# US 2018/0024239 A1 · Systems and methods for radar localization in autonomous vehicles > Agents: this data is available as JSON from the patentgrep API and MCP server — see https://patentgrep.com/llms.txt - Number: US 2018/0024239 A1 - Document ID: US20180024239A1 - Status: Published application - Kind: A1 - Application number: 15/714,738 - Filed: 2017-09-25 - Published: 2018-01-25 - Earliest claimed: 2017-09-25 - Assignees: GM Global Technology Operations LLC - Applicants: GM Global Technology Operations LLC - Inventors: Elliot Branson - Web page: https://patentgrep.com/patent/US20180024239A1 ## CPC - G01S 13/931: of land vehicles - G01S 7/4808: {Evaluating distance, position or velocity data} - G01S 13/52: Discriminating between fixed and moving objects or between objects moving at different speeds - G01S 13/86: Combinations of radar systems with non-radar systems, e.g. sonar, direction finder - G01S 17/58: Velocity or trajectory determination systems; Sense-of-movement determination systems - G01S 17/931: of land vehicles - G08G 1/165: {for passive traffic, e.g. including static obstacles, trees} - G08G 1/166: {for active traffic, e.g. moving vehicles, pedestrians, bikes} - G01S 2013/9318: {Controlling the steering} - G01S 2013/93185: {Controlling the brakes} - G01S 2013/9319: {Controlling the accelerator} - 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 - 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 method are provided for controlling a vehicle. In one embodiment, a localization method includes receiving sensor data relating to an environment of a vehicle, the sensor data including a plurality of sensor returns associated with objects in the environment, each of the sensor returns having a plurality of corresponding attributes, and constructing a first plurality of sensor data groups, each including a self-consistent subset of the plurality of sensor returns based on their corresponding attributes. The method further includes defining, for each of the first plurality of sensor data groups, a first set of features, wherein each feature is based on at least one of the corresponding attributes and each has an associated feature location, and determining, with a processor, a feature correlation between the first set of features and a second, previously determined set of features. ## Claims ### Claim 1 A localization method comprising: receiving sensor data relating to an environment of a vehicle, the sensor data including a plurality of sensor returns associated with objects in the environment, each of the sensor returns having a plurality of corresponding attributes; constructing a first plurality of sensor data groups, each including a self-consistent subset of the plurality of sensor returns based on their corresponding attributes; defining, for each of the first plurality of sensor data groups, a first set of features, wherein each feature is based on at least one of the corresponding attributes and each has an associated feature location; determining, with a processor, a feature correlation between the first set of features and a second, previously determined set of features; and estimating a position of the vehicle based on the feature correlation. ### Claim 2 (depends on 1) The method of claim 1, wherein the plurality of corresponding attributes includes at least one of Doppler shift, return power, and neighborhood similarity. ### Claim 3 (depends on 1) The method of claim 1, wherein the sensor data includes at least radar data. ### Claim 4 (depends on 1) The method of claim 1, wherein the first set of features includes a histogram of one of the corresponding attributes. ### Claim 5 (depends on 4) The method of claim 4, wherein the first set of features is a convex hull of the histogram. ### Claim 6 (depends on 1) The method of claim 1, wherein the first set of features includes a summary statistic of one of the corresponding attributes. ### Claim 7 (depends on 6) The method of claim 6, wherein the summary statistic is a mean value. ### Claim 8 (depends on 6) The method of claim 6, wherein the summary statistic is a measure of variance. ### Claim 9 (depends on 1) The method of claim 1, further including classifying each of the sensor data groups as being associated with one of a dynamic object, a static-moveable object, or a static-nonmoveable object, and determining the feature correlation based only on the sensor data groups associated with static-nonmoveable objects. ### Claim 10 A system for controlling a vehicle, comprising: a feature determination module, including a processor, configured to: receive sensor data relating to an environment of a vehicle, the sensor data including a plurality of sensor returns associated with objects in the environment, each of the sensor returns having a plurality of corresponding attributes; construct a first plurality of sensor data groups, each including a self-consistent subset of the plurality of sensor returns based on their corresponding attributes; and define, for each of the first plurality of sensor data groups, a first set of features, wherein each feature is based on at least one of the corresponding attributes and each has an associated feature location; and a feature correlation module configured to determine, with a processor, a feature correlation between the first set of features and a second, previously determined set of features. ### Claim 11 (depends on 10) The system of claim 10, wherein: the plurality of corresponding attributes includes at least one of Doppler shift, return power, and neighborhood similarity; and the sensor data is at least one of radar data and lidar data. ### Claim 12 (depends on 10) The system of claim 10, wherein the first set of features includes a histogram of one of the corresponding attributes. ### Claim 13 (depends on 10) The system of claim 10, wherein the first set of features includes a summary statistic of one of the corresponding attributes. ### Claim 14 (depends on 13) The system of claim 13, wherein the summary statistic is a mean value. ### Claim 15 (depends on 13) The system of claim 13, wherein the summary statistic is a measure of variance. ### Claim 16 (depends on 10) The system of claim 10, wherein the feature determination module classifies each of the sensor data groups as being associated with one of a dynamic object, a static-moveable object, or a static-nonmoveable object, and the feature correlation module determines the feature correlation based only on the sensor data groups associated with static-nonmoveable objects. ### Claim 17 An autonomous vehicle, comprising: at least one sensor that provides sensor data relating to an environment of the autonomous vehicle, the sensor data including a plurality of sensor returns associated with objects in the environment, each of the sensor returns having a plurality of corresponding attributes; and a controller that, by a processor: receives the sensor data; constructs a first plurality of sensor data groups, each including a self-consistent subset of the plurality of sensor returns based on their corresponding attributes; defines, for each of the first plurality of sensor data groups, a first set of features, wherein each feature is based on at least one of the corresponding attributes and each has an associated feature location; determines, with a processor, a feature correlation between the first set of features and a second, previously determined set of features; and estimates a position of the vehicle based on the feature correlation. ### Claim 18 (depends on 17) The autonomous vehicle of claim 17. wherein the first set of features includes at least one of a histogram or a summary statistic of one of the corresponding attributes. ### Claim 19 (depends on 18) The autonomous vehicle of claim 18, summay statistic is a mean value. ### Claim 20 (depends on 17) The autonomous vehicle of claim 17, wherein the plurality of corresponding attributes includes at least one of Doppler shift, return power, and neighborhood similarity; and the sensor data includes radar data.