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US 12,737,909 B2Granted
Systems and methods for using image data to identify lane width
AssigneeTORC Robotics, Inc.
InventorJoseph Stamenkovich
Life of this patent
Estimating the term…
Abstract
A method, comprises identifying a set of image data captured by at least one autonomous vehicle when the at least one autonomous vehicle was positioned in a lane of a roadway, and respective ground truth localization data of the at least one autonomous vehicle; determining a plurality of lane width values for the set of image data; labeling the set of image data with the plurality of lane width values, the plurality of lane width values representing a width of a lane in which the at least one autonomous vehicle was positioned; and training using the labeled set of image data, a machine learning model, such that the machine learning model is configured to predict a new lane width value for a new lane as output.
Claims20
A method, comprising:
- identifying, by one or more processors coupled to non-transitory memory, a set of image data captured by at least one autonomous vehicle when the at least one autonomous vehicle was positioned in a lane of a roadway, and respective ground truth localization data of the at least one autonomous vehicle;
- determining, by the one or more processors, a plurality of lane width values for the set of image data, the plurality of lane width values including a first lane width value of the lane in which the at least one autonomous vehicle was positioned and a second lane width value of a lane not being traveled by the at least one autonomous vehicle, determining the plurality of lane width values further comprising:
- determining distances between i) lane lines of the lane in which the at least one autonomous vehicle was positioned and ii) the ground truth localization data of the at least one autonomous vehicle, wherein determining the distances further comprises:
- generating one or more confidence values of one or more ground truth information sources;
- and
- selecting a ground truth information source of the ground truth localization data from the one or more ground truth information sources, based on the one or more confidence values;
- determining the first lane width value, based on the determined distances and a width of the at least one autonomous vehicle;
- and
- determining the second lane width value by comparing distances of lane lines of the lane not being traveled by the at least one autonomous vehicle to a same object within the set of image data;
- labeling, by the one or more processors, the set of image data with the plurality of lane width values;
- and
- training, by the one or more processors, using the labeled set of image data, a machine learning model, such that the machine learning model is configured to predict a new lane width value for a new lane as output.
9 more elements
A non-transitory machine-readable storage medium having computer-executable instructions stored thereon that, when executed by one or more processors, cause the one or more processors to perform operations comprising:
- identify a set of image data captured by at least one autonomous vehicle when the at least one autonomous vehicle was positioned in a lane of a roadway, and respective ground truth localization data of the at least one autonomous vehicle;
- determine a plurality of lane width values for the set of image data, the plurality of lane width values including a first lane width value of the lane in which the at least one autonomous vehicle was positioned and a second lane width value of a lane not being traveled by the at least one autonomous vehicle, determine the plurality of lane width values further comprising:
- determine distances between i) lane lines of the lane in which the at least one autonomous vehicle was positioned and ii) the ground truth localization data of the at least one autonomous vehicle, wherein determine the distances further comprises:
- generate one or more confidence values of one or more ground truth information sources;
- and
- select a ground truth information source of the ground truth localization data from the one or more ground truth information sources, based on the one or more confidence values;
- determine the first lane width value, based on the determined distances and a width of the at least one autonomous vehicle;
- and
- determine the second lane width value by comparing distances of lane lines of the lane not being traveled by the at least one autonomous vehicle to a same object within the set of image data,
- label the set of image data with the plurality of lane width values;
- and
- train, using the labeled set of image data, a machine learning model, such that the machine learning model is configured to predict a new lane width value for a new lane as output.
9 more elements
A system comprising a processor configured to:
- identify a set of image data captured by at least one autonomous vehicle when the at least one autonomous vehicle was positioned in a lane of a roadway, and respective ground truth localization data of the at least one autonomous vehicle;
- determine a plurality of lane width values for the set of image data, the plurality of lane width values including a first lane width value of the lane in which the at least one autonomous vehicle was positioned and a second lane width value of a lane not being traveled by the at least one autonomous vehicle, determine the plurality of lane width values further comprising:
- determine distances between i) lane lines of the lane in which the at least one autonomous vehicle was positioned and ii) the ground truth localization data of the at least one autonomous vehicle, wherein determine the distances further comprises:
- generate one or more confidence values of one or more ground truth information sources;
- and
- select a ground truth information source of the ground truth localization data from the one or more ground truth information sources, based on the one or more confidence values;
- determine the first lane width value, based on the determined distances and a width of the at least one autonomous vehicle;
- and
- determine the second lane width value by comparing distances of lane lines of the lane not being traveled by the at least one autonomous vehicle to a same object within the set of image data,
- label the set of image data with the plurality of lane width values;
- and
- train, using the labeled set of image data, a machine learning model, such that the machine learning model is configured to predict a new lane width value for a new lane as output.
9 more elements
Dependencies are inferred from the claim text.