Patent Description:
Sensors have been mounted on vehicle tires to monitor certain tire parameters, such as pressure and temperature. Systems that include sensors which monitor tire pressure are known in the art as tire pressure monitoring systems (TPMS). For example, a tire may have a TPMS sensor that transmits a pressure signal to a processor, which generates a low pressure warning when the pressure of the tire falls below a predetermined threshold. It is desirable that systems including pressure sensors be capable of identifying the specific tire that is experiencing low air pressure, rather than merely alerting the vehicle operator or a fleet manager that one of the vehicle tires is low in pressure.

The process of identifying which sensor sent a particular signal and, therefore, which tire may have low pressure, is referred to as auto-location or localization. Effective and efficient auto-location or localization is a challenge in TPMS, as tires may be replaced, rotated, and/or changed between summer and winter tires, altering the position of each tire on the vehicle. Additionally, power constraints typically make frequent communications and auto-location or localization of signal transmissions impractical.

Prior art techniques to achieve signal auto-location or localization have included various approaches. For example, low frequency (LF) transmitters have been installed in the vicinity of each wheel of the tire, two-axis acceleration sensors have been employed which recognize a rotation direction of the tire for left or right tire location determination, as well as methods distinguishing front tires from rear tires using radio frequency (RF) signal strength. The prior art techniques have deficiencies that make location of a sensor mounted in a tire on a vehicle either expensive or susceptible to inaccuracies.

As a result, there is a need in the art for a system that provides economical and accurate identification of the location of a position of a tire on a vehicle.

<CIT> and <CIT> describe an auto-location system for locating a position of a tire supporting a vehicle comprising a sensor unit that may be mounted on the tire and that may determine a footprint length. The system also employs the footprint length to determine the position of the tire on the vehicle. Furthermore, there is an output block executed on a processor for generating a message correlating the sensor unit to the position of the tire on the vehicle.

Further tire or wheel localization systems and methods are described in <CIT> and <CIT>.

The invention relates to an auto-location system in accordance with claim <NUM>, to a use of such a system in accordance with claim <NUM>, and to a method in accordance with claim <NUM>.

According to an aspect of an exemplary embodiment of the invention, an auto-location system for locating a position of a tire supporting a vehicle is provided. The system includes a sensor unit that is mounted on the tire, and which includes a footprint length measurement sensor to measure a length of a footprint of the tire. A processor is in electronic communication with the sensor unit and receives the measured footprint length. A driving event classifier is executed on the processor and employs the measured footprint length to determine the position of the tire on the vehicle. An auto-location output block is executed on the processor and receives the determined position of the tire on the vehicle and generates a message correlating the sensor unit to the position of the tire on the vehicle.

In a preferred aspect of the invention, the driving event classifier is configured to determine from parameters sensed by the sensor unit whether the vehicle is executing a turn, and if the vehicle is executing a turn (which may be a right turn or a left turn), to input the determined mean footprint length into a turn-based auto-locator when a predetermined number of turn events has been met. Preferably, a left tire position is distinguished from a right tire position in the turn-based locator using a change from the determined mean footprint length. Preferably, a left tire position is distinguished from a right tire position in the right turn-based auto-locator using a speed difference between a wheel revolution time and a speed of the vehicle.

"ANN" or "artificial neural network" is an adaptive tool for non-linear statistical data modeling that changes its structure based on external or internal information that flows through a network during a learning phase. ANN neural networks are non-linear statistical data modeling tools used to model complex relationships between inputs and outputs or to find patterns in data.

"CAN bus" is an abbreviation for controller area network.

"Equatorial centerplane (CP)" means the plane perpendicular to the tire's axis of rotation and passing through the center of the tread.

"Footprint" means the contact patch or area of contact created by the tire tread with a flat surface as the tire rotates or rolls.

"Rib" means a circumferentially extending strip of rubber on the tread which is defined by at least one circumferential groove and either a second such groove or a lateral edge, the strip being laterally undivided by full-depth grooves.

