@unpublished{AfrajVaculinBoehmlaenderetal.2023, author = {Afraj, Shahabaz and Vaculin, Ondrej and B{\"o}hml{\"a}nder, Dennis and Hynč{\´i}k, Luděk}, title = {Vehicle Crash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} } @inproceedings{PlaschkiesVaculin2020, author = {Plaschkies, Franz and Vaculin, Ondrej}, title = {Estimation of the Impact of Human Body Variation on Its Crash Behavior Using Machine Learning Methods}, booktitle = {FISITA Web Congress 2020}, publisher = {Fisita}, address = {Bishops Stortford}, url = {https://www.fisita.com/library/f2020-pif-051}, year = {2020}, language = {en} } @inproceedings{SlavikVaculin2021, author = {Slav{\´i}k, Martin and Vaculin, Ondrej}, title = {Concept of Mission Control System for IN2Lab testing field for Automated Driving}, booktitle = {FISITA World Congress 2021}, publisher = {FISITA}, address = {Bishops Stortford}, url = {https://www.fisita.com/library/f2021-acm-119}, year = {2021}, language = {en} } @inproceedings{Vaculin2019, author = {Vaculin, Ondrej}, title = {Assessment of a pilot deployment of an automated shuttle bus}, booktitle = {Autosympo 2019}, doi = {http://www.cas-sae.cz/autosympo.php}, year = {2019}, language = {en} } @inbook{Vaculin2022, author = {Vaculin, Ondrej}, title = {Automatizovan{\´e} ř{\´i}zen{\´i}}, booktitle = {Automatizovan{\´e} ř{\´i}zen{\´i} vozidel a autonomn{\´i} doprava: Technick{\´e} a humanitn{\´i} perspektivy}, editor = {Čern{\´y}, David and Vaculin, Ondrej and Z{\´a}mečn{\´i}k, Petr}, publisher = {Academia}, address = {Prag}, isbn = {978-80-200-3358-1}, pages = {131 -- 152}, year = {2022}, language = {mul} } @inproceedings{VanderschurenVaculinNewlands2025, author = {Vanderschuren, Maria and Vaculin, Ondrej and Newlands, Alexandra}, title = {Vehicle rescue sheets: opportunities and barriers in the South African context}, booktitle = {43rd Annual Southern African Transport Conference 2025}, publisher = {SATC}, address = {Johannesburg}, isbn = {978-0-0370-8021-0}, url = {http://hdl.handle.net/2263/104908}, year = {2025}, language = {en} } @inproceedings{VaculinHaryantodeBorba2025, author = {Vaculin, Ondrej and Haryanto, Aditya and de Borba, Thiago}, title = {Potential of infrastructure-based sensors to road safety}, booktitle = {43rd Annual Southern African Transport Conference 2025}, publisher = {SATC}, address = {Johannesburg}, isbn = {978-0-0370-8021-0}, url = {http://hdl.handle.net/2263/104958}, year = {2025}, language = {en} } @unpublished{HaryantoVaculin2025, author = {Haryanto, Aditya and Vaculin, Ondrej}, title = {YoFlow Method for Scenario Based Automatic Accident Detection}, publisher = {TechRxiv}, address = {Piscataway}, doi = {https://doi.org/10.36227/techrxiv.175099959.99453472/v1}, year = {2025}, abstract = {Recent advances in sensor and computing technologies have enabled roadside units (RSUs) to not only monitor traffic flow but also process data in real time to improve road safety. However, leveraging RSUs for proactive accident detection remains a challenging and underexplored task, partly due to the lack of diverse accident data. To address this, this study proposes two key contributions: (i) a scenario based synthetic data generation framework, and (ii) YoFlow, a novel system for vehicle-to-vehicle accident detection from a simulated RSU camera perspective. The proposed framework leverages the PEGASUS methodology and the BeamNG.tech simulation platform to create the SB-SIF dataset, which includes five representative intersection crash scenarios derived from German accident data. SB-SIF dataset contains 914 crash videos, 123 near-miss events, and 924 normal traffic instances and is publicly available at: https://doi.org/10.5281/zenodo.15267252. The proposed YoFlow system identifies accidents by analyzing temporal variations in vehicle speed vectors, using YOLO for vehicle classification and CUDA-accelerated dense optical flow to capture abrupt motion changes. Extracted features are processed and classified using an XGBoost model, achieving 94\% recall and 90\% precision in accident detection.