TY - CHAP A1 - Schmid, Maximilian A1 - Momberg, Marcel A1 - Kettelgerdes, Marcel A1 - Elger, Gordon T1 - Transient thermal analysis for VCSEL Diodes T2 - 2023 29th International Workshop on Thermal Investigations of ICs and Systems (THERMINIC) UR - https://doi.org/10.1109/THERMINIC60375.2023.10325906 Y1 - 2023 UR - https://doi.org/10.1109/THERMINIC60375.2023.10325906 SN - 979-8-3503-1862-3 PB - IEEE CY - Piscataway ER - TY - JOUR A1 - Theissler, Andreas A1 - Pérez-Velázquez, Judith A1 - Kettelgerdes, Marcel A1 - Elger, Gordon T1 - Predictive maintenance enabled by machine learning: Use cases and challenges in the automotive industry JF - Reliability Engineering & System Safety N2 - 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. UR - https://doi.org/10.1016/j.ress.2021.107864 KW - predictive maintenance KW - artificial intelligence KW - machine learning KW - deep learning KW - vehicle KW - automotive KW - reliability KW - lifetime prediction KW - condition monitoring Y1 - 2021 UR - https://doi.org/10.1016/j.ress.2021.107864 UR - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:573-9673 SN - 0951-8320 VL - 2021 IS - 215 PB - Elsevier CY - Amsterdam ER - TY - CHAP A1 - Kettelgerdes, Marcel A1 - Böhm, Lena A1 - Elger, Gordon T1 - Correlating Intrinsic Parameters and Sharpness for Condition Monitoring of Automotive Imaging Sensors T2 - 2021 5th International Conference on System Reliability and Safety (ICSRS) UR - https://doi.org/10.1109/ICSRS53853.2021.9660665 Y1 - 2021 UR - https://doi.org/10.1109/ICSRS53853.2021.9660665 SN - 978-1-6654-0049-7 SP - 298 EP - 306 PB - IEEE CY - Piscataway ER - TY - CHAP A1 - Tavakolibasti, M. A1 - Meszmer, P. A1 - Böttger, Gunnar A1 - Kettelgerdes, Marcel A1 - Elger, Gordon A1 - Erdogan, Hüseyin A1 - Seshaditya, A. A1 - Wunderle, Bernhard T1 - Thermo-mechanical-optical coupling within a digital twin development for automotive LiDAR T2 - 2021 22nd International Conference on Thermal, Mechanical and Multi-Physics Simulation and Experiments in Microelectronics and Microsystems (EuroSimE) UR - https://doi.org/10.1109/EuroSimE52062.2021.9410831 KW - Couplings KW - Laser radar KW - Digital twin KW - Thermomechanical processes KW - Adaptive optics KW - Optical coupling KW - Real-time systems Y1 - 2021 UR - https://doi.org/10.1109/EuroSimE52062.2021.9410831 SN - 978-1-6654-1373-2 PB - IEEE CY - Piscataway ER - TY - JOUR A1 - Kettelgerdes, Marcel A1 - Elger, Gordon T1 - In-Field Measurement and Methodology for Modeling and Validation of Precipitation Effects on Solid-State LiDAR Sensors JF - IEEE Journal of Radio Frequency Identification UR - https://doi.org/10.1109/JRFID.2023.3234999 KW - LiDAR KW - adverse weather KW - sensor model KW - automotive KW - simulation KW - virtual validation KW - ROS KW - ADAS Y1 - 2023 UR - https://doi.org/10.1109/JRFID.2023.3234999 SN - 2469-7281 VL - 7 SP - 192 EP - 202 PB - Institute of Electrical and Electronics Engineers (IEEE) CY - New York ER - TY - CHAP A1 - Kettelgerdes, Marcel A1 - Elger, Gordon T1 - Modeling Methodology and In-field Measurement Setup to Develop Empiric Weather Models for Solid-State LiDAR Sensors T2 - 2022 IEEE 2nd International Conference on Digital Twins and Parallel Intelligence (DTPI) UR - https://doi.org/10.1109/DTPI55838.2022.9998918 KW - LiDAR KW - adverse weather KW - sensor model KW - auto-motive KW - simulation KW - virtual validation KW - ROS KW - ADAS Y1 - 2022 UR - https://doi.org/10.1109/DTPI55838.2022.9998918 SN - 978-1-6654-9227-0 PB - IEEE CY - Piscataway ER - TY - CHAP A1 - Tavakolibasti, M. A1 - Meszmer, P. A1 - Kettelgerdes, Marcel A1 - Böttger, Gunnar A1 - Elger, Gordon A1 - Erdogan, Hüseyin A1 - Seshaditya, A. A1 - Wunderle, Bernhard T1 - Structural-thermal-optical-performance (STOP) analysis of a lens stack for realization of a digital twin of an automotive LiDAR T2 - 2022 23rd International Conference on Thermal, Mechanical and Multi-Physics Simulation and Experiments in Microelectronics and Microsystems (EuroSimE) UR - https://doi.org/10.1109/EuroSimE54907.2022.9758897 KW - thermo-mechanical simulation KW - optical simulation KW - digital twin KW - structural thermal optical performance analysis Y1 - 2022 UR - https://doi.org/10.1109/EuroSimE54907.2022.9758897 SN - 978-1-6654-5836-8 PB - IEEE CY - Piscataway, NJ ER - TY - JOUR A1 - Kettelgerdes, Marcel A1 - Sarmiento, Nicolas A1 - Erdogan, Hüseyin A1 - Wunderle, Bernhard A1 - Elger, Gordon T1 - Precise Adverse Weather Characterization by Deep-Learning-Based Noise Processing in Automotive LiDAR Sensors JF - Remote Sensing N2 - 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. UR - https://doi.org/10.3390/rs16132407 Y1 - 2024 UR - https://doi.org/10.3390/rs16132407 UR - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:573-49599 SN - 2072-4292 VL - 16 IS - 13 PB - MDPI CY - Basel 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 - TY - CHAP A1 - Kettelgerdes, Marcel A1 - Mezmer, Peter A1 - Haeussler, Michael J. A1 - Böttger, Gunnar A1 - Tavakolibasti, Majid A1 - Pandey, Amit A1 - Erdogan, Hüseyin A1 - Elger, Gordon A1 - Schacht, Ralph A1 - Wunderle, Bernhard T1 - Realization, multi-field coupled simulation and characterization of a thermo-mechanically robust LiDAR front end on a copper coated glass substrate T2 - Proceedings: IEEE 73rd Electronic Components and Technology Conference, ECTC 2023 UR - https://doi.org/10.1109/ECTC51909.2023.00131 KW - LiDAR KW - Reliability KW - Thermo-mechanical Simulation KW - Optical Simulation KW - VCSEL KW - Lifetime Testing KW - Glass Packaging Y1 - 2023 UR - https://doi.org/10.1109/ECTC51909.2023.00131 SN - 979-8-3503-3498-2 SN - 2377-5726 SP - 753 EP - 760 PB - IEEE CY - Piscataway ER -