Dynamic layers for navigation database systems

• US 10,991,241 B2
• Filed: 07/18/2017
• Issued: 04/27/2021
• Est. Priority Date: 07/20/2016
• Status: Active Grant

• Current Assignee: Harman Becker Automotive Systems GmbH (Samsung Electronics Co. Ltd.), Harman International Industries Incorporated (Samsung Electronics Co. Ltd.)
• Original Assignee: Harman Becker Automotive Systems GmbH (Samsung Electronics Co. Ltd.)
• Inventors: Ivanov, Volodymyr, Pryakhin, Alexey, Borshch, Ievgenii
• Primary Examiner(s): Ahmed, Masud
• Application Number:
  US15/653,335
• Publication Number:
  US 20180023964A1
• Time in Patent Office: 1,379 Days
• CPC Class Codes: G01C 15/002 Active optical surveying means optical plumbing G01C15/105
• CPC Class Codes: G01C 21/26 specially adapted for navigation in a road network
• CPC Class Codes: G01C 21/28 with correlation of data from several navigational instruments
• CPC Class Codes: G01C 21/30 Map- or contour-matching
• CPC Class Codes: G01C 21/3415 Dynamic re-routing, e.g. recalculating the route when the user deviates from calculated route or after detecting real-time traffic data or accidents
• CPC Class Codes: G01C 21/36 Input/output arrangements for on-board computers
• CPC Class Codes: G01C 21/3697 Output of additional, non-guidance related information, e.g. low fuel level G01C21/3679 takes precedence
• CPC Class Codes: G01C 21/3815 Road data
• CPC Class Codes: G01S 19/51 Relative positioning
• CPC Class Codes: G01S 19/53 Determining attitude
• CPC Class Codes: G06F 16/284 Relational databases
• CPC Class Codes: G06F 16/29 Geographical information databases
• CPC Class Codes: G06F 17/10 Complex mathematical operations function generation by table look-up G06F1/03; evaluation of elementary functions by calculation G06F7/544
• CPC Class Codes: G06F 8/65 Updates security arrangements therefor G06F21/57
• CPC Class Codes: G06F 9/44526 Plug-ins; Add-ons
• CPC Class Codes: G06T 17/05 Geographic models
• CPC Class Codes: G06T 7/12 Edge-based segmentation
• CPC Class Codes: G08G 1/0104 Measuring and analyzing of parameters relative to traffic conditions
• CPC Class Codes: G08G 1/0112 from the vehicle, e.g. floating car data [FCD]
• CPC Class Codes: G08G 1/052 with provision for determining speed or overspeed speed measuring in general G01P
• CPC Class Codes: G08G 1/093 : Data selection, e.g. prioritizing information, managing message queues, selecting the information to be output
• CPC Class Codes: G08G 1/09623 : Systems involving the acquisition of information from passive traffic signs by means mounted on the vehicle G08G1/0967 takes precedence
• CPC Class Codes: G08G 1/0967 : Systems involving transmission of highway information, e.g. weather, speed limits transmission of navigation instructions to the vehicle G08G1/0968
• CPC Class Codes: G08G 1/096716 : where the received information does not generate an automatic action on the vehicle control
• CPC Class Codes: G08G 1/20 : Monitoring the location of vehicles belonging to a group, e.g. fleet of vehicles, countable or determined number of vehicles

Abstract

In one embodiment, a layer application generates navigation-related data based on observations associated with vehicles. In operation, the layer application generates an attribute based on a first observation associated with a vehicle and an observation type. The layer application then compares the attribute to an existing road database to generate a dynamic layer. Subsequently, the layer application causes the dynamic layer to be transmitted to a navigation subsystem. After receiving the dynamic layer, the navigation subsystem performs navigation operation(s) based on the road database and the dynamic layer. Because the layer application may receive and operate on real-time observations, providers of road databases may produce dynamic layers at a frequency that enables navigation applications to continually provide accurate navigation data.

Claims

1. A method for generating navigation-related data, the method comprising:

storing a plurality of attributes to a layer data, each attribute being based on an observation associated with a vehicle, wherein the plurality of attributes includes a first attribute based on a first observation associated with a first vehicle and a first observation type, and each attribute in the plurality of attributes has an associated confidence level;

removing all stored attributes from the layer data having an associated confidence level that does not satisfy a threshold confidence level;

performing one or more comparison operations between the stored attributes remaining in the layer data after removing all the stored attributes from the layer data having an associated confidence level that does not satisfy the threshold confidence; and

causing the first dynamic layer to be transmitted to a first navigation system, wherein the first navigation system performs at least one navigation operation based on the road database and the first dynamic layer.

2. The method of claim 1, wherein the first observation type comprises one of road signs, traffic lights, pavement markings, emergency vehicles, or road conditions.

3. The method of claim 1, wherein the first attribute associates first navigation-related data associated with the first observation with a first road segment included in the road database.

