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    <title language="eng">Reinforcement Learning for Traffic Signal Control Optimization: A Concept for Real-World Implementation</title>
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      <value>MARL</value>
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      <value>traffic optimization</value>
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    <subject>
      <language>eng</language>
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      <value>multimodal traffic</value>
    </subject>
    <subject>
      <language>eng</language>
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      <value>DRL</value>
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    <title language="eng">Reliability Maximization with Location-Based Scheduling for Cellular-V2X Communications in Highway Scenarios</title>
    <parentTitle language="eng">2018 16th International Conference on Intelligent Transportation Systems Telecommunications (ITST) Proceedings</parentTitle>
    <identifier type="isbn">978-1-5386-5544-3</identifier>
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      <first_name>Richard</first_name>
      <last_name>Fritzsche</last_name>
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      <last_name>Festag</last_name>
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    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Optimal scheduling</value>
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    <subject>
      <language>eng</language>
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      <value>Power system reliability</value>
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    <subject>
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      <value>Probability</value>
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      <value>Throughput</value>
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      <value>Signal to noise ratio</value>
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      <value>Road transportation</value>
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    <completedDate>2023-12-06</completedDate>
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    <title language="eng">Intelligent Roadside Infrastructure for Connected Mobility</title>
    <parentTitle language="eng">Smart Cities, Green Technologies, and Intelligent Transport Systems: 11th International Conference, SMARTGREENS 2022 and 8th International Conference, VEHITS 2022: Revised Selected Papers</parentTitle>
    <identifier type="isbn">978-3-031-37470-8</identifier>
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      <first_name>Shiva</first_name>
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      <first_name>Cornel</first_name>
      <last_name>Klein</last_name>
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      <first_name>Rui</first_name>
      <last_name>Song</last_name>
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      <first_name>Kristina</first_name>
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      <first_name>Jeroen</first_name>
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      <first_name>Alois</first_name>
      <last_name>Knoll</last_name>
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      <first_name>Markus</first_name>
      <last_name>Helfert</last_name>
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      <first_name>Gordon</first_name>
      <last_name>Elger</last_name>
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      <first_name>Karsten</first_name>
      <last_name>Berns</last_name>
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      <first_name>Oleg</first_name>
      <last_name>Gusikhin</last_name>
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    <title language="eng">Infra-3DRC-FusionNet: Deep Fusion of Roadside Mounted RGB Mono Camera and Three-Dimensional Automotive Radar for Traffic User Detection</title>
    <abstract language="eng">Mono RGB cameras and automotive radar sensors provide a complementary information set that makes them excellent candidates for sensor data fusion to obtain robust traffic user detection. This has been widely used in the vehicle domain and recently introduced in roadside-mounted smart infrastructure-based road user detection. However, the performance of the most commonly used late fusion methods often degrades when the camera fails to detect road users in adverse environmental conditions. The solution is to fuse the data using deep neural networks at the early stage of the fusion pipeline to use the complete data provided by both sensors. Research has been carried out in this area, but is limited to vehicle-based sensor setups. Hence, this work proposes a novel deep neural network to jointly fuse RGB mono-camera images and 3D automotive radar point cloud data to obtain enhanced traffic user detection for the roadside-mounted smart infrastructure setup. Projected radar points are first used to generate anchors in image regions with a high likelihood of road users, including areas not visible to the camera. These anchors guide the prediction of 2D bounding boxes, object categories, and confidence scores. Valid detections are then used to segment radar points by instance, and the results are post-processed to produce final road user detections in the ground plane. The trained model is evaluated for different light and weather conditions using ground truth data from a lidar sensor. It provides a precision of 92%, recall of 78%, and F1-score of 85%. The proposed deep fusion methodology has 33%, 6%, and 21% absolute improvement in precision, recall, and F1-score, respectively, compared to object-level spatial fusion output.</abstract>
    <parentTitle language="eng">Sensors</parentTitle>
    <identifier type="issn">1424-8220</identifier>
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    <author>
      <first_name>Shiva</first_name>
      <last_name>Agrawal</last_name>
    </author>
    <author>
      <first_name>Savankumar</first_name>
