TY - INPR A1 - Novicki Neto, Leonardo A1 - Reway, Fabio A1 - Poledna, Yuri A1 - Funk Drechsler, Maikol A1 - Parente Ribeiro, Eduardo A1 - Huber, Werner A1 - Icking, Christian T1 - TWICE Dataset: Digital Twin of Test Scenarios in a Controlled Environment N2 - 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. UR - https://doi.org/10.48550/arXiv.2310.03895 Y1 - 2023 UR - https://doi.org/10.48550/arXiv.2310.03895 UR - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:573-59887 PB - arXiv CY - Ithaca ER - TY - CHAP A1 - Cristófoli Duarte Silva, Letícia A1 - Funk Drechsler, Maikol A1 - Poledna, Yuri A1 - Huber, Werner A1 - Antonio Fiorentin, Thiago ED - Ariwa, Ezendu ED - Fong, Simon T1 - Synthetic Extreme Weather for AI Training: Concept and Validation T2 - Proceedings: 2023 Third International Conference on Digital Data Processing, DDP 2023 UR - https://doi.org/10.1109/DDP60485.2023.00044 Y1 - 2023 UR - https://doi.org/10.1109/DDP60485.2023.00044 SN - 979-8-3503-2901-8 SP - 188 EP - 194 PB - IEEE CY - Piscataway 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 - Capdevila, Marc Gonzàlez A1 - Vitorino, Natanael A1 - Poledna, Yuri A1 - Malena, Bruno A1 - Funk Drechsler, Maikol A1 - Albuquerque, Gustavo G. A1 - Furlan, Tales A1 - Netto, Roberto S. ED - Mattos, Diogo Menezes Ferrazani ED - Moraes, Igor Monteiro ED - Nguyen, Thi Mai Trang ED - de Souza Couto, Rodrigo ED - Rubinstein, Marcelo Goncalves T1 - Smart&Safe Mobility Lab: Mixed Reality Environment with HIL, CV2X for VRU Detection T2 - 2024 IEEE 13th International Conference on Cloud Networking (CloudNet) UR - https://doi.org/10.1109/CloudNet62863.2024.10815791 Y1 - 2024 UR - https://doi.org/10.1109/CloudNet62863.2024.10815791 SN - 979-8-3503-7656-2 PB - IEEE CY - Piscataway ER - TY - CHAP A1 - Funk Drechsler, Maikol A1 - Poledna, Yuri A1 - Hjort, Mattias A1 - Kharrazi, Sogol A1 - Huber, Werner T1 - Vehicle Dynamics Parameter Estimation Methodology for Virtual Automated Driving Testing T2 - 2024 IEEE International Automated Vehicle Validation Conference (IAVVC), Proceedings UR - 10.1109/IAVVC63304.2024.10786416 Y1 - 2024 UR - 10.1109/IAVVC63304.2024.10786416 SN - 979-8-3503-5407-2 PB - IEEE CY - Piscataway ER - TY - CHAP A1 - Funk Drechsler, Maikol A1 - Peintner, Jakob A1 - Seifert, Georg A1 - Huber, Werner A1 - Riener, Andreas T1 - Mixed Reality Environment for Testing Automated Vehicle and Pedestrian Interaction T2 - Adjunct Proceedings: 13th International ACM Conference on Automotive User Interfaces and Interactive Vehicular Applications UR - https://doi.org/10.1145/3473682.3481878 KW - Automated Driving Systems KW - Test Procedures KW - Vehicle-in-the-Loop KW - External Human-Machine Interfaces KW - Sensor stimulation Y1 - 2021 UR - https://doi.org/10.1145/3473682.3481878 SN - 978-1-4503-8641-8 SP - 229 EP - 232 PB - ACM CY - New York ER - TY - JOUR A1 - Funk Drechsler, Maikol A1 - Fiorentin, Thiago Antonio A1 - Göllinger, Harald T1 - Actor-Critic Traction Control Based on Reinforcement Learning with Open-Loop Training JF - Modelling and Simulation in Engineering N2 - 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. UR - https://doi.org/10.1155/2021/4641450 Y1 - 2021 UR - https://doi.org/10.1155/2021/4641450 UR - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:573-15862 SN - 1687-5605 VL - 2021 PB - Hindawi CY - New York 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 - 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 - TY - CHAP A1 - Peintner, Jakob A1 - Funk Drechsler, Maikol A1 - Manger, Carina A1 - Seifert, Georg A1 - Reway, Fabio A1 - Huber, Werner A1 - Riener, Andreas T1 - Comparing Different Pedestrian Representations for Testing Automated Driving Functions in Mixed Reality Environments T2 - Proceedings of the International Conference on Vehicle Electronics and Safety (ICVES 2022) UR - https://doi.org/10.1109/ICVES56941.2022.9986669 KW - Target tracking KW - Roads KW - Mixed reality KW - Virtual environments KW - Motion capture KW - Complexity theory KW - Safety Y1 - 2022 UR - https://doi.org/10.1109/ICVES56941.2022.9986669 SN - 978-1-6654-7698-0 PB - IEEE CY - Piscataway ER - TY - CHAP A1 - Reway, Fabio A1 - Funk Drechsler, Maikol A1 - Murthy, Ravikiran A1 - Poledna, Yuri A1 - Huber, Werner A1 - Icking, Christian T1 - Simulation-based test methods with an automotive camera-in-the-loop for automated driving algorithms T2 - 2022 International Conference on