This aircraft inspection support device includes a first imaging unit configured to capture a measurement information image displayed on a measurement instrument-side display unit of a specific measurement instrument associated with a model of an aircraft or an inspection target of an aircraft component, the measurement instrument-side display unit being configured to display measurement information on the inspection target, and an operator-side display unit configured to display the measurement information image so as to be visible to an inspection operator who is performing an inspection operation near the inspection target.

This aircraft inspection support device includes a first imaging unit configured to capture a measurement information image displayed on a measurement instrument-side display unit of a specific measurement instrument associated with a model of an aircraft or an inspection target of an aircraft component, the measurement instrument-side display unit being configured to display measurement information on the inspection target, and an operator-side display unit configured to display the measurement information image so as to be visible to an inspection operator who is performing an inspection operation near the inspection target.

To identify sources of data resulting from an execution flow in a robotic device such as an autonomous vehicle, an operating system receives sensor data from various sensors of the robotic device. For each sensor, the system generates a data log comprising an identifier of a first checkpoint associated with that sensor, as well as a first timestamp. The system performs an execution flow on the sensor data from that sensor. The system updates the data log to include an identifier and timestamp for one or more additional checkpoints during the execution flow. The system then fuses results, uses the fused data as an input for a decision process, and causes a component of the robotic device to take an action in response to an output of the decision process. The system may record the action, an action timestamp and the data logs for each sensor in a memory.

A system and method preserves raw vibration data for a physical event involving a transport vehicle such as a helicopter, plane, boat, car, or truck. The event may involve unexpected mechanical stresses on the vehicle. The system and method preserves raw vibration data for parts of the transport vehicle, such as from multiple points along the drive train. The preserved raw vibration data includes data from time prior to the physical event. In an embodiment, the system and method continuously detects vibration data, and stores the most recent vibration data in a circular memory buffer. The buffer is continually updated with the most current vibration data. When an event is automatically detected or manually triggered, the most recently saved vibration data is transferred from the buffer to permanent storage, along with vibration data obtained subsequent to the event. This allows for a more thorough post-event analysis.

A system and method preserves raw vibration data for a physical event involving a transport vehicle such as a helicopter, plane, boat, car, or truck. The event may involve unexpected mechanical stresses on the vehicle. The system and method preserves raw vibration data for parts of the transport vehicle, such as from multiple points along the drive train. The preserved raw vibration data includes data from time prior to the physical event. In an embodiment, the system and method continuously detects vibration data, and stores the most recent vibration data in a circular memory buffer. The buffer is continually updated with the most current vibration data. When an event is automatically detected or manually triggered, the most recently saved vibration data is transferred from the buffer to permanent storage, along with vibration data obtained subsequent to the event. This allows for a more thorough post-event analysis.

A system may comprise a device. The device may be configured to receive, from one or more sensor devices of the machine, sensor data associated with wear of one or more components of an undercarriage of the machine; and predict, using a machine learning model and the sensor data, an amount wear of the one or more components based on a wear rate of the one or more components. The machine learning model is trained, using training data, to predict the wear rate of the one or more components. The training data includes two or more of: historical sensor data, historical inspection data, or simulation data, of a simulation model, from one or more third devices. The device may perform an action based on the amount of wear.

Systems, apparatuses, and methods disclosed provide for tailoring responses to fault data generated during a service event. A method includes determining that a service event for a vehicle has started based on an indication from an off-board diagnostic service tool, interrupting transmission of a fault message during a time period after the service event for the vehicle has started and before the service event for the vehicle has ended, and determining that the service event for the vehicle has ended.

A method for evaluating performance of a self-driving vehicle oriented to full parameter space of a logical scenario is provided. After the test logic scenario of the self-driving vehicle system and its matched parameter space are given, the tested self-driving vehicle system is put into the logic scenario for testing, and the driving data under each specific test condition is obtained. After the driving trajectory of the tested self-driving vehicle system in the whole test logic scenario parameter space is obtained, the logic scenario is divided into two parts according to the ideal vehicle motion curve, namely, a safe region and a dangerous region. The key points and indicators of evaluation in the two regions are determined.

A method for evaluating performance of a self-driving vehicle oriented to full parameter space of a logical scenario is provided. After the test logic scenario of the self-driving vehicle system and its matched parameter space are given, the tested self-driving vehicle system is put into the logic scenario for testing, and the driving data under each specific test condition is obtained. After the driving trajectory of the tested self-driving vehicle system in the whole test logic scenario parameter space is obtained, the logic scenario is divided into two parts according to the ideal vehicle motion curve, namely, a safe region and a dangerous region. The key points and indicators of evaluation in the two regions are determined.

A method for correcting a first light pattern provided by a lighting device with a matrix of light sources. The method includes steps of providing some resolution data of the light sources, simulating a test map of the light pattern using the resolution data, and simulating distortion maps of the test map. Each distortion map is associated to a distortion factor. Also included is obtaining a real distorted light pattern, comparing the real distorted light pattern with the distortion maps, finding a distortion map which is the most similar to the real distorted map and applying a correction factor to correct the real distorted light pattern, thus obtaining a corrected light pattern. The correction factor is related to the distortion factor of the distortion map which is the most similar to the real distorted map.