SYSTEMS AND METHODS FOR LANE-MARKER DETECTION

• US 20180189577A1
• Filed: 07/25/2017
• Published: 07/05/2018
• Est. Priority Date: 01/04/2017
• Status: Active Grant

• Current Assignee: Qualcomm, Inc.
• Original Assignee: Qualcomm, Inc.
• Granted Patent: US 10,867,189 B2
• Time in Patent Office: Days
• CPC Class Codes: G06T 2207/10012 Stereo images
• CPC Class Codes: G06T 2207/30256 Lane; Road marking
• CPC Class Codes: G06T 7/13 Edge detection
• CPC Class Codes: G06T 7/181 involving edge growing; involving edge linking
• CPC Class Codes: G06T 7/593 from stereo images
• CPC Class Codes: G06V 10/44 Local feature extraction by analysis of parts of the pattern, e.g. by detecting edges, contours, loops, corners, strokes or intersections; Connectivity analysis, e.g. of connected components
• CPC Class Codes: G06V 10/457 by analysing connectivity, e.g. edge linking, connected component analysis or slices
• CPC Class Codes: G06V 20/588 Recognition of the road, e.g. of lane markings; Recognition of the vehicle driving pattern in relation to the road
• CPC Class Codes: H04N 13/133 Equalising the characteristics of different image components, e.g. their average brightness or colour balance
• CPC Class Codes: H04N 2013/0092 Image segmentation from stereoscopic image signals

Abstract

An electronic device is described. The electronic device includes a memory and a processor in communication with the memory. The processor is configured to receive an image. The processor is also configured to extract a lane response map from the image. The processor is further configured to estimate one or more lane markers based on the lane response map.

Claims

1. An electronic device, comprising:

a memory; and

a processor in communication with the memory, the processor configured to;

receive an image;

extract a lane response map from the image; and

estimate one or more lane markers based on the lane response map.

2. The electronic device of claim 1, wherein the processor is further configured to transform the lane response map to an inverse-perspective-mapping (IPM) domain.

3. The electronic device of claim 1, wherein the processor configured to estimate the one or more lane markers based on the lane response map further comprises the processor configured to:

define one or more sections in the image;

detect one or more straight lane-marker segments in each of the one or more sections; and

cluster the one or more straight lane-marker segments to obtain one or more lane markers.

4. The electronic device of claim 3, wherein defining the one or more sections is based on at least one of a lane-marker segment width assumption, a pre-defined detection distance range, or calibration data.

5. The electronic device of claim 3, wherein the processor is further configured to:

perform a transform in each of the one or more sections; and

obtain a lane-marker position and a lane-marker orientation based on the transform.

6. The electronic device of claim 3, wherein the processor is further configured to filter one or more potential straight line segments to reduce false positives.

7. The electronic device of claim 3, wherein the processor is further configured to refine the one or more lane markers.

8. The electronic device of claim 7, wherein the processor is further configured to optimize a score function to obtain the one or more lane markers.

9. The electronic device of claim 3, wherein the processor is further configured to apply a fitting to the one or more lane markers.

10. The electronic device of claim 1, wherein the processor configured to extract the lane response map from the image comprises the processor configured to perform row-wise one-dimensional (1D) filtering on the image.

11. The electronic device of claim 1, wherein the processor is further configured to:

convert the image from a red-green-blue (RGB) color model to a grayscale color model; and

apply a trained projection model that maximizes intensity distances between a color of a lane marker and a color of a road.

12. The electronic device of claim 1, wherein the processor is further configured to apply an ego-lane mask to the image to restrict a lane-marker search region.

13. A method, comprising:

receiving an image;

extracting a lane response map from the image; and

estimating one or more lane markers based on the lane response map.

14. The method of claim 13, further comprising transforming the lane response map to an inverse-perspective-mapping (IPM) domain.

15. The method of claim 13, wherein estimating the one or more lane markers based on the lane response map further comprises:

defining one or more sections in the image;

detecting one or more straight lane-marker segments in each of the one or more sections; and

clustering the one or more straight lane-marker segments to obtain one or more lane markers.

16. The method of claim 13, wherein extracting the lane response map from the image comprises performing row-wise one-dimensional (1D) filtering on the image.

17. The method of claim 13, further comprising:

converting the image from a red-green-blue (RGB) color model to a grayscale color model; and

applying a trained projection model that maximizes intensity distances between a color of a lane marker and a color of a road.

18. The method of claim 13, further comprising applying an ego-lane mask to the image to restrict a lane-marker search region.

19. A non-transitory computer readable medium storing computer executable code, comprising:

code for causing an electronic device to receive an image;

code for causing the electronic device to extract a lane response map from the image; and

code for causing the electronic device to estimate one or more lane markers based on the lane response map.

20. The computer readable medium of claim 19, further comprising code for causing the electronic device to transform the lane response map to an inverse-perspective-mapping (IPM) domain.

21. The computer readable medium of claim 19, wherein the code for causing the electronic device to estimate the one or more lane markers based on the lane response map further comprises:

code for causing the electronic device to define one or more sections in the image;

code for causing the electronic device to detect one or more straight lane-marker segments in each of the one or more sections; and

code for causing the electronic device to cluster the one or more straight lane-marker segments to obtain one or more lane markers.

22. The computer readable medium of claim 19, wherein the code for causing the electronic device to extract the lane response map from the image comprises performing row-wise one-dimensional (1D) filtering on the image.

23. The computer readable medium of claim 19, further comprising:

code for causing the electronic device to convert the image from a red-green-blue (RGB) color model to a grayscale color model; and

code for causing the electronic device to apply a trained projection model that maximizes intensity distances between a color of a lane marker and a color of a road.

24. The computer readable medium of claim 19, further comprising code for causing the electronic device to apply an ego-lane mask to the image to restrict a lane-marker search region.

25. An apparatus, comprising:

means for receiving an image;

means for extracting a lane response map from the image; and

means for estimating one or more lane markers based on the lane response map.

26. The apparatus of claim 25, further comprising means for transforming the lane response map to an inverse-perspective-mapping (IPM) domain.

27. The apparatus of claim 25, wherein the means for estimating the one or more lane markers based on the lane response map further comprise:

means for defining one or more sections in the image;

means for detecting one or more straight lane-marker segments in each of the one or more sections; and

means for clustering the one or more straight lane-marker segments to obtain one or more lane markers.

28. The apparatus of claim 25, wherein the means for extracting the lane response map from the image comprise means for performing row-wise one-dimensional (1D) filtering on the image.

29. The apparatus of claim 25, further comprising:

means for converting the image from a red-green-blue (RGB) color model to a grayscale color model; and

means for applying a trained projection model that maximizes intensity distances between a color of a lane marker and a color of a road.

30. The apparatus of claim 25, further comprising means for applying an ego-lane mask to the image to restrict a lane-marker search region.