Electrical, Computer, Communications and Mechatronics Engineering (ICECCME) UR - https://doi.org/10.1109/ICECCME55909.2022.9988437 KW - sensor-in-the-loop KW - automate driving KW - testing Y1 - 2022 UR - https://doi.org/10.1109/ICECCME55909.2022.9988437 SN - 978-1-6654-7095-7 PB - IEEE CY - Piscataway ER - TY - CHAP A1 - Poledna, Yuri A1 - Funk Drechsler, Maikol A1 - Donzella, Valentina A1 - Chan, Pak Hung A1 - Duthon, Pierre A1 - Huber, Werner T1 - REHEARSE: adveRse wEatHEr datAset for sensoRy noiSe modEls T2 - 2024 IEEE Intelligent Vehicles Symposium (IV) UR - https://doi.org/10.1109/IV55156.2024.10588491 Y1 - 2024 UR - https://doi.org/10.1109/IV55156.2024.10588491 SN - 979-8-3503-4881-1 SP - 2451 EP - 2457 PB - IEEE CY - Piscataway ER - TY - CHAP A1 - Reway, Fabio A1 - Funk Drechsler, Maikol A1 - Wachtel Granado, Diogo A1 - Huber, Werner T1 - Validity Analysis of Simulation-based Testing concerning Free-space Detection in Autonomous Driving T2 - Proceedings of the 6th International Conference on Vehicle Technology and Intelligent Transport Systems UR - https://doi.org/10.5220/0009573705520558 KW - automated driving KW - testing KW - validation KW - simulation Y1 - 2020 UR - https://doi.org/10.5220/0009573705520558 UR - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:573-18804 SN - 978-989-758-419-0 SN - 2184-495X SP - 552 EP - 558 PB - SciTePress CY - Setúbal ER - TY - JOUR A1 - Novicki Neto, Leonardo A1 - Reway, Fabio A1 - Poledna, Yuri A1 - Funk Drechsler, Maikol A1 - Icking, Christian A1 - Huber, Werner A1 - Parente Ribeiro, Eduardo T1 - TWICE dataset: digital twin of test scenarios in a controlled environment JF - International Journal of Vehicle Systems Modelling and Testing (IJVSMT) UR - https://doi.org/10.1504/IJVSMT.2025.147353 Y1 - 2025 UR - https://doi.org/10.1504/IJVSMT.2025.147353 SN - 1745-6436 SN - 1745-6444 VL - 19 IS - 2 SP - 152 EP - 170 PB - Inderscience CY - Genf ER - TY - CHAP A1 - Herranen, Tuomas A1 - Henriksson, Erik A1 - Chan, Pak Hung A1 - Poledna, Yuri A1 - Duthon, Pierre A1 - Ben-Daoued, Amine A1 - Funk Drechsler, Maikol A1 - Donzella, Valentina ED - Dijk, Judith ED - Sanchez-Lopez, Jose Luis T1 - Creation of digital models for accelerated and reliable testing of automated systems in adverse weather T2 - Autonomous Systems for Security and Defence UR - https://doi.org/10.1117/12.3031473 Y1 - 2024 UR - https://doi.org/10.1117/12.3031473 SN - 978-1-5106-8123-1 PB - SPIE CY - Bellingham ER - TY - CHAP A1 - Ulreich, Fabian A1 - Funk Drechsler, Maikol A1 - Poledna, Yuri A1 - Chan, Pak Hung A1 - Herraren, Tuomas A1 - Ebert, Martin A1 - Kaup, André A1 - Huber, Werner T1 - Validating Camera Sensor Models for Virtual Testing of Vision Systems in Automated Driving T2 - 2025 IEEE International Conference on Vehicular Electronics and Safety (ICVES) UR - https://doi.org/10.1109/ICVES65691.2025.11376043 Y1 - 2026 UR - https://doi.org/10.1109/ICVES65691.2025.11376043 SN - 978-1-6654-7778-9 SP - 57 EP - 64 PB - IEEE CY - Piscataway ER - TY - CHAP A1 - Funk Drechsler, Maikol A1 - Sell, Christoph Dominic A1 - Poledna, Yuri A1 - Huber, Werner T1 - Accelerating the Approval of Automated Driving Vehicles through standardized XiL test environments T2 - 2025 IEEE International Conference on Vehicular Electronics and Safety (ICVES) UR - https://doi.org/10.1109/ICVES65691.2025.11376566 Y1 - 2026 UR - https://doi.org/10.1109/ICVES65691.2025.11376566 SN - 978-1-6654-7778-9 SP - 183 EP - 188 PB - IEEE CY - Piscataway ER - TY - INPR A1 - Aksoy, Eren Erdal A1 - Raisuddin, Abu Mohammed A1 - Holmblad, Jesper A1 - Haghighi, Hamed A1 - Poledna, Yuri A1 - Funk Drechsler, Maikol A1 - Donzella, Valentina T1 - Rehearse-3d: A Multi-Modal Emulated Rain Dataset for 3d Point Cloud De-Raining T2 - SSRN UR - https://dx.doi.org/10.2139/ssrn.5360105 Y1 - 2025 UR - https://dx.doi.org/10.2139/ssrn.5360105 PB - Elsevier CY - Amsterdam ER - TY - JOUR A1 - Raisuddin, Abu Mohammed A1 - Holmblad, Jesper A1 - Haghighi, Hamed A1 - Poledna, Yuri A1 - Funk Drechsler, Maikol A1 - Donzella, Valentina A1 - Aksoy, Eren Erdal T1 - REHEARSE-3D: A Multi-Modal Emulated Rain Dataset for 3D Point Cloud De-Raining JF - Sensors N2 - 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. UR - https://doi.org/10.3390/s26020728 Y1 - 2026 UR - https://doi.org/10.3390/s26020728 UR - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:573-66060 SN - 1424-8220 VL - 26 IS - 2 PB - MDPI CY - Basel ER -