TY - RPRT A1 - Brandmeier, Thomas T1 - CISS.S Seitencrash-Erkennung mit Körperschall BT - Abschlussbericht N2 - Das Projekt CISS.S-Seitencrash-Erkennung mit Körperschall knüpfte an die Ergebnisse aus dem Vorgänger-projekt CISS an bzw. baute auf diesen auf. Im Projekt CISS wurde ein Verfahren für die Frontalcrash-Erkennung erforscht und zur Serienreife gebracht, das auf der Erfassung hochfrequenter Schwingungssignale basiert. Im Forschungsprojekt CISS.S sollten diese Erkenntnisse aus der Frontalcrash-Erkennung nun auf den Seitencrash übertragen werden, da dieser aufgrund der geringen Knautschzone verglichen mit anderen Lastfallgruppen besonders gefährlich ist. Ziel von CISS.S war deshalb die frühzeitige Erkennung des Seitencrashs zur verbesserten Airbag-Auslösung mittels Körperschallmessung während eines Unfalls. Ein weiteres Projektziel war zudem die Weiterentwicklung eines Simulationsverfahrens für die Ausbreitung von Körperschall im Fahrzeug. Darüber hinaus war mit CISS.S auch die Bestrebung verbunden, einen Transfer der Forschungsergebnisse in Querschnittsbereiche wie die Produktions- oder die Medizintechnik zu realisieren. KW - Fahrzeugsicherheit KW - Körperschall Y1 - 2012 UR - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:573-5754 ER - TY - JOUR A1 - Sequeira, Gerald Joy A1 - Lugner, Robert A1 - Jumar, Ulrich A1 - Brandmeier, Thomas T1 - A validation sensor based on carbon-fiber-reinforced plastic for early activation of automotive occupant restraint systems JF - Journal of sensors and sensor systems (JSSS) N2 - In the automotive industry, sensors and sensor systems are one of the most important components in upcoming challenges like highly automated and autonomous driving. Forward-looking sensors (radar, lidar and cameras) have the technical capability to already provide important (pre-)crash information, such as the position of contact, relative crash velocity and overlap (width of contact) before the crash occurs. Future safety systems can improve crash mitigation with sophisticated vehicle safety strategies based on this information. One such strategy is an early activation of restraint systems compared with conventional passive safety systems. These integrated safety systems consist of a combination of predictive forward-looking sensors and occupant restraint systems (airbags, belt tensioners, etc.) to provide the best occupant safety in inevitable crash situations. The activation of the restraint systems is the most critical decision process and requires a very robust validation system to avoid false activation. Hence, the information provided by the forward-looking sensor needs to be highly reliable. A validation sensor is required to check the plausibility of crucial information from forward-looking sensors used in integrated safety systems for safe automated and autonomous driving. This work presents a CFRP-based (carbon-fiber-reinforced plastic) validation sensor working on the principle of change in electrical resistance when a contact occurs. This sensor detects the first contact, gives information on impact position (where the contact occurs) and provides information on the overlap. The aim is to activate the vehicle restraint systems at near T0 (time of first contact). Prototypes of the sensor were manufactured in house and manually and were evaluated. At first, the sensor and its working principle were tested with a pendulum apparatus. In the next stage, the sensor was tested in a real crash test. The comparison of the signals from the CFRP-based sensor with presently used crash sensors in the vehicle highlights its advantages. The crash event can be identified at 0.1 ms after the initial contact. The sensor also provides information on impact position at 1.2 ms and enables a validation of the overlap development. Finally, a possible algorithm for the vehicle safety system using forward-looking sensors with a validation sensor is described. UR - https://doi.org/10.5194/jsss-8-19-2019 Y1 - 2019 UR - https://doi.org/10.5194/jsss-8-19-2019 UR - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:573-9578 SN - 2194-878X VL - 8 IS - 1 SP - 19 EP - 35 PB - Copernicus Publ. CY - Göttingen ER - TY - JOUR A1 - Sequeira, Gerald Joy A1 - Patel, Akshay A1 - Afraj, Shahabaz A1 - Lugner, Robert A1 - Brandmeier, Thomas T1 - FEM-based methodology for crash severity estimation in frontal crash scenarios JF - IOP Conference Series: Materials Science and Engineering N2 - 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. UR - https://doi.org/10.1088/1757-899X/831/1/012019 Y1 - 2020 UR - https://doi.org/10.1088/1757-899X/831/1/012019 UR - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:573-13159 SN - 1757-899X SN - 1757-8981 VL - 2020 IS - 831 PB - IOP CY - Bristol ER - TY - CHAP A1 - Fadl, Islam A1 - Schön, Torsten A1 - Behret, Valentino A1 - Brandmeier, Thomas A1 - Palme, Frank A1 - Helmer, Thomas ED - Bashford-Rogers, Thomas ED - Meneveaux, Daniel ED - Ammi, Mehdi ED - Ziat, Mounia ED - Jänicke, Stefan ED - Purchase, Helen ED - Radeva, Petia ED - Furnari, Antonino ED - Bouatouch, Kadi ED - Sousa, A. Augusto T1 - Environment Setup and Model Benchmark of the MuFoRa Dataset T2 - Proceedings of the 20th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - (Volume 3) N2 - 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. UR - https://doi.org/10.5220/0013307900003912 Y1 - 2025 UR - https://doi.org/10.5220/0013307900003912 UR - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:573-58070 SN - 978-989-758-728-3 SP - 729 EP - 737 PB - SciTePress CY - Setúbal ER - TY - CHAP A1 - Sezgin, Fatih A1 - Vriesman, Daniel A1 - Steinhauser, Dagmar A1 - Lugner, Robert A1 - Brandmeier, Thomas T1 - Safe Autonomous Driving in Adverse Weather: Sensor Evaluation and Performance Monitoring T2 - IEEE IV 2023: Symposium Proceedings UR - https://doi.org/10.1109/IV55152.2023.10186596 Y1 - 2023 UR - https://doi.org/10.1109/IV55152.2023.10186596 SN - 979-8-3503-4691-6 PB - IEEE CY - Piscataway ER - TY - CHAP A1 - Böhmländer, Dennis A1 - Hasirlioglu, Sinan A1 - Yano, Vitor A1 - Lauerer, Christian A1 - Brandmeier, Thomas A1 - Zimmer, Alessandro T1 - Advantages in Crash Severity Prediction Using Vehicle to Vehicle Communication T2 - Proceedings: 2015 45th Annual IEEE/IFIP International Conference on Dependable Systems and Networks Workshops UR - https://doi.org/10.1109/DSN-W.2015.23 Y1 - 2015 UR - https://doi.org/10.1109/DSN-W.2015.23 SN - 978-1-4673-8044-7 SP - 112 EP - 117 PB - IEEE CY - Los Alamitos ER - TY - CHAP A1 - Vriesman, Daniel A1 - Junior, Alceu Britto A1 - Zimmer, Alessandro A1 - Brandmeier, Thomas T1 - Multimodal Early Fusion of Automotive Sensors based on Autoencoder Network: An anchor-free approach for Vehicle 3D Detection T2 - 2023 26th International Conference on Information Fusion (FUSION) UR - https://doi.org/10.23919/FUSION52260.2023.10224140 KW - early fusion KW - lidar KW - camera KW - radar KW - vehicle detection Y1 - 2023 UR - https://doi.org/10.23919/FUSION52260.2023.10224140 SN - 979-8-89034-485-4 PB - IEEE CY - Piscataway ER - TY - CHAP A1 - Graf, Michael A1 - Vriesman, Daniel A1 - Brandmeier, Thomas T1 - Testmethodik zur Untersuchung, Validierung und Absicherung von Störeinflüssen auf Umfeldsensoren durch Witterung unter reproduzierbaren Bedingungen T2 - 14. VDI-Tagung Fahrzeugsicherheit, Fahrzeugsicherheit im Kontext neuer, nachhaltiger Mobilitätskonzepte UR - https://doi.org/10.51202/9783181024263-231 Y1 - 2023 UR - https://doi.org/10.51202/9783181024263-231 SN - 978-3-18-102426-3 SN - 978-3-18-092426-7 SP - 231 EP - 248 PB - VDI Verlag CY - Düsseldorf ER - TY - CHAP A1 - Chan, Lap Yan A1 - Zimmer, Alessandro A1 - Lopes da Silva, Joed A1 - Brandmeier, Thomas T1 - European Union dataset and annotation tool for real time automatic license plate detection and blurring T2 - 2020 IEEE 23rd International Conference on Intelligent Transportation Systems (ITSC) UR - https://doi.org/10.1109/ITSC45102.2020.9294240 KW - european license plate dataset KW - machine learning KW - license plate detector Y1 - 2020 UR - https://doi.org/10.1109/ITSC45102.2020.9294240 SN - 978-1-7281-4149-7 PB - IEEE CY - Piscataway ER - TY - CHAP A1 - Lopes da Silva, Joed A1 - Tabata, Alan Naoto A1 - Cardoso Broto, Lucas A1 - Pereira Cocron, Marta A1 - Zimmer, Alessandro A1 - Brandmeier, Thomas ED - Campilho, Aurélio ED - Karray, Fakhri ED - Wang, Zhou T1 - Open Source Multipurpose Multimedia Annotation Tool T2 - Image Analysis and Recognition: 17th International Conference, ICIAR 2020, Proceedings, Part I UR - https://doi.org/10.1007/978-3-030-50347-5_31 KW - open source KW - video annotation KW - multipurpose KW - generic annotation Y1 - 2020 UR - https://doi.org/10.1007/978-3-030-50347-5_31 SN - 978-3-030-50346-8 SN - 978-3-030-50347-5 SN - 0302-9743 SN - 1611-3349 SP - 356 EP - 367 PB - Springer CY - Cham ER - TY - CHAP A1 - Cardoso Broto, Lucas A1 - Okimoto, Maria L.R. A1 - Pereira Cocron, Marta A1 - Zimmer, Alessandro A1 - Brandmeier, Thomas ED - Yurish, Sergey Y. T1 - Application of Low-cost Pulse Radar for Heart Rate Detection in Vehicle Interior T2 - 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 Y1 - 2020 UR - https://www.seia-conference.com/publications.html SN - 978-84-09-23483-7 SP - 99 EP - 104 PB - IFSA CY - Castelldefels ER -