>Faria</last_name>
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    <author>
      <first_name>Torsten</first_name>
      <last_name>Schön</last_name>
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    <completedDate>2025-06-12</completedDate>
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    <title language="eng">Gradient Derivation for Learnable Parameters in Graph Attention Networks</title>
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      <first_name>Marion</first_name>
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    <author>
      <first_name>Andreas</first_name>
      <last_name>Tollkühn</last_name>
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    <author>
      <first_name>Sebastian</first_name>
      <last_name>Dorn</last_name>
    </author>
    <author>
      <first_name>Michael</first_name>
      <last_name>Botsch</last_name>
    </author>
    <author>
      <first_name>Wolfgang</first_name>
      <last_name>Utschick</last_name>
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    <collection role="persons" number="26835">Botsch, Michael</collection>
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    <publishedYear>2023</publishedYear>
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    <completedDate>2025-06-11</completedDate>
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    <title language="eng">Emotion GaRage Vol. IV: Creating Empathic In-Vehicle Interfaces with Generative AIs for Automated Vehicle Contexts</title>
    <parentTitle language="eng">15th International ACM Conference on Automotive User Interfaces: Adjunct Conference Proceedings</parentTitle>
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    <author>
      <first_name>Mungyeong</first_name>
      <last_name>Choe</last_name>
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      <first_name>Esther</first_name>
      <last_name>Bosch</last_name>
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      <first_name>Jiayuan</first_name>
      <last_name>Dong</last_name>
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      <first_name>Ignacio</first_name>
      <last_name>Alvarez</last_name>
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      <first_name>Michael</first_name>
      <last_name>Oehl</last_name>
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    <author>
      <first_name>Christophe</first_name>
      <last_name>Jallais</last_name>
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    <author>
      <first_name>Areen</first_name>
      <last_name>Alsaid</last_name>
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    <author>
      <first_name>Chihab</first_name>
      <last_name>Nadri</last_name>
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    <author>
      <first_name>Myounghoon</first_name>
      <last_name>Jeon</last_name>
    </author>
    <collection role="persons" number="62036">Alvarez, Ignacio</collection>
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    <id>5969</id>
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    <publishedYear>2024</publishedYear>
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    <language>eng</language>
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    <pageNumber>16</pageNumber>
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    <publisherName>arXiv</publisherName>
    <publisherPlace>Ithaca</publisherPlace>
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    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>2025-06-10</completedDate>
    <publishedDate>--</publishedDate>
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    <title language="eng">"When TwoWrongs Don’t Make a Right" - Examining Confirmation Bias and the Role of Time Pressure During Human-AI Collaboration in Computational Pathology</title>
    <enrichment key="THI_furtherversion">https://opus4.kobv.de/opus4-haw/frontdoor/index/index/docId/5879</enrichment>
    <enrichment key="THI_relatedIdentifier">https://doi.org/10.48550/arXiv.2411.01007</enrichment>
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    <author>
      <first_name>Emely</first_name>
      <last_name>Rosbach</last_name>
    </author>
    <author>
      <first_name>Jonas</first_name>
      <last_name>Ammeling</last_name>
    </author>
    <author>
      <first_name>Sebastian</first_name>
      <last_name>Krügel</last_name>
    </author>
    <author>
      <first_name>Angelika</first_name>
      <last_name>Kießig</last_name>
    </author>
    <author>
      <first_name>Alexis</first_name>
      <last_name>Fritz</last_name>
    </author>
    <author>
      <first_name>Jonathan</first_name>
      <last_name>Ganz</last_name>
    </author>
    <author>
      <first_name>Chloé</first_name>
      <last_name>Puget</last_name>
    </author>
    <author>
      <first_name>Taryn</first_name>
      <last_name>Donovan</last_name>
    </author>
    <author>
      <first_name>Andrea</first_name>
      <last_name>Klang</last_name>
    </author>
    <author>
      <first_name>Maximilian C.</first_name>
      <last_name>Köller</last_name>
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    <author>
