@unpublished{NovickiNetoRewayPolednaetal.2023, author = {Novicki Neto, Leonardo and Reway, Fabio and Poledna, Yuri and Funk Drechsler, Maikol and Parente Ribeiro, Eduardo and Huber, Werner and Icking, Christian}, title = {TWICE Dataset: Digital Twin of Test Scenarios in a Controlled Environment}, publisher = {arXiv}, address = {Ithaca}, doi = {https://doi.org/10.48550/arXiv.2310.03895}, year = {2023}, abstract = {Ensuring the safe and reliable operation of autonomous vehicles under adverse weather remains a significant challenge. To address this, we have developed a comprehensive dataset composed of sensor data acquired in a real test track and reproduced in the laboratory for the same test scenarios. The provided dataset includes camera, radar, LiDAR, inertial measurement unit (IMU), and GPS data recorded under adverse weather conditions (rainy, night-time, and snowy conditions). We recorded test scenarios using objects of interest such as car, cyclist, truck and pedestrian -- some of which are inspired by EURONCAP (European New Car Assessment Programme). The sensor data generated in the laboratory is acquired by the execution of simulation-based tests in hardware-in-the-loop environment with the digital twin of each real test scenario. The dataset contains more than 2 hours of recording, which totals more than 280GB of data. Therefore, it is a valuable resource for researchers in the field of autonomous vehicles to test and improve their algorithms in adverse weather conditions, as well as explore the simulation-to-reality gap.}, language = {en} } @inproceedings{CristofoliDuarteSilvaFunkDrechslerPolednaetal.2023, author = {Crist{\´o}foli Duarte Silva, Let{\´i}cia and Funk Drechsler, Maikol and Poledna, Yuri and Huber, Werner and Antonio Fiorentin, Thiago}, title = {Synthetic Extreme Weather for AI Training: Concept and Validation}, booktitle = {Proceedings: 2023 Third International Conference on Digital Data Processing, DDP 2023}, editor = {Ariwa, Ezendu and Fong, Simon}, publisher = {IEEE}, address = {Piscataway}, isbn = {979-8-3503-2901-8}, doi = {https://doi.org/10.1109/DDP60485.2023.00044}, pages = {188 -- 194}, year = {2023}, 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{CapdevilaVitorinoPolednaetal.2024, author = {Capdevila, Marc Gonz{\`a}lez and Vitorino, Natanael and Poledna, Yuri and Malena, Bruno and Funk Drechsler, Maikol and Albuquerque, Gustavo G. and Furlan, Tales and Netto, Roberto S.}, title = {Smart\&Safe Mobility Lab: Mixed Reality Environment with HIL, CV2X for VRU Detection}, booktitle = {2024 IEEE 13th International Conference on Cloud Networking (CloudNet)}, editor = {Mattos, Diogo Menezes Ferrazani and Moraes, Igor Monteiro and Nguyen, Thi Mai Trang and de Souza Couto, Rodrigo and Rubinstein, Marcelo Goncalves}, publisher = {IEEE}, address = {Piscataway}, isbn = {979-8-3503-7656-2}, doi = {https://doi.org/10.1109/CloudNet62863.2024.10815791}, year = {2024}, language = {en} } @inproceedings{FunkDrechslerPolednaHjortetal.2024, author = {Funk Drechsler, Maikol and Poledna, Yuri and Hjort, Mattias and Kharrazi, Sogol and Huber, Werner}, title = {Vehicle Dynamics Parameter Estimation Methodology for Virtual Automated Driving Testing}, booktitle = {2024 IEEE International Automated Vehicle Validation Conference (IAVVC), Proceedings}, publisher = {IEEE}, address = {Piscataway}, isbn = {979-8-3503-5407-2}, doi = {10.1109/IAVVC63304.2024.10786416}, year = {2024}, 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} } @article{FunkDrechslerFiorentinGoellinger2021, author = {Funk Drechsler, Maikol and Fiorentin, Thiago Antonio and G{\"o}llinger, Harald}, title = {Actor-Critic Traction Control Based on Reinforcement Learning with Open-Loop Training}, volume = {2021}, pages = {4641450}, journal = {Modelling and Simulation in Engineering}, publisher = {Hindawi}, address = {New York}, issn = {1687-5605}, doi = {https://doi.org/10.1155/2021/4641450}, year = {2021}, abstract = {The use of