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 - CHAP A1 - Plaschkies, Franz A1 - Vaculin, Ondrej T1 - Estimation of the Impact of Human Body Variation on Its Crash Behavior Using Machine Learning Methods T2 - FISITA Web Congress 2020 Y1 - 2020 UR - https://www.fisita.com/library/f2020-pif-051 PB - Fisita CY - Bishops Stortford ER - TY - CHAP A1 - Slavík, Martin A1 - Vaculin, Ondrej T1 - Concept of Mission Control System for IN2Lab testing field for Automated Driving T2 - FISITA World Congress 2021 Y1 - 2021 UR - https://www.fisita.com/library/f2021-acm-119 PB - FISITA CY - Bishops Stortford ER - TY - CHAP A1 - Vaculin, Ondrej T1 - Assessment of a pilot deployment of an automated shuttle bus T2 - Autosympo 2019 UR - http://www.cas-sae.cz/autosympo.php Y1 - 2019 UR - http://www.cas-sae.cz/autosympo.php ER - TY - CHAP A1 - Vaculin, Ondrej ED - Černý, David ED - Vaculin, Ondrej ED - Zámečník, Petr T1 - Automatizované řízení T2 - Automatizované řízení vozidel a autonomní doprava: Technické a humanitní perspektivy Y1 - 2022 SN - 978-80-200-3358-1 SP - 131 EP - 152 PB - Academia CY - Prag ER - TY - CHAP A1 - Vanderschuren, Maria A1 - Vaculin, Ondrej A1 - Newlands, Alexandra T1 - Vehicle rescue sheets: opportunities and barriers in the South African context T2 - 43rd Annual Southern African Transport Conference 2025 Y1 - 2025 UR - http://hdl.handle.net/2263/104908 SN - 978-0-0370-8021-0 PB - SATC CY - Johannesburg ER - TY - CHAP A1 - Vaculin, Ondrej A1 - Haryanto, Aditya A1 - de Borba, Thiago T1 - Potential of infrastructure-based sensors to road safety T2 - 43rd Annual Southern African Transport Conference 2025 Y1 - 2025 UR - http://hdl.handle.net/2263/104958 SN - 978-0-0370-8021-0 PB - SATC CY - Johannesburg ER - TY - INPR A1 - Haryanto, Aditya A1 - Vaculin, Ondrej T1 - YoFlow Method for Scenario Based Automatic Accident Detection N2 - 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. UR - https://doi.org/10.36227/techrxiv.175099959.99453472/v1 Y1 - 2025 UR - https://doi.org/10.36227/techrxiv.175099959.99453472/v1 UR - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:573-60556 PB - TechRxiv CY - Piscataway ER - TY - JOUR A1 - Haryanto, Aditya A1 - Vaculin, Ondrej T1 - YoFlow Method for Scenario Based Automatic Accident Detection JF - IEEE Open Journal of Intelligent Transportation Systems N2 - 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. UR - https://doi.org/10.1109/OJITS.2025.3639557 KW - accident detection KW - traffic accident KW - surveillance camera KW - optical flow KW - traffic scenarios Y1 - 2025 UR - https://doi.org/10.1109/OJITS.2025.3639557 SN - 2687-7813 VL - 7 SP - 61 EP - 73 PB - IEEE CY - New York ER - TY - JOUR A1 - Graf, Michael A1 - Steinhauser, Dagmar A1 - Vaculin, Ondrej A1 - Brandmeier, Thomas T1 - 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 JF - IEEE Access N2 - 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. UR - https://doi.org/10.1109/ACCESS.2025.3622501 Y1 - 2025 UR - https://doi.org/10.1109/ACCESS.2025.3622501 UR - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:573-63190 SN - 2169-3536 VL - 13 SP - 179817 EP - 179838 PB - IEEE CY - New York ER - TY - JOUR A1 - de Borba, Thiago A1 - Vaculin, Ondrej A1 - Marzbani, Hormoz A1 - Jazar, Reza T1 - Increasing Safety of Automated Driving by Infrastructure-Based Sensors JF - IEEE Access N2 - 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. UR - https://doi.org/10.1109/ACCESS.2023.3311136 KW - Automated vehicles KW - infrastructure-based sensors KW - safety KW - test field Y1 - 2023 UR - https://doi.org/10.1109/ACCESS.2023.3311136 UR - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:573-39623 