}, language = {en} } @article{HaryantoVaculin2025, author = {Haryanto, Aditya and Vaculin, Ondrej}, title = {YoFlow Method for Scenario Based Automatic Accident Detection}, volume = {7}, journal = {IEEE Open Journal of Intelligent Transportation Systems}, publisher = {IEEE}, address = {New York}, issn = {2687-7813}, doi = {https://doi.org/10.1109/OJITS.2025.3639557}, pages = {61 -- 73}, year = {2025}, abstract = {Recent advances in sensor and computing technologies have enabled road side units (RSUs) to not only monitor traffic flow but also process data in real time to improve road safety. However, leveraging RSUs for proactive accident detection remains a challenging and underexplored task, partly due to the lack of diverse accident data. To address this, this study proposes two key contributions: (i) a scenario-based synthetic data generation framework, and (ii) YoFlow, a novel system for vehicle-tovehicle accident detection from a simulated RSU camera perspective. The proposed framework leverages the PEGASUS method for scenario generation strategy and BeamNG.tech for generating synthetic traffic videos. This approach led to the development of the SB-SIF dataset, which includes five representative intersection crash scenarios derived from German accident data. The SB-SIF dataset contains 914 crash videos, 123 near-miss events, and 924 normal traffic instances and is publicly available at: https://doi.org/10.5281/zenodo.15267252. The proposed YoFlow system identifies accidents by analyzing temporal variations in vehicle speed vectors, using YOLO for vehicle classification and CUDA-accelerated dense optical flow to capture abrupt motion changes. The extracted features are processed and classified using an XGBoost model, achieving 94\% recall and 90\% precision in accident detection.}, language = {en} } @article{GrafSteinhauserVaculinetal.2025, author = {Graf, Michael and Steinhauser, Dagmar and Vaculin, Ondrej and Brandmeier, Thomas}, title = {Impact of Adverse Weather on Road Safety: A Survey of Test Methods for Enhancing Safety of Automated Vehicles and Sensor Robustness in Challenging Environmental Conditions}, volume = {13}, journal = {IEEE Access}, publisher = {IEEE}, address = {New York}, issn = {2169-3536}, doi = {https://doi.org/10.1109/ACCESS.2025.3622501}, pages = {179817 -- 179838}, year = {2025}, abstract = {Adverse weather conditions can significantly affect environmental sensors and reduce the ability of automated vehicles to interpret the environment. This can lead to the failure of driving and safety functions. To validate and increase the robustness of these, several adverse weather test methods have been introduced in recent years. The survey first gives an overview of the current traffic and accident analysis with a focus on adverse weather influences to assess the most relevant weather phenomena. Overall, rain and glare are the statistically most important adverse weather phenomena in terms of accidents. Heavy fog is rare but can cause very serious accidents. In a second step, meteorological knowledge is incorporated and the key environmental indicators for the respective weather conditions are determined, particularly taking the environmental sensor characteristics into account. Both sets of information are then utilized to provide a qualified overview of simulative and physical test methods used to reproduce critical adverse weather situations. These methods are employed to investigate the disturbance effects of inclement weather on sensors and to test the robustness of automated vehicles. The paper reveals that various approaches are used to reproduce weather effects and that test methods, each with different advantages and disadvantages, have been developed to varying degrees depending on the type of the weather phenomena. Overall, this review integrates perspectives from road safety research, meteorology, and testing methodologies to provide new insights into the validation of automated driving systems in challenging weather conditions.