"Tread element" or "traction element" means a rib or a block element defined by a shape having adjacent grooves.

With reference to <FIG>, an exemplary embodiment of an auto-location system <NUM> of the present invention is presented. With particular reference to <FIG>, the system <NUM> locates the position of each tire <NUM> supporting a vehicle <NUM>. The position of each tire <NUM> shall be referred to herein by way of example as left front 12a, right front 12b, left rear 12c, and right rear 12d. While the vehicle <NUM> is depicted as a passenger car, the invention is not to be so restricted. The principles of the invention find application in other vehicle categories, such as commercial trucks, in which vehicles may be supported by more or fewer tires than those shown in <FIG>.

The tires <NUM> are preferably of conventional construction, and each tire is mounted on a respective wheel <NUM> as known to those skilled in the art. Each tire <NUM> includes a pair of sidewalls <NUM> (only one shown) that extend to a circumferential tread <NUM>. An innerliner <NUM> is disposed on the inner surface of the tire <NUM>, and when the tire is mounted on the wheel <NUM>, an internal cavity <NUM> is formed, which is filled with a pressurized fluid, such as air.

A sensor unit <NUM> is attached to the innerliner <NUM> of each tire <NUM> by means such as an adhesive, and measures certain parameters or conditions of the tire as will be described in greater detail below. It is to be understood that the sensor unit <NUM> may be attached in such a manner, or to other components of the tire <NUM>, such as on or in one of the sidewalls <NUM>, on or in the tread <NUM>, on the wheel <NUM>, and/or a combination thereof. For the purpose of convenience, reference herein shall be made to mounting of the sensor unit <NUM> on the tire <NUM>, with the understanding that such mounting includes all such types of attachment.

The sensor unit <NUM> is mounted on each tire <NUM> for the purpose of detecting certain real-time tire parameters, such as tire pressure <NUM> and tire temperature <NUM>. For this reason, the sensor unit <NUM> preferably includes a pressure sensor and a temperature sensor and may be of any known configuration. The sensor unit <NUM> may be referred to as a tire pressure monitoring system (TPMS) sensor. The sensor unit <NUM> preferably also includes electronic memory capacity for storing identification (ID) information for the sensor unit mounted in each tire <NUM>, known as sensor ID information, which includes a unique identifying number or code for each sensor unit.

The electronic memory capacity in the sensor unit may also store ID information for each tire <NUM>, known as tire ID information. Alternatively, tire ID information may be included in another sensor unit, or in a separate tire ID storage medium, such as a tire ID tag, which preferably is in electronic communication with the sensor unit <NUM>. The tire ID information may be correlated to specific construction data for each tire <NUM>, including: the tire type; tire model; size information, such as rim size, width, and outer diameter; manufacturing location; manufacturing date; a treadcap code that includes or correlates to a compound identification; and a mold code that includes or correlates to a tread structure identification.

As described above, the phrases sensor ID and sensor ID information refer to identification of the tire-mounted sensor unit <NUM>. The system <NUM> employs sensor ID and sensor ID information to identify each sensor unit <NUM>, and analyses data from each sensor unit to determine the location of each respective tire <NUM> on the vehicle <NUM>, as will be described in detail below. In the art, the phrase tire ID is sometimes used in connection with identification of the location of each tire <NUM> on the vehicle <NUM>. However, as described above, the phrases tire ID and tire ID information as used herein refer to specific construction data for each tire <NUM>, rather than locating the position of each tire on the vehicle <NUM>.

