@article{KleinerKomsiyskaElgeretal.2019, author = {Kleiner, Jan and Komsiyska, Lidiya and Elger, Gordon and Endisch, Christian}, title = {Thermal modelling of a prismatic lithium-ion cell in a battery electric vehicle environment}, volume = {13}, pages = {62}, journal = {Energies}, subtitle = {influences of the experimental validation setup}, number = {1}, publisher = {MDPI}, address = {Basel}, issn = {1996-1073}, doi = {https://doi.org/10.3390/en13010062}, year = {2019}, abstract = {In electric vehicles with lithium-ion battery systems, the temperature of the battery cells has a great impact on performance, safety, and lifetime. Therefore, developing thermal models of lithium-ion batteries to predict and investigate the temperature development and its impact is crucial. Commonly, models are validated with experimental data to ensure correct model behaviour. However, influences of experimental setups or comprehensive validation concepts are often not considered, especially for the use case of prismatic cells in a battery electric vehicle. In this work, a 3D electro-thermal model is developed and experimentally validated to predict the cell's temperature behaviour for a single prismatic cell under battery electric vehicle (BEV) boundary conditions. One focus is on the development of a single cell's experimental setup and the investigation of the commonly neglected influences of an experimental setup on the cell's thermal behaviour. Furthermore, a detailed validation is performed for the laboratory BEV scenario for spatially resolved temperatures and heat generation. For validation, static and dynamic loads are considered as well as the detected experimental influences. The validated model is used to predict the temperature within the cell in the BEV application for constant current and Worldwide harmonized Light vehicles Test Procedure (WLTP) load profile.}, language = {en} } @article{LiuContiBhogarajuetal.2020, author = {Liu, E and Conti, Fosca and Bhogaraju, Sri Krishna and Signorini, Raffaella and Pedron, Danilo and Wunderle, Bernhard and Elger, Gordon}, title = {Thermomechanical stress in GaN-LEDs soldered onto Cu substrates studied using finite element method and Raman spectroscopy}, volume = {51}, journal = {Journal of Raman Spectroscopy}, number = {10}, publisher = {Wiley}, address = {Chichester}, issn = {1097-4555}, doi = {https://doi.org/10.1002/jrs.5947}, pages = {2083 -- 2094}, year = {2020}, abstract = {Local thermomechanical stress can cause failures in semiconductor packages during long-time operation under harsh environmental conditions. This study helps to explain the packaging-induced stress in blue GaN-LEDs soldered onto copper substrates using AuSn alloy as lead-free interconnect material. Based on the finite element method, a virtual prototype is developed to simulate the thermomechanical behavior and stress in the LED and in the complete LED/AuSn/Cu assembly considering plastic and viscoplastic strain. The investigations were performed by varying the temperature between -50°C and 180°C. To validate the model, the simulation results are compared to experimental data collected with Raman spectroscopy. Studies of the urn:x-wiley:03770486:media:jrs5947:jrs5947-math-0003 phonon mode of GaN semiconductor are elaborated to understand the induced thermomechanical stress. The model enables evaluation of the stress in the interfaces of the assembly, which otherwise cannot be accessed by measurements. It serves to predict how assemblies would perform, before committing resources to build a physical prototype.}, language = {en} } @inproceedings{AgrawalElger2021, author = {Agrawal, Shiva and Elger, Gordon}, title = {Concept of infrastructure based environment perception for IN2Lab test field for automated driving}, booktitle = {2021 IEEE International Smart Cities Conference (ISC2)}, publisher = {IEEE}, address = {Piscataway}, isbn = {978-1-6654-4919-9}, issn = {2687-8860}, doi = {https://doi.org/10.1109/ISC253183.2021.9562894}, year = {2021}, language = {en} } @inproceedings{LiuContiBhogarajuetal.2021, author = {Liu, E and Conti, Fosca and Bhogaraju, Sri Krishna and Elger, Gordon}, title = {Simulations and experiments to analyze stress phenomena in soldered and sintered interconnections between silicon nitride chips and copper