TY - CHAP A1 - Olcay, Ertug A1 - Meeß, Henri A1 - Elger, Gordon T1 - Dynamic Obstacle Avoidance for UAVs using MPC and GP-Based Motion Forecast T2 - 2024 European Control Conference (ECC) UR - https://doi.org/10.23919/ECC64448.2024.10591083 Y1 - 2024 UR - https://doi.org/10.23919/ECC64448.2024.10591083 SN - 978-3-9071-4410-7 SP - 1024 EP - 1031 PB - IEEE CY - Piscataway ER - TY - CHAP A1 - Paret, Paul A1 - Bhogaraju, Sri Krishna A1 - Busse, Dirk A1 - Dahlbüdding, Alexander A1 - Elger, Gordon A1 - Narumanchi, Sreekant T1 - Thermomechanical Degradation of Sintered Copper under High-Temperature Thermal Shock T2 - Proceedings: IEEE 74th Electronic Components and Technology Conference, ECTC 2024 UR - https://doi.org/10.1109/ECTC51529.2024.00196 Y1 - 2024 UR - https://doi.org/10.1109/ECTC51529.2024.00196 SN - 979-8-3503-7598-5 SP - 1219 EP - 1224 PB - IEEE CY - Piscataway ER - TY - CHAP A1 - Steinberger, Fabian A1 - Mohan, Nihesh A1 - Rämer, Olaf A1 - Elger, Gordon T1 - Low temperature die-attach bonding using copper particle free inks T2 - 2024 IEEE 10th Electronics System-Integration Technology Conference (ESTC), Proceedings UR - https://doi.org/10.1109/ESTC60143.2024.10712150 Y1 - 2024 UR - https://doi.org/10.1109/ESTC60143.2024.10712150 SN - 979-8-3503-9036-0 PB - IEEE CY - Piscataway ER - TY - CHAP A1 - Agrawal, Shiva A1 - Song, Rui A1 - Kohli, Akhil A1 - Korb, Andreas A1 - Andre, Maximilian A1 - Holzinger, Erik A1 - Elger, Gordon ED - Ploeg, Jeroen ED - Helfert, Markus ED - Berns, Karsten ED - Gusikhin, Oleg T1 - Concept of Smart Infrastructure for Connected Vehicle Assist and Traffic Flow Optimization T2 - Proceedings of the 8th International Conference on Vehicle Technology and Intelligent Transport Systems N2 - The smart infrastructure units can play a vital role to develop smart cities of the future and in assisting automated vehicles on the road by providing extended perception and timely warnings to avoid accidents. This paper focuses on the development of such an infrastructure unit, that is specifically designed for a pedestrian crossing junction. It can control traffic lights at the junction by real-time environment perception through its sensors and can optimize the flow of vehicles and passing vulnerable road users (VRUs). Moreover, it can assist on-road vehicles by providing real-time information and critical warnings via a v2x module. This paper further describes different use-cases of the work, all major hardware components involved in the development of smart infrastructure unit, referred to as an edge, different sensor fusion approaches using the camera, radar, and lidar mounted on the edge for environment perception, various modes of communication including v2x, system design UR - https://doi.org/10.5220/0011068800003191 KW - Road Side Unit (RSU) KW - Smart Infrastructure KW - Radar KW - LiDAR KW - Camera KW - Perception KW - Sensor Data Fusion KW - Vehicle-to-everything (V2X) KW - Communication KW - Simulation KW - ITS Y1 - 2022 UR - https://doi.org/10.5220/0011068800003191 UR - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:573-30926 SN - 978-989-758-573-9 SN - 2184-495X SP - 360 EP - 367 PB - SciTePress CY - Setúbal ER - TY - CHAP A1 - Streck, Egor A1 - Herschel, Reinhold A1 - Wallrath, Patrick A1 - Sunderam, M. A1 - Elger, Gordon ED - Prasad, Venkatesha ED - Pesch, Dirk ED - Ansari, Nirwan ED - Benavente-Peces, César T1 - Comparison of Two Different Radar Concepts for Pedestrian Protection on Bus Stops T2 - Proceedings of the 11th International Conference on Sensor Networks N2 - 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. UR - https://doi.org/10.5220/0010777100003118 KW - Sensor Data Fusion KW - Radar Sensor KW - Multiple-Sensor Systems KW - Machine Learning Y1 - 2022 UR - https://doi.org/10.5220/0010777100003118 UR - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:573-30953 SN - 978-989-758-551-7 SN - 2184-4380 SP - 89 EP - 96 PB - SciTePress CY - Setúbal