Turning to <FIG>, the sensor unit <NUM> (<FIG>) preferably also measures a length <NUM> of a centerline <NUM> of a footprint <NUM> of the tire <NUM>. More particularly, as the tire <NUM> contacts the ground, the area of contact created by the tread <NUM> with the ground is known as the footprint <NUM>. The centerline <NUM> of the footprint <NUM> corresponds to the equatorial centerplane of the tire <NUM>, which is the plane that is perpendicular to the axis of rotation of the tire and which passes through the center of the tread <NUM>. The sensor unit <NUM> thus measures the length <NUM> of the centerline <NUM> of the tire footprint <NUM>, which is referred to herein as the footprint length <NUM>. Any suitable technique for measuring the footprint length <NUM> may be employed by the sensor unit <NUM>. For example, the sensor unit <NUM> may include a strain sensor or piezoelectric sensor that measures deformation of the tread <NUM> and thus indicates the footprint length <NUM>.

The sensor unit <NUM> may also include an accelerometer for measuring wheel acceleration <NUM>, and a revolution counter to measure wheel revolution time <NUM>. It is to be understood that the pressure sensor, the temperature sensor, the sensor ID capacity, the tire ID capacity, the footprint length sensor, the accelerometer, and/or the revolution counter may be incorporated into the single sensor unit <NUM>, or may be incorporated into multiple units. For the purpose of convenience, reference herein shall be made to a single sensor unit <NUM>.

With reference to <FIG>, the parameters of tire pressure <NUM>, tire temperature <NUM>, footprint length <NUM>, the wheel acceleration <NUM>, and the wheel revolution time <NUM> are collectively referred to as sensed parameters <NUM>. The sensor unit <NUM> includes wireless transmission means <NUM>, such as an antenna, for wirelessly sending the sensed parameters <NUM> to a processor <NUM>. The processor <NUM> may be integrated into the sensor unit <NUM>, or may be a remote processor, which may be mounted on the vehicle <NUM> or be cloud-based. For the purpose of convenience, the processor <NUM> will be described as a cloud-based processor, with the understanding that the processor may alternatively be integrated into the sensor unit <NUM> or mounted on the vehicle <NUM>.

Aspects of the auto-location system <NUM> preferably are executed on the processor <NUM>, which enables input of the sensed parameters <NUM> and execution of specific analysis techniques, to be described below, which are stored in a suitable storage medium and are also in electronic communication with the processor. For preliminary treatment, the sensed parameters <NUM> are input into a data converter <NUM>, which processes and normalizes the data from the sensed parameters for analysis.

Turning to <FIG>, after the data converter <NUM>, output data <NUM> from the sensed parameters <NUM> are analyzed by an initial assessment module <NUM> to determine if the incoming data is for an ongoing trip, or if a new trip by the vehicle <NUM> is in progress <NUM>. The output data <NUM> may include, by way of example, tire footprint length <NUM>, lateral acceleration of the vehicle <NUM>, longitudinal acceleration of the vehicle, yaw rate of the vehicle, a time stamp, a revolution time of the tire <NUM>, a vehicle speed from a global positioning system (GPS), a received signal strength indication (RSSI) from each sensor unit <NUM>, and/or sensor ID information.

If the data <NUM> from the sensed parameters <NUM> indicates that a new trip by the vehicle <NUM> is in progress, the system <NUM> proceeds to an initial system diagnosis module <NUM>. If the data <NUM> from the sensed parameters <NUM> indicates that a new trip by the vehicle <NUM> is not in progress, an ongoing trip is in progress, and the data is reviewed to determine if new sensor ID detection has been completed <NUM>. If the new sensor ID detection has not been completed, the system <NUM> again proceeds to the initial system diagnosis module <NUM>. If the new sensor ID detection has been completed, the assessment module determines if auto-location for the current trip of the vehicle <NUM> has already been performed <NUM>. If auto-location for the current vehicle trip has already been performed, the system <NUM> proceeds to an auto-location assessment module <NUM>. If auto-location for the current vehicle trip has not been performed, the system proceeds to a location determination pre-assessment module <NUM>.