substrates}, booktitle = {2021 22nd International Conference on Thermal, Mechanical and Multi-Physics Simulation and Experiments in Microelectronics and Microsystems (EuroSimE)}, publisher = {IEEE}, address = {Piscataway}, isbn = {978-1-6654-1373-2}, doi = {https://doi.org/10.1109/EuroSimE52062.2021.9410869}, year = {2021}, language = {en} } @article{SchmidBhogarajuLiuetal.2020, author = {Schmid, Maximilian and Bhogaraju, Sri Krishna and Liu, E and Elger, Gordon}, title = {Comparison of Nondestructive Testing Methods for Solder, Sinter, and Adhesive Interconnects in Power and Opto-Electronics}, volume = {10}, pages = {8516}, journal = {Applied Sciences}, number = {23}, publisher = {MDPI}, address = {Basel}, issn = {2076-3417}, doi = {https://doi.org/10.3390/app10238516}, year = {2020}, abstract = {Reliability is one of the major requirements for power and opto-electronic devices across all segments. High operation temperature and/or high thermomechanical stress cause defects and degradation of materials and interconnects, which may lead to malfunctions with costly or even life-threatening consequences. To avoid or at least reduce failures, nondestructive testing (NDT) methods are common within development and production of power and opto-electronics. Currently, the dominating NDT methods are X-ray, scanning acoustic microscopy (SAM), and transient thermal analysis (TTA). However, they have different strengths and weaknesses with respect to materials and mechanical designs. This paper compares these NDT methods for different interconnect technologies, i.e., reflow soldering, adhesive, and sintered interconnection. While X-ray provided adequate results for soldered interfaces, inspection of adhesives and sintered interconnects was not possible. With SAM, evaluation of adhesives and sintered interconnects was also feasible, but quality depended strongly on the sample under test. TTA enabled sufficiently detailed results for all the interconnect applications. Automated TTA equipment, as the in-house developed tester used within this investigation, enabled measurement times compatible with SAM and X-ray. In the investigations, all methods revealed their pros and cons, and their selection has to depend on the sample under tests and the required analysis depth and data details. In the paper, guidelines are formulated for an appropriate decision on the NDT method depending on sample and requirements.}, language = {en} } @unpublished{HanXuKefferpuetzetal.2024, author = {Han, Longfei and Xu, Qiuyu and Kefferp{\"u}tz, Klaus and Elger, Gordon and Beyerer, J{\"u}rgen}, title = {Applying Extended Object Tracking for Self-Localization of Roadside Radar Sensors}, publisher = {arXiv}, address = {Ithaca}, doi = {https://doi.org/10.48550/arXiv.2407.03084}, year = {2024}, abstract = {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.}, language = {en} } @inproceedings{MeessGernerHeinetal.2022, author = {Meess, Henri and Gerner, Jeremias and Hein, Daniel and Schmidtner, Stefanie and Elger, Gordon}, title = {Reinforcement Learning for Traffic Signal Control Optimization: A Concept for Real-World Implementation}, booktitle = {AAMAS '22: Proceedings of the 21st International Conference on Autonomous Agents and Multiagent Systems}, publisher = {International Foundation for Autonomous Agents and Multiagent Systems}, address = {Richland}, isbn = {978-1-4503-9213-6}, doi = {https://dl.acm.org/doi/10.5555/3535850.3536081}, pages = {1699 -- 1701}, year = {2022}, language = {en} } @inproceedings{AgrawalSongDoychevaetal.2023, author = {Agrawal, Shiva and Song, Rui and Doycheva, Kristina and Knoll, Alois and Elger, Gordon}, title = {Intelligent Roadside Infrastructure for Connected Mobility}, booktitle = {Smart Cities, Green Technologies, and Intelligent Transport Systems: 11th International Conference, SMARTGREENS 2022 and 8th International Conference, VEHITS 2022: Revised Selected Papers}, editor = {Klein, Cornel and Jarke, Matthias and Ploeg, Jeroen and Helfert, Markus and Berns, Karsten and Gusikhin, Oleg}, publisher = {Springer}, address = {Cham}, isbn = {978-3-031-37470-8}, issn = {1865-0937}, doi = {https://doi.org/10.1007/978-3-031-37470-8_6}, pages = {134 -- 157}, year = {2023}, language = {en} } @inproceedings{AgrawalBhanderiAmanagietal.2023, author = {Agrawal, Shiva