System and method for tread depth reader with modular sensor unit

Abstract

Various systems and methods for measuring the tread depth of a tire supported by a vehicle are disclosed. In particular, the present disclosure relates to measuring tread depth using an improved tread depth reader having one or more modular sensor units. Each modular sensor unit includes a light source, a sensor, and a control circuit. The color of the light source of the first modular sensor unit is different from the second color of the light source of the second modular sensor unit to avoid interference between adjacent sensor units.

Description

BACKGROUND

[0001] Tires support vehicles and transmit driving and braking forces from the vehicle to the road surface. It is beneficial to periodically measure the wear of the tires because tire wear plays an important role in vehicle factors such as safety, reliability, and performance. Tread wear, which refers to the loss of material from the tire tread, directly impacts such vehicle factors. Therefore, it is desirable to monitor and / or measure the amount of tread wear experienced by a tire, which is indicated by the tire wear state. It should be understood that the terms “tread wear” and “tire wear” can be used interchangeably for convenience purposes. SUMMARY

[0002] One method of monitoring and / or measuring tread wear is to measure the tread depth of a tire installed on a vehicle as the vehicle drives over a station and the tire passes over a sensor installed in the station, which is referred to in the art as a drive-over reader. The tread depth is measured when the tire is positioned on or adjacent to the sensor, depending on the sensor employed.

[0003] Advantages of drive-over readers include the static positioning of the tire tread on the reader contact surface during a short time interval, which enables the use of contact or non-contact methods to determine the tread depth. Examples of such methods include ultrasonic, radar reflection, or other optical methods such as laser triangulation or light-section processing, which generate an image of the tire footprint or an image of the tire tread along a lateral line or cross-section. The tread depth is determined from the image.

[0004] The present invention provides the following technical solutions: 1. A system for estimating a tread depth of a tire supporting a vehicle, the system comprising: a tread depth reader housing; and a plurality of modular sensor units disposed within the tread depth reader housing, each modular sensor unit comprising: a light source; a sensor; and a modular sensor unit control circuit, wherein a first color of the light source of a first modular sensor unit is different than a second color of the light source of a second modular sensor unit, the first modular sensor unit directly adjacent to the second modular sensor unit.

[0005] 2. The system of technical solution 1, further comprising: a computing device comprising a processor and a memory; and at least one application stored in the memory, wherein, when executed by the processor, the at least one application causes the computing device to at least: obtain a plurality of images from the plurality of modular sensor units; estimating a tread depth of a tire supporting a vehicle based at least in part on the analysis of the plurality of images.

[0006] 3. The system of claim 2, wherein the plurality of images correspond to images of a tread of the tire along a lateral line or cross-section of the tire.

[0007] 4. The system of claim 2, wherein the plurality of images include images of a footprint of the tire.

[0008] 5. The system of claim 1, wherein the plurality of modular sensor units are communicatively coupled to one another via a chain-link configuration.

[0009] 6. The system of claim 1, wherein each modular sensor unit includes a sensor housing and a first cover disposed on the sensor housing.

[0010] 7. The system of claim 6, further comprising a plurality of second covers, each second cover disposed on a respective modular sensor unit of the plurality of modular sensor units disposed within the tread depth reader housing.

[0011] 8. The system of claim 7, wherein the plurality of second covers are stainless steel.

[0012] 9. The system of claim 1, further comprising a measurement control circuit, the plurality of modular sensor units communicatively coupled to the measurement control circuit via a modular sensor unit circuit.

[0013] 10. The system of claim 1, wherein the sensor of the first modular sensor unit is configured to capture images corresponding to the first color and the sensor of the second modular sensor unit is configured to capture images corresponding to a second color.

[0014] 11. The system of claim 1, wherein each modular sensor unit is removably attached to the tread depth reader housing.

[0015] 12. A method for estimating a tread depth of a tire supporting a vehicle, the method comprising: obtaining a plurality of images of the tire from a plurality of modular sensor units of a tread depth reader in response to the vehicle traveling over the tread depth reader, a first modular sensor unit of the plurality of modular sensor units including a first light source and a first sensor, a second modular sensor unit of the plurality of modular sensor units including a second light source and a second sensor, a first color of the first light source being different than a second color of the second light source; and estimating a tread depth of the tire based at least in part on the analysis of the plurality of images.