Referring to <FIG>, in the initial system diagnosis module <NUM>, a self-diagnosis of the system <NUM> is executed. As described in greater detail below, the system <NUM> is in communication with a cloud-based server <NUM>, which saves data from the system. The initial system diagnosis module <NUM> checks for sensor ID information <NUM> in the saved data. If no sensor ID information is present in the saved data, the module generates a message that sensor ID information is not available <NUM>. If sensor ID information is detected in the saved data, the system <NUM> proceeds to an identification review module <NUM>.

As shown in <FIG>, the identification review module <NUM> detects a new tire <NUM>. For the detection, the sensor ID information is reviewed for a predetermined period of time <NUM>. Within the predetermined period of time, the review module <NUM> receives additional data <NUM> to continue to review the sensor ID information. When the predetermined period of time has elapsed, the system <NUM> proceeds to the location determination pre-assessment module <NUM>. Also when the predetermined period of time has elapsed, the review module <NUM> determines if the sensor ID information matches previously received and stored sensor identification information <NUM> associated with the vehicle <NUM>.

If the current sensor ID information matches sensor ID information identified for the vehicle <NUM> by the identification review module <NUM> when a previous iteration of the system <NUM> was running, the review module <NUM> generates a message that no new sensor ID information was found <NUM>, as consistent sensor ID information corresponds to each tire <NUM> remaining in the same location on the vehicle from prior determinations. If the current sensor ID information does not match previously received and stored identification information, the review module <NUM> generates a message that auto location is being executed <NUM>, as replacement or repositioning of one or more tires <NUM> may have occurred. It is to be understood that the system <NUM> may execute auto-location when the current sensor ID information matches sensor ID information identified for the vehicle <NUM> by the identification review module <NUM> when a previous iteration of the system <NUM> was running, as tire repositioning or rotation on the vehicle may have occurred.

Turning to <FIG>, the location determination pre-assessment module <NUM> verifies if all sensed parameter signals <NUM> are available <NUM>. If the sensed parameter signals <NUM> are not available, the pre-assessment module <NUM> generates an error message that not all signals are available, so location cannot be performed <NUM>. If the sensed parameter signals <NUM> are available, the system <NUM> proceeds to a sensor ID monitoring module <NUM>.

As shown in <FIG>, the system <NUM> includes the sensor ID monitoring module <NUM>. The sensor ID monitoring module <NUM> compares <NUM> the most recently received sensor ID information with the sensor ID information from the identification review module <NUM> (<FIG>). If the most recently received sensor ID information and the sensor ID information from the identification review module <NUM> match, the sensor ID information is maintained <NUM>. If the most recently received sensor ID information and the sensor ID information from the identification review module <NUM> do not match, the most recently received sensor ID information is added to the stored data as described above, and the sensor ID information from the identification review module <NUM> that does not match the most recently received sensor ID information is removed or dropped <NUM>. After the sensor ID information is compared in the sensor ID monitoring module, the system <NUM> proceeds to a location determination module <NUM>.

Referring to <FIG>, the location determination module <NUM> executes a driving event classifier <NUM>. The driving event classifier <NUM> determines from the sensed parameters <NUM> and the output data <NUM>, such as the lateral acceleration of the vehicle <NUM>, the longitudinal acceleration of the vehicle, and the yaw rate of the vehicle, whether the vehicle is traveling straight and at a steady speed, referred to as cruising <NUM>. If the vehicle is traveling straight and at a steady speed, the data is labeled as cruising <NUM>, which enables the determination of a mean footprint length <NUM>. When the vehicle is cruising, the driving event classifier <NUM> checks whether a predetermined number of cruising events has been met <NUM>. If so, a mean footprint length <NUM> for each tire <NUM> is determined <NUM>. If the predetermined number of cruising events has not been met, the driving event classifier <NUM> waits for additional sensed parameters <NUM> to be received <NUM>.

