An anti-tampering trigger system design apparatus includes an equipment simulation device that generates driving simulation data by simulating data output by equipment that is mounted on an unmanned aerial vehicle, a virtual unmanned aerial vehicle simulation device that generates a function result value by simulating a function of the unmanned aerial vehicle based on the driving simulation data, and generates a mission result value by simulating mission performance of the unmanned aerial vehicle using the function of the simulated unmanned aerial vehicle, and a machine learning device that performs machine learning of an anti-tampering trigger using the driving simulation data, the function result value, and the mission result value.

Disclosed is a test device and method for remote control parking, falling within the technical field of testing. The method provided by the present disclosure includes the following steps: responding to a remote control parking instruction sent by a portable terminal, and acquiring position and pose data of a vehicle in real-time; injecting function loss conditions; determining a stopping position of the vehicle according to the position and pose data if an alarm signal or a parking success signal sent by an in-vehicle infotainment system is received; and determining a test result of remote control parking according to the stopping position of the vehicle and the function loss conditions. The present disclosure is used to test the response of a vehicle parking under the function loss condition.

Disclosed is a test device and method for remote control parking, falling within the technical field of testing. The method provided by the present disclosure includes the following steps: responding to a remote control parking instruction sent by a portable terminal, and acquiring position and pose data of a vehicle in real-time; injecting function loss conditions; determining a stopping position of the vehicle according to the position and pose data if an alarm signal or a parking success signal sent by an in-vehicle infotainment system is received; and determining a test result of remote control parking according to the stopping position of the vehicle and the function loss conditions. The present disclosure is used to test the response of a vehicle parking under the function loss condition.

The management server 3 acquires the image information indicating the image that includes at least a part of the sign attached in advance in the port P where the UAV 1 is placed and is captured by the camera of the UAV 1, and detects the airframe orientation of the UAV 1 on the basis of at least one of the position of the sign and the indication of the sign in the image indicated by the image information.

The management server 3 acquires the image information indicating the image that includes at least a part of the sign attached in advance in the port P where the UAV 1 is placed and is captured by the camera of the UAV 1, and detects the airframe orientation of the UAV 1 on the basis of at least one of the position of the sign and the indication of the sign in the image indicated by the image information.

Systems and methods for automatic multi-modality sensor calibration with near-infrared images (NIR). Image keypoints from collected images and NIR keypoints from NIR can be detected. A deep-learning-based neural network that learns relation graphs between the image keypoints and the NIR keypoints can match the image keypoints and the NIR keypoints. Three dimensional (3D) points from 3D point cloud data can be filtered based on corresponding 3D points from the NIR keypoints (NIR-to-3D points) to obtain filtered NIR-to-3D points. An extrinsic calibration can be optimized based on a reprojection error computed from the filtered NIR-to-3D points to obtain an optimized extrinsic calibration for an autonomous entity control system. An entity can be controlled by employing the optimized extrinsic calibration for the autonomous entity control system.

Systems and methods for automatic multi-modality sensor calibration with near-infrared images (NIR). Image keypoints from collected images and NIR keypoints from NIR can be detected. A deep-learning-based neural network that learns relation graphs between the image keypoints and the NIR keypoints can match the image keypoints and the NIR keypoints. Three dimensional (3D) points from 3D point cloud data can be filtered based on corresponding 3D points from the NIR keypoints (NIR-to-3D points) to obtain filtered NIR-to-3D points. An extrinsic calibration can be optimized based on a reprojection error computed from the filtered NIR-to-3D points to obtain an optimized extrinsic calibration for an autonomous entity control system. An entity can be controlled by employing the optimized extrinsic calibration for the autonomous entity control system.

A method of controlling an autonomous vehicle includes: under the control of a processor, receiving external information from an external source and updating the external information into navigation map information; predicting an avoidance area, which is an electromagnetic disturbance area, based on the updated navigation map information; determining whether the autonomous vehicle enters the predicted avoidance area while the autonomous vehicle drives on a road; and generating a warning signal related to an occurrence of an error in the autonomous vehicle based on a result of the determining.

A method of controlling an autonomous vehicle includes: under the control of a processor, receiving external information from an external source and updating the external information into navigation map information; predicting an avoidance area, which is an electromagnetic disturbance area, based on the updated navigation map information; determining whether the autonomous vehicle enters the predicted avoidance area while the autonomous vehicle drives on a road; and generating a warning signal related to an occurrence of an error in the autonomous vehicle based on a result of the determining.

System, methods, and machine-readable media may facilitate control of an aircraft. An actuator may be controlled based on the following. A first control signal may be output, with a first command module, to control the actuator. The first command module may include a first electronic configuration. Commands generated by the first command module and/or system response signals may be monitored with a monitor module. The monitor module may include a third electronic configuration that is different from the first electronic configuration. A second control signal may be output, with a second command module, to control the actuator when the first command module is deactivated or malfunctioning. The second command module may include a second electronic configuration that is different from the first electronic configuration and the third electronic configuration. The actuator may be controlled using the second control signal when the first command module is deactivated or malfunctioning.