      <first_name>Pompei</first_name>
      <last_name>Bolfa</last_name>
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    <author>
      <first_name>Marco</first_name>
      <last_name>Tecilla</last_name>
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    <author>
      <first_name>Daniela</first_name>
      <last_name>Denk</last_name>
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    <author>
      <first_name>Matti</first_name>
      <last_name>Kiupel</last_name>
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    <author>
      <first_name>Georgios</first_name>
      <last_name>Paraschou</last_name>
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    <author>
      <first_name>Mun Keong</first_name>
      <last_name>Kok</last_name>
    </author>
    <author>
      <first_name>Alexander F. H.</first_name>
      <last_name>Haake</last_name>
    </author>
    <author>
      <first_name>Ronald R.</first_name>
      <last_name>de Krijger</last_name>
    </author>
    <author>
      <first_name>Andreas F.-P.</first_name>
      <last_name>Sonnen</last_name>
    </author>
    <author>
      <first_name>Tanit</first_name>
      <last_name>Kasantikul</last_name>
    </author>
    <author>
      <first_name>Gerry M.</first_name>
      <last_name>Dorrestein</last_name>
    </author>
    <author>
      <first_name>Rebecca C.</first_name>
      <last_name>Smedley</last_name>
    </author>
    <author>
      <first_name>Nikolas</first_name>
      <last_name>Stathonikos</last_name>
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    <author>
      <first_name>Matthias</first_name>
      <last_name>Uhl</last_name>
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    <author>
      <first_name>Christof</first_name>
      <last_name>Bertram</last_name>
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    <author>
      <first_name>Andreas</first_name>
      <last_name>Riener</last_name>
    </author>
    <author>
      <first_name>Marc</first_name>
      <last_name>Aubreville</last_name>
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    <collection role="institutes" number="19309">Fakultät Informatik</collection>
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    <collection role="persons" number="26627">Riener, Andreas</collection>
    <collection role="institutes" number="19588">Human-Computer Interaction Group (HCIG)</collection>
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    <id>5968</id>
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    <publishedYear>2024</publishedYear>
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    <completedDate>2025-06-10</completedDate>
    <publishedDate>--</publishedDate>
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    <title language="eng">Automation Bias in AI-Assisted Medical Decision-Making under Time Pressure in Computational Pathology</title>
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    <author>
      <first_name>Emely</first_name>
      <last_name>Rosbach</last_name>
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    <author>
      <first_name>Jonathan</first_name>
      <last_name>Ganz</last_name>
    </author>
    <author>
      <first_name>Jonas</first_name>
      <last_name>Ammeling</last_name>
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    <author>
      <first_name>Andreas</first_name>
      <last_name>Riener</last_name>
    </author>
    <author>
      <first_name>Marc</first_name>
      <last_name>Aubreville</last_name>
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    <id>5967</id>
    <completedYear/>
    <publishedYear>2024</publishedYear>
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    <pageLast/>
    <pageNumber>6</pageNumber>
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    <publisherName>arXiv</publisherName>
    <publisherPlace>Ithaca</publisherPlace>
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    <completedDate>2025-06-10</completedDate>
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    <title language="eng">Is Self-Supervision Enough? Benchmarking Foundation Models Against End-to-End Training for Mitotic Figure Classification</title>
    <abstract language="eng">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.</abstract>
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    <author>
      <first_name>Jonathan</first_name>
      <last_name>Ganz</last_name>
    </author>
    <author>
      <first_name>Jonas</first_name>
      <last_name>Ammeling</last_name>
    </author>
    <author>
      <first_name>Emely</first_name>
      <last_name>Rosbach</last_name>
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    <author>
      <first_name>Ludwig</first_name>
      <last_name>Lausser</last_name>
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    <author>
      <first_name>Christof</first_name>
      <last_name>Bertram</last_name>
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    <author>
      <first_name>Katharina</first_name>
      <last_name>Breininger</last_name>
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    <author>
      <first_name>Marc</first_name>