If the vehicle is not traveling straight and at a steady speed, the driving event classifier <NUM> determines, based on the sensed parameters <NUM>, whether the vehicle <NUM> is accelerating <NUM>. If the vehicle <NUM> is accelerating, the sensed parameters <NUM> are designated as acceleration data <NUM>. The driving event classifier <NUM> then checks whether a predetermined number of acceleration events has been met <NUM>. If the predetermined number of acceleration events has not been met, the driving event classifier <NUM> waits for additional sensed parameters <NUM> to be received <NUM>. If the predetermined number of acceleration events has been met, the determined mean footprint length <NUM> is input into an acceleration-based auto-locator <NUM>.

In the acceleration-based auto-locator <NUM>, the front tire positions 12A and 12B are distinguished from the rear tire positions 12C and 12D. More particularly, when the vehicle <NUM> accelerates, there is typically a load transfer from the front tires 12A and 12B to the rear tires 12C and 12D. This load transfer results in a positive change or gain in the footprint length <NUM> for the rear tires 12C and 12D relative to the mean footprint length, and a negative change or reduction in the footprint length for the front tires 12A and 12B relative to the mean footprint length. This positive change in the footprint length <NUM> for the rear tires 12C and 12D and negative change in the footprint length for the front tires 12A and 12B enables the front tires to be distinguished from the rear tires. Once the front tires 12A and 12B are distinguished from the rear tires 12C and 12D, the relative front and rear positions are sent to an acceleration output block <NUM>.

If the vehicle <NUM> is not accelerating, the driving event classifier <NUM> determines, based on the sensed parameters <NUM>, whether the vehicle <NUM> is braking <NUM>. If the vehicle <NUM> is braking, the sensed parameters <NUM> are designated as braking data <NUM>. The driving event classifier <NUM> checks whether a predetermined number of braking events has been met <NUM>. If the predetermined number of braking events has not been met, the driving event classifier <NUM> waits for additional sensed parameters <NUM> to be received <NUM>. If the predetermined number of braking events has been met, the determined mean footprint length <NUM> is input into a braking-based auto-locator <NUM>.

In the braking-based auto-locator <NUM>, the front tire positions 12A and 12B are distinguished from the rear tire positions 12C and 12D. When the vehicle <NUM> brakes, there is typically a load transfer from the rear tires 12C and 12D to the front tires 12A and 12B. This load transfer results in a positive change or gain in the footprint length <NUM> for the front tires 12A and 12B relative to the mean footprint length, and a negative change or reduction in the footprint length for the rear tires 12C and 12D relative to the mean footprint length. This positive change in the footprint length <NUM> for the front tires 12A and 12B and negative change in the footprint length for the rear tires 12C and 12C enables the front tires to be distinguished from the rear tires. Once the front tires 12A and 12B are distinguished from the rear tires 12C and 12D, the relative front and rear positions are sent to a braking output block <NUM>.

If the vehicle <NUM> is not braking, the driving event classifier <NUM> determines, based on the sensed parameters <NUM>, whether the vehicle is executing a right turn <NUM>. If the vehicle <NUM> is executing a right turn, the sensed parameters <NUM> are designated as right turn data <NUM>. The driving event classifier <NUM> then checks whether a predetermined number of right turn events has been met <NUM>. If the predetermined number of right turn events has not been met, the driving event classifier <NUM> waits for additional sensed parameters <NUM> to be received <NUM>. If the predetermined number of right turn events has been met, the determined mean footprint length <NUM> is input into a right turn based auto-locator <NUM>.

In the right turn based auto-locator <NUM>, the left tire positions 12A and 12C are distinguished from the right tire positions 12B and 12D. More particularly, when the vehicle <NUM> executes a right turn, there is lateral load transfer from the inside or right side tires 12B and 12D to the outside or left side tires 12A and 12C. This load transfer results in a positive change or gain in the footprint length <NUM> for the left side tires 12A and 12C relative to the mean footprint length, and a negative change or reduction in the footprint length for right side tires 12B and 12D relative to the mean footprint length, which enables the left side tires to be distinguished from the right side tires.