4. The method of claim 1, further comprising, prior to performing the one or more comparison operations, generating a first confidence level associated with the first attribute, and determining that the first confidence level indicates that a reliability of the first attribute is greater than a minimum acceptable confidence level.

5. The method of claim 1, further comprising generating the first attribute by causing a first plugin associated with the first observation type to execute a first plurality of processing operations based on the first observation, wherein the first plurality of processing operations implement a data processing flow for the first observation type.

6. The method of claim 5, further comprising causing the first plugin to perform one or more learning operations based on ground truth data.

7. The method of claim 5, wherein the data processing flow includes at least one of a spatial referencing phase, a clustering phase, a matching phase, an interpolation phase, or a generate phase.

8. The method of claim 5, further comprising:

while the first plugin executes a first set of one or more collection operations to receive a second observation associated with the first observation type, identifying a second plugin associated with a second observation type; and

causing the second plugin to execute a second set of one or more collection operations to receive a third observation associated with the second observation type.

9. The method of claim 8, further comprising:

causing the first plugin to execute the first plurality of processing operations based on the second observation to generate a second attribute;

causing the second plugin to execute a second plurality of processing operations based on the third observation to generate a third attribute, wherein the second plurality of processing operations implement the data processing flow for a second observation type;

performing one or more comparison operations between the second attribute, the third attribute, and the road database to generate a second dynamic layer; and

causing the second dynamic layer to be transmitted to the first navigation system.

10. The method of claim 1, wherein the first dynamic layer specifies the one or more differences between the layer data and the road database via one or more attributes.

11. One or more non-transitory computer-readable media including instructions that, when executed by a one or more processors, cause the one or more processors to generate navigation-related data by performing the steps of:

storing a plurality of attributes to a layer data, each attribute being based on an observation associated with a vehicle, wherein the plurality of attributes includes a first attribute based on a first observation associated with a first vehicle and a first observation type, and each attribute in the plurality of attributes has an associated confidence level;

removing all stored attributes from the layer data having an associated confidence level that does not satisfy a threshold confidence level;

performing one or more comparison operations between the stored attributes remaining in the layer data after removing all the stored attributes from the layer data having an associated confidence level that does not satisfy the threshold confidence; and

causing the first dynamic layer to be transmitted to a first navigation system, wherein the first navigation system performs at least one navigation operation based on the road database and the first dynamic layer.

12. The one or more non-transitory computer-readable media of claim 11, wherein the first observation type comprises one of road signs, traffic lights, pavement markings, emergency vehicles, or road conditions.

13. The one or more non-transitory computer-readable media of claim 11, wherein the first attribute associates a speed limit with a first road segment included in the road database.

14. The one or more non-transitory computer-readable media of claim 11, further comprising, prior to performing the one or more comparison operations, determining that a confidence level associated with a second attribute indicates that a reliability of the second attribute is less than a minimum acceptable confidence level and, in response, removing the second attribute.

15. The one or more non-transitory computer-readable media of claim 11, further comprising generating the first attribute by causing a first plugin associated with the first observation type to execute a first plurality of processing operations based on the first observation, wherein the first plurality of processing operations implement a data processing flow for the first observation type.

16. The one or more non-transitory computer-readable media of claim 15, further comprising causing the first plugin to perform one or more learning operations based on ground truth data.

17. The one or more non-transitory computer-readable media of claim 15, wherein the data processing flow includes at least one of a spatial referencing phase, a clustering phase, a matching phase, an interpolation phase, or a generate phase.

18. The one or more non-transitory computer-readable media of claim 15, further comprising:

while the first plugin executes a first set of one or more collection operations to receive a second observation associated with the first observation type, identifying a second plugin associated with a second observation type; and

causing the second plugin to execute a second set of one or more collection operations to receive a third observation associated with the second observation type.

19. The one or more non-transitory computer-readable media of claim 11, wherein the first observation is received from the first vehicle or a manufacturer of the first vehicle.

20. A system for generating navigation-related data, the system comprising:

a memory storing a layer application; and

a processor coupled to the memory, wherein when executed by the processor, the layer application causes the processor to;

store a plurality of attributes to a layer data, each attribute being based on an observation associated with a vehicle, wherein the plurality of attributes includes a first attribute based on one or more observations associated with a road specified in a road database, and each attribute in the plurality of attributes has an associated confidence level;

remove all stored attributes from the layer data having an associated confidence level that does not satisfy a threshold confidence level;

perform one or more comparison operations between the stored attributes remaining in the layer data after removing all the stored attributes from the layer data having an associated confidence level that does not satisfy the threshold confidence; and

cause the first dynamic layer to be transmitted to a first navigation system, wherein the first navigation system performs at least one navigation operation based on the road database and the first dynamic layer.

21. The system of claim 20, wherein a first observation included in the one or more observations is received from a first vehicle or a manufacturer of the first vehicle.