[0016] 13. The method of claim 12, further comprising: determining a baseline of the tire tread based at least in part on the analysis of the plurality of images; and identifying a deepest line of a groove of the tire based at least in part on the analysis of the plurality of images, the tread depth estimated based at least in part on the baseline and the deepest line of the groove.

[0017] 14. The method of claim 12, wherein a first portion of the plurality of images is captured by a first sensor and a second portion of the plurality of images is captured by a second sensor, the first sensor configured to capture images associated with a first color and the second sensor configured to capture images associated with a second color.

[0018] 15. The method of claim 12, wherein the plurality of images correspond to images of the tread along a side line or cross section of the tire.

[0019] 16. The method of claim 12, wherein the plurality of images include images of a footprint of the tire.

[0020] 17. The method of claim 12, wherein the plurality of modular sensor units are communicatively coupled to one another via a chain-link configuration. BRIEF DESCRIPTION OF DRAWINGS

[0021] Many aspects of the disclosure can be better understood with reference to the following drawings. The components in the drawings are not necessarily to scale, emphasis instead being placed upon clearly illustrating the principles of the present disclosure. Furthermore, in the drawings, like reference numerals designate corresponding parts throughout the several views.

[0022] Figure 1 is an example scenario of a vehicle in accordance with various embodiments of the present disclosure driving over a tread depth reader.

[0023] Figures 2A-2C is an example perspective view of a tread depth reader in accordance with various embodiments of the present disclosure. Figure 1

[0024] Figures 3A-3B is an example perspective view of a modular sensor unit of a tread depth reader in accordance with various embodiments of the present disclosure. Figure 1

[0025] Figure 4 is a perspective view of modular sensor units connected to one another via a chain-link configuration in accordance with various embodiments of the present disclosure.

[0026] ​​Figure 5 is an example point cloud image of a tire tread according to various examples of the present disclosure, which can be formed based on images captured by sensors of each modular sensor unit of a tread depth reader.

[0027] Figure 6 is a diagram of a network environment according to various embodiments of the present disclosure.

[0028] Figure 7 is a flow diagram of one example illustrating functionality implemented as part of an application executing in a computing environment in a network environment of Figure 6

[0029] Definitions “Axial” and “axially” mean a line or direction parallel to the axis of rotation of the tire.

[0030] “Circumferential” means a line or direction along the circumference of the annular tread surface perpendicular to the axial direction.

[0031] “Equatorial plane (EP)” means a plane perpendicular to the axis of rotation of the tire and passing through the center of the tire’s tread.

[0032] “Footprint” means the contact patch or contact area made by the tire’s tread with a flat surface, such as the ground, as the tire rotates or rolls.

[0033] “Lateral” means an axial direction.

[0034] “Lateral edge” means a line tangent to the axially outermost tread ground contact patch or footprint, measured under normal load and tire inflation, that is parallel to the equatorial center plane.

[0035] “Net contact area” means the total area of ground contact tread elements between the lateral edges around the entire circumference of the tire’s tread divided by the total area of the entire tread between the lateral edges.

[0036] “Outboard” means the side of the tire farthest from the vehicle when the tire is mounted on a wheel and the wheel is mounted on a vehicle.

[0037] “Radial” and “radially” mean a line or direction perpendicular to the axis of rotation of the tire.

[0038] “Rib” means a circumferentially extending strip of rubber on the tread defined by at least one circumferential groove and either a second such groove or a lateral edge, the strip not being laterally separated by a full-depth groove.

