@article{ShirurBirknerHenzeetal.2021, author = {Shirur, Naveen and Birkner, Christian and Henze, Roman and Deserno, Thomas Martin}, title = {Tactile Occupant Detection Sensor for Automotive Airbag}, volume = {14}, pages = {5288}, journal = {Energies}, number = {17}, publisher = {MDPI}, address = {Basel}, issn = {1996-1073}, doi = {https://doi.org/10.3390/en14175288}, year = {2021}, abstract = {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.}, language = {en} } @article{DaSilvaJuniorBirknerJazaretal.2024, author = {Da Silva Junior, Amauri and Birkner, Christian and Jazar, Reza and Marzbani, Hormoz}, title = {Crash-Prone Fault Combination Identification for Over-Actuated Vehicles During Evasive Maneuvers}, volume = {12}, journal = {IEEE Access}, publisher = {IEEE}, address = {New York}, issn = {2169-3536}, doi = {https://doi.org/10.1109/ACCESS.2024.3374524}, pages = {37256 -- 37275}, year = {2024}, abstract = {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.}, language = {en} } @article{daSilvaJuniorBirknerJazaretal.2023, author = {da Silva Junior, Amauri and Birkner, Christian and Jazar, Reza and Marzbani, Hormoz}, title = {Coupled Lateral and Longitudinal Controller for Over-Actuated Vehicle in Evasive Maneuvering with Sliding Mode Control Strategy}, volume = {11}, journal = {IEEE Access}, publisher = {IEEE}, address = {New York}, issn = {2169-3536}, doi = {https://doi.org/10.1109/ACCESS.2023.3264277}, pages = {33792 -- 33811}, year = {2023}, abstract = {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.}, language = {en} } @article{ShirurBirknerBleecketal.2023, author = {Shirur, Naveen and Birkner, Christian and Bleeck, Matthias and Forster, Andreas and Deserno, Thomas Martin and Henze, Roman}, title = {3D simulations and laboratory experiments to evaluate a dynamic airbag valve}, volume = {29}, journal = {International Journal of Crashworthiness}, number = {2}, publisher = {Taylor \& Francis}, address = {London}, issn = {1754-2111}, doi = {https://doi.org/10.1080/13588265.2023.2252214}, pages = {378 -- 388}, year = {2023}, abstract = {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.}, language = {en} } @article{WeihmayrBirknerMarzbanietal.2025, author = {Weihmayr, Daniel and Birkner, Christian and Marzbani, Hormoz and Jazar, Reza}, title = {Data-Driven Vehicle Dynamics: Lever-Aging SINDy for Optimization-Based Vehicular Motion Planning}, volume = {13}, journal = {IEEE Access}, publisher = {IEEE}, address = {New York}, issn = {2169-3536}, doi = {https://doi.org/10.1109/ACCESS.2025.3594892}, pages = {136584 -- 136597}, year = {2025}, abstract = {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.}, 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} } @article{DaSilvaJuniorMuellerBirkneretal.2026, author = {Da Silva Junior, Amauri and M{\"u}ller, Steffen and Birkner, Christian and Jazar, Reza and Marzbani, Hormoz}, title = {Fault Tolerant Control With Reinforcement Learning for Evasive Maneuvers Using a Scaled Vehicle}, volume = {14}, journal = {IEEE Access}, publisher = {IEEE}, address = {New York}, issn = {2169-3536}, doi = {https://doi.org/10.1109/ACCESS.2026.3661179}, pages = {21353 -- 21383}, year = {2026}, abstract = {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.}, language = {en} }