TY - RPRT A1 - Bálint, András A1 - Schindler, Ron A1 - Löffler, Christian A1 - Wimmer, Peter A1 - Kirschbichler, Stefan A1 - Kolk, Harald A1 - Klein, Christoph A1 - Schories, Lars A1 - Hay, Julian A1 - Becker, Julian A1 - Birkner, Christian A1 - Lopes da Silva, Joed A1 - Zimmer, Alessandro A1 - Mensa, Genís A1 - Parera, Núria A1 - Rokova, Simona A1 - Castells, Jacint A1 - Lorente Mallada, Jorge A1 - Nikolaou, Stella A1 - Vogl, Carina A1 - Stoll, Johann T1 - D5.2 Safety impact assessment - intermediate report Y1 - 2021 UR - https://www.safe-up.eu/resources ER - TY - RPRT A1 - Mensa, Genís A1 - Wimmer, Peter A1 - Schories, Lars A1 - Bálint, András A1 - Djukic, Tamara A1 - Östling, Martin A1 - Löffler, Christian A1 - Nikolaou, Stella A1 - Rial, Sergio A1 - Merdivan, David A1 - Birkner, Christian A1 - Weihmayr, Daniel A1 - Kirschbichler, Stefan T1 - D5.1 Requirements for impact assessment KW - safety impact assessment KW - Bayesian framework KW - requirements definition KW - virtual and physical demonstrators KW - benefit evaluation KW - safety-critical scenarios Y1 - 2021 UR - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:573-28483 ER - TY - JOUR A1 - Shirur, Naveen A1 - Birkner, Christian A1 - Henze, Roman A1 - Deserno, Thomas Martin T1 - Tactile Occupant Detection Sensor for Automotive Airbag JF - Energies N2 - Automotive airbags protect occupants from crash forces during severe vehicle collisions. They absorb energy and restrain the occupants by providing a soft cushion effect known as the restraint effect. Modern airbags offer partial restraint effect control by controlling the bag’s vent holes and providing multi-stage deployment. Full restraint effect control is still a challenge because the closed-loop restraint control system needs airbag–occupant contact and interaction feedback. In this work, we have developed novel single and matrix capacitive tactile sensors to measure the occupant’s contact data. They can be integrated with the airbag surface and folded to follow the dynamic airbag shape during the deployment. The sensors are tested under a low-velocity pendulum impact and benchmarked with high-speed test videos. The results reveal that the single sensor can successfully measure occupant–airbag contact time and estimate the area, while the contact position is additionally identified from the matrix sensor. UR - https://doi.org/10.3390/en14175288 KW - automotive airbag KW - capacitive tactile sensor KW - occupant detection KW - passive safety KW - vehicle crash Y1 - 2021 UR - https://doi.org/10.3390/en14175288 UR - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:573-9210 SN - 1996-1073 VL - 14 IS - 17 PB - MDPI CY - Basel ER - TY - JOUR A1 - Da Silva Junior, Amauri A1 - Birkner, Christian A1 - Jazar, Reza A1 - Marzbani, Hormoz T1 - Crash-Prone Fault Combination Identification for Over-Actuated Vehicles During Evasive Maneuvers JF - IEEE Access N2 - Throughout a vehicle’s lifecycle, systems may fail during operation, requiring effective fault management by the vehicle controller. Various system faults affect vehicle handling differently. Additionally, vehicle velocity and road friction directly impact handling and stability. Thus, it is essential to investigate relevant factors, such as actuator faults, vehicle velocity, road friction, and their combinations, before developing a fault-tolerant controller to mitigate potential critical situations. Our work thus focuses on identifying faults and fault combinations that might lead to crashes for over-actuated vehicles during evasive maneuvers and those impacting comfort parameters. We employ a state-of-the-art vehicle controller optimized for evasive lane