AND DETECTING TIRE CHARACTERISTICS

PRIORITY

[0001] This application claims priority to and the benefit of U.S. Provisional Application No. 62/827,330, filed April 1, 2019, the disclosure of which is incorporated herein by reference in its entirety.

BACKGROUND

[0002] Modern technology has made it possible for automobile owners and operators to remain informed as to the current operational condition of their vehicle. A simple example of this is the tire pressure monitoring system (TPMS) common to most modem vehicles. Low tire pressure can decrease fuel efficiency, handling performance, and tread life, while simultaneously increasing braking distance. Consequently, automobile owners and operators benefit from the TPMS and other automated systems included in the makeup of modern vehicles that inform the user when certain aspects of the may require attention.

[0003] As is often the case, however, automobile owners and operators are not engaged sufficiently to monitor all operational aspects of their vehicle. Instead, skilled technicians, such as automobile mechanics, are regularly relied upon to identify and diagnose most vehicle irregularities. For instance, although traditional plunger- type tire tread gauges are relatively inexpensive, most automobile owners and operators will rely on skilled technicians at service stations, auto repair shops, or tire retail establishments to measure tread depth and assess the current tread condition. While skilled technicians often use the inexpensive tire tread gauges, more complex and accurate tire tread readers are also available, such as tire mats and laser readers. Such complex devices, however, are commonly used only for highly specific industrial cases, and are otherwise time and cost prohibitive for large-scale deployment.

[0004] What is needed is a platform or model that combines a system of physical and digital components configured to collect and analyze data on vehicles and tires, such as tread depth, and autonomously inform automobile owners and operators of the current condition of their vehicle.

BRIEF DESCRIPTION OF THE DRAWINGS

[0005] The following figures are included to illustrate certain aspects of the present disclosure, and should not be viewed as exclusive embodiments. The subject matter disclosed is capable of considerable modifications, alterations, combinations, and equivalents in form and function, without departing from the scope of this disclosure.

[0006] FIG. 1 is a schematic diagram of a high traffic location where a vehicle monitoring system according to one or more embodiments of the present disclosure may be employed. [0007] FIGS. 2A and 2B are top and cross-sectional side views of an example system for training a machine-learning model that may be used in accordance with the principles of the present disclosure

[0008] FIG. 3 A is an example 3D image of a tire obtained by the first sensors of FIGS. 2A-2B.

[0009] FIG. 3B is an example 2D image of a tire obtained by the second sensor of FIG. 2A- 2B.

[0010] FIGS. 4A-4B depict an example prediction of tire tread depth for an unknown tire.

[0011] FIGS. 5A-5B depict another example prediction of tire tread depth for an unknown tire.

[0012] FIG. 6 is an example method of identifying a vehicle and determining one or more tire characteristics of the vehicle, according to one or more embodiments.

[0013] FIG. 7 is a schematic diagram of the computer system of FIG. 1, according to one or more embodiments.

DETAILED DESCRIPTION

[0014] The present disclosure is related to vehicle maintenance and, more particularly, to systems that identify vehicles and detect one or more tire characteristics associated with the tires mounted to the vehicles.

[0015] Disclosed herein is a digital platform that combines a system of physical and digital components that collects and analyzes data on vehicles and tires. More specifically, the systems disclosed herein employ a plurality of sensors at high traffic locations, such as service stations, drive-through retail, and parking garages, to obtain a significant volume of vehicle and tire specific information. The systems employ unique vehicle identification techniques to accurately identify vehicles, and the obtained tire specific information may provide an owner or driver of the vehicle with valuable tire wear characteristics over time, which allows the vehicle owner to be proactive in maintaining tire performance and condition. In some cases, the tire specific information may be aggregated for use in tire research and design, or in conjunction with other data-based business models.

[0016] FIG. 1 is a schematic diagram of a high traffic location 100 where a vehicle monitoring system 102 according to one or more embodiments of the present disclosure may be employed. The high traffic location 100 may comprise any location accessible by a plurality of vehicles, either simultaneously or in succession. Example high traffic locations 100 include, but are not limited to, a service station (e.g., a gas or fueling station, a vehicle dealership service area, etc.), a drive- through retail establishment (e.g., fast food or coffee drive through lane, drive through bank or ATM, etc.), a parking garage, a roundabout, a freeway toll booth, a traffic junction, or any combination thereof. In the illustrated embodiment, the high traffic location 100 comprises a service station used primarily for fuel purchase. Accordingly, the high traffic location 100 will alternately be referred to herein as“the service station 100.” It will be appreciated, however, the service station 100 is merely one example of high traffic locations that may incorporate the principles of the present disclosure and, therefore, the following description of the service station 100 should not be considered particularly limiting to the scope of the present disclosure.

[0017] As illustrated, the service station 100 may include a service station building 104 and one or more fuel pumps, shown as fuel pumps 106a, 106b, 106c, and 106d capable of dispensing fuel simultaneously to different vehicles. In some embodiments, the service station 100 may further include an air compressor 108 that discharges compressed air to inflate automobile tires.

[0018] In the depicted view, a first vehicle 110a and a second vehicle 110b are present at the service station 100. The vehicles 110a, b may include any vehicle having two or more tires mounted thereto to facilitate mobility. Example vehicles 110a,b that may benefit from the vehicle monitoring system 102 include, but are not limited to, passenger cars, commuter cars, trucks, tractor- trailers, delivery vehicles, fleet vehicles, commercial vehicles, motorcycles, or any combination thereof. In at least one embodiment, the pumps 106a-d may include a display screen and one or more audio output devices (e.g., speakers) to communicate with a user (e.g., driver, owner, occupant, etc.) of the vehicles 110a,b. The vehicles 110a,b may enter the service station 100 via an entrance 112a, and depart the service station 100 via an exit 112b.

[0019] The vehicle monitoring system 102 may be configured to autonomously identify the vehicles 110a, b entering the service station 100 and detect one or more tire characteristics and wear patterns corresponding to the tires mounted to the vehicles 110a, b. To accomplish this, the vehicle monitoring system 102 may include a computer system 114 in communication with a plurality of physical and digital components that collect and analyze data corresponding to each vehicle 110a,b. As described in more detail below, the computer system 114 may include a computer-readable medium programmed with computer executable instructions that, when executed by a processor, performs tasks that help identify the vehicles 110a,b and detect and report various tire characteristics associated with the tires of the vehicles 110a,b.

[0020] The computer system 114 may communicate with the various physical and digital components of the vehicle monitoring system 102 via any wired or wireless telecommunications means. In at least one embodiment, for example, the computer system 114 may communicate with the physical and digital components over a wide area network, such as communicating with or through a local Wi-Fi router connected to the wide area network. In some embodiments, as illustrated, all or a portion of the computer system 114 may be located onsite and otherwise housed within the service station building 104. In other embodiments, however, the computer system 114 may be remotely located, without departing from the scope of the disclosure.

