Anchor lane selection method using navigation input in road change scenarios

• US 8,706,417 B2
• Filed: 07/30/2012
• Issued: 04/22/2014
• Est. Priority Date: 07/30/2012
• Status: Active Grant

• Current Assignee: GM Global Technology Operations LLC (General Motors Company)
• Original Assignee: GM Global Technology Operations LLC (General Motors Company)
• Inventors: Zeng, Shuqing, Litkouhi, Bakhtiar Brian, Chen, Xingping, Nickolaou, James N., Gandhi, Daniel, Pazhayampallil, Joel
• Primary Examiner(s): Cheung, Mary
• Assistant Examiner(s): BUTLER, RODNEY ALLEN
• Application Number:
  US13/561,755
• Publication Number:
  US 20140032108A1
• Time in Patent Office: 631 Days
• Field of Search: 701/23, 701 25- 28, 701/468, 701/523, 701533-536, 348/113, 34099516-99519, 340/995.22
• US Class Current: 701/533
• CPC Class Codes: B60W 2554/4041 Position
• CPC Class Codes: B60W 2555/60 Traffic rules, e.g. speed limits or right of way
• CPC Class Codes: B60W 2556/50 of positioning data, e.g. GPS [Global Positioning System] data
• CPC Class Codes: B60W 30/12 Lane keeping
• CPC Class Codes: G08G 1/167 Driving aids for lane monitoring, lane changing, e.g. blind spot detection

Abstract

A method for selecting an anchor lane for tracking in a vehicle lane tracking system. Digital map data and leading vehicle trajectory data are used to predict lane information ahead of a vehicle. Left and right lane boundary markers are also detected, where available, using a vision system. The lane marker data from the vision system is combined with the lane information from the digital map data and the leading vehicle trajectory data in a lane curvature fusion calculation. The left and right lane marker data from the vision system are also evaluated for conditions such as parallelism and sudden jumps in offsets, while considering the presence of entrance or exit lanes as indicated by the map data. An anchor lane for tracking is selected based on the evaluation of the vision system data, using either the fused curvature calculation or the digital map and leading vehicle trajectory data.

Claims

1. A method for selecting an anchor lane in a vehicle lane tracking system, said method comprising:

predicting lane information for a vehicle using digital map data, including determining whether an exit or entrance lane is present;

using a controller to predict lane information for the vehicle using leading vehicle trajectory data;

obtaining from a vision system at least one of left lane marker data and/or right lane marker data;

performing a lane curvature fusion calculation using the digital map data, the leading vehicle trajectory data and the lane marker data from the vision system, where the lane curvature fusion calculation is performed on a microprocessor in the vehicle lane tracking system;

comparing the lane marker data from the vision system to the digital map data and the leading vehicle trajectory data; and

selecting an anchor lane for tracking based on whether the exit or entrance lane is present and the comparison of the lane marker data from the vision system to the digital map data and the leading vehicle trajectory data.

2. The method of claim 1 wherein the lane information includes lane heading angle and lane curvature.

3. The method of claim 1 wherein the digital map data includes navigational route data for the vehicle.

4. The method of claim 1 wherein the lane curvature fusion calculation is a weighted average of lane curvature from the vision system data, lane curvature from the digital map data and lane curvature from the leading vehicle trajectory data.

5. The method of claim 4 wherein the lane curvature from the vision system data is chosen as a median value of recent measurements.

6. The method of claim 4 wherein the lane curvature from the digital map data is calculated using a least-squares curve fit to road waypoint data from a navigation &

digital map system.

7. The method of claim 4 wherein the lane curvature from the leading vehicle trajectory data is calculated by transposing object tracks from an object detection sensor into trajectory paths and fitting a curve to each of the trajectory paths.

8. The method of claim 1 wherein comparing the lane marker data from the vision system to the digital map data and the leading vehicle trajectory data includes discarding the lane marker data from the vision system on a side of the vehicle where the exit or entrance lane is present.

