A lane identification method for identifying a lane whereon a vehicle is traveling, which includes: providing a drive video; generating an identification result of recognizing a plurality of lane lines corresponding to the line according the drive video, wherein the identification result is generated based on inside borderlines of the lane lines; and determining the lane according to the identification result.

Determining three-dimensional structure in a road environment using a system mountable in a host vehicle including a camera connectible to a processor. Multiple image frames are captured in the field of view of the camera. In the image frames, a line is selected below which the road is imaged. The line separates between upper images essentially excluding images of the road and lower images essentially including images of the road. One or more of the lower images is warped, according to a road homography to produce at least one warped lower image. The three-dimensional structure may be provided from motion of a matching feature within the upper images or from motion of a matching feature within at least one of the lower images and at least one warped lower image.

A vehicular control system includes a camera that captures image data. The system includes an electronic control unit (ECU) for processing image data captured by the camera. The ECU, via processing by an image processor of image data captured by the camera, determines lane information of a traffic lane along a road being traveled by the equipped vehicle. The ECU determines a lane quality value that represents a confidence in the determined lane information. When the lane quality value exceeds a threshold value, and based at least in part on the determined lane information, the ECU provides a steering command to a steering system of the equipped vehicle to adjust a heading of the equipped vehicle to center the equipped vehicle within the traffic lane of the road being traveled by the equipped vehicle.

A system for striping a roadway marking onto roadway surface either for rehabilitating the current roadway marking or duplicating a previously recorded roadway marking with a computer having a machine learning program to process the roadway marking image and position the marker at the desired roadway marking location.

A ground plane detection system including an imaging device operable to generate at least two images of a scene and an image processing assembly operable to receive the at least two images of the scene and to generate a model of each image and to perform homography computations through use of corresponding features in the models of each image.

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.

A method is described for interpreting a surrounding environment of a vehicle. The method includes a step of providing an image of the surrounding environment of the vehicle, a step of forming an item of relational information using a first position of a first object in the image and a second position of a second object in the image, the item of relational information representing a spatial relation between the first object and the second object in the surrounding environment of the vehicle, and a step of use of the item of relational information in order to interpret the surrounding environment of the vehicle.

Disclosed relates to a driving support apparatus including at least: a detection unit that detects a driving lane on which a user's vehicle and a front vehicle located in front of the user's vehicle are driving based on image data output from a camera; a first calculation unit that calculates a second front vehicle width for the front vehicle based on a first front vehicle width for the front vehicle measured on the image data, a first driving lane width for the driving lane measured on the image data, and a second driving lane width predetermined according to a characteristic of the driving lane; and a second calculation unit that calculates a distance from the front vehicle based on a focal length of the camera, the first front vehicle width, and the second front vehicle width, thereby precisely measuring the distance from the front vehicle based on camera image data.

A three-dimensional object detection device can enhance the accuracy in detecting a three-dimensional object regardless of the brightness in the detection environment. The device has an image capture means that captures an image of a predetermined area and an image conversion means that converts the image through a viewpoint conversion into birds-eye view image. A first three-dimensional object detection means aligns positions of bird's-eye view images at different times obtained by the image conversion means, counts the number of pixels that exhibit a predetermined difference on a differential image of the aligned bird's-eye view images to generate a frequency distribution thereby creating differential waveform information, and detects a three-dimensional object on the basis of the differential waveform information. A second three-dimensional object detection means detects edge information from the bird's-eye view image obtained by the image conversion means and detects a three-dimensional object on the basis of the edge information.

Intersection guide systems, methods, and programs acquire information on a path of a vehicle and acquire a travel direction of the vehicle at a guide intersection ahead of the vehicle on the basis of the information on the path. The systems, methods, and programs display a guide image that represents the travel direction superimposed on a portion of a forward scene ahead of the vehicle other than an image of the guide intersection, and a connection line image as superimposed on the forward scene, the connection line image connecting between the image of the guide intersection in the forward scene and the guide image. The connection line image is superimposed on the forward scene such that a length of the connection line image becomes shorter as a degree of approach of the vehicle to the guide intersection becomes larger.