Vision method of detecting lane boundaries and obstacles

Claim 2

In an automotive vehicle having a computer vision system and an associated camera for viewing a scene ahead of the vehicle, a method of detecting lane markers on a roadway comprising the steps of: obtaining an image of the scene and digitizing the image, normalizing the image, defining a search area in the image, searching for lane markers in the search area of the image to locate lane marker positions, estimating the position of any missing lane marker from said located lane marker positions, and defining lane boundaries from the said located lane marker positions and the estimated position of the missing lane marker.

Claim 3

The invention as defined in claim 1 wherein the step of estimating the position of missing lane markers comprises averaging the corresponding marker positions from previous images.

Claim 4

In an automotive vehicle having a computer vision system and an associated camera for viewing a scene ahead of the vehicle and obtaining a series of images of the scene, a method of locating lane boundaries on a roadway comprising for each image the steps of: digitizing the image and normalizing the image, dynamically defining a search area in the image based on the lane boundaries located in a previous image, searching for lane markers in the search area of the image to locate lane marker positions, estimating the position of any missing lane marker based on information selected from the current image and previous images, and defining lane boundaries from the said located lane marker positions and the estimated position of the missing lane marker.

Claim 5

In an automotive vehicle having a computer vision system and an associated camera for viewing a scene ahead of the vehicle and obtaining a series of images of the scene, a method of locating lane boundaries on a roadway comprising for each image the steps of: digitizing the image and normalizing the image, dynamically defining a search area in the image based on the lane boundaries located in a previous image, searching for lane markers in the search area of the image to locate lane marker positions, estimating the position of any missing lane marker based on information selected from the current image and previous images, and defining lane boundaries from the said located lane marker positions and the estimated position of the missing lane marker.

Claim 6

The invention as defined in claim 3 wherein the step of estimating the position of missing lane markers comprises the steps of: determining the lane width from said previous image, and calculating the estimated position of a missing lane marker from the position of a marker on the opposite side of the lane and the lane width.

Claim 7

The invention as defined in claim 2 including the steps of: checking the validity of a current lane boundary position by comparing a current lane boundary angle and lane width with that of the previous image wherein a discrepancy of said current lane boundary angle or said current lane width greater than threshold values reveals an invalid boundary, and when said current lane boundary position is invalid, substituting a preceding boundary position.

Claim 8

The invention as defined in claim 2 wherein the step of searching for lane markers comprises the steps of: generating edge data from the image, correlating a template with the image in the search area, and if insufficient match points are found to define a marker then correlating said template with the edge data in the search area.

Claim 9

The invention as defined in claim 2 wherein the steps of defining a search area and searching for lane markers comprise the steps of: generating edge data from the image, performing a boundary tracing operation on the edge data to generate a boundary traced image, performing a line Hough transform on the boundary traced image to yield pixels in Hough space, defining a search area in Hough space based on lane boundaries defined in a previous frame, and searching for lane markers in Hough space to locate said lane marker positions.

Claim 10

The invention as defined in claim 7 wherein the step of search for lane markers in Hough space comprises the steps of: reducing the number of said pixels in Hough space by filtering said pixels in Hough space with a non-maximum suppression filter, deleting pixels in Hough space representing vertical and horizontal lines, removing pixels in Hough space closer than a set amount, and selecting pixels in Hough space with the most contributing edge elements as said lane markers.

Claim 11

In an automotive vehicle having a computer vision system and an associated camera for viewing a scene ahead of the vehicle and obtaining a series of images of the scene, a method of locating lane boundaries and close obstacles on a roadway comprising for each image the steps of: digitizing the image and normalizing the image, generating edge data from the image, determining an edge pixel count by counting edge pixels between lane boundaries defined in the previous image ad in the image portion representing space close to the vehicle, comparing the edge pixel count to a threshold value and issuing an obstacle warning when the count is above the threshold value, when the edge pixel count is below the threshold value, dynamically defining a search area in the image based on the lane boundaries located in said previous image, searching for lane markers in the search area of the image to locate lane marker positions, estimating the position of any missing lane marker based on information selected from the current image and previous images, and defining lane boundaries from the said located lane marker positions and the estimated position of the missing lane marker.

Claim 12

In an automotive vehicle having a computer vision system and an associated camera for viewing a scene ahead of the vehicle and obtaining a series of images of the scene, a method of locating lane boundaries and close obstacles on a roadway comprising for each image the steps of: digitizing the image and normalizing the image, generating edge data from the image, determining an edge pixel count by counting edge pixels between lane boundaries defined in the previous image ad in the image portion representing space close to the vehicle, comparing the edge pixel count to a threshold value and issuing an obstacle warning when the count is above the threshold value, when the edge pixel count is below the threshold value, dynamically defining a search area in the image based on the lane boundaries located in said previous image, searching for lane markers in the search area of the image to locate lane marker positions, estimating the position of any missing lane marker based on information selected from the current image and previous images, and defining lane boundaries from the said located lane marker positions and the estimated position of the missing lane marker.

Claim 13

The invention as defined in claim 9 including the step of: before searching for said lane markers, effectively deleting obstacles from far field portions of the image by removing vertical and horizontal lines within the said search area in the image.