      <last_name>Bhanderi</last_name>
    </author>
    <author>
      <first_name>Gordon</first_name>
      <last_name>Elger</last_name>
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  </doc>
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    <id>952</id>
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    <publishedYear>2020</publishedYear>
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    <language>eng</language>
    <pageFirst>174931</pageFirst>
    <pageLast>174942</pageLast>
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    <issue/>
    <volume>8</volume>
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    <publisherPlace>New York</publisherPlace>
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    <completedDate>2020-09-18</completedDate>
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    <title language="eng">Tensor-based framework with model order selection and high accuracy factor decomposition for time-delay estimation in dynamic multipath scenarios</title>
    <abstract language="eng">Global Navigation Satellite Systems (GNSS) are crucial for applications that demand very accurate positioning. Tensor-based time-delay estimation methods, such as CPD-GEVD, DoA/KRF, and SECSI, combined with the GPS3 L1C signal, are capable of, significantly, mitigating the positioning degradation caused by multipath components. However, even though these schemes require an estimated model order, they assume that the number of multipath components is constant. In GNSS applications, the number of multipath components is time-varying in dynamic scenarios. Thus, in this paper, we propose a tensor-based framework with model order selection and high accuracy factor decomposition for time-delay estimation in dynamic multipath scenarios. Our proposed approach exploits the estimates of the model order for each slice by grouping the data tensor slices into sub-tensors to provide high accuracy factor decomposition. We further enhance the proposed approach by incorporating the tensor-based Multiple Denoising (MuDe).</abstract>
    <parentTitle language="eng">IEEE Access</parentTitle>
    <identifier type="issn">2169-3536</identifier>
    <identifier type="urn">urn:nbn:de:bvb:573-9528</identifier>
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    <author>
      <first_name>Mateus</first_name>
      <last_name>Da Rosa Zanatta</last_name>
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    <author>
      <first_name>Joao Paulo</first_name>
      <last_name>Carvalho Lustosa da Costa</last_name>
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    <author>
      <first_name>Felix</first_name>
      <last_name>Antreich</last_name>
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    <author>
      <first_name>Martin</first_name>
      <last_name>Haardt</last_name>
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    <author>
      <first_name>Gordon</first_name>
      <last_name>Elger</last_name>
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    <author>
      <first_name>Fábio Lúcio</first_name>
      <last_name>Lopes de Mendonça</last_name>
    </author>
    <author>
      <first_name>Rafael Timóteo</first_name>
      <last_name>de Sousa Junior</last_name>
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    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Global navigation satellite systems (GNSS)</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>global positioning system (GPS)</value>
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    <subject>
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      <value>GPS3</value>
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    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>time-delay estimation (TDE)</value>
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    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>multipath components</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>model order selection (MOS)</value>
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    <volume>10</volume>
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    <completedDate>2022-09-30</completedDate>
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    <title language="eng">Analysis and Evaluation of Information Redundancy Mitigation for V2X Collective Perception</title>
    <abstract language="eng">Sensor data sharing enables vehicles to exchange locally perceived sensor data among each other and with the roadside infrastructure to increase their environmental awareness. It is commonly regarded as a next-generation vehicular communication service beyond the exchange of highly aggregated messages in the first generation. The approach is being considered in the European standardization process, where it relies on the exchange of locally detected objects representing anything safety-relevant, such as other vehicles or pedestrians, in periodically broadcasted messages to vehicles in direct communication range. Objects filtering methods for inclusion in a message are necessary to avoid overloading a channel and provoking unnecessary data processing. Initial studies provided in a pre-standardization report about sensor data sharing elaborated a first set of rules to filter objects based on their characteristics, such as their dynamics or type. However, these rules still lack the consideration of information received by other stations to operate. Specifically, to address the problem of information redundancy, several rules have been proposed, but their performance has not been evaluated yet comprehensively. In the present work, the rules are further analyzed, assessed, and compared. Functional and operational requirements are investigated. A performance evaluation is realized by discrete-event simulations in a scenario for a representative city with realistic vehicle densities and mobility patterns. A score and other redundancy-level metrics are elaborated to ease the evaluation and comparison of the filtering rules. Finally, improvements and future works to the filtering methods are proposed.</abstract>