ER - TY - CHAP A1 - Conti, Fosca A1 - Lux, Kerstin A1 - Bhogaraju, Sri Krishna A1 - Liu, E A1 - Lenz, Christoph A1 - Seitz, Roland A1 - Elger, Gordon ED - Baldini, Francesco ED - Homola, Jiri ED - Lieberman, Robert A. T1 - Raman spectroscopy to investigate gallium nitride light emitting diodes after assembling onto copper substrates T2 - Optical Sensors 2021 UR - https://doi.org/10.1117/12.2591947 Y1 - 2021 UR - https://doi.org/10.1117/12.2591947 SN - 978-1-5106-4379-6 N1 - Poster + Paper PB - SPIE CY - Bellingham ER - TY - JOUR A1 - Bhanderi, Savankumar A1 - Agrawal, Shiva A1 - Elger, Gordon T1 - Deep segmentation of 3+1D radar point cloud for real-time roadside traffic user detection JF - Scientific Reports N2 - 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). UR - https://doi.org/10.1038/s41598-025-23019-6 Y1 - 2025 UR - https://doi.org/10.1038/s41598-025-23019-6 UR - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:573-63458 SN - 2045-2322 VL - 15 PB - Springer Nature CY - London ER - TY - INPR A1 - Bhanderi, Savankumar A1 - Agrawal, Shiva A1 - Elger, Gordon T1 - Deep Segmentation of 3+1D Radar Point Cloud for Real-Time Roadside Traffic User Detection T2 - Research Square N2 - 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). UR - https://doi.org/10.21203/rs.3.rs-7222130/v1 Y1 - 2025 UR - https://doi.org/10.21203/rs.3.rs-7222130/v1 UR - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:573-61996 SN - 2693-5015 PB - Research Square CY - Durham ER - TY - JOUR A1 - Kleiner, Jan A1 - Heider, Alexander A1 - Komsiyska, Lidiya A1 - Elger, Gordon A1 - Endisch, Christian T1 - Thermal behavior of intelligent automotive lithium-ion batteries: Experimental study with switchable cells and reconfigurable modules JF - Journal of Energy Storage UR - https://doi.org/10.1016/j.est.2021.103274 KW - lithium-ion KW - intelligent battery KW - switchable cell KW - reconfiguration KW - smart cell KW - thermal management Y1 - 2021 UR - https://doi.org/10.1016/j.est.2021.103274 SN - 2352-1538 VL - 2021 IS - 44, Part A PB - Elsevier CY - Amsterdam ER - TY - JOUR A1 - Kleiner, Jan A1 - Lechermann, Lorenz A1 - Komsiyska, Lidiya A1 - Elger, Gordon A1 - Endisch, Christian T1 - Thermal behavior of intelligent automotive lithium-ion batteries BT - operating strategies for adaptive thermal balancing by reconfiguration JF - Journal of energy storage UR - https://doi.org/10.1016/j.est.2021.102686 KW - intelligent battery KW - thermal management KW - balancing KW - cell-to-cell variations KW - inhomogeneities KW - thermal modeling Y1 - 2021 UR - https://doi.org/10.1016/j.est.2021.102686 SN - 2352-1538 VL - 2021 IS - 40 PB - Elsevier CY - Amsterdam ER - TY - JOUR A1 - Schmid, Maximilian A1 - Zippelius, Andreas A1 - Hanß, Alexander A1 - Böckhorst, Stephan A1 - Elger, Gordon T1 - Investigations on High-Power LEDs and Solder Interconnects in Automotive Application: Part II - Reliability JF - IEEE Transactions on Device and Materials Reliability UR - https://doi.org/10.1109/TDMR.2023.3300355 KW - LED KW - non-destructive testing KW - reliability KW - solder KW - scanning acoustic microscopy (SAM) KW - thermal impedance (Zth) KW - thermal resistant (Rth) KW - X-ray KW - transient thermal analysis (TTA) Y1 - 2023 UR - https://doi.org/10.1109/TDMR.2023.3300355 UR - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:573-39651 SN - 1558-2574 SN - 1530-4388 VL - 23 IS - 3 SP - 419 EP - 429 PB - IEEE CY - New York ER - TY - JOUR A1 - Schmid, Maximilian A1 - Zippelius, Andreas A1 - Hanss, Alexander A1 - Böckhorst, Stephan A1 - Elger, Gordon T1 - Investigations on High-Power LEDs and Solder Interconnects in Automotive Application: Part I - Initial Characterization JF - IEEE Transactions on Device and Materials Reliability N2 - Thermo-mechanical reliability is one major issue in solid-state lighting. Mismatches in the coefficients of thermal expansion (CTE) between high-power LED packages and substrates paired with temperature