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 - 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 - Sequeira, Gerald Joy A1 - Elnagdy, Elnagdy A1 - Danapal, Gokulesh A1 - Lugner, Robert A1 - Jumar, Ulrich A1 - Brandmeier, Thomas T1 - Investigation of Different Classification Algorithms for Predicting Occupant Injury Criterion to Decide the Required Restraint Strategy T2 - 2021 IEEE International Intelligent Transportation Systems Conference (ITSC) UR - https://doi.org/10.1109/ITSC48978.2021.9564868 KW - pre-crash injury prediction KW - Advanced Vehicle Safety KW - machine learning Y1 - 2021 UR - https://doi.org/10.1109/ITSC48978.2021.9564868 SN - 978-1-7281-9142-3 SP - 204 EP - 210 PB - IEEE CY - Piscataway ER - TY - CHAP A1 - Sequeira, Gerald Joy A1 - Harlapur, Bhuvan A1 - Lugner, Robert A1 - Brandmeier, Thomas T1 - Investigation of three-arc contour prediction algorithm on KITTI LiDAR raw data set T2 - 2021 IEEE 94th Vehicular Technology Conference (VTC2021-Fall): Proceedings UR - https://doi.org/10.1109/VTC2021-Fall52928.2021.9625355 KW - vehicular and wireless technologies KW - laser radar KW - trajectory planning KW - shape KW - roads KW - image edge detection KW - transportation Y1 - 2021 UR - https://doi.org/10.1109/VTC2021-Fall52928.2021.9625355 SN - 978-1-6654-1368-8 PB - IEEE CY - Piscataway ER - TY - CHAP A1 - Sequeira, Gerald Joy A1 - Afraj, Shahabaz A1 - Lugner, Robert A1 - Brandmeier, Thomas T1 - LiDAR based prediction and contact based validation of crash parameters for a preemptive restraint strategy T2 - 2019 IEEE International Conference on Vehicular Electronics and Safety (ICVES) UR - https://doi.org/10.1109/ICVES.2019.8906354 KW - vehicle safety KW - integrated safety KW - predictive safety system KW - contour estimation KW - crash scenario prediction KW - crash validation Y1 - 2019 UR - https://doi.org/10.1109/ICVES.2019.8906354 SN - 978-1-7281-3473-4 PB - IEEE CY - Piscataway ER - TY - CHAP A1 - Schneider, Kilian A1 - Lugner, Robert A1 - Brandmeier, Thomas T1 - LiDAR-based contour estimation of oncoming vehicles in pre-crash scenarios T2 - 2019 IEEE Intelligent Vehicles Symposium (IV) UR - https://doi.org/10.1109/IVS.2019.8813825 Y1 - 2019 UR - https://doi.org/10.1109/IVS.2019.8813825 SN - 978-1-7281-0560-4 SP - 2272 EP - 2277 PB - IEEE CY - Piscataway ER - TY - CHAP A1 - Lugner, Robert A1 - Inderst, Maximilian A1 - Sequeira, Gerald Joy A1 - Schneider, Kilian A1 - Brandmeier, Thomas T1 - Collision Prediction for Irreversible Pre-Crash Safety Measures T2 - FISITA Web Congress 2020 Y1 - 2021 UR - https://www.fisita.com/library/f2020-pif-033 N1 - Paper + Video PB - FISITA CY - Bishops Stortford ER - TY - CHAP A1 - Held, Patrick A1 - Steinhauser, Dagmar A1 - Kamann, Alexander A1 - Lugner, Robert A1 - Brandmeier, Thomas A1 - Koch, Andreas T1 - Radarbasierte Klassifikation von Fußgängern für Pre-Crash Systeme T2 - Fahrerassistenzsysteme und automatisiertes Fahren 2018, VDI Berichte 2335 UR - https://doi.org/10.51202/9783181023358-137 Y1 - 2018 UR - https://doi.org/10.51202/9783181023358-137 SN - 978-3-18-092335-2 SN - 978-3-18-102335-8 SP - 137 EP - 150 PB - VDI Verlag GmbH CY - Düsseldorf ER - TY - CHAP A1 - Schneider, Kilian A1 - Lugner, Robert A1 - Inderst, Maximilian A1 - Weigl, Tobias A1 - Brandmeier, Thomas T1 - Verification of Crash Severity Estimation Algorithm for Autonomous Driving Using Real Crash Data T2 - ICVISP 2020: Proceedings of the 2020 4th International Conference on Vision, Image and Signal Processing UR - https://doi.org/10.1145/3448823.3448850 KW - crash severity KW - pre-crash KW - autonomous driving KW - vehicle safety KW - driver assistant systems Y1 - 2021 UR - https://doi.org/10.1145/3448823.3448850 SN - 978-1-4503-8953-2 PB - ACM CY - New York ER -