Multi-sensor precipitation-classification apparatus and method
First Claim
1. A vehicle comprising:
- a camera outputting image data;
a windshield;
an accelerometer outputting accelerometer data characterizing vibration of the windshield;
an artificial neural network trained to classify meteorological precipitation in an environment of the vehicle using the image data and the accelerometer data as inputs; and
at least one actuator controlling at least one function of the vehicle based on classifications made by the artificial neural network;
wherein the artificial neural network includes a plurality of classifiers, each classifier of the plurality of classifiers being trained to detect a type of precipitation corresponding to the each classifier, each classifier being configured to take as inputs the accelerometer data and the image data and configured to output a decision indicating whether the type of precipitation corresponding to the each classifier is present; and
wherein each classifier is further configured to apply weights of the each classifier to the accelerometer data and the image data such that the weights of one classifier of the plurality of classifier are different from the weights of another classifier of the plurality of classifiers.
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Abstract
A vehicle is disclosed that uses data from different types of on-board sensors to determine whether meteorological precipitation is failing near the vehicle. The vehicle may include an on-board camera capturing image data and an on-board accelerometer capturing accelerometer data. The image data may characterize an area in front of or behind the vehicle. The accelerometer data may characterize vibrations of a windshield of the vehicle. An artificial neural network may run on computer hardware carried on-board the vehicle. The artificial neural network may be trained to classify meteorological precipitation in an environment of the vehicle using the image data and the accelerometer data as inputs. The classifications of the artificial neural network may be used to control one or more functions of the vehicle such as windshield-wiper speed, traction-control settings, or the like.
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Citations
20 Claims
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1. A vehicle comprising:
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a camera outputting image data; a windshield; an accelerometer outputting accelerometer data characterizing vibration of the windshield; an artificial neural network trained to classify meteorological precipitation in an environment of the vehicle using the image data and the accelerometer data as inputs; and at least one actuator controlling at least one function of the vehicle based on classifications made by the artificial neural network; wherein the artificial neural network includes a plurality of classifiers, each classifier of the plurality of classifiers being trained to detect a type of precipitation corresponding to the each classifier, each classifier being configured to take as inputs the accelerometer data and the image data and configured to output a decision indicating whether the type of precipitation corresponding to the each classifier is present; and wherein each classifier is further configured to apply weights of the each classifier to the accelerometer data and the image data such that the weights of one classifier of the plurality of classifier are different from the weights of another classifier of the plurality of classifiers. - View Dependent Claims (2, 3, 4, 5, 6, 7, 8, 9, 10, 11)
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12. A method comprising:
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receiving, by a computer system carried on-board a vehicle, image data captured by a camera oriented so as to be forward or rearward facing with respect to the vehicle, wherein the image data comprises at least one image captured by the camera within a period of time; receiving, by the computer system, accelerometer data output by an accelerometer secured to a windshield of the vehicle, wherein the accelerometer data characterizes vibration of the windshield during the period of time; receiving, by an artificial neural network run on the computer system, the image data and the accelerometer data as inputs; generating, by the artificial neural network, classification scores based on the inputs with respect to at least three classes of meteorological precipitation, the at least three classes comprising rain, hail, and snow; and controlling, by the computer system, at least one function of the vehicle based on the classification scores generated by the artificial neural network; wherein generating the classification scores based on the inputs comprises; inputting the image data and accelerometer data to a plurality of classifiers, each classifier of the plurality of classifiers being trained to detect a class of the at least three classes of meteorological precipitation corresponding to the each classifier; weighting, by each classifier of the plurality of classifiers, the image data and accelerometer data according to weights of the each classifier such that the weights of one classifier of the plurality of classifiers are different from the weights of another classifier of the plurality of classifiers; generating, by each classifier of the plurality of classifiers, a classification score of the classifications scores according to the weights of the each classifier, the image data, and the accelerometer data. - View Dependent Claims (13, 14, 15, 16, 17, 18, 19, 20)
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Specification