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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.
Crash-Prone Fault Combination Identification for Over-Actuated Vehicles During Evasive Maneuvers
(2024)
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.
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.