Weight based occupant classification system
First Claim
1. An occupant classification system for a vehicle, the system comprising:
- a plurality of sensors, each sensor configured to produce an output related to a characteristic of an occupant in a seat of the vehicle; and
a controller configured to receive the output of each of the sensors and to perform a maximum likelihood ratio test to provide a signal indicative of the classification of the occupant, wherein the maximum likelihood ratio test incorporates the output of each individual sensor relating to the occupant, and further incorporates a mean and a covariance of each of a pair of data sets, wherein each data set is related to a specific class and includes the outputs of each of the sensors in response to a plurality of test cases.
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Accused Products
Abstract
A system and method of classifying an occupant in a seat. The system can include a plurality of sensors, and each sensor can be configured to produce an output related to a weight of an object in a seat of the vehicle. A controller can be configured to receive the output of each of the sensors when an occupant is in the seat, and perform a maximum likelihood ratio test to provide a signal indicative of the classification of the occupant. The maximum likelihood ratio test can incorporate the output of each sensor relating to the occupant, and can further incorporate data sets related to the outputs of each of the sensors in response to a plurality of test cases, for a specific class of occupants.
36 Citations
28 Claims
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1. An occupant classification system for a vehicle, the system comprising:
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a plurality of sensors, each sensor configured to produce an output related to a characteristic of an occupant in a seat of the vehicle; and a controller configured to receive the output of each of the sensors and to perform a maximum likelihood ratio test to provide a signal indicative of the classification of the occupant, wherein the maximum likelihood ratio test incorporates the output of each individual sensor relating to the occupant, and further incorporates a mean and a covariance of each of a pair of data sets, wherein each data set is related to a specific class and includes the outputs of each of the sensors in response to a plurality of test cases. - View Dependent Claims (2, 3, 4, 5, 6, 7, 8, 9)
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10. A method for classifying an occupant in a seat of a vehicle, comprising:
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providing a plurality of sensors, wherein each sensor provides an output related to a characteristic of an occupant in the seat; obtaining a first data set including the outputs from each sensor under a plurality of test cases representing a first class of occupants, and further computing from the first data set a covariance and a mean; obtaining a second data set including the outputs from each sensor under a plurality of test cases representing a second class of occupants, and further computing from the second data set a covariance and a mean; obtaining the outputs from each individual sensor; and using a maximum likelihood ratio test to classify the occupant, wherein the maximum likelihood ratio test incorporates the outputs from each individual sensor, the covariance and the mean of the data set relating to the first class of occupants, and the covariance and the mean of the data set relating to the second class of occupants. - View Dependent Claims (11, 12, 13, 14, 15, 16, 17)
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18. An occupant classification system for a vehicle, the system comprising:
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a plurality of sensors, each sensor configured to produce an output related to a weight of an occupant in a seat of a vehicle; a controller configured to receive the output of each of the sensors and to arrange the outputs into a matrix, the controller further configured with a plurality of matrices of pre-stored data, at least a portion of the pre-stored data related to mechanical loading caused by occupant restraints, and to perform a maximum likelihood ratio test using the matrix of outputs of the plurality of sensors and the matrices of pre-stored data; wherein the matrices of pre-stored data include a mean matrix and a covariance matrix related to a plurality of classes of occupants. - View Dependent Claims (19, 20, 21, 22, 23, 24, 25, 26, 28)
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27. A method for classifying an occupant in a seat of a vehicle, comprising:
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providing a plurality of sensors, wherein each sensor provides an output related to a weight of an occupant in the seat; obtaining a plurality of data sets, the plurality of data sets including the outputs from each sensor under a plurality of test cases representing a plurality of classes of occupants; obtaining the outputs indicative of the weight of the occupant from each individual sensor; and using a maximum likelihood ratio test to classify the occupant, wherein the maximum likelihood ratio test incorporates the outputs from each individual sensor relating to the weight of the occupant, at least a portion of the data sets relating to the weights of the occupants of the plurality of test cases, and a mean matrix and a covariance matrix for each of the portion of the data sets related to the plurality of classes of occupants.
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Specification