actor-critic algorithms can improve the controllers currently implemented in automotive applications. This method combines reinforcement learning (RL) and neural networks to achieve the possibility of controlling nonlinear systems with real-time capabilities. Actor-critic algorithms were already applied with success in different controllers including autonomous driving, antilock braking system (ABS), and electronic stability control (ESC). However, in the current researches, virtual environments are implemented for the training process instead of using real plants to obtain the datasets. This limitation is given by trial and error methods implemented for the training process, which generates considerable risks in case the controller directly acts on the real plant. In this way, the present research proposes and evaluates an open-loop training process, which permits the data acquisition without the control interaction and an open-loop training of the neural networks. The performance of the trained controllers is evaluated by a design of experiments (DOE) to understand how it is affected by the generated dataset. The results present a successful application of open-loop training architecture. The controller can maintain the slip ratio under adequate levels during maneuvers on different floors, including grounds that are not applied during the training process. The actor neural network is also able to identify the different floors and change the acceleration profile according to the characteristics of each ground.}, 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{RewayFunkDrechslerMurthyetal.2022, author = {Reway, Fabio and Funk Drechsler, Maikol and Murthy, Ravikiran and Poledna, Yuri and Huber, Werner and Icking, Christian}, title = {Simulation-based test methods with an automotive camera-in-the-loop for automated driving algorithms}, booktitle = {2022 International Conference on Electrical, Computer, Communications and Mechatronics Engineering (ICECCME)}, publisher = {IEEE}, address = {Piscataway}, isbn = {978-1-6654-7095-7}, doi = {https://doi.org/10.1109/ICECCME55909.2022.9988437}, year = {2022}, language = {en} } @inproceedings{PolednaFunkDrechslerDonzellaetal.2024, author = {Poledna, Yuri and Funk Drechsler, Maikol and Donzella, Valentina and Chan, Pak Hung and Duthon, Pierre and Huber, Werner}, title = {REHEARSE: adveRse wEatHEr datAset for sensoRy noiSe modEls}, booktitle = {2024 IEEE Intelligent Vehicles Symposium (IV)}, publisher = {IEEE}, address = {Piscataway}, isbn = {979-8-3503-4881-1}, doi = {https://doi.org/10.1109/IV55156.2024.10588491}, pages = {2451 -- 2457}, year = {2024}, language = {en} } @inproceedings{RewayFunkDrechslerWachtelGranadoetal.2020, author = {Reway, Fabio and Funk Drechsler, Maikol and Wachtel Granado, Diogo and Huber, Werner}, title = {Validity Analysis of Simulation-based Testing concerning Free-space Detection in Autonomous Driving}, booktitle = {Proceedings of the 6th International Conference on Vehicle Technology and Intelligent Transport Systems}, publisher = {SciTePress}, address = {Set{\´u}bal}, isbn = {978-989-758-419-0}, issn = {2184-495X}, doi = {https://doi.org/10.5220/0009573705520558}, pages = {552 -- 558}, year = {2020}, language = {en} } @article{NovickiNetoRewayPolednaetal.2025, author = {Novicki Neto, Leonardo and Reway, Fabio and Poledna, Yuri and Funk Drechsler, Maikol and Icking, Christian and Huber, Werner and Parente Ribeiro, Eduardo}, title = {TWICE dataset: digital twin of test scenarios in a controlled environment}, volume = {19}, journal = {International Journal of Vehicle Systems Modelling and Testing (IJVSMT)}, number = {2}, publisher = {Inderscience}, address = {Genf}, issn = {1745-6436}, doi = {https://doi.org/10.1504/IJVSMT.2025.147353}, pages = {152 -- 170}, year = {2025}, language = {en} } @inproceedings{HerranenHenrikssonChanetal.2024, author = {Herranen, Tuomas and Henriksson, Erik and Chan, Pak Hung and Poledna, Yuri and Duthon, Pierre and