# US 2018/0023964 A1 · Dynamic layers for navigation database systems > Agents: this data is available as JSON from the patentgrep API and MCP server — see https://patentgrep.com/llms.txt - Number: US 2018/0023964 A1 - Document ID: US20180023964A1 - Status: Published application - Kind: A1 - Application number: 15/653,335 - Filed: 2017-07-18 - Published: 2018-01-25 - Earliest claimed: 2016-07-20 - Assignees: Harman International Industries, Incorporated - Applicants: Harman International Industries, Incorporated - Inventors: Volodymyr Ivanov; Alexey Pryakhin; Ievgenii Borshch - Web page: https://patentgrep.com/patent/US20180023964A1 ## CPC - G08G 1/0104: {Measuring and analyzing of parameters relative to traffic conditions} - G01C 15/002: {Active optical surveying means (optical plumbing G01C15/105)} - G01C 21/26: specially adapted for navigation in a road network - G01C 21/28: with correlation of data from several navigational instruments - G01C 21/30: Map- or contour-matching - 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} - G01C 21/36: Input/output arrangements for on-board computers - G01C 21/3697: {Output of additional, non-guidance related information, e.g. low fuel level (G01C21/3679 takes precedence)} - G01C 21/3815: {Road data} - G01S 19/51: Relative positioning - G01S 19/53: Determining attitude - G06F 8/65: Updates (security arrangements therefor G06F21/57) - G06F 9/44526: {Plug-ins; Add-ons} - G06F 16/284: {Relational databases} - G06F 16/29: Geographical information databases - G06F 17/10: Complex mathematical operations {(function generation by table look-up G06F1/03; evaluation of elementary functions by calculation G06F7/544)} - G06T 7/12: Edge-based segmentation - G06T 17/05: Geographic models - G08G 1/0112: {from the vehicle, e.g. floating car data [FCD]} - G08G 1/052: with provision for determining speed or overspeed {(speed measuring in general G01P)} - G08G 1/093: {Data selection, e.g. prioritizing information, managing message queues, selecting the information to be output} - G08G 1/09623: {Systems involving the acquisition of information from passive traffic signs by means mounted on the vehicle (G08G1/0967 takes precedence)} - G08G 1/0967: Systems involving transmission of highway information, e.g. weather, speed limits (transmission of navigation instructions to the vehicle G08G1/0968) - G08G 1/096716: {where the received information does not generate an automatic action on the vehicle control} - G08G 1/20: {Monitoring the location of vehicles belonging to a group, e.g. fleet of vehicles, countable or determined number of vehicles} - G08G: TRAFFIC CONTROL SYSTEMS (guiding railway traffic, ensuring the safety of railway traffic B61L; arrangement of road signs or traffic signals E01F9/00; radar or analogous systems, sonar systems, lidar systems specially adapted for traffic control G01S13/91, G01S15/88, G01S17/88; {radar or analogous systems, sonar systems, lidar systems specially adapted for anti-collision purposes G01S13/93, G01S15/93, G01S17/93}) - G01C: MEASURING DISTANCES, LEVELS OR BEARINGS; SURVEYING; NAVIGATION; GYROSCOPIC INSTRUMENTS; PHOTOGRAMMETRY OR VIDEOGRAMMETRY (measuring liquid level G01F; radio navigation, determining distance or velocity by use of propagation effects, e.g. Doppler effects, propagation time, of radio waves, analogous arrangements using other waves G01S) - G01S: RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES - G06F: ELECTRIC DIGITAL DATA PROCESSING (computer systems based on specific computational models G06N) - G06T: IMAGE DATA PROCESSING OR GENERATION, IN GENERAL ## 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 ### Claim 1 A method for generating navigation-related data, the method comprising: generating a first attribute based on a first observation associated with a first vehicle and a first observation type; performing one or more comparison operations between the first attribute and a road database to generate a first dynamic layer; 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. ### Claim 2 (depends on 1) The method of claim 1, wherein the first observation type comprises one of road signs, traffic lights, pavement markings, emergency vehicles, or road conditions. ### Claim 3 (depends on 1) 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. ### Claim 4 (depends on 1) 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. ### Claim 5 (depends on 1) The method of claim 1, wherein generating the first attribute comprises 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. ### Claim 6 (depends on 5) The method of claim 5, further comprising causing the first plugin to perform one or more learning operations based on ground truth data. ### Claim 7 (depends on 5) 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, and a generate phase. ### Claim 8 (depends on 5) 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. ### Claim 9 (depends on 8) 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. ### Claim 10 A non-transitory computer-readable storage medium including instructions that, when executed by a processor, cause the processor to generate navigation-related data by performing the steps of: generating first navigation-related data based on a first observation of a first object, wherein the first observation is associated with a first vehicle and a first observation type; performing one or more comparison operations between the first navigation-related data and a road database to generate a first dynamic layer; 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. ### Claim 11 (depends on 10) The computer-readable storage medium of claim 10, wherein the first observation type comprises one of road signs, traffic lights, pavement markings, emergency vehicles, or road conditions. ### Claim 12 (depends on 10) The computer-readable storage medium of claim 10, wherein the first navigation related data comprises an attribute that associates a speed limit with a first road segment included in the road database. ### Claim 13 (depends on 10) The computer-readable storage medium of claim 10, further comprising, prior to performing the one or more comparison operations, determining that a second confidence level associated with second navigation-related data indicates that a reliability of the second navigation-related data is less than the minimum acceptable confidence level and, in response, discarding the second navigation-related data. ### Claim 14 (depends on 10) The computer-readable storage medium of claim 10, wherein generating the first navigation-related data comprises 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. ### Claim 15 (depends on 14) The computer-readable storage medium of claim 14, further comprising causing the first plugin to perform one or more learning operations based on ground truth data. ### Claim 16 (depends on 14) The computer-readable storage medium of claim 14, wherein the data processing flow includes at least one of a spatial referencing phase, a clustering phase, a matching phase, an interpolation phase, and a generate phase. ### Claim 17 (depends on 14) The computer-readable storage medium of claim 14, 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. ### Claim 18 (depends on 10) The computer-readable storage medium of claim 10, wherein the first observation is received from the first vehicle or a manufacturer of the first vehicle. ### Claim 19 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: generate a first attribute based on one or more observations associated with a road specified in a road database; perform one or more comparison operations between the first attribute and the road database to generate a first dynamic layer; 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. ### Claim 20 (depends on 19) The system of claim 19, wherein a first observation included in the one or more observations is received from a first vehicle or a manufacturer of the first vehicle.