Training a machine-learned model to detect low variance regions

Low variance detection training is described herein. In an example, annotated data can be determined based on sensor data received from a sensor associated with a vehicle. The annotated data can comprise an annotated low variance region and/or an annotated high variance region. The sensor data can b...

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Bibliographic Details
Main Authors Tariq, Sarah, Philbin, James William Vaisey, Goel, Kratarth
Format Patent
LanguageEnglish
Published 14.03.2023
Subjects
CALCULATING
COMPUTING
CONTROL OR REGULATING SYSTEMS IN GENERAL
CONTROLLING
COUNTING
ELECTRIC DIGITAL DATA PROCESSING
FUNCTIONAL ELEMENTS OF SUCH SYSTEMS
MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS ORELEMENTS
PHYSICS
REGULATING
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Summary:Low variance detection training is described herein. In an example, annotated data can be determined based on sensor data received from a sensor associated with a vehicle. The annotated data can comprise an annotated low variance region and/or an annotated high variance region. The sensor data can be input into a model, and the model can determine an output comprising a high variance output and a low variance output. In an example, a difference between the annotated data and the output can be determined and one or more parameters associated with the model can be altered based at least in part on the difference. The model can be transmitted to a vehicle configured to be controlled by another output of the model.
Bibliography:Application Number: US201916706623