System and method for estimating tire wear using acoustic footprint analysis

Abstract

Disclosed herein are systems and methods for estimating tire wear based on acoustic footprint analysis. A sensor mounted on the tire provides a signal corresponding to the dynamic mechanical behavior of the tire, and a computing device graphically constructs a spectrum including the time and frequency content of the signal, and implements a model including predetermined spectra associated with un-worn versions of the tire for comparison with the graphically constructed spectrum of the footprint region of the tire. Based on the comparison, graphical features of the spectrum associated with the footprint region of the tire are extracted as predefined indicators of stiffness variations, wherein the tire wear status is estimated based on the extracted graphical features. Exemplary graphical features can include high frequency energy features at the footprint region, signal damping features at the trailing edge of the footprint region, increased energy features in the tread pass band at the footprint region, etc.

Description

Technical Field

[0001] This disclosure generally relates to the estimation of tire wear on wheeled vehicles. More specifically, the systems, methods, and related algorithms disclosed herein can collect and analyze physical parameters such as, for example, physics-based vibration indices for, for example, to make an improved estimate of the tread depth of tires on wheeled vehicles, including but not limited to motorcycles, consumer vehicles (e.g., passenger cars and light trucks), commercial and off-road (OTR) vehicles. Background Technology

[0002] Tire wear prediction is an important tool for anyone who owns or operates a vehicle, especially in the context of fleet management. As tires are used, the tread typically becomes thinner, and overall tire performance changes. Generally speaking, when driving in rain, snow, or other adverse weather conditions, the thinner the tire tread, the more easily the driver loses traction.

[0003] In addition, irregular tread wear can occur for a variety of reasons, which may cause users to replace tires earlier than originally intended. Vehicles, drivers, and individual tires are all different from each other, and this can cause tires to wear at very different rates. For example, high-performance tires used in sports cars wear out faster than tires used in family cars. However, a wide variety of factors can cause tires to wear out earlier than expected, and / or cause irregular tire wear and produce noise or vibration. Two common causes of premature and / or irregular tire wear are improper inflation pressure and out-of-specification alignment conditions.

[0004] However, tire wear is a complex phenomenon to model. Accurate models using finite element analysis (FEA) exist, but these simulations typically take weeks to complete. If it is desired to simulate wear rates at several different tread depths, this would further require computationally expensive simulations lasting several months.

[0005] The goal is to provide users with essentially real-time predictions about the performance and capabilities of their tires.

[0006] It is also expected that these services will be provided as part of a distributed and relatively automated tire-as-a-service model, without the need for manual tread depth measurements (such as those typically provided by field engineers and / or with specialized equipment).

[0007] It is also desirable to estimate the tire tread depth and provide such feedback as input to models for other useful / effective predictions, such as traction, fuel efficiency, durability, etc. Accurate tread depth prediction is the first step in predicting many other areas of tire performance. Summary of the Invention

[0008] Generally, various embodiments of the systems and methods disclosed herein can achieve the detected mechanical response, namely the displacement, velocity, and / or acceleration of the rolling tire. Preferably, the response can be measured directly using a data acquisition system (such as an accelerometer mounted, on, or associated with the tire). The data acquisition system can continuously collect data as the tire rolls on different roads and surfaces.

[0009] The various embodiments disclosed herein may more specifically relate to the dynamic response of the vehicle body measured at the imprint (also known as a “contact patch”) and / or near the imprint at its trailing edge. Therefore, accelerometer data are analyzed when the mounted device is in the imprint (e.g., where the tread surface at a location radially outward of the mounting point of the device is in contact with the road surface) or when the device is in the trailing edge region (i.e., where the mounted device is located away from the road surface).