      <last_name>Aubreville</last_name>
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    <collection role="persons" number="49455">Lausser, Ludwig</collection>
    <thesisPublisher>Technische Hochschule Ingolstadt</thesisPublisher>
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    <publisherName>arXiv</publisherName>
    <publisherPlace>Ithaca</publisherPlace>
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    <completedDate>2025-06-10</completedDate>
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    <title language="eng">Decentralized Fusion of 3D Extended Object Tracking based on a B-Spline Shape Model</title>
    <abstract language="eng">Extended Object Tracking (EOT) exploits the high resolution of modern sensors for detailed environmental perception. Combined with decentralized fusion, it contributes to a more scalable and robust perception system. This paper investigates the decentralized fusion of 3D EOT using a B-spline curve based model. The spline curve is used to represent the side-view profile, which is then extruded with a width to form a 3D shape. We use covariance intersection (CI) for the decentralized fusion and discuss the challenge of applying it to EOT. We further evaluate the tracking result of the decentralized fusion with simulated and real datasets of traffic scenarios. We show that the CI-based fusion can significantly improve the tracking performance for sensors with unfavorable perspective.</abstract>
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    <author>
      <first_name>Longfei</first_name>
      <last_name>Han</last_name>
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    <author>
      <first_name>Klaus</first_name>
      <last_name>Kefferpütz</last_name>
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    <author>
      <first_name>Jürgen</first_name>
      <last_name>Beyerer</last_name>
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    <abstract language="eng">Precise vehicle state estimation is crucial for safe and reliable autonomous driving. The number of measurable states and their precision offered by the onboard vehicle sensor system are often constrained by cost. For instance, measuring critical quantities such as the Vehicle Sideslip Angle (VSA) poses significant commercial challenges using current optical sensors. This paper addresses these limitations by focusing on the development of high-performance virtual sensors to enhance vehicle state estimation for active safety. The proposed Uncertainty-Aware Hybrid Learning (UAHL) architecture integrates a machine learning model with vehicle motion models to estimate VSA directly from onboard sensor data. A key aspect of the UAHL architecture is its focus on uncertainty quantification for individual model estimates and hybrid fusion. These mechanisms enable the dynamic weighting of uncertainty-aware predictions from machine learning and vehicle motion models to produce accurate and reliable hybrid VSA estimates. This work also presents a novel dataset named Real-world Vehicle State Estimation Dataset (ReV-StED), comprising synchronized measurements from advanced vehicle dynamic sensors. The experimental results demonstrate the superior performance of the proposed method for VSA estimation, highlighting UAHL as a promising architecture for advancing virtual sensors and enhancing active safety in autonomous vehicles.</abstract>
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    <abstract language="eng">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.</abstract>
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    <title language="eng">Antifragile Control Systems: The case of mobile robot trajectory tracking in the presence of uncertainty</title>
    <abstract language="eng">Mobile robots are ubiquitous. Such vehicles benefit from well-designed and calibrated control algorithms ensuring their task execution under precise uncertainty bounds. Yet, in tasks involving humans in the loop, such as elderly or mobility impaired, the problem takes a new dimension. In such cases, the system needs not only to compensate for uncertainty and volatility in its operation but at the same time to anticipate and offer responses that go beyond robust. Such robots operate in cluttered, complex environments, akin to human residences, and need to face during their operation sensor and, even, actuator faults, and still operate. This is where our thesis comes into the foreground. We propose a new control design framework based on the principles of antifragility. Such a design is meant to offer a high uncertainty anticipation given previous exposure to failures and faults, and exploit this anticipation capacity to provide performance beyond robust. In the current instantiation of antifragile control applied to mobile robot trajectory tracking, we provide controller design steps, the analysis of performance under parametrizable uncertainty and faults, as well as an extended comparative evaluation against state-of-the-art controllers. We believe in the potential antifragile control has in achieving closed-loop performance in the face of uncertainty and volatility by using its exposures to uncertainty to increase its capacity to anticipate and compensate for such events.</abstract>
