Lane sensing through lane marker identification for lane centering/keeping

• US 9,751,529 B2
• Filed: 06/09/2011
• Issued: 09/05/2017
• Est. Priority Date: 06/09/2011
• Status: Expired due to Fees

• Current Assignee: GM Global Technology Operations LLC (General Motors Company)
• Original Assignee: GM Global Technology Operations LLC (General Motors Company)
• Inventors: Zhang, Wende, Litkouhi, Bakhtiar Brian, Lee, Jin-Woo
• Primary Examiner(s): Beaulieu, Yonel
• Assistant Examiner(s): WEEKS, MARTIN A
• Application Number:
  US13/156,974
• Publication Number:
  US 20120316730A1
• Time in Patent Office: 2,280 Days
• Field of Search: 701 29, 701 41, 701300, 701 93, 701 96, 701301, 382104
• CPC Class Codes: B60W 10/20 including control of steering systems
• CPC Class Codes: B60W 2420/403 Image sensing, e.g. optical camera
• CPC Class Codes: B60W 2710/202 Steering torque
• CPC Class Codes: B60W 2710/207 Steering angle of wheels
• CPC Class Codes: B60W 2720/14 Yaw
• CPC Class Codes: B60W 30/12 Lane keeping
• CPC Class Codes: B60W 40/06 Road conditions

Abstract

A method for adjusting a vehicle'"'"'s position in a roadway lane. A camera mounted on the vehicle generates a current image of the lane and the method identifies a current lane-center line in the current image. A reference image is generated and the method identifies a reference lane-center line in the reference image. The method then calculates an error between the current lane-center line and the reference lane-center line and provide steering commands to adjust the position of the vehicle so that the error is reduced.

Claims

1. A method for correcting a position of a vehicle in a roadway lane, said method comprising:

generating a reference image of the roadway lane using a camera mounted on the vehicle, where the reference image is a two-dimensional image;

identifying a reference lane-center line on the two-dimensional reference image;

generating a current image of the lane using the camera, where the current image is a two-dimensional image;

identifying a current lane-center line on the two-dimensional current image;

calculating an error between the current lane-center line and the reference lane-center line; and

adjusting the position of the vehicle with a controller so that the error is reduced, wherein identifying the current lane center line and the reference lane-center line includes identifying the current lane-center line and the reference lane-center line by lane-center parameters including an angle of the lane-center line and a location where the lane-center line touches an edge line of the image.

2. The method of claim 1 wherein adjusting the position of the vehicle includes providing a steering adjustment command signal to a vehicle steering system.

3. The method of claim 1 wherein identifying the current lane-center line includes using camera-based lane marker detection to identify a left lane-boundary and a right lane-boundary in the current image, and then averaging the left and right lane-boundaries to identify the lane-center line.

4. The method of claim 1 wherein generating the reference image includes generating the reference image when the vehicle is in a geometric center of the lane or the geometric center of the lane plus a bias.

5. The method of claim 1 wherein generating the reference image includes identifying the reference image by machine learning techniques that are based on a driver'"'"'s preferred bias.

6. The method of claim 1 further comprising determining a desired driver offset relative to a center line in the roadway lane, and modifying the reference lane-center line based on the offset.

7. The method of claim 1 wherein adjusting the position of the vehicle includes generating a sequence of steering angle commands that minimizes a cost function J as:

8. The method of claim 7 wherein adjusting the position of the vehicle includes generating a sequence of steering torques or yaw moments instead of steering angle commands to minimize the cost function J.

9. A lane centering system for centering a vehicle in a roadway lane that the vehicle is traveling in, said system comprising:

a camera that generates images of the roadway lane, where the images are two-dimensional images;

a reference image processor that identifies a reference lane-center line on a two-dimensional reference image selected from the camera that is identified as an image when the vehicle was in a geometric center of the lane plus a predetermined bias using machine learning techniques;

a current image processor that identifies a current lane-center line on a two-dimensional current image from the camera;

a comparator that compares the reference lane-center line and the current lane-center line and provides an error therebetween; and

a lane-centering controller that receives the error and calculates a steering command that centers the vehicle in the lane by reducing the error.

10. The centering system of claim 9 further comprising a vehicle steering system, said vehicle steering system receiving the steering command from the lane-centering controller to steer the vehicle in the lane.

11. The centering system of claim 9 wherein the reference image processor and the current image processor identify the current lane-center line and the reference lane-center line by lane-center parameters that include an angle of the lane-center line and a location that the lane-center line touches an edge line of the image.

12. The centering system of claim 9 wherein the reference image processor and the current image processor use camera-based lane marker detection to identify a left lane-boundary and a right lane-boundary in the reference image and the current image, and then average the left and right lane-boundaries to identify the lane-center line.

13. The centering system of claim 9 wherein the reference image processor selects the reference image from the camera when the vehicle is in a geometric center of the lane or the geometric center of the lane plus a bias.

14. The centering system of claim 9 wherein the reference image processor selects the reference image from the camera by machine learning techniques when the vehicle is in a geometric center of the lane.

15. The centering system of claim 9 wherein the reference image processor selects the reference image from the camera by machine learning techniques that are for the same driver representing that driver'"'"'s preferred bias.

16. The centering system of claim 9 further comprising a preference processor that determines a desired driver offset relative to a center of the lane, said preference processor providing the driver offset to the reference image processor.

17. The centering system of claim 16 wherein the preference processor generates the reference image when the vehicle is in a preferred reference location within the lane.

18. A method for correcting a position of a vehicle in a roadway lane, said method comprising:

generating a reference image of the roadway lane using a camera mounted on the vehicle, where the reference image is a two-dimensional image;

identifying a reference lane-center line on the two-dimensional reference image;

generating a current image of the lane using the camera, where the current image is a two-dimensional image;

identifying a current lane-center line on the two-dimensional current image;

calculating an error between the current lane-center line and the reference lane-center line; and

adjusting the position of the vehicle with a controller so that the error is reduced, wherein generating the reference image includes identifying the reference image by machine learning techniques when the vehicle is in a geometric center of the lane.