@article{SequeiraPatelAfrajetal.2020, author = {Sequeira, Gerald Joy and Patel, Akshay and Afraj, Shahabaz and Lugner, Robert and Brandmeier, Thomas}, title = {FEM-based methodology for crash severity estimation in frontal crash scenarios}, volume = {2020}, pages = {012019}, journal = {IOP Conference Series: Materials Science and Engineering}, number = {831}, publisher = {IOP}, address = {Bristol}, issn = {1757-899X}, doi = {https://doi.org/10.1088/1757-899X/831/1/012019}, year = {2020}, abstract = {With the technological development of forward-looking sensors, researchers are exploring their use not only for advanced driver assistance systems but also to gain important pre-crash information. Based on this pre-crash information, if the occupant motion inside the vehicle structure can be predicted for the oncoming crash scenario, then an optimal restraint strategy can be planned before the crash. This paper introduces a two-step FEM simulation based methodology for predicting the occupant severity in head-on crash scenarios. In the first step, we simulate the vehicle level model with different impact positions and relative approach angles. The results of these simulations, linear velocities in the longitudinal and lateral direction and angular velocities (roll, pitch, and yaw) during in-crash phase are the loading conditions for next simulation step (occupant level). This step simulates the motion of the driver in different crash scenarios. In this paper, we investigate the head, neck, and chest injury risks from vehicle-to-vehicle crash both traveling at 50 kilometers per hour. Prediction of the head injury criterion, identifying the cases where additional deployment of side-airbags and discussion of injury criteria with contour plots are the main outcome of this paper.}, language = {en} } @inproceedings{FadlSchoenBehretetal.2025, author = {Fadl, Islam and Sch{\"o}n, Torsten and Behret, Valentino and Brandmeier, Thomas and Palme, Frank and Helmer, Thomas}, title = {Environment Setup and Model Benchmark of the MuFoRa Dataset}, booktitle = {Proceedings of the 20th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - (Volume 3)}, editor = {Bashford-Rogers, Thomas and Meneveaux, Daniel and Ammi, Mehdi and Ziat, Mounia and J{\"a}nicke, Stefan and Purchase, Helen and Radeva, Petia and Furnari, Antonino and Bouatouch, Kadi and Sousa, A. Augusto}, publisher = {SciTePress}, address = {Set{\´u}bal}, isbn = {978-989-758-728-3}, doi = {https://doi.org/10.5220/0013307900003912}, pages = {729 -- 737}, year = {2025}, abstract = {Adverse meteorological conditions, particularly fog and rain, present significant challenges to computer vision algorithms and autonomous systems. This work presents MuFoRa a novel, controllable, and measured multimodal dataset recorded at CARISSMA's indoor test facility, specifically designed to assess perceptual difficulties in foggy and rainy environments. The dataset bridges research gap in the public benchmarking datasets, where quantifiable weather parameters are lacking. The proposed dataset comprises synchronized data from two sensor modalities: RGB stereo cameras and LiDAR sensors, captured under varying intensities of fog and rain. The dataset incorporates synchronized meteorological annotations, such as visibility through fog and precipitation levels of rain, and the study contributes a detailed explanation of the diverse weather effects observed during data collection in the methods section. The dataset's utility is demonstrated through a baseline evaluation example, asse ssing the performance degradation of state-of-the-art YOLO11 and DETR 2D object detection algorithms under controlled and quantifiable adverse weather conditions. The public release of the dataset (https://doi.org/10.5281/zenodo.14175611) facilitates various benchmarking and quantitative assessments of advanced multimodal computer vision and deep learning models under the challenging conditions of fog and rain.