changes for over-actuated vehicles. The driving scenario encompasses critical conditions defined in ISO 26262 with ASIL-D, including velocities up to 130 km/h and requiring steering away from obstacles. Failure Mode and Effects Analysis, Design of Experiments, and statistical tools are used to determine fault combinations most likely to lead to crashes during evasive maneuvers. Our results indicate that the vehicle controller successfully handled the maneuver in over 53% of investigated cases, reaching up to 75.1% on dry surfaces. Road friction emerges as the most critical parameter for collision avoidance and comfort. Brake faults exhibit a higher influence on vehicle handling than other actuator faults, while single motor faults do not significantly impact vehicle parameters. Regarding two-factor interactions, brake actuators dominate, followed by steering and motor. These findings provide valuable insights for developing fault-tolerant controllers for over-actuated vehicles, guiding decisions on addressing specific faults to enhance safety and comfort parameters. UR - https://doi.org/10.1109/ACCESS.2024.3374524 Y1 - 2024 UR - https://doi.org/10.1109/ACCESS.2024.3374524 UR - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:573-46165 SN - 2169-3536 VL - 12 SP - 37256 EP - 37275 PB - IEEE CY - New York ER - TY - CHAP A1 - da Silva Junior, Amauri A1 - Birkner, Christian A1 - Jazar, Reza A1 - Marzbani, Hormoz T1 - From Design to Application: Emergency Maneuver Control in a 1:3.33 Scaled Vehicle T2 - 2024 18th International Conference on Control, Automation, Robotics and Vision (ICARCV) UR - https://doi.org/10.1109/ICARCV63323.2024.10821589 Y1 - 2024 UR - https://doi.org/10.1109/ICARCV63323.2024.10821589 SN - 979-8-3315-1849-3 SP - 170 EP - 177 PB - IEEE CY - Piscataway ER - TY - JOUR A1 - da Silva Junior, Amauri A1 - Birkner, Christian A1 - Jazar, Reza A1 - Marzbani, Hormoz T1 - Coupled Lateral and Longitudinal Controller for Over-Actuated Vehicle in Evasive Maneuvering with Sliding Mode Control Strategy JF - IEEE Access N2 - Coupled controllers are vital for safely handling vehicles, especially in critical driving situations that include changing lanes to avoid obstacles. Controllers specialized in emergencies must keep road users safe in critical situations. In this paper, we develop the coupled controller to handle evasive maneuvers for an over-actuated vehicle. The controller is based on the second-order sliding mode control theory. We use the bicycle model to establish the equivalent and robust steering equations as a control-oriented model. The lateral and longitudinal vehicle motions are coupled to each other by the lateral vehicle information on the longitudinal sliding surface, and the dependence of the lateral sliding surface on the longitudinal velocity. The torque vectoring method based on fuzzy logic adjusts the yaw moment. We address the tire slip circle on the slip controller to stabilize the vehicle while maneuvering. We simulate and evaluate our controller in a rear-end collision situation with a short time window to maneuver the vehicle. The ego vehicle detects the preceding vehicle and performs an evasive lane change while simultaneously applying brakes to bring the vehicle to a halt. Our research is the earliest in providing an ultimate emergency control to successfully avoid crashes up to 130 km/h in short time crash detection. UR - https://doi.org/10.1109/ACCESS.2023.3264277 KW - autonomous vehicle KW - crash avoidance KW - evasive maneuvers KW - over-actuated vehicle KW - sliding mode control KW - vehicle coupled controllers Y1 - 2023 UR - https://doi.org/10.1109/ACCESS.2023.3264277 UR - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:573-34336 SN - 2169-3536 VL - 11 SP - 33792 EP - 33811 PB - IEEE CY - New York ER - TY - CHAP A1 - da Silva Junior, Amauri A1 - Birkner, Christian A1 - Jazar, Reza A1 - Marzbani, Hormoz ED - Mehdi, Driss ED - Farza, Mondher ED - M'Saad, Mohammed ED - Aitouche, Abdelouahab T1 - Vehicle lateral dynamics with sliding mode control strategy for evasive maneuvering T2 - Proceedings of the 2021 9th International Conference on Systems and Control UR - https://doi.org/10.1109/ICSC50472.2021.9666598 KW - vehicle lateral control KW - evasive maneuvering KW - vehicle stability KW - trajectory control KW - closed-loop control system Y1 - 2022 UR - https://doi.org/10.1109/ICSC50472.2021.9666598 SN - 978-1-6654-0782-3 SP - 165 EP - 172 PB - IEEE CY - Piscataway ER - TY - CHAP A1 - Shirur, Naveen A1 - Birkner, Christian A1 - Henze, Roman A1 - Deserno, Thomas Martin A1 - Dudhat, Darshankumar T1 - Effect of airbag deployment phases on tactile occupant detection sensor T2 - 2020 XII International Science-Technical Conference AUTOMOTIVE SAFETY UR - https://doi.org/10.1109/AUTOMOTIVESAFETY47494.2020.9435283 KW - accidents KW - airbag deployment KW - capacitive tactile sensor KW - passive safety KW - restraint effect KW - vehicle crash testing Y1 - 2021 UR - https://doi.org/10.1109/AUTOMOTIVESAFETY47494.2020.9435283 SN - 978-1-7281-5812-9 PB - IEEE CY - Piscataway ER - TY - CHAP A1 - da Silva Junior, Amauri A1 - Birkner, Christian A1 - Shirur, Naveen T1 - Development of Lateral Control for Different Electric Vehicle Drive and Steering Systems T2 - FISITA Web Congress 2020 Y1 - 2020 UR - https://www.fisita.com/library/f2020-vdc-075 PB - FISITA CY - Bishops Stortford ER - TY - CHAP A1 - Shirur, Naveen A1 - Birkner, Christian A1 - Foster, Andreas A1 - Bleeck, Matthias T1 - Active control of airbag filling: sensor, valve, test bench, best-practice of industry-university-cooperation T2 - Fahrzeugsicherheit 2022: Auf dem Weg zur Fahrzeugsicherheit 2030 UR - https://doi.org/10.51202/9783181023877-35 KW - Kraftfahrzeug KW - Verkehrssicherheit KW - Technische Sicherheit KW - Passive Sicherheit KW - Fahrerassistenzsystem KW - Elektrofahrzeug Y1 - 2022 UR - https://doi.org/10.51202/9783181023877-35 SN - 978-3-18-092387-1 SN - 0083-5560 SP - 35 EP - 46 PB - VDI-Verlag GmbH CY - Düsseldorf ER - TY - JOUR A1 - Shirur, Naveen A1 - Birkner, Christian A1 - Bleeck, Matthias A1 - Forster, Andreas A1 - Deserno, Thomas Martin A1 - Henze, Roman T1 - 3D simulations and laboratory experiments to evaluate a dynamic airbag valve JF - International Journal of Crashworthiness N2 - Airbag pressure determines the restraint effect during a vehicle crash. The pressure required to restrain the occupant depends on pre-crash detection, collision parameters and the occupant’s mass and position. This work modulated airbag pressure for optimum safety using a novel airbag control valve for cold-gas inflators. This paper evaluates the valve’s stationary and dynamic performances for Helium by 3D flow simulations using a pressure-based solver in ANSYS Fluent® and SAE J2238 laboratory tank tests. The predicted and measured tank pressures for the fully open (stationary) valve were agreed by an average 93.73% with an excellent correlation (correlation coefficient, R = 0.9995). For the first dynamic operation with 10 ms switching time, the results agreed by 92.78% with R = 0.9975. In the second test with 30 ms switching, 83.67% agreement was observed with R = 0.9893. The research concluded that the valve modulates the bag pressure and is implementable in vehicles. UR - https://doi.org/10.1080/13588265.2023.2252214 KW - Automotive airbag KW - cold-gas