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 - JOUR A1 - Mohan, Nihesh A1 - Ahuir-Torres, Juan Ignacio A1 - Bhogaraju, Sri Krishna A1 - Webler, Ralf A1 - Kotadia, Hiren R. A1 - Erdogan, Hüseyin A1 - Elger, Gordon T1 - Decomposition mechanism and morphological evolution of in situ realized Cu nanoparticles in Cu complex inks JF - New Journal of Chemistry UR - https://doi.org/10.1039/D3NJ05185D Y1 - 2024 UR - https://doi.org/10.1039/D3NJ05185D SN - 1369-9261 VL - 48 IS - 15 SP - 6796 EP - 6808 PB - RSC CY - London ER - TY - CHAP A1 - Bhogaraju, Sri Krishna A1 - Mohan, Nihesh A1 - Steinberger, Fabian A1 - Erdogan, Hüseyin A1 - Hadrava, Philipp A1 - Elger, Gordon T1 - Novel Low Temperature and Low Pressure Sintering of ADAS Radar Sensor Antenna Stack T2 - 2023 24th European Microelectronics and Packaging Conference & Exhibition (EMPC) UR - https://doi.org/10.23919/EMPC55870.2023.10418277 Y1 - 2024 UR - https://doi.org/10.23919/EMPC55870.2023.10418277 SN - 978-0-9568086-9-1 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 -