TY - INPR A1 - Fidelis, Eduardo A1 - Reway, Fabio A1 - Ribeiro, Herick Y. S. A1 - Campos, Pietro A1 - Huber, Werner A1 - Icking, Christian A1 - Faria, Lester A1 - Schön, Torsten T1 - Generation of Realistic Synthetic Raw Radar Data for Automated Driving Applications using Generative Adversarial Networks N2 - 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. UR - https://doi.org/10.48550/arXiv.2308.02632 Y1 - 2023 UR - https://doi.org/10.48550/arXiv.2308.02632 UR - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:573-59872 PB - arXiv CY - Ithaca ER - TY - CHAP A1 - Poledna, Yuri A1 - Reway, Fabio A1 - Funk Drechsler, Maikol A1 - Huber, Werner A1 - Icking, Christian A1 - Parente Ribeiro, Eduardo ED - Juárez-Ramírez, Reyes ED - Fernández y Fernández, Carlos ED - Perez Gonzalez, Hector G. ED - Ramírez-Noriega, Alan ED - Jiménez, Samantha ED - Guerra-García, César ED - Licea Sandoval, Guillermo T1 - An Open-Source High-Level Fusion Algorithm in ROS for Automated Driving Applications T2 - Proceedings: 2022 10th International Conference in Software Engineering Research and Innovation (CONISOFT 2022) UR - https://doi.org/10.1109/CONISOFT55708.2022.00031 Y1 - 2023 UR - https://doi.org/10.1109/CONISOFT55708.2022.00031 SN - 978-1-6654-6126-9 SP - 174 EP - 181 PB - IEEE CY - Los Alamitos ER - TY - JOUR A1 - Funk Drechsler, Maikol A1 - Sharma, Varun A1 - Reway, Fabio A1 - Schütz, Christoph A1 - Huber, Werner T1 - Dynamic Vehicle-in-the-Loop: A Novel Method for Testing Automated Driving Functions JF - SAE International Journal of Connected and Automated Vehicles N2 - 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. UR - https://doi.org/10.4271/12-05-04-0029 KW - automated driving KW - safety KW - test KW - track KW - vehicle dynamics KW - virtual KW - driving functions KW - automated emergency braking Y1 - 2022 UR - https://doi.org/10.4271/12-05-04-0029 UR - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:573-24952 SN - 2574-0741 SN - 2574-075X VL - 5 IS - 4 PB - SAE International CY - Warrendale ER - TY - CHAP A1 - Wachtel Granado, Diogo A1 - Schröder, Sabine A1 - Reway, Fabio A1 - Huber, Werner A1 - Vossiek, Martin T1 - Validation of a radar sensor model under non-ideal conditions for testing automated driving systems T2 - 2021 IEEE Intelligent Vehicles Symposium Workshops (IV Workshops) UR - https://doi.org/10.1109/IVWorkshops54471.2021.9669205 KW - meteorological radar KW - radar cross-sections KW - rain KW - conferences KW - virtual environments KW - safety KW - reliability Y1 - 2022 UR - https://doi.org/10.1109/IVWorkshops54471.2021.9669205 SN - 978-1-6654-7921-9 SP - 83 EP - 89 PB - IEEE CY - Piscataway ER - TY - CHAP A1 - Reway, Fabio A1 - Hoffmann, Abdul A1 - Wachtel Granado, Diogo A1 - Huber, Werner A1 - Knoll, Alois A1 - Parente Ribeiro, Eduardo T1 - Test Method for Measuring the Simulation-to-Reality Gap of Camera-based Object Detection Algorithms for Autonomous Driving T2 - 2020 IEEE Intelligent Vehicles Symposium (IV) UR - https://doi.org/10.1109/IV47402.2020.9304567 KW - Roads KW - Cameras KW - Rain KW - Object detection KW - Software KW - Sensors KW - Testing Y1 - 2021 UR - https://doi.org/10.1109/IV47402.2020.9304567 SN - 978-1-7281-6673-5 SP - 1249 EP - 1256 PB - IEEE CY - Piscataway ER - TY - CHAP A1 - Hasirlioglu, Sinan A1 - Reway, Fabio A1 - Klingenberg, Tim A1 - Riener, Andreas A1 - Huber, Werner T1 - Raindrops on the Windshield: Performance Assessment of Camera-based Object Detection T2 - 2019 IEEE International Conference on Vehicular Electronics and Safety (ICVES) UR - https://doi.org/10.1109/ICVES.2019.8906344 Y1 - 2019 UR - https://doi.org/10.1109/ICVES.2019.8906344 SN - 978-1-7281-3473-4 PB - IEEE CY - Piscataway ER - TY - CHAP A1 - Peintner, Jakob A1 - Funk Drechsler, Maikol A1 - Reway, Fabio A1 - Seifert, Georg A1 - Huber, Werner A1 - Riener, Andreas ED - Schneegass, Stefan ED - Pfleging, Bastian ED - Kern, Dagmar T1 - Mixed Reality Environment for Complex Scenario Testing T2 - Tagungsband Mensch & Computer 2021 UR - https://doi.org/10.1145/3473856.3474034 Y1 - 2021 UR - https://doi.org/10.1145/3473856.3474034 SN - 978-1-4503-8645-6 SP - 605 EP - 608 PB - ACM CY - New York ER - TY - CHAP A1 - Reway, Fabio A1 - Huber, Werner A1 - Parente Ribeiro, Eduardo T1 - Test Methodology for Vision-Based ADAS Algorithms with an Automotive Camera-in-the-Loop T2 - 2018 IEEE International Conference on Vehicular Electronics and Safety (ICVES) UR - https://doi.org/10.1109/ICVES.2018.8519598 KW - cameras KW - sensors KW - testing KW - automobiles KW - automotive engineering KW - radar KW - accidents Y1 - 2018 UR - https://doi.org/10.1109/ICVES.2018.8519598 SN - 978-1-5386-3543-8 PB - IEEE CY - New York ER - TY - CHAP A1 - Funk Drechsler, Maikol A1 - Seifert, Georg A1 - Peintner, Jakob A1 - Reway, Fabio A1 - Riener, Andreas A1 - Huber, Werner T1 - How Simulation based Test Methods will substitute the Proving Ground Testing? T2 - 2022 IEEE Intelligent Vehicles Symposium (IV) UR - https://doi.org/10.1109/IV51971.2022.9827394 KW - Wireless LAN KW - Actuators KW - Virtual environments KW - Systems architecture KW - Cameras KW - Software KW - Delays Y1 - 2022 UR - https://doi.org/10.1109/IV51971.2022.9827394 UR - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:573-27177 SN - 978-1-6654-8821-1 SP - 903 EP - 908 PB - IEEE CY - Piscataway ER - TY - JOUR A1 - Funk Drechsler, Maikol A1 - Peintner, Jakob A1 - Reway, Fabio A1 - Seifert, Georg A1 - Riener, Andreas A1 - Huber, Werner T1 - MiRE, A Mixed Reality Environment for Testing of Automated Driving Functions JF - IEEE Transactions on Vehicular Technology UR - https://doi.org/10.1109/TVT.2022.3160353 KW - human factors KW - road vehicle testing KW - vehicle safety KW - virtual reality Y1 - 2022 UR - https://doi.org/10.1109/TVT.2022.3160353 SN - 0018-9545 SN - 1939-9359 VL - 71 IS - 4 SP - 3443 EP - 3456 PB - IEEE CY - New York ER -