Patent Description:
Automotive vehicles include one or more seat assemblies having a seat cushion and a seat back for supporting a passenger or occupant above a vehicle floor. The seat assembly is commonly mounted to the vehicle floor by a riser assembly. The seat back is typically operatively coupled to the seat cushion by a recliner assembly for providing selective pivotal adjustment of the seat back relative to the seat cushion.

Front passenger seat assemblies for automotive vehicles typically include an occupant classification system for determining the weight of an occupant in the seat assembly. Occupant classification systems are useful to optimize vehicle safety systems, such as airbag deployment systems. For example, an occupant classification system may send the weight of an occupant to an occupant restraint controller, which may alter the intensity at which an airbag deploys depending on the weight of the occupant. For smaller individuals, the airbag may deploy at a lower intensity or not deploy at all.

Occupant classification systems typically include a pressure sensing device, such as a plurality of sensing cells or a bladder system, located in the seat cushion, which determines the weight of an occupant by measuring the amount of force applied to the seat cushion. However, the amount of force applied to the seat cushion varies depending on the occupant's posture because the occupant's posture affects the weight distribution between the vehicle floor, the seat cushion and the seat back. In addition, each occupant has a distinct manner of sitting that may affect their weight distribution on the seat.

For example, the amount of force measured on a seat cushion for a person sitting upright with their feet on the floor and their lower legs extended as depicted in <FIG> may be <NUM>. If that same individual leans forward as depicted in <FIG>, the amount of force decreases to <NUM>. Similarly, the amount of force measured on a seat cushion for a person sitting upright with their feet on the floor and their lower legs extended as depicted in <FIG> may be <NUM>, but when the individual raises his/her legs as depicted in <FIG>, the amount of force increases to <NUM>.

Conventional occupant classification systems often misclassify the weight of seat occupants because they do not distinguish between different sitting postures, which can greatly affect the accuracy of the weight measurements. It is desirable, therefore, to provide an occupant classification system that factors an occupant's posture into the weight analysis.

<CIT> discloses a vehicle passenger classification system for determining whether a passenger is in a seat or which type of passenger is in the seat. The system uses three weight sensors and one single dummy sensor installed under the seat frame to support the load of the vehicle seat along with the weight sensors, and a passenger classification logic unit for classifying a passenger as either a first class or a second class by comparing a total passenger value obtained by summing sensor values of the plurality of weight sensors with a first reference and a second reference.

<CIT> discloses a method of recognizing and classifying the occupancy in a vehicle seat having an occupancy sensing system, including the steps of sensing the output of an array of sensors that detect a physical presence in a seat and applying the sensor array output as a vector representation to a neural net that was trained using a learning vector quantization algorithm. The known method also includes the step of recognizing the sensor array output as falling within one of a group of predetermined classification patterns that represent a physical presence in the seat defined by size, weight, and physical orientation.

The present invention significantly improves accuracy in assigning weight classes to occupants in a vehicle seat assembly by providing an occupant classification system with the features of claim <NUM> and a method associated with classifying an occupant of a seat assembly with the features of claim <NUM>.

<FIG> illustrates one embodiment of a seat assembly <NUM> for use in an automotive vehicle. The seat assembly <NUM> includes a seat cushion <NUM> and a seat back <NUM> operatively coupled to the seat cushion <NUM> for supporting a seat occupant in a generally upright seating position. The seat back <NUM> is typically operatively coupled to the seat cushion <NUM> by a recliner assembly <NUM> for providing pivotal movement between an upright seating position and a plurality of reclined seating positions.

The seat assembly <NUM> includes an occupant classification system <NUM> for determining the posture <NUM> and the weight class <NUM> of an occupant in the seat assembly <NUM>. Rather than trying to identify the precise weight of an occupant, the occupant classification system <NUM> of the present invention identifies the likelihood that the occupant belongs to a certain weight class. For example, the system <NUM> may distinguish between four standard adult weight classes: feather weight, light weight, middle weight and heavy weight. Feather weight is defined as an adult that falls below the <NUM>th percentile. Light weight is defined as an adult between the <NUM>th and <NUM>th percentile. Middle weight is defined as an adult between the <NUM>th and <NUM>th percentile. Heavy weight is defined as an adult above the <NUM>th percentile.