inflator KW - airbag control valve KW - pressure modulation KW - restraint effect Y1 - 2023 UR - https://doi.org/10.1080/13588265.2023.2252214 UR - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:573-39673 SN - 1754-2111 SN - 1358-8265 VL - 29 IS - 2 SP - 378 EP - 388 PB - Taylor & Francis CY - London ER - TY - CHAP A1 - Shirur, Naveen A1 - Birkner, Christian A1 - Forster, Andreas A1 - Deserno, Thomas Martin A1 - Henze, Roman T1 - Smart airbag: an innovative adaptive airbag considering out-of-position (OOP) for restraint effect optimization and injury mitigation T2 - 15th International Symposium and Exhibition on Sophisticated Car Safety Systems Y1 - 2022 UR - https://publica.fraunhofer.de/entities/publication/c890596b-4b7e-4406-a5f6-d2b5e588f2e7/details SN - 0722-4087 VL - 2022 PB - Fraunhofer ICT CY - Pfinztal ER - TY - JOUR A1 - Weihmayr, Daniel A1 - Birkner, Christian A1 - Marzbani, Hormoz A1 - Jazar, Reza T1 - Data-Driven Vehicle Dynamics: Lever-Aging SINDy for Optimization-Based Vehicular Motion Planning JF - IEEE Access N2 - Motion planning remains a crucial challenge for the widespread adoption of autonomous vehicles. This paper presents a novel approach that integrates an empirical plant model within an optimization-based motion planning architecture. The model prioritizes performance and efficiency while maintaining interpretability. We introduce a methodology that utilizes a data-driven approach to derive an interpretable description of the evolution of vehicle states over time using sparse regression. This method allows effective learning from limited datasets, eliminating the need for extensive and expensive data collection. Our approach addresses the trade-off between performance and accuracy, enabling adaptation to diverse driving scenarios. We affirm the efficacy of our methodology via an extensive analysis, evaluating the independent prediction performance across diverse metrics. Additionally, we examine the overall tracking performance when incorporated into an optimization-based framework. Finally, we present a comparative analysis and discuss the subsequent impact on overall motion planning and decision-making in relation to a state-of-the-art single-track model. UR - https://doi.org/10.1109/ACCESS.2025.3594892 Y1 - 2025 UR - https://doi.org/10.1109/ACCESS.2025.3594892 UR - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:573-61981 SN - 2169-3536 VL - 13 SP - 136584 EP - 136597 PB - IEEE CY - New York ER - TY - CHAP A1 - Rozek, Lukas A1 - Harrison, Andrew A1 - Birkner, Christian T1 - Influence of Autonomous Vehicle Interior Design on Occupant Injuries T2 - 2025 IEEE Intelligent Vehicles Symposium (IV) UR - https://doi.org/10.1109/IV64158.2025.11097519 Y1 - 2025 UR - https://doi.org/10.1109/IV64158.2025.11097519 SN - 979-8-3315-3803-3 SP - 1517 EP - 1522 PB - IEEE CY - Piscataway 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 - JOUR A1 - Da Silva Junior, Amauri A1 - Müller, Steffen A1 - Birkner, Christian A1 - Jazar, Reza A1 - Marzbani, Hormoz T1 - Fault Tolerant Control With Reinforcement Learning for Evasive Maneuvers Using a Scaled Vehicle JF - IEEE Access N2 - Autonomous vehicle controllers are responsible for handling the vehicle at all times in any situation, including emergency conditions. An emergency might arise from, e.g., adverse weather conditions, short-time detection, and system faults. In this paper, we develop a fault-tolerant controller to handle actuator faults for an over-actuated autonomous vehicle based on reinforcement learning. A worst-case scenario is selected for the controller development, involving short-time detection of the preceding objects, high velocity, and