Abstract: The present invention discloses an unmanned aerial vehicle and a method for controlling a gimbal thereof. The method for controlling a gimbal includes: generating, by a flight control system, a yaw angular speed instruction of the unmanned aerial vehicle; and controlling, by a gimbal control system, a yaw axis motor of the gimbal according to the yaw angular speed instruction of the unmanned aerial vehicle. In the present invention, the yaw axis motor of the gimbal is jointly controlled by the flight control system and the gimbal control system, so that advantages of high-precision control and quick response of the gimbal control system are maximized. The advantages are used for compensating for deficiencies of the flight control system in yaw control, thereby improving the stability of a yaw channel of the gimbal, and completely resolving frame freezing of an aerial video when the unmanned aerial vehicle yaws at a low speed.

Abstract: Systems, methods, and autonomous vehicles may obtain sensor data associated with an environment surrounding an autonomous vehicle; provide the sensor data to a plurality of plugins; independently determine, with each plugin, based on the sensor data, whether to request a remote guidance session for the autonomous vehicle, each plugin of the plurality of plugins including a different model that is applied by that plugin to the sensor data to determine whether to request the remote guidance session; receive, from at least one plugin, a request to initiate the remote guidance session; and in response to receiving the request to initiate the remote guidance session, communicate with a computing device external to the autonomous vehicle to establish the remote guidance session.

Abstract: In order to solve the problem in which a flight vehicle flies only in locations having poor communication quality and retained data cannot necessarily reach the ground, this device communicates with a flight vehicle, wherein the device is provided with: a storage means that associates and stores first position information and a first communication rate at which it is possible to communicate at the position of the first position information; an extraction means that, upon receiving information that corresponds to a prescribed second communication rate at which the flight vehicle transmits data, extracts the first position information corresponding to the first communication rate equal to or greater than the second communication rate from the storage means; and an output means that outputs the first position information extracted by the extraction means to the flight vehicle, or outputs the inputted first position information to an instrument that notifies the flight vehicle.

Abstract: Enclosed are embodiments for sampling driving scenarios for training machine learning models. In an embodiment, a method comprises: assigning, using at least one processor, a set of initial physical states to a set of objects in a map for a set of simulated driving scenarios, wherein the set of initial physical states are assigned according to one or more outputs of a random number generator; generating, using the at least one processor, the set of simulated driving scenarios in the map using the initial physical states of the objects in the set of objects; selecting, using the at least one processor, samples of the simulated driving scenarios; training, using the at least one processor, a machine learning model using the selected samples; and operating, using a control circuit, a vehicle in an environment using the trained machine learning model.

Abstract: An unmanned device used in a wireless communication relay system includes: a surrounding environment information detector acquiring information on a surrounding environment of a moving object; a processor generating a control command for the moving object based on the acquired information on the surrounding environment; and a communication interface for transmitting the control command generated by the processor to the moving object, wherein the unmanned device is located at a predetermined height and relays communication between the moving object and a control center, and thus, even when wireless communication between the control center and the moving object is difficult due to an obstacle or the like, the connection of the wireless communication may be maintained without disconnection of the wireless communication.

Abstract: A machine-learning (ML) architecture for determining three or more outputs, such as a two and/or three-dimensional region of interest, semantic segmentation, direction logits, depth data, and/or instance segmentation associated with an object in an image. The ML architecture may output these outputs at a rate of 30 or more frames per second on consumer grade hardware.

Abstract: There is provided a motorcycle-drive assistance apparatus that makes rapid deceleration by an engine brake possible so that braking control unintended by a rider can be avoided. The motorcycle-drive assistance apparatus is configured in such a way that a deceleration-start inter-vehicle distance calculation unit calculates a deceleration-start inter-vehicle distance, by use of an acceleration value read from an engine-brake acceleration value storage unit, based on an own-vehicle speed calculated by an own-vehicle speed calculation unit and a road-surface gradient angle calculated by a road-surface gradient angle calculation unit and in such a way that when the inter-vehicle distance between an own vehicle and another vehicle, detected by an object detection unit, becomes smaller than the calculated deceleration-start inter-vehicle distance, deceleration of the own vehicle through the engine brake is started.

