# US 10,120,381 B2 · Identifying significant locations based on vehicle probe data > Agents: this data is available as JSON from the patentgrep API and MCP server — see https://patentgrep.com/llms.txt - Number: US 10,120,381 B2 - Document ID: US10120381B2 - Status: Granted - Kind: B2 - Application number: 14/976,405 - Filed: 2015-12-21 - Published: 2016-09-15 - Granted: 2018-11-06 - Earliest claimed: 2015-03-13 - Assignees: Nissan North America, Inc. - Applicants: Nissan North America, Inc. - Inventors: Siddharth Thakur - Web page: https://patentgrep.com/patent/US10120381B2 ## CPC - G01C 21/3617: {using user history, behaviour, conditions or preferences, e.g. predicted or inferred from previous use or current movement} - G05D 1/00: Control of position, course, altitude or attitude of land, water, air or space vehicles, e.g. using automatic pilots (drive control systems specially adapted for autonomous road vehicles B60W60/00) - 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) - G05D: SYSTEMS FOR CONTROLLING OR REGULATING NON-ELECTRIC VARIABLES ## Abstract A method, a vehicle and a system that identify and use a location significant to a person associated with a vehicle based on vehicle probe data are described. Vehicle transportation network information representing a vehicle transportation network is identified, the vehicle transportation network information including destinations of a vehicle obtained from vehicle probe data. For at least some of the destinations, density-based clustering is performed using temporal data associated with the at least some of the destinations to form at least two spatio-temporal clusters. A contextual label is assigned to a location associated with a first spatio-temporal cluster of the at least two spatial-temporal clusters based on the temporal data associated with the first spatio-temporal cluster, the location being a labeled location, and the labeled location is used for vehicle operation. The labeled location may be used for navigation or service, for example. ## Claims ### Claim 1 A vehicle, comprising: a processor configured to execute instructions stored on a non-transitory computer readable medium to: identify vehicle transportation network information representing a vehicle transportation network, the vehicle transportation network information including destinations of a vehicle obtained by spatially clustering vehicle probe data; for at least some of the destinations, perform density-based clustering using temporal data associated with the at least some of the destinations to form at least two spatio-temporal clusters; and assign a contextual label to a location associated with a first spatio-temporal cluster of the at least two spatio-temporal clusters based on the temporal data associated with the first spatio-temporal cluster, the location being a labeled location; and a vehicle operation device configured to operate the vehicle using the labeled location. ### Claim 2 (depends on 1) The vehicle of claim 1, wherein the processor is configured to execute instructions stored on the non-transitory computer readable medium to perform density-based clustering by: performing two-dimensional density-based clustering using an arrival time of day and a stay time of trip data associated with the at least some of the destinations. ### Claim 3 (depends on 2) The vehicle of claim 2, wherein the processor is configured to execute instructions stored on the non-transitory computer readable medium to perform density-based clustering by: grouping trip data associated with each destination of the at least some of the destinations by stay type; and performing the two-dimensional density-based clustering by the stay type. ### Claim 4 (depends on 3) The vehicle of claim 3, wherein the stay type comprises at least two of: a weekday stay type having an arrival day of a week and a departure day of the week both falling on a weekday; a weekend stay type having an arrival day of the week and a departure day of the week both falling on a weekend; or a mixed stay type having an arrival day of the week falling on a weekday and a departure day of the week falling on a weekend or having an arrival day of the week falling on a weekend and a departure day of the week falling on a weekday. ### Claim 5 (depends on 1) The vehicle of claim 1, wherein the processor is configured to execute instructions stored on the non-transitory computer readable medium to identify vehicle transportation network information representing a vehicle transportation network by, before the density-based clustering: transforming point data from the vehicle probe data into trip data, each trip forming the trip data comprising a start point with a start timestamp and a stop point with a stop timestamp; and performing the spatial clustering using the trip data to form spatial clusters to identify the destinations of the vehicle. ### Claim 6 (depends on 5) The vehicle of claim 5, wherein the processor is configured to execute instructions stored on the non-transitory computer readable medium to identify vehicle transportation network information representing a vehicle transportation network by, before the density-based clustering: storing the trip data to form stored trip data; assigning a cluster identifier to each unique vehicle transportation network location resulting from performing the spatial clustering; and appending the cluster identifier to any of the start points and any of the stop points forming a respective one of the spatial clusters within the stored trip data. ### Claim 7 (depends on 6) The vehicle of claim 6, wherein the processor is configured to execute instructions stored on the non-transitory computer readable medium to identify vehicle transportation network information representing a vehicle transportation network by, before the density-based clustering: determining a stay time for a trip of the trip data as a difference between a stop timestamp of the trip and a start timestamp of a subsequent trip; filtering continuous records by comparing a cluster identifier of the trip with a cluster identifier of the subsequent trip to generate a location record for the unique vehicle transportation network location; and adding an arrival timestamp at the unique vehicle transportation network location and a departure timestamp from the unique vehicle transportation network location to the location record. ### Claim 8 (depends on 5) The vehicle of claim 5, wherein the processor is configured to execute instructions stored on the non-transitory computer readable medium to identify vehicle transportation network information representing a vehicle transportation network by, before the density-based clustering: sorting each entry within the vehicle probe data based on a measurement timestamp to form a sequence of records; determining a time difference of consecutive records within the sequence; identifying the start of a trip when the time difference exceeds a threshold value; and identifying the start point, the start timestamp, the stop point, and the stop timestamp for the