[0021] In some embodiments, the vehicle monitoring system 102 may autonomously identify the vehicles 110a,b upon entering or approaching the service station 100. In such embodiments, the vehicle monitoring system 102 may include one or more image capture devices, shown as a first image capture device 116a and a second image capture device 116b. In at least one embodiment, as illustrated, one or both of the image capture devices 116a,b may be arranged at or near the entrance 112a. In other embodiments, however, one or both of the image capture devices 116a, b may be arranged at or near the pumps 106a-d or another designated location at the service station 100, without departing from the scope of the disclosure.

[0022] The image capture devices 116a,b may each comprise a camera (e.g., a high-resolution camera) capable of capturing still images or video of the vehicles 110a, b or certain parts or sections of the vehicles 110a, b. In one embodiment, for example, the first image capture device 116a may be configured to capture one or more images of a license plate (front and/or back) pertaining to the vehicle 110a,b, and the second image capture device 116b may be configured to capture an image of the vehicle 110a,b from one or more angles. The resulting images derived from the image capture devices 116a,b may be transmitted to the computer system 114 for processing. It should be noted that in all of the embodiments described herein, the computer system 114 may be configured to facilitate cloud and/or edge computing capabilities when retrieving, processing, and/or transmitting data.

[0023] The computer system 114 may be configured to receive and process the image(s) of the license plate and determine the characters printed (displayed) thereon. This may be done, for example, using automatic number-plate recognition (ANPR) techniques that employ optical character recognition (OCR) to read vehicle registration plates. Once the license plate characters are identified, the computer system 114 may be programmed and otherwise configured to query a private or public database, such as an application programming interface (API), to obtain particulars corresponding to the vehicle 110a,b based on the deciphered (interpreted) license plate characters. Example vehicle particulars that may be obtained by the computer system 114 include, but are not limited to, year, make, model, color, and vehicle identification number (VIN) for the vehicle 110a,b.

[0024] In some embodiments, recall information associated with a particular vehicle 110a,b may also be obtained by the computer system 114 upon querying the database. Some API’s, for example, maintain outstanding recall notice information for vehicles based on VIN number or other vehicle particulars. Accordingly, upon matching the license plate of the vehicle 110a,b with corresponding vehicle particulars, the computer system 114 may be programmed or otherwise configured to determine if there are any outstanding recall notices for the particular vehicle 110a, b and push this information to the user (driver or owner) in a variety of forms, as discussed in more detail below.

[0025] In some embodiments, upon deciphering (interpreting) the license plate for the vehicle 110a,b, the computer system 114 may be programmed to query an internal (or external) database for a vehicle profile corresponding to or otherwise matching the license plate. The internal database may have stored therein a plurality of vehicle profiles associated with vehicles already known to the computer system 114. The vehicle profiles stored in the database may include vehicle information and user account information associated with the known vehicles. Example vehicle information includes, but is not limited to, license plate number, year, make, model, color, VIN, tires (e.g., make, model, photos, 3D model, tread depth, etc.), maintenance history, current estimated retail/resale value of vehicle, photos of the vehicle’s exterior, and mileage of the vehicle. Example user account information may include, but is not limited to, the name of the owner or driver, credit card or other payment information, user preferences, fuel preferences, email information, social network information, reward programs, phone numbers, home address, work address, and a record of computing devices associated with the user.

[0026] If a vehicle profile for the particular vehicle 110a, b has not been created, the computer system 114 may be programmed to autonomously generate a new vehicle profile for the particular vehicle 110a,b currently being monitored. If a vehicle profile does exist, however, the computer system 114 may be programmed to compare the vehicle information against the image(s) obtained by the second image capture device 116b of the vehicle 110a, b to determine if they align (match). More specifically, the image(s) obtained by the second image capture device 116b will provide the color and general make and model of the vehicle 110a,b, which the computer system 114 may be programmed to ascertain.

[0027] A confidence score for risk management purposes may be generated by the computer system 114 based on whether the image(s) obtained by the second image capture device 116b substantially match(es) the known color and general make and model of the vehicle 110a,b based on the vehicle profile. If the images match the particulars stored in the vehicle profile associated with the particular vehicle 110a, b, the computer system 114 may issue a high confidence score. If, however, the images fail to match the particulars stored in the vehicle profile, the computer system 114 may issue a low confidence score, which may be an indication of fraud (e.g., stolen license plates, etc.). As described in more detail below, a high or low confidence score may determine whether a user may be authorized to use additional benefits available at the service station 100, such as using automated or“frictionless” payment methods.

[0028] As mentioned above, the vehicle monitoring system 102 may also be configured to detect one or more characteristics of the tires associated with the vehicles 110a,b. Example tire characteristics that may be detected or otherwise ascertained include, but are not limited to, tire pressure, tread depth, uneven wear patterns, damage, photos of the tire, a 3D point cloud model of the tire, and the make and model of the tires.

[0029] In some embodiments, one or more tire characteristics may be detected or ascertained by driving the vehicle 110a, b over a tire mat 118. In the illustrated embodiment, the tire mat 118 is located at the entrance 112a to the service station 110, but could alternatively be placed adjacent one or more of the pumps 106a-d or at a separate area in the service station 100 designated for tire evaluation. As the vehicle 110a,b drives over the tire mat 118, sensor readings for each tire may be obtained. The tire mat 118 may include, for example, one or more three-dimensional (3D) cameras or laser scanners configured to detect the tread depth of the tires.

[0030] Sensor readings obtained by the tire mat 118 may be transmitted to the computer system 114 for processing. Alternatively, the sensor readings may be processed in an internal computer system included in the tire mat 118 and an output of the internal computer system may be transmitted to the computer system 114 or directly to the user of the vehicle 110a, b. Suitable tire mats that may be used in accordance with the principles of the present disclosure are described in U.S. Patent No. 5,557,268, entitled,“Automatic vehicle recognition and customer automobile diagnostic system,” and U.S. Patent No. 8,312,766, entitled“Method for ascertaining the pressure and the profile depth in a vehicle tire.”

[0031] In other embodiments, one or more tire characteristics for the vehicles 110a, b may be obtained using one or more sensors 120 positioned at or near each pump 106a-d. In the illustrated embodiment, the sensors 120 are arranged at the first pump 106a to detect tire characteristics of each tire of the first vehicle 110a (or any vehicle near the first pump 106a). Similar sensors, however, may be arranged at or near the other pumps 106b-d to detect tire characteristics of other vehicles, such as the second vehicle 110b at the third pump 106c. While six sensors 120 are illustrated in FIG. 1, more or less than six may be used. Moreover, while the sensors 120 are depicted generally arranged about the periphery of the vehicle 110a, one or more of the sensors 120 may be positioned beneath the vehicle 110a, without departing from the scope of the disclosure.

