Navigating a vehicle using a crowdsourced sparse map

Patent metadata

Field	Value
Publication number	US-10558222-B2
Application number	US-201715656308-A
Country	US
Kind code	B2
Filing date	Jul 21, 2017
Priority date	Jul 21, 2016
Publication date	Feb 11, 2020
Grant date	Feb 11, 2020

Abstract

Systems and methods are provided for navigating a vehicle using a crowdsourced sparse map. In one implementation, a method of autonomously navigating a vehicle along a road segment may include receiving a sparse map model, receiving at least one image representative of an environment of the vehicle, analyzing the sparse map model and the at least one image, and determining an autonomous navigational response for the vehicle based on the analysis of the sparse map model and the at least one image. The at least one image may be received from a camera, and the sparse map model may include at least one line representation of a road surface feature extending along the road segment, each line representation representing a path along the road segment substantially corresponding with the road surface feature.

First claim

What is claimed is:

1. A system for autonomously navigating a vehicle along a road segment, the system comprising:
at least one processing device configured to:
receive a sparse map model, wherein the sparse map model includes at least one line representation of a road surface feature extending along the road segment, each line representation representing a path along the road segment substantially corresponding with the road surface feature;
receive from a camera, at least one image representative of an environment of the vehicle;
analyze the sparse map model and the at least one image received from the camera to determine a current position of the vehicle relative to a longitudinal position along the at least one line representation of the road surface feature extending along the road segment; and
determine an autonomous navigational response for the vehicle based on the analysis of the sparse map model and the at least one image received from the camera.

2. The system of claim 1 , wherein the road surface feature includes a road edge or a lane marking.

3. The system of claim 1 , wherein determining a current position of the vehicle relative to a longitudinal position along the at least one line representation of a road surface feature extending along the road segment is based on identification of at least one recognized landmark in the at least one image.

4. The system of claim 3 , wherein the at least one processing device is further configured to determine an estimated offset based on an expected position of the vehicle relative to the longitudinal position and the current position of the vehicle relative to the longitudinal position.

5. The system of claim 4 , wherein the autonomous navigational response is further based on the estimated offset.

6. The system of claim 1 , wherein the at least one processing device is further configured to adjust a steering system of the vehicle based on the autonomous navigational response.

7. A method for autonomously navigating a vehicle along a road segment, the method comprising:
receiving a sparse map model, wherein the sparse map model includes at least one line representation of a road surface feature extending along the road segment, each line representation representing a path along the road segment substantially corresponding with the road surface feature;
receiving from a camera, at least one image representative of an environment of the vehicle;
analyzing the sparse map model and the at least one image received from the camera to determine a current position of the vehicle relative to a longitudinal position along the at least one line representation of the road surface feature extending along the road segment; and
determining an autonomous navigational response for the vehicle based on the analysis of the sparse map model and the at least one image received from the camera.

8. The method of claim 7 , wherein determining a current position of the vehicle relative to a longitudinal position along the at least one line representation of a road surface feature extending along the road segment is based on identification of at least one recognized landmark in the at least one image.

9. The method of claim 8 , further comprising determining an estimated offset based on an expected position of the vehicle relative to the longitudinal position and the current position of the vehicle relative to the longitudinal position.

10. The method of claim 9 , wherein the autonomous navigational response is further based on the estimated offset.

11. The method of claim 7 , further comprising adjusting a steering system of the vehicle based on the autonomous navigational response.

12. A non-transitory, computer-readable medium storing instructions that, when executed by at least one processing device, cause the device to:
receive a sparse map model, wherein the sparse map model includes at least one line representation of a road surface feature extending along the road segment, each line representation representing a path along the road segment substantially corresponding with the road surface feature;
receive from a camera, at least one image representative of an environment of the vehicle;
analyze the sparse map model and the at least one image received from the camera to determine a current position of the vehicle relative to a longitudinal position along the at least one line representation of the road surface feature extending along the road segment; and
determine an autonomous navigational response for the vehicle based on the analysis of the sparse map model and the at least one image received from the camera.

13. The non-transitory, computer-readable medium of claim 12 , wherein the road feature includes a road edge or a lane marking.

14. The non-transitory, computer-readable medium of claim 12 , wherein determining a current position of the vehicle relative to a longitudinal position along the at least one line representation of a road surface feature extending along the road segment is based on identification of at least one recognized landmark in the at least one image.

15. The non-transitory, computer-readable medium of claim 14 , further storing instructions to determine an estimated offset based on an expected position of the vehicle relative to the longitudinal position and the current position of the vehicle relative to the longitudinal position.

16. The non-transitory, computer-readable medium of claim 15 , wherein the autonomous navigational response is further based on the estimated offset.

17. The non-transitory, computer-readable medium of claim 12 , further storing instructions to adjust a steering system of the vehicle based on the autonomous navigational response.

Assignees

• Mobileye Vision Technologies Ltd

Inventors

• Fridman Ofer

Classifications

• G06T11/60

  Creating or editing images; Combining images with text · CPC title

• G06T7/12

  Edge-based segmentation · CPC title

• G08G1/096708

  where the received information might be used to generate an automatic action on the vehicle control · CPC title

• G06T7/37

  using transform domain methods · CPC title

• G06T2207/30256

  Lane; Road marking · CPC title