    <parentTitle language="eng">IEEE Access</parentTitle>
    <identifier type="issn">2169-3536</identifier>
    <identifier type="urn">urn:nbn:de:bvb:573-28662</identifier>
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    <author>
      <first_name>Quentin</first_name>
      <last_name>Delooz</last_name>
    </author>
    <author>
      <first_name>Alexander</first_name>
      <last_name>Willecke</last_name>
    </author>
    <author>
      <first_name>Keno</first_name>
      <last_name>Garlichs</last_name>
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    <author>
      <first_name>Andreas-Christian</first_name>
      <last_name>Hagau</last_name>
    </author>
    <author>
      <first_name>Lars</first_name>
      <last_name>Wolf</last_name>
    </author>
    <author>
      <first_name>Alexey</first_name>
      <last_name>Vinel</last_name>
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    <author>
      <first_name>Andreas</first_name>
      <last_name>Festag</last_name>
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    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Collective perception</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>information redundancy mitigation</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>road safety</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>sensor data sharing</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>V2X communications</value>
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    <file>https://opus4.kobv.de/opus4-haw/files/2866/Analysis_Evaluation_Information_Redundancy.pdf</file>
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    <title language="eng">ResFed: Communication-Efficient Federated Learning With Deep Compressed Residuals</title>
    <abstract language="eng">Federated learning allows for cooperative training among distributed clients by sharing their locally learned model parameters, such as weights or gradients. However, as model size increases, the communication bandwidth required for deployment in wireless networks becomes a bottleneck. To address this, we propose a residual-based federated learning framework (ResFed) that transmits residuals instead of gradients or weights in networks. By predicting model updates at both clients and the server, residuals are calculated as the difference between updated and predicted models and contain more dense information than weights or gradients. We find that the residuals are less sensitive to an increasing compression ratio than other parameters, and hence use lossy compression techniques on residuals to improve communication efficiency for training in federated settings. With the same compression ratio, ResFed outperforms current methods (weight- or gradient-based federated learning) by over 1.4× on federated data sets, including MNIST, FashionMNIST, SVHN, CIFAR-10, CIFAR-100, and FEMNIST, in client-to-server communication, and can also be applied to reduce communication costs for server-to-client communication.</abstract>
    <parentTitle language="eng">IEEE Internet of Things Journal</parentTitle>
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    <author>
      <first_name>Rui</first_name>
      <last_name>Song</last_name>
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    <author>
      <first_name>Liguo</first_name>
      <last_name>Zhou</last_name>
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    <author>
      <first_name>Lingjuan</first_name>
      <last_name>Lyu</last_name>
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      <first_name>Andreas</first_name>
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      <last_name>Knoll</last_name>
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  <doc>
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    <publishedYear>2023</publishedYear>
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    <language>eng</language>
    <pageFirst>87225</pageFirst>
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    <title language="eng">Radio Resource Allocation in 5G-NR V2X: A Multi-Agent Actor-Critic Based Approach</title>
    <abstract language="eng">The efficiency of radio resource allocation and scheduling procedures in Cellular Vehicle-to-X (Cellular V2X) communication networks directly affects link quality in terms of latency and reliability. However, owing to the continuous movement of vehicles, it is impossible to have a centralized coordinating unit at all times to manage the allocation of radio resources. In the unmanaged mode of the fifth generation new radio (5G-NR) V2X, the sensing-based semi-persistent scheduling (SB-SPS) loses its effectiveness when V2X data messages become aperiodic with varying data sizes. This leads to misinformed resource allocation decisions among vehicles and frequent resource collisions. To improve resource selection, this study formulates the Cellular V2X communication network as a decentralized multi-agent networked markov decision process (MDP) where each vehicle agent executes an actor-critic-based radio resource scheduler. Developing further the actor-critic methodology for the radio resource allocation problem in Cellular V2X, two variants are derived: independent actor-critic (IAC) and shared experience actor-critic (SEAC). Results from simulation studies indicate that the actor-critic schedulers improve reliability, achieving a 15-20% higher probability of reception under high vehicular density scenarios with aperiodic traffic patterns.</abstract>
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      <first_name>Anupama</first_name>
      <last_name>Hegde</last_name>
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    <author>
      <first_name>Rui</first_name>
      <last_name>Song</last_name>
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      <first_name>Andreas</first_name>
      <last_name>Festag</last_name>
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    <subject>
