# US 2018/0024565 A1 · Navigating a vehicle using a crowdsourced sparse map > Agents: this data is available as JSON from the patentgrep API and MCP server — see https://patentgrep.com/llms.txt - Number: US 2018/0024565 A1 - Document ID: US20180024565A1 - Status: Published application - Kind: A1 - Application number: 15/656,308 - 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/US20180024565A1 ## 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 navigating a vehicle using a crowdsourced sparse map. In one implementation, a method of autonomously navigating a vehicle along a road segment may include receiving a sparse map model, receiving at least one image representative of an environment of the vehicle, analyzing the sparse map model and the at least one image, and determining an autonomous navigational response for the vehicle based on the analysis of the sparse map model and the at least one image. The at least one image may be received from a camera, and the sparse map model may include at least one line representation of a road surface feature extending along the road segment, each line representation representing a path along the road segment substantially corresponding with the road surface feature. ## Claims ### Claim 1 A system for autonomously navigating a vehicle along a road segment, the system comprising: at least one processing device configured to: receive a sparse map model, wherein the sparse map model includes at least one line representation of a road surface feature extending along the road segment, each line representation representing a path along the road segment substantially corresponding with the road surface feature; and receive from a camera, at least one image representative of an environment of the vehicle; analyze the sparse map model and the at least one image received from the camera; and determine an autonomous navigational response for the vehicle based on the analysis of the sparse map model and the at least one image received from the camera. ### Claim 2 (depends on 1) The system of claim 1, wherein the road surface feature includes a road edge or a lane marking. ### Claim 3 (depends on 1) The system of claim 1, wherein analysis of the sparse map model and the at least one image received from the camera includes determining a current position of the vehicle relative to a longitudinal position along the at least one line representation of a road surface feature extending along the road segment. ### Claim 4 (depends on 3) The system of claim 3, wherein determining a current position of the vehicle relative to a longitudinal position along the at least one line representation of a road surface feature extending along the road segment is based on identification of at least one recognized landmark in the at least one image. ### Claim 5 (depends on 4) The system of claim 4, wherein the at least one processing device is further configured to determine an estimated offset based on an expected position of the vehicle relative to the longitudinal position and the current position of the vehicle relative to the longitudinal position. ### Claim 6 (depends on 5) The system of claim 5, wherein the autonomous navigational response is further based on the estimated offset. ### Claim 7 (depends on 1) The system of claim 1, wherein the at least one processing device is further configured to adjust a steering system of the vehicle based on the autonomous navigational response. ### Claim 8 A method for autonomously navigating a vehicle along a road segment, the method comprising: receiving a sparse map model, wherein the sparse map model includes at least one line representation of a road surface feature extending along the road segment, each line representation representing a path along the road segment substantially corresponding with the road surface feature; and receiving from a camera, at least one image representative of an environment of the vehicle; analyzing the sparse map model and the at least one image received from the camera; and determining an autonomous navigational response for the vehicle based on the analysis of the sparse map model and the at least one image received from the camera. ### Claim 9 (depends on 8) The method of claim 8, wherein analyzing the sparse map model and the at least one image received from the camera includes determining a current position of the vehicle relative to a longitudinal position along the at least one line representation of a road surface feature extending along the road segment. ### Claim 10 (depends on 9) The method of claim 9, wherein determining a current position of the vehicle relative to a longitudinal position along the at least one line representation of a road surface feature extending along the road segment is based on identification of at least one recognized landmark in the at least one image. ### Claim 11 (depends on 10) The method of claim 10, further comprising determining an estimated offset based on an expected position of the vehicle relative to the longitudinal position and the current position of the vehicle relative to the longitudinal position. ### Claim 12 (depends on 11) The method of claim 11, wherein the autonomous navigational response is further based on the estimated offset. ### Claim 13 (depends on 8) The method of claim 8, further comprising adjusting a steering system of the vehicle based on the autonomous navigational response. ### Claim 14 A non-transitory, computer-readable medium storing instructions that, when executed by at least one processing device, cause the device to: receive a sparse map model, wherein the sparse map model includes at least one line representation of a road surface feature extending along the road segment, each line representation representing a path along the road segment substantially corresponding with the road surface feature; and receive from a camera, at least one image representative of an environment of the vehicle; analyze the sparse map model and the at least one image received from the camera; and determine an autonomous navigational response for the vehicle based on the analysis of the sparse map model and the at least one image received from the camera. ### Claim 15 (depends on 14) The non-transitory, computer-readable medium of claim 14, wherein the road feature includes a road edge or a lane marking. ### Claim 16 (depends on 14) The non-transitory, computer-readable medium of claim 14, wherein analysis of the sparse map model and the at least one image received from the camera includes determining a current position of the vehicle relative to a longitudinal position along the at least one line representation of a road surface feature extending along the road segment. ### Claim 17 (depends on 16) The non-transitory, computer-readable medium of claim 16, wherein determining a current position of the vehicle relative to a longitudinal position along the at least one line representation of a road surface feature extending along the road segment is based on identification of at least one recognized landmark in the at least one image. ### Claim 18 (depends on 17) The non-transitory, computer-readable medium of claim 17, further storing instructions to determine an estimated offset based on an expected position of the vehicle relative to the longitudinal position and the current position of the vehicle relative to the longitudinal position. ### Claim 19 (depends on 18) The non-transitory, computer-readable medium of claim 18, wherein the autonomous navigational response is further based on the estimated offset. ### Claim 20 (depends on 14) The non-transitory, computer-readable medium of claim 14, further storing instructions to adjust a steering system of the vehicle based on the autonomous navigational response.