@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{SequeiraAfrajLugneretal.2019, author = {Sequeira, Gerald Joy and Afraj, Shahabaz and Lugner, Robert and Brandmeier, Thomas}, title = {LiDAR based prediction and contact based validation of crash parameters for a preemptive restraint strategy}, booktitle = {2019 IEEE International Conference on Vehicular Electronics and Safety (ICVES)}, publisher = {IEEE}, address = {Piscataway}, isbn = {978-1-7281-3473-4}, doi = {https://doi.org/10.1109/ICVES.2019.8906354}, year = {2019}, language = {en} } @inproceedings{SequeiraSurveAfrajetal.2020, author = {Sequeira, Gerald Joy and Surve, Manasi and Afraj, Shahabaz and Brandmeier, Thomas}, title = {A novel concept for validation of pre-crash perception sensor information using contact sensor}, 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/ITSC45102.2020.9294242}, year = {2020}, language = {en} } @inproceedings{SequeiraAfrajSurveetal.2020, author = {Sequeira, Gerald Joy and Afraj, Shahabaz and Surve, Manasi and Brandmeier, Thomas}, title = {LiDAR point cloud analysis for vehicle contour estimation using polynomial approximation and curvature breakdown}, booktitle = {2020 IEEE 92nd Vehicular Technology Conference (VTC2020-Fall) Proceedings}, publisher = {IEEE}, address = {Piscataway (NJ)}, isbn = {978-1-7281-9484-4}, issn = {2577-2465}, doi = {https://doi.org/10.1109/VTC2020-Fall49728.2020.9348457}, year = {2020}, language = {en} } @article{MothershedLugnerAfrajetal.2020, author = {Mothershed, David Michael and Lugner, Robert and Afraj, Shahabaz and Sequeira, Gerald Joy and Schneider, Kilian and Brandmeier, Thomas and Soloiu, Valentin}, title = {Comparison and Evaluation of Algorithms for LiDAR-Based Contour Estimation in Integrated Vehicle Safety}, volume = {23}, journal = {IEEE Transactions on Intelligent Transportation Systems}, number = {5}, publisher = {IEEE}, address = {New York}, issn = {1558-0016}, doi = {https://doi.org/10.1109/TITS.2020.3044753}, pages = {3925 -- 3942}, year = {2020}, language = {en} } @unpublished{AfrajVaculinBoehmlaenderetal.2023, author = {Afraj, Shahabaz and Vaculin, Ondrej and B{\"o}hml{\"a}nder, Dennis and Hynč{\´i}k, Luděk}, title = {Vehicle Crash Simulation Models for Reinforcement Learning driven crash-detection algorithm calibration}, titleParent = {Research Square}, publisher = {Research Square}, address = {Durham}, doi = {https://doi.org/10.21203/rs.3.rs-3004299/v1}, year = {2023}, abstract = {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.}, language = {en} } @article{AfrajVaculinBoehmlaenderetal.2025, author = {Afraj, Shahabaz and Vaculin, Ondrej and B{\"o}hml{\"a}nder, Dennis and Hynč{\´i}k, Luděk}, title = {Vehicle crash simulation models for reinforcement learning driven crash-detection algorithm calibration}, volume = {12}, pages = {17}, journal = {Advanced Modeling and Simulation in Engineering Sciences}, number = {1}, publisher = {SpringerOpen}, address = {Berlin}, issn = {2213-7467}, doi = {https://doi.org/10.1186/s40323-025-00288-4}, year = {2025}, abstract = {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.}, language = {en} } @inproceedings{AfrajBoehmlaenderVaculinetal.2021, author = {Afraj, Shahabaz and B{\"o}hml{\"a}nder, Dennis and Vaculin, Ondrej and Hynč{\´i}k, Luděk}, title = {Quantification methodology for crash behavior comparison between virtual crash simulations and real-time crash tests}, booktitle = {FISITA World Congress 2021}, publisher = {FISITA}, address = {Bishops Stortford}, url = {https://www.fisita.com/library/f2021-pif-072}, year = {2021}, language = {en} }