In addition, during turning of the vehicle <NUM>, each outer wheel turns <NUM> slower than the inner wheel. The speed difference between the wheel revolution time <NUM> (TREV) for each tire <NUM> and the speed of the vehicle <NUM> is expected to be positive for the tires on the outer wheels <NUM> and negative for the tires on the inner wheels, further enabling the left side tires 12A and 12C to be distinguished from the right side tires 12B and 12D. Once the left side tires 12A and 12C are distinguished from the right side tires 12B and 12D, the relative left and right positions are sent to a right turn output block <NUM>.

If the vehicle <NUM> is not executing a right turn, the driving event classifier <NUM> determines, based on the sensed parameters <NUM>, whether the vehicle is executing a left turn <NUM>. If the vehicle <NUM> is executing a left turn, the sensed parameters <NUM> are designated as left turn data <NUM>. The driving event classifier <NUM> then checks whether a predetermined number of left turn events has been met <NUM>. If the predetermined number of left turn events has not been met, the driving event classifier <NUM> waits for additional sensed parameters <NUM> to be received <NUM>. If the predetermined number of left turn events has been met, the determined mean footprint length <NUM> is input into a left turn based auto-locator <NUM>.

In the left turn based auto-locator <NUM>, the left tire positions 12A and 12C are distinguished from the right tire positions 12B and 12D. When the vehicle <NUM> executes a left turn, there is lateral load transfer from the inside or left side tires 12A and 12C to the outside or right side tires 12B and 12D. This load transfer results in a positive change or gain in the footprint length <NUM> for the right side tires 12B and 12D relative to the mean footprint length, and a negative change or reduction in the footprint length for left side tires 12A and 12C relative to the mean footprint length, which enables the left side tires to be distinguished from the right side tires.

In addition, during turning, the speed difference between the wheel revolution time <NUM> (TREV) for each tire <NUM> and the speed of the vehicle <NUM> is expected to be positive for the tires on the outer wheels <NUM> and negative for the tires on the inner wheels, further enabling the left side tires 12A and 12C to be distinguished from the right side tires 12B and 12D. Once the left side tires 12A and 12C are distinguished from the right side tires 12B and 12D, the relative left and right positions are sent to a left turn output block <NUM>.

If the vehicle <NUM> is not executing a left turn, the driving event classifier <NUM> labels the sensed parameters <NUM> as a non-event <NUM>, and the data are not used as inputs for auto-location based on footprint length <NUM> and TREV <NUM> methodology.

Optionally, the driving event classifier <NUM> may include a received signal strength indicator (RSSI) auto-locator <NUM>. For example, when a vehicle-based processor or receiver is employed, it may be placed closer to the rear tires 12C and 12D than the front tires 12A and 12B. In such a case, the signal received from the sensor unit <NUM> in each of the rear tires 12C and 12D will be stronger than the strength of the signal received from the sensor unit in each of the front tires 12A and 12B, enabling the front tires to be distinguished from the rear tires. Once the front tires 12A and 12B are distinguished from the rear tires 12C and 12D, the relative front and rear positions are sent to an RSSI output block <NUM>.

The front tire position data 12A and 12B and the rear tire position data 12C and 12D from the acceleration output block <NUM>, the front tire position data and the rear tire position data from the braking output block <NUM>, the left side tire position data and the right side tire position data from the right turn output block <NUM>, the left side tire position data and the right side tire position data from the left turn output block <NUM>, and optionally, the front tire position data and the rear tire position data from the RSSI output block <NUM>, are sent to a combined auto-location mapping function <NUM>. The combined auto-location mapping function <NUM> executes a comparison between the data from all of the output blocks, isolating the front tires 12A and 12B from the rear tires 12C and 12D, and the left tires from the right tires. In this manner, the position of each respective front left tire 12A, front right tire 12B, rear left tire 12C and rear right tire 12D is identified.