SN - 2169-3536 VL - 11 SP - 94974 EP - 94991 PB - IEEE CY - New York 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 - de Borba, Thiago A1 - Vaculin, Ondrej A1 - Marzbani, Hormoz A1 - Jazar, Reza T1 - Cooperative Maneuver Coordination: Smart Infrastructure for VRUs Collision Avoidance with Trajectory Planning T2 - Proceedings: 2025 IEEE 3rd International Conference on Mobility, Operations, Services and Technologies, MOST 2025 UR - https://doi.org/10.1109/MOST65065.2025.00015 Y1 - 2025 UR - https://doi.org/10.1109/MOST65065.2025.00015 SN - 979-8-3315-1160-9 SP - 51 EP - 58 PB - IEEE CY - Piscataway ER - TY - JOUR A1 - de Borba, Thiago A1 - Vaculin, Ondrej A1 - Marzbani, Hormoz A1 - Jazar, Reza T1 - Increasing Safety of Vulnerable Road Users in Scenarios With Occlusion: A Collaborative Approach for Smart Infrastructures and Automated Vehicles JF - IEEE Access N2 - 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. UR - https://doi.org/10.1109/ACCESS.2025.3527865 Y1 - 2025 UR - https://doi.org/10.1109/ACCESS.2025.3527865 UR - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:573-56466 SN - 2169-3536 VL - 13 SP - 8851 EP - 8885 PB - IEEE CY - New York ER - TY - JOUR A1 - Dönmez, Ömer A1 - Vaculin, Ondrej A1 - de Borba, Thiago T1 - A Cost Effective Solution to an Automated Valet Parking System JF - International Journal of Automotive Technology N2 - 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. UR - https://doi.org/10.1007/s12239-024-00031-9 KW - Automated driving KW - Automated valet parking KW - Trajectory planning KW - Smart parking KW - System architecture Y1 - 2024 UR - https://doi.org/10.1007/s12239-024-00031-9 UR - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:573-46333 SN - 1976-3832 SN - 1229-9138 VL - 25 IS - 2 SP - 369 EP - 380 PB - Springer CY - Heidelberg ER - TY - CHAP A1 - Plaschkies, Franz A1 - Possoli, Ketlen A1 - Vaculin, Ondrej A1 - Schumacher, Axel A1 - de Andrade Junior, Pedro T1 - Evaluation Approach for Machine Learning Concepts in Occupant Protection Based on Multi-Attribute Decision Making T2 - Proceedings of the 27th International Technical Conference on the Enhanced Safety of Vehicles Y1 - 2023 UR - https://www-nrd.nhtsa.dot.gov/departments/esv/27th/TOC.htm PB - NHTSA CY - Washington ER - TY - CHAP A1 - de Borba, Thiago A1 - Vaculin, Ondrej A1 - Patel, Parth T1 - Concept of a Vehicle Platform for Development and Testing of Low-Speed Automated Driving Functions T2 - FISITA World Congress 2021 Y1 - 2021 UR - https://www.fisita.com/library/f2021-acm-118 PB - FISITA CY - Bishops Stortford ER - TY - CHAP A1 - Vaculin, Ondrej ED - Černý, David ED - Vaculin, Ondrej ED - Zámečník, Petr T1 - Senzory pro automatizované řízení T2 - Automatizované řízení vozidel a autonomní doprava: Technické a humanitní perspektivy Y1 - 2022 SN - 978-80-200-3358-1 SP - 153 EP - 178 PB - Academia CY - Prag ER - TY - JOUR A1 - Vaculin, Ondrej A1 - Gellrich, Michael A1 - Matawa, Robert A1 - Witschass, Steffen T1 - Testing of automated driving systems JF - MECCA : Journal of Middle European Construction and Design of Cars N2 - 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. N2 - Automatizované rízení vyžaduje nové testovací prístupy, které jsou daleko komplexnejší než soucasné testovací systémy. Komplexnost a požadavky na presnost jsou duležité z pohledu na propojení fyzického a virtuálního testování. Tento clánek prezentuje obecný prístup k testování funkcí automatizovaného rízení a demonstruje jeho implementaci na mereních dvou scénáru. UR - https://doi.org/10.14311/mecdc.2020.01.02 KW - automated driving KW - testing KW - testing