}, language = {en} } @article{deBorbaVaculinMarzbanietal.2023, author = {de Borba, Thiago and Vaculin, Ondrej and Marzbani, Hormoz and Jazar, Reza}, title = {Increasing Safety of Automated Driving by Infrastructure-Based Sensors}, volume = {11}, journal = {IEEE Access}, publisher = {IEEE}, address = {New York}, issn = {2169-3536}, doi = {https://doi.org/10.1109/ACCESS.2023.3311136}, pages = {94974 -- 94991}, year = {2023}, abstract = {This paper describes the development of an intelligent infrastructure, a test field, for the safety assurance of automated vehicles within the research project Ingolstadt Innovation Laboratory (IN2Lab). It includes a description of the test field architecture, the RoadSide Units (RSU) concept based on infrastructure-based sensors, the environment perception system, and the mission control system. The study also proposes a global object fusion method to fuse objects detected by different RSUs and investigate the overall measurement accuracy obtained from the usage of different infrastructure-based sensors. Furthermore, it presents four use cases: traffic monitoring, assisted perception, collaborative perception, and extended perception. The traffic monitoring, based on the perception information provided by each roadside unit, generates a global fused object list and monitors the state of the traffic participants. The assisted perception, using vehicle-to-infrastructure communication, broadcasts the state information of the traffic participants to the connected vehicles. The collaborative perception creates a global fused object list with the local detections of connected vehicles and the detections provided by the roadside units, making it available for all connected vehicles. Lastly, the extended environment perception monitors specific locations, recognizes critical scenarios involving vulnerable road users and automated vehicles, and generates a suitable avoidance maneuver to avoid or mitigate the occurrence of collisions.}, 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{deBorbaVaculinMarzbanietal.2025, author = {de Borba, Thiago and Vaculin, Ondrej and Marzbani, Hormoz and Jazar, Reza}, title = {Cooperative Maneuver Coordination: Smart Infrastructure for VRUs Collision Avoidance with Trajectory Planning}, booktitle = {Proceedings: 2025 IEEE 3rd International Conference on Mobility, Operations, Services and Technologies, MOST 2025}, publisher = {IEEE}, address = {Piscataway}, isbn = {979-8-3315-1160-9}, doi = {https://doi.org/10.1109/MOST65065.2025.00015}, pages = {51 -- 58}, year = {2025}, language = {en} } @article{deBorbaVaculinMarzbanietal.2025, author = {de Borba, Thiago and Vaculin, Ondrej and Marzbani, Hormoz and Jazar, Reza}, title = {Increasing Safety of Vulnerable Road Users in Scenarios With Occlusion: A Collaborative Approach for Smart Infrastructures and Automated Vehicles}, volume = {13}, journal = {IEEE Access}, publisher = {IEEE}, address = {New York}, issn = {2169-3536}, doi = {https://doi.org/10.1109/ACCESS.2025.3527865}, pages = {8851 -- 8885}, year = {2025}, abstract = {The impact of Automated Vehicles (AVs) on road traffic safety has become the focus of discussions among governmental organizations, academia, stakeholders, and OEMs. Questions about how safe the automated driving features should be and how the road infrastructure should be improved for the arrival of this new technology must be clarified to enable full acceptance by the customers and society and prepare the mobility of future cities. The fundamental architecture of automated vehicles comprises perception, planning, decision, and actuation. The operation of the perception system, which is responsible for understanding the environment in which the vehicle is inserted, relies mainly on the onboard sensors. However, the available ranging and vision sensors, e.g., LiDAR, radar, and camera, have several limitations. Scenarios