The identification of the position of respective front left tire 12A, front right tire 12B, rear left tire 12C and rear right tire 12D locations is output from the combined auto-location mapping function <NUM> to an auto-location output block <NUM>. The output block <NUM> generates a message correlating each sensor unit <NUM>, and thus its sensed parameters, to a respective front left tire 12A, front right tire 12B, rear left tire 12C and rear right tire 12D location.

Returning to <FIG>, the identified location or positions of each sensor unit <NUM> and its respective tire 12A, 12B, 12C and 12D is transmitted from the output block <NUM> to a cloud-based server <NUM>. The cloud-based server <NUM> may be in electronic communication with control systems of the vehicle <NUM>, a fleet management device, or a vehicle operator device. In this manner, the parameters sensed by each sensor unit <NUM> may be correlated to each respective tire 12A, 12B, 12C and 12D for use in vehicle control systems, a fleet manager, and/or an operator of the vehicle <NUM>.

With reference to <FIG>, the auto-location assessment module <NUM> provides an analysis of historical data to ensure a satisfactory level of statistical confidence is achieved by
the system <NUM>. Location data as determined above, along with sensed parameter data <NUM>, is input from the cloud-based server <NUM> into the assessment module <NUM>. The assessment module <NUM> employs statistical tests to determine the level of statistical confidence reached by the system <NUM>. The statistical test that is employed is an inferential statistical analysis, which is referred to as a T-test.

For example, an acceleration T-test <NUM> employs the change in footprint length <NUM> as described above from the acceleration data <NUM> to compare footprint-length based position determinations <NUM> for the front left tire 12A versus the rear left tire 12C, the front left tire versus the rear right tire 12D, the front right tire 12B versus the rear left tire, and the front right tire versus the rear right tire. The T-test <NUM> outputs a confidence value or level <NUM>. The output confidence value <NUM> is compared to a predetermined threshold value <NUM>. If the confidence value <NUM> is less than the threshold, the assessment module <NUM> generates a message that the auto-location confidence threshold of the system <NUM> has been achieved <NUM>. If the confidence value <NUM> is not less than the threshold, the assessment module <NUM> generates a message that the auto-location confidence threshold of the system <NUM> has not been achieved <NUM>.

A braking-based T-test <NUM> employs the change in footprint length <NUM> as described above from the braking data <NUM> to compare footprint-length based position determinations <NUM> for the front left tire 12A versus the rear left tire 12C, the front left tire versus the rear right tire 12D, the front right tire 12B versus the rear left tire, and the front right tire versus the rear right tire. The T-test <NUM> outputs a confidence value or level <NUM>. The output confidence value <NUM> is compared to a predetermined threshold value <NUM>. If the confidence value <NUM> is less than the threshold, the assessment module <NUM> generates the message that the auto-location confidence threshold of the system <NUM> has been achieved <NUM>. If the confidence value <NUM> is not less than the threshold, the assessment module <NUM> generates the message that the auto-location confidence threshold of the system <NUM> has not been achieved <NUM>.

A right-turn based T-test <NUM> employs labeled data points from the right turn data <NUM> to compare right turn determinations <NUM>, including the change in footprint length <NUM> and the speed difference based determinations described above for the front left tire 12A versus the front right tire 12B and the rear left tire 12C versus the rear right tire 12D. The T-test <NUM> outputs a confidence value or level <NUM>. The output confidence value <NUM> is compared to a predetermined threshold value <NUM>. If the confidence value <NUM> is less than the threshold, the assessment module <NUM> generates the message that the auto-location confidence threshold of the system <NUM> has been achieved <NUM>. If the confidence value <NUM> is not less than the threshold, the assessment module <NUM> generates the message that the auto-location confidence threshold of the system <NUM> has not been achieved <NUM>.

