# US 11,906,311 B2 · Pavement marking map change detection, reaction, and live tile shipping > Agents: this data is available as JSON from the patentgrep API and MCP server — see https://patentgrep.com/llms.txt - Number: US 11,906,311 B2 - Document ID: US11906311B2 - Status: Granted - Kind: B2 - Application number: 18/118,937 - Filed: 2023-03-08 - Published: 2023-07-13 - Granted: 2024-02-20 - Earliest claimed: 2020-06-29 - Assignees: GM Cruise Holdings LLC - Applicants: GM Cruise Holdings LLC - Inventors: Juan Fasola; Harman Kumar; Shreyans Kushwaha; Xiaoyu Zhou; Yu-Cheng Lin - Web page: https://patentgrep.com/patent/US11906311B2 ## CPC - G01C 21/32: Structuring or formatting of map data - G01C 21/3807: {characterised by the type of data} - G01S 7/4802: {using analysis of echo signal for target characterisation; Target signature; Target cross-section} - G01S 17/89: for mapping or imaging - G01S 17/931: of land vehicles - G05D 1/0246: {using a video camera in combination with image processing means} - G05D 1/0274: {using mapping information stored in a memory device (navigation using map-matching G01C21/30)} - G05D 1/246: using environment maps, e.g. simultaneous localisation and mapping [SLAM] - G05D 1/0088: {characterized by the autonomous decision making process, e.g. artificial intelligence, predefined behaviours (using knowledge based models G06N5/00)} - G05D 1/228: Command input arrangements located on-board unmanned vehicles - 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 - G05D: SYSTEMS FOR CONTROLLING OR REGULATING NON-ELECTRIC VARIABLES ## Abstract Systems, methods, and computer-readable media are provided for detecting a pavement marking around an autonomous vehicle, comparing the detected pavement marking with a pavement marking present in a semantic data map, determining whether a change has occurred between the detected pavement marking and the pavement marking present in the semantic data map, and updating the semantic data map based on the determining of whether the change has occurred between the detected pavement marking and the pavement marking present in the semantic data map. ## Claims ### Claim 1 A computer-implemented method comprising: determining, by an autonomous vehicle, that light detection and ranging (LiDAR) sensor data captured using at least one LiDAR sensor associated with the autonomous vehicle includes a detected pavement marking; comparing, by the autonomous vehicle, the LiDAR sensor data that includes the detected pavement marking with corresponding data map points of a pavement marking present in a semantic data map; determining, by the autonomous vehicle and based on the comparing, that a change has occurred between the detected pavement marking and the pavement marking present in the semantic data map; determining, by the autonomous vehicle, a severity of the change of the detected pavement marking; in response to determining that the severity of the change of the detected pavement marking is greater than an acceptable threshold, updating, by the autonomous vehicle, the semantic data map based on the change to produce an updated semantic data map, wherein the semantic data map is updated by selecting between generating local LiDAR intensity tiles or generating colorized tiles for geospatial tiles based on a category of the detected pavement marking; and navigating the autonomous vehicle based on the updated semantic data map. ### Claim 2 (depends on 1) The computer-implemented method of claim 1, wherein the detected pavement marking is a lane line or a crosswalk. ### Claim 3 (depends on 1) The computer-implemented method of claim 1, wherein the change that has occurred between the detected pavement marking and the pavement marking present in the semantic data map is a percentage of change between the detected pavement marking and the pavement marking present in the semantic data map. ### Claim 4 (depends on 1) The computer-implemented method of claim 1, further comprising providing the updated semantic data map to an autonomous vehicle fleet server. ### Claim 5 (depends on 1) The computer-implemented method of claim 1, further comprising: generating geospatial tiles around the autonomous vehicle; and designating each geospatial tile that is associated with the detected pavement marking. ### Claim 6 (depends on 5) The computer-implemented method of claim 5, wherein the geospatial tile is a local LIDAR intensity tile or a colorized tile. ### Claim 7 (depends on 1) The computer-implemented method of claim 1, further comprising: in response to determining that the severity of the change of the detected pavement marking is greater than the acceptable threshold, determining an updated route for the autonomous vehicle. ### Claim 8 (depends on 1) The computer-implemented method of claim 1, further comprising: determining that the change of the detected pavement marking is outside of a current route of the autonomous vehicle; and in response, proceeding along the current route of the autonomous vehicle. ### Claim 9 An autonomous vehicle comprising: one or more processors; and at least one computer-readable storage medium having stored therein instructions which, when executed by