System and method for generating precise road lane map data

Patent metadata

Field	Value
Publication number	US-12298149-B2
Application number	US-202418418752-A
Country	US
Kind code	B2
Filing date	Jan 22, 2024
Priority date	Nov 6, 2017
Publication date	May 13, 2025
Grant date	May 13, 2025

Abstract

An in-vehicle system for generating precise, lane-level road map data includes a GPS receiver operative to acquire positional information associated with a track along a road path. An inertial sensor provides time local measurement of acceleration and turn rate along the track, and a camera acquires image data of the road path along the track. A processor is operative to receive the local measurement from the inertial sensor and image data from the camera over time in conjunction with multiple tracks along the road path, and improve the accuracy of the GPS receiver through curve fitting. One or all of the GPS receiver, inertial sensor and camera are disposed in a smartphone. The road map data may be uploaded to a central data repository for post processing when the vehicle passes through a WiFi cloud to generate the precise road map data, which may include data collected from multiple drivers.

First claim

What is claimed is:

1. A method of generating precise lane-level road map data, comprising the steps of:
collecting raw GPS coordinates, via a plurality of GPS receivers associated with each respective vehicle of a plurality of vehicles as the plurality of vehicles travels along a road path defining the same road path traveled by each respective vehicle;
defining raw road track data via the raw GPS coordinates acquired by each vehicle of the plurality of vehicles;
receiving, via a processor connected to memory, the raw road track data from the plurality of vehicles;
applying, via the processor, an algorithm to the raw road track data to generate improved road track data defining the road path; and
storing, via the processor, the improved road track data in a database for subsequent use by the same or different vehicles when traveling along the road path.

2. The method of claim 1 , further comprising:
measuring acceleration and turn rate of the plurality of vehicles, via an inertial sensor, as the plurality of vehicles travels along the road path; and
generating the raw road track data via the acceleration and turn rate of each vehicle of the plurality of vehicles.

3. The method of claim 1 , further comprising:
acquiring image data from each vehicle of the plurality of vehicles, via a video camera, of the road path as the plurality of vehicles travel along the road path; and
generating, via the image data, raw road track data for each vehicle of the plurality of vehicles.

4. The method of claim 3 , wherein the video camera is integrated with the vehicle.

5. The method of claim 1 , further comprising:
uploading, via the processor, the improved road track data to a central data repository for post processing.

6. The method of claim 5 , further comprising:
uploading, via the processor, the improved road track data to a central data repository for post processing whenever at least one vehicle of the plurality of vehicles passes through a WiFi cloud.

7. The method of claim 2 , further comprising:
improving the raw road track data with curve fitting using the acceleration and turn rate of the plurality of vehicles.

8. The method of claim 1 , wherein the plurality of vehicles are autonomous vehicles.

9. A method of generating precise lane-level road map data, comprising the steps of:
collecting raw GPS coordinates, via a plurality of GPS receivers associated with each respective vehicle of a plurality of vehicles;
measuring acceleration and turn rate data, via an inertial sensor, as each vehicle of the plurality of vehicles travels along a road path;
defining raw road track data via the raw GPS coordinates and the acceleration and turn rate data, where the raw road track data defines the same road path travelled by each respective vehicle;
receiving, via a processor connected to memory, the raw road track data from the plurality of vehicles;
applying, via the processor, a curve fitting algorithm to the raw road track data to generate improved road track data defining the road path; and
storing, via the processor, the improved road track data in a database for subsequent use by the same or different vehicles when traveling along the road path.

10. The method of claim 9 , further comprising:
uploading, via the processor, the improved road track data to a central data repository for post processing.

11. The method of claim 10 , further comprising:
uploading, via the processor, the improved road track data to a central data repository for post processing whenever the vehicle passes through a WiFi cloud.

12. The method of claim 9 , wherein the plurality of vehicles are autonomous vehicles.

13. The method of claim 9 , wherein the improved road track data includes transient road features.

14. The method of claim 13 , wherein the transient road features include at least one of construction sites, traffic diversions, and newly opened road or lane paths.

15. A method of generating precise lane-level road map data, comprising the steps of:
collecting raw GPS coordinates, via a plurality of GPS receivers associated with each respective vehicle of a plurality of vehicles;
acquiring image data from each vehicle of the plurality of vehicles, via a video camera, of a road path as the plurality of vehicles travel along the road path;
defining raw road track data via the raw GPS coordinates and image data, wherein the raw road track data defines the same road path travelled by the plurality of vehicles;
receiving, via a processor connected to memory, the raw road track data from the plurality of vehicles;
applying, via the processor, a curve fitting algorithm to the raw road track data to generate improved road track data defining the road path; and
storing, via the processor, the improved road track data in a database for subsequent use by the same or different vehicles when traveling along the road path.

16. The method of claim 15 , wherein the video camera is integrated with the vehicle.

17. The method of claim 15 , further comprising:
uploading, via the processor, the improved road track data to a central data repository for post processing.

18. The method of claim 15 , further comprising:
uploading, via the processor, the improved road track data to a central data repository for post processing whenever the vehicle passes through a WiFi cloud.

19. The method of claim 15 , wherein the plurality of vehicles are autonomous vehicles.

20. The method of claim 15 , wherein the improved road track data includes transient road features.

Assignees

• Cybernet Systems Corp

Inventors

• Jacobus Charles J
• Beach Glenn J
• Haanpaa Douglas
• Cohen Charles J

Classifications

• G05D1/246

  using environment maps, e.g. simultaneous localisation and mapping [SLAM] · CPC title

• G05D1/248

  generated by satellites, e.g. GPS · CPC title

• G05D1/249

  from positioning sensors located off-board the vehicle, e.g. from cameras · CPC title

• G01C21/1656

  with passive imaging devices, e.g. cameras · CPC title

• G01C21/3815

  Road data · CPC title