Systems and methods use cameras to provide autonomous navigation features. In one implementation, a method for navigating a user vehicle may include acquiring, using at least one image capture device, a plurality of images of an area in a vicinity of the user vehicle; determining from the plurality of images a first lane constraint on a first side of the user vehicle and a second lane constraint on a second side of the user vehicle opposite to the first side of the user vehicle; enabling the user vehicle to pass a target vehicle if the target vehicle is determined to be in a lane different from the lane in which the user vehicle is traveling; and causing the user vehicle to abort the pass before completion of the pass, if the target vehicle is determined to be entering the lane in which the user vehicle is traveling.

Systems and methods use cameras to provide autonomous navigation features. In one implementation, a method for navigating a user vehicle may include acquiring, using at least one image capture device, a plurality of images of an area in a vicinity of the user vehicle; determining from the plurality of images a first lane constraint on a first side of the user vehicle and a second lane constraint on a second side of the user vehicle opposite to the first side of the user vehicle; enabling the user vehicle to pass a target vehicle if the target vehicle is determined to be in a lane different from the lane in which the user vehicle is traveling; and causing the user vehicle to abort the pass before completion of the pass, if the target vehicle is determined to be entering the lane in which the user vehicle is traveling.

A large-scale forest height remote sensing retrieval method includes: acquiring Ice, Cloud and land Elevation Satellite (ICESAT-2) tree height data, Landsat data, Shuttle Radar Topography Mission (SRTM) data, Worldclim data, forest type data and ecological zoning data within a target zone, and preprocessing the data; carrying out georeferencing on the processed data to generate a first data set; calculating spectral features, terrain features and climatic factor features of an image, and combining the calculated features with the ecological zoning data and the forest type data to obtain a second data set; extracting eigenvalues of a same geographical location from the second data set, and combining the extracted eigenvalues with the tree height data to generate training data; constructing a random forest model covering a large zone as an ecological zoning tree height retrieval model, and dividing the obtained training data into a training sample and a verification sample.

A large-scale forest height remote sensing retrieval method includes: acquiring Ice, Cloud and land Elevation Satellite (ICESAT-2) tree height data, Landsat data, Shuttle Radar Topography Mission (SRTM) data, Worldclim data, forest type data and ecological zoning data within a target zone, and preprocessing the data; carrying out georeferencing on the processed data to generate a first data set; calculating spectral features, terrain features and climatic factor features of an image, and combining the calculated features with the ecological zoning data and the forest type data to obtain a second data set; extracting eigenvalues of a same geographical location from the second data set, and combining the extracted eigenvalues with the tree height data to generate training data; constructing a random forest model covering a large zone as an ecological zoning tree height retrieval model, and dividing the obtained training data into a training sample and a verification sample.

An example method performed by a processing system includes obtaining a light detecting and ranging point cloud of a building, where the point cloud includes a plurality of points, and where each point is associated with a set of (x,y,z) coordinates. A first point of the plurality of points is assigned to a subset of the plurality of points that is associated with the building, where the subset includes points whose (x,y) coordinates fall within a footprint of the building. The first point is grouped into a first cluster according to at least one of: a (z) coordinate of the first point and a gradient to which the first point belongs. A first prism formed by the first cluster is constructed. A model of the building is stored as a plurality of connected prisms, where the plurality of connected prisms includes the first prism.

A camera is provided that has an image sensor having a plurality of pixel elements for recording image data of an object stream moved in a direction of movement relative to the camera and having objects of a height, and a control and evaluation unit that is configured to determine the height of a respective object with reference to the image data. The image sensor here is an event-based image sensor and the control and evaluation unit is configured to detect a measurement series of points in time at which pixel elements adjacent in the direction of movement consecutively register an event to determine a speed of an object image of the object on the image sensor from said measurement series and from this the height.

A method for autonomously maneuvering a tow vehicle towards a trailer positioned behind the tow vehicle is provided. The method includes receiving one or more images from one or more cameras positioned on a back portion of the tow vehicle. The method also includes identifying a trailer representation within the one or more images. The trailer representation being indicative of the trailer positioned behind the tow vehicle. The method also includes setting a vertical center of the trailer representation as a target. The method also includes determining a first steering wheel angle to turn the tow vehicle such that the vehicle autonomously maneuvers in a direction towards the target. The method also includes transmitting instructions to a drive system causing the tow vehicle to maneuver based on the first steering wheel angle.

A cooking assistance appliance can include a body, at least one camera, at least one sensor, and an imaging device for capturing an image of a cooking appliance. The cooking appliance can provide for monitoring use of the cooking appliance, as well as assisting the user in operating the cooking appliance. Additionally, the cooking assistance appliance can be integrated into a hood or a microwave oven and vent combination above a stovetop.

An autonomous vehicle control system is provided. The control system may include a plurality of cameras to acquire a plurality of images of an area in a vicinity of a vehicle; and at least one processing device configured to: recognize a curve to be navigated based on map data and vehicle position information; determine an initial target velocity for the vehicle based on at least one characteristic of the curve as reflected in the map data; adjust a velocity of the vehicle to the initial target velocity; determine, based on the plurality of images, observed characteristics of the curve; determine an updated target velocity based on the observed characteristics of the curve; and adjust the velocity of the vehicle to the updated target velocity.

An autonomous vehicle control system is provided. The control system may include a plurality of cameras to acquire a plurality of images of an area in a vicinity of a vehicle; and at least one processing device configured to: recognize a curve to be navigated based on map data and vehicle position information; determine an initial target velocity for the vehicle based on at least one characteristic of the curve as reflected in the map data; adjust a velocity of the vehicle to the initial target velocity; determine, based on the plurality of images, observed characteristics of the curve; determine an updated target velocity based on the observed characteristics of the curve; and adjust the velocity of the vehicle to the updated target velocity.