@inproceedings{UlreichMoserOlbrichetal., author = {Ulreich, Fabian and Moser, Elisabeth and Olbrich, Florian and Ebert, Martin and Bierl, Rudolf and Kaup, Andr{\´e}}, title = {Luminance Simulation in CARLA under Cloud Coverage - Model Validation and Implications}, series = {2023 IEEE International Workshop on Metrology for Automotive (MetroAutomotive), -30 June 2023, Modena, Italy}, booktitle = {2023 IEEE International Workshop on Metrology for Automotive (MetroAutomotive), -30 June 2023, Modena, Italy}, editor = {Keil, Rudolf and Tschorn, Jan Alexander and T{\"u}mler, Johannes and Altinsoy, Mehmet Ercan}, publisher = {IEEE}, isbn = {979-8-3503-2187-6}, doi = {10.1109/MetroAutomotive57488.2023.10219098}, pages = {228 -- 233}, abstract = {To decrease the number of kilometers driven during the development of autonomous cars or driving assistance systems, performant simulation tools are necessary. Currently, domain distance effects between simulation and reality are limiting the successful application of rendering engines in data-driven perception tasks. In order to mitigate those domain distance effects, simulation tools have to be as close to reality as possible for the given task. For optical sensors like cameras, the luminance of the scene is essential. We provide within this paper a method to measure the luminance of rendered scenes within CARLA, an often used open-source simulation environment. Thereby, it is possible to validate the environment and weather models by taking real-world measurements with photometric sensors or with the help of open-source weather data, published e.g. by the German federal service for weather data (DWD - "Deutscher Wetterdienst"). Employing our proposed luminance measurement, the domain gap resulting from the simulation can be specified, which makes it possible to evaluate the statements about the safety of the automated driving system determined within the simulation. We show that the ratio between global and diffuse radiation modeled by the default atmosphere models within CARLA are under limited conditions similar to real-world measurements taken by the DWD. Nevertheless, we show, that the ratio's temporal variability in real-world situations is not modeled by CARLA.}, language = {en} } @article{MoserJobstBierletal., author = {Moser, Elisabeth and Jobst, Simon and Bierl, Rudolf and Jenko, Frank}, title = {A Deep Learning System to Transform Cross-Section Spectra to Varying Environmental Conditions}, series = {Vibrational Spectroscopy}, volume = {122}, journal = {Vibrational Spectroscopy}, number = {September}, publisher = {Elsevier}, issn = {0924-2031}, doi = {10.1016/j.vibspec.2022.103410}, abstract = {Absorption cross-sections provide a basis for many gas sensing applications. Therefore, any error in molecular cross-sections caused by varying environmental conditions propagates to spectroscopic applications. Original molecular cross-sections in varying environmental conditions can only be simulated for some molecules, whereas for most multi-atom molecules, one must rely on high-precision measurements at certain environmental configurations. In this study, a deep learning system trained with simulated absorption cross-sections for predicting cross-sections at a different pressure configuration is presented. The system's capability to transfer to measured, multi-atom cross-sections is demonstrated. Thus, it provides an alternative to (pseudo-) line lists whenever the required information for simulation is unavailable. The predictive performance of the system was evaluated on validation data via simulation, and its transfer learning capabilities were demonstrated on actual measurement chlorine nitrate data. From the comparison between the system and line lists, the system shows slightly worse performance than pseudo-line lists but its predictive quality is still deemed acceptable with less than 5\% relative integral change with a highly localized error around the peak center. This opens a promising way for further research to use deep learning to simulate the effect of varying environmental conditions on absorption cross-sections.}, language = {en} }