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    <author>
      <first_name>Cristian</first_name>
      <last_name>Axenie</last_name>
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    <author>
      <first_name>Matteo</first_name>
      <last_name>Saveriano</last_name>
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    <collection role="institutes" number="19382">Audi Konfuzius-Institut Ingolstadt (AKII)</collection>
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    <id>5843</id>
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    <title language="eng">Influence of Blind Spot Assistance Systems in Heavy Commercial Vehicles on Accident Reconstruction</title>
    <abstract language="eng">Accidents between right-turning commercial vehicles and crossing vulnerable road users (VRU) in urban environments often lead to serious or fatal injuries and therefore play a significant role in forensic accident analysis. To reduce the risk of accidents, blind spot assistance systems have been installed in commercial vehicles for several years, among other things, to detect VRUs and warn the driver in time. However, since such systems cannot reliably prevent all turning accidents, an investigation by experts must clarify how the accident occurred and to what extent the blind spot assistance system influenced the course of the accident. The occurrence of the acoustic warning message can be defined as an objective reaction prompt for the driver, so that the blind spot assistance system can significantly influence the avoidability assessment. In order to be able to integrate the system into forensic accident analysis, a precise knowledge of how the system works and its limitations is required. For this purpose, tests with different systems and accident constellations were conducted and evaluated. It was found that the type of sensor used for the assistance systems has a great influence on the system’s performance. The lateral distance between the right side of the commercial vehicle and the VRU as well as obstacles between them and the speed difference can take great influence on the reliability of the assistance system. Depending on the concrete time of the system’s warning signal the accident can be avoided or not by the driver when reacting on this signal.</abstract>
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      <first_name>Stefan</first_name>
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    <publishedYear>2023</publishedYear>
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    <title language="eng">A Comprehensive Review of EV Lithium-Ion Battery Degradation</title>
    <abstract language="eng">Lithium-ion batteries with improved energy densities have made understanding the Solid Electrolyte Interphase (SEI) generation mechanisms that cause mechanical, thermal, and chemical failures more complicated. SEI processes reduce battery capacity and power. Thus, a review of this area's understanding is important. It is essential to know how batteries degrade in EVs to estimate battery lifespan as it goes, predict, and minimize losses, and determine the ideal time for a replacement. Lithium-ion batteries used in EVs mainly suffer two types of degradation: calendar degradation and cycling degradation. Despite the existence of several existing works in the literature, several aspects of battery degradation remain unclear or have not been analyzed in detail. This work presents a systematic review of existing works in the literature. The results of the present investigation provide insight into the complex relationships among various factors affecting battery degradation mechanisms. Specifically, this systematic review examined the effects of time, side reactions, temperature fluctuations, high charge/discharge rates, depth of discharge, mechanical stress, thermal stress, and the voltage relationship on battery performance and longevity. The results revealed that these factors interact in complex ways to influence the degradation mechanisms of batteries. For example, high charge currents and deep discharges were found to accelerate degradation, while low temperatures and moderate discharge depths were shown to be beneficial for battery longevity. Additionally, the results showed that the relationship between cell voltage and State-of-Charge (SOC) plays a critical role in determining the rate of degradation. Overall, these findings have important implications for the design and operation of battery systems, as they highlight the need to carefully manage a range of factors to maximize battery performance and longevity. The result is an analysis of the main articles published in this field in recent years. This work aims to present new knowledge about fault detection, diagnosis, and management of lithium-ion batteries based on battery degradation concepts. The new knowledge is presented and discussed in a structured and comprehensive way.</abstract>
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    <author>
      <first_name>Carlos Antônio</first_name>