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    <title language="eng">Semi-Automatic Annotation of 3D Radar and Camera for Smart Infrastructure-Based Perception</title>
    <abstract language="eng">Environment perception using camera, radar, and/or lidar sensors has significantly improved in the last few years because of deep learning-based methods. However, a large group of these methods fall into the category of supervised learning, which requires a considerable amount of annotated data. Due to uncertainties in multi-sensor data, automating the data labeling process is extremely challenging; hence,&#13;
it is performed manually to a large extent. Even though full automation of such a process is difficult, semiautomation can be a significant step to ease this process. However, the available work in this regard is still very limited; hence, in this paper, a novel semi-automatic annotation methodology is developed for labeling RGB camera images and 3D automotive radar point cloud data using a smart infrastructure-based sensor setup. This paper also describes a new method for 3D radar background subtraction to remove clutter and a new object category, GROUP, for radar-based object detection for closely located vulnerable road users. To validate the work, a dataset named INFRA-3DRC is created using this methodology, where 75% of the&#13;
labels are automatically generated. In addition, a radar cluster classifier and an image classifier are developed, trained, and tested on this dataset, achieving accuracy of 98.26% and 94.86%, respectively. The dataset and Python scripts are available at https://fraunhoferivi.github.io/INFRA-3DRC-Dataset/.</abstract>
    <parentTitle language="eng">IEEE Access</parentTitle>
    <identifier type="issn">2169-3536</identifier>
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    <title language="eng">Edge-Aided Sensor Data Sharing in Vehicular Communication Networks</title>
    <parentTitle language="eng">2022 IEEE 95th Vehicular Technology Conference: (VTC2022-Spring) Proceedings</parentTitle>
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      <value>sensor data sharing and fusion</value>
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      <value>distributed estimation</value>
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      <value>measurement noise</value>
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    <title language="eng">Priority Maneuver (PriMa) Coordination for Connected and Automated Vehicles</title>
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    <title language="eng">Congestion Aware Objects Filtering for Collective Perception</title>
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      <first_name>Alexey</first_name>
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    <subject>
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      <value>V2X</value>
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      <language>eng</language>
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      <value>Decentralized Congestion Control</value>
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    <title language="eng">Towards Realistic Evaluation of Collective Perception for Connected and Automated Driving</title>
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    <title language="eng">Mode Switching Performance in Cellular-V2X</title>
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      <value>Switches</value>
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    <title language="eng">Multi-Sensor Data Fusion for Real-Time Multi-Object Tracking</title>
    <abstract language="eng">Sensor data fusion is essential for environmental perception within smart traffic applications. By using multiple sensors cooperatively, the accuracy and probability of the perception are increased, which is crucial for critical traffic scenarios or under bad weather conditions. In this paper, a modular real-time capable multi-sensor fusion framework is presented and tested to fuse data on the object list level from distributed automotive sensors (cameras, radar, and LiDAR). The modular multi-sensor fusion architecture receives an object list (untracked objects) from each sensor. The fusion framework combines classical data fusion algorithms, as it contains a coordinate transformation module, an object association module (Hungarian algorithm), an object tracking module (unscented Kalman filter), and a movement compensation module. Due to the modular design, the fusion framework is adaptable and does not rely on the number of sensors or their types. Moreover, the method continues to operate because of this adaptable design in case of an individual sensor failure. This is an essential feature for safety-critical applications. The architecture targets environmental perception in challenging time-critical applications. The developed fusion framework is tested using simulation and public domain experimental data. Using the developed framework, sensor fusion is obtained well below 10 milliseconds of computing time using an AMD Ryzen 7 5800H mobile processor and the Python programming language. Furthermore, the object-level multi-sensor approach enables the detection of changes in the extrinsic calibration of the sensors and potential sensor failures. A concept was developed to use the multi-sensor framework to identify sensor malfunctions. This feature will become extremely important in ensuring the functional safety of the sensors for autonomous driving.</abstract>
    <parentTitle language="eng">Processes</parentTitle>
    <identifier type="issn">2227-9717</identifier>
    <identifier type="urn">urn:nbn:de:bvb:573-31989</identifier>
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      <first_name>Numan</first_name>
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    <author>
      <first_name>Klaus</first_name>
      <last_name>Kefferpütz</last_name>
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    <author>
      <first_name>Kristina</first_name>
      <last_name>Doycheva</last_name>
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      <first_name>Gordon</first_name>