}, language = {en} } @inproceedings{SezginVriesmanSteinhauseretal.2023, author = {Sezgin, Fatih and Vriesman, Daniel and Steinhauser, Dagmar and Lugner, Robert and Brandmeier, Thomas}, title = {Safe Autonomous Driving in Adverse Weather: Sensor Evaluation and Performance Monitoring}, booktitle = {IEEE IV 2023: Symposium Proceedings}, publisher = {IEEE}, address = {Piscataway}, isbn = {979-8-3503-4691-6}, doi = {https://doi.org/10.1109/IV55152.2023.10186596}, year = {2023}, language = {en} } @inproceedings{VriesmanJuniorZimmeretal.2023, author = {Vriesman, Daniel and Junior, Alceu Britto and Zimmer, Alessandro and Brandmeier, Thomas}, title = {Multimodal Early Fusion of Automotive Sensors based on Autoencoder Network: An anchor-free approach for Vehicle 3D Detection}, booktitle = {2023 26th International Conference on Information Fusion (FUSION)}, publisher = {IEEE}, address = {Piscataway}, isbn = {979-8-89034-485-4}, doi = {https://doi.org/10.23919/FUSION52260.2023.10224140}, year = {2023}, language = {en} } @inproceedings{GrafVriesmanBrandmeier2023, author = {Graf, Michael and Vriesman, Daniel and Brandmeier, Thomas}, title = {Testmethodik zur Untersuchung, Validierung und Absicherung von St{\"o}reinfl{\"u}ssen auf Umfeldsensoren durch Witterung unter reproduzierbaren Bedingungen}, booktitle = {14. VDI-Tagung Fahrzeugsicherheit, Fahrzeugsicherheit im Kontext neuer, nachhaltiger Mobilit{\"a}tskonzepte}, publisher = {VDI Verlag}, address = {D{\"u}sseldorf}, isbn = {978-3-18-102426-3}, doi = {https://doi.org/10.51202/9783181024263-231}, pages = {231 -- 248}, year = {2023}, language = {de} } @inproceedings{ChanZimmerLopesdaSilvaetal.2020, author = {Chan, Lap Yan and Zimmer, Alessandro and Lopes da Silva, Joed and Brandmeier, Thomas}, title = {European Union dataset and annotation tool for real time automatic license plate detection and blurring}, booktitle = {2020 IEEE 23rd International Conference on Intelligent Transportation Systems (ITSC)}, publisher = {IEEE}, address = {Piscataway}, isbn = {978-1-7281-4149-7}, doi = {https://doi.org/10.1109/ITSC45102.2020.9294240}, year = {2020}, language = {en} } @inproceedings{LopesdaSilvaTabataCardosoBrotoetal.2020, author = {Lopes da Silva, Joed and Tabata, Alan Naoto and Cardoso Broto, Lucas and Pereira Cocron, Marta and Zimmer, Alessandro and Brandmeier, Thomas}, title = {Open Source Multipurpose Multimedia Annotation Tool}, booktitle = {Image Analysis and Recognition: 17th International Conference, ICIAR 2020, Proceedings, Part I}, editor = {Campilho, Aur{\´e}lio and Karray, Fakhri and Wang, Zhou}, publisher = {Springer}, address = {Cham}, isbn = {978-3-030-50346-8}, issn = {0302-9743}, doi = {https://doi.org/10.1007/978-3-030-50347-5_31}, pages = {356 -- 367}, year = {2020}, language = {en} } @inproceedings{CardosoBrotoOkimotoPereiraCocronetal.2020, author = {Cardoso Broto, Lucas and Okimoto, Maria L.R. and Pereira Cocron, Marta and Zimmer, Alessandro and Brandmeier, Thomas}, title = {Application of Low-cost Pulse Radar for Heart Rate Detection in Vehicle Interior}, booktitle = {Sensors and Electronic Instrumentation Advances: Proceedings of the 6th International Conference on Sensors and Electronic Instrumentation Advances and Frequency \& Time: Proceedings of the 2nd IFSA Frequency \& Time Conference}, editor = {Yurish, Sergey Y.}, publisher = {IFSA}, address = {Castelldefels}, isbn = {978-84-09-23483-7}, url = {https://www.seia-conference.com/publications.html}, pages = {99 -- 104}, year = {2020}, language = {en} } @inproceedings{SequeiraElnagdyDanapaletal.2021, author = {Sequeira, Gerald Joy and Elnagdy, Elnagdy and Danapal, Gokulesh and Lugner, Robert and Jumar, Ulrich and Brandmeier, Thomas}, title = {Investigation of Different Classification Algorithms for Predicting Occupant Injury Criterion to Decide the Required Restraint Strategy}, booktitle = {2021 IEEE International Intelligent Transportation Systems Conference (ITSC)}, publisher = {IEEE}, address = {Piscataway}, isbn = {978-1-7281-9142-3}, doi = {https://doi.org/10.1109/ITSC48978.2021.9564868}, pages = {204 -- 210}, year = {2021}, language = {en} } @inproceedings{TomalRibasPereiraCocronLopesDaSilvaetal.2021, author = {Tomal Ribas, Luis Gustavo and Pereira Cocron, Marta and Lopes Da Silva, Joed and Zimmer, Alessandro and Brandmeier, Thomas}, title = {In-Cabin vehicle synthetic data to test Deep Learning based human pose estimation models}, booktitle = {2021 IEEE Intelligent Vehicles Symposium (IV)}, publisher = {IEEE}, address = {Piscataway}, isbn = {978-1-7281-5394-0}, doi = {https://doi.org/10.1109/IV48863.2021.9576020}, pages = {610 -- 615}, year = {2021}, language = {en} } @inproceedings{SequeiraHarlapurLugneretal.2021, author = {Sequeira, Gerald Joy and Harlapur, Bhuvan and Lugner, Robert and Brandmeier, Thomas}, title = {Investigation of three-arc contour prediction algorithm on KITTI LiDAR raw data set}, booktitle = {2021 IEEE 94th Vehicular Technology Conference (VTC2021-Fall): Proceedings}, publisher = {IEEE}, address = {Piscataway}, isbn = {978-1-6654-1368-8}, doi = {https://doi.org/10.1109/VTC2021-Fall52928.2021.9625355}, year = {2021}, language = {en} }