# US 9,437,053 B2 · Determining portions of a roadway model requiring updating > Agents: this data is available as JSON from the patentgrep API and MCP server — see https://patentgrep.com/llms.txt - Number: US 9,437,053 B2 - Document ID: US9437053B2 - Status: Granted - Kind: B2 - Application number: 14/589,616 - Filed: 2015-01-05 - Published: 2015-07-09 - Granted: 2016-09-06 - Earliest claimed: 2014-01-06 - Assignees: Geodigital International Inc. - Applicants: Geodigital International Inc. - Inventors: Alastair Nigel Jenkins; Richard James Pollock - Web page: https://patentgrep.com/patent/US9437053B2 ## CPC - G07C 5/008: {communicating information to a remotely located station (transmission systems for measured values G08C)} - G05D 1/0274: {using mapping information stored in a memory device (navigation using map-matching G01C21/30)} - G09B 29/106: {using electronic means} - G07C: TIME OR ATTENDANCE REGISTERS; REGISTERING OR INDICATING THE WORKING OF MACHINES; GENERATING RANDOM NUMBERS; VOTING OR LOTTERY APPARATUS; ARRANGEMENTS, SYSTEMS OR APPARATUS FOR CHECKING NOT PROVIDED FOR ELSEWHERE - G05D: SYSTEMS FOR CONTROLLING OR REGULATING NON-ELECTRIC VARIABLES - G09B: EDUCATIONAL OR DEMONSTRATION APPLIANCES; APPLIANCES FOR TEACHING, OR COMMUNICATING WITH, THE BLIND, DEAF OR MUTE; MODELS; PLANETARIA; GLOBES; MAPS; DIAGRAMS (devices for psychotechnics or for testing reaction times A61B5/16; games, sports, amusements A63; projectors, projector screens G03B) ## Abstract A computer-implemented method for determining which portions of a roadway model used by self-driving road vehicles require updating uses discrepancy data derived from the sensors of a plurality of self-driving road vehicles. The discrepancy data may indicate discrepancies between the sensor data and the roadway model, or may indicate portions of the roadway where a self-driving road vehicle underperformed. The discrepancy data is aggregated, and the aggregated discrepancy data is used to identify, as the portions of the roadway model which require updating, those portions of the roadway model corresponding to portions of the roadway for which the aggregated discrepancy data exceeds a threshold. ## Claims ### Claim 1 A computer-implemented method for determining which portions of a roadway model representing a real-world roadway require updating, the method comprising: maintaining a roadway model of the real-world roadway, the roadway model being sufficient to enable autonomous driverless operation of a self-driving road vehicle; receiving, from a plurality of self-driving road vehicles on the roadway, discrepancy data for known positions on the roadway; aggregating the discrepancy data into aggregated discrepancy data, wherein the aggregated discrepancy data comprises, for at least some portions of the roadway, discrepancy data from a plurality of individual self-driving road vehicles; using the aggregated discrepancy data to identify, as the portions of the roadway model which require updating, those portions of the roadway model corresponding to portions of the roadway for which the aggregated discrepancy data exceeds a threshold; and responsive to identifying the portions of the roadway model which require updating, doing at least one of: dispatching one or more survey vehicles to the portions of the roadway for which the aggregated discrepancy data exceeds the threshold; and modifying an existing plan for updating the roadway model to include the portions of the roadway for which the aggregated discrepancy data exceeds the threshold. ### Claim 2 (depends on 1) The method of claim 1, wherein the discrepancy data identifies positions on the roadway where the self-driving road vehicles experienced underperformance. ### Claim 3 (depends on 1) The method of claim 1, wherein the discrepancy data represents discrepancies, at known positions on the roadway, between information in the roadway model and sensor data from at least one sensor carried by each self-driving road vehicle. ### Claim 4 (depends on 3) The method of claim 3, wherein the sensor data includes at least one of image data, radar data, LIDAR data, GNSS data, INS data, IMU data, accelerometer data, inclinometer data, vibration sensor data, compass data, magnetometer data, color sensor data, weather sensor data and sound data. ### Claim 5 (depends on 1) The method of claim 1, wherein the threshold is a predetermined, fixed threshold. ### Claim 6 (depends on 1) The method of claim 1, wherein the threshold is an adaptive threshold. ### Claim 7 (depends on 1) The method of claim 1, wherein the threshold is generated by applying a statistical model to the discrepancy data. ### Claim 8 A computer-implemented method for determining which portions of a roadway model representing a real-world roadway require updating, the method comprising: maintaining a roadway model of the real-world roadway, the roadway model being sufficient to enable autonomous driverless operation of a self-driving road vehicle; receiving, from a plurality of self-driving road vehicles on the roadway, from at least one sensor carried by each self-driving road vehicle, sensor data associated with known positions on the roadway, the sensor data representing at least one feature of the roadway corresponding to a model feature in the roadway model; using the sensor data to determine discrepancy data; aggregating the discrepancy data into aggregated discrepancy data, wherein the aggregated discrepancy data comprises, for at least some portions of the roadway, discrepancy data from a plurality of individual self-driving road vehicles; using the aggregated discrepancy data to identify, as the portions of the roadway model which require updating, those portions of the roadway model corresponding to portions of the roadway for which the aggregated discrepancy data exceeds a threshold; and responsive to identifying the portions of the roadway model which require updating, doing at least one of: dispatching one or more survey vehicles to the portions of the roadway for which the aggregated discrepancy data exceeds the threshold; and modifying an existing plan for updating the roadway model to include the portions of the roadway for which the aggregated discrepancy data exceeds the threshold. ### Claim 9 (depends on 8) The method of claim 8, wherein the discrepancy data identifies positions on the roadway where the self-driving road vehicles experienced underperformance. ### Claim 10 (depends on 8) The method of claim 8, wherein the discrepancy data represents discrepancies, at known positions on the roadway, between information in the roadway model and sensor data from the at least one sensor carried by each self-driving road vehicle. ### Claim 11 (depends on 8) The method of claim 8, wherein the sensor data includes at least one of image data, radar data, LIDAR data, GNSS data, INS data, IMU data, accelerometer data, inclinometer data, vibration sensor data, compass data, magnetometer data, color sensor data, weather sensor data and sound data. ### Claim 12 (depends on 8) The method of claim 8, wherein the threshold is a predetermined, fixed threshold. ### Claim 13 (depends on 8) The method of claim 8, wherein the threshold is an adaptive threshold. ### Claim 14 (depends on 8) The method of claim 8, wherein the threshold is generated by applying a statistical model to the discrepancy data.