@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} } @inproceedings{daSilvaJuniorBirknerJazaretal.2024, author = {da Silva Junior, Amauri and Birkner, Christian and Jazar, Reza and Marzbani, Hormoz}, title = {From Design to Application: Emergency Maneuver Control in a 1:3.33 Scaled Vehicle}, booktitle = {2024 18th International Conference on Control, Automation, Robotics and Vision (ICARCV)}, publisher = {IEEE}, address = {Piscataway}, isbn = {979-8-3315-1849-3}, doi = {https://doi.org/10.1109/ICARCV63323.2024.10821589}, pages = {170 -- 177}, year = {2024}, 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} } @inproceedings{daSilvaJuniorBirknerJazaretal.2022, author = {da Silva Junior, Amauri and Birkner, Christian and Jazar, Reza and Marzbani, Hormoz}, title = {Vehicle lateral dynamics with sliding mode control strategy for evasive maneuvering}, booktitle = {Proceedings of the 2021 9th International Conference on Systems and Control}, editor = {Mehdi, Driss and Farza, Mondher and M'Saad, Mohammed and Aitouche, Abdelouahab}, publisher = {IEEE}, address = {Piscataway}, isbn = {978-1-6654-0782-3}, doi = {https://doi.org/10.1109/ICSC50472.2021.9666598}, pages = {165 -- 172}, year = {2022}, 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} } @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} } @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{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} } @inproceedings{WeihmayrSezginTolksdorfetal.2024, author = {Weihmayr, Daniel and Sezgin, Fatih and Tolksdorf, Leon and Birkner, Christian and Jazar, Reza}, title = {Predicting the Influence of Adverse Weather on Pedestrian Detection with Automotive Radar and Lidar Sensors}, booktitle = {2024 IEEE Intelligent Vehicles Symposium (IV)}, publisher = {IEEE}, address = {Piscataway}, isbn = {979-8-3503-4881-1}, doi = {https://doi.org/10.1109/IV55156.2024.10588472}, pages = {2591 -- 2597}, year = {2024}, language = {en} }