trip. ### Claim 9 (depends on 1) The vehicle of claim 1, wherein the processor is configured to execute instructions stored on the non-transitory computer readable medium to identify vehicle transportation network information representing a vehicle transportation network by, before the density-based clustering: performing the spatially clustering using a plurality of trips comprising GPS coordinates associated with each of a start location and an end location from the vehicle probe data to form spatial clusters; and assigning a cluster identifier to each location associated with a spatial cluster; and wherein the processor is configured to execute instructions stored on the non-transitory computer readable medium to perform density-based clustering by: for each location assigned to a cluster identifier, performing two-dimensional density-based clustering of arrival time of day at location versus a stay time at the location to form temporal clusters; and forming the at least two spatio-temporal clusters by combining the spatial clusters with the temporal clusters. ### Claim 10 (depends on 1) The vehicle of claim 1, wherein the processor is configured to execute instructions stored on the non-transitory computer readable medium to assign a contextual label to a location by: selecting records from the first spatio-temporal cluster within a defined time window that have an overnight stay; identifying the location that is the most common within the records; and assigning the contextual label of home to the location. ### Claim 11 (depends on 1) The vehicle of claim 1, wherein the processor is configured to execute instructions stored on the non-transitory computer readable medium to assign a contextual label to a location by: selecting records from the first spatio-temporal cluster within a defined time window that have no overnight stay; identifying the location that is the most common within the records; and assigning the contextual label of work to the location. ### Claim 12 (depends on 1) The vehicle of claim 1, wherein the vehicle operation device is a navigation system; and wherein the processor is configured to execute instructions stored on the non-transitory computer readable medium to: generate a route to the labeled location using the navigation system. ### Claim 13 (depends on 12) The vehicle of claim 12, wherein the vehicle is an autonomous vehicle; and wherein the processor is configured to execute instructions stored on the non-transitory computer readable medium to: operate the vehicle to traverse the route. ### Claim 14 A system for identifying and using a location significant to a person associated with a vehicle based on vehicle probe data, the system including: a memory including a non-transitory computer readable medium; and a processor configured to execute instructions stored on the non-transitory computer readable medium to: identify vehicle transportation network information representing a vehicle transportation network, the vehicle transportation network information including destinations of a vehicle obtained by spatially clustering vehicle probe data; for at least some of the destinations, perform density-based clustering using temporal data associated with the at least some of the destinations to form at least two spatio-temporal clusters; assign a contextual label to a location associated with a first spatio-temporal cluster of the at least two spatio-temporal clusters based on the temporal data associated with the first spatio-temporal cluster, the location being a labeled location; and use the labeled location for vehicle operation. ### Claim 15 (depends on 14) The system of claim 14, wherein the processor is configured to execute instructions stored on the non-transitory computer readable medium to use the labeled location for vehicle operation by: performing density-based clustering using temporal data associated with destinations determined over a period of time subsequent to the assigning the contextual label to form at least two updated spatio-temporal clusters; assigning the contextual label to an updated location associated with a first updated spatio-temporal cluster; comparing the location with the updated location; and changing the labeled location to the updated location when the updated location is different from the location. ### Claim 16 (depends on 14) The system of claim 14, wherein the processor is configured to execute instructions stored on the non-transitory computer readable medium to perform density-based clustering using temporal data by: performing two-dimensional density-based clustering using an arrival time of day and a stay time of trip data associated with those of the destinations having a same stay type based on an arrival day of a week and a departure day of the week. ### Claim 17 (depends on 16) The system of claim 16, wherein the processor is configured to execute instructions stored on the non-transitory computer readable medium to identify vehicle transportation network information by, before the density-based clustering: performing the spatially clustering using location data within the vehicle probe data to identify the destinations. ### Claim 18 A system for identifying and using a location significant to a person associated with a vehicle based on vehicle probe data, the system including: a memory including a non-transitory computer readable medium; and a processor configured to execute instructions stored on the non-transitory computer readable medium to: identify vehicle transportation network information representing a vehicle transportation network by performing spatial clustering of vehicle location information obtained from vehicle probe data of a vehicle, the vehicle transportation network information including destinations of the vehicle; perform two-dimensional density-based clustering using an arrival time of day and a stay time associated with at least some of the destinations to form a spatio-temporal cluster; assign a contextual label to a location associated with the spatio-temporal cluster based on temporal data associated with the spatio-temporal cluster, the location being a labeled location; and use the labeled location for vehicle operation. ### Claim 19 (depends on 18) The system of claim 18, wherein the processor is configured to execute instructions stored on the non-transitory computer readable medium to: group trip data associated with the destinations by stay type; and wherein the processor is configured to execute instructions stored on the non-transitory computer readable medium to perform two-dimensional density-based clustering by: performing the two-dimensional density-based clustering for the at least some of the destinations associated with a single stay type. ### Claim 20 (depends on 19) The system of claim 19, wherein the single stay type comprises one of: a weekday stay type having an arrival day of a week and a departure day of the week both falling on a weekday; or a weekend stay type having an arrival day of the week and a departure day of the week both falling on a weekend.