Conventional occupant classification systems commonly mistake child seats for adults because the weight measured on a seat cushion includes not only the weight of the child seat and the weight of a child in the child seat, but also may be affected by seat belt tension. The present invention solves this problem by treating a child seat as a posture <NUM>. Once categorized as a posture <NUM>, the system <NUM> may distinguish between different child seat weight classes <NUM>. For example, the system <NUM> may distinguish between a <NUM>-month old, a <NUM>-year old and a <NUM>-year old.

In addition to a child seat, the system <NUM> may distinguish between any number of postures <NUM>. For example, referring to <FIG>, the system <NUM> may distinguish between a person sitting upright with their feet on the floor and their lower legs extended <NUM>, a person sitting in a slouched position <NUM>, a person sitting upright with their feet on the floor and their lower legs pulled in toward the seat <NUM>, a person sitting with their legs spread apart with their feet on the floor and their lower legs pulled in toward the seat <NUM>, a person sitting with their legs spread apart with their feet on the floor and their lower legs extended <NUM>, a person sitting on the left side of the seat with their lower legs pulled in toward the seat <NUM>, a person sitting on the right side of the seat with their lower legs pulled in toward the seat <NUM>, a person sitting with their legs angled to the left <NUM>, a person sitting with their legs angled to the right <NUM>, a person sitting on the front edge of the seat with their legs angled to the left <NUM>, a person sitting on the front edge of the seat with their legs angled to the right <NUM>, a person sitting with their legs crossed <NUM>, a person sitting with their hands beneath their thighs <NUM>, a person sitting with their legs crossed and angled to the left <NUM>, a person sitting with their legs crossed and angled to the right <NUM>, a person sitting with their right foot tucked under their left thigh <NUM>, and a person sitting with their left foot tucked under their right thigh <NUM>.

The occupant classification system <NUM> may be used to optimize vehicle safety systems, such as an airbag deployment system. For example, the occupant classification system <NUM> may provide the posture <NUM> of the occupant to an occupant restraint controller so that the occupant restraint controller will not deploy an airbag under certain conditions, such as if there is a child seat in the seat assembly <NUM> or if the occupant is sitting in a vulnerable position that is not ideal for airbag deployment. The occupant classification system <NUM> also may provide the weight class <NUM> of the occupant to the occupant restraint controller so that the occupant restraint controller may alter the intensity at which the airbag deploys. For example, for feather weight individuals, the occupant restraint controller may deploy the airbag at a lower intensity.

Referring to <FIG>, the occupant classification system <NUM> of the present invention includes an array <NUM> of sensing cells <NUM> in the seat cushion <NUM>. Each sensing cell <NUM> measures the amount of force applied to the cell <NUM>. In a preferred embodiment, the system <NUM> also includes an array <NUM> of sensing cells <NUM> in the seat back <NUM>. Including the sensing cells <NUM> in both the seat cushion <NUM> and the seat back <NUM> increases overall performance of the system <NUM>. Although the seat cushion <NUM> is depicted as including <NUM> rows of <NUM> sensing cells, and the seat back <NUM> is depicted as including <NUM> rows of <NUM> sensing cells, the number of sensing cells <NUM> in each array <NUM> is customizable.

Each sensing cell <NUM> provides a voltage based on the magnitude of force applied to each individual sensing cell <NUM>. Using a <NUM>,<NUM>-ohm bias resistor and a <NUM>-bit analog-to-digital converter, the dynamic range of reliable force measured on each cell <NUM> may vary between <NUM> and <NUM>. The system <NUM> may output an array <NUM> of values <NUM> times per second.