[0039] “Tread element” or “traction element” means a rib or block element defined by a shape having an adjacent groove. ​

[0040] “tread arc width” means the arc length of the tire tread measured between the lateral edges of the tire tread. DETAILED DESCRIPTION

[0041] Various systems and methods for measuring the tread depth of a tire supported by a vehicle are disclosed. In particular, the present disclosure relates to measuring tread depth using an improved tread depth reader having one or more modular sensor units that can be individually installed and removed within the tread depth reader in a plug-and-play manner. Conventional drive-over readers for tread depth measurement have challenges related to obsolescence of electronic hardware, deformation of sensor bodies, misalignment of lasers, and water ingress corroding sensor housings and damaging electrical components. According to various examples, the tread depth reader of the present disclosure minimizes load forces and vibrations that can be transferred to the electronics and / or deform the sensor bodies of the modular sensor units, improves the sealing capabilities of the sensor units to minimize water ingress, and eliminates the need for mutual calibration of the lasers of the sensor units within the tread depth reader.

[0042] Turning now to Figure 1 , an example scenario is shown in which a vehicle 100 is driving over a drive-over tread depth reader 103 for tread depth measurement. According to various examples, the tread depth reader 103 obtains measurements that can be analyzed to estimate the tread depth of each tire 106 supporting the vehicle 100. It should be appreciated that the vehicle 100 can be any type of vehicle, and is shown by way of example as a commercial vehicle.

[0043] The tires 106 are of conventional construction, and each tire 106 is mounted on a respective wheel 109 known to those skilled in the art. Each tire 106 includes a pair of sidewalls 112 that extend to a circumferential tread 115 that wears over time as a result of road abrasion. As each tire 106 rolls over a ground surface 118, an impression is created that is the area of contact of the tread 115 with the ground surface.

[0044] With additional reference to Figures 2A-3B , a schematic diagram of a tread depth reader 103 according to various embodiments is shown. Figure 1An example perspective view of a tire depth reader 103. The tire depth reader 103 can be mounted in or on the ground. The tire depth reader 103 includes a housing 124 and at least one modular sensor unit 127 (e.g., 127a, 127b, 127c, 127d, 127e, 127f, collectively referred to as modular sensor unit 127, and generally referred to as modular sensor unit 127) mounted in the housing 124. In various examples, the housing 124 comprises a concrete housing. The driver of vehicle 100 guides the vehicle onto the tire depth reader 103, causing each tire 106 to roll on the tire depth reader 103. When the tire 106 is on or adjacent to one or more modular sensor units 127, each modular sensor unit 127 can capture one or more images of the imprint or tread 115 along a lateral line or cross-section. The sensors can generate images using techniques such as ultrasonic waves, radar reflection, laser triangulation, or optical sectioning. The depth of the tread 115 of tire 106 is determined from an image. Some techniques for generating images and measuring the depth of the tread 115 from images are described by way of example in U.S. Patent Nos. 8,621,919, 8,312,766, and 7,942,048, all of which are owned by the assignee of this invention, Goodyear Tire & Rubber Company, and are incorporated herein by reference.

[0045] Figure 2A The illustration shows a perspective view of a tread depth reader 103, which includes multiple modular sensor units 127 disposed within a tread depth reader housing 124 and covered by corresponding sensor plates 130 (e.g., 130a, 130b, 130c, 130d, 130e, 130f, collectively referred to as sensor plates 130, and generally referred to as sensor plates 130). Figure 2B The illustration shows a perspective view of a tread depth reader 103, which includes multiple modular sensor units 127 disposed within a tread depth reader housing 124, without corresponding sensor plates 130. Figure 2C The illustration shows a perspective view of a tread depth reader 103 according to various examples, illustrating light projections 133 (e.g., 133a, 133b, 133c, 133d, 133e, 133f, collectively referred to as light projection 133, and generally referred to as light projection 133) emitted from a light source of the modular sensor unit 127. It should be noted that, although... Figures 2A-2C The illustration shows three pairs of six modular sensor units 127, but the tread depth reader 103 is not limited to this configuration. In various examples, the number of modular sensor units 127 disposed within the tread depth reader housing 124 can be modified to include more than... Figures 2A-2CThe modular sensor units 127 shown in FIG. 1 are more or less modular sensor units 127. In some examples, the number of modular sensor units 127s can be based at least in part on the type of tire 106 being analyzed (e.g., commercial, passenger), the number of tires 106 on each axle, the number of images obtained, and / or other factors.

