@unpublished{FidelisRewayRibeiroetal.2023, author = {Fidelis, Eduardo and Reway, Fabio and Ribeiro, Herick Y. S. and Campos, Pietro and Huber, Werner and Icking, Christian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{PolednaRewayFunkDrechsleretal.2023, author = {Poledna, Yuri and Reway, Fabio and Funk Drechsler, Maikol and Huber, Werner and Icking, Christian and Parente Ribeiro, Eduardo}, title = {An Open-Source High-Level Fusion Algorithm in ROS for Automated Driving Applications}, booktitle = {Proceedings: 2022 10th International Conference in Software Engineering Research and Innovation (CONISOFT 2022)}, editor = {Ju{\´a}rez-Ram{\´i}rez, Reyes and Fern{\´a}ndez y Fern{\´a}ndez, Carlos and Perez Gonzalez, Hector G. and Ram{\´i}rez-Noriega, Alan and Jim{\´e}nez, Samantha and Guerra-Garc{\´i}a, C{\´e}sar and Licea Sandoval, Guillermo}, publisher = {IEEE}, address = {Los Alamitos}, isbn = {978-1-6654-6126-9}, doi = {https://doi.org/10.1109/CONISOFT55708.2022.00031}, pages = {174 -- 181}, year = {2023}, language = {en} } @article{FunkDrechslerSharmaRewayetal.2022, author = {Funk Drechsler, Maikol and Sharma, Varun and Reway, Fabio and Sch{\"u}tz, Christoph and Huber, Werner}, title = {Dynamic Vehicle-in-the-Loop: A Novel Method for Testing Automated Driving Functions}, volume = {5}, pages = {12-05-04-0029}, journal = {SAE International Journal of Connected and Automated Vehicles}, number = {4}, publisher = {SAE International}, address = {Warrendale}, issn = {2574-0741}, doi = {https://doi.org/10.4271/12-05-04-0029}, year = {2022}, abstract = {In automated driving functions (ADF) testing, novel methods have been developed to allow the combination of hardware and simulation to ensure safety in usage even at an early stage of development. This article proposes an architecture to integrate an entire test vehicle—denominated Dynamic Vehicle-in-the-Loop (DynViL)—in a virtual environment. This approach enables the interaction of a real vehicle with virtual traffic participants. The vehicle is physically tested on an empty track, but connected to the CARLA simulator, in which virtual driving scenarios are created. The simulated environment is transmitted to the vehicle driving function which controls the real vehicle in reaction to the virtual objects perceived in simulation. Furthermore, the performance of the DynViL in different test scenarios is evaluated. The results show that the reproducibility of the tests with the DynViL is satisfactory. Furthermore, the results indicate that the deviation between simulation and DynViL variates according to the vehicle speed and the complexity of the scenario. Based on the performance of the DynViL in comparison to simulation, the DynViL can be implemented as a complementary test method to be added to the transition between hardware in the loop (HiL) and proving ground. In this test method, erratic or unexpected behavior generated by the driving function and controllers can be detected in the real vehicle dynamics in a risk-free manner.}, language = {en} } @inproceedings{WachtelGranadoSchroederRewayetal.2022, author = {Wachtel Granado, Diogo and Schr{\"o}der, Sabine and Reway, Fabio and Huber, Werner and Vossiek, Martin}, title = {Validation of a radar sensor model under non-ideal conditions for testing automated driving systems}, booktitle = {2021 IEEE Intelligent Vehicles Symposium Workshops (IV Workshops)}, publisher = {IEEE}, address = {Piscataway}, isbn = {978-1-6654-7921-9}, doi = {https://doi.org/10.1109/IVWorkshops54471.2021.9669205}, pages = {83 -- 89}, year = {2022}, language = {en} } @inproceedings{RewayHoffmannWachtelGranadoetal.2021, author = {Reway, Fabio and Hoffmann, Abdul and Wachtel Granado, Diogo and Huber, Werner and Knoll, Alois and Parente Ribeiro, Eduardo}, title = {Test Method for Measuring the Simulation-to-Reality Gap of Camera-based Object Detection Algorithms for Autonomous Driving}, booktitle = {2020 IEEE Intelligent Vehicles Symposium (IV)}, publisher = {IEEE}, address = {Piscataway}, isbn = {978-1-7281-6673-5}, doi = {https://doi.org/10.1109/IV47402.2020.9304567}, pages = {1249 -- 1256}, year = {2021}, language = {en} } @inproceedings{HasirliogluRewayKlingenbergetal.2019, author = {Hasirlioglu, Sinan and Reway, Fabio and Klingenberg, Tim and Riener, Andreas and Huber, Werner}, title = {Raindrops on the Windshield: Performance Assessment of Camera-based Object Detection}, booktitle = {2019 IEEE International Conference on Vehicular Electronics and Safety (ICVES)}, publisher = {IEEE}, address = {Piscataway}, isbn = {978-1-7281-3473-4}, doi = {https://doi.org/10.1109/ICVES.2019.8906344}, year = {2019}, 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{RewayHuberParenteRibeiro2018, author = {Reway, Fabio and Huber, Werner and Parente Ribeiro, Eduardo}, title = {Test Methodology for Vision-Based ADAS Algorithms with an Automotive Camera-in-the-Loop}, booktitle = {2018 IEEE International Conference on Vehicular Electronics and Safety (ICVES)}, publisher = {IEEE}, address = {New York}, isbn = {978-1-5386-3543-8}, doi = {https://doi.org/10.1109/ICVES.2018.8519598}, year = {2018}, 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} }