# US 2018/0023960 A1 · Distributing a crowdsourced sparse map for autonomous vehicle navigation > Agents: this data is available as JSON from the patentgrep API and MCP server — see https://patentgrep.com/llms.txt - Number: US 2018/0023960 A1 - Document ID: US20180023960A1 - Status: Published application - Kind: A1 - Application number: 15/656,314 - Filed: 2017-07-21 - Published: 2018-01-25 - Earliest claimed: 2016-07-21 - Assignees: Mobileye Vision Technologies Ltd. - Applicants: Mobileye Vision Technologies Ltd. - Inventors: Ofer Fridman - Web page: https://patentgrep.com/patent/US20180023960A1 ## CPC - H04N 7/18: Closed-circuit television [CCTV] systems, i.e. systems in which the video signal is not broadcast - B60W 60/0016: {of the vehicle or its occupants} - G01C 21/28: with correlation of data from several navigational instruments - G01C 21/3822: {Road feature data, e.g. slope data} - G01C 21/3848: {Data obtained from both position sensors and additional sensors} - G01C 21/3852: {Data derived from aerial or satellite images} - G05D 1/0246: {using a video camera in combination with image processing means} - G05D 1/0251: {extracting 3D information from a plurality of images taken from different locations, e.g. stereo vision (stereoscopic image analysis H04N13/00; depth recovery from images G06T7/593)} - G05D 1/0274: {using mapping information stored in a memory device (navigation using map-matching G01C21/30)} - G05D 1/0278: {using satellite positioning signals, e.g. GPS} - G05D 1/2435: {Extracting 3D information} - G05D 1/246: using environment maps, e.g. simultaneous localisation and mapping [SLAM] - G05D 1/248: generated by satellites, e.g. GPS - G05D 1/249: from positioning sensors located off-board the vehicle, e.g. from cameras - G06T 7/11: Region-based segmentation - G06T 7/12: Edge-based segmentation - G06T 7/337: {involving reference images or patches} - G06T 7/37: using transform domain methods - G06T 7/70: Determining position or orientation of objects or cameras (camera calibration G06T7/80) - G06T 7/74: {involving reference images or patches} - G06T 11/65: on geographic maps - G06V 20/56: exterior to a vehicle by using sensors mounted on the vehicle - G06V 20/588: {Recognition of the road, e.g. of lane markings; Recognition of the vehicle driving pattern in relation to the road} - G08G 1/096708: {where the received information might be used to generate an automatic action on the vehicle control} - G08G 1/096725: {where the received information generates an automatic action on the vehicle control} - H04N 13/239: using two two-dimensional [2D] image sensors having a relative position equal to or related to the interocular distance (H04N13/243 takes precedence) - H04N 23/90: Arrangement of cameras or camera modules, e.g. multiple cameras in TV studios or sports stadiums - B60W 2420/403: Image sensing, e.g. optical camera - B60W 2552/10: Number of lanes - B60W 2552/25: Road altitude - B60W 2552/30: Road curve radius - B60W 2552/35: Road bumpiness, e.g. potholes - B60W 2552/50: Barriers - B60W 2552/53: Road markings, e.g. lane marker or crosswalk - B60W 2554/20: Static objects - B60W 2554/4026: Cycles - B60W 2554/4029: Pedestrians - B60W 2554/4041: Position - B60W 2554/60: Traversable objects, e.g. speed bumps or curbs - B60W 2554/802: Longitudinal distance - B60W 2554/804: Relative longitudinal speed - G05D 1/0231: {using optical position detecting means (position-fixing by using electromagnetic waves other than radio waves, e.g. optical position detecting means G01S5/16)} - G06T 2207/10024: Color image - G06T 2207/10032: Satellite or aerial image; Remote sensing - G06T 2207/30256: Lane; Road marking - H04N 2013/0081: {Depth or disparity estimation from stereoscopic image signals} - H04N: PICTORIAL COMMUNICATION, e.g. TELEVISION - B60W: CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT - 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 - G06T: IMAGE DATA PROCESSING OR GENERATION, IN GENERAL - G06V: IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING - 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}) ## Abstract Systems and methods are provided for distributing a crowdsourced sparse map for autonomous vehicle navigation. In one implementation, a method of generating a road navigation model for use in autonomous vehicle navigation may include receiving navigation information associated with a common road segment from a plurality of vehicles; storing the navigation information associated with the common road segment; generating at least a portion of an autonomous vehicle road navigation model for the common road segment based on the navigation information; and distributing the autonomous vehicle road navigation model to one or more autonomous vehicles for use in autonomously navigating along the common road segment. The autonomous vehicle road navigation model may include at least one line representation of a road surface feature extending along the common road segment, and each line representation may representing a path along the common road segment substantially corresponding with the road surface feature. ## Claims ### Claim 1 A method of generating a road navigation model for use in autonomous vehicle navigation, the method comprising: receiving, by a server, navigation information from a plurality of vehicles, wherein the navigation information from the plurality of vehicles is associated with a common road segment; storing, by the server, the navigation information associated with the common road segment; generating, by the server, at least a portion of an autonomous vehicle road navigation model for the common road segment based on the navigation information from the plurality of vehicles, the autonomous vehicle road navigation model for the common road segment including at least one line representation of a road surface feature extending along the common road segment, each line representation representing a path along the common road segment substantially corresponding with the road surface feature, and wherein the road surface feature is identified through image analysis of a plurality of images acquired as the plurality of vehicles traverse the common road segment; and distributing, by the server, the autonomous vehicle road navigation model to one or more autonomous vehicles for use in autonomously navigating the one or more autonomous vehicles along the common road segment. ### Claim 2 (depends on 1) The method of claim 1, wherein the autonomous vehicle road navigation