[0046] Each sensor plate 130 is configured to cover a corresponding modular sensor unit 127. For example, the sensor plate 130a is placed over the sensor unit 127a. In various examples, the sensor plate 130 is mounted to and / or within the housing 124 to cover each corresponding modular sensor unit 127a to allow any load or vibration caused by the vehicle 100 driving over the tread depth reader 103 to be transferred to the housing 124, rather than to the corresponding modular sensor unit 127 and / or electronics within the corresponding modular sensor unit 127. In various examples, the sensor plate 130 is manufactured using stainless steel to provide additional strength and protection to the modular sensor unit 127 from loads and vibrations caused by the vehicle 100 driving over the tread depth reader 103.

[0047] Figure 3A and 3B Additional reference is provided to the modular sensor units 127 mounted within the tread depth reader 103. In various examples, the modular sensor units 127 include a sensor housing 136, a cover plate 139, a sensor 142, a light source 145, a modular sensor circuit 148, and / or other components as can be appreciated. In various examples, the sensor housing 136 includes an aluminum body and is configured to contain the sensor 142, the light source 145, and the modular sensor unit circuit 148, which are necessary components for obtaining images of the tire 106 of the vehicle 100 driving over the reader 103. The cover plate 139 covers the top of the sensor housing 136 to protect the sensor 142, the light source 145, the modular unit circuit 148, and / or other components included within the sensor housing 136.

[0048] The sensor 142 includes a camera for obtaining images of the tire 106 based at least in part on the light projection 133 emitted from the corresponding light source 145. In various examples, the camera can include a high-speed monochrome camera. In various examples, for each modular sensor unit 127, a fan of light rays (e.g., the light projection 133) is produced from the light source 145 that shines transverse to the direction of movement of the tire 106 driving over the tread depth reader 103. The sensor 142 obtains data (e.g., images) for obtaining a tread depth measurement of a given tire 106.

[0049] In various examples, the light source 145 comprises a laser. According to various embodiments, and as illustrated in Figure 2C When the first modular sensor unit 127a and the second modular sensor unit 127b are installed directly adjacent to one another, as illustrated in the example of FIG. 6, the color of the first light projection 133a of the first light source 145a of the first modular sensor unit 127a is different than the second light projection of the second light source 145b of the second modular sensor unit 127b. In this example, the first sensor 142a of the first modular sensor unit 127a is configured to only capture images associated with the color emitted from the first light source 145a, and the second sensor 142b of the second modular sensor unit 127b is configured to only capture images associated with the color emitted from the second light source 145b. Thus, adjacent light sources 145 do not interfere with one another, and each sensor 142 can capture as many images as possible without interference from an adjacent light source 145. In various examples, the sensor 142 and light source 145 for a given modular sensor unit 127 only need to be calibrated to one another, and do not need to be calibrated between adjacent modular sensor units 127, as the sensor 142 only obtains data associated with the corresponding light source 145 of the given modular sensor unit 127.

[0050] Turning now to Figure 4 , a perspective view of modular sensor units 127 connected to one another via a daisy chain configuration is shown. For example, as illustrated in Figure 4 , each modular sensor unit 127 can include a power outlet connector, a power inlet connector, a data outlet connector, and a data inlet connector, each of which can be connected via a corresponding power cable 152 or data cable 154. The last modular sensor unit 127 of the daisy chain connection can then be connected to the measurement unit circuit 157 of the tread depth reader 103 (FIG. 1). Figure 6 The daisy chain configuration between modular sensor units 127 allows for easy installation and removal of a given modular sensor unit 127 within the tread depth reader 103.

[0051] Referring next to Figure 5 , an example point cloud image 160 of a tire tread 115 is shown, which can be formed based on the images captured by the sensors 142 of each modular sensor unit 127 of the tread depth reader 103. Figure 5 The illustration of multiple images obtained from the sensors 142 allows the point cloud image 160 to include lateral tread lines 163 that can be used to accurately measure the tread depth of a given tire 106.