[0032] One or more of the sensors 120 may be arranged at or near ground level. In some embodiments, for example, one or more of the sensors 120 may be mounted in the ground so that they are substantially flush with the upper surface of the ground level (i.e., concrete, asphalt, etc.). In other embodiments, however, one or more of the sensors 120 may be mounted within a low- profile device (e.g., a speed bump or the like) mounted to the ground adjacent the pump 106a. Some of the sensors 120 may be generally directed toward the tires of the vehicle 110a to detect tire characteristics. Other sensors 120, however, may be directed toward the front and/or back of the vehicle 110a. In some embodiments, the sensors 120 directed toward the front and/or back of the vehicle 110a may be configured to detect tire characteristics, but may alternatively be configured to capture an image of the license plate (front and/or back) of the vehicle 110a. In such embodiments, the sensors 120 at the front/back of the vehicle 110a may comprise cameras that replace operation of the first image capture device 116a, as described above, or may otherwise provide a redundant feature to capture images of the license plate.

[0033] The sensors 120 may comprise a variety of sensing or imaging devices capable of obtaining tire characteristics. Example types of the sensors 120 include, but are not limited to, an image capture device (e.g., a 2D or 3D camera), a laser scanner, a structured light source (both visible and non- visible), a light detection and ranging (LIDAR) device, a thermal camera, an acoustic sensor, a software defined radio, other Radio Frequency (RF) devices, or any combination thereof.

[0034] In some embodiments, one or more of the sensors 120 may comprise an image capture device configured to help determine the make, model, and tread design of the tires. In such embodiments, the image capture device may be configured to capture an image of the tire identification number printed on the sidewall of each tire; e.g., the tire identification number required by the U.S. Department of Transportation (DOT). The captured image(s) may be transmitted to the computer system 114, which may employ optical character recognition (OCR) to interpret (decipher) the tire identification number. Once the tire identification number is ascertained, the computer system 114 may be programmed and otherwise configured to query a private or public database, such as an API, to obtain the make and model of the particular tire based on the deciphered tire identification number.

[0035] If the tire identification number is not ascertainable (e.g., the tire is soiled, the number is worn off, etc.), however, the make, model, and tread design of each tire may nonetheless be determined with the sensors 120. In such embodiments, an image of the tire tread (2D or 3D) may be captured using one or more of the sensors 120, and the image may be transmitted to the computer system 114 for processing. The computer system 114 may be programmed to compare the captured image of the tire tread against known tire tread images stored in an internal (or external) database. Upon matching the captured image with an image of a known tire tread, the make, model, and tread design of the particular tire may be ascertained. As described in more detail below, the computer system 114 may employ a model trained in machine learning to ascertain such tire characteristics.

[0036] In some embodiments, one or more of the tires mounted to the vehicle 110a as detected by the sensors 120 may not match the vehicle profile data for the vehicle 110a. More specifically, it is possible that the tires were changed since the last time the vehicle 110a was detected at the service station 100. In such embodiments, the vehicle profile for the vehicle 110a may be updated to reflect the newly mounted tires and/or other tire characteristics.

[0037] In some embodiments, the sensors 120 may further be capable of ascertaining (determining) the current (real-time) depth of the tread for each tire. In such embodiments, a two- dimensional (2D) image of the tire tread may be captured using one or more of the sensors 120 and transmitted to the computer system 114 for processing. The computer system 114 may compare the captured 2D image of the tire tread against numerous images of tire treads with known tread depths stored in an internal (or external) database. Based on a model trained using machine learning, as will be discussed below, the computer system 114 may match the captured image with an image of a tire tread having a known tread depth and assign the present tire with that known tread depth.

[0038] In some embodiments, the sensors 120 include a handheld form. The handheld sensor may compromise an image capture device attached wired or wirelessly to a mobile computer, such as a tablet, smartphone, or microprocessor. The image can be processed on that mobile computer and display results to the user on a screen. The results may be sent to computer system 114 or directly to a cloud/server for storage or further processing or reporting. This may be done wired or wirelessly. The handheld sensor may also send the image directly to computer system 114 for processing and reporting. The handheld sensor would be configured in such a way for easy transport around the station. The image capture device will be positioned to read the tire tread, sidewall, or other vehicle information (e.g. license plate, VIN number, visible anomalies). The handheld sensor can allow the end user to take notes on the mobile computer screen/keyboard to record observations of the tires and vehicle. All these data will be collected and aggregated as a report for the vehicle being observed. The mobile computer screen can also be used to display alerts or messages, such as advertising or directions on how to recall the reports. The handheld sensor will be an additional sensor to capture and provide tire/vehicle information as part of the system described herein.

[0039] Once the tread depth for one or more of the tires is ascertained, and if the vehicle profile for the vehicle 110a includes the tires currently being examined, the vehicle profile may then be updated with the current tread depth. As will be appreciated, this may prove advantageous in - Si - allowing a user to compare tire usage against previous wear data, determine tire wear progress, and identify uneven wear patterns.

[0040] In some embodiments, the vehicle monitoring system 102 may further be configured to detect and/or measure tire pressure in each of the tires in the vehicle 110a. To accomplish this, the vehicle monitoring system 102 may include a tire pressure monitoring module 122 that may communicate (wired or wirelessly) with the computer system 114. In some embodiments, as illustrated, the module 122 may be located at or near the service station building 104 (e.g., inside or on the roof), but may alternatively be located at another location at the service station 100, such as at the entrance 112a, without departing from the scope of the disclosure.

[0041] The module 122 may be configured to communicate with the tire pressure monitoring system (TPMS) included in most vehicles (e.g., vehicles 110a, b). The TPMS is a system designed to warn drivers when one or more tires are under-inflated, possibly creating unsafe driving conditions. When the TPMS sensors detect low-pressure conditions, a wireless signal is sent from the sensors to an onboard computer, which triggers illumination of a low tire pressure indicator on the dashboard instrument panel. The low tire pressure indicator is universally recognized as the yellow symbol in the shape of a tire cross-section (that resembles a horseshoe) with an exclamation point.

[0042] Per U.S. government regulations, the majority of TPMS sensors are activated with a low frequency (LF) signal at around 125 KHz, and the TPMS sensors transmit sensor signals via ultra-high frequency (UHF) signals ranging between about 315 MHz and about 433 MHz. The LF activation signal can vary from vehicle to vehicle (i.e., some require more power than others), but causes the TPMS sensors to transmit tire pressure data and communicate with the onboard computer over the UHF frequency. TPMS sensor signals can be detected and captured within a certain physical proximity and they can be triggered with suitable hardware devices.

