# US 12,181,584 B2 · Systems and methods for monitoring LiDAR sensor health > Agents: this data is available as JSON from the patentgrep API and MCP server — see https://patentgrep.com/llms.txt - Number: US 12,181,584 B2 - Document ID: US12181584B2 - Status: Granted - Kind: B2 - Application number: 17/148,691 - Filed: 2021-01-14 - Published: 2022-07-14 - Granted: 2024-12-31 - Earliest claimed: 2021-01-14 - Assignees: Volkswagen Group of America Investments, LLC - Applicants: Argo AI, LLC - Inventors: Hsin Miao; Willibald Brems; Dikpal Reddy - Web page: https://patentgrep.com/patent/US12181584B2 ## CPC - G01S 17/931: of land vehicles - G01S 7/4808: {Evaluating distance, position or velocity data} - G01S 7/4873: {by deriving and controlling a threshold value} - G01S 7/497: Means for monitoring or calibrating - G01S 17/89: for mapping or imaging - 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 ## Abstract Systems and methods for generating operating an autonomous vehicle. The methods comprise: obtaining LiDAR point cloud data generated by a LiDAR system of the autonomous vehicle; inspecting the LiDAR point cloud data to infer a health of LiDAR beams; identifying bad quality point cloud data based on the inferred health of the LiDAR beams; removing the bad quality point cloud data from the LiDAR point cloud data to generate modified LiDAR point cloud data; and causing the autonomous vehicle to perform at least one autonomous driving operation or mode change based on the modified LiDAR point cloud data. ## Claims ### Claim 1 A method for operating an autonomous vehicle, comprising: obtaining, by a computing device, LiDAR point cloud data generated by a LiDAR system of the autonomous vehicle; inspecting, by the computing device, the LiDAR point cloud data to infer a health of LiDAR beams; identifying, by the computing device, bad quality point cloud data based on the inferred health of the LiDAR beams; removing, by the computing device, the bad quality point cloud data from the LiDAR point cloud data to generate modified LiDAR point cloud data; and causing, by the computing device, the autonomous vehicle to perform at least one autonomous driving operation or mode change based on the modified LiDAR point cloud data; wherein the health of the LiDAR beam is inferred by computing a plurality of metrics based on characteristics of a LiDAR point cloud defined by the LiDAR point cloud data; and wherein a metric of the plurality of metrics is generated by: determining a first number representing a total number of data points in the LIDAR point cloud that have a first intensity value of a plurality of intensity values, and a second number representing a total number of data points in the LIDAR point cloud that have a different second intensity value of the plurality of intensity values; combining the first number and the second number to obtain a third number; and setting a value of the metric equal to the third number. ### Claim 2 (depends on 1) The method according to claim 1, wherein the health of the LiDAR beam is inferred by further computing a confidence score based on the plurality of metrics. ### Claim 3 (depends on 2) The method according to claim 2, wherein the health of the LiDAR beam is inferred by further classifying the LiDAR beam as a faulty beam or a good beam based on the confidence score. ### Claim 4 (depends on 3) The method according to claim 3, wherein the LiDAR beam is classified as a faulty beam when the confidence score is less than a threshold value. ### Claim 5 (depends on 1) The method according to claim 1, wherein the plurality of metrics comprise at least one of a shape context metric, and a height metric. ### Claim 6 (depends on 1) The method according to claim 1, wherein the plurality of metrics are determined based on at least one of a total number of data points in the LiDAR point cloud, intensity values of the LiDAR point cloud, and z-coordinate values for data points in the LiDAR point cloud. ### Claim 7 (depends on 1) The method according to claim 1, wherein another metric of the plurality of metrics is equal to a total number of data points in the LiDAR point cloud. ### Claim 8 (depends on 1) The method according to claim 1, wherein another metric of the plurality of metrics is generated by: using a z-coordinate value to compute a distance from each given data point to an average z-coordinate value; determining a standard deviation of the z-coordinate values for data points in a LiDAR point cloud; dividing each distance by the standard deviation to obtain a value; computing an average of the values. ### Claim 9 A system, comprising: a processor; a non-transitory computer-readable storage medium comprising programming instructions that are configured to cause the processor to implement a method for operating an autonomous vehicle, wherein the programming instructions comprise instructions to: obtain LiDAR point cloud data generated by a LiDAR system of the autonomous vehicle; inspect the LiDAR point cloud data to infer a health of LiDAR beams; identify bad quality point cloud data based on the inferred health of the LiDAR beams; remove the bad quality point cloud data from the LiDAR point cloud data to generate modified LiDAR point cloud data; and cause the autonomous vehicle to perform at least one autonomous driving operation or mode change based on the modified LiDAR point cloud data; wherein the health of the LiDAR beam is inferred by computing a plurality of metrics based on characteristics of a LiDAR point cloud defined by the LiDAR point cloud data; and wherein a metric of the plurality of metrics is generated by: determining a first number representing a total number of data points in the LIDAR point cloud that have a first intensity value of a plurality of intensity values, and a second number representing a total number of data points in the LIDAR point cloud that have a different second intensity value of the plurality of intensity values; combining the first number and the second number to obtain a third number; and setting a value of the metric equal to the third number. ### Claim 10 (depends on 9) The system according to claim 9, wherein the health of the LiDAR beam is inferred by further computing a confidence score based on the plurality of metrics. ### Claim 11 (depends on 10) The system according to claim 10, wherein the health of the LiDAR beam is inferred by further classifying the LiDAR beam as a faulty beam or a good beam based on the confidence score. ### Claim 12 (depends on 11) The system according to claim 11, wherein the LiDAR beam is classified as a faulty beam when the confidence score is less than a threshold value. ### Claim 13 (depends on 9) The system according to claim 9, wherein the plurality of metrics comprise at least one of an outlier metric, a shape context metric, and a height metric. ### Claim 14 (depends on 9) The system according to claim 9, wherein the plurality of metrics are determined based on at least one of a total number of data points in the LiDAR point cloud, intensity values of the LiDAR point cloud, and z-coordinate values for data points in the LiDAR point cloud. ### Claim 15 (depends on 9) The system according to claim 9, wherein another metric of the plurality of metrics is equal to a total number of data points in the LiDAR point cloud. ### Claim 16 (depends on 9) The system according to claim 9, wherein another metric of the plurality of metrics is generated by: using a z-coordinate value to compute a distance from each given data point to an average z-coordinate value; determining a standard deviation of the z-coordinate values for data points in a LiDAR point cloud; dividing each distance by the standard deviation to obtain a value; computing an average of the values.