[0052] Referring to Figure 6FIG. 6 illustrates a network environment 600, in accordance with various embodiments. The network environment 600 can include a computing environment 603 and a tread depth reader 103, which can be in data communication with each other via a network 606.

[0053] The network 606 can include wide area networks (WANs), local area networks (LANs), personal area networks (PANs), or a combination thereof. These networks can include wired or wireless components or a combination thereof. Wired networks can include Ethernet, cable networks, fiber-optic networks, and telephone networks such as dial-up, digital subscriber line (DSL), and integrated services digital network (ISDN) networks. Wireless networks can include cellular networks, satellite networks, Institute of Electrical and Electronics Engineers (IEEE) 802.11 wireless networks (i.e., WI-FI®), BLUETOOTH® networks, microwave transmission networks, and other networks that rely on radio broadcasts. The network 606 can also include a combination of two or more networks 606. Examples of the network 606 can include the Internet, intranets, extranets, virtual private networks (VPNs), and similar networks.

[0054] The computing environment 603 can include one or more computing devices that include a processor, a memory, and / or a network interface. For example, a computing device can be configured to perform computations on behalf of other computing devices or applications. As another example, such a computing device can host content and / or provide content to other computing devices in response to a request for the content.

[0055] Further, the computing environment 603 can employ multiple computing devices, which can be arranged in one or more server banks or computer banks or other arrangements. Such computing devices can be located in a single installation or can be distributed among many different geographical locations. For example, the computing environment 603 can include multiple computing devices that together can comprise a hosted computing resource, a grid computing resource, or any other distributed computing arrangement. In some cases, the computing environment 603 can correspond to an elastic computing resource, in which the allocated capacity of processes, networks, storage, or other computing-related resources can vary over time.

[0056] Various applications or other functionality can be executed in the computing environment 603. Components executed in the computing environment 603 include a tread depth estimator service 609, as well as other applications, services, processes, systems, engines, or functionality not discussed in detail herein.

[0057] A tread depth estimator service 609 can be executed to measure the tread depth of the tire 106 based at least in part on measurement data 610 received from the tread depth reader 103. In various examples, the measurement data 610 corresponds to images captured by each of the sensors 142 of the modular sensor unit 127 in the tread depth reader 103. In various examples, the tread depth estimator service 609 can obtain the measurement data 610 and generate a footprint or tread point cloud image 160 of the tire 106. In various examples, the tread depth estimator service 609 can determine a tread depth baseline and a tread depth from the point cloud image 160 and can use a comparison of the baseline and the tread depth to estimate the tread depth.

[0058] Further, various data is stored in a data store 612 accessible to the tread depth estimator service 609. As can be appreciated, the data store 612 can represent a plurality of data stores 612. For example, the data stored in the data store 612 is associated with various applications and / or the operation of functional entities associated with the tread depth reader 103 and / or the tread depth estimator service 609. For example, the data store 612 can include tire data 615, tread depth estimator rules 618, and / or other information.

[0059] The tire data 615 can include information for each particular tire 106. For example, the tire data 615 can include a tire identifier, manufacturing information for the tire 106 (e.g., manufacturer name, tire model, etc.), tire size information (e.g., rim size, width, and overall diameter, etc.), manufacturing location, manufacturing date, a tread crown code including or associated with a compound identification, a mold code including or associated with a tread structure identification, and / or other information. The vehicle tire data 615 can also include service history or other information to identify particular characteristics and parameters of each tire 106.

[0060] The tread depth estimation rules 618 include rules, models, and / or configuration data for various algorithms or methods employed by the tread depth estimator service 609 and / or other applications or devices. In some examples, the tread depth estimation rules 618 can include various models, formulas, equations, and / or algorithms used to generate the point cloud image 160, analyze the measurement data 610, estimate the tread depth, and / or other factors.

[0061] It should be noted that while the computing environment 603 and the tread depth reader 103 are illustrated as separate from and distinct from one another in Figure 6 some examples, the tread depth reader 103 and / or the measurement unit circuit 151 of the tread depth reader 103 includes functionality and components of the computing environment 603. Thus, functionality and components described with respect to the computing environment 603 can be included as part of the tread depth reader 103.