[0043] According to embodiments of the present disclosure, the module 122 may include a transmitter 124a and a receiver 124b, each configured to operate based on radio frequency (RF) signals. The transmitter 124a may be configured to transmit LF activation signals at or around 125 KHz to trigger operation of the TPMS sensors in the vehicles 110a,b, and the receiver 124b may be configured to receive UHF sensor signals between about 315 MHz and about 433 MHz and transmitted by the TPMS sensors in the vehicles 110a,b. The sensor signals may be received at the module 122 and transmitted to the computer system 114 for processing and determining the real time air pressure in each tire. If any of the tires are below the manufacturer suggested limits, the computer system 114 may be configured to send out an alert or otherwise inform the user, as described in more detail below. [0044] In some embodiments, one or more of the tires on the vehicle 110a,b may include a radio frequency identification (RFID) chip coupled thereto. The RFID chip may be configured to transmit identifying information about the corresponding tire to the module 122, such as the make, model, and tread design of the tire. In such embodiments, the receiver 124b or a separate receiver (not shown) included in the module 122 may be configured to receive such signals from the RFID chips and transmit those signals to the computer system 114 for processing. Alternatively, the computer system 114 may include a receiver (not shown) configured to receive the signals directly from the RFID chip for processing. In such embodiments, one or more tire characteristics may be ascertained in real-time via the onboard RFID chip. In at least one embodiment, the onboard RFID chip may also be configured to provide other sensor data, e.g., temperature, pressure, etc.

[0045] In one or more embodiments, the vehicle monitoring system 102 may be configured to communicate with the user (owner or driver) of the vehicle 110a, b for a variety of purposes. The vehicle monitoring system 102 may communicate with the user via a variety of platforms including, but not limited to, a display screen at the pump 106a-d, a digital advertisement screen adjacent the pump 106a-d, speaker(s) at the pump 106a-d, a hardcopy printout received at the pump 106a-d, a smartphone or laptop (e.g., app notification, email, text, phone call recording, digital coupons, QR codes, etc.), a vehicle automation system, social media, a fleet management system, a mail parcel sent to the user’s home or work address, or any combination thereof.

[0046] In some embodiments, the communications provided by the vehicle monitoring system 102 to the user may comprise one or more alerts corresponding to the user’s vehicle 110a, b. More specifically, an alert may be sent if one or more tire characteristics, such as tire pressure, tread depth, or another tire characteristic, fall outside recommended ranges. The alert may provide, for example, the current air pressure and tread depth for each tire contrasted against the manufacturer recommended air pressure and tread depth. If needed, a recommendation may be provided to the user to update the tires and/or add additional air, such as at the onsite air compressor 108. In at least one embodiment, the alert may provide the user with statistical information, such that gas mileage could be improved by 5% if the tires are inflated to 35 psi, for example.

[0047] In some embodiments, the vehicle monitoring system 102 may be configured to automatically make an appointment for the user to purchase new tires or to have the alignment of the vehicle 110a, b adjusted. After checking vendor availability and the user’s calendar for a time and date that is mutually available, an appointment suggestion could be presented through the user’s smart phone, the pump 106a-d, or any of the aforementioned communication platforms. In at least one embodiment, previous maintenance records or actions can be analyzed to suggest a vendor for the user. For example, an appointment could be suggested for the user’s typical mechanic or dealer service station.

[0048] If no vehicle characteristics were measured as falling outside recommended ranges, then an alert may not be generated by the vehicle monitoring system 102. However, a communication may optionally be generated by the vehicle monitoring system 102 indicating that measurements have been taken and that the vehicle characteristics appear to be within optimal operating ranges. For example, specific tire pressures measured could be shared on the display screen at pump 106a,b indicating that the tire pressure is within recommended limits.

[0049] In some embodiments, the communications provided by the vehicle monitoring system 102 to the user may comprise recall information associated with the particular vehicle 110a,b. Consequently, the user may be informed as to whether the vehicle 110a,b requires manufacturer service to remedy a recall issue. In some embodiments, the vehicle monitoring system 102 may be configured to automatically make an appointment with a preferred mechanic or dealership service station to remedy the recall issue for the vehicle 110a, b.

[0050] In some embodiments, the communications provided by the vehicle monitoring system 102 to the user may comprise one or more advertisements, offers, and/or personal information tailored to the user of the vehicle 110a,b and based on the current status of the vehicle 110a,b. Advertisements and offers, for example, may be triggered based on the measurements taken by the vehicle monitoring system 102. Low tire tread depth, for instance, may trigger advertisements and/or discount coupons for tires from local vendors. Personal information provided by the vehicle monitoring system 102 may include the results of the testing for the user’s general information.

[0051] In some embodiments, the vehicle monitoring system 102 may communicate with and/or support one or more online applications, such as SPEEDPASS®, SPEEDPASS+®, or any other rewards or general apps available from ExxonMobil of Houston, Texas, USA or any of its affiliates to provide additional information that was collected and function as a mechanism to further engage the user.

[0052] In some embodiments, the vehicle monitoring system 102 may be configured to generate one or more reports that may be available to third parties for data harvesting purposes. Aggregated tire data, for example, may be generated and shared with tire manufacturers, auto manufacturers, auto service stations, tire retailers, commercial data aggregators, the Department of Transportation (DOT) and other organizations for tire research and development (R&D) as well as commercial opportunities. Such reports may include, but are not limited to information and analytics which show how tires are performing and wearing in various climates and seasons, differences between tire wear on different vehicles, average mileage and wear of a tire on different vehicles, the most common tire replacement(s) for a particular tire/vehicle, the average tread depth on tires before they get replaced and how it differs on various vehicle make/models, etc. These reports may be made available on a per request basis or through an automated reporting system through APIs.

[0053] In some embodiments, the vehicle monitoring system 102 may facilitate automatic payment at the pump 106a-d, alternately referred to as “frictionless” vehicle payment. As discussed above, based on whether the physical characteristics of the particular vehicle 110a,b currently at the service station 100 match a vehicle profile saved in a database accessible by the computer system 114, a confidence score may be assigned to the vehicle 110a,b. In some embodiments, the confidence score may be calculated, at least in part, by using a machine-learning model. If the calculated confidence score is at or above a predetermined limit, and the user of the vehicle 110a,b is enrolled in a pre-registered automatic payment program or process (e.g., credit card, debit card, electronic fund transfer, etc.), the vehicle monitoring system 102 may authorize automatic payment by the user. If, however, the calculated confidence score is below a predetermined limit, the vehicle monitoring system 102 may prevent automatic payment by the user, who may instead pay for fuel or other purchases manually or by alternative means.

