Systems and methods for collecting, monitoring, and analyzing vehicle data from a plurality of vehicles using edge computing are disclosed. The method may include: generating a plurality of edge computing microservices modules, each edge computing microservices module being generated based on different vehicle system details of the plurality of vehicles; installing a respective edge computing microservices module onto a vehicle data gateway of a respective vehicle based on the vehicle system details of the respective vehicle, each edge computing microservices module is configured to perform vehicle data computation of vehicle data received at the vehicle data gateway to generate computed vehicle data; receiving the computed vehicle data from the vehicle data gateway of each of the plurality of vehicles; storing the received computed vehicle data in one or more databases; and transmitting select computed vehicle data to an end user via a web platform.

Systems and methods for collecting, monitoring, and analyzing vehicle data from a plurality of vehicles using edge computing are disclosed. The method may include: generating a plurality of edge computing microservices modules, each edge computing microservices module being generated based on different vehicle system details of the plurality of vehicles; installing a respective edge computing microservices module onto a vehicle data gateway of a respective vehicle based on the vehicle system details of the respective vehicle, each edge computing microservices module is configured to perform vehicle data computation of vehicle data received at the vehicle data gateway to generate computed vehicle data; receiving the computed vehicle data from the vehicle data gateway of each of the plurality of vehicles; storing the received computed vehicle data in one or more databases; and transmitting select computed vehicle data to an end user via a web platform.

Systems and methods for autonomous vehicle service assignment simulation are provided. In one example embodiment, a computer-implemented method includes obtaining data indicative of an autonomous vehicle to be tested within a simulation associated with a service entity, and generating a simulation environment for the simulation and a simulated autonomous vehicle within the simulation environment based at least in part on the data indicative of the autonomous vehicle. The method includes accessing one or more backend systems of the service entity via an application programming interface platform having one or more functional calls defined to be accessed by a third-party autonomous vehicle or a managing entity of third-party autonomous vehicles. The method includes initiating the simulation of the simulated autonomous vehicle within the simulation environment, and providing the simulated autonomous vehicle access to one or more services of the one or more backend systems of the service entity during the simulation.

Systems and methods for autonomous vehicle service assignment simulation are provided. In one example embodiment, a computer-implemented method includes obtaining data indicative of an autonomous vehicle to be tested within a simulation associated with a service entity, and generating a simulation environment for the simulation and a simulated autonomous vehicle within the simulation environment based at least in part on the data indicative of the autonomous vehicle. The method includes accessing one or more backend systems of the service entity via an application programming interface platform having one or more functional calls defined to be accessed by a third-party autonomous vehicle or a managing entity of third-party autonomous vehicles. The method includes initiating the simulation of the simulated autonomous vehicle within the simulation environment, and providing the simulated autonomous vehicle access to one or more services of the one or more backend systems of the service entity during the simulation.

A vehicle operation data collection apparatus includes a vehicle operation history DB which accumulates vehicle operation data acquired from a vehicle; and a processor programmed to evaluate excess or deficiency of vehicle operation data accumulated in the vehicle operation history DB for each of abnormality types, on the basis of accuracy information of classification obtained when classifying the abnormality types occurring in the vehicle by machine learning, using vehicle operation data accumulated in the vehicle operation history DB, extract a vehicle suitable for acquiring data of an abnormality type evaluated as data deficiency from a vehicle maintenance history DB as a collection target vehicle, and distribute a collection command instructing collection of operation data to the extracted collection target vehicle.

Embodiments of the disclosure provide systems and methods for testing of separating systems between a space vehicle and a launch vehicle, at a component level and/or at a system level, using High Speed Digital Image Correlation (HSDIC). Proposed embodiments integrate HSDIC into the separation testing processes to better determine system function without contacting the object under test. By applying HSDIC to objects under test, the previously immeasurable quantities may be measured and may provide results leading to more complete and more understandable measurements of interest. As one example, local coordinate systems may be generated indicating symmetry and/or lack of symmetry. Furthermore, stay out volumes may be verified. In some embodiments, trajectories associated with measure targets, (e.g., features of the object under test and/or patterns such as a speckle pattern) may be utilized to track and analysis movements as well as verify stay out volumes.

Embodiments of the disclosure provide systems and methods for testing of separating systems between a space vehicle and a launch vehicle, at a component level and/or at a system level, using High Speed Digital Image Correlation (HSDIC). Proposed embodiments integrate HSDIC into the separation testing processes to better determine system function without contacting the object under test. By applying HSDIC to objects under test, the previously immeasurable quantities may be measured and may provide results leading to more complete and more understandable measurements of interest. As one example, local coordinate systems may be generated indicating symmetry and/or lack of symmetry. Furthermore, stay out volumes may be verified. In some embodiments, trajectories associated with measure targets, (e.g., features of the object under test and/or patterns such as a speckle pattern) may be utilized to track and analysis movements as well as verify stay out volumes.

A transmission testing device that can highly accurately reproduce behavior of an actual engine includes a drive dynamometer DM1 connected to an input shaft of a transmission, absorption dynamometers DM2 and DM3 that are connected to output shafts of the transmission, a shaft torque detection unit that detects a shaft torque value generated at the input shaft of the transmission, and a control unit that controls the drive dynamometer DM1. The control unit uses the shaft torque value detected by the shaft torque detection unit to generate a shaft torque correction value for the drive dynamometer DM1, receives an engine torque input value and uses the received engine torque input value to generate an engine torque correction value for the drive dynamometer DM1, and controls the drive dynamometer DM1 on the basis of a torque command value generated from the shaft torque correction value and the engine torque correction value.

A transmission testing device that can highly accurately reproduce behavior of an actual engine includes a drive dynamometer DM1 connected to an input shaft of a transmission, absorption dynamometers DM2 and DM3 that are connected to output shafts of the transmission, a shaft torque detection unit that detects a shaft torque value generated at the input shaft of the transmission, and a control unit that controls the drive dynamometer DM1. The control unit uses the shaft torque value detected by the shaft torque detection unit to generate a shaft torque correction value for the drive dynamometer DM1, receives an engine torque input value and uses the received engine torque input value to generate an engine torque correction value for the drive dynamometer DM1, and controls the drive dynamometer DM1 on the basis of a torque command value generated from the shaft torque correction value and the engine torque correction value.

A computer-implemented method and system for controlling an aircraft based on detecting and mitigating fatiguing conditions and aircraft damage conditions is disclosed. According to one example, a computer-implemented method includes detecting, by a processing system, a health condition of a component of the aircraft. The method further includes determining, by the processing system, whether the health condition is one of a fatigue condition or a damage condition. The method further includes implementing, by the processing system, a first action based at least in part on determining that the health condition is a fatigue condition to mitigate the fatigue condition. The method further includes implementing, by the processing system, a second action based at least in part on determining that the health condition is a damage condition to mitigate the damage condition.