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 - 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 - 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 - 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 - 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 -