[0054] In some embodiments, the confidence score assigned to the particular vehicle 110a,b may be tailored to geography and/or locale where the vehicle 110a,b is currently located. For example, in locations where there might be a greater likelihood of fraud and/or crime, the computer system 114 may be programmed to increase the scrutiny in assigning a confidence score. This feature may be especially useful at retail drive-through lanes, where the vehicle monitoring system 102 may be able to autonomously recognize the vehicle 110a,b and no credit card information or cash need be exchanged to receive goods.

Machine-Learning Model

[0055] The computer system 114 may at least partially operate based on machine learning to ascertain various tire characteristics, such as tread design (pattern) and tread depth. More specifically, the computer system 114 may include a model trained through multi-class classification machine learning. In multi-class classification, image classifiers use details such as feature and edge detection at different layers of a neural network to identify the difference between different tread designs and tread depths (levels). Once the model is adequately (sufficiently) trained through machine learning, the computer system 114 is able to quickly and efficiently predict tread depth and design using the trained model. The trained model may be used in conjunction with databases accessible by the computer system 114. [0056] FIGS. 2A and 2B are top and cross-sectional side views of an example system 200 for training a machine-learning model that may be used in accordance with the principles of the present disclosure. As illustrated, the system 200 includes one or more first sensors 202 mounted to a structure 204 and one or more second sensors 206 positioned adjacent the one or more first sensors 202. The structure 204 may comprise any rigid structure capable of withstanding vertical and lateral loading applied by a tire traversing the structure 204 in the direction A. In one embodiment, for instance, the structure 204 may be a speed barrier device (e.g., a speed bump) or the like. In other embodiments, the structure 204 may comprise the ground and the sensors 202 may be mounted within the ground.

[0057] Each of the sensors 202, 206 may be configured to obtain data corresponding to a tire as the tire traverses (rolls over) the structure 204 in the direction A. Data obtained by the sensors 202, 206 may be cooperatively used to help train the machine-learning model for use in the computer system 114 of FIG. 1 to predict tread design and/or tread depth of unknown tires; e.g., the tires mounted to the vehicles 110a, b of FIG. 1.

[0058] The first sensors 202 mounted to the structure 204 may comprise any device or system capable of producing a three-dimensional (3D) image from which tread depth and/or tread design may be ascertained. Examples of the first sensors 202 include, but are not limited to, a 3D camera, a laser scanner, a structured light source, a light detection and ranging (LIDAR) device, a tread depth gauge, or any combination thereof. In some embodiments, one or more of the sensors 202 may be different from the other sensors 202. Moreover, while three sensors 202 are depicted in FIGS. 2A-2B, more or less than three sensors 202 may be employed, without departing from the scope of the disclosure.

[0059] The first sensors 202 may be placed either horizontally (i.e., landscape shot) or vertically (i.e., portrait shot) within the structure 204, depending on space constraints and/or allowances. As a tire traverses the structure 204, the first sensors 202 capture a 3D point cloud of the tire, from which tire characteristics such as tread depth and tread design may be ascertained. In some embodiments, one or more of the first sensors 202 may be configured to capture images at 30 frames per second (fps). In other embodiments, however, one or more of the first sensors 202 may be operable to capture images at elevated frame rates, such as at 60 fps or 90 fps, which may allow the first sensors 202 to capture images of tires traversing the structure 204 at up to 60 miles per hour.

[0060] FIG. 3A is an example 3D image 302a of a tire obtained by the first sensors 202 of FIGS. 2A-2B. Tread data may be ascertained and collected based on the 3D image 302a. In some embodiments, tread depth of the 3D image may be obtained in x/32 of an inch (or other units). [0061] Referring again to FIGS. 2A-2B, the second sensor(s) 206 adjacent the structure 204 may comprise a traditional two-dimensional (2D) camera. As the tire traverses the structure 204 during data gathering, the second sensor 206 may be configured to capture a standard close up image of the tire tread.

[0062] FIG. 3B is an example 2D image 302b of the tire tread obtained by the second sensor 206 of FIGS. 2A-2B. The 2D image 302b depicts a particular tread design, which may be known and classified in a database accessible by the computer system 114. The 2D image 302b may be used in conjunction with the 3D image 302a to train the model in multi-class image classifying for use by the computer system 114 (FIG. 1). More specifically, the tread depth and design of the tire may be ascertained based on the 3D image 302a (FIG. 3A), and the 2D image 302b constitutes a real-life image of the tread depth and design. These images 302a, b may be paired and saved in the database for access by the computer system 114 during operation of the vehicle monitoring system 102 of FIG. 1.

[0063] In an example application, the 3D image 302a depicts a tire having a known tread depth of 8/32”, for example, and the 2D image provides a standard 2D image of that tire exhibiting the known tread depth and design. This data may be paired and stored in the database for reference by the computer system 114 (FIG. 1) in predicting tread depth and/or tread design of unknown tires. The system 200 (FIGS. 2A-2B) may be used to obtain additional 3D and 2D images of numerous other tires, thus populating the database with several different data pairings of various tires having known tread depths and known tread designs. The model is effectively trained by storing these numerous examples of known tread depths and/or known tread designs in the database. In some embodiments, the database may be populated with more than 1,000 data pairings of 3D and 2D images to adequately train the model. In other embodiments, however, the database may be populated with more than 10,000 data pairings, more than 100,000 data pairings, or more than 1,000,000 data pairings to adequately train the model.

[0064] As will be appreciated, the more data pairings used to train the model will result in more accurate predictions of tread depth and/or design of unknown tires provided by the model. More specifically, as additional tire treads and data pairings are added to the database, the mathematical weighting of the algorithm applied by the model increases, thus making the model more accurate.

[0065] The 2D images may be taken of different tires with different tread designs, in different light, and at different angles to ensure the database has a robust library of data pairings to search. For each data pairing included in the database, the tread depth and tread design is known and searchable by the computer system 114 (FIG. 1) in predicting the tread depth and/or tread design of unknown tires, such as the tire mounted to the vehicles 110a, b in FIG. 1. Accordingly, once the database has been populated with sufficient data pairings, and the machine-learning model has thus been trained to a sufficient degree of accuracy, the model may be used by the computer system 114 to predict tread depth and design for unknown tires. When a 2D image of an unknown tire is obtained, such as by one of the sensors 120 of FIG. 1, the computer system 114 may be programmed to query the model for a prediction of the unknown tire. When the matching data pair is identified, one or both of the tire tread depth and tread design of the unknown tire may be ascertained to a high degree of accuracy.

[0066] In some embodiments, the model may be trained by grouping data pairings of known tire tread depths into a plurality of tread depth categories, such as Very Low = 0/32” to 2/32” of tread depth, Low = 3/32” to 4/32” of tread depth, Medium = 5/32” to 6/32” of tread depth, High 7/32” to 8/32” of tread depth, and New 9/32” to 10/32” of tread depth. In such embodiments, the computer system 114 (FIG. 1) may be configured to use the model for prediction using a 2D image of an unknown tread with one of these categories.