dry to wet road conditions. The design of the vehicle controller is performed in three steps. First, a robust controller based on sliding mode control with lateral and longitudinal coupled strategy was built to ensure stability in emergency scenarios. Secondly, a strategy was proposed to identify the most critical vehicle faults that might lead to a crash. Building on these foundations, this study extends the vehicle controller to handle vehicle faults with a reinforcement learning strategy, enabling adaptive and robust fault handling in complex fault scenarios. The vehicle controller is designed and optimized in IPG-Carmaker®, and proof of concept is carried out in a scaled 1:3.33 test vehicle. The results demonstrate the robustness of the proposed controller in an emergency single-lane change with a velocity of up to 130 km/h. Tests in the scaled vehicle demonstrate the vehicle controller’s accuracy against simulation, with the application of reinforcement learning strategy in real-case scenarios. UR - https://doi.org/10.1109/ACCESS.2026.3661179 Y1 - 2026 UR - https://doi.org/10.1109/ACCESS.2026.3661179 UR - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:573-66839 SN - 2169-3536 VL - 14 SP - 21353 EP - 21383 PB - IEEE CY - New York ER - TY - CHAP A1 - Tolksdorf, Leon A1 - Birkner, Christian A1 - Tejada, Arturo A1 - Van De Wouw, Nathan T1 - Fast Collision Probability Estimation for Automated Driving using Multi-circular Shape Approximations T2 - 2024 IEEE Intelligent Vehicles Symposium (IV) UR - https://doi.org/10.1109/IV55156.2024.10588731 Y1 - 2024 UR - https://doi.org/10.1109/IV55156.2024.10588731 SN - 979-8-3503-4881-1 SP - 2529 EP - 2536 PB - IEEE CY - Piscataway ER - TY - CHAP A1 - Tolksdorf, Leon A1 - Tejada, Arturo A1 - van de Wouw, Nathan A1 - Birkner, Christian T1 - Risk in Stochastic and Robust Model Predictive Path-Following Control for Vehicular Motion Planning T2 - IEEE IV 2023: Symposium Proceedings UR - https://doi.org/10.1109/IV55152.2023.10186708 KW - autonomous vehicles KW - motion planning KW - path following KW - robust model predictive control KW - stochastic model predictive control KW - risk assessment Y1 - 2023 UR - https://doi.org/10.1109/IV55152.2023.10186708 SN - 979-8-3503-4691-6 PB - IEEE CY - Piscataway ER - TY - CHAP A1 - Weihmayr, Daniel A1 - Sezgin, Fatih A1 - Tolksdorf, Leon A1 - Birkner, Christian A1 - Jazar, Reza T1 - Predicting the Influence of Adverse Weather on Pedestrian Detection with Automotive Radar and Lidar Sensors T2 - 2024 IEEE Intelligent Vehicles Symposium (IV) UR - https://doi.org/10.1109/IV55156.2024.10588472 Y1 - 2024 UR - https://doi.org/10.1109/IV55156.2024.10588472 SN - 979-8-3503-4881-1 SP - 2591 EP - 2597 PB - IEEE CY - Piscataway ER - TY - INPR A1 - Tolksdorf, Leon A1 - Tejada, Arturo A1 - Bauernfeind, Jonas A1 - Birkner, Christian A1 - van de Wouw, Nathan T1 - Risk Estimation for Automated Driving UR - https://doi.org/10.48550/arXiv.2601.15018 Y1 - 2026 UR - https://doi.org/10.48550/arXiv.2601.15018 PB - arXiv CY - Ithaca ER - TY - INPR A1 - Kern, Tobias A1 - Tolksdorf, Leon A1 - Birkner, Christian T1 - Comparison of Localization Algorithms between Reduced-Scale and Real-Sized Vehicles Using Visual and Inertial Sensors UR - https://doi.org/10.48550/arXiv.2507.11241 Y1 - 2025 UR - https://doi.org/10.48550/arXiv.2507.11241 PB - arXiv CY - Ithaca ER - TY - INPR A1 - Tolksdorf, Leon A1 - Tejada, Arturo A1 - Birkner, Christian A1 - van de Wouw, Nathan T1 - Collision Probability Estimation for Optimization-based Vehicular Motion Planning UR - https://doi.org/10.48550/arXiv.2505.21161 Y1 - 2025 UR - https://doi.org/10.48550/arXiv.2505.21161 PB - arXiv CY - Ithaca ER -