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 - 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 - Sequeira, Gerald Joy A1 - Surve, Manasi A1 - Afraj, Shahabaz A1 - Brandmeier, Thomas T1 - A novel concept for validation of pre-crash perception sensor information using contact sensor T2 - 2020 IEEE 23rd International Conference on Intelligent Transportation Systems (ITSC) UR - https://doi.org/10.1109/ITSC45102.2020.9294242 KW - Contacts KW - Accidents KW - Resistance KW - Safety KW - Electron tubes KW - Pins KW - Laser radar Y1 - 2020 UR - https://doi.org/10.1109/ITSC45102.2020.9294242 SN - 978-1-7281-4149-7 PB - IEEE CY - Piscataway ER - TY - CHAP A1 - Sequeira, Gerald Joy A1 - Afraj, Shahabaz A1 - Surve, Manasi A1 - Brandmeier, Thomas T1 - LiDAR point cloud analysis for vehicle contour estimation using polynomial approximation and curvature breakdown T2 - 2020 IEEE 92nd Vehicular Technology Conference (VTC2020-Fall) Proceedings UR - https://doi.org/10.1109/VTC2020-Fall49728.2020.9348457 KW - Three-dimensional displays KW - Laser radar KW - Electric breakdown KW - Shape KW - Estimation KW - Sensors KW - Vehicles Y1 - 2020 UR - https://doi.org/10.1109/VTC2020-Fall49728.2020.9348457 SN - 978-1-7281-9484-4 SN - 2577-2465 PB - IEEE CY - Piscataway (NJ) ER - TY - JOUR A1 - Mothershed, David Michael A1 - Lugner, Robert A1 - Afraj, Shahabaz A1 - Sequeira, Gerald Joy A1 - Schneider, Kilian A1 - Brandmeier, Thomas A1 - Soloiu, Valentin T1 - Comparison and Evaluation of Algorithms for LiDAR-Based Contour Estimation in Integrated Vehicle Safety JF - IEEE Transactions on Intelligent Transportation Systems UR - https://doi.org/10.1109/TITS.2020.3044753 KW - contour estimation KW - curve similarity KW - integrated safety KW - intelligent vehicles KW - inverse analysis KW - light detection and ranging (LiDAR) Y1 - 2020 UR - https://doi.org/10.1109/TITS.2020.3044753 SN - 1558-0016 VL - 23 IS - 5 SP - 3925 EP - 3942 PB - IEEE CY - New York ER - TY - INPR A1 - Afraj, Shahabaz A1 - Vaculin, Ondrej A1 - Böhmländer, Dennis A1 - Hynčík, Luděk T1 - Vehicle Crash Simulation Models for Reinforcement Learning driven crash-detection algorithm calibration T2 - Research Square N2 - The development of finite element vehicle models for crash simulations is a highly complex task. The main aim of these models is to simulate a variety of crash scenarios and assess all the safety systems for their respective performances. These vehicle models possess a substantial amount of data pertaining to the vehicle's geometry, structure, materials, etc., and are used to estimate a large set of system and component level characteristics using crash simulations. It is understood that even the most well-developed simulation models are prone to deviations in estimation when compared to real-world physical test results. This is generally due to our inability to model the chaos and uncertainties introduced in the real world. Such unavoidable deviations render the use of virtual simulations ineffective for the calibration process of the algorithms that activate the restraint systems in the event of a crash (crash-detection algorithm). In the scope of this research, authors hypothesize the possibility of accounting for such variations introduced in the real world by creating a feedback loop between real-world crash tests and crash simulations. To accomplish this, a Reinforcement Learning (RL) compatible virtual surrogate model is used, which is adapted from crash simulation models. Hence, a conceptual methodology is illustrated in this paper for developing an RL-compatible model that can be trained using the results of crash simulations and crash tests. As the calibration of the crash-detection algorithm is fundamentally dependent upon the crash pulses, the scope of the expected output is limited to advancing the ability to estimate crash pulses. Furthermore, the real-time implementation of the methodology is illustrated using an actual vehicle model. UR - https://doi.org/10.21203/rs.3.rs-3004299/v1 KW - virtual vehicle models KW - crash tests KW - crash simulations KW - surrogate model KW - crash-detection algorithm KW - reinforcement Learning Y1 - 2023 UR - https://doi.org/10.21203/rs.3.rs-3004299/v1 UR - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:573-37632 SN - 2693-5015 PB - Research Square CY - Durham ER - TY - JOUR A1 - Afraj, Shahabaz A1 - Vaculin, Ondrej A1 - Böhmländer, Dennis A1 - Hynčík, Luděk T1 - Vehicle crash simulation models for reinforcement learning driven crash-detection algorithm calibration JF - Advanced Modeling and Simulation in Engineering Sciences N2 - The development of finite element vehicle models for crash simulations is a highly complex task. The main aim of these models is to simulate a variety of crash scenarios and assess all the safety systems for their respective performances. These vehicle models possess a substantial amount of data pertaining to the vehicle’s geometry, structure, materials, etc., and are used to estimate a large set of system and component level characteristics using crash simulations. It is understood that even the most well-developed simulation models are prone to deviations in estimation when compared to real-world physical test results. This is generally due to our inability to model the chaos and uncertainties introduced in the real world. Such unavoidable deviations render the use of virtual simulations ineffective for the calibration process of the algorithms that activate the restraint systems in the event of a crash (crash-detection algorithm). In the scope of this research, authors hypothesize the possibility of accounting for such variations introduced in the real world by creating a feedback loop between real-world crash tests and crash simulations. To accomplish this, a Reinforcement Learning (RL) compatible virtual surrogate model is used, which is adapted from crash simulation models. Hence, a conceptual methodology is illustrated in this paper for developing an RL-compatible model that can be trained using the results of crash simulations and crash tests. As the calibration of the crash-detection algorithm is fundamentally dependent upon the crash pulses, the scope of the expected output is limited to advancing the ability to estimate crash pulses. Furthermore, the real-time implementation of the methodology is illustrated using an actual vehicle model. UR - https://doi.org/10.1186/s40323-025-00288-4 Y1 - 2025 UR - https://doi.org/10.1186/s40323-025-00288-4 UR - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:573-60861 SN - 2213-7467 VL - 12 IS - 1 PB - SpringerOpen CY - Berlin ER - TY - CHAP A1 - Afraj, Shahabaz A1 - Böhmländer, Dennis A1 - Vaculin, Ondrej A1 - Hynčík, Luděk T1 - Quantification methodology for crash behavior comparison between virtual crash simulations and real-time crash tests T2 - FISITA World Congress 2021 Y1 - 2021 UR - https://www.fisita.com/library/f2021-pif-072 PB - FISITA CY - Bishops Stortford ER -