changes induce mechanical stress. This leads to a thermal degradation of LED modules by crack formation in the solder interconnect and/or delamination in the substrate, which in turn increases junction temperature and thus decreases light output and reduces lifetime. To investigate degradation and understand influence of LED package design and solder material, a reliability study with a total of 1800 samples − segmented in nine LED types and five solder pastes − is performed. First of all, in this paper a state-of-the-art review of high-power LED packages is performed by analyzing and categorizing the packaging technologies. Second, the quality inspection after assembly is realized by transient thermal analysis (TTA), scanning acoustic microscopy (SAM) and X-ray. For TTA, a new method is introduced to separate the thermal resistance of the LED package from solder interconnect and substrate by applying the transient dual interface method (TDI) on samples with different solder interconnect void ratios. Further measurement effort is not required. The datasheet values for thermal resistance are verified and the different LED package types are benchmarked. The void ratio of the solder interconnects is determined by X-ray inspection combined with an algorithm to suppress disruptive internal LED package structures. TTA and TDI revealed that initial thermal performance is independent of solder paste type and that voiding is more critical to smaller LED packages. In addition, lower silver proportion in the paste is found to increase voiding. SAM is less sensitive for initial void detection than X-ray, but it’s applied to monitor crack propagation while aging in combination with TTA. The results of the reliability study, i.e., the crack growth under temperature shock test for the different SAC solders, will be presented in a second independent paper. UR - https://doi.org/10.1109/TDMR.2022.3152590 KW - LED KW - non-destructive testing KW - reliability KW - solder KW - scanning acoustic microscopy (SAM) KW - thermal impedance (Zth) KW - thermal resistant (Rth) KW - transient thermal analysis (TTA) KW - X-ray Y1 - 2022 UR - https://doi.org/10.1109/TDMR.2022.3152590 UR - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:573-28799 SN - 1530-4388 SN - 1558-2574 VL - 22 IS - 2 SP - 175 EP - 186 PB - IEEE CY - New York ER - TY - JOUR A1 - Mohd, Zubair Akhtar A1 - Schmid, Maximilian A1 - Zippelius, Andreas A1 - Elger, Gordon T1 - Solder joint lifetime model using AI framework operating on FEA data JF - Engineering Failure Analysis N2 - The thermo-mechanical reliability of electronic systems is often limited by the crack growth within the solder joints. Addressing this issue requires careful consideration of the design of the package and solder pads. Finite Element Analysis (FEA) is widely used to predict crack growth and to model their lifetime. Traditionally, FEA post-processing methods rely on human expertise to select appropriate regions for evaluating plastic and creep strain at critical locations and correlating these values with experimental data using the Coffin-Manson equation, which predicts fatigue lifetime based on cyclic plastic strain. This study introduces a novel method for FEA post-processing of surface-mounted devices (SMD) on printed circuit boards (PCB) using artificial intelligence. The method transforms the FEA data into a 2D grid map of creep strain values and employs a Convolutional Neural Network (CNN) for automatic feature extraction. Afterwards, a fully connected layer correlates the extracted features with the experimental measured solder joint lifetime, effectively capturing nonlinear relationships. The study focuses on the development of the concept of crack formation in the solder interconnects of ceramic based high-power LED packages used in the automotive industry for headlights. The validated FEA model is based on an extensive data set of 1800 LED packages including seven