with occlusion present a real challenge for state-of-the-art perception systems. The occlusion, caused by obstructing the sensors' detection field, limits the vehicle's perception ability and inhibits the detection of other road users in the surroundings, especially Vulnerable Road Users (VRUs). Infrastructure composed of Roadside Units (RSUs) equipped with infrastructure-based sensors can overcome the perception limitations of a system based solely on onboard sensors by monitoring the road environment with a larger field of view and reduced sensitivity to occlusion. This paper presents a collaborative approach for smart infrastructures and automated vehicles for vulnerable road users' collision avoidance. The proposed extended perception system comprises four main modules: traffic monitoring, long-term motion prediction, collision risk assessment, and trajectory planning. In the event of a safety-critical scenario, the infrastructure generates a safe and comfortable evasive maneuver to avoid a possible collision. Hence, the proposed approach provides a complete solution to overcome scenarios with occluded VRUs. It allows AVs to react to a critical situation with a longer time-to-collision than other systems relying only on onboard sensors, increasing the chance of successful avoidance even when implementing smoother maneuvers. This contributes considerably to the safe and comfortable operation of automated vehicles.}, language = {en} } @article{DoenmezVaculindeBorba2024, author = {D{\"o}nmez, {\"O}mer and Vaculin, Ondrej and de Borba, Thiago}, title = {A Cost Effective Solution to an Automated Valet Parking System}, volume = {25}, journal = {International Journal of Automotive Technology}, number = {2}, publisher = {Springer}, address = {Heidelberg}, issn = {1976-3832}, doi = {https://doi.org/10.1007/s12239-024-00031-9}, pages = {369 -- 380}, year = {2024}, abstract = {Automated Valet Parking Systems (AVPS) relieve the driver of the entire parking process. Many of the systems known today rely on a combination of automotive sensors with sensors of the infrastructure. For this purpose, parking facilities are equipped with comprehensive sensor technology to support the vehicles in environment sensing and route planning. This approach is comparatively expensive which is why many parking operators don't provide that technology to their customers. This paper proposes a lean AVPS system architecture that requires minimal effort to adapt the infrastructure. At the same time, state-of-the-art vehicle technology is used to make AVPS more profitable overall. At the beginning, an overview will be given describing the state of the art of AVPS. Subsequently, requirements for the AVPS will be elaborated, whereby the system can be designed and implemented in the following. Finally, the presentation of simulation results shows that one doesn't have to extend the infrastructure with sensors to develop a safe and reliable AVPS.}, language = {en} } @inproceedings{PlaschkiesPossoliVaculinetal.2023, author = {Plaschkies, Franz and Possoli, Ketlen and Vaculin, Ondrej and Schumacher, Axel and de Andrade Junior, Pedro}, title = {Evaluation Approach for Machine Learning Concepts in Occupant Protection Based on Multi-Attribute Decision Making}, pages = {23-0055}, booktitle = {Proceedings of the 27th International Technical Conference on the Enhanced Safety of Vehicles}, publisher = {NHTSA}, address = {Washington}, url = {https://www-nrd.nhtsa.dot.gov/departments/esv/27th/TOC.htm}, year = {2023}, language = {en} } @inproceedings{deBorbaVaculinPatel2021, author = {de Borba, Thiago and Vaculin, Ondrej and Patel, Parth}, title = {Concept of a Vehicle Platform for Development and Testing of Low-Speed Automated Driving Functions}, booktitle = {FISITA World Congress 2021}, publisher = {FISITA}, address = {Bishops Stortford}, url = {https://www.fisita.com/library/f2021-acm-118}, year = {2021}, language = {en} } @inbook{Vaculin2022, author = {Vaculin, Ondrej}, title = {Senzory pro automatizovan{\´e} ř{\´i}zen{\´i}}, booktitle = {Automatizovan{\´e} ř{\´i}zen{\´i} vozidel a autonomn{\´i} doprava: Technick{\´e} a humanitn{\´i} perspektivy}, editor = {Čern{\´y}, David and Vaculin, Ondrej and Z{\´a}mečn{\´i}k, Petr}, publisher = {Academia}, address = {Prag}, isbn = {978-80-200-3358-1}, pages = {153 -- 178}, year = {2022}, language = {mul} } @article{VaculinGellrichMatawaetal.2020, author = {Vaculin, Ondrej and Gellrich, Michael and Matawa, Robert and Witschass, Steffen}, title = {Testing of automated driving systems}, volume = {17}, journal = {MECCA : Journal of Middle European Construction and Design of Cars}, number = {1}, publisher = {Czech Technical University}, address = {Prag}, issn = {1804-9338}, doi = {https://doi.org/10.14311/mecdc.2020.01.02}, pages = {7 -- 13}, year = {2020}, abstract = {The automated driving requires new testing approaches, which are more complex than the current testing systems. The complexity and requirements for accuracy is important, because of interconnection of virtual with physical testing. This paper presents a generic approach to testing of automated driving functions and demonstrates its implementation on measurement of two scenarios.}, language = {en} } @inproceedings{Vaculin2023, author = {Vaculin, Ondrej}, title = {Holistic Environment for Development and Testing of Cooperative, Connected and Automated Mobility Functions}, booktitle = {FISITA World Congress 2023}, publisher = {FISITA}, address = {Bishops Stortford}, url = {https://www.fisita.com/library/fwc2023-sca-025}, year = {2023}, language = {en} } @inproceedings{BruehlShanmuganathanEweckeretal.2025, author = {Br{\"u}hl, Tim and Shanmuganathan, Aravind Kumar and Ewecker, Lukas and Schwager, Robin and Sohn, Tin Stribor and Vaculin, Ondrej and Hohmann, S{\"o}ren}, title = {Consideration of Safety Aspects in a Camera-Aided, Radar-Based Free Space Detection}, booktitle = {2024 IEEE 27th International Conference on Intelligent Transportation Systems (ITSC)}, publisher = {IEEE}, address = {Piscataway}, isbn = {979-8-3315-0592-9}, doi = {https://doi.org/10.1109/ITSC58415.2024.10920194}, pages = {3997 -- 4004}, year = {2025}, language = {en} } @inproceedings{PlaschkiesVaculinPelissonetal.2022, author = {Plaschkies, Franz and Vaculin, Ondrej and Pelisson, Angelo A. and Schumacher, Axel}, title = {Schnelle Absch{\"a}tzung des Crashverhaltens von Insassen unter Ber{\"u}cksichtigung der Vielfalt des Menschen}, booktitle = {Fahrzeugsicherheit 2022: Auf dem Weg zur Fahrzeugsicherheit 2030}, subtitle = {Robustheit, Datenintensit{\"a}t und Vorhersagekraft von Metamodellen}, publisher = {VDI Verlag}, address = {D{\"u}sseldorf}, isbn = {978-3-18-092387-1}, doi = {https://doi.org/10.51202/9783181023877-313}, pages = {313 -- 326}, year = {2022}, language = {de} } @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} } @inproceedings{NegrideAzeredoVaculindaCostaOliveira2020, author = {Negri de Azeredo, Rodrigo and Vaculin, Ondrej and da Costa Oliveira, Gustavo Henrique}, title = {Automatic Car Reverse Braking System Based on a ToF Camera Sensor}, booktitle = {FISITA Web Congress 2020}, publisher = {FISITA}, address = {Bishops Stortford}, url = {https://www.fisita.com/library/f2020-pif-049}, year = {2020}, language = {en} } @inproceedings{PlaschkiesVaculinSchumacher2021, author = {Plaschkies, Franz and Vaculin, Ondrej and Schumacher, Axel}, title = {Assessment of the Influence of Human Body Diversity on Passive Safety Systems}, booktitle = {FISITA World Congress 2021}, subtitle = {A State-of-the-art Overview}, publisher = {FISITA}, address = {Bishops Stortford}, url = {https://www.fisita.com/library/f2021-pif-071}, year = {2021}, language = {en} } @inproceedings{NietoOtaeguiPanouetal.2025, author = {Nieto, Marcos and Otaegui, Oihana and Panou, Maria and Birkner, Christian and Vaculin, Ondrej and Rodr{\´i}guez, Ariadna}, title = {AWARE2ALL: Human Centric Interaction and Safety Systems for Increasing the Share of