Generalization factor based generation of navigation data

• US 10,451,429 B2
• Filed: 08/03/2017
• Issued: 10/22/2019
• Est. Priority Date: 08/04/2016
• Status: Active Grant

• Current Assignee: HERE Global B.V. (HERE Holding Corporation)
• Original Assignee: HERE Global B.V. (HERE Holding Corporation)
• Inventors: Pfeifle, Martin, Gessner, Yong, His, Aurelien
• Primary Examiner(s): Shaawat, Mussa A
• Application Number:
  US15/667,874
• Publication Number:
  US 20180038707A1
• Time in Patent Office: 810 Days
• CPC Class Codes: G01C 21/36 Input/output arrangements for on-board computers
• CPC Class Codes: G01C 21/3811 Point data, e.g. Point of Interest [POI]
• CPC Class Codes: G01C 21/3826 Terrain data
• CPC Class Codes: G01C 21/3867 Geometry of map features, e.g. shape points, polygons or for simplified maps
• CPC Class Codes: G08G 1/0112 from the vehicle, e.g. floating car data [FCD]
• CPC Class Codes: G08G 1/012 from other sources than vehicle or roadside beacons, e.g. mobile networks
• CPC Class Codes: G08G 1/0129 for creating historical data or processing based on historical data
• CPC Class Codes: G08G 1/0141 for traffic information dissemination
• CPC Class Codes: G08G 1/0968 Systems involving transmission of navigation instructions to the vehicle

Abstract

A method is disclosed comprising: determining, using a processor, a generalization factor associated with a data element of a database comprising navigation data, the generalization factor depending on a usage information associated with the data element, wherein the generalization factor is indicative to a level of details of a new data element to be generated based on the data element. Further disclosed are a corresponding apparatus, a corresponding system and a corresponding computer readable storage medium.

Claims

1. A method, comprising:

determining, using a processor, a generalization factor associated with a data element of a database comprising navigation data, the generalization factor depending on usage information associated with the data element, wherein the generalization factor is indicative of a level of details of a new data element to be generated based on the data element; and

generating the new data element based on the data element and the generalization factor wherein a lower generalization factor results in generating the new data element with a relatively lower level of detail than the new data element generated in response to a higher generalization factor.

2. The method according to claim 1, wherein the higher generalization factor results in generating the new data element with a relatively higher level of detail.

3. The method according to claim 1, further comprising:

determining the usage information based on data associated with the data element, wherein the data element represents at least one map region.

4. The method according to claim 3, wherein said data associated with the data element represents data being indicative of a likelihood of usage of the data element.

5. The method according to claim 3, wherein the data associated with the data element comprises at least one of:

a representative of a number of links associated with the data element, and/or

a representative of a number of nodes associated with the data element, and/or

a representative of a number of inhabitants associated with the data element, and/or

a representative of a number of point-of-interests associated with the data element, and/or

a representative of importance of at least one road associated with the data element, and/or

an area identifier associated with the data element, and/or

traffic related usage information associated with the data element.

6. The method according to claim 5, wherein the traffic related usage information are obtained from at least one of:

mobile devices, and/or

cars, and/or

traffic providers.

7. The method according to claim 1, wherein the generalization factor further depends on a zooming level associated with at least one map region intended to comprise the new data element.

8. The method according to claim 1, further comprising:

compiling, using the processor, a generalized database comprising a plurality of generated data elements, wherein the plurality of generated data elements represent navigation data, and each data element of the plurality of data elements represents at least one map region; and

storing, in a memory, the compiled generalized database.

9. The method according to claim 8, wherein the generated new data element comprises data for different zooming levels.

10. The method according to claim 1, wherein the processor generates the new data element using a Douglas-Peucker algorithm.

11. The method according to claim 1, wherein the data element comprises at least one of:

a representative of a polygon, and/or

a representative of a line, and/or

a representative of a point, and/or

a representative of a basic map display feature, and/or

a representative of a digital terrain model feature, and/or

a representative of a satellite image, and/or

a representative of an orthoimage, and/or

a representative of 2D data, and/or

a representative of 3D data, and/or

a representative of a junction view.

12. An apparatus comprising at least one processor and at least one memory including computer program code for one or more programs;

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

determine a generalization factor associated with a data element of a database comprising navigation data, the generalization factor depending on usage information associated with the data element, wherein the generalization factor is indicative of a level of details of a new data element to be generated based on the data element; and

generate the new data element based on the data element and the generalization factor wherein a lower generalization factor results in generating the new data element with a relatively lower level of detail than the new data element generated in response to a higher generalization factor.

13. The apparatus according to claim 12, wherein the higher generalization factor results in generating the new data element with a relatively higher level of detail.

14. The apparatus according to claim 12, wherein the at least one memory and the computer program code are further configured to, with the at least one processor, cause the apparatus to:

determine the usage information based on data associated with the data element, wherein the data element represents at least one map region.

15. The apparatus according to claim 14, wherein said data associated with the data element represents data being indicative of a likelihood of usage of the data element.

16. The apparatus according to claim 12, wherein the generalization factor further depends on a zooming level associated with at least one map region intended to comprise the new data element.

17. The apparatus according to claim 12, wherein the at least one memory and the computer program code are further configured to, with the at least one processor, cause the apparatus to:

compile a generalized database comprising a plurality of generated data elements, wherein the plurality of generated data elements represent navigation data, and each data element of the plurality of data elements represents at least one map region; and

store the compiled generalized database.

18. The apparatus according to claim 12, wherein the generated new data element comprises data for different zooming levels.

19. The apparatus according to claim 12, wherein the new data element is generated using a Douglas-Peucker algorithm.

20. A non-transitory computer readable storage medium including one or more sequences of one or more instructions which, when executed by one or more processors, cause an apparatus to at least perform:

determining a generalization factor associated with a data element of a database comprising navigation data, the generalization factor depending on usage information associated with the data element, wherein the generalization factor is indicative of a level of details of a new data element to be generated based on the data element; and

generating the new data element based on the data element and the generalization factor wherein a lower generalization factor results in generating the new data element with a relatively lower level of detail than the new data element generated in response to a higher generalization factor.