@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{TavakolibastiMeszmerKettelgerdesetal.2022, author = {Tavakolibasti, M. and Meszmer, P. and Kettelgerdes, Marcel and B{\"o}ttger, Gunnar and Elger, Gordon and Erdogan, H{\"u}seyin and Seshaditya, A. and Wunderle, Bernhard}, title = {Structural-thermal-optical-performance (STOP) analysis of a lens stack for realization of a digital twin of an automotive LiDAR}, booktitle = {2022 23rd International Conference on Thermal, Mechanical and Multi-Physics Simulation and Experiments in Microelectronics and Microsystems (EuroSimE)}, publisher = {IEEE}, address = {Piscataway, NJ}, isbn = {978-1-6654-5836-8}, doi = {https://doi.org/10.1109/EuroSimE54907.2022.9758897}, year = {2022}, language = {en} } @article{KettelgerdesSarmientoErdoganetal.2024, author = {Kettelgerdes, Marcel and Sarmiento, Nicolas and Erdogan, H{\"u}seyin and Wunderle, Bernhard and Elger, Gordon}, title = {Precise Adverse Weather Characterization by Deep-Learning-Based Noise Processing in Automotive LiDAR Sensors}, volume = {16}, pages = {2407}, journal = {Remote Sensing}, number = {13}, publisher = {MDPI}, address = {Basel}, issn = {2072-4292}, doi = {https://doi.org/10.3390/rs16132407}, year = {2024}, abstract = {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.}, language = {en} } @unpublished{KettelgerdesHillmannHirmeretal.2023, author = {Kettelgerdes, Marcel and Hillmann, Tjorven and Hirmer, Thomas and Erdogan, H{\"u}seyin and Wunderle, Bernhard and Elger, Gordon}, title = {Accelerated Real-Life (ARL) Testing and Characterization of Automotive LiDAR Sensors to facilitate the Development and Validation of Enhanced Sensor Models}, publisher = {arXiv}, address = {Ithaca}, doi = {https://doi.org/10.48550/arXiv.2312.04229}, year = {2023}, abstract = {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.}, language = {en} } @inproceedings{KettelgerdesMezmerHaeussleretal.2023, author = {Kettelgerdes, Marcel and Mezmer, Peter and Haeussler, Michael J. and B{\"o}ttger, Gunnar and Tavakolibasti, Majid and Pandey, Amit and Erdogan, H{\"u}seyin and Elger, Gordon and Schacht, Ralph and Wunderle, Bernhard}, title = {Realization, multi-field coupled simulation and characterization of a thermo-mechanically robust LiDAR front end on a copper coated glass substrate}, booktitle = {Proceedings: IEEE 73rd Electronic Components and Technology Conference, ECTC 2023}, publisher = {IEEE}, address = {Piscataway}, isbn = {979-8-3503-3498-2}, issn = {2377-5726}, doi = {https://doi.org/10.1109/ECTC51909.2023.00131}, pages = {753 -- 760}, year = {2023}, language = {en} } @inproceedings{KettelgerdesPandeyUnruhetal.2024, author = {Kettelgerdes, Marcel and Pandey, Amit and Unruh, Denis and Erdogan, H{\"u}seyin and Wunderle, Bernhard and Elger, Gordon}, title = {Automotive LiDAR Based Precipitation State Estimation Using Physics Informed Spatio-Temporal 3D Convolutional Neural Networks (PIST-CNN)}, booktitle = {2023 29th International Conference on Mechatronics and Machine Vision in Practice (M2VIP)}, publisher = {IEEE}, address = {Piscataway}, isbn = {979-8-3503-2562-1}, doi = {https://doi.org/10.1109/M2VIP58386.2023.10413394}, year = {2024}, language = {en} } @inproceedings{PandeyUnruhKettelgerdesetal.2023, author = {Pandey, Amit and Unruh, Denis and Kettelgerdes, Marcel and Wunderle, Bernhard and Elger, Gordon}, title = {Evaluation of thermally-induced change in sharpness of automotive cameras by coupled thermo-mechanical and optical simulation}, pages = {123270U}, booktitle = {SPIE Future Sensing Technologies 2023}, editor = {Matoba, Osamu and Shaw, Joseph A. and Valenta, Christopher R.}, publisher = {SPIE}, address = {Bellingham}, isbn = {978-1-5106-5723-6}, doi = {https://doi.org/10.1117/12.2665475}, year = {2023}, language = {en} }