Automated Vehicles}, booktitle = {Transport Transitions: Advancing Sustainable and Inclusive Mobility, Proceedings of the 10th TRA Conference, 2024, Dublin, Ireland-Volume 1: Safe and Equitable Transport}, editor = {McNally, Ciaran and Carroll, P{\´a}raic and Martinez-Pastor, Beatriz and Ghosh, Bidisha and Efthymiou, Marina and Valantasis-Kanellos, Nikolaos}, publisher = {Springer}, address = {Cham}, isbn = {978-3-031-88974-5}, doi = {https://doi.org/10.1007/978-3-031-88974-5_112}, pages = {779 -- 785}, year = {2025}, abstract = {The AWARE2ALL project is designed to address the new challenges of Highly Automated Vehicles (HAVs) from a human-centric perspective. These vehicles will allow occupants to engage in non-driving activities, rising research questions about occupant behavior, activities, and Human-Machine Interfaces (HMI) to keep them aware of the situation and the automation mode. The project aims to ensure safe operation of HAVs by developing safety and HMI systems that provide a holistic understanding of the scene. This includes continuous monitoring of the interior situation and advanced passive safety systems for occupant safety, as well as a surround perception system and external HMI for the safety of Human Road Users (HRUs). AWARE2ALL is paving the way for HAV deployment by effectively addressing changes in road safety and interactions between different road users caused by the emergence of HAVs. It is developing innovative technologies, assessment tools, and methodologies to adapt to new scenarios in mixed traffic. The project builds on previous research and aims to mitigate new safety risks associated with the introduction of HAVs.}, language = {en} } @unpublished{DoenmezTejerodelaPiedraKloseetal.2026, author = {D{\"o}nmez, {\"O}mer and Tejero de la Piedra, Ricardo and Klose, Simona and Riolet, Matthieu and Rozek, Lukas and Vaculin, Ondrej and Hach, Christian}, title = {Approach for Passive Safety Assessment of Rearward-Sitting Occupants}, publisher = {Zenodo}, address = {Genf}, doi = {https://doi.org/10.5281/zenodo.18338626}, year = {2026}, abstract = {The introduction of highly automated vehicles (HAVs) will allow vehicle occupants to take advantage of new seating configurations, such as sitting rearward in the first row. One critical aspect of assessing occupant safety during high-speed impacts is the lack of a dedicated safety framework for rearward-facing passengers in the first row. This paper introduces a method to develop new assessment criteria for these novel seat configurations. Thus, this research presents some preliminary results of rearward-facing occupant injury biomechanics analyses carried out employing a variety of anthropomorphic test devices (ATDs) and the VIVA+ 50M human body model (HBM), restrained with different belt configurations and considering different seat typologies. It reviews the suitability of 50th percentile male ATDs to capture a biofidelic engagement with the seat structure and belt system and evaluates the reaction loads on the occupant, along with the energy management resulting from seat back rotational stiffness and energy-absorbing foams layered behind the seat cushion. Based on the results, the THOR-AV-50M is a suitable candidate for further biofidelity analysis. Torso occupant loads can be effectively reduced utilizing seat back rotation but pelvis load management requires further studies.}, language = {en} } @inproceedings{DoenmezTejerodelaPiedraKloseetal.2026, author = {D{\"o}nmez, {\"O}mer and Tejero de la Piedra, Ricardo and Klose, Simona and Riolet, Matthieu and Rozek, Lukas and Vaculin, Ondrej and Hach, Christian}, title = {Approach for Passive Safety Assessment of Rearward-Sitting Occupants}, booktitle = {2025 IEEE International Conference on Vehicular Electronics and Safety (ICVES)}, publisher = {IEEE}, address = {Piscataway}, isbn = {978-1-6654-7778-9}, doi = {https://doi.org/10.1109/ICVES65691.2025.11376179}, pages = {445 -- 452}, year = {2026}, language = {en} }