      <last_name>Rufino Júnior</last_name>
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      <first_name>Eleonora</first_name>
      <last_name>Riva Sanseverino</last_name>
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      <first_name>Pierluigi</first_name>
      <last_name>Gallo</last_name>
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      <first_name>Murilo Machado</first_name>
      <last_name>Amaral</last_name>
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      <first_name>Daniel</first_name>
      <last_name>Koch</last_name>
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      <first_name>Sergej</first_name>
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    <author>
      <first_name>Gero</first_name>
      <last_name>Walter</last_name>
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    <author>
      <first_name>Hans-Georg</first_name>
      <last_name>Schweiger</last_name>
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    <author>
      <first_name>Hudson</first_name>
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    <title language="eng">Towards AI-enabled Cyber Threat Assessment in the Health Sector</title>
    <abstract language="eng">Cyber attacks on the healthcare industry can have tremendous consequences and the attack surface expands continuously. In order to handle the steadily rising workload, an expanding amount of analog processes in healthcare institutions is digitized. Despite regulations becoming stricter, not all existing infrastructure is sufficiently protected against cyber attacks. With an increasing number of devices and digital processes, the system and network landscape becomes more complex and harder to manage and therefore also more difficult to protect. The aim of this project is to introduce an AI-enabled platform that collects security relevant information from the outside of a health organization, analyzes it, delivers a risk score and supports decision makers in healthcare institutions to optimize investment choices for security measures. Therefore, an architecture of such a platform is designed, relevant information sources are identified, and AI methods for relevant data collection, selection, and risk scoring are explored.</abstract>
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    <author>
      <first_name>Patrizia</first_name>
      <last_name>Heinl</last_name>
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    <author>
      <first_name>Andrius</first_name>
      <last_name>Patapovas</last_name>
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    <author>
      <first_name>Michael</first_name>
      <last_name>Pilgermann</last_name>
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    <completedDate>2025-04-15</completedDate>
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    <title language="eng">Cybersecurity as a Service</title>
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    <author>
      <first_name>John</first_name>
      <last_name>Morris</last_name>
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    <author>
      <first_name>Stefan</first_name>
      <last_name>Tatschner</last_name>
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    <author>
      <first_name>Michael P.</first_name>
      <last_name>Heinl</last_name>
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    <author>
      <first_name>Patrizia</first_name>
      <last_name>Heinl</last_name>
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      <first_name>Thomas</first_name>
      <last_name>Newe</last_name>
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    <author>
      <first_name>Sven</first_name>
      <last_name>Plaga</last_name>
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    <id>5836</id>
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    <pageLast/>
    <pageNumber>7</pageNumber>
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    <completedDate>2025-04-15</completedDate>
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    <title language="eng">Applying Extended Object Tracking for Self-Localization of Roadside Radar Sensors</title>
    <abstract language="eng">Intelligent Transportation Systems (ITS) can benefit from roadside 4D mmWave radar sensors for large-scale traffic monitoring due to their weatherproof functionality, long sensing range and low manufacturing cost. However, the localization method using external measurement devices has limitations in urban environments. Furthermore, if the sensor mount exhibits changes due to environmental influences, they cannot be corrected when the measurement is performed only during the installation. In this paper, we propose self-localization of roadside radar data using Extended Object Tracking (EOT). The method analyses both the tracked trajectories of the vehicles observed by the sensor and the aerial laser scan of city streets, assigns labels of driving behaviors such as "straight ahead", "left turn", "right turn" to trajectory sections and road segments, and performs Semantic Iterative Closest Points (SICP) algorithm to register the point cloud. The method exploits the result from a down stream task -- object tracking -- for localization. We demonstrate high accuracy in the sub-meter range along with very low orientation error. The method also shows good data efficiency. The evaluation is done in both simulation and real-world tests.</abstract>