different ceramic-based LED packages and five different solders. The design of the ceramic LED package covers two-pad and three-pad footprint for soldering and thin film and thick film metallized ceramic carriers. Results show a strong agreement (R2 Score is 99.867 %) between simulations and experimental data for ceramic LED packages. This automatic feature extraction from FEA data sets a new benchmark for improving solder reliability predictions, and it has proved to be better than established methods for lifetime prediction of solder joints. UR - https://doi.org/10.1016/j.engfailanal.2024.109032 Y1 - 2024 UR - https://doi.org/10.1016/j.engfailanal.2024.109032 UR - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:573-53803 SN - 1350-6307 VL - 2025 IS - 167, Part B PB - Elsevier CY - Oxford ER - TY - CHAP A1 - Mohd, Zubair Akhtar A1 - Kreiner, Christian A1 - Schmid, Maximilian A1 - Zippelius, Andreas A1 - Tetzlaff, Ulrich A1 - Elger, Gordon T1 - Fully Connected Neural Network (FCNN) Based Validation Framework for FEA Post Processing to Improve SAC Solder Reliability Analysis T2 - 2024 IEEE 10th Electronics System-Integration Technology Conference (ESTC), Proceedings UR - https://doi.org/10.1109/ESTC60143.2024.10712023 Y1 - 2024 UR - https://doi.org/10.1109/ESTC60143.2024.10712023 SN - 979-8-3503-9036-0 PB - IEEE CY - Piscataway ER - TY - CHAP A1 - Mohd, Zubair Akhtar A1 - Schmid, Maximilian A1 - Zippelius, Andreas A1 - Elger, Gordon T1 - LEDs Lifetime Prediction Modeling: Thermomechanical Simulation for SAC305 and SAC105 T2 - 2024 25th International Conference on Thermal, Mechanical and Multi-Physics Simulation and Experiments in Microelectronics and Microsystems (EuroSimE) UR - https://doi.org/10.1109/EuroSimE60745.2024.10491530 Y1 - 2024 UR - https://doi.org/10.1109/EuroSimE60745.2024.10491530 SN - 979-8-3503-9363-7 PB - IEEE CY - Piscataway ER - TY - JOUR A1 - Mohd, Zubair Akhtar A1 - Schmid, Maximilian A1 - Elger, Gordon T1 - AI-driven point cloud framework for predicting solder joint reliability using 3D FEA data JF - Scientific Reports N2 - Crack propagation in solder joints remains a major challenge impacting the thermo-mechanical reliability of electronic devices, underscoring the importance of optimizing package and solder pad designs. Traditional Finite Element Analysis (FEA) techniques for predicting solder joint lifespan often rely on manual post-processing to identify high-risk regions for plastic strain accumulation. However, this manual process can fail to detect complex and subtle failure mechanisms and purely based on averaging the creep strain and correlating it to lifetime values collected from experiments using Coffin Manson equation. To address these limitations, this study presents an Artificial Intelligence (AI) framework designed for automated 3D FEA post-processing of surface-mounted devices (SMDs) assembled to Printed Circuit Board (PCB). This framework integrates 3D Convolutional Neural Networks (CNNs) and PointNet architectures to automatically extract complex spatial features from 3D FEA data. These learned features are then linked to experimentally measured solder joint lifetimes through fully connected neural network layers, allowing the model to capture complex and nonlinear failure behaviours. The research specifically targets crack development in solder joints of ceramic-based high-power LED packages used in automotive lighting systems. This dataset included variations in two-pad and three-pad configurations, as well as thin and thick film metallized ceramic substrates. Results from the study demonstrate that the PointNet model outperforms the 3D CNN, achieving a high correlation with experimental data (R2 = 99.91%). This AI-driven, automated feature extraction approach significantly improves the accuracy and provide the more reliable models for solder joint lifetime predictions, offering a substantial improvement over traditional method. UR - https://doi.org/10.1038/s41598-025-06902-0 Y1 - 2025 UR - https://doi.org/10.1038/s41598-025-06902-0 UR - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:573-60961 SN - 2045-2322 VL - 15 PB - Springer Nature CY - London ER - TY - INPR A1 - Mohd, Zubair Akhtar A1 - Schmid, Maximilian A1 - Elger, Gordon T1 - AI-Driven Point Cloud Framework for Predicting Solder Joint Reliability using 3D FEA Data T2 - Research Square N2 - 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. 