Referring to <FIG>, the occupant classification system <NUM> of the present invention also includes a posture classifier <NUM> and a plurality of weight classifier systems <NUM>. Each posture <NUM> corresponds to a unique weight classifier system <NUM>. The posture classifier <NUM> determines the posture <NUM> of the occupant in the seat assembly <NUM> based on the distribution of forces on the array <NUM> of sensing cells <NUM>. After determining the occupant's posture <NUM>, the corresponding weight classifier system <NUM> determines the weight class <NUM> of the occupant based on the magnitude of force on each sensing cell <NUM> in the array <NUM>.

The posture classifier <NUM> comprises a probabilistic model. A probabilistic model is preferred over a deterministic model because it allows for more significant handling of output ambiguities, it is quicker to develop, it is more easily adapted and scaled, and it more easily accommodates complex user types and behaviors. In addition, because it uses a multiple signal input array, it accommodates complex user types and behaviors. In other words, it uses a higher dimensional analysis (i.e., spatial 3D sensing) compared to a one-dimensional deterministic model.

Preferably, the probabilistic model comprises a neural network. However, other probabilistic models may be used, including support vector machines, logistic regression, decision trees, Naïve-Bayes or nearest neighbors. The posture classifier <NUM> depicted in <FIG> comprises a neural network. Various algorithms may be used to train the neural network to differentiate between the different postures <NUM>. For example, a supervised batch learning method may be used to adjust the weights and bias parameters that feed every node of the neural network and regulates its output. Although probabilistic in nature, once the weights and bias terms have been optimized during the learning process, the system becomes deterministic. In other words, it becomes predicable once it receives a different set of data.

The input layer of the posture classifier <NUM> comprises the array <NUM> of sensing cells <NUM> (X = [x<NUM>, x<NUM>,. x,]), where n represents the number of sensing cells <NUM>. The output layer of the posture classifier <NUM> comprises the different postures <NUM> [k<NUM>, k<NUM>,. ko] that the system has been trained to recognize. The posture classifier <NUM> includes a hidden layer with m transfer functions <NUM> [y<NUM>, y<NUM>,. ym], where the weights <NUM> of the transfer functions <NUM> are represented by [w<NUM>, w<NUM>,. Although depicted with a single hidden layer, the type and structure of the neural network may be modified to optimize the system, for example by using more than one hidden layer or by changing the number of nodes in the hidden layer.

The weight classifier system <NUM> includes a deterministic component <NUM> and a plurality of probabilistic components <NUM>, <NUM>, <NUM>. For example, the deterministic component <NUM> may comprise a weight band based on the total sum <NUM> of the values from the sensing cells <NUM> for each weight class <NUM>. As depicted in the example in <FIG>, for a given posture, the feather weight band <NUM> extends from below <NUM> to b, the light weight band <NUM> extends from a to d, the middle weight band <NUM> extend from c to f, and the heavy weight band <NUM> extends from e to over <NUM>.

There may be an overlap between adjacent weight bands. For the example depicted in <FIG>, the overlap <NUM> between the feather weight band <NUM> and the light weight band <NUM> occurs when the total sum <NUM> of the values from the sensing cells <NUM> falls between a and b. The overlap <NUM> between the light weight band <NUM> and the middle weight band <NUM> occurs when the total sum <NUM> of the values from the sensing cells <NUM> falls between c and d. The overlap <NUM> between the middle weight band <NUM> and the heavy weight band <NUM> occurs when the total sum <NUM> of the values from the sensing cells <NUM> falls between e and f.

Threshold values may be identified for each weight class in which the total sum <NUM> of the values from the sensing cells <NUM> could only reflect one weight class and no other because between or beyond these threshold values, there is no overlap with an adjacent class. For example, if the total sum <NUM> of the values from the sensing cells <NUM> is less than a, then the occupant is a feather weight. If the total sum <NUM> of the values from the sensing cells <NUM> falls between b and c, then the occupant is a light weight. If the total sum <NUM> of the values from the sensing cells <NUM> falls between d and e, then the occupant is a middle weight. And if the total sum <NUM> of the values from the sensing cells <NUM> is greater than f, then the occupant is a heavy weight.