scenarios Y1 - 2020 UR - https://doi.org/10.14311/mecdc.2020.01.02 UR - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:573-16888 SN - 1804-9338 VL - 17 IS - 1 SP - 7 EP - 13 PB - Czech Technical University CY - Prag ER - TY - CHAP A1 - Vaculin, Ondrej T1 - Holistic Environment for Development and Testing of Cooperative, Connected and Automated Mobility Functions T2 - FISITA World Congress 2023 Y1 - 2023 UR - https://www.fisita.com/library/fwc2023-sca-025 PB - FISITA CY - Bishops Stortford ER - TY - CHAP A1 - Brühl, Tim A1 - Shanmuganathan, Aravind Kumar A1 - Ewecker, Lukas A1 - Schwager, Robin A1 - Sohn, Tin Stribor A1 - Vaculin, Ondrej A1 - Hohmann, Sören T1 - Consideration of Safety Aspects in a Camera-Aided, Radar-Based Free Space Detection T2 - 2024 IEEE 27th International Conference on Intelligent Transportation Systems (ITSC) UR - https://doi.org/10.1109/ITSC58415.2024.10920194 Y1 - 2025 UR - https://doi.org/10.1109/ITSC58415.2024.10920194 SN - 979-8-3315-0592-9 SP - 3997 EP - 4004 PB - IEEE CY - Piscataway ER - TY - CHAP A1 - Plaschkies, Franz A1 - Vaculin, Ondrej A1 - Pelisson, Angelo A. A1 - Schumacher, Axel T1 - Schnelle Abschätzung des Crashverhaltens von Insassen unter Berücksichtigung der Vielfalt des Menschen BT - Robustheit, Datenintensität und Vorhersagekraft von Metamodellen T2 - Fahrzeugsicherheit 2022: Auf dem Weg zur Fahrzeugsicherheit 2030 UR - https://doi.org/10.51202/9783181023877-313 Y1 - 2022 UR - https://doi.org/10.51202/9783181023877-313 SN - 978-3-18-092387-1 SN - 978-3-18-102387-7 SP - 313 EP - 326 PB - VDI Verlag CY - Düsseldorf 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 - TY - CHAP A1 - Negri de Azeredo, Rodrigo A1 - Vaculin, Ondrej A1 - da Costa Oliveira, Gustavo Henrique T1 - Automatic Car Reverse Braking System Based on a ToF Camera Sensor T2 - FISITA Web Congress 2020 Y1 - 2020 UR - https://www.fisita.com/library/f2020-pif-049 PB - FISITA CY - Bishops Stortford ER - TY - CHAP A1 - Plaschkies, Franz A1 - Vaculin, Ondrej A1 - Schumacher, Axel T1 - Assessment of the Influence of Human Body Diversity on Passive Safety Systems BT - A State-of-the-art Overview T2 - FISITA World Congress 2021 Y1 - 2021 UR - https://www.fisita.com/library/f2021-pif-071 PB - FISITA CY - Bishops Stortford ER - TY - CHAP A1 - Nieto, Marcos A1 - Otaegui, Oihana A1 - Panou, Maria A1 - Birkner, Christian A1 - Vaculin, Ondrej A1 - Rodríguez, Ariadna ED - McNally, Ciaran ED - Carroll, Páraic ED - Martinez-Pastor, Beatriz ED - Ghosh, Bidisha ED - Efthymiou, Marina ED - Valantasis-Kanellos, Nikolaos T1 - AWARE2ALL: Human Centric Interaction and Safety Systems for Increasing the Share of Automated Vehicles T2 - Transport Transitions: Advancing Sustainable and Inclusive Mobility, Proceedings of the 10th TRA Conference, 2024, Dublin, Ireland-Volume 1: Safe and Equitable Transport N2 - 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. UR - https://doi.org/10.1007/978-3-031-88974-5_112 Y1 - 2025 UR - https://doi.org/10.1007/978-3-031-88974-5_112 UR - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:573-62356 SN - 978-3-031-88974-5 SP - 779 EP - 785 PB - Springer CY - Cham ER - TY - INPR A1 - Dönmez, Ömer A1 - Tejero de la Piedra, Ricardo A1 - Klose, Simona A1 - Riolet, Matthieu A1 - Rozek, Lukas A1 - Vaculin, Ondrej A1 - Hach, Christian T1 - Approach for Passive Safety Assessment of Rearward-Sitting Occupants N2 - 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. UR - https://doi.org/10.5281/zenodo.18338626 Y1 - 2026 UR - https://doi.org/10.5281/zenodo.18338626 UR - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:573-66254 PB - Zenodo CY - Genf ER -