    <identifier type="urn">urn:nbn:de:bvb:573-58361</identifier>
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    <author>
      <first_name>Longfei</first_name>
      <last_name>Han</last_name>
    </author>
    <author>
      <first_name>Qiuyu</first_name>
      <last_name>Xu</last_name>
    </author>
    <author>
      <first_name>Klaus</first_name>
      <last_name>Kefferpütz</last_name>
    </author>
    <author>
      <first_name>Gordon</first_name>
      <last_name>Elger</last_name>
    </author>
    <author>
      <first_name>Jürgen</first_name>
      <last_name>Beyerer</last_name>
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    <collection role="persons" number="49574">Kefferpütz, Klaus</collection>
    <thesisPublisher>Technische Hochschule Ingolstadt</thesisPublisher>
    <file>https://opus4.kobv.de/opus4-haw/files/5836/2407.03084v1.pdf</file>
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    <id>5833</id>
    <completedYear/>
    <publishedYear>2025</publishedYear>
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    <language>eng</language>
    <pageFirst/>
    <pageLast/>
    <pageNumber>8</pageNumber>
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    <publisherName>arXiv</publisherName>
    <publisherPlace>Ithaca</publisherPlace>
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    <completedDate>2025-04-15</completedDate>
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    <title language="eng">3D Extended Object Tracking based on Extruded B-Spline Side View Profiles</title>
    <abstract language="eng">Object tracking is an essential task for autonomous systems. With the advancement of 3D sensors, these systems can better perceive their surroundings using effective 3D Extended Object Tracking (EOT) methods. Based on the observation that common road users are symmetrical on the right and left sides in the traveling direction, we focus on the side view profile of the object. In order to leverage of the development in 2D EOT and balance the number of parameters of a shape model in the tracking algorithms, we propose a method for 3D extended object tracking (EOT) by describing the side view profile of the object with B-spline curves and forming an extrusion to obtain a 3D extent. The use of B-spline curves exploits their flexible representation power by allowing the control points to move freely. The algorithm is developed into an Extended Kalman Filter (EKF). For a through evaluation of this method, we use simulated traffic scenario of different vehicle models and realworld open dataset containing both radar and lidar data.</abstract>
    <identifier type="urn">urn:nbn:de:bvb:573-58331</identifier>
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    <author>
      <first_name>Longfei</first_name>
      <last_name>Han</last_name>
    </author>
    <author>
      <first_name>Klaus</first_name>
      <last_name>Kefferpütz</last_name>
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    <author>
      <first_name>Jürgen</first_name>
      <last_name>Beyerer</last_name>
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    <collection role="persons" number="49574">Kefferpütz, Klaus</collection>
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    <id>5831</id>
    <completedYear/>
    <publishedYear>2025</publishedYear>
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    <language>eng</language>
    <pageFirst/>
    <pageLast/>
    <pageNumber>19</pageNumber>
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    <publisherName>arXiv</publisherName>
    <publisherPlace>Ithaca</publisherPlace>
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    <completedDate>2025-04-14</completedDate>
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    <title language="eng">CoDa-4DGS: Dynamic Gaussian Splatting with Context and Deformation&#13;
Awareness for Autonomous Driving</title>
    <abstract language="eng">Dynamic scene rendering opens new avenues in autonomous driving by enabling closed-loop simulations with photorealistic data, which is crucial for validating end-to-end algorithms. However, the complex and highly dynamic nature of traffic environments presents significant challenges in accurately rendering these scenes. In this paper, we introduce a novel 4D Gaussian Splatting (4DGS) approach, which incorporates context and temporal deformation awareness to improve dynamic scene rendering. Specifically, we employ a 2D semantic segmentation foundation model to self-supervise the 4D semantic features of Gaussians, ensuring meaningful contextual embedding. Simultaneously, we track the temporal deformation of each Gaussian across adjacent frames. By aggregating and encoding both semantic and temporal deformation features, each Gaussian is equipped with cues for potential deformation compensation within 3D space, facilitating a more precise representation of dynamic scenes. Experimental results show that our method improves 4DGS's ability to capture fine details in dynamic scene rendering for autonomous driving and outperforms other self-supervised methods in 4D reconstruction and novel view synthesis. Furthermore, CoDa-4DGS deforms semantic features with each Gaussian, enabling broader applications.</abstract>