and Bhanderi, Savankumar and Amanagi, Sumit and Doycheva, Kristina and Elger, Gordon}, title = {Instance Segmentation and Detection of Children to Safeguard Vulnerable Traffic User by Infrastructure}, booktitle = {Proceedings of the 9th International Conference on Vehicle Technology and Intelligent Transport Systems}, editor = {Vinel, Alexey and Ploeg, Jeroen and Berns, Karsten and Gusikhin, Oleg}, publisher = {SciTePress}, address = {Set{\´u}bal}, isbn = {978-989-758-652-1}, issn = {2184-495X}, doi = {https://doi.org/10.5220/0011825400003479}, pages = {206 -- 214}, year = {2023}, abstract = {Cameras mounted on intelligent roadside infrastructure units and vehicles can detect humans on the road using state-of-the-art perception algorithms, but these algorithms are presently not trained to distinguish between human and adult. However, this is a crucial requirement from a safety perspective because a child may not follow all the traffic rules, particularly while crossing the road. Moreover, a child may stop or may start playing on the road. In such situations, the separation of a child from an adult is necessary. The work in this paper targets to solve this problem by applying a transfer-learning-based neural network approach to classify child and adult separately in camera images. The described work is comprised of image data collection, data annotation, transfer learning-based model development, and evaluation. For the work, Mask-RCNN (region-based convolutional neural network) with different backbone architectures and two different baselines are investigated and the perception precision of the architectures after transfer-learning is compared. The results reveal that the best performing trained model is able to detect and classify children and adults separately in different road scenarios with segmentation mask AP (average precision) of 85\% and bounding box AP of 92\%.}, language = {en} } @article{AgrawalBhanderiElger2025, author = {Agrawal, Shiva and Bhanderi, Savankumar and Elger, Gordon}, title = {Infra-3DRC-FusionNet: Deep Fusion of Roadside Mounted RGB Mono Camera and Three-Dimensional Automotive Radar for Traffic User Detection}, volume = {25}, pages = {3422}, journal = {Sensors}, number = {11}, publisher = {MDPI}, address = {Basel}, issn = {1424-8220}, doi = {https://doi.org/10.3390/s25113422}, year = {2025}, abstract = {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.}, language = {en} } @article{DaRosaZanattaCarvalhoLustosadaCostaAntreichetal.2020, author = {Da Rosa Zanatta, Mateus and Carvalho Lustosa da Costa, Joao Paulo and Antreich, Felix and Haardt, Martin and Elger, Gordon and Lopes de Mendon{\c{c}}a, F{\´a}bio L{\´u}cio and de Sousa Junior, Rafael Tim{\´o}teo}, title = {Tensor-based framework with model order selection and high accuracy factor decomposition for time-delay estimation in dynamic multipath scenarios}, volume = {8}, journal = {IEEE Access}, publisher = {IEEE}, address = {New York}, issn = {2169-3536}, doi = {https://doi.org/10.1109/ACCESS.2020.3024597}, pages = {174931 -- 174942}, year = {2020}, abstract = {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).}, language = {en} } @article{MohanAhuirTorresKotadiaetal.2025, author = {Mohan, Nihesh and Ahuir-Torres, Juan Ignacio and Kotadia, Hiren R. and Elger, Gordon}, title = {Laser sintering of Cu particle-free inks for high-performance printed electronics}, volume = {9}, pages = {18}, journal = {npj Flexible Electronics}, number = {1}, publisher = {Springer}, address = {London}, issn = {2397-4621}, doi = {https://doi.org/10.1038/s41528-025-00389-5}, year = {2025}, abstract = {This study investigates laser sintering of Cu particle-free ink (Cu formate tetrahydrate—amino-2-propanol complex) as an alternative to conventional sintering in an oven (under inert/reducing atmosphere). Utilizing benefits of high-speed localized heating using laser, substrate damage can be prevented for low-melting substrates such as Polyethylene Terephthalate (PET). Firstly, a suitable sintering process window is achieved based on energy density for two different flexible polymeric susbtrates: Polyimide and PET using different laser parameters (laser power, scan rate and spot diameter). Subsequently, characterization of laser sintered traces are also made using different laser optic profiles (Gaussian and top hat). Different methodologies for fabrication of metallized Cu layer were also demonstrated. A very low bulk resistivity of 3.24 µΩcm (1.87 times of bulk Cu) was achieved on trace thickness of 0.85 ± 0.15 µm exhibiting good adherence to polymeric substrates. A promising fabrication process of low-cost and reliable flexible printed electronic devices is demonstrated.