Path curve confidence factors

• US 10,197,404 B2
• Filed: 08/23/2016
• Issued: 02/05/2019
• Est. Priority Date: 02/19/2013
• Status: Active Grant

• Current Assignee: HERE Global B.V. (Nokia Corporation)
• Original Assignee: HERE Global B.V. (Nokia Corporation)
• Inventors: Dorum, Ole Henry, Lynch, James D.
• Primary Examiner(s): Shaawat, Mussa A
• Assistant Examiner(s): Jha, Abdhesh K
• Application Number:
  US15/244,480
• Publication Number:
  US 20160356609A1
• Time in Patent Office: 896 Days
• Field of Search: None
• CPC Class Codes: G01C 21/34 Route searching; Route guidance
• CPC Class Codes: G01C 21/3819 Road shape data, e.g. outline of a route
• CPC Class Codes: G01C 21/3841 Data obtained from two or more sources, e.g. probe vehicles
• CPC Class Codes: G01C 21/3844 Data obtained from position sensors only, e.g. from inertial navigation

Abstract

In one embodiment, navigational features of a navigation device are activated or deactivated according to the accuracy of the geographic data. The navigation features may be predictive features related to upcoming portions of a path curve. The path curve is compared to measured data. For example, a first curve is accessed from a map database and a second curve is based on measured position data collected while traversing the path. The first curve and the second curve correspond to the same geographic area. A difference of an attribute between a section of the first curve and a section of the second curve is used to assign a confidence factor to the section of the first polycurve based on the difference. The attribute may be heading, position, curvature, or another aspect of the path curves.

Claims

1. A method comprising:

obtaining, from a map database, a first spline representing a path, wherein the first spline is formed with piecewise polynomial functions and comprises a plurality of sections defined by at least one control point or at least one knot;

generating a second spline based on measured position data collected from one or more sensors while traversing the path represented by the first spline, wherein the second spline is formed with piecewise polynomial functions and comprises a plurality of sections;

performing, with a processor, an alignment between each section of the first spline and each section of the second spline, wherein the each section of the first spline corresponds geographically to the each section of the second spline;

generating a plurality of confidence factors by comparing the each section of the first spline to the each corresponding section of the second spline;

influencing at least one advanced driving system when the plurality of confidence factors meet or exceed a confidence threshold, wherein a plurality of advanced driving assist systems are assigned individual confidence thresholds and wherein the plurality of advanced driving assist systems are individually enabled or disabled when the plurality of confidence factors meet or exceed the corresponding confidence thresholds.

2. The method of claim 1, further comprising:

displaying the confidence threshold.

3. The method of claim 1, wherein the plurality of confidence factors are generated by comparing a curvature of the each section of the first spline to a curvature of the each corresponding section of the second spline in succession.

4. The method of claim 1, wherein the measured position data is collected by at least one satellite based positioning sensor.

5. The method of claim 1, wherein the measured position data is collected by at least one inertial sensor.

6. The method of claim 1, further comprising:

determining a correlation value for the plurality of sections of the first spline by calculating the differences between the plurality of sections of the second spline and the plurality of sections of the first spline, wherein the first spline corresponds to link geometry in the map database; and

comparing the correlation value to a correlation threshold.

7. The method of claim 6, further comprising:

excluding at least one of the plurality of sections of the first spline when the correlation value is less than the correlation threshold.

8. The method of claim 6, wherein the correlation value is based on shape, position, and/or heading.

9. The method of claim 1, wherein the at least one knot represents a point where the polynomial functions piece together;

and

wherein the at least one control point represents a point on a line segment that defines a shape of the first spline.

10. The method of claim 1, wherein the alignment between the section of the first spline and the section of the second spline is supplemented using heading data.

11. The method of claim 1, wherein the alignment between the each section of the first spline and the each corresponding section of the second spline is supplemented using shape data.

12. An apparatus comprising:

at least one processor; and

at least one non-transitory memory including computer program code for one or more programs;

the at least one non-transitory memory and the computer program code configured to, with the at least one processor, cause the apparatus to at least;

obtaining a first spline representing a path from a map database, wherein the first spline is formed with piecewise polynomial functions and comprises a plurality of sections defined by at least one control point or at least one knot;

generating a second spline based on measured position data collected while traversing the path, wherein the second spline is formed with piecewise polynomial functions and comprises a plurality of sections;

generating a plurality of confidence factors by comparing each section of the first spline to each section of the second spline; and

activate or deactivate at least one advanced driving assist system if the plurality of confidence factors meet or exceed a confidence threshold.

13. The apparatus of claim 12, wherein the at least one control point defines a first polynomial and the at least one knot joins the first polynomial with a second polynomial.

14. The apparatus of claim 12, the at least one non-transitory memory and the computer program code configured to, with the at least one processor, cause the apparatus to at least:

performing an alignment between the each section of the first spline spanning a geographic distance and the each section of the second spline corresponding to the geographic distance, wherein the each section of the first spline corresponds geographically to the each section of the second spline.

15. A non-transitory computer readable medium including instructions that when executed by a processor are operable to:

access a first spline representing a path in a map database, wherein the first spline is formed with piecewise polynomial functions and comprises a plurality of sections defined by at least one control point or at least one knot;

generate a second spline based on measured data collected while traversing the path, wherein the second spline is formed with piecewise polynomial functions and comprises a plurality of sections;

align each section of the first spline spanning a geographic distance and each section of the second spline corresponding to the geographic distance, wherein the each section of the first spline corresponds geographically to the each section of the second spline;

assign a plurality of confidence factors by comparing the each section of the first spline to the each corresponding section of the second spline; and

activate or deactivate at least one advanced driving assist system if the plurality of confidence factors meet or exceed a confidence threshold.

16. The non-transitory computer readable medium of claim 15, wherein a plurality of advanced driving assist systems are assigned individual confidence thresholds;

and

wherein the plurality of advanced driving assist systems are individually activated or deactivated when the plurality of confidence factors meet or exceed the corresponding confidence thresholds.

17. The non-transitory computer readable medium of claim 15, the instructions when executed by the processor are operable to:

determining a correlation value for the plurality of sections of the first spline by calculating the differences between the plurality of sections of the second spline and the plurality of sections of the first spline, wherein the first spline corresponds to link geometry in the map database;

comparing the correlation value to a correlation threshold; and

excluding at least one of the plurality of sections of the first spline when the correlation value is less than the correlation threshold.

18. The non-transitory computer readable medium of claim 15,

wherein the at least one knot represents a point where the polynomial functions piece together;

and

wherein the at least one control point represents a point on a line segment that defines a shape of the first spline.