    <identifier type="urn">urn:nbn:de:bvb:573-58313</identifier>
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    <author>
      <first_name>Rui</first_name>
      <last_name>Song</last_name>
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    <author>
      <first_name>Chenwei</first_name>
      <last_name>Liang</last_name>
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    <author>
      <first_name>Yan</first_name>
      <last_name>Xia</last_name>
    </author>
    <author>
      <first_name>Walter</first_name>
      <last_name>Zimmer</last_name>
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    <author>
      <first_name>Hu</first_name>
      <last_name>Cao</last_name>
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    <author>
      <first_name>Holger</first_name>
      <last_name>Caesar</last_name>
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    <author>
      <first_name>Andreas</first_name>
      <last_name>Festag</last_name>
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    <author>
      <first_name>Alois</first_name>
      <last_name>Knoll</last_name>
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    <pageNumber>28</pageNumber>
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    <publisherName>Research Square</publisherName>
    <publisherPlace>Durham</publisherPlace>
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    <completedDate>2025-04-14</completedDate>
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    <title language="eng">AI-Driven Point Cloud Framework for Predicting Solder Joint Reliability using 3D FEA Data</title>
    <abstract language="eng">Crack propagation in solder joints remains a critical challenge affecting the thermo-mechanical reliability of electronic devices, emphasizing the need for optimized package and solder pad designs. Traditional Finite Element Analysis (FEA) methods for predicting solder joint lifespan rely heavily on manual post-processing, where high-risk regions for plastic strain accumulation are identified. However, these approaches often overlook intricate failure mechanisms, as they primarily average creep strain and correlate it with experimental lifetime data using the Coffin-Manson equation, limiting their predictive accuracy. &#13;
&#13;
To overcome these limitations, this study introduces a novel AI-driven framework that automates 3D FEA post-processing for surface-mounted devices (SMDs) connected to printed circuit boards (PCBs). Unlike traditional methods, this framework leverages deep learning architectures—specifically, 3D Convolutional Neural Networks (CNNs) and PointNet—to extract complex spatial features directly from 3D FEA data, eliminating the need for manual interpretation. These learned features are then mapped to experimentally measured solder joint lifetimes through fully connected neural network layers, allowing the model to capture nonlinear failure behaviours that conventional methods fail to recognize. &#13;
&#13;
The research focuses on crack propagation in ceramic-based high-power LED packages used in automotive lighting systems, incorporating variations in two-pad and three-pad configurations, as well as thin and thick film metallized ceramic substrates with validated FEA models. Comparative analysis shows that PointNet significantly outperforms 3D CNNs, achieving an exceptionally high correlation with experimental data (R² = 99.99%). This AI-driven automated feature extraction and lifetime prediction approach marks a major advancement over traditional FEA-based methods, offering superior accuracy, reliability, and scalability for predicting solder joint reliability in microelectronics.</abstract>
    <parentTitle language="eng">Research Square</parentTitle>
    <identifier type="issn">2693-5015</identifier>
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    <licence>Creative Commons BY 4.0</licence>
    <author>
      <first_name>Zubair Akhtar</first_name>
      <last_name>Mohd</last_name>
    </author>
    <author>
      <first_name>Maximilian</first_name>
      <last_name>Schmid</last_name>
    </author>
    <author>
      <first_name>Gordon</first_name>
      <last_name>Elger</last_name>
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    <collection role="open_access" number="">open_access</collection>
    <collection role="institutes" number="19311">Fakultät Elektro- und Informationstechnik</collection>
    <collection role="institutes" number="19320">Institut für Innovative Mobilität (IIMo)</collection>
    <collection role="persons" number="26589">Elger, Gordon</collection>
    <collection role="persons" number="26880">Schmid, Maximilian</collection>
    <thesisPublisher>Technische Hochschule Ingolstadt</thesisPublisher>
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