      <last_name>Elger</last_name>
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      <value>object tracking</value>
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    <subject>
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      <value>roadside units</value>
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    <title language="eng">Crack Growth Prediction in High-Power LEDs from TTA, SAM and Simulated Data</title>
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      <first_name>Maximilian</first_name>
      <last_name>Schmid</last_name>
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      <value>solder</value>
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    <subject>
      <language>eng</language>
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      <value>crack</value>
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    <title language="eng">Precise Adverse Weather Characterization by Deep-Learning-Based Noise Processing in Automotive LiDAR Sensors</title>
    <abstract language="eng">With current advances in automated driving, optical sensors like cameras and LiDARs are playing an increasingly important role in modern driver assistance systems. However, these sensors face challenges from adverse weather effects like fog and precipitation, which significantly degrade the sensor performance due to scattering effects in its optical path. Consequently, major efforts are being made to understand, model, and mitigate these effects. In this work, the reverse research question is investigated, demonstrating that these measurement effects can be exploited to predict occurring weather conditions by using state-of-the-art deep learning mechanisms. In order to do so, a variety of models have been developed and trained on a recorded multiseason dataset and benchmarked with respect to performance, model size, and required computational resources, showing that especially modern vision transformers achieve remarkable results in distinguishing up to 15 precipitation classes with an accuracy of 84.41% and predicting the corresponding precipitation rate with a mean absolute error of less than 0.47 mm/h, solely based on measurement noise. Therefore, this research may contribute to a cost-effective solution for characterizing precipitation with a commercial Flash LiDAR sensor, which can be implemented as a lightweight vehicle software feature to issue advanced driver warnings, adapt driving dynamics, or serve as a data quality measure for adaptive data preprocessing and fusion.</abstract>
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    <title language="eng">Concept of Smart Infrastructure for Connected Vehicle Assist and Traffic Flow Optimization</title>
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      <first_name>Shiva</first_name>
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      <first_name>Jeroen</first_name>
      <last_name>Ploeg</last_name>
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    <author>
      <first_name>Rui</first_name>
      <last_name>Song</last_name>
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      <first_name>Markus</first_name>
      <last_name>Helfert</last_name>
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    <author>
      <first_name>Akhil</first_name>
      <last_name>Kohli</last_name>
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      <first_name>Karsten</first_name>
      <last_name>Berns</last_name>
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    <author>
      <first_name>Andreas</first_name>
      <last_name>Korb</last_name>
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    <editor>
      <first_name>Oleg</first_name>
      <last_name>Gusikhin</last_name>
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    <author>
      <first_name>Maximilian</first_name>
      <last_name>Andre</last_name>
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    <author>
      <first_name>Erik</first_name>
      <last_name>Holzinger</last_name>
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    <author>
      <first_name>Gordon</first_name>
      <last_name>Elger</last_name>
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      <value>Road Side Unit (RSU)</value>
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      <value>Smart Infrastructure</value>
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    <subject>
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      <value>Radar</value>
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      <value>LiDAR</value>
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    <subject>
      <language>eng</language>
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      <value>Camera</value>
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    <subject>
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      <value>Perception</value>
    </subject>
    <subject>
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      <value>Sensor Data Fusion</value>
    </subject>
    <subject>
      <language>eng</language>
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      <value>Vehicle-to-everything (V2X)</value>
    </subject>
    <subject>
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      <value>Communication</value>
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    <subject>
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      <value>Simulation</value>
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    <subject>
      <language>eng</language>
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      <value>ITS</value>
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    <thesisPublisher>Technische Hochschule Ingolstadt</thesisPublisher>
    <file>https://opus4.kobv.de/opus4-haw/files/3092/110688.pdf</file>
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    <title language="eng">Comparison of Two Different Radar Concepts for Pedestrian Protection on Bus Stops</title>
