Method for increasing waypoint accuracies for crowd-sourced routes

• US 8,818,717 B2
• Filed: 03/28/2013
• Issued: 08/26/2014
• Est. Priority Date: 03/28/2012
• Status: Expired due to Fees

• Current Assignee: Pioneer Corporation
• Original Assignee: Pioneer Advanced Solutions, Inc. (Pioneer Corporation)
• Inventors: Cardoso, Augusto C. Jr., Cole, Robert Curtis, Chernetsky, Victor, Dunmire, Jerry, Morel, Charles Paul, Phan, Dan
• Primary Examiner(s): Olszewski, John R
• Assistant Examiner(s): Ziaeianmehdizadeh, Navid
• Application Number:
  US13/852,049
• Publication Number:
  US 20130261961A1
• Time in Patent Office: 516 Days
• Field of Search: None
• US Class Current: 701/426
• CPC Class Codes: G01C 21/3841 Data obtained from two or more sources, e.g. probe vehicles

Abstract

A method for increasing route accuracies, comprising receiving GPS waypoints associated position accuracy prediction (PAP) parameters, comparing the PAP parameters to corpus PAP parameters associated with corpus GPS waypoints, and updating the corpus PAP parameters and the corpus GPS waypoints based on the comparison of the PAP parameters to the corpus PAP parameters.

Claims

1. A method for increasing route accuracies, the method comprising:

recording a waypoint while moving along a route;

recording a positional-accuracy measurement for the recorded waypoint;

calculating a position accuracy prediction (PAP) parameter for the recorded waypoint based on the recorded positional-accuracy measurement wherein the PAP parameter comprises a first weight value, a first confidence value, and an-a first error distance; and

comparing, with a computer processor, the recorded waypoint and the calculated PAP parameter to a corpus waypoint and an associated corpus PAP parameter stored on a computer-based system, wherein the corpus PAP parameter comprises a second weight value, a second confidence value, and a second error distance, and wherein the comparing step comprises;

calculating a weight average based on the first and second weight values;

computationally extending the first error distance from the PAP parameter of the recorded waypoint to provide a first probability distribution circle;

computationally extending the second error distance from the PAP parameter of the corpus waypoint to provide a second probability distribution circle; and

computationally generating a vector line between the recorded waypoint and the corpus waypoint; and

determining, with the computer processor, an updated waypoint along the vector line between the recorded waypoint and the corpus waypoint and within an overlapping area of the first and second probability distribution circles based on the calculated weight average.

2. The method of claim 1, wherein the recorded waypoint comprises GPS waypoint, and wherein recording the GPS waypoint while moving along the route comprises reading GPS signals with a GPS receiver, and storing data of the GPS waypoint on a portable electronic device that communicates with the GPS receiver.

3. The method of claim 1, wherein the recorded positional-accuracy measurement for the recorded waypoint is selected from the group consisting of GPS dilution of precision effects, GPS error distances, satellite signal to noise ratios, a number of satellites utilized, satellite configurations, and combinations thereof.

4. The method of claim 1, and further comprising recording environment information for each recorded waypoint, wherein the recorded environment information is selected from the group consisting of humidity, precipitation, temperature, barometric pressure, and combinations thereof, and wherein the PAP parameter is also based on the recorded environment information.

5. The method of claim 4, and further comprising receiving geographic information, wherein the PAP parameter is further based on the geographic information.

6. The method of claim 4, wherein calculating the PAP parameter comprises:

generating a quality tuple having the first weight value, the first confidence value, and the first error distance; and

adjusting at least one of the first confidence value and the first error distance based on the environment information.

