Abstract: A control method for a mobile robot includes: obtaining indication information of a target object, wherein the indication information includes position information of the target object in a reference image output by a photographic apparatus of a mobile robot; determining the position information of the target object according to the indication information; and controlling, according to the position information of the target object, the mobile robot to move around the target object. With the provided control methods and apparatuses, devices, and storage media, the mobile robot may move around the target object without needing to move to a circling center to record a position of the circling center.

Abstract: Provided are a computer system and method using image analysis for controlling a flight path of a surface inspection unmanned aerial vehicle, wherein the computer system is configured to: acquire an image captured by a drone; perform image analysis on the acquired image; extract, in a result of the image analysis, a point whose an edge variation amount is equal to or greater than a predetermined threshold; acquire a position coordinate of the extracted point; in a case where there are a plurality of points, set a flight path of the drone in a manner of flying in an order of edge variation amounts of the plurality of points from large to small; and control the drone to fly towards the acquired position coordinate and perform capturing with a camera using light other than visible light.

Abstract: Aircraft, fly-by-wire systems, and controllers are provided. An aircraft includes a trim control system and a fly-by-wire system. The trim control system is configured for controlling surfaces of the aircraft. The fly-by-wire system is communicatively coupled with the trim control system and includes an input device and a controller. The input device is configured to receive a re-trim input from a user. The controller is communicatively coupled with the input device and is configured to control the trim control system, to obtain the re-trim input from the user, and to set a pitch trim of the aircraft based on a stable flight condition at a present airspeed of the aircraft in response to the re-trim input from the input device.

Abstract: Techniques for presenting a user interface on a display for monitoring and/or controlling a vehicle. The user interface may include a digital representation of an environment in which the vehicle is operation. Additionally, the user interface may include system interface that is configured to display one or more notifications associated with systems or components of the vehicle. The system interface may additionally, or alternatively, comprise one or more control inputs for controlling systems or components of the vehicle. The user interface may also include a mission interface that includes a map interface and a mission selection interface for selecting routes the vehicle is to navigate. The user interface may additionally cause presentation of visual indicators that indicate the presence of an object that is not shown on the user interface, but that is moving in a direction such that the object will soon be visibly displayed on the user interface.

Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium that generates path prediction data for agents in the vicinity of an autonomous vehicle using machine learning models. One method includes identifying an agent in a vicinity of an autonomous vehicle navigating an environment and determining that the agent is within a vicinity of a crossing zone, which can be marked or unmarked. In response to determining that the agent is within a vicinity of a crossing zone: (i) features of the agent and of the crossing zone can be obtained; (ii) a first input that includes the features can be processed using a first machine learning model that is configured to generate a first crossing prediction that characterizes future crossing behavior of the agent, and (iii) a predicted path for the agent for crossing the roadway can be determined from at least the first crossing prediction.

Abstract: The present invention relates to a method and device for estimating a distance to a target, and a unmanned aerial vehicle. The method is applicable to a unmanned aerial vehicle including a photographing device. The method includes: acquiring a current frame of image of the target captured by the photographing device; acquiring location information of the target according to the current frame of image, where the location information includes a height of the target and two-dimensional pixel coordinates of the target in the image; acquiring attitude information of the photographing device, where the attitude information includes a pitch angle of the photographing device; and acquiring a distance between the target and the unmanned aerial vehicle according to the location information of the target and the attitude information of the photographing device.

Abstract: A manual control system includes input devices to receive user inputs to control the system according to the user inputs when operated in a manual mode. The user input devices are movable between a first configuration to receive the user input by being physically manipulated by the user, and a second configuration in which the user input is not receivable.

Abstract: A vehicle may include a camera configured to obtain an image of at least one surrounding vehicle; and a controller configured to determine object recognition data including at least one of full area data, wheel area data, and bumper area data of the surrounding vehicle from the image of the at least one surrounding vehicle, based on the object recognition data, to set a reference point in the at least one of the full area data, the wheel area data, and the bumper area data of the surrounding vehicle from the image of the surrounding vehicle, and to predict a driving speed of the surrounding vehicle based on a change in a position of the reference point.

Abstract: Techniques are provided for implementing automated contagion detection and remediation (ACDR) features to detect and remediate environmental contagion conditions. For example, ACDR techniques can be used to target contagion contamination on surfaces in a trafficked area, rather than focusing detecting and remediating human symptoms. ACDR systems can include swarms of specially configured drones under control of one or more centralized controllers to detect presence of one or more types of pathogens on surfaces and to classify detected contagion events. In some embodiments, upon such detection, the same or other specially configured drones can be triggered to remediate the detected condition by removing the pathogen by disinfecting surfaces, by cordoning off infected areas, and/or in other ways. Some embodiments can further log and aggregate data relating to detected contagion events to support tracking, remediation, enforcement, protocol updating, research, and/or other efforts.

Abstract: A method for quantifying vehicle path following performance, the method comprising; obtaining samples of path following performance (I), selecting a subset of the path following performance samples such that the selected samples follow a pre-determined statistical extreme value distribution, parameterizing the pre-determined statistical extreme value distribution based on the selected samples of path following performance, and quantifying vehicle path following performance based on the parameterized statistical extreme value distribution.