[0067] FIGS. 4A-4B depict an example prediction of tire tread depth for an unknown tire. More specifically, FIG. 4A is a 2D image 402 of the tread of an unknown tire 404, and FIG. 4B is the resulting prediction 406 generated by the computer system 114 (FIG. 1) and based on the trained model. The computer system 114 may be configured to match the 2D image 402 with one or more data pairings of known tire tread depths and thereby provide a prediction of the tread depth of the unknown tire 404. As shown in FIG. 4B, the prediction 406 shows an approximately 98% chance that the tread depth of the unknown tire 404 is Low, which equates to 3/32” to 4/32” of tread depth. The trained model has been tested with a greater than 80% accuracy rate.

[0068] FIGS. 5A-5B depict another example prediction of tire tread depth for an unknown tire. More specifically, FIG. 5A is a 2D image 502 of the tread of an unknown tire 504, and FIG. 5B is the resulting prediction 506 generated by the computer system 114 (FIG. 1) and based on the trained model. To obtain the prediction 506, the computer system 114 queries the trained model to get the most likely tread depth. As shown in FIG. 5B, the prediction 506 shows an approximately 58% chance that the tread depth of the unknown tire 504 is Very Low, which equates to 0/32” to 2/32” of tread depth, but further shows an approximately 41% chance that the tread depth of the unknown tire 504 is Low, which equates to 3/32” to 4/32” of tread depth. Hybrid predictions, where two or more tread depths are predicted with a high level of confidence, may be an indication of uneven wear on the unknown tire 504, which is evident from the 2D image 502. Hybrid predictions may also be used to infer issues such as under inflation or over inflation of the unknown tire 504, or wheel alignment issues. [0069] In some embodiments, the trained model may be programmed into and otherwise included in a smartphone app downloadable by a user on a personal smartphone or tablet. In such embodiments, the user may be able to take a picture of the tire tread for an unknown tire and the model may be used to predict the tire tread depth. This allows for a user of the app to predict their tire tread without having a tire gauge or any other specialized equipment other than their smartphone (and its camera). This functionality may be included in mobile apps provided by ExxonMobil or its affiliates.

[0070] FIG. 6 is an example method 600 of identifying a vehicle and determining one or more tire characteristics of the vehicle, according to one or more embodiments. The method 600 may be best understood with reference to FIG. 1, where like numerals refer to like system components not described in detail again. A vehicle 110a,b may drive into a high traffic location, such as the service station 100, as at 602. Upon entering the service station 100, one or more images of the vehicle 110a,b may be captured, as at 604. More specifically, one or more images of the vehicle 110a,b and one or more images of the license plate of the vehicle 110a,b may be captured. The vehicle 110a, b may then be identified based on the license plate, as at 606. More specifically, the image of the license plate may be processed by the computer system 114 to decipher the characters, which may then be used by the computer system 114 to query a database and determine one or more of the year, make, model, color, and VIN for the vehicle 110a,b. In some embodiments, recall information associated with the vehicle 110a,b may also be obtained using the license plate characters.

[0071] The vehicle 110a,b may then be matched to a vehicle profile, as at 608. More specifically, the computer system 114 may be programmed to query an internal (or external) database for a vehicle profile corresponding to or otherwise matching the license plate. Alternatively, if a vehicle profile for the vehicle 110a,b does not exist, a new vehicle profile for the vehicle 110a, b may be generated, as at 610. The vehicle profile may be then be updated, as at 612, to include any new information or data.

[0072] In some embodiments, the method 600 may further include obtaining one or more two- dimensional (2D) images of a tire of the vehicle 110a, b, as at 614. In some embodiments, the 2D image of the tire may be used to determine the make and/or model of the tire, as at 616. In such embodiments, the 2D image of the tire may encompass the identification number printed on the sidewall of the tire, and the computer system 114 may interpret (decipher) the tire identification number and query a private or public database to obtain the make and model of the particular tire based on the deciphered tire identification number. [0073] In other embodiments, the 2D image of the tire may be used to determine the current tire tread depth, as at 618. As described above, this may be accomplished by comparing the captured 2D image of the tire tread against numerous images of tire treads with known tread depths stored in an internal (or external) database. Based on a machine-learning model, the computer system 114 may match the captured image with an image of a tire tread having a known tread depth and assign the tire with that known tread depth. Once the tread depth for the tire is ascertained, and if a valid vehicle profile exists, the vehicle profile may then be updated with the updated tread depth, as at 612. This may prove advantageous in providing a comparison against previous tread depth, thus updating the user as to the tire wear progress and identifying uneven wear patterns, if any.

[0074] The method 600 may further include sending an alert (communication) to the user regarding the vehicle 110a, b, as at 620. In some embodiments, the alert may inform the user if one or more tire characteristics, such as tire pressure or tread depth, fall outside recommended ranges. In other embodiments, or in addition thereto, the alert may inform the user of any recall information associated with the vehicle 110a, b. In yet other embodiments, the alert may provide the user with one or more advertisements or offers tailored to the user and based on the current status of the user’s vehicle 110a,b.

[0075] The method may further include calculating a confidence score assigned to the vehicle, as at 622. The confidence score may be assigned to the vehicle 110a,b based, at least in part, on whether the physical characteristics of the vehicle 110a, b (e.g., the color, make, model, etc.) match the data pertaining to the vehicle 110a, b stored in the vehicle profile. If the confidence score is at or above a predetermined limit, automatic payment by the user may be authorized, as at 624. However, if the confidence score is below a predetermined limit, automatic payment by the user may be prevented.

[0076] FIG. 7 is a schematic diagram of the computer system 114 of FIG. 1, according to one or more embodiments. The computer system 114 is but one example of a suitable computing environment and is not intended to suggest any limitation as to the scope of use of the technology described herein. Neither should the computer system 114 be interpreted as having any dependency or requirement relating to any one or combination of components illustrated.

[0077] The technology described herein may be described in the general context of computer code or machine-useable instructions, including computer-executable instructions such as program components, being executed by a computer or other machine, such as a personal data assistant or other handheld device. Generally, program components, including routines, programs, objects, components, data structures, and the like, refer to code that performs particular tasks or implements particular abstract data types. The technology described herein may be practiced in a variety of system configurations, including handheld devices, consumer electronics, general-purpose computers, specialty computing devices, etc. Aspects of the technology described herein may also be practiced in distributed computing environments where tasks are performed by remote processing devices that are linked through a communications network.

