Learned state covariances
AssigneeZoox, Inc.
InventorsSubhasis Das, Shida Shen
Life of this patent
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Abstract
Techniques are disclosed for a covariance model that may generate observation covariances based on observation data of object detections. Techniques may include determining observation data for an object detection of an object represented in sensor data, determining that track data of a track is associated with the object, and inputting the observation data associated with the object detection into a machine-learned model configured to output a covariance (a covariance model). The covariance model may output one or more observation covariance values for the observation data. In some examples, the techniques may include determining updated track data based on the track data, the one or more observation covariance values, and the observation data.
Claims20
A method comprising:
- determining, based at least in part on sensor data, observation data for an object detection of an object represented in the sensor data;
- inputting one or more of the sensor data or the observation data associated with the object detection into a covariance machine-learning (ML) model;
- receiving, as output from the covariance ML model:
- an observation covariance associated with the observation data, the observation covariance representing a measure of joint variability of two or more instances of the observation data;
- and
- model observation data;
- receiving ground truth data associated with the object detection;
- determining a loss based at least in part on the model observation data and the ground truth data;
- altering, as a trained ML model, one or more parameters of the covariance ML model to reduce the loss;
- and
- transmitting the trained ML model to an autonomous vehicle, the trained ML model being configured to control operation of the autonomous vehicle.
A system comprising:
- one or more processors;
- and
- a memory storing processor-executable instructions that, when executed by the one or more processors, cause the system to perform operations comprising:
- inputting, into a covariance ML model, one or more of sensor data associated with an object detection of an object represented in the sensor data or observation data for an object detection of an object represented in the sensor data;
- receiving, as output from the covariance ML model, an observation covariance associated with the observation data, the observation covariance representing a measure of joint variability of two or more instances of the observation data;
- determining a loss based at least in part on the output;
- altering, as a trained ML model, one or more parameters of the covariance ML model to reduce the loss;
- and
- transmitting the trained ML model to an autonomous vehicle, the trained ML model being configured to control operation of the autonomous vehicle.
One or more non-transitory computer-readable media storing processor-executable instructions that, when executed by one or more processors, cause n to perform operations comprising:
- determining, based at least in part on sensor data, observation data for an object detection of an object represented in the sensor data;
- inputting one or more of the sensor data or the observation data associated with the object detection into a covariance ML model;
- receiving, as output from the covariance ML model an observation covariance associated with the observation data, the observation covariance representing a measure of joint variability of two or more instances of the observation data;
- determining a loss based at least in part on the output;
- altering, as a trained ML model, one or more parameters of the covariance ML model to reduce the loss;
- and
- transmitting the trained ML model to an autonomous vehicle, the trained ML model being configured to control operation of the autonomous vehicle.
Dependencies are inferred from the claim text.