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 - 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 - TY - CHAP A1 - Kettelgerdes, Marcel A1 - Pandey, Amit A1 - Unruh, Denis A1 - Erdogan, Hüseyin A1 - Wunderle, Bernhard A1 - Elger, Gordon T1 - Automotive LiDAR Based Precipitation State Estimation Using Physics Informed Spatio-Temporal 3D Convolutional Neural Networks (PIST-CNN) T2 - 2023 29th International Conference on Mechatronics and Machine Vision in Practice (M2VIP) UR - https://doi.org/10.1109/M2VIP58386.2023.10413394 Y1 - 2024 UR - https://doi.org/10.1109/M2VIP58386.2023.10413394 SN - 979-8-3503-2562-1 PB - IEEE CY - Piscataway ER - TY - CHAP A1 - Pandey, Amit A1 - Unruh, Denis A1 - Kettelgerdes, Marcel A1 - Wunderle, Bernhard A1 - Elger, Gordon ED - Matoba, Osamu ED - Shaw, Joseph A. ED - Valenta, Christopher R. T1 - Evaluation of thermally-induced change in sharpness of automotive cameras by coupled thermo-mechanical and optical simulation T2 - SPIE Future Sensing Technologies 2023 UR - https://doi.org/10.1117/12.2665475 Y1 - 2023 UR - https://doi.org/10.1117/12.2665475 SN - 978-1-5106-5723-6 SN - 978-1-5106-5722-9 PB - SPIE CY - Bellingham ER -