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 - 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 - 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 -