TY - THES A1 - Volkan, Taylan Delil T1 - Raytracing-based and phenomenological modelling of precipitation effects on automotive flash LiDAR sensors N2 - For the accurate environmental perception in simulation environments, high-quality sensor models are needed. This is especially the case for the simulation of adverse weather conditions. Traditional physics-based approaches appear to have limited scalability. Because of this, two phenomenological approaches have developed in this thesis, which model the adverse effects on a Flash LiDAR sensor in a generative manner. The main contributions are the following: 1. The development of a statistical model. For this model, a data extraction strategy has been developed. On the extracted data, the model can then be fitted. 2. The development of a generative adversarial network (GAN), which models the adverse weather effects on a LiDAR, by implicit distribution modelling. 3. The development of of validation strategies. Additionally the models are compared to a ray-tracing based model, which serves as a benchmark. Y1 - 2024 UR - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:573-50635 CY - Ingolstadt ER - TY - CHAP A1 - Hartung, Kai A1 - Herygers, Aaricia A1 - Kurlekar, Shubham Vijay A1 - Zakaria, Khabbab A1 - Volkan, Taylan A1 - Gröttrup, Sören A1 - Georges, Munir ED - Ekštein, Kamil ED - Pártl, František ED - Konopík, Miloslav T1 - Measuring Sentiment Bias in Machine Translation T2 - Text, Speech, and Dialogue: 26th International Conference: Proceedings UR - https://doi.org/10.1007/978-3-031-40498-6_8 KW - Machine translation KW - sentiment classification KW - bias Y1 - 2023 UR - https://doi.org/10.1007/978-3-031-40498-6_8 SN - 978-3-031-40498-6 SN - 1611-3349 SP - 82 EP - 93 PB - Springer CY - Cham ER -