[0010] An exemplary embodiment of the tire wear prediction method disclosed herein includes collecting signals corresponding to the dynamic mechanical behavior of the tire via at least one data acquisition device mounted on the tire of a motor vehicle. The method also includes graphically constructing a spectrum including the time and frequency content of the signals, and implementing at least one model including a predetermined spectrum associated with an unworn version of the tire for comparison with a graphically constructed spectrum of the tire's track area. Based on this comparison, one or more graphical features of the spectrum associated with the tire's track area are extracted as a predefined indicator of stiffness change. The tire wear state can be estimated based on the extracted one or more graphical features, and an output signal can be selectively generated based on the estimated tire wear state.

[0011] In one exemplary aspect of the above implementation, acoustic imprint analysis is used to compare a predetermined spectrum associated with an unworn version of the tire with a spectrum graphically constructed from the imprinted regions of the tire.

[0012] In another exemplary aspect of the above-described embodiments, the extracted one or more graphic features may include one or more of the following: high-frequency energy features identified at the imprint region; signal damping features at the trailing edge of the imprint region; and increased energy features in the tread pass band at the imprint region.

[0013] In another exemplary aspect of the above implementation scheme, graphically constructing the spectrum includes deconvolving the signal into its respective time, frequency, and amplitude contents. For example, time-frequency short-time Fourier transform (STFT) analysis can be used.

[0014] In another exemplary aspect of the above implementation scheme, at least one model for implementation can be selected and retrieved based on the determined tire type.

[0015] In another embodiment disclosed herein, the above method can be implemented via a system for predicting tire wear, the system comprising at least one data acquisition device mounted on a tire of a motor vehicle and configured to generate a signal corresponding to the dynamic mechanical behavior of the tire, and a computing device communicatively linked to the at least one data acquisition device to receive the generated signal therefrom and configured to process the received signal accordingly.

[0016] In an exemplary aspect of the system described above, the output signal can be provided to the display unit for selectively displaying one or more markers corresponding to the estimated tire wear condition.

[0017] In another exemplary aspect of the system described above, the output signal can be provided to the vehicle control unit for automatically intervening in one or more vehicle control attributes based on an estimated tire wear condition.

[0018] In another exemplary aspect of the above-described embodiments, one or more data storage media are communicatively linked to a computing device and store at least one model thereon for selective retrieval and implementation based on a determined tire type.

[0019] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains. The invention disclosed herein may be embodied in other specific forms without departing from the spirit or essential attributes of the invention, and therefore various embodiments are intended to be illustrative rather than restrictive in all respects. Any headings used in this specification are for convenience only and not for legal or restrictive effect. Many objects, features, and advantages of the embodiments described herein will become readily apparent to those skilled in the art when read in conjunction with the accompanying drawings. Attached Figure Description

[0020] Figure 1 This is a block diagram illustrating an implementation scheme of a system for tread depth estimation as disclosed herein.

[0021] Figure 2 This is a flowchart illustrating an implementation of the method for estimating tire tread depth as disclosed herein.

[0022] Figure 3A This is a graphical schematic diagram representing an exemplary spectrum of an unworn tire, including acoustic imprint features based on a first physics, according to an embodiment disclosed herein.

[0023] Figure 3B It means corresponding to Figure 3AThe spectrum is a graphical representation of an exemplary spectrum of a worn tire according to an embodiment disclosed herein.

[0024] Figure 4A This is a graphical representation of an exemplary spectrum of an unworn tire, including acoustic imprint features based on a second physics, according to an embodiment disclosed herein.

[0025] Figure 4B It means corresponding to Figure 4A The spectrum is a graphical representation of an exemplary spectrum of a worn tire according to an embodiment disclosed herein. Detailed Implementation

[0026] General Reference Figures 1 to 4B Various exemplary embodiments of the invention will now be described in detail. Where various figures may depict embodiments that share various common elements and features with other embodiments, similar elements and features are given the same reference numerals, and their redundant descriptions may be omitted below.

[0027] Various implementations of the system disclosed herein may include centralized computing nodes (e.g., cloud servers) that functionally communicate with multiple distributed data collectors, and computing nodes (e.g., associated with individual users and / or vehicles) for efficiently implementing at least the tire wear model disclosed herein.

