DISTRIBUTING A CROWDSOURCED SPARSE MAP FOR AUTONOMOUS VEHICLE NAVIGATION

• US 20180023960A1
• Filed: 07/21/2017
• Published: 01/25/2018
• Est. Priority Date: 07/21/2016
• Status: Active Grant

• Current Assignee: MobilEye Vision Technologies Ltd. (Intel Corporation)
• Original Assignee: MobilEye Vision Technologies Ltd. (Intel Corporation)
• Inventors: Fridman, Ofer
• Granted Patent: US 10,838,426 B2
• Time in Patent Office: Days
• CPC Class Codes: B60W 2420/403 Image sensing, e.g. optical camera
• CPC Class Codes: B60W 2552/10 Number of lanes
• CPC Class Codes: B60W 2552/25 Road altitude
• CPC Class Codes: B60W 2552/30 Road curve radius
• CPC Class Codes: B60W 2552/35 Road bumpiness, e.g. potholes
• CPC Class Codes: B60W 2552/50 Barriers
• CPC Class Codes: B60W 2552/53 Road markings, e.g. lane marker or crosswalk
• CPC Class Codes: B60W 2554/20 Static objects
• CPC Class Codes: B60W 2554/4026 Cycles
• CPC Class Codes: B60W 2554/4029 Pedestrians
• CPC Class Codes: B60W 2554/4041 Position
• CPC Class Codes: B60W 2554/60 Traversable objects, e.g. speed bumps or curbs
• CPC Class Codes: B60W 2554/802 Longitudinal distance
• CPC Class Codes: B60W 2554/804 Relative longitudinal speed
• CPC Class Codes: B60W 60/0016 of the vehicle or its occupants
• CPC Class Codes: G01C 21/28 with correlation of data from several navigational instruments
• CPC Class Codes: G01C 21/3822 Road feature data, e.g. slope data
• CPC Class Codes: G01C 21/3848 Data obtained from both position sensors and additional sensors
• CPC Class Codes: G01C 21/3852 Data derived from aerial or satellite images
• CPC Class Codes: G05D 1/0231 using optical position detecting means position-fixing by using electromagnetic waves other than radio waves, e.g. optical position detecting means G01S5/16
• CPC Class Codes: G05D 1/0246 : using a video camera in combination with image processing means
• CPC Class Codes: G05D 1/0251 : extracting 3D information from a plurality of images taken from different locations, e.g. stereo vision stereoscopic image analysis H04N13/00; depth recovery from images G06T7/593
• CPC Class Codes: G05D 1/0274 : using mapping information stored in a memory device navigation using map-matching G01C21/30
• CPC Class Codes: G05D 1/0278 : using satellite positioning signals, e.g. GPS
• CPC Class Codes: G06T 11/60 : Editing figures and text; Combining figures or text
• CPC Class Codes: G06T 2207/10024 : Color image
• CPC Class Codes: G06T 2207/10032 : Satellite or aerial image; Remote sensing
• CPC Class Codes: G06T 2207/30256 : Lane; Road marking
• CPC Class Codes: G06T 7/11 : Region-based segmentation
• CPC Class Codes: G06T 7/12 : Edge-based segmentation
• CPC Class Codes: G06T 7/337 : involving reference images or patches
• CPC Class Codes: G06T 7/37 : using transform domain methods
• CPC Class Codes: G06T 7/70 : Determining position or orientation of objects or cameras camera calibration G06T7/80
• CPC Class Codes: G06T 7/74 : involving reference images or patches
• CPC Class Codes: G06V 20/56 : exterior to a vehicle by using sensors mounted on the vehicle
• 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: G08G 1/096708 : where the received information might be used to generate an automatic action on the vehicle control
• CPC Class Codes: G08G 1/096725 : where the received information generates an automatic action on the vehicle control
• CPC Class Codes: H04N 13/239 : using two 2D image sensors having a relative position equal to or related to the interocular distance H04N13/243 takes precedence
• CPC Class Codes: H04N 2013/0081 : Depth or disparity estimation from stereoscopic image signals
• CPC Class Codes: H04N 23/90 : Arrangement of cameras or camera modules, e.g. multiple cameras in TV studios or sports stadiums
• CPC Class Codes: H04N 7/18 : Closed-circuit television [CCTV] systems, i.e. systems in which the video signal is not broadcast

Abstract

Systems and methods are provided for distributing a crowdsourced sparse map for autonomous vehicle navigation. In one implementation, a method of generating a road navigation model for use in autonomous vehicle navigation may include receiving navigation information associated with a common road segment from a plurality of vehicles; storing the navigation information associated with the common road segment; generating at least a portion of an autonomous vehicle road navigation model for the common road segment based on the navigation information; and distributing the autonomous vehicle road navigation model to one or more autonomous vehicles for use in autonomously navigating along the common road segment. The autonomous vehicle road navigation model may include at least one line representation of a road surface feature extending along the common road segment, and each line representation may representing a path along the common road segment substantially corresponding with the road surface feature.

Claims

1. A method of generating a road navigation model for use in autonomous vehicle navigation, the method comprising:

receiving, by a server, navigation information from a plurality of vehicles, wherein the navigation information from the plurality of vehicles is associated with a common road segment;

storing, by the server, the navigation information associated with the common road segment;

generating, by the server, at least a portion of an autonomous vehicle road navigation model for the common road segment based on the navigation information from the plurality of vehicles, the autonomous vehicle road navigation model for the common road segment including at least one line representation of a road surface feature extending along the common road segment, each line representation representing a path along the common road segment substantially corresponding with the road surface feature, and wherein the road surface feature is identified through image analysis of a plurality of images acquired as the plurality of vehicles traverse the common road segment; and

distributing, by the server, the autonomous vehicle road navigation model to one or more autonomous vehicles for use in autonomously navigating the one or more autonomous vehicles along the common road segment.

2. The method of claim 1, wherein the autonomous vehicle road navigation model is configured to be superimposed over a map, an image, or a satellite image.

3. The method of claim 1, wherein the plurality of road features include road edges or lane markings.

4. The method of claim 1, wherein generating at least a portion of the autonomous vehicle road navigation model includes identifying, based on image analysis of the plurality of images, a plurality of landmarks associated with the common road segment.

5. The method of claim 4, wherein generating at least a portion of the autonomous vehicle road navigation model further includes accepting potential landmarks when a ratio of images in which the landmark does appear to images in which the landmark does not appear exceeds a threshold.

6. The method of claim 4, wherein generating at least a portion of the autonomous vehicle road navigation model further includes rejecting potential landmarks when a ratio of images in which the landmark does not appear to images in which the landmark does appear exceeds a threshold.

7. A system for generating a road navigation model for use in autonomous vehicle navigation, the system comprising:

at least one network interface;

at least one non-transitory storage medium; and

at least one processing device, wherein the at least one processing device is configured to;

receive, using the network interface, navigation information from a plurality of vehicles, wherein the navigation information from the plurality of vehicles is associated with a common road segment;

store, on the non-transitory storage medium, the navigation information associated with the common road segment;

generate at least a portion of an autonomous vehicle road navigation model for the common road segment based on the navigation information from the plurality of vehicles, the autonomous vehicle road navigation model for the common road segment including at least one line representation of a road surface feature extending along the common road segment, each line representation representing a path along the common road segment substantially corresponding with the road surface feature, and wherein the road surface feature is identified through image analysis of a plurality of images acquired as the plurality of vehicles traverse the common road segment; and

distribute, using the network interface, the autonomous vehicle road navigation model to one or more autonomous vehicles for use in autonomously navigating the one or more autonomous vehicles along the common road segment.

8. The system of claim 7, wherein the autonomous vehicle road navigation model is configured to be superimposed over a map, an image, or a satellite image.

9. The system of claim 7, wherein the plurality of road features include road edges or lane markings.

10. The system of claim 7, wherein generating at least a portion of the autonomous vehicle road navigation model includes identifying, based on image analysis of the plurality of images, a plurality of landmarks associated with the common road segment.

11. The system of claim 10, wherein generating at least a portion of the autonomous vehicle road navigation model further includes accepting potential landmarks when a ratio of images in which the landmark does appear to images in which the landmark does not appear exceeds a threshold.

12. The system of claim 10, wherein generating at least a portion of the autonomous vehicle road navigation model further includes rejecting potential landmarks when a ratio of images in which the landmark does not appear to images in which the landmark does appear exceeds a threshold.

13. A non-transitory, computer-readable medium storing instructions that, when executed by at least one processing device, cause the server to:

receive navigation information from a plurality of vehicles, wherein the navigation information from the plurality of vehicles is associated with a common road segment;

store the navigation information associated with the common road segment;

generate at least a portion of an autonomous vehicle road navigation model for the common road segment based on the navigation information from the plurality of vehicles, the autonomous vehicle road navigation model for the common road segment including at least one line representation of a road surface feature extending along the common road segment, each line representation representing a path along the common road segment substantially corresponding with the road surface feature, and wherein the road surface feature is identified through image analysis of a plurality of images acquired as the plurality of vehicles traverse the common road segment; and

distribute the autonomous vehicle road navigation model to one or more autonomous vehicles for use in autonomously navigating the one or more autonomous vehicles along the common road segment.

14. The non-transitory, computer-readable medium of claim 13, wherein the autonomous vehicle road navigation model is configured to be superimposed over a map, an image, or a satellite image.

15. The non-transitory, computer-readable medium of claim 13, wherein the plurality of road features include road edges or lane markings.

16. The non-transitory, computer-readable medium of claim 13, wherein the instructions to generate at least a portion of the autonomous vehicle road navigation model include instructions to identify, based on image analysis of the plurality of images, a plurality of landmarks associated with the common road segment.

17. The non-transitory, computer-readable medium of claim 16, wherein the instructions to generate at least a portion of the autonomous vehicle road navigation model further include instructions to accept potential landmarks when a ratio of images in which the landmark does appear to images in which the landmark does not appear exceeds a threshold.

18. The non-transitory, computer-readable medium of claim 16, wherein the instructions to generate at least a portion of the autonomous vehicle road navigation model further include instructions to reject potential landmarks when a ratio of images in which the landmark does not appear to images in which the landmark does appear exceeds a threshold.