@unpublished{FidelisRewayRibeiroetal.2023, author = {Fidelis, Eduardo and Reway, Fabio and Ribeiro, Herick Y. S. and Campos, Pietro and Huber, Werner and Icking, Christian and Faria, Lester and Sch{\"o}n, Torsten}, title = {Generation of Realistic Synthetic Raw Radar Data for Automated Driving Applications using Generative Adversarial Networks}, publisher = {arXiv}, address = {Ithaca}, doi = {https://doi.org/10.48550/arXiv.2308.02632}, year = {2023}, abstract = {The main approaches for simulating FMCW radar are based on ray tracing, which is usually computationally intensive and do not account for background noise. This work proposes a faster method for FMCW radar simulation capable of generating synthetic raw radar data using generative adversarial networks (GAN). The code and pre-trained weights are open-source and available on GitHub. This method generates 16 simultaneous chirps, which allows the generated data to be used for the further development of algorithms for processing radar data (filtering and clustering). This can increase the potential for data augmentation, e.g., by generating data in non-existent or safety-critical scenarios that are not reproducible in real life. In this work, the GAN was trained with radar measurements of a motorcycle and used to generate synthetic raw radar data of a motorcycle traveling in a straight line. For generating this data, the distance of the motorcycle and Gaussian noise are used as input to the neural network. The synthetic generated radar chirps were evaluated using the Frechet Inception Distance (FID). Then, the Range-Azimuth (RA) map is calculated twice: first, based on synthetic data using this GAN and, second, based on real data. Based on these RA maps, an algorithm with adaptive threshold and edge detection is used for object detection. The results have shown that the data is realistic in terms of coherent radar reflections of the motorcycle and background noise based on the comparison of chirps, the RA maps and the object detection results. Thus, the proposed method in this work has shown to minimize the simulation-to-reality gap for the generation of radar data.}, language = {en} } @inproceedings{BiankHuberMeyeretal.2019, author = {Biank, Sanjana and Huber, Werner and Meyer, Matthias and Hof, Hans-Joachim and Hempen, Thomas}, title = {Model-based security and safety assurance for automotive safety systems}, booktitle = {Proceedings of 3. ACM Computer Science in Cars Symposium (CSCS 2019)}, subtitle = {Extended Abstract}, publisher = {ACM}, address = {New York}, url = {https://acm-cscs.org/_/2019/}, year = {2019}, language = {en} } @inproceedings{FunkDrechslerPeintnerSeifertetal.2021, author = {Funk Drechsler, Maikol and Peintner, Jakob and Seifert, Georg and Huber, Werner and Riener, Andreas}, title = {Mixed Reality Environment for Testing Automated Vehicle and Pedestrian Interaction}, booktitle = {Adjunct Proceedings: 13th International ACM Conference on Automotive User Interfaces and Interactive Vehicular Applications}, publisher = {ACM}, address = {New York}, isbn = {978-1-4503-8641-8}, doi = {https://doi.org/10.1145/3473682.3481878}, pages = {229 -- 232}, year = {2021}, language = {en} } @inproceedings{PeintnerFunkDrechslerRewayetal.2021, author = {Peintner, Jakob and Funk Drechsler, Maikol and Reway, Fabio and Seifert, Georg and Huber, Werner and Riener, Andreas}, title = {Mixed Reality Environment for Complex Scenario Testing}, booktitle = {Tagungsband Mensch \& Computer 2021}, editor = {Schneegass, Stefan and Pfleging, Bastian and Kern, Dagmar}, publisher = {ACM}, address = {New York}, isbn = {978-1-4503-8645-6}, doi = {https://doi.org/10.1145/3473856.3474034}, pages = {605 -- 608}, year = {2021}, language = {en} } @inproceedings{FunkDrechslerSeifertPeintneretal.2022, author = {Funk Drechsler, Maikol and Seifert, Georg and Peintner, Jakob and Reway, Fabio and Riener, Andreas and Huber, Werner}, title = {How Simulation based Test Methods will substitute the Proving Ground Testing?}, booktitle = {2022 IEEE Intelligent Vehicles Symposium (IV)}, publisher = {IEEE}, address = {Piscataway}, isbn = {978-1-6654-8821-1}, doi = {https://doi.org/10.1109/IV51971.2022.9827394}, pages = {903 -- 908}, year = {2022}, language = {en} } @article{FunkDrechslerPeintnerRewayetal.2022, author = {Funk Drechsler, Maikol and Peintner, Jakob and Reway, Fabio and Seifert, Georg and Riener, Andreas and Huber, Werner}, title = {MiRE, A Mixed Reality Environment for Testing of Automated Driving Functions}, volume = {71}, journal = {IEEE Transactions on Vehicular Technology}, number = {4}, publisher = {IEEE}, address = {New York}, issn = {0018-9545}, doi = {https://doi.org/10.1109/TVT.2022.3160353}, pages = {3443 -- 3456}, year = {2022}, language = {en} } @inproceedings{PeintnerFunkDrechslerMangeretal.2022, author = {Peintner, Jakob and Funk Drechsler, Maikol and Manger, Carina and Seifert, Georg and Reway, Fabio and Huber, Werner and Riener, Andreas}, title = {Comparing Different Pedestrian Representations for Testing Automated Driving Functions in Mixed Reality Environments}, booktitle = {Proceedings of the International Conference on Vehicle Electronics and Safety (ICVES 2022)}, publisher = {IEEE}, address = {Piscataway}, isbn = {978-1-6654-7698-0}, doi = {https://doi.org/10.1109/ICVES56941.2022.9986669}, year = {2022}, language = {en} } @inproceedings{WachtelGranadoQueirozSchoenetal.2024, author = {Wachtel Granado, Diogo and Queiroz, Samuel and Sch{\"o}n, Torsten and Huber, Werner and Faria, Lester}, title = {A novel Conditional Generative Adversarial Networks for Automotive Radar Range-Doppler Targets Synthetic Generation}, booktitle = {2023 IEEE 26th International Conference on Intelligent Transportation Systems (ITSC)}, publisher = {IEEE}, address = {Piscataway}, isbn = {979-8-3503-9946-2}, doi = {https://doi.org/10.1109/ITSC57777.2023.10422067}, pages = {3964 -- 3969}, year = {2024}, language = {en} } @unpublished{ChandraSekaranGeislerRoessleetal.2025, author = {Chandra Sekaran, Karthikeyan and Geisler, Markus and R{\"o}ßle, Dominik and Mohan, Adithya and Cremers, Daniel and Utschick, Wolfgang and Botsch, Michael and Huber, Werner and Sch{\"o}n, Torsten}, title = {UrbanIng-V2X: A Large-Scale Multi-Vehicle, Multi-Infrastructure Dataset Across Multiple Intersections for Cooperative Perception}, publisher = {arXiv}, address = {Ithaca}, doi = {https://doi.org/10.48550/arXiv.2510.23478}, year = {2025}, language = {en} } @inproceedings{DenkHimmelsAndreevetal.2024, author = {Denk, Florian and Himmels, Chantal and Andreev, Vladislav and Lindner, Johannes and Syed, Arslan Ali and Riener, Andreas and Huber, Werner and Kates, Ronald}, title = {Studying Interactions of Motorists and Vulnerable Road Users: Empirical Comparison of Test Track and Simulator Experiments}, booktitle = {2023 IEEE 26th International Conference on Intelligent Transportation Systems (ITSC)}, publisher = {IEEE}, address = {Piscataway}, isbn = {979-8-3503-9946-2}, doi = {https://doi.org/10.1109/ITSC57777.2023.10421865}, pages = {992 -- 999}, year = {2024}, language = {en} }