Abstract: A roadmanship system comprises a computational device and a vehicle comprising a plurality of sensors and a vehicle control system in communication with the computational device and the plurality of sensors. The computational device can be configured to: (i) receive driving data from a group of vehicles; (ii) calculate a regression curve based on the driving data; (iii) calculate a threshold value of an engineering parameter based on the regression curve and a predetermined roadmanship level; and (iv) output the threshold value to the vehicle control system. The vehicle control system can be configured to: (a) receive the threshold value from the computational device; (b) receive operational information associated with at least one of the vehicle and a driving environment surrounding the vehicle from the plurality of sensors; and (c) cause the vehicle to perform a vehicle maneuver based on the threshold value and the operational information.

Abstract: A method for three-dimensional modeling. The method may include: acquiring coordinate points of obstacles in a surrounding environment of an autonomous driving vehicle in a vehicle coordinate system; determining a position of eyes of a passenger in the autonomous driving vehicle, and establishing an eye coordinate system using the position of the eyes as a coordinate origin; converting the coordinate points of the obstacles in the vehicle coordinate system to coordinate points in the eye coordinate system, and determining a visualization distance between the obstacles in the surrounding environment based on an observation angle of the eyes; and performing three-dimensional modeling of the surrounding environment, based on visualization distance between the coordinate points of the obstacles in the eye coordinate system and the obstacles.

Abstract: A vehicle controller includes a processor configured to detect an object region including another vehicle near a vehicle from each of time series images obtained by a camera mounted on the vehicle; detect a predetermined action taken by the other vehicle, based on a trajectory of the other vehicle estimated from the object region of each image; identify the state of a signal light of the other vehicle, based on characteristics obtained from pixel values of the object region of each image; extract information indicating characteristics of an action of the other vehicle or the state of a signal light at the predetermined action taken by the other vehicle, based on the predetermined action detected in a tracking period and the state of the signal light related to the predetermined action; and predict behavior of the other vehicle, using the extracted information.

Abstract: A vehicle control method for platooning vehicles includes determining, for a first following vehicle, a predicted movement state information, and a lead vehicle predicted movement state information of a lead vehicle corresponding to a number of time points in the future. In the vehicle control method, when there is an adjacent second following vehicle, second following vehicle predicted movement state information corresponding to the number of time points is determined, and also includes determining optimized control quantities corresponding to respective ones of the number of time points, including determining an overall constraint model based on constraints between the first following vehicle and the lead vehicle, and constraints between the first following vehicle and the second following vehicle; determining the optimized control quantities; and performing longitudinal control in accordance with the optimized control quantities.

Abstract: The control device for forward collision avoidance in a vehicle includes a forward object determination unit configured to recognize an object in front of the vehicle and to determine an attribute of the recognized object, a gear position detection unit configured to detect a gear position of the vehicle, and a forward collision-avoidance assist (FCA) control unit configured to finally determine the attribute of the object determined by the forward object determination unit according to the gear position input from the gear position detection unit and to set an FCA control range based on the finally determined object attribute.

Abstract: An apparatus for providing drone data provides a method of matching a user who needs drone data of a certain area with at least one provider capable of providing drone data of a part or the entirety of the certain area.

Abstract: A navigation apparatus determines an estimated position of an object. Navigation information that includes a sequence of quantized measurements is received. Each quantized measurement has a corresponding quantization error that is negatively correlated with the quantization error of a prior quantized measurement. The apparatus iteratively performs a navigation update operation that includes determining a state and a covariance matrix of the object for a current iteration based on a state and covariance matrix in a prior iteration. The state includes an end quantization error and a start quantization error. The covariance matrix includes end and start covariance values corresponding to the end and start quantization errors, respectively. Determining the state and the covariance matrix for the current iteration includes replacing the start quantization error with the end quantization error determined in a prior iteration, and updating the state and the covariance matrix via a Kalman filter update operation.

Abstract: An automated valet parking system, an automated valet parking method, and an automated valet parking infrastructure, and a vehicle having an automated valet parking feature are disclosed. In particular, the vehicle can autonomously move to and park in a designated parking spot by communicating with the infrastructure. In addition, the vehicle can autonomously move to a pickup area from a parking spot by communicating with the infrastructure.

Abstract: In one embodiment, a method of controlling adaptive window transparency includes calculating a buffer period having a start time and an end time. The method also includes adjusting transparency on a set of windows based on the start time of the buffer period and transferring control of the vehicle to the vehicle or driver based on the end time. In response to detecting an emergency, the method further includes removing all tinting on the set of windows. In another embodiment, a method of controlling adaptive window transparency includes calculating a buffer period based on a navigation route.

Abstract: Techniques for controlling a vehicle based on height data and/or classification data being determined utilizing multi-channel image data are discussed herein. The vehicle can capture lidar data as it traverses an environment. The lidar data can be associated with a voxel space as three-dimensional data. Semantic information can be determined and associated with the lidar data and/or the three-dimensional voxel space. A multi-channel input image can be determined based on the three-dimensional voxel space and input into a machine learned (ML) model. The ML model can output data to determine height data and/or classification data associated with a ground surface of the environment. The height data and/or classification data can be utilized to determine a mesh associated with the ground surface. The mesh can be used to control the vehicle and/or determine additional objects proximate the vehicle.