A left-turn based T-test <NUM> employs labeled data points from the left turn data <NUM> to compare left turn determinations <NUM>, including the change in footprint length <NUM> and the speed difference based determinations described above for the front left tire 12A versus the front right tire 12B and the rear left tire 12C versus the rear right tire 12D. The T-test <NUM> outputs a confidence value or level <NUM>. The output confidence value <NUM> is compared to a predetermined threshold value <NUM>. If the confidence value <NUM> is less than the threshold, the assessment module <NUM> generates the message that the auto-location confidence threshold of the system <NUM> has been achieved <NUM>. If the confidence value <NUM> is not less than the threshold, the assessment module <NUM> generates the message that the auto-location confidence threshold of the system <NUM> has not been achieved <NUM>.

An RSSI-based T-test <NUM> employs the RSSI determinations <NUM> to compare position determinations for the front left tire 12A versus the rear left tire 12C, the front left tire versus the rear right tire 12D, the front right tire 12B versus the rear left tire, and the front right tire versus the rear right tire. The T-test <NUM> outputs a confidence value or level <NUM>. The output confidence value <NUM> is compared to a predetermined threshold value <NUM>. If the confidence value <NUM> is less than the threshold, the assessment module <NUM> generates the message that the auto-location confidence threshold of the system <NUM> has been achieved <NUM>. If the confidence value <NUM> is not less than the threshold, the assessment module <NUM> generates the message that the auto-location confidence threshold of the system <NUM> has not been achieved <NUM>.

In this manner, the auto-location system <NUM> of the present invention employs sensed parameters <NUM>, including the tire footprint length <NUM>, to identify and locate the position of each tire <NUM> on a vehicle <NUM>. As described above, the auto-location system <NUM> generates notifications when a newly mounted tire <NUM> on the vehicle <NUM> is detected, accompanied by the tire location or mounting position. The system <NUM> also generates notifications when a mounting position or location of a tire <NUM> has been changed, such as in a tire rotation procedure, accompanied by the new tire position or location. The system <NUM> provides economical and accurate identification of the location of each tire <NUM> on the vehicle <NUM> with self-diagnosis, and optionally includes an assessment module <NUM> that analyzes historical data to ensure a satisfactory level of statistical confidence is achieved by the system.

The present invention also includes a method for locating the position of a tire <NUM> on a vehicle <NUM>. The method includes steps in accordance with the description that is presented above and shown in <FIG>.

Claim 1:
An auto-location system for locating a position of a tire (<NUM>) supporting a vehicle (<NUM>), the system (<NUM>) comprising:
a sensor unit (<NUM>) being mounted on the tire (<NUM>), the sensor unit (<NUM>) including a footprint length measurement sensor to measure a length of a footprint of the tire;
a processor (<NUM>) in electronic communication with the sensor unit (<NUM>), the processor (<NUM>) being configured for receiving the measured footprint length;
a driving event classifier (<NUM>) for execution on the processor (<NUM>), the driving event classifier (<NUM>) operationally employing the measured footprint length to determine the position of the tire (<NUM>) on the vehicle (<NUM>); and
an auto-location output block (<NUM>, <NUM>, <NUM>, <NUM>, <NUM>) for executing on the processor (<NUM>), the auto-location output block being configured for receiving the determined position of the tire (<NUM>) on the vehicle (<NUM>) and for generating a message correlating the sensor unit (<NUM>) to the position of the tire (<NUM>) on the vehicle (<NUM>); characterized in that
the system further comprises at least one of an acceleration T-test (<NUM>) for employing acceleration data to compare footprint-length based position determinations, a braking-based T-test (<NUM>) for employing braking data to compare footprint-length based position determinations, a right-turn based T-test (<NUM>) for employing right turn data to compare right turn determinations, a left-turn based T-test (<NUM>) for employing left turn data to compare left turn determinations, and a received signal strength indicator based T-test for employing received signal strength indicators to compare position determinations.