# US 2021/0241612 A1 · Systems and methods for zone tagging > Agents: this data is available as JSON from the patentgrep API and MCP server — see https://patentgrep.com/llms.txt - Number: US 2021/0241612 A1 - Document ID: US20210241612A1 - Status: Published application - Kind: A1 - Application number: 16/782,655 - Filed: 2020-02-05 - Published: 2021-08-05 - Earliest claimed: 2020-02-05 - Assignees: Toyota Motor Engineering & Manufacturing North America, Inc. - Applicants: Toyota Motor Engineering & Manufacturing North America, Inc. - Inventors: Seyhan Ucar; Takamasa Higuchi; Onur Altintas - Web page: https://patentgrep.com/patent/US20210241612A1 ## CPC - G08G 1/0129: {for creating historical data or processing based on historical data} - G08G 1/0133: {for classifying traffic situation} - G01C 21/3461: {Preferred or disfavoured areas, e.g. dangerous zones, toll or emission zones, intersections, manoeuvre types or segments such as motorways, toll roads or ferries} - G01C 21/3804: {Creation or updating of map data} - G06N 5/022: Knowledge engineering; Knowledge acquisition - G08G 1/0141: {for traffic information dissemination} - G08G 1/04: using optical or ultrasonic detectors - G06N 7/01: Probabilistic graphical models, e.g. probabilistic networks - G06N 20/00: Machine learning - G08G 1/0112: {from the vehicle, e.g. floating car data [FCD]} - G08G 1/0116: {from roadside infrastructure, e.g. beacons} - 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) - G06N: COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS ## Abstract System, methods, and other embodiments described herein relate to improving awareness about aspects of traveling through a transportation network. In one embodiment, a method includes analyzing observations about a location to correlate the observations into knowledge about the location. The method includes defining a tag for a zone associated with the location according to a pattern within a knowledge set that includes the knowledge for the zone. The pattern corresponding with a zone attribute of the zone. The method includes distributing a mapping of tags including at least the tag to one or more entities that are to travel through the zone. ## Claims ### Claim 1 A tagging system for improving awareness about aspects of traveling through a transportation network, comprising: one or more processors; a memory communicably coupled to the one or more processors and storing: a zone module including instructions that when executed by the one or more processors cause the one or more processors to: analyze observations about a location to correlate the observations into knowledge about the location, and define a tag for a zone associated with the location according to a pattern within a knowledge set that includes the knowledge for the zone, the pattern corresponding with a zone attribute of the zone; and a mapping module including instructions that when executed by the one or more processors cause the one or more processors to distribute a mapping of tags including at least the tag to one or more entities that are to travel through the zone. ### Claim 2 (depends on 1) The tagging system of claim 1, wherein the zone module includes instructions to define the tag including instructions to identify the pattern by analyzing the knowledge set to determine whether the zone attribute associated with the knowledge set occurs with a sufficient frequency to influence travel of the one or more entities through the zone, and wherein the zone module includes instructions to analyze the knowledge set including instructions to apply a model to the knowledge set and the observations from which the knowledge set is derived to infer the pattern from correlations between characteristics and events for the location that are described by the knowledge set. ### Claim 3 (depends on 1) The tagging system of claim 1, wherein the zone module includes instructions to define the tag including instructions to generate the tag with one or more of: an identifier that characterizes the zone attribute, a location of the zone, a contextual indicator specifying whether the zone attribute is one of dynamic and static, dynamic conditions that identify when the zone attribute associated with the tag is present, and suggested actions for mitigating an effect of the zone attribute on navigating through the zone. ### Claim 4 (depends on 1) The tagging system of claim 1, wherein the zone module includes instructions to define the tag including instructions to determine boundaries of the zone according to an extent of impact associated with the zone attribute relative to the location, wherein the zone includes at least the location and additional portions of the transportation network that are effected by the zone attribute, and wherein the location corresponds with one or more zones including the zone. ### Claim 5 (depends on 1) The tagging system of claim 1, wherein the knowledge characterizes at least one of an event and a characteristic associated with the location, wherein the event includes one of confusion navigating through the zone, construction of the location, school in progress near the location, a presence of pedestrians near the location, and congestion of the location, and wherein the characteristic includes one of a roadway geometry associated with the location, at least one traffic signal proximate to the location, dangerous conditions of the location, and effects on the location due to weather or time of day. ### Claim 6 (depends on 1) The tagging system of claim 1, wherein the zone module includes instructions to analyze the observations about the location including instructions to apply a knowledge model to the observations to extract relationships corresponding to the knowledge and indicating at least one of a characteristic and an event associated with the location, wherein the observations are information about the location derived from sensor data of at least one sensor, and wherein the observations are provided by at least one of a vehicle, a roadside unit, and a pedestrian. ### Claim 7 (depends on 1) The tagging system of claim 1, wherein the mapping module includes instructions to distribute the mapping including instructions to electronically communicate the tag in response to a query from the one or more entities about a characteristic that matches the tag. ### Claim 8 (depends on 1) The tagging system of claim 1, wherein the mapping module includes instructions to distribute the mapping with the tag including instructions to electronically communicate the mapping in response to the one or more entities approaching the zone, and wherein the tag causes the one or more entities to adapt travel through the zone to improve progress of the one or more entities, the travel including