}, language = {en} } @article{TheisslerPerezVelazquezKettelgerdesetal.2021, author = {Theissler, Andreas and P{\´e}rez-Vel{\´a}zquez, Judith and Kettelgerdes, Marcel and Elger, Gordon}, title = {Predictive maintenance enabled by machine learning: Use cases and challenges in the automotive industry}, volume = {2021}, pages = {107864}, journal = {Reliability Engineering \& System Safety}, number = {215}, publisher = {Elsevier}, address = {Amsterdam}, issn = {0951-8320}, doi = {https://doi.org/10.1016/j.ress.2021.107864}, year = {2021}, abstract = {Recent developments in maintenance modelling fuelled by data-based approaches such as machine learning (ML), have enabled a broad range of applications. In the automotive industry, ensuring the functional safety over the product life cycle while limiting maintenance costs has become a major challenge. One crucial approach to achieve this, is predictive maintenance (PdM). Since modern vehicles come with an enormous amount of operating data, ML is an ideal candidate for PdM. While PdM and ML for automotive systems have both been covered in numerous review papers, there is no current survey on ML-based PdM for automotive systems. The number of publications in this field is increasing — underlining the need for such a survey. Consequently, we survey and categorize papers and analyse them from an application and ML perspective. Following that, we identify open challenges and discuss possible research directions. We conclude that (a) publicly available data would lead to a boost in research activities, (b) the majority of papers rely on supervised methods requiring labelled data, (c) combining multiple data sources can improve accuracies, (d) the use of deep learning methods will further increase but requires efficient and interpretable methods and the availability of large amounts of (labelled) data.}, language = {en} } @article{AgrawalBhanderiElger2024, author = {Agrawal, Shiva and Bhanderi, Savankumar and Elger, Gordon}, title = {Semi-Automatic Annotation of 3D Radar and Camera for Smart Infrastructure-Based Perception}, volume = {12}, journal = {IEEE Access}, publisher = {IEEE}, address = {New York}, issn = {2169-3536}, doi = {https://doi.org/10.1109/ACCESS.2024.3373310}, pages = {34325 -- 34341}, year = {2024}, abstract = {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, 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 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/.}, language = {en} } @article{MohanSteinbergerWaechteretal.2025, author = {Mohan, Nihesh and Steinberger, Fabian and W{\"a}chter, Sonja and Erdogan, H{\"u}seyin and Elger, Gordon}, title = {Additive Manufacturing for Automotive Radar Sensors Using Copper Inks and Pastes}, volume = {15}, pages = {2676}, journal = {Applied Sciences}, number = {5}, publisher = {MDPI}, address = {Basel}, issn = {2076-3417}, doi = {https://doi.org/10.3390/app15052676}, year = {2025}, abstract = {Radar sensors are critical for obstacle detection and navigation, especially for automated driving. Using the use-case "printing of heating coils on the inside of the front housing (primary radome)" needed for de-icing in winter, it is demonstrated that additive manufacturing (AM) can provide economic and functional benefits for manufacturing of the sensors. AM will allow significant cost reduction by eliminating parts and simplifying the manufacturing process. Different AM technologies for the coils were investigated, first, by applying the conductive traces by fused deposition modeling (FDM), and, second, by printing copper particle-free inks and pastes. The metal layers were electrically and mechanically characterized using a profilometer to measure the trace dimension and a four-point probe to measure the resistance. It was revealed that low-cost conductive filaments with low resistivity and current carrying capacity are commercially still not available. The