the one or more processors, cause the autonomous vehicle to: determine that light detection and ranging (LiDAR) sensor data captured using at least one LiDAR sensor associated with the autonomous vehicle includes a detected pavement marking; compare the LiDAR sensor data that includes the detected pavement marking with corresponding data map points of a pavement marking present in a semantic data map; determine, based on the comparing, that a change has occurred between the detected pavement marking and the pavement marking present in the semantic data map; determine a severity of the change of the detected pavement marking; in response to determining that the severity of the change of the detected pavement marking is greater than an acceptable threshold, update the semantic data map based on the change to produce an updated semantic data map, wherein the semantic data map is updated by selecting between generating local LiDAR intensity tiles or generating colorized tiles for geospatial tiles based on a category of the detected pavement marking; and navigate the autonomous vehicle based on the updated semantic data map. ### Claim 10 (depends on 9) The autonomous vehicle of claim 9, wherein the detected pavement marking is a lane line or a crosswalk. ### Claim 11 (depends on 9) The autonomous vehicle of claim 9, wherein the change that has occurred between the detected pavement marking and the pavement marking present in the semantic data map is a percentage of change between the detected pavement marking and the pavement marking present in the semantic data map. ### Claim 12 (depends on 9) The autonomous vehicle of claim 9, wherein the instructions which, when executed by the one or more processors, cause the autonomous vehicle to provide the updated semantic data map to an autonomous vehicle fleet server. ### Claim 13 (depends on 9) The autonomous vehicle of claim 9, wherein the instructions which, when executed by the one or more processors, cause the autonomous vehicle to: generate geospatial tiles around the autonomous vehicle; and designate each geospatial tile that is associated with the detected pavement marking. ### Claim 14 (depends on 13) The autonomous vehicle of claim 13, wherein the geospatial tile is a local LIDAR intensity tile or a colorized tile. ### Claim 15 (depends on 9) The autonomous vehicle of claim 9, wherein the instructions which, when executed by the one or more processors, cause the autonomous vehicle to: determine that the change of the detected pavement marking is outside of a current route of the autonomous vehicle; and in response, proceed along the current route of the autonomous vehicle. ### Claim 16 A non-transitory computer-readable storage medium comprising: instructions stored on the non-transitory computer-readable storage medium, the instructions, when executed by one or more processors, cause the one or more processors to: determine that light detection and ranging (LiDAR) sensor data captured using at least one LiDAR sensor associated with an autonomous vehicle includes a detected pavement marking; compare the LiDAR sensor data that includes the detected pavement marking with corresponding data map points of a pavement marking present in a semantic data map; determine, based on the comparing, that a change has occurred between the detected pavement marking and the pavement marking present in the semantic data map; determine a severity of the change of the detected pavement marking; in response to determining that the severity of the change of the detected pavement marking is greater than an acceptable threshold, update, the semantic data map based on the change to produce an updated semantic data map, wherein the semantic data map is updated by selecting between generating local LiDAR intensity tiles or generating colorized tiles for geospatial tiles based on a category of the detected pavement marking; and navigate the autonomous vehicle based on the updated semantic data map. ### Claim 17 (depends on 16) The non-transitory computer-readable storage medium of claim 16, wherein the detected pavement marking is a lane line or a crosswalk. ### Claim 18 (depends on 16) The non-transitory computer-readable storage medium of claim 16, wherein the change that has occurred between the detected pavement marking and the pavement marking present in the semantic data map is a percentage of change between the detected pavement marking and the pavement marking present in the semantic data map. ### Claim 19 (depends on 16) The non-transitory computer-readable storage medium of claim 16, wherein the instructions, when executed by the one or more processors, cause the one or more processors to provide the updated semantic data map to an autonomous vehicle fleet server. ### Claim 20 (depends on 16) The non-transitory computer-readable storage medium of claim 16, wherein the instructions, when executed by the one or more processors, cause the one or more processors to: generate geospatial tiles around the autonomous vehicle; and designate each geospatial tile that is associated with the detected pavement marking, wherein the geospatial tile is a local LIDAR intensity tile or a colorized tile.