    <abstract language="eng">This paper presents the joint work from the “HORIS” project, with a focus on pedestrian detection at bus-stops by radar sensors mounted in the infrastructure to support future autonomous driving and protecting pedestrians in critical situations. Two sensor systems are investigated and evaluated. The first based on single radar sensor phase-sensitive raw data analysis and the second based on sensor data fusion of cluster data with two radar sensors using neural networks to predict the position of pedestrians.</abstract>
    <parentTitle language="eng">Proceedings of the 11th International Conference on Sensor Networks</parentTitle>
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    <author>
      <first_name>Egor</first_name>
      <last_name>Streck</last_name>
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      <first_name>Gordon</first_name>
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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 D 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">Scientific Reports</parentTitle>
    <identifier type="issn">2045-2322</identifier>
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      <first_name>Savankumar</first_name>
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    <author>
      <first_name>Shiva</first_name>
      <last_name>Agrawal</last_name>
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    <title language="eng">A Survey on Decentralized Cooperative Maneuver Coordination for Connected and Automated Vehicles</title>
    <abstract language="eng">V2X communications can be applied for maneuver coordination of automated vehicles, where the vehicles exchange messages to inform each other of their driving intentions and to negotiate for joint maneuvers. For motion and maneuver planning of automated vehicles, the cooperative maneuver coordination extends the perception range of the sensors, enhances the planning horizon and allows complex interactions among the vehicles. For specific scenarios, various schemes for maneuver coordination of connected automated vehicles exist. Recently, several proposals for maneuver coordination have been made that address generic instead of specific scenarios and apply different schemes for the message exchange of driving intentions and maneuver negotiation. This paper presents use cases for maneuver coordination and classifies existing generic approaches for decentralized maneuver coordination considering implicit and explicit trajectory broadcast, cost values and space-time reservation. We systematically describe the approaches, compare them and derive future research topics.</abstract>
    <parentTitle language="eng">Proceedings of the 7th International Conference on Vehicle Technology and Intelligent Transport Systems</parentTitle>
    <identifier type="isbn">978-989-758-513-5</identifier>
    <identifier type="issn">2184-495X</identifier>
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    <author>
      <first_name>Daniel</first_name>
      <last_name>Maksimovski</last_name>
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      <first_name>Andreas</first_name>
      <last_name>Festag</last_name>
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    <author>
      <first_name>Christian</first_name>
      <last_name>Facchi</last_name>
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    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>V2X Communications</value>
    </subject>
    <subject>
      <language>eng</language>
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      <value>Cooperative Driving</value>
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    <subject>
      <language>eng</language>
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      <value>Maneuver Coordination</value>
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    <subject>
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    <title language="eng">Modeling Cellular Network Infrastructure in SUMO</title>
    <abstract language="deu">Communication networks are becoming an increasingly important part of the mobility system. They allow traffic participants to be connected and to exchange information related to traffic and roads. The information exchange impacts the behavior of traffic participants, such as the selection of travel routes or their mobility dynamics. Considering infrastructure-based networks, the information exchange depends on the availability of the network infrastructure and the quality of the communication links. Specifically in urban areas, today’s 4G and 5G networks deploy small cells of high capacity, which do not provide ubiquitous cellular coverage due to their small range, signal blocking, etc. Therefore, the accurate modeling of the network infrastructure and its integration in simulation scenarios in microscopic traffic simulation software is gaining relevance.&#13;
&#13;
Unlike traffic infrastructure, such as traffic lights, the simulation of a cellular network infrastructure is not natively supported in SUMO. Instead, the protocols, functions and entities of the communication system with the physical wireless transmission are modeled in a dedicated and specialized network simulator that is coupled with SUMO. The disadvantage of this approach is that the simulated SUMO entities, typically vehicles, are not aware which portions of the roads are covered by wireless cells and what quality the wireless communication links have.&#13;
&#13;