best option sourced was a copper-polyester-based filament with 6000 µΩcm after printing. Therefore, low-cost particle-free copper inks and commercial copper flake paste were selected to print the heating coil. The Cu particle-free inks were amine-based Cu (II) formate complexes, where the Cu exists in an ionic form. Using contactless printing processes such as ink-jet printing or pneumatic dispensing, the traces could be deposited onto the low-melting temperature (225 °C) polymeric radome structure. After printing, the material needed to be sintered to form the conductive copper traces. To avoid damaging the polymer radome during sintering, two different processes were investigated: low-temperature (<150 °C) sintering in an oven for 30 min or fast laser sintering. The sintered Cu layers achieved the following specific electric resistivities when slowly sintered in the oven: paste 4 µΩcm and ink 8.8 µΩcm. Using laser sintering, the ink achieved 3.2 µΩcm because the locally high temperature provides better sintering. Also, the adhesion was significantly increased to (5 B). Therefore, laser sintering is the preferred technology. In addition, it allows fast processing directly after printing. Commercial equipment is available where printing and laser sintering is integrated. The potential of low-cost copper material and the integration in additive manufacturing of electronic systems using radar sensors as an example are demonstrated in this paper.}, language = {en} } @inproceedings{KettelgerdesBoehmElger2021, author = {Kettelgerdes, Marcel and B{\"o}hm, Lena and Elger, Gordon}, title = {Correlating Intrinsic Parameters and Sharpness for Condition Monitoring of Automotive Imaging Sensors}, booktitle = {2021 5th International Conference on System Reliability and Safety (ICSRS)}, publisher = {IEEE}, address = {Piscataway}, isbn = {978-1-6654-0049-7}, doi = {https://doi.org/10.1109/ICSRS53853.2021.9660665}, pages = {298 -- 306}, year = {2021}, language = {en} } @inproceedings{TavakolibastiMeszmerBoettgeretal.2021, author = {Tavakolibasti, M. and Meszmer, P. and B{\"o}ttger, Gunnar and Kettelgerdes, Marcel and Elger, Gordon and Erdogan, H{\"u}seyin and Seshaditya, A. and Wunderle, Bernhard}, title = {Thermo-mechanical-optical coupling within a digital twin development for automotive LiDAR}, booktitle = {2021 22nd International Conference on Thermal, Mechanical and Multi-Physics Simulation and Experiments in Microelectronics and Microsystems (EuroSimE)}, publisher = {IEEE}, address = {Piscataway}, isbn = {978-1-6654-1373-2}, doi = {https://doi.org/10.1109/EuroSimE52062.2021.9410831}, year = {2021}, language = {en} } @inproceedings{PascucciContiBhogarajuetal.2021, author = {Pascucci, Jacopo and Conti, Fosca and Bhogaraju, Sri Krishna and Signorini, Raffaella and Liu, E and Pedron, Danilo and Elger, Gordon}, title = {Micro-Raman to detect stress phenomena in Si-chips bonded onto Cu substrates}, booktitle = {Integrated Optics: Design, Devices, Systems and Applications VI}, editor = {Cheben, Pavel and Čtyrok{\´y}, Jiř{\´i} and Molina-Fern{\´a}ndez, I{\~n}igo}, publisher = {SPIE}, address = {Bellingham}, isbn = {978-1-5106-4385-7}, doi = {https://doi.org/10.1117/12.2576414}, year = {2021}, language = {en} } @inproceedings{BhogarajuSchmidHufnageletal.2021, author = {Bhogaraju, Sri Krishna and Schmid, Maximilian and Hufnagel, Elias and Conti, Fosca and Kotadia, Hiren R. and Elger, Gordon}, title = {Low temperature and low pressure die-attach bonding of high power light emitting diodes with self reducing copper complex paste}, booktitle = {IEEE 71st Electronic Components and Technology Conference ECTC 2021, Proceedings}, publisher = {IEEE}, address = {Piscataway}, isbn = {978-1-6654-4097-4}, doi = {https://doi.org/10.1109/ECTC32696.2021.00094}, pages = {526 -- 531}, year = {2021}, language = {en} } @inproceedings{SchmidBhogarajuElger2021, author = {Schmid, Maximilian and Bhogaraju, Sri Krishna and Elger, Gordon}, title = {Characterization of copper sintered interconnects by transient thermal analysis}, booktitle = {2021 International Conference on Electronics Packaging (ICEP 2021)}, publisher = {IEEE}, address = {Piscataway}, isbn = {978-4-9911-9111-4}, doi = {https://doi.org/10.23919/ICEP51988.2021.9451966}, pages = {71 -- 72}, year = {2021}, language = {en} }