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. 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. UR - https://doi.org/10.21203/rs.3.rs-6173485/v1 Y1 - 2025 UR - https://doi.org/10.21203/rs.3.rs-6173485/v1 UR - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:573-58308 SN - 2693-5015 PB - Research Square CY - Durham ER - TY - CHAP A1 - Liu, E A1 - Mohd, Zubair Akhtar A1 - Steinberger, Fabian A1 - Wunderle, Bernhard A1 - Elger, Gordon T1 - Using µ-RAMAN Spectroscopy to Inspect Sintered Interconnects T2 - 2024 IEEE 10th Electronics System-Integration Technology Conference (ESTC), Proceedings UR - https://doi.org10.1109/ESTC60143.2024.10712149 Y1 - 2024 UR - https://doi.org10.1109/ESTC60143.2024.10712149 SN - 979-8-3503-9036-0 PB - IEEE CY - Piscataway ER - TY - JOUR A1 - Zippelius, Andreas A1 - Mohd, Zubair Akhtar A1 - Schmid, Maximilian A1 - Elger, Gordon T1 - Comparison of different Input data for the prediction of LED solder joints using Artificial Neural Networks JF - IEEE Transactions on Device and Materials Reliability N2 - Scarcity of raw data is a major issue for applying data driven methods to reliability prediction, so making the best use of what is available is critical. This paper studies how different aspects of measurement data can be used best. Specifically, the reliability of the solder joint of LED packages is predicted based on Transient Thermal Analysis and Scanning Acoustic Microscopy data from a large measurement campaign. The impact of using full temporal information vs measurements at individual datapoints is investigated as well as the benefit of including the SAM data, and different ways of presenting the TTA information, either as a full curve or as expert-selected features. The impact of formatting categorical information of solder and LED package type as one-hot encoding or using embeddings is considered. Finally, the performance for Pass/Fail predictions of the best identified model with a model architecture developed on a similar dataset is compared. We identified the most relevant sources of information for predicting the behavior and the best format for the data, which helps guide the choice for future model architectures. UR - https://doi.org/10.1109/TDMR.2025.3633876 Y1 - 2025 UR - https://doi.org/10.1109/TDMR.2025.3633876 SN - 1558-2574 PB - IEEE CY - New York ER - TY - JOUR A1 - Meess, Henri A1 - Gerner, Jeremias A1 - Hein, Daniel A1 - Schmidtner, Stefanie A1 - Elger, Gordon A1 - Bogenberger, Klaus T1 - First steps towards real-world traffic signal control optimisation by reinforcement learning JF - Journal of Simulation N2 - 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. UR - https://doi.org/10.1080/17477778.2024.2364715 Y1 - 2024 UR - https://doi.org/10.1080/17477778.2024.2364715 UR - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:573-48585 SN - 1747-7778 VL - 18 IS - 6 SP - 957 EP - 972 PB - Taylor & Francis CY - London ER - TY - INPR A1 - Kettelgerdes, Marcel A1 - Hillmann, Tjorven A1 - Hirmer, Thomas A1 - Erdogan, Hüseyin A1 - Wunderle, Bernhard A1 - Elger, Gordon T1 - Accelerated Real-Life (ARL) Testing and Characterization of Automotive LiDAR Sensors to facilitate the Development and Validation of Enhanced Sensor Models N2 - 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. UR - https://doi.org/10.48550/arXiv.2312.04229 Y1 - 2023 UR - https://doi.org/10.48550/arXiv.2312.04229 UR - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:573-59856 PB - arXiv CY - Ithaca ER -