The present invention improves the accuracy of detection of a three-dimensional object. A three-dimensional object detecting device generates a mask image 90 that masks regions outside a three-dimensional object candidate region in a difference image G of a first overhead image F1 and a second overhead view image F2 for which the imaging locations O are mutually aligned, identifies a near ground contact line L1 of a three-dimensional object based on a masked difference image wherein the difference image G is masked with the mask image 90, finds an end point of the three-dimensional object based on the masked difference image Gm, identifies the width of the three-dimensional object based on a distance between a non-masking region boundary N and the end point V of the three-dimensional object in the mask image 90, identifies a far ground contact line L2 of the three-dimensional object based on the width of the three-dimensional object and the near ground contact line L1, and identifies the location of the three-dimensional object in the difference image G based on the near ground contact line L1 and the far ground contact line L2.

A method and an electronic device for determining a presence of an obstacle in a surrounding area of a self-driving car (SDC) are provided. The method comprises receiving sensor data representative of the surrounding area of the SDC in a form of 3D point cloud data; generating, by an MLA, based on the 3D point cloud data, a set of feature vectors representative of the surrounding area; generating, by the MLA, a grid representation of the surrounding area, each given cell of the grid representation including a predicted distance parameter indicative of a distance from the given cell to a closest cell with the obstacle; and using, by the electronic device, the distance parameter to determine presence of the obstacle in the surrounding area of the SDC.

Disclosed is a control assistance device that includes a radar system that detects surrounding objects, and generates radar tracking information tracking a position of the surrounding object relative to a vehicle, and a camera system, that includes cameras, and generates image information, and camera tracking information tracking the position of the surrounding object. The control assistance device also includes processors that determine whether the radar tracking information is generated in real time when the surrounding object approaches within a preset distance from the vehicle based on generated tracking information that includes the radar tracking information or the camera tracking information, determine whether the camera tracking information is generated in real time, when the radar tracking information is determined as generated in real time, and control a braking system of the vehicle based on the generated tracking information, when the camera tracking information is determined as generated in real time.

A method includes obtaining, using at least one processing device, a vanishing point and a boundary based on image data associated with a scene, where the boundary is associated with a detected object within the scene. The method also includes repeatedly, during multiple iterations and using the at least one processing device, (i) identifying multiple patches within the boundary and (ii) determining a similarity of the image data contained within the multiple patches. The method further includes identifying, using the at least one processing device, a modification to be applied to the boundary based on the identified patches and the determined similarities. In addition, the method includes generating, using the at least one processing device, a refined boundary based on the modification, where the refined boundary identifies a specified portion of the detected object.

A method for analyzing of a road transport route for transport of a heavy load from an origin to a destination includes i) obtaining images of the transport route, the images being images taken by a drone or satellite camera system, where each of the images includes a different road section of the complete transport route and an peripheral area adjacent to the respective road section; ii) determining objects and their location in the peripheral area of the road section by processing each of the images by a first trained data driven model, where the images are as a digital input to the first trained data driven model and where the first trained data driven model provides the objects, if any, and their location as a digital output; and iii) determining critical objects from the number of determined objects along the road transport route

A target vehicle recognition apparatus for recognizing a target vehicle as a target to be avoided by steering control of a host vehicle detects a stopped vehicle located in front of the host vehicle, determines whether the stopped vehicle is in a forward-facing state to the host vehicle or a rearward-facing state to the host vehicle, determines whether the stopped vehicle is in a right boundary line deviation state crossing a right boundary line of a travel lane of the host vehicle, a left boundary line deviation state crossing a left boundary line of the travel lane, and recognizes the target vehicle for steering control based on a captured image captured by a front camera of the host vehicle and each of the determination results.

An apparatus for estimating distance according to the present disclosure extracts feature maps from respective images including a reference image and a source image; projects a source feature map extracted from the source image onto hypothetical planes to generate a cost volume; sets sampling points on a ray extending from the viewpoint of the reference image in a direction corresponding to a target pixel in the reference image; interpolates features of the respective sampling points, using features associated with nearby coordinates; inputs the features corresponding to the respective sampling points into a classifier to calculate occupancy probabilities corresponding to the respective sampling points; and adds up products of the occupancy probabilities of the respective sampling points and the distances from the viewpoint of the reference image to the corresponding sampling points to estimate the distance from the viewpoint of the reference image to a surface of the object.

Aspects of the subject technology relate to a vehicle backup warning system. A rearview image is received from a rearview camera capturing images of an area behind an own vehicle. The rearview image is determined to include a plurality of white pixels each having a luminance value equal to or above a luminance threshold. Two or more white pixels within a first distance of one another are grouped from the plurality of white pixels. The rearview image is determined to include two groups of the two or more white pixels. A distance between centers of the two groups is determined to be equal to or less than a second distance of each other. The two groups are identified as a pair of illuminated backup lights of a vehicle in the area behind the own vehicle. A warning is provided to alert that the vehicle's intention to backup.

A vehicular lighting system includes a light projecting device disposed at each headlight of a vehicle. The light projecting device is part of the respective headlight. The light projecting device, when operated, projects an animated sequence of visual images or a sequence of visual light patterns on a wall in front of the vehicle. The projected animated sequence of visual images or projected sequence of visual light patterns is viewable at the wall by a driver of the vehicle. The light projecting device may include a matrix of light emitting diodes. The visual images or visual light pattern projected by the light projecting device, when operated, may change as distance between the vehicle and the wall decreases.

A method for estimating time to collision (TTC) of a detected object in a computer vision system is provided that includes determining a three dimensional (3D) position of a camera in the computer vision system, determining a 3D position of the detected object based on a 2D position of the detected object in an image captured by the camera and an estimated ground plane corresponding to the image, computing a relative 3D position of the camera, a velocity of the relative 3D position, and an acceleration of the relative 3D position based on the 3D position of the camera and the 3D position of the detected object, wherein the relative 3D position of the camera is relative to the 3D position of the detected object, and computing the TTC of the detected object based on the relative 3D position, the velocity, and the acceleration.