7. A method for increasing route accuracies, the method comprising:

receiving a first GPS waypoint and a first position accuracy prediction (PAP) parameter associated with the first GPS waypoint, wherein the first PAP parameter comprises a first quality parameter set having a first weight value, a first confidence value for the first GPS waypoint, and a first error distance for the first GPS waypoint;

comparing, with a computer processor, the first PAP parameter to a corpus PAP parameter associated with a corpus GPS waypoint, wherein the corpus PAP parameter comprises a second quality parameter set having a second weight value, a second confidence value for the corpus GPS waypoint, and a second error distance for the corpus GPS waypoint, wherein the comparing step comprises;

calculating a weight average based on the first and second weight values;

computationally extending the first error distance from the PAP parameter of the first GPS waypoint to provide a first probability distribution circle;

computationally extending the second error distance from the PAP parameter of the corpus GPS waypoint to provide a second probability distribution circle; and

computationally generating a vector line between the first GPS waypoint and the corpus GPS waypoint; and

determining, with the computer processor, an updated GPS waypoint along the vector line between the first GPS waypoint and the corpus waypoint and within an overlapping area of the first and second probability distribution circles based on the calculated weight average.

8. The method of claim 7, and further comprising replacing the corpus GPS waypoint with the updated GPS waypoint.

9. The method of claim 8, and further comprising:

calculating an updated confidence value from the first confidence value of the first quality parameter set and the second confidence value of the second quality parameter set based at least in part on the calculated weight average; and

replacing the second confidence value of the second quality parameter set with the updated confidence value.

10. The method of claim 8, and further comprising:

calculating an updated error distance from the first error distance of the first quality parameter set and the second error distance of the second quality parameter set based at least in part on the calculated weight average; and

replacing the second error distance of the second quality parameter set with the updated error distance.

11. The method of claim 8, and further comprising:

adding the first weight value and the second weight value to attain a summed weight value; and

replacing the second weight value in the second quality parameter set with the summed weight value.

12. The method of claim 7, wherein the first PAP parameter is based on data comprising positional-accuracy measurements for the first GPS waypoint and environment information, the environment information being selected from the group consisting of humidity, precipitation, temperature, barometric pressure, and combinations thereof.

13. The method of claim 7, and further comprising determining, with the computer processor, whether the first GPS waypoint is within a predetermined coordinate area from the corpus GPS waypoint.

14. A method for increasing route accuracies, the method comprising:

receiving a first GPS waypoint and a first position accuracy prediction (PAP) parameter, wherein the first PAP parameter comprises a first quality parameter set having a first weight value, a first confidence value for the first GPS waypoint, and a first error distance for the first GPS waypoint;

reading, with a computer processor, corpus route data from a computer readable medium, the corpus route data comprising a corpus GPS waypoint and an associated corpus PAP parameter, wherein the corpus PAP parameter comprises a second quality parameter set having a second weight value, a second confidence value for the corpus GPS waypoint, and a second error distance for the corpus GPS waypoint;

calculating a weight average based on a ratio of the first weight value of the first quality parameter set and the second weight value of the second quality parameter set;

computationally extending the first error distance from the PAP parameter of the first GPS waypoint to provide a first probability distribution circle;

computationally extending the second error distance from the PAP parameter of the corpus GPS waypoint to provide a second probability distribution circle; and

computationally generating a vector line between the first GPS waypoint and the corpus GPS waypoint;

determining, with the computer processor, an updated GPS waypoint along the vector line between the first GPS waypoint and the corpus waypoint and within an overlapping area of the first and second probability distribution circles based on the calculated weight average;

replacing the corpus GPS waypoint with the updated GPS waypoint;

adding the first weight value and the second weight value to attain a summed weight value; and

replacing the second weight value in the second quality parameter set with the summed weight value.

15. The method of claim 14, and further comprising:

comparing the first confidence value of the first quality parameter set to the second confidence value of the second quality parameter set; and

comparing the first error distance of the first quality parameter set to the first error distance of the second quality parameter set.

16. The method of claim 14, and further comprising:

calculating an updated confidence value from the first confidence value of the first quality parameter set and the second confidence value of the second quality parameter set based at least in part on the calculated weight average; and

replacing the second confidence value of the second quality parameter set with the updated confidence value.

17. The method of claim 14, wherein the first PAP parameter is based on data comprising positional-accuracy measurements for the first GPS waypoint and environment information, the environment information being selected from the group consisting of humidity, precipitation, temperature, barometric pressure, and combinations thereof.