one or more of navigation and behavior while moving relative to the zone. ### Claim 9 A non-transitory computer-readable medium storing instructions for improving awareness about aspects of traveling through a transportation network and that when executed by one or more processors cause the one or more processors to: analyze observations about a location to correlate the observations into knowledge about the location; define a tag for a zone associated with the location according to a pattern within a knowledge set that includes the knowledge for the zone, the pattern corresponding with a zone attribute of the zone; and distribute a mapping of tags including at least the tag to one or more entities that are to travel through the zone. ### Claim 10 (depends on 9) The non-transitory computer-readable medium of claim 9, wherein the instructions to define the tag include instructions to identify the pattern by analyzing the knowledge set to determine whether the zone attribute associated with the knowledge set occurs with a sufficient frequency to influence travel of the one or more entities through the zone, and wherein the instructions to analyze the knowledge set include instructions to apply a model to the knowledge set and the observations from which the knowledge set is derived to infer the pattern from correlations between characteristics and events for the location that are described by the knowledge set. ### Claim 11 (depends on 9) The non-transitory computer-readable medium of claim 9, wherein the instructions to define the tag include instructions to generate the tag with one or more of: an identifier that characterizes the zone attribute, a location of the zone, a contextual indicator specifying whether the zone attribute is one of dynamic and static, dynamic conditions that identify when the zone attribute associated with the tag is present, and suggested actions for mitigating an effect of the zone attribute on traveling through the zone. ### Claim 12 (depends on 9) The non-transitory computer-readable medium of claim 9, wherein the instructions to define the tag include instructions to determine boundaries of the zone according to an extent of impact associated with the zone attribute relative to the location, wherein the zone includes at least the location and additional portions of the transportation network that are effected by the zone attribute, and wherein the location corresponds with one or more zones including the zone. ### Claim 13 (depends on 9) The non-transitory computer-readable medium of claim 9, wherein the knowledge characterizes at least one of an event and a characteristic of the location, wherein the event includes one of confusion navigating through the zone, construction of the location, school in progress near the location, a presence of pedestrians near the location, and congestion of the location, and wherein the characteristic includes one of a roadway geometry associated with the location, at least one traffic signal proximate to the location, dangerous conditions of the location, and effects on the location due to weather or time of day. ### Claim 14 A method of improving awareness about aspects of traffic associated with a transportation network, comprising: analyzing observations about a location to correlate the observations into knowledge about the location; defining a tag for a zone associated with the location according to a pattern within a knowledge set that includes the knowledge for the zone, the pattern corresponding with a zone attribute of the zone; and distributing a mapping of tags including at least the tag to one or more entities that are to travel through the zone. ### Claim 15 (depends on 14) The method of claim 14, wherein defining the tag includes identifying the pattern by analyzing the knowledge set to determine whether the zone attribute associated with the knowledge set occurs with a sufficient frequency to influence travel of the one or more entities through the zone, and wherein analyzing the knowledge set includes applying a model to the knowledge set and the observations from which the knowledge set is derived to infer the pattern from correlations between characteristics and events for the location that are described by the knowledge set. ### Claim 16 (depends on 14) The method of claim 14, wherein defining the tag includes generating the tag with one or more of: an identifier that characterizes the zone attribute, a location of the zone, a contextual indicator specifying whether the zone attribute is one of dynamic and static, dynamic conditions that identify when the zone attribute associated with the tag is present, and suggested actions for mitigating an effect of the zone attribute on traveling through the zone. ### Claim 17 (depends on 14) The method of claim 14, wherein defining the tag includes determining boundaries of the zone according to an extent of impact associated with the zone attribute relative to the location, wherein the zone includes at least the location and additional portions of the transportation network that are effected by the zone attribute, and wherein the location corresponds with one or more zones including the zone. ### Claim 18 (depends on 14) The method of claim 14, wherein the knowledge characterizes at least one of an event and a characteristic of the location, wherein the event includes one of confusion navigating through the zone, construction of the location, school in progress near the location, a presence of pedestrians near the location, and congestion of the location, and wherein the characteristic includes one of a roadway geometry associated with the location, at least one traffic signal proximate to the location, dangerous conditions of the location, and effects on the location due to weather or time of day. ### Claim 19 (depends on 14) The method of claim 14, wherein analyzing the observations about the location includes applying a knowledge model to the observations to extract relationships corresponding to the knowledge and indicating at least one of a characteristic and an event associated with the location, wherein the observations are information about the location derived from sensor data of at least one sensor, and wherein the observations are provided by at least one of a vehicle, a roadside unit, and a pedestrian. ### Claim 20 (depends on 14) The method of claim 14, wherein distributing the mapping with the tag includes electronically communicating the mapping in response to the one or more entities approaching the zone, and wherein the tag causes the one or more entities to adapt travel through the zone to improve progress of the one or more entities through the zone, the travel including one or more of navigation and behavior while moving relative to the zone.