[0028] Initial Reference Figure 1 An exemplary embodiment of system 100 includes a computing device 102 located in the vehicle and configured to at least acquire data and transmit the data to a remote server 130 and / or perform relevant calculations as disclosed herein. The computing device may be portable or otherwise modular as part of a distributed vehicle data collection and control system (as shown), or it may be provided integrally with respect to a central vehicle data collection and control system (not shown). The device may include a processor 104 and a memory 106 on which program logic 108 resides. The computing device 102 in various embodiments may be a vehicle electronic control unit (ECU) or a component thereof, or it may be discrete in nature, for example, permanently or removably provided relative to a vehicle mount.

[0029] Generally, the system 100 disclosed herein can implement numerous components distributed across one or more vehicles, such as, but not necessarily, associated with a fleet management entity, and can also implement a central server 130 or server network that communicates functionally with each vehicle via a communication network. Vehicle components typically include, for example, one or more sensors linked to a Controller Area Network (CAN) bus network and thereby providing signals to a local processing unit, such as, for example, vehicle accelerometers, gyroscopes, inertial measurement units (IMUs), position sensors such as a Global Positioning System (GPS) transponder 112, a Tire Pressure Monitoring System (TPMS) sensor transmitter 118, and associated onboard receivers. For illustrative purposes and without further limiting the scope of the invention, the illustrated embodiments include an ambient temperature sensor 116, a vehicle speed sensor 114 configured to collect, for example, acceleration data associated with the vehicle, and a DC power supply 110. One or more of the sensors disclosed herein may be integrated or otherwise co-located within a given modular structure, rather than being discrete and distributed within the structure. For example, the tire-mounted TPMS sensor referred to herein can be configured to generate an output signal corresponding to each of a plurality of tire-specific conditions (e.g., acceleration, pressure, temperature).

[0030] Various bus interfaces, protocols, and associated networks are well known in the art for communication between the respective data source and the local computing device, and those skilled in the art will recognize a wide range of such tools and means for implementing these tools.

[0031] It should be noted that Figure 1 The embodiments described herein do not limit the scope of the systems or methods disclosed herein, and in alternative embodiments, one or more of models 134 may be implemented locally at the on-board computing device 102 (e.g., electronic control unit) rather than at the server level. For example, models 134 may be generated and trained over time at the server level and downloaded to the on-board computing device 102 for local execution of one or more steps or operations as disclosed herein.

[0032] In other alternative embodiments, one or more of the various sensors 112, 114, 116, 118 may be configured to communicate directly with the remote server 130, or via a mobile computing device (not shown) carried by a user of the vehicle instead of via the onboard computing device 102.

[0033] The system 100 may include additional distributed program logic (such as residing, for example, on a fleet management server or other user computing device 140), or a user interface residing in the vehicle or associated with its driver (not shown) for providing real-time notifications (e.g., via visual and / or audio indicators), wherein the fleet management device is functionally linked to the on-board device via a communication network in some embodiments. System programming information may be provided, for example, by the driver in the vehicle or from the fleet manager.

[0034] Unless otherwise stated, the term "user interface" as used herein can include any input-output module through which a user device facilitates user interaction with respect to servers and / or devices as disclosed herein, including but not limited to downloaded or otherwise residing application programs; web browsers; web portals, such as individual web pages or those collectively defining a hosting website; etc. The user interface can also be described in the context of buttons and displays on a personal mobile computing device, which may be arranged independently of, for example, a touchscreen or otherwise associated with each other, and may also include audio and / or visual input / output functionality, even without explicit user interaction.

[0035] In implementations, vehicle and tire sensors 112, 114, 116, and 118 are also provided with unique identifiers, allowing the onboard processor 104 to distinguish between signals provided from corresponding sensors on the same vehicle. Furthermore, