# US 12,118,883 B2 · Utilization of reflectivity to determine changes to traffic infrastructure elements > Agents: this data is available as JSON from the patentgrep API and MCP server — see https://patentgrep.com/llms.txt - Number: US 12,118,883 B2 - Document ID: US12118883B2 - Status: Granted - Kind: B2 - Application number: 16/849,657 - Filed: 2020-04-15 - Published: 2021-10-21 - Granted: 2024-10-15 - Earliest claimed: 2020-04-15 - Assignees: GM Cruise Holdings LLC - Applicants: GM Cruise Holdings LLC - Inventors: Nestor Grace; Dogan Gidon; Diego Plascencia-Vega; Srishti Iyer Koduvayur; Hooman Barekatain - Web page: https://patentgrep.com/patent/US12118883B2 ## CPC - G01C 21/3822: {Road feature data, e.g. slope data} - G08G 1/096725: {where the received information generates an automatic action on the vehicle control} - G01C 21/30: Map- or contour-matching - 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 1/0088: {characterized by the autonomous decision making process, e.g. artificial intelligence, predefined behaviours (using knowledge based models G06N5/00)} - G05D 1/0274: {using mapping information stored in a memory device (navigation using map-matching G01C21/30)} - G05D 1/0285: {using signals transmitted via a public communication network, e.g. GSM network} - G05D 1/246: using environment maps, e.g. simultaneous localisation and mapping [SLAM] - G05D 1/247: using signals provided by artificial sources external to the vehicle, e.g. navigation beacons - G05D 1/81: Handing over between on-board automatic and on-board manual control - G06V 20/582: {of traffic signs} - G08G 1/0112: {from the vehicle, e.g. floating car data [FCD]} - G08G 1/0129: {for creating historical data or processing based on historical data} - G08G 1/0141: {for traffic information dissemination} - G08G 1/04: using optical or ultrasonic detectors - G08G 1/096741: {where the source of the transmitted information selects which information to transmit to each vehicle} - G08G 1/09675: {where a selection from the received information takes place in the vehicle} - G08G 1/096775: {where the origin of the information is a central station} - H04W 4/44: for communication between vehicles and infrastructures, e.g. vehicle-to-cloud [V2C] or vehicle-to-home [V2H] - 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) - 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}) - 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 - G06V: IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING - H04W: WIRELESS COMMUNICATION NETWORKS (broadcast communication H04H; communication systems using wireless links for non-selective communication, e.g. wireless extensions H04M1/72) ## Abstract Systems, methods, and computer-readable media are provided for analyzing a traffic infrastructure element, determining reflectivity information of the traffic infrastructure element based on the analyzing of the traffic infrastructure element, comparing the reflectivity information of the traffic infrastructure element with semantic map information of the traffic infrastructure element, and providing instructions to an autonomous vehicle based on the comparing of the reflectivity information of the traffic infrastructure element with the semantic map information of the traffic infrastructure element. ## Claims ### Claim 1 A computer-implemented method comprising: analyzing sensor data returned from a traffic infrastructure element, wherein the sensor data includes LiDAR intensity information corresponding to visual characteristics of the traffic infrastructure element; determining reflectivity information of the traffic infrastructure element based on the analyzing of the sensor data; comparing the reflectivity information of the traffic infrastructure element with semantic map information of the traffic infrastructure element, wherein: the semantic map information is part of a semantic map that is generated based on sensor data captured by a plurality of vehicles and is provided to the plurality of vehicles in response to the sensor data matching across the plurality of vehicles; and the semantic map information includes blacklisted road segment information indicating degraded infrastructure that is identified from reflectivity information of the degraded infrastructure in the sensor data captured by the plurality of vehicles; determining that the reflectivity information of the traffic infrastructure element does not match the semantic map information of the traffic infrastructure element based on a detection that the traffic infrastructure element has been tampered with based on the reflectivity information indicating a change in material of the traffic infrastructure element; providing instructions to an autonomous vehicle based on the reflectivity information of the traffic infrastructure element not matching the semantic map information of the traffic infrastructure element, wherein the instructions are effective to cause the autonomous vehicle to operate based on the semantic map information instead of the reflectivity information; and controlling the autonomous vehicle to operate based on the semantic map information according to the instructions and a road utilization quality index that is identified based on the blacklisted road segment information included in the semantic map information. ### Claim 2 (depends on 1) The computer-implemented method of claim 1, wherein the traffic infrastructure element is a road sign, a traffic light, or signage. ### Claim 3 (depends on 1) The computer-implemented method of claim 1, wherein the (Original) semantic map information of the traffic infrastructure element includes previous reflectivity information of the traffic infrastructure element. ### Claim 4 (depends on 1) The computer-implemented method of claim 1, wherein the providing of the instructions to the autonomous vehicle includes logging a difference between the reflectivity information of the traffic infrastructure element and the semantic map information of the traffic infrastructure element. ### Claim 5 (depends on 4) The computer-implemented method of claim 4, wherein the logging the difference for the traffic infrastructure element is performed by a first autonomous vehicle in a fleet of autonomous vehicles, the method further comprises: logging at least a second difference for at least a second traffic infrastructure element received from at least a second autonomous vehicle in the fleet of autonomous vehicles; compiling a city-wide state of traffic infrastructure from the difference from the first autonomous vehicle and the at least the second difference from the at least the second autonomous vehicle in the fleet of autonomous vehicles. ### Claim 6 (depends on 1) The computer-implemented method of claim 1, further comprising comparing visual information of the traffic infrastructure element with the semantic map information of the traffic infrastructure element. ### Claim 7 (depends on 6) The computer-implemented method of claim 6, wherein the visual information of the traffic infrastructure element includes color information of the traffic infrastructure element. ### Claim 8 (depends on 1) The computer-implemented method of claim 1, wherein the reflectivity information is indicative of a first speed limit, and wherein the semantic map information indicates a second speed limit different from the first speed limit, and wherein the autonomous vehicle operates based on the second speed limit indicated in the semantic map information. ### Claim 9 (depends on 1) The computer-implemented method of claim 1, wherein the determining that the reflectivity information of the traffic infrastructure element does not match the semantic map information of the traffic infrastructure element is performed by a first autonomous vehicle in a fleet of autonomous vehicles, the method further comprising: confirming that the reflectivity information of the traffic infrastructure element does not match the semantic map information of the traffic infrastructure element by receiving communications from at least a second autonomous vehicle in the fleet of autonomous vehicles indicating that the reflectivity information of the traffic infrastructure element does not match the semantic map information of the traffic infrastructure element. ### Claim 10 (depends on 1) The computer-implemented method of claim 1, wherein the road utilization quality index is generated for each road segment in a region based on analyzing differences between multiple LiDAR data collected at different times and analyzing times and locations where the autonomous vehicles have difficulty navigating. ### Claim 11 A system 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 system to: analyze sensor data returned from a traffic infrastructure element, wherein the sensor data includes LiDAR intensity information corresponding to visual characteristics of the traffic infrastructure element; determine reflectivity information of the traffic infrastructure element based on the analysis of the sensor data; compare the reflectivity information of the traffic infrastructure element with semantic map information of the traffic infrastructure element, wherein: the semantic map information is part of a semantic map that is generated based on sensor data captured by a plurality of vehicles and is provided to the plurality of vehicles in response to the sensor data matching across the plurality of vehicles; and the semantic map information includes blacklisted road segment information indicating degraded infrastructure that is identified from reflectivity information of the degraded infrastructure in the sensor data captured by the plurality of vehicles; determine that the reflectivity information of the traffic infrastructure element does not match the semantic map information of the traffic infrastructure element based on a detection that the traffic infrastructure element has been tampered with based on the reflectivity information indicating a change in material of the traffic infrastructure element; provide instructions to an autonomous vehicle based on the reflectivity information of the traffic infrastructure element not matching-the semantic map information of the traffic infrastructure element, wherein the instructions are effective to cause the autonomous vehicle to operate based on the semantic map information instead of the reflectivity information; and control the autonomous vehicle to operate based on the semantic map information according to the instructions and a road utilization quality index that is identified based on the blacklisted road segment information included in the semantic map information. ### Claim 12 (depends on 11) The system of claim 11, wherein the traffic infrastructure element is a road sign, a traffic light, or signage. ### Claim 13 (depends on 11) The system of claim 11, wherein the reflectivity information of the traffic infrastructure element is a 3D light detection and ranging (LIDAR) point cloud. ### Claim 14 (depends on 11) The system of claim 11, wherein the semantic map information of the traffic infrastructure element includes previous reflectivity information of the traffic infrastructure element. ### Claim 15 (depends on 11) The system of claim 11, wherein the instructions provided to the autonomous vehicle includes logging a difference between the reflectivity information of the traffic infrastructure element and the semantic map information of the traffic infrastructure element. ### Claim 16 (depends on 11) The system of claim 11, wherein the instructions which, when executed by the one or more processors, cause the system to compare visual information of the traffic infrastructure element with the semantic map information of the traffic infrastructure element. ### Claim 17 (depends on 16) The system of claim 16, wherein the visual information of the traffic infrastructure element includes color information of the traffic infrastructure element. ### Claim 18 A non-transitory computer-readable storage medium comprising: instructions stored on the non-transitory computer-readable storage medium, the instructions, when executed by one more processors, cause the one or more processors to: analyze sensor data returned from a traffic infrastructure element, wherein the sensor data includes LiDAR intensity information corresponding to visual characteristics of the traffic infrastructure element; determine reflectivity information of the traffic infrastructure element based on the analysis of the sensor information; compare the reflectivity information of the traffic infrastructure element with semantic map information of the traffic infrastructure element, wherein: the semantic map information is part of a semantic map that is generated based on sensor data captured by a plurality of vehicles and is provided to the plurality of vehicles in response to the sensor data matching across the plurality of vehicles; and the semantic map information includes blacklisted road segment information indicating degraded infrastructure that is identified from reflectivity information of the degraded infrastructure in the sensor data captured by the plurality of vehicles; determine that the reflectivity information of the traffic infrastructure element does not match the semantic map information of the traffic infrastructure element based on a detection that the traffic infrastructure element has been tampered with based on the reflectivity information indicating a change in material of the traffic infrastructure element; provide instructions to an autonomous vehicle based on the reflectivity information of the traffic infrastructure element not matching the semantic map information of the traffic infrastructure element, wherein the instructions are effective to cause the autonomous vehicle to operate based on the semantic map information instead of the reflectivity information; and control the autonomous vehicle to operate based on the semantic map information according to the instructions and a road utilization quality index that is identified based on the blacklisted road segment information included in the semantic map information. ### Claim 19 (depends on 18) The non-transitory computer-readable storage medium of claim 18, wherein the traffic infrastructure element is a road sign, a traffic light, or signage. ### Claim 20 (depends on 18) The non-transitory computer-readable storage medium of claim 18, wherein the reflectivity information of the traffic infrastructure element is a 3D light detection and ranging (LiDAR) point cloud. ### Claim 21 (depends on 18) The non-transitory computer-readable storage medium of claim 18, wherein the semantic map information of the traffic infrastructure element includes previous reflectivity information of the traffic infrastructure element.