Ben-Daoued, Amine and Funk Drechsler, Maikol and Donzella, Valentina}, title = {Creation of digital models for accelerated and reliable testing of automated systems in adverse weather}, pages = {1320705}, booktitle = {Autonomous Systems for Security and Defence}, editor = {Dijk, Judith and Sanchez-Lopez, Jose Luis}, publisher = {SPIE}, address = {Bellingham}, isbn = {978-1-5106-8123-1}, doi = {https://doi.org/10.1117/12.3031473}, year = {2024}, language = {en} } @inproceedings{UlreichFunkDrechslerPolednaetal.2026, author = {Ulreich, Fabian and Funk Drechsler, Maikol and Poledna, Yuri and Chan, Pak Hung and Herraren, Tuomas and Ebert, Martin and Kaup, Andr{\´e} and Huber, Werner}, title = {Validating Camera Sensor Models for Virtual Testing of Vision Systems in Automated Driving}, booktitle = {2025 IEEE International Conference on Vehicular Electronics and Safety (ICVES)}, publisher = {IEEE}, address = {Piscataway}, isbn = {978-1-6654-7778-9}, doi = {https://doi.org/10.1109/ICVES65691.2025.11376043}, pages = {57 -- 64}, year = {2026}, language = {en} } @inproceedings{FunkDrechslerSellPolednaetal.2026, author = {Funk Drechsler, Maikol and Sell, Christoph Dominic and Poledna, Yuri and Huber, Werner}, title = {Accelerating the Approval of Automated Driving Vehicles through standardized XiL test environments}, booktitle = {2025 IEEE International Conference on Vehicular Electronics and Safety (ICVES)}, publisher = {IEEE}, address = {Piscataway}, isbn = {978-1-6654-7778-9}, doi = {https://doi.org/10.1109/ICVES65691.2025.11376566}, pages = {183 -- 188}, year = {2026}, language = {en} } @unpublished{AksoyRaisuddinHolmbladetal.2025, author = {Aksoy, Eren Erdal and Raisuddin, Abu Mohammed and Holmblad, Jesper and Haghighi, Hamed and Poledna, Yuri and Funk Drechsler, Maikol and Donzella, Valentina}, title = {Rehearse-3d: A Multi-Modal Emulated Rain Dataset for 3d Point Cloud De-Raining}, titleParent = {SSRN}, publisher = {Elsevier}, address = {Amsterdam}, doi = {https://dx.doi.org/10.2139/ssrn.5360105}, year = {2025}, language = {en} } @article{RaisuddinHolmbladHaghighietal.2026, author = {Raisuddin, Abu Mohammed and Holmblad, Jesper and Haghighi, Hamed and Poledna, Yuri and Funk Drechsler, Maikol and Donzella, Valentina and Aksoy, Eren Erdal}, title = {REHEARSE-3D: A Multi-Modal Emulated Rain Dataset for 3D Point Cloud De-Raining}, volume = {26}, pages = {728}, journal = {Sensors}, number = {2}, publisher = {MDPI}, address = {Basel}, issn = {1424-8220}, doi = {https://doi.org/10.3390/s26020728}, year = {2026}, abstract = {Sensor degradation poses a significant challenge in autonomous driving. During heavy rainfall, interference from raindrops can adversely affect the quality of LiDAR point clouds, resulting in, for instance, inaccurate point measurements. This, in turn, can potentially lead to safety concerns if autonomous driving systems are not weather-aware, i.e., if they are unable to discern such changes. In this study, we release a new, large-scale, multi-modal emulated rain dataset, REHEARSE-3D, to promote research advancements in 3D point cloud de-raining. Distinct from the most relevant competitors, our dataset is unique in several respects. First, it is the largest point-wise annotated dataset (9.2 billion annotated points), and second, it is the only one with high-resolution LiDAR data (LiDAR-256) enriched with 4D RADAR point clouds logged in both daytime and nighttime conditions in a controlled weather environment. Furthermore, REHEARSE-3D involves rain-characteristic information, which is of significant value not only for sensor noise modeling but also for analyzing the impact of weather at the point level. Leveraging REHEARSE-3D, we benchmark raindrop detection and removal in fused LiDAR and 4D RADAR point clouds. Our comprehensive study further evaluates the performance of various statistical and deep learning models, where SalsaNext and 3D-OutDet achieve above 94\% IoU for raindrop detection.}, language = {en} }