9. The method of claim 1 wherein selecting an anchor lane for tracking includes determining whether left and right lane boundaries are parallel, performing a curvature consistency calculation if the left and right lane boundaries are not parallel but no exit or entrance lane is present, and using a lower curvature consistency value to identify the anchor lane for tracking.

10. The method of claim 1 wherein selecting an anchor lane for tracking includes discarding the lane marker data from the vision system on a side of the vehicle where a sudden jump in offsets is detected.

11. The method of claim 1 further comprising performing a lane fusion calculation using the lane curvature fusion and vehicle dynamics data as input to a Kalman filter computation, where the lane fusion calculation determines lane offset and heading error.

12. A method for selecting an anchor lane in a vehicle lane tracking system, said method comprising:

predicting lane information for a vehicle using digital map data including navigational route data for the vehicle;

using a controller to predict lane information for the vehicle using leading vehicle trajectory data;

obtaining from a vision system at least one of left lane marker data and/or right lane marker data;

performing a lane curvature fusion calculation using the digital map data, the leading vehicle trajectory data and the lane marker data from the vision system, where the lane curvature fusion calculation is performed on a microprocessor in the vehicle lane tracking system;

comparing the lane marker data from the vision system to the digital map data and the leading vehicle trajectory data, including discarding the lane marker data from the vision system on a side of the vehicle where an exit or entrance lane is present; and

selecting an anchor lane for tracking based on the comparison of the lane marker data from the vision system to the digital map data and the leading vehicle trajectory data, including determining whether left and right lane boundaries are parallel, performing a curvature consistency calculation if the left and right lane boundaries are not parallel but no exit or entrance lane is present, and using a lower curvature consistency value to identify the anchor lane for tracking.

13. The method of claim 12 wherein the lane curvature fusion calculation is a weighted average of lane curvature from the vision system data, lane curvature from the digital map data and lane curvature from the leading vehicle trajectory data, and where the lane curvature from the vision system data is chosen as a median value of recent measurements, the lane curvature from the digital map data is calculated using a least-squares curve fit to road waypoint data from a navigation &

digital map system, and the lane curvature from the leading vehicle trajectory data is calculated by transposing object tracks from an object detection sensor into trajectory paths and fitting a curve to each of the trajectory paths.

14. The method of claim 12 wherein selecting an anchor lane for tracking includes discarding the lane marker data from the vision system on a side of the vehicle where a sudden jump in offsets is detected.

15. A lane tracking system for a vehicle comprising:

input channels for receiving data from a navigation &

digital map system, a vision system, an object detection sensor and a vehicle dynamics system;

a memory module for storing the received data and any calculated data; and

a processor configured to perform a lane curvature fusion calculation using lane data from the navigation &

digital map system, leading vehicle trajectory data from the object detection sensor and lane marker data from the vision system, said processor being further configured to select an anchor lane for tracking based on whether an exit or entrance lane is present and a comparison of the lane marker data from the vision system to the lane data from the navigation &

digital map system and the leading vehicle trajectory data from the object detection sensor.

16. The system of claim 15 wherein the lane curvature fusion calculation is a weighted average of lane curvature from the vision system lane marker data, lane curvature from the navigation &

digital map lane data and lane curvature from the leading vehicle trajectory data.

17. The system of claim 15 wherein the comparison of the lane marker data from the vision system to the lane data from the navigation &

digital map system and the leading vehicle trajectory data from the object detection sensor includes discarding the lane marker data from the vision system on a side of the vehicle where the exit or entrance lane is present.

18. The system of claim 15 wherein selecting an anchor lane for tracking includes determining whether left and right lane boundaries are parallel, performing a curvature consistency calculation if the left and right lane boundaries are not parallel but no exit or entrance lane is present, and using a lower curvature consistency value to identify the anchor lane for tracking.

19. The system of claim 15 wherein selecting an anchor lane for tracking includes discarding the lane marker data from the vision system on a side of the vehicle where a sudden jump in offsets is detected.

20. The system of claim 15 wherein the processor is also configured to perform a lane fusion calculation using the lane curvature fusion and vehicle dynamics data as input to a Kalman filter computation, where the lane fusion calculation determines lane offset and heading error.