Self-supervised learning
AssigneeNVIDIA Corporation
InventorsPatrik Gebhardt, Alexander Popov, Shane Murray
Life of this patent
Estimating the term…
Abstract
In some embodiments, the method may include determining, using a machine learning model, an estimated velocity corresponding to an object based at least on measured RADAR data, where the measured RADAR data may correspond to RADAR detections associated with the object. In some embodiments, the method may further include determining expected RADAR data corresponding to the object based at least on the estimated velocity. Some embodiments may additionally include updating one or more parameters of the machine learning model based on the difference between the measured RADAR data and the expected RADAR data.
Claims20
A method comprising:
- obtaining, using a machine learning (ML) model, an output of the ML model that includes an estimated velocity corresponding to an object as a whole, the estimated velocity being based at least on providing measured RADAR data to the ML model as an input to the ML model, the measured RADAR data corresponding to a plurality of RADAR detections associated with different portions of the object;
- reconstructing, to generate expected RADAR data as a reconstruction of the measured RADAR data and formatted according to a format of the measured RADAR data, RADAR data corresponding to the different portions of the object based at least on one or more reverse calculations performed with respect to the estimated velocity;
- and
- updating one or more parameters of the ML model based at least on a difference between the measured RADAR data and the expected RADAR data, wherein the ML model is used by a machine to determine a detected velocity of a detected object detected using one or more RADAR sensors of the machine.
A system comprising:
- one or more processors to perform operations comprising:
- obtaining RADAR data corresponding to an object, the RADAR data captured within a time range and corresponding to a plurality of RADAR sensors;
- estimating, using a machine learning (ML) model, a velocity corresponding to the object based at least on the RADAR data corresponding to the plurality of RADAR sensors, wherein the ML model was trained based at least on reconstructing, to generate expected RADAR data, previously measured RADAR data from one or more previously estimated velocities previously estimated by the ML model as one or more previous outputs generated using the ML model based at least on the previously measured RADAR data being provided to the ML model as one or more inputs to the ML model, the expected RADAR data being of a same data type as the previously measured RADAR data;
- and
- associating the velocity as estimated with the object detected using one or more object tracking techniques.
One or more processors comprising processing circuitry to perform one or more operations using a velocity estimate generated using a machine learning (ML) model, wherein the ML model is trained by:
- determining, using the ML model, an estimated velocity corresponding to an object as a whole based at least on providing, as an input to the ML model, measured RADAR data corresponding to a plurality of RADAR detections associated with different portions of the object;
- reconstructing the measured RADAR data according to a format of the measured RADAR data based at least on the estimated velocity to determine expected RADAR data corresponding to the different portions of the object;
- and
- updating one or more parameters of the ML model based at least on a difference between the measured RADAR data and the expected RADAR data, wherein the ML model is used by a machine to determine a detected velocity of a detected object detected using one or more RADAR sensors of the machine.
Dependencies are inferred from the claim text.