A lane line recognition method, a lane line recognition device and a non-volatile storage medium. The method includes obtaining a first image which is a road image; dividing the first image from a middle of the first image to determine a first sub-image at a left part of the first image and a second sub-image at a right part of the first image; and performing respectively a recognition operation on the first sub-image and the second sub-image to determine a first lane line in the first sub-image and a second lane line in the second sub-image.

Aspects of the disclosure relate to dynamic driving metric output platforms that utilize improved computer vision methods to determine vehicle metrics from video footage. A computing platform may receive video footage from a vehicle camera. The computing platform may determine that a reference marker in the video footage has reached a beginning and an end of a road marker based on brightness transitions, and may insert time stamps into the video accordingly. Based on the time stamps, the computing platform may determine an amount of time during which the reference marker covered the road marking. Based on a known length of the road marking and the amount of time during which the reference marker covered the road marking, the computing platform may determine a vehicle speed. The computing platform may generate driving metric output information, based on the vehicle speed, which may be displayed by an accident analysis platform. Based on known dimensions of pavement markings the computing platform may obtain the parameters of the camera (e.g., focal length, camera height above ground plane and camera tilt angle) used to generate the video footage and use the camera parameters to determine the distance between the camera and any object in the video footage.

Disclosed are an electronic device, and a method for controlling same. Particularly, the present disclosure relates to an electronic device, and a method for controlling same, which can secure high visibility of a subject by acquiring, from an original image, a plurality of sub images having a smaller bit number than the original image, and acquiring a synthesized image on the basis of information about the shapes of objects included in the plurality of sub images.

A method for displaying stop bar information to an aircrew member of an aircraft includes the steps of capturing a light signature emitted from an area surrounding the aircraft, processing the captured light signature to detect a lighted stop bar, and providing information to the aircrew member regarding the detection of the lighted stop bar.

An on-vehicle control device includes: an image acquiring unit that acquires an image from an image-capturing device that captures the image of surroundings of a vehicle; an overhead view image generating unit that generates an overhead view image from the image acquired by the image acquiring unit, the overhead view image being a plan view of the surroundings of the vehicle seen from above the vehicle; a white line recognizing unit that recognizes possible white lines on a road surface in the overhead view image; a white line information saving unit that saves information including positional information of the possible white lines recognized by the white line recognizing unit; a white line position predicting unit that predicts a position of a possible white line to be reached, based on information about a previous possible white line saved by the white line information saving unit and vehicle behavior information; and a white line judging unit that excludes, among the possible white lines recognized by the white line recognizing unit, a possible white line that extends in a radial direction from the image-capturing device in the overhead view image and satisfies a predetermined condition.

A road surface detection device according to an embodiment includes an image acquisition unit that acquires captured image data output from a stereo camera that captures an imaging area including a road surface on which a vehicle travels, a three-dimensional model generation unit that generates a three-dimensional model of the imaging area including a surface shape of the road surface from a viewpoint of the stereo camera based on the captured image data, and a correction unit that estimates a plane from the three-dimensional model, and corrects the three-dimensional model so as to match an orientation of a normal vector of the plane and a height position of the plane with respect to the stereo camera with a correct value of an orientation of a normal vector of the road surface and a correct value of a height position of the road surface with respect to the stereo camera, respectively.

A method for determining the roadway plane in the surrounding area of a vehicle, the vehicle comprising a stereo camera system for capturing stereo images of the surrounding area of the vehicle and an artificial neural network for processing the image information provided by the stereo camera system, wherein the neural network determines disparity information of the surrounding area of the vehicle, wherein, on the basis of the disparity information, distance information is calculated which contains information regarding the distance of the objects displayed on the image information from the stereo camera system or the vehicle, wherein roadway distance information is extracted from the distance information, wherein the roadway plane is determined on the basis of the roadway distance information.

A vehicle as a moving body is provided with an imaging device as first object detecting means and a radar apparatus as second object detecting means. The object recognition apparatus is provided with axis displacement learning means for learning an axis displacement of the reference axis X1 of the first object detecting means; axis displacement determining means for performing, based on an object detecting result of the first object detecting means and the second object detecting means, an axis displacement determination process for determining whether or not an axis displacement has occurred in the reference axis of the second object detecting means; and disabling means for disabling, based on a learning result of the axis displacement by the axis displacement learning means, information about an axis displacement of the second object detecting means acquired by the axis displacement determination process.

A method to detect a roadway edge includes calculating a first likelihood of a roadway edge from an aerial image of a roadway by shifting a centerline of the roadway perpendicular to the centerline and overlapping the centerline with image gradients. A second likelihood of the roadway edge is determined using a vehicle telemetry fitting a probability distribution to telemetry points along the roadway. The first likelihood of the roadway edge and the second likelihood of the roadway edge are fused to identify a final likelihood of the roadway edge.

An axial deviation estimation apparatus of an in-vehicle sensor includes a road surface recognition unit configured to recognize a road surface by a laser radar installed in a vehicle, an estimation unit configured to estimate an axial deviation amount of the in-vehicle sensor mounted on the vehicle, and a plane creation unit configured to create a virtual plane of the road surface based on a recognition result of the road surface recognition unit. The estimation unit estimates the axial deviation amount based on an inclination of the virtual plane with respect to a reference plane of the laser radar.