[0062] Referring next Figure 7 to the flowchart of FIG. 6, an example of a process to provide a portion of the operation of the tread depth estimator service 609 is shown. Figure 7 The flowchart of FIG. 6 provides only an example of the many different types of functional arrangements that can be taken to implement the depicted portion of the operation of the tread depth estimator service 609. As an alternative, Figure 7 The flowchart of FIG. 6 can be viewed as depicting an example of elements of a method implemented within the network environment 600.

[0063] Beginning with block 703, the tread depth estimator service 609 obtains an image from the modular sensor unit 137 of the tread depth reader 103. The image can be included in the measurement data 610 and can correspond to an image of the footprint and / or tread 115 of a tire 106 of a vehicle 100 driving on the tread depth reader 103. In various examples, each sensor 142 within the modular sensor unit 127 captures a plurality of images of the tread of the tire 106 in response to a corresponding light fan (e.g., light projection 133) associated with the light source 145 of a given modular sensor unit 127. The tread of the tire 106 can be optically sensed by the sensors 142 transverse to the rolling direction of the tire 106. Each sensor 142 can be configured to obtain an image of the tire tread corresponding to one or more portions of the tire tread 115. In some examples, the sensors 142 can obtain overlapping images, but because the light sources 145 of adjacent modular sensor units 127 emit different colors, the data obtained by a given sensor 142 will not be interfered with by the adjacent light source 145.

[0064] At block 706, the tread depth estimator service 609 determines a baseline of the tire tread 115. For example, the tread depth estimator service 609 can analyze the images obtained from the modular sensor unit 127 and generate a point cloud 160 representing the tire tread 115. The baseline can be estimated based at least in part on the analysis of the point cloud image 160. At block 709, the tread depth estimator service 609 identifies a deepest line of the tread groove. In various examples, the deepest line can be identified based at least in part on the analysis of the point cloud image 160.

[0065] At block 709, the tread depth estimator service 609 estimates the tread depth of the tire 106. For example, the tread depth can be estimated by comparing the baseline to the deepest line of the tread groove. Thereafter, this portion of the process continues to complete.

[0066] The various software components discussed above are stored in the memory of the respective computing device and are executable by a processor in the respective computing device. In this regard, the term "executable" means a program file in any executable form suitable for execution by a processor. The memory includes both volatile and nonvolatile memory as well as data storage components.

[0067] While the applications and systems described herein can be implemented in software or code executed by general purpose hardware as described above, as an alternative they can also be implemented in dedicated hardware or a combination of software and dedicated hardware. If implemented in software, each can be embodied as a module of code, and / or a component of a module of code. If implemented in hardware, each can be embodied as a circuit or any number of circuits are state machines that execute the logic.

[0068] The flow diagrams illustrate the functionality and operation of various embodiments of present disclosure. If implemented in software, each block can represent a module, segment, or portion of code that comprises program instructions to implement the specified logical function(s). The program instructions can be embodied in the form of source code that includes human- readable statements written in a programming language or machine code that includes numerical instructions recognizable by a suitable execution system such as a processor in a computer system. The machine code can be converted from the source code, etc., using a variety of processes. For example, a compiler can generate machine code from source code prior to execution of the corresponding application. As another example, an interpreter can generate machine code from source code while the application is executed. Other methods can also be used. If implemented in hardware, each block can represent a circuit or a number of interconnected circuits that implement the specified logical function(s).

[0069] While the flow diagrams illustrate a particular order of execution, it is to be understood that the order of execution can differ from that which is depicted. For example, two or more blocks shown in succession can in fact be executed concurrently or with partial concurrence. Also, a number of additional or intervening processes can be employed, and the order of execution can be rearranged. Further, in some embodiments, one or more blocks illustrated in the flow diagrams can be skipped or omitted. Additionally, any number of counters, state variables, warning semaphores, or messages can be added to the logic for purposes of enhanced utility, statistical gathering, performance measurement, or fault diagnosis, etc. It is understood that all such variations are within the scope of the present disclosure.