[0078] As illustrated, the computer system 114 includes a bus 702 that directly or indirectly couples the following devices: a memory 704, one or more processors 706, one or more presentation components 708, input/output (I/O) ports 710, I/O components 712, a power supply 714, and a radio 716. The bus 702 represents what may be one or more busses (such as an address bus, data bus, or a combination thereof). Although the various blocks of FIG. 7 are shown with lines for the sake of clarity, in reality, delineating various components is not so clear, and metaphorically, the lines would more accurately be grey and fuzzy. For example, one may consider a presentation component such as a display device to be an I/O component. In addition, processors have memory. The inventors hereof recognize that such is the nature of the art and reiterate that the diagram of FIG. 7 is merely illustrative of an exemplary computing device that can be used in connection with one or more aspects of the technology described herein. Distinction is not made between such categories as“workstation,”“server,”“laptop,”“handheld device,” etc., as all are contemplated within the scope of FIG. 7 and refer to “computer,” “computing device,” or “computer system.”

[0079] The computer system 114 typically includes a variety of computer-readable media. Computer-readable media can be any available media that can be accessed by the computer system 114 and includes both volatile and nonvolatile, removable and non-removable media. By way of example, and not limitation, computer-readable media may comprise computer storage media and communication media. Computer storage media includes both volatile and nonvolatile, removable and non-removable media implemented in any method or technology for storage of information such as computer-readable instructions, data structures, program modules, or other data.

[0080] Computer storage media includes RAM, ROM, EEPROM, flash memory or other memory technology, CD-ROM, digital versatile disks (DVD) or other optical disk storage, magnetic cassettes, magnetic tape, magnetic disk storage or other magnetic storage devices. Computer storage media does not comprise a propagated data signal.

[0081] Communication media typically embodies computer-readable instructions, data structures, program modules, or other data in a modulated data signal such as a carrier wave or other transport mechanism and includes any information delivery media. The term“modulated data signal” means a signal that has one or more of its characteristics set or changed in such a manner as to encode information in the signal. By way of example, and not limitation, communication media includes wired media such as a wired network or direct-wired connection, and wireless media such as acoustic, RF, infrared, and other wireless media. Combinations of any of the above should also be included within the scope of computer-readable media.

[0082] The memory 704 includes computer storage media in the form of volatile and/or nonvolatile memory. The memory 704 may be removable, non-removable, or a combination thereof. Exemplary memory includes solid-state memory, hard drives, optical-disc drives, etc. The processors 706 may be configured to read data from various entities such as the bus 702, the memory 704, or the I/O components 712. The presentation component(s) 708 present data indications to a user or other device. Exemplary presentation components 708 include a display device, speaker, printing component, vibrating component, etc. The I/O ports 710 allow the computer system 114 to be logically coupled to other devices, including the I/O components 712, some of which may be built in.

[0083] Illustrative I/O components include a microphone, joystick, game pad, satellite dish, scanner, printer, display device, wireless device, a controller (such as a stylus, a keyboard, and a mouse), a natural user interface (NUI), and the like. In some aspects, a pen digitizer (not shown) and accompanying input instrument (also not shown but which may include, by way of example only, a pen or a stylus) are provided in order to digitally capture freehand user input. The connection between the pen digitizer and the processor(s) 706 may be direct or via a coupling utilizing a serial port, parallel port, and/or other interface and/or system bus known in the art. Furthermore, the digitizer input component may be a component separated from an output component such as a display device, or in some aspects, the useable input area of a digitizer may coexist with the display area of a display device, be integrated with the display device, or may exist as a separate device overlaying or otherwise appended to a display device. Any and all such variations, and any combination thereof, are contemplated to be within the scope of aspects of the technology described herein.

[0084] An NUI processes air gestures, voice, or other physiological inputs generated by a user. Appropriate NUI inputs may be interpreted as ink strokes for presentation in association with the computer system 114. These requests may be transmitted to the appropriate network element for further processing. An NUI implements any combination of speech recognition, touch and stylus recognition, facial recognition, biometric recognition, gesture recognition both on screen and adjacent to the screen, air gestures, head and eye tracking, and touch recognition associated with displays on the computer system 114. The radio 716 transmits and receives radio communications. [0085] The computing device may be a wireless terminal adapted to receive communications and media over various wireless networks. The computer system 114 may communicate via wireless protocols, such as code division multiple access (“CDMA”), global system for mobiles (“GSM”), or time division multiple access (“TDMA”), as well as others, to communicate with other devices. The radio communications may be a short-range connection, a long-range connection, or a combination of both a short-range and a long-range wireless telecommunications connection. When we refer to“short” and“long” types of connections, we do not mean to refer to the spatial relation between two devices. Instead, we are generally referring to short range and long range as different categories, or types, of connections (i.e., a primary connection and a secondary connection). A short-range connection may include a Wi-Fi® connection to a device (e.g., mobile hotspot) that provides access to a wireless communications network, such as a WLAN connection using the 802.11 protocol. A Bluetooth connection to another computing device is a second example of a short-range connection. A long-range connection may include a connection using one or more of CDMA, GPRS, GSM, TDMA, and 802.16 protocols.

[0086] Embodiments disclosed herein include:

[0087] A. A method that includes capturing an image of a license plate of a vehicle and transmitting the image of the license plate to a computer system, identifying the vehicle with the computer system based on characters deciphered from the license plate, locating with the computer system a vehicle profile for the vehicle corresponding to the characters of the license plate and thereby identifying a user of the vehicle, detecting one or more tire characteristics of a tire mounted to the vehicle, and sending with the computer system a communication to the user based on the one or more tire characteristics.

[0088] B. A vehicle monitoring system that includes a computer system, one or more image capture devices in communication with the computer system and operable to capture an image of a license plate of a vehicle and transmit the image of the license plate to the computer system, and one or more sensors in communication with the computer system and operable to detect one or more tire characteristics of a tire mounted and transmit the image of the one or more tire characteristics to the computer system, wherein the computer system includes a computer-readable medium programmed with computer executable instructions that, when executed by a processor, performs the steps of identifying the vehicle by deciphering characters of the license plate, locating a vehicle profile for the vehicle corresponding to the characters of the license plate and thereby identifying a user of the vehicle, and sending a communication to the user based on the one or more tire characteristics. [0089] C. A method that includes capturing a two-dimensional image of an unknown tire tread for a tire mounted to a vehicle and transmitting the two-dimensional image of the unknown tire tread to a computer system, using a machine learning model to compare the two-dimensional image of the unknown tire tread against a plurality of two-dimensional images of known tire treads, wherein each known tire tread has a known tread depth, and predicting a tread depth of the unknown tire tread by matching the two-dimensional image of the unknown tire tread to one or more of the plurality of two-dimensional images of the known tire treads.