<FIG> illustrates the importance of factoring posture into determining weight classification. If one were to compare the total sum <NUM> of the values from the sensing cells <NUM> for all postures collectively, the weight bands <NUM>, <NUM>, <NUM>, <NUM> for each weight class will expand because for any given individual, the sensor readings in the different postures may vary significantly. The greater variation in individual sensor readings results in a wider weight band for all individuals within that weight band, and a greater likelihood of overlap between different weight bands. Thus, as depicted, there is an area of overlap <NUM>, not only between adjacent weight classes, but between all four weight classes. By contrast, viewing the sensor readings on a posture-by-posture basis, as illustrated by the deterministic component <NUM> in <FIG>, fine-tunes the weight class bands <NUM>, <NUM>, <NUM>, <NUM> in such a way that overlap is reduced and limited to adjacent classes.

Returning to <FIG>, if the total sum <NUM> of the values from the sensing cells <NUM> falls within overlap <NUM>, then probabilistic component <NUM> may be used to distinguish between the feather and light weight classes. If the total sum <NUM> of the values from the sensing cells <NUM> falls within overlap <NUM>, then probabilistic component <NUM> may be used to distinguish between the light and middle weight classes. If the total sum <NUM> of the values from the sensing cells <NUM> falls within overlap <NUM>, then probabilistic component <NUM> may be used to distinguish between the middle and heavy weight classes.

Preferably, each probabilistic component <NUM>, <NUM>, <NUM> of the weight classifier system <NUM> comprises a neural network. However, other probabilistic models may be used, including support vector machines, logistic regression, decision trees, Naïve-Bayes, nearest neighbors, regression-based models or a radial basis network. Similar to the posture classifier <NUM>, the probabilistic components <NUM>, <NUM>, <NUM> are trained to differentiate between their respective adjacent weight classes.

Additional modifications may be made to improve the accuracy of the occupant classification system <NUM>. For example, the system <NUM> may determine the centroid of the occupant and use it to enhance one or more of the probabilistic models <NUM>, <NUM>, <NUM>, <NUM>. The centroid also may be useful to identify transitions in postures <NUM> and to identify slight variations based on the occupant's specific manner of sitting.

The deterministic component <NUM> of the weight classifier system <NUM> may use metrics different from the total sum <NUM> of the values from the sensing cells <NUM> to identify the weight classes. For example, the deterministic component <NUM> may be based on the centroid of the occupant or the average of the values measured from the sensing cells <NUM>. Likewise, these metrics may be used to enhance one or more of the probabilistic models <NUM>, <NUM>, <NUM>, <NUM>. The system <NUM> also may use the temperature of the sensors <NUM> to enhance one or more of the probabilistic models <NUM>, <NUM>, <NUM>, <NUM>.

There may be circumstances in which one or more of the probabilistic models <NUM>, <NUM>, <NUM>, <NUM> may not be able to clearly identify a single posture <NUM> or weight class <NUM> into which an occupant falls. In these circumstances, the system <NUM> can apply a deterministic model to help distinguish which posture <NUM> or weight class <NUM> is most appropriate for this occupant.

Claim 1:
An occupant classification system (<NUM>) for a seat assembly (<NUM>), wherein the seat assembly (<NUM>) includes a seat cushion (<NUM>) and a seat back (<NUM>), the system comprising:
a plurality of sensors (<NUM>), wherein each of the plurality of sensors (<NUM>) measures a force applied to the seat cushion (<NUM>) by an occupant of the seat assembly (<NUM>);
a posture classifier (<NUM>) for identifying a posture (<NUM>) of the occupant based on the distribution of forces applied to each of the plurality of sensors (<NUM>), the posture classifier (<NUM>) comprising a probabilistic model ; and
a weight classification system (<NUM>) for identifying a weight class (<NUM>) of the occupant based on the posture (<NUM>) and the magnitude of forces applied to each of the plurality of sensors (<NUM>), wherein the weight classification system (<NUM>) comprises a deterministic component (<NUM>) with a deterministic method to identify the weight class (<NUM>) and a probabilistic component (<NUM>, <NUM>, <NUM>) with a probabilistic method to identify the weight class (<NUM>) in case the deterministic method is unable to identify a single weight class (<NUM>).