model is configured to be superimposed over a map, an image, or a satellite image. ### Claim 3 (depends on 1) The method of claim 1, wherein the plurality of road features include road edges or lane markings. ### Claim 4 (depends on 1) The method of claim 1, wherein generating at least a portion of the autonomous vehicle road navigation model includes identifying, based on image analysis of the plurality of images, a plurality of landmarks associated with the common road segment. ### Claim 5 (depends on 4) The method of claim 4, wherein generating at least a portion of the autonomous vehicle road navigation model further includes accepting potential landmarks when a ratio of images in which the landmark does appear to images in which the landmark does not appear exceeds a threshold. ### Claim 6 (depends on 4) The method of claim 4, wherein generating at least a portion of the autonomous vehicle road navigation model further includes rejecting potential landmarks when a ratio of images in which the landmark does not appear to images in which the landmark does appear exceeds a threshold. ### Claim 7 A system for generating a road navigation model for use in autonomous vehicle navigation, the system comprising: at least one network interface; at least one non-transitory storage medium; and at least one processing device, wherein the at least one processing device is configured to: receive, using the network interface, navigation information from a plurality of vehicles, wherein the navigation information from the plurality of vehicles is associated with a common road segment; store, on the non-transitory storage medium, the navigation information associated with the common road segment; generate at least a portion of an autonomous vehicle road navigation model for the common road segment based on the navigation information from the plurality of vehicles, the autonomous vehicle road navigation model for the common road segment including at least one line representation of a road surface feature extending along the common road segment, each line representation representing a path along the common road segment substantially corresponding with the road surface feature, and wherein the road surface feature is identified through image analysis of a plurality of images acquired as the plurality of vehicles traverse the common road segment; and distribute, using the network interface, the autonomous vehicle road navigation model to one or more autonomous vehicles for use in autonomously navigating the one or more autonomous vehicles along the common road segment. ### Claim 8 (depends on 7) The system of claim 7, wherein the autonomous vehicle road navigation model is configured to be superimposed over a map, an image, or a satellite image. ### Claim 9 (depends on 7) The system of claim 7, wherein the plurality of road features include road edges or lane markings. ### Claim 10 (depends on 7) The system of claim 7, wherein generating at least a portion of the autonomous vehicle road navigation model includes identifying, based on image analysis of the plurality of images, a plurality of landmarks associated with the common road segment. ### Claim 11 (depends on 10) The system of claim 10, wherein generating at least a portion of the autonomous vehicle road navigation model further includes accepting potential landmarks when a ratio of images in which the landmark does appear to images in which the landmark does not appear exceeds a threshold. ### Claim 12 (depends on 10) The system of claim 10, wherein generating at least a portion of the autonomous vehicle road navigation model further includes rejecting potential landmarks when a ratio of images in which the landmark does not appear to images in which the landmark does appear exceeds a threshold. ### Claim 13 A non-transitory, computer-readable medium storing instructions that, when executed by at least one processing device, cause the server to: receive navigation information from a plurality of vehicles, wherein the navigation information from the plurality of vehicles is associated with a common road segment; store the navigation information associated with the common road segment; generate at least a portion of an autonomous vehicle road navigation model for the common road segment based on the navigation information from the plurality of vehicles, the autonomous vehicle road navigation model for the common road segment including at least one line representation of a road surface feature extending along the common road segment, each line representation representing a path along the common road segment substantially corresponding with the road surface feature, and wherein the road surface feature is identified through image analysis of a plurality of images acquired as the plurality of vehicles traverse the common road segment; and distribute the autonomous vehicle road navigation model to one or more autonomous vehicles for use in autonomously navigating the one or more autonomous vehicles along the common road segment. ### Claim 14 (depends on 13) The non-transitory, computer-readable medium of claim 13, wherein the autonomous vehicle road navigation model is configured to be superimposed over a map, an image, or a satellite image. ### Claim 15 (depends on 13) The non-transitory, computer-readable medium of claim 13, wherein the plurality of road features include road edges or lane markings. ### Claim 16 (depends on 13) The non-transitory, computer-readable medium of claim 13, wherein the instructions to generate at least a portion of the autonomous vehicle road navigation model include instructions to identify, based on image analysis of the plurality of images, a plurality of landmarks associated with the common road segment. ### Claim 17 (depends on 16) The non-transitory, computer-readable medium of claim 16, wherein the instructions to generate at least a portion of the autonomous vehicle road navigation model further include instructions to accept potential landmarks when a ratio of images in which the landmark does appear to images in which the landmark does not appear exceeds a threshold. ### Claim 18 (depends on 16) The non-transitory, computer-readable medium of claim 16, wherein the instructions to generate at least a portion of the autonomous vehicle road navigation model further include instructions to reject potential landmarks when a ratio of images in which the landmark does not appear to images in which the landmark does appear exceeds a threshold.