In this paper, we propose a method for modeling the cellular infrastructure in SUMO that introduces a cellular coverage layer to SUMO. This layer models cell sites in a regular hexagonal grid, where each site is served by a base station. Following commonly accepted guidelines for the evaluation of cellular communication system, the method facilitates standardized and realistic modeling of the cellular coverage, including cell sites, antenna characteristics, cell association and handover. In order to ease the applicability of the method, we describe the work flow to create cell sites. As a representative case, we have applied the method to InTAS, the SUMO Ingolstadt traffic scenario and applied real data for the cellular infrastructure. We validate the approach by simulating a Cellular V2X system with sidelink connectivity in an urban macro cell environment by coupling SUMO enhanced by the proposed connectivity sublayer with ARTERY-C, a network simulator for Cellular V2X. As a proof-of-concept, we present a signal-to-interference noise ratio (SINR) coverage map and further evaluate the impact of different types of interference. We also demonstrate the effect of advanced features of cellular networks such as inter-cell interference coordination (ICIC) and sidelink communication modes of Cellular V2X with dynamic switching between the in-coverage and out-of-coverage mode.</abstract>
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      <first_name>Anupama</first_name>
      <last_name>Hegde</last_name>
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      <first_name>Ringo</first_name>
      <last_name>Stahl</last_name>
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      <first_name>Silas</first_name>
      <last_name>Lobo</last_name>
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      <first_name>Andreas</first_name>
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    <title language="eng">Revisiting Message Generation Strategies for Collective Perception in Connected and Automated Driving</title>
    <parentTitle language="eng">VEHICULAR 2020: The Ninth International Conference on Advances in Vehicular Systems, Technologies and Applications</parentTitle>
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    <title language="eng">Investigations on High-Power LEDs and Solder Interconnects in Automotive Application: Part II - Reliability</title>
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    <title language="eng">First steps towards real-world traffic signal control optimisation by reinforcement learning</title>
    <abstract language="eng">Enhancing traffic signal optimisation has the potential to improve urban traffic flow without the need for expensive infrastructure modifications. While reinforcement learning (RL) techniques have demonstrated their effectiveness in simulations, their real-world implementation is still a challenge. Real-world systems need to be developed that guarantee a deployable action definition for real traffic systems while prioritising safety constraints and robust policies. This paper introduces a method to overcome this challenge by introducing a novel action definition that optimises parameter-level control programmes designed by traffic engineers. The complete proposed framework consists of a traffic situation estimation, a feature extractor, and a system that enables training on estimates of real-world traffic situations. Further multimodal optimisation, scalability, and continuous training after deployment could be achieved. The first simulative tests using this action definition show an average improvement of more than 20% in traffic flow compared to the baseline – the corresponding pre-optimised real-world control.</abstract>
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    <title language="eng">Transient thermal analysis for VCSEL Diodes</title>
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    <title language="eng">Laser Stimulated Transient Thermal Analysis of Semiconductors</title>
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      <value>reliability</value>
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      <value>LED</value>
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    <title language="eng">Evaluating optical performance degradation of automotive cameras under accelerated aging</title>
    <abstract language="eng">Automotive cameras are subject to environmental stress, which degrades performance by reducing image sharpness. To qualify for automotive use and to ensure that the cameras maintain sharpness according to the hard requirements of end-of-line testing, cameras have to undergo standardized accelerated aging tests. These tests are performed to demonstrate reliability and functional safety over lifetime. Few studies have been published that demonstrate how aging contributes to the degradation of optical performance. This study addresses this gap by combining accelerated thermal aging with sharpness tracking to investigate degradation over time. To quantify sharpness degradation, six series-production cameras were subjected to accelerated thermal aging between −40◦𝐶 and +85◦𝐶. Each camera underwent 2000 aging cycles, equivalent to 80% of their lifetime based on the Coffin-Manson model of the LV124 standard. Sharpness was measured by calculating the Spatial Frequency Response (SFR) from images captured of a double-cross reticle projected by a virtual object generator with three illumination wavelengths (625nm, 520nm, and 470nm). The change in sharpness was evaluated with SFR50 and SFR at 60 line pairs per millimeter (SFR@60). During the first 250 cycles, a wear-in effect was observed, where sharpness increased before leveling off, as seen previously. The results also indicated a slow decline in sharpness showing long-term stability. Analysis indicated that before aging, the best focal plane was located closer to the focal position of the red wavelength, which lies furthest from the objective. By the end of the aging process, the best focal plane had shifted toward the focal position of the blue wavelength, which is located closer to the objective. This suggests a forward movement of the image sensor due to aging. Even after 2000 cycles, all cameras maintained an SFR@60 above 0.5. A Random Forest regression model was trained to predict the age based on the SFR curves, achieving a mean absolute error of 126 cycles and a 𝑅2 score of 0.96.</abstract>
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    <title language="eng">Realization, multi-field coupled simulation and characterization of a thermo-mechanically robust LiDAR front end on a copper coated glass substrate</title>
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