[0070] Furthermore, any of the logic or applications described herein can be implemented as software that operates across various platforms, such as a platform that includes a plurality of computing devices or a platform that includes a plurality of computing environments. For example, the logic or applications described herein can be implemented as a platform that includes a plurality of computing devices or a platform that includes a plurality of computing environments.

[0071] A computer-readable medium can include any of a number of physical media, including, for example, magnetic, optical, or semiconductor media. More specific examples of a suitable computer-readable medium would include, but are not limited to, magnetic tapes, magnetic floppy diskettes, magnetic hard disks, memory cards, solid- state RAM, USB flash drives, or optical discs. Also, computer- readable medium can be random access memory (RAM) including static random access memory (SRAM) and dynamic random access memory (DRAM), such as synchronous DRAM (SDRAM), magnetic random access memory (MRAM), other type of RAM, or other types of memory. In addition, computer-readable medium can be read-only memory (ROM), programmable ROM (PROM), erasable programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), or other type of ROM. The computer-readable medium can also be a compact disc read-only memory (CD-ROM), or other type of optical storage, or a magnetic cassette.

[0072] Furthermore, any of the logic or applications described herein can be implemented as software that operates across various platforms, such as a platform that includes a plurality of computing devices or a platform that includes a plurality of computing environments. For example, the logic or applications described herein can be implemented as a platform that includes a plurality of computing devices or a platform that includes a plurality of computing environments.

[0073] Unless specifically stated otherwise, conjunctive language such as the phrase “at least one of X, Y, or Z,” is to be understood as typically meaning that items, terms, etc. can be either X, Y, or Z, or any combination of them (e.g., X; Y; Z; X or Y; X or Z; Y or Z; X, Y, or Z; etc.). Thus, such conjunctive language is generally not intended to imply that certain embodiments require at least one of X, at least one of Y, or at least one of Z each to exist.

[0074] It should be emphasized that the above-described embodiments of the present disclosure are merely possible examples of implementations, set forth for the purpose of clarity and understanding of the principles of the present disclosure. Many changes and modifications can be made to the above-described embodiments, with out departing substantially from the spirit and principles of the present disclosure. All such modifications and changes are intended to be included within the scope of the present disclosure and protected by the following claims.

Claims

1. A system for estimating a tread depth of a tire supporting a vehicle, the system comprising: a tread depth reader housing; and a plurality of modular sensor units disposed within the tread depth reader housing, each modular sensor unit comprising: a light source; a sensor; and a modular sensor unit control circuit, wherein a first color of the light source of a first modular sensor unit is different than a second color of the light source of a second modular sensor unit, the first modular sensor unit directly adjacent to the second modular sensor unit.

2. The system of claim 1, further comprising: a computing device comprising a processor and a memory; and at least one application stored in the memory, wherein, when executed by the processor, the at least one application causes the computing device to at least: obtain a plurality of images from the plurality of modular sensor units; estimate a tread depth of a tire supporting a vehicle based at least in part on an analysis of the plurality of images.

3. The system of claim 2, wherein, the plurality of images correspond to images of a tread along a lateral line or cross section of the tire.

4. The system of claim 2, wherein, the plurality of images include images of a footprint of the tire.

5. The system of claim 1, wherein, the plurality of modular sensor units are communicatively coupled to one another via a daisy chain configuration.

6. The system of claim 1, wherein, each modular sensor unit comprises a sensor housing and a first cover plate disposed on the sensor housing.

7. The system of claim 6, further comprising a plurality of second cover plates, each second cover plate disposed on a respective modular sensor unit of the plurality of modular sensor units disposed within the tread depth reader housing.

8. The system of claim 7, wherein, the plurality of second cover plates are stainless steel.

9. The system of claim 1, further comprising a measurement control circuit, the plurality of modular sensor units communicatively coupled to the measurement control circuit via the modular sensor unit circuit.

10. The system of claim 1, wherein, the sensor of the first modular sensor unit is configured to capture images corresponding to the first color, and the sensor of the second modular sensor unit is configured to capture images corresponding to the second color.

Images