[0090] Each of embodiments A, B, and C may have one or more of the following additional elements in any combination: Element 1: wherein identifying the vehicle based on the characters deciphered from the license plate comprises querying a database with the computer system based on the characters of the license plate to obtain at least one of a year, a make, a model, a color, and a vehicle identification number for the vehicle. Element 2: further comprising querying a database with the computer system based on the characters of the license plate to obtain recall information associated with the vehicle, and sending an alert to the user regarding the recall information. Element 3: wherein the vehicle profile includes vehicle information selected from the group consisting of make, model, and color of the vehicle, and wherein identifying the vehicle further comprises capturing an image of the vehicle, comparing the image of the vehicle with the vehicle information, and generating a confidence score for the vehicle with the computer system based on comparison of the image of the vehicle with the vehicle information. Element 4: further comprising authorizing automated payment for the user when the confidence score is at or above a predetermined limit. Element 5: further comprising denying automated payment for the user when the confidence score is below a predetermined limit. Element 6: further comprising updating the vehicle profile for the vehicle based on the one or more tire characteristics. Element 7: wherein detecting the one or more tire characteristics comprises driving the vehicle over a tire mat including one or more sensors and thereby obtaining one or more sensor readings corresponding to the tire, and processing the one or more sensor readings to obtain the one or more tire characteristics. Element 8: wherein the one or more tire characteristics includes tread depth, and detecting the one or more tire characteristics comprises capturing a two-dimensional image of a tire tread of the tire and transmitting the two-dimensional image of the tire tread to the computer system, using a machine learning model to compare the two-dimensional image of the tread against a plurality of two-dimensional images of tire treads having known tread depths, and predicting a tread depth of the tire tread by matching the two-dimensional image of the tire tread of the tire to one or more of the plurality of two-dimensional images of the tire treads having known tread depths. Element 9: wherein detecting the one or more tire characteristics comprises capturing an image of a tire identification number printed on a sidewall of the tire, deciphering characters of the tire identification number with the computer system, and querying a database with the computer system to determine at least one of a make and a model of the tire based on the tire identification number. Element 10: wherein the one or more tire characteristics includes tire pressure, and detecting the one or more tire characteristics comprises sending a signal with a transmitter included in a tire pressure monitoring module in communication with the computer system, receiving the signal with a tire pressure monitoring system (TPMS) sensor included in the tire and thereby triggering activation of the TPMS sensor, sending a sensor signal with the TPMS sensor corresponding to a current tire pressure of the tire, receiving the sensor signal with a receiver included in the tire pressure monitoring module, and determining the current tire pressure of the tire with the computer system based on the sensor signal. Element 11: wherein sending the communication to the user comprises sending the communication via a platform selected from the group consisting of a display screen at a fuel pump, a digital advertisement screen, a speaker, a hardcopy printout, a smartphone, a laptop, a vehicle automation system, a fleet management system, a mail parcel, and any combination thereof. Element 12: wherein sending the communication to the user based on the one or more tire characteristics comprises sending an alert that at least one of tire pressure and tread depth is outside a recommended range. Element 13: wherein sending the communication to the user based on the one or more tire characteristics comprises sending at least one of an advertisement and an offer tailored to the user and based on the one or more tire characteristics.

[0091] Element 14: wherein the vehicle is located at a high traffic location selected from the group selected from a service station, a drive-through retail establishment, a drive-through ATM, a parking garage, a roundabout, a freeway toll booth, a traffic junction, and any combination thereof. Element 15: wherein the one or more sensors are selected from the group consisting of an image capture device, a laser scanner, a structured light source, a light detection and ranging sensor, a thermal camera, an acoustic sensor, and any combination thereof. Element 16: further comprising a tire pressure monitoring module in communication with the computer system and including a transmitter operable to transmit a signal receivable by a tire pressure monitoring system (TPMS) sensor included in the tire, and a receiver included in the tire pressure monitoring module to receive sensor signals transmitted by the TPMS sensor upon being activated by the signal transmitted by the transmitter, wherein the computer system determines a current tire pressure of the tire based on the sensor signal.

[0092] Element 17: further comprising capturing an image of a tire identification number printed on a sidewall of the unknown tire, deciphering characters of the tire identification number with the computer system, and querying a database with the computer system to determine at least one of a make and a model of the tire based on the tire identification number.

[0093] By way of non-limiting example, exemplary combinations applicable to A, B, and C include: Element 1 with Element 2; Element 3 with Element 4; Element 3 with Element 5; Element 8 with Element 9; Element 8 with Element 10; Element 8 with Element 11; Element 11 with Element 12; Element 11 with Element 13; Element 14 with Element 15; and Element 14 with Element 16.

[0094] Therefore, the disclosed systems and methods are well adapted to attain the ends and advantages mentioned as well as those that are inherent therein. The particular embodiments disclosed above are illustrative only, as the teachings of the present disclosure may be modified and practiced in different but equivalent manners apparent to those skilled in the art having the benefit of the teachings herein. Furthermore, no limitations are intended to the details of construction or design herein shown, other than as described in the claims below. It is therefore evident that the particular illustrative embodiments disclosed above may be altered, combined, or modified and all such variations are considered within the scope of the present disclosure. The systems and methods illustratively disclosed herein may suitably be practiced in the absence of any element that is not specifically disclosed herein and/or any optional element disclosed herein. While compositions and methods are described in terms of “comprising,” “containing,” or “including” various components or steps, the compositions and methods can also “consist essentially of’ or“consist of’ the various components and steps. All numbers and ranges disclosed above may vary by some amount. Whenever a numerical range with a lower limit and an upper limit is disclosed, any number and any included range falling within the range is specifically disclosed. In particular, every range of values (of the form,“from about a to about b,” or, equivalently,“from approximately a to b,” or, equivalently,“from approximately a-b”) disclosed herein is to be understood to set forth every number and range encompassed within the broader range of values. Also, the terms in the claims have their plain, ordinary meaning unless otherwise explicitly and clearly defined by the patentee. Moreover, the indefinite articles“a” or“an,” as used in the claims, are defined herein to mean one or more than one of the elements that it introduces. If there is any conflict in the usages of a word or term in this specification and one or more patent or other documents that may be incorporated herein by reference, the definitions that are consistent with this specification should be adopted.

[0095] As used herein, the phrase“at least one of’ preceding a series of items, with the terms “and” or“or” to separate any of the items, modifies the list as a whole, rather than each member of the list (i.e., each item). The phrase“at least one of’ allows a meaning that includes at least one of any one of the items, and/or at least one of any combination of the items, and/or at least one of each of the items. By way of example, the phrases“at least one of A, B, and C” or“at least one of A, B, or C” each refer to only A, only B, or only C; any combination of A, B, and C; and/or at least one of each of A, B, and C.