A non-impact method for testing either or both the rigidity and/or the stability of one or more loads placed on a carriage by subjecting the carriage to an acceleration single pulse profile including displacing the carriage in a first direction and accelerating the carriage according to the acceleration single pulse profile such that along part of the acceleration single pulse profile the carriage is displaced in a second direction opposite to the first direction. A device for testing stability and/or rigidity of one or more loads is also provided.

Systems, apparatuses, and methods disclosed provide for tailoring response to fault data generated during a service event. The method comprises determining fault data for a vehicle continually, determining that a service event for the vehicle has started, interrupting transmission of 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.

The present subject matter provides various technical solutions to technical problems facing sensor-based vehicle safety technology. To address problems facing identification of safety features (e.g., safety sensors) for a particular vehicle, a vehicle safety feature identification system may be used to identify a vehicle and the safety features that are installed on that vehicle. To address problems facing identification of which safety sensors require maintenance, a vehicle safety feature maintenance system may be used to identify vehicle safety sensors based on information received about one or more vehicle repairs, such as structural repairs following a vehicle collision. The vehicle safety feature maintenance system may use image data or other inputs to identify a vehicle repair area, identify other vehicle components that must be removed or adjusted to complete the vehicle repair, and identify all vehicle safety sensors and other safety features that will need to be repaired, replaced, or recalibrated.

An example method for estimating vehicle repair costs includes receiving, via one or more sensors coupled to a damaged vehicle involved in a crash, crash information of the damaged vehicle, the crash information including vehicle operating information of the damaged vehicle; analyzing the crash information with respective collision data of a vehicle type that includes or is similar to the vehicle type of the damaged vehicle, the analyzing including comparing at least one of a velocity or an acceleration included in the vehicle operating information to vehicle operating characteristics indicated by the respective collision data of the vehicle type; determining one or more damaged vehicle parts of the damaged vehicle based on the analyzed crash information; determining a cost estimate to repair the damaged vehicle based on the one or more damaged vehicle parts; and transmitting an indication of the cost estimate.

A simulation test method for autonomous driving vehicle includes steps of: obtaining source data; constructing a simulation scene according to the source data; controlling a virtual vehicle to perform a simulation test in the simulation scene; detecting a virtual driving trajectory of the virtual vehicle at a predetermined time interval while performing the simulation test; calculating difference between a first position and a second position; adjusting perceptual information related to a coordinate of the first position as perceptual information related to a coordinate of the second position to obtain modified perceptual information when the difference exceeds a first preset value; controlling the virtual vehicle to perform the simulation test based on the modified perceptual information, and detecting the virtual driving trajectory of the virtual vehicle at the predetermined time interval again. Furthermore, a computer equipment and a medium are also provided.

A simulation test method for autonomous driving vehicle includes steps of: obtaining source data; constructing a simulation scene according to the source data; controlling a virtual vehicle to perform a simulation test in the simulation scene; detecting a virtual driving trajectory of the virtual vehicle at a predetermined time interval while performing the simulation test; calculating difference between a first position and a second position; adjusting perceptual information related to a coordinate of the first position as perceptual information related to a coordinate of the second position to obtain modified perceptual information when the difference exceeds a first preset value; controlling the virtual vehicle to perform the simulation test based on the modified perceptual information, and detecting the virtual driving trajectory of the virtual vehicle at the predetermined time interval again. Furthermore, a computer equipment and a medium are also provided.

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.

Pre-flight and episodic aircraft inspections to ensure operational safety and effectiveness are made faster, less costly and more effective by utilizing mobile model and data-driven sensor platforms inspecting known-state target vehicles. For autonomous aircraft, an inspection unmanned aerial vehicle (I-UAV) containing sensors inspects a utility unmanned aerial vehicle (U-UAV). Failure and system models of the U-UAV and the flight history of the U-UAV carried in the sensor data captured by the U-UAV's aircraft health management system are combined to guide the I-UAV to appropriately attend to the systems of the U-UAV and to drive the U-UAV systems into target states that will reveal otherwise hidden or latent failures.

An apparatus for determining a rollover condition of a vehicle may include: a rate sensing unit configured to sense one or more rates of a pitch rate PitchRate and a yaw rate YawRate of a vehicle and a roll rate RollRate_IN; an acceleration sensing unit configured to sense horizontal acceleration and vertical acceleration of the vehicle; a conversion unit configured to convert the horizontal acceleration and vertical acceleration into a pitch rate PitchRate_ACC and a yaw rate YawRate_ACC; a combination unit configured to calculate a pitch rate PitchRate_IN and a yaw rate YawRate_IN by combining the one or more rates with the pitch rate PitchRate_ACC and the yaw rate YawRate_ACC; and a determination unit configured to calculate a roll angle RollAngle, and determine whether the vehicle has rolled over, based on the roll rate RollRate_IN and the roll angle RollAngle.

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.