Abstract: A LIDAR (Light Detection and Ranging) apparatus for a vehicle including a light emitting unit configured to emit laser light; an angle adjusting unit configured to adjust an emission angle of the laser light emitted from the light emitting unit by reflecting the laser light; a window cover unit installed to discharge the laser light emitted through the angle adjusting unit to the outside of the vehicle; a light receiving unit configured to receive reflected light introduced into the vehicle through the window cover unit; and a moisture removing unit configured to emit far-infrared light using a far-infrared light source to heat the window cover unit through the angle adjusting unit.

Abstract: Systems and methods coordinate drone flights in an operating windfarm. A drone flight path that is removed from any location where the drone is conducting observations of a wind turbine and that extends through at least part of a windfarm to a destination location is determined. A respective wake pattern along at least one portion of the drone fight path is determined based on respective operating parameters for each of at least one wind turbine in the windfarm. Flight time commands to adjust at least one respective operating parameter of the respective wind turbine to reduce the effect of the respective wake pattern at a point ahead of the drone on the drone flight path are sent by a windfarm controller controlling the at least one wind turbine.

Abstract: A system for training an operator of a vehicle includes a processor and a memory in communication with the processor, which includes a safety module and a training module. The safety module has instructions that, when executed by the processor, cause the processor to determine when the vehicle is operating within a safe area based on at least one of: a location of the vehicle and a location of one or more objects with respect to the vehicle. The training module has instructions that, when executed by the processor, cause the processor to apply at least one brake of the vehicle when the vehicle is operating within the safe area to cause the vehicle to engage in an oversteer event, and collect operator response information when the vehicle engages in the oversteer event.

Abstract: A drone is deployed from a vehicle, e.g., an autonomous or semi-autonomous vehicle, to assist in vehicle control, e.g. in situations in which the vehicle's embedded sensors may not provide sufficient information to perform a desired operation safely, e.g. backing up, parking in a tight environment, traversing a very narrow road, navigating a sharp corner, or bypassing an obstruction, etc. The deployed drone includes sensors, e.g. cameras, radars, LIDARs, etc, which capture sensor data from a position offset from the vehicle. Captured sensor data is communicated from the drone to a vehicle control system in the vehicle and/or to a remote control system, e.g., including an operator who can make decisions. Based on the captured sensor data, which supplements sensor data collected by the vehicle's embedded sensors, vehicle movement is controlled.

Abstract: To provide a work machine having a blade provided to a track structure, and a swing structure provided swingably on the upper side of the track structure, which work machine allows for computation of the horizontal coordinates of the blade. The work machine includes: a swing-structure-position acquiring device that acquires a horizontal coordinate and an orientation of the swing structure; a swing sensor that senses a swing of the swing structure; a travel sensor that senses travelling of the track structure; and a controller that computes an orientation of the track structure, and a horizontal coordinate of the blade.

Abstract: In some embodiments, techniques are provided for analyzing time series data to detect anomalies. In some embodiments, the time series data is processed using a machine learning model. In some embodiments, the machine learning model is trained in an unsupervised manner on large amounts of previous time series data, thus allowing highly accurate models to be created from novel data. In some embodiments, training of the machine learning model alternates between a fitting optimization and a trimming optimization to allow large amounts of training data that includes untagged anomalous records to be processed. Because a machine learning model is used, anomalies can be detected within complex systems, including but not limited to autonomous vehicles such as unmanned aerial vehicles. When anomalies are detected, commands can be transmitted to the monitored system (such as an autonomous vehicle) to respond to the anomaly.

Abstract: An apparatus for post-processing of a decision-making model of an autonomous vehicle receives a decision-making model including a plurality of states. The model is processed using multivariate data that comprises values for at least three observations of a vehicle operational scenario. A slice of the model decision space is generated by fixing values of all except two observations, and modifying the values of the two observations to obtain multiple alternative solutions for the model. The alternative solutions and the modified values form the slice. Each alternative solution is associated with a respective first value of a first observation and a respective second value of a second observation. The apparatus also generates a solution to a modified decision-making model that is the model modified by, for at least one state and at least one of the two observations, modifying a probabilistic transition matrix, a probabilistic observation matrix, or both.

Abstract: Systems, apparatuses, and methods for autonomously estimating the position and orientation (“pose”) of an aircraft relative to a target site are disclosed herein, including a system including a plurality of beacons arranged about the target site, wherein the plurality of beacons collectively comprise at least one electromagnetic radiation source and at least one beacon ranging radio, a sensor system coupled to the aircraft including an electromagnetic radiation sensor and a ranging radio configured to determine a range of the aircraft relative to the target site, and a processor configured to determine an estimated pose of the aircraft based on at least: (i) detected electromagnetic radiation, and (ii) time-stamped range data for the aircraft relative to the target site.

Abstract: The disclosure relates to a method and a corresponding execution device. The method includes determining a position of a vehicle by a satellite navigation system, establishing a guaranteed position range, where the guaranteed position range describes the geographical region around the actual position of the vehicle, in which the determined position has to be located according to a specified minimum integrity, matching the determined position with an electronically stored road map and corresponding allocation of the position of the vehicle to a road on the road map, where the road map includes information regarding open spaces without a drivable infrastructure as well as an allocation of roads according to road classes, and validating the road class of the allocated road according to the road map on the basis of the guaranteed position range as well as the information contained in the road map regarding open spaces without a drivable infrastructure.

Abstract: A method, including: acquiring a point cloud frame including point cloud data of a pedestrian; projecting the point cloud data of the pedestrian to a ground coordinate system to obtain projection point data of the pedestrian; determining a direction of a connection line between the two shoulders of the pedestrian, based on a location distribution of the projection point data; extracting a point cloud of a stable region from the point cloud data of the pedestrian based on the direction of the connection line between the two shoulders of the pedestrian, a form change range of the stable region when the pedestrian moves being smaller than form change ranges of other regions of the pedestrian; and determining movement information of the pedestrian based on a coordinate of a center point of the point cloud of the stable region in a plurality of consecutive point cloud frames.

Abstract: Camera data normalization for an autonomous vehicle are described herein, including: receiving, from one or more cameras of the autonomous vehicle, camera data; applying a color normalization to the camera data; applying a spherical reprojection to the camera data; and applying, based on a registration point, a stabilization to the camera data.

Abstract: Embodiments of the present disclosure sets forth a computer-implemented method comprising receiving, from at least one sensor, sensor data associated with an environment, computing, based on the sensor data, a cognitive load associated with a user within the environment, computing, based on the sensor data, an affective load associated with an emotional state of the user, determining, based on both the cognitive load at the affective load, an affective-cognitive load, determining, based on the affective-cognitive load, a user readiness state associated with the user, and causing one or more actions to occur based on the user readiness state.

Abstract: A method of transferring data between an autonomous vehicle and a vehicle traffic control infrastructure system. The method includes receiving, by a communication device of a vehicle, a data payload for a smart traffic control infrastructure node. The method includes, by a computing device of the vehicle: determining that the vehicle is or will be within a communication range of the smart traffic control infrastructure node, determining a length of time that the vehicle will be in the communication range of the smart traffic control infrastructure node, and assembling a communication package with at least a portion of the data payload that can be transferred in the determined length of time. The method includes, by a communication device of the vehicle when the vehicle has an ad hoc communication link with the smart traffic control infrastructure node, transmitting the assembled communication package to the smart node.