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US 11,238,370 B2Granted
Approaches for determining sensor calibration
AssigneeWoven Planet North America, Inc.
InventorsAshesh Jain, Lei Zhang
Life of this patent
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Abstract
Systems, methods, and non-transitory computer-readable media can determine first sensor data captured by a first sensor of a vehicle. Second sensor data captured by a second sensor of the vehicle can be determined. Information describing the first sensor data and the second sensor data can be provided to a machine learning model trained to predict whether a pair of sensors are calibrated or mis-calibrated based on sensor data captured by the pair of sensors. A determination is made whether the first sensor and the second sensor are calibrated or mis-calibrated based on an output from the machine learning model.
Claims20
A computer-implemented method comprising:
- determining, by a computing system, first sensor data captured by a first sensor of a vehicle;
- determining, by the computing system, second sensor data captured by a second sensor of the vehicle;
- providing, by the computing system, information describing the first sensor data and the second sensor data to a machine learning model,
- wherein the machine learning model is trained based on at least first training data that is associated with a first category of positive training examples and second training data that is associated with a second category of negative training examples, and the second training data corresponds to a transformation of the first training data based on one or more offsets applied to the first training data, and wherein the second training data corresponds to the one or more off sets producing inaccuracies in the first training data;
- determining, by the computing system, whether the first sensor and the second sensor are calibrated or mis-calibrated based on an output from the machine learning model that includes applying a regression analysis to the first sensor data and the second sensor data;
- and
- automatically calibrating the first sensor and the second sensor in real-time according to the output.
4 more elements
A system comprising:
- at least one processor;
- and
- a memory storing instructions that, when executed by the at least one processor, cause the system to perform:
- determining first sensor data captured by a first sensor of a vehicle;
- determining second sensor data captured by a second sensor of the vehicle;
- providing information describing the first sensor data and the second sensor data to a machine learning model to predict whether the first sensor and the second sensor are calibrated or mis-calibrated based on at least the first sensor data and the second sensor data, wherein the machine learning model is trained based on at least first training data that is associated with a first category of positive training examples and second training data that is associated with a second category of negative training examples, and the second training data corresponds to a transformation of the first training data based on one or more offsets applied to the first training data, wherein the second training data corresponds to the one or more offsets producing inaccuracies in the first training data;
- determining whether the first sensor and the second sensor are calibrated or mis-calibrated based on an output from the machine learning model that includes applying a regression analysis to the first sensor data and the second sensor data;
- and
- automatically calibrating the first sensor and the second sensor in real-time according to the output.
6 more elements
A non-transitory computer-readable storage medium including instructions that, when executed by at least one processor of a computing system, cause the computing system to perform:
- determining first sensor data captured by a first sensor of a vehicle;
- determining second sensor data captured by a second sensor of the vehicle;
- providing information describing the first sensor data and the second sensor data to a machine learning model to predict whether the first sensor and the second sensor are calibrated or mis-calibrated based on at least the first sensor data and the second sensor data, wherein the machine learning model is trained based on at least first training data that is associated with a first category of positive training examples and second training data that is associated with a second category of negative training examples, and the second training data corresponds to a transformation of the first training data based on one or more offsets applied to the first training data, wherein the second training data corresponds to the one or more off sets producing inaccuracies in the first training data;
- and
- determining whether the first sensor and the second sensor are calibrated or mis-calibrated based on an output from the machine learning model that includes applying a regression analysis to the first sensor data and the second sensor data;
- and
- automatically calibrating the first sensor and the second sensor in real-time according to the output.
4 more elements
Dependencies are inferred from the claim text.