A lane line detection method, an electronic device, and a storage medium, related to the field of artificial intelligence, and particularly related to computer vision and deep learning technologies, which can be applied to intelligent traffic scenes, are provided. The method includes: dividing an image into a foreground region and a background region; determining a solid line and a dotted line included in the foreground region; determining, according to the solid line and the dotted line comprised in the foreground region, whether a dotted-and-solid line is included in the foreground region; and determining a lane line detection result according to the solid line, the dotted line, and whether a dotted-and-solid line is comprised in the foreground region. According to the technical solution, the accuracy of lane line detection can be improved.

An information processing apparatus includes an acquisition unit configured to acquire a plurality of captured images of a traveling surface where a movable apparatus travels, each of the captured images including distance information in a depth direction transverse to the traveling surface, the plurality of captured images having been captured using a plurality of stereo image capture devices, and an image processing unit configured to stitch together the plurality of images of the traveling surface captured by the plurality of stereo image capture devices by identifying partially overlapping portions of one or more pairs of the images captured by respective stereo image capture devices which are adjacent in a width direction of the traveling surface.

The present disclosure provides a method for identifying a plurality of lane lines on a road segment. The method may include obtaining a plurality of images of the road segment having one or more lanes and vehicles, and identifying a plurality of reference straight lines from the images. The method may include determining a vanishing point based on the reference straight lines, and determining a plurality of candidate lane lines among the reference straight lines based on the vanishing point. The method may also include determining at least one travel trajectory of at least one of the vehicles based on the images. The method may further include determining a target travel trajectory among the travel trajectory based on the vanishing point or a count of the lanes, and determining the lane lines among the candidate lane lines based on the target travel trajectory.

Disclosed are a method and apparatus for recognizing a landmark in a panoramic image. The method includes steps of performing projection transformation on the panoramic image so as to generate a projection image; conducting semantic segmentation on the projection image so as to determine a landmark region and a road surface region; correcting distortion in the landmark region so as to produce a corrected landmark region; and recognizing the landmark in the corrected landmark region.

An operating method of a slope estimating apparatus is provided. The operating method of the slope estimating apparatus including at least one camera includes obtaining a forward image through the at least one camera, detecting a lane included in the forward image, dividing the forward image into a plurality of smaller regions in a horizontal direction, identifying a plurality of lane segments included in each of the plurality of smaller regions, obtaining a plurality of coordinate values forming each of the plurality of lane segments, and obtaining a pitch angle of each of the plurality of smaller regions based on the obtained plurality of coordinate values.

A road shape recognizer includes a peripheral information recognizer that recognizes at least two items of peripheral information based on an output of a periphery detector. A reliability assigner assigns a reliability level to each of the peripheral information. A point sequence generator generates and places a point sequence representing a shape of a road on which the own vehicle travels, based on at least two items of peripheral information and the reliability level. The point sequence generator generates and places a point sequence by generating and placing points one by one toward a distant place from a point located at a prescribed relative position to the own vehicle. The point sequence generator generates and places the next point corresponding to an amount of change in shape and a position of a point generated and placed at the end of the point sequence. The amount of change in shape is represented by the peripheral information and determined per section having a prescribed distance.

An image processing apparatus according to the present invention includes: input means for inputting a distance information distribution calculated from an image captured by using an optical system that forms a field image on an imaging element of imaging means; estimation means for estimating a depth direction in the image from an imaging condition of the imaging means; and decision means for deciding, from a relationship between the distance information distribution and the estimated depth direction, an evaluation value indicating a deviation degree of the optical system and the imaging element from design positions.

A vehicle capable of autonomous driving includes a lane detection system. The lane detection system is trained to predict lane lines using training images. The training images are automatically processed by a training module of the lane detection system in order to create ground truth data. The ground truth data is used to train the lane detection system to predict lane lines that are occluded in real-time images of roadways. The lane detection system predicts lane lines of a roadway in a real-time image even though the lane lines maybe indiscernible due to objects on the roadway or due to the position of the lane lines being in the horizon.

A computer-implemented method for road layout prediction is provided. The method includes segmenting, by a first processor-based element, an RGB image to output pixel-level semantic segmentation results for the RGB image in a perspective view for both visible and occluded pixels in the perspective view based on contextual clues. The method further includes learning, by a second processor-based element, a mapping from the pixel-level semantic segmentation results for the RGB image in the perspective view to a top view of the RGB image using a road plane assumption. The method also includes generating, by a third processor-based element, an occlusion-aware parametric road layout prediction for road layout related attributes in the top view.

A method for determining blockage of a vehicular camera includes providing a camera and mounting the camera at a vehicle so as to view exterior of the vehicle. The control determines at least one selected from the group consisting of (i) that the imaging sensor is totally blocked by determining that the count of bright photosensor pixels of the camera's imaging sensor remains below a threshold, and (ii) that the imaging sensor is partially blocked by determining continuity of intensity variations in different regions of the imaging sensor. The control, responsive to determination of either total blockage or partial blockage of the imaging sensor of the camera, adapts image processing by the image processor of frames of image data captured by the camera to accommodate (i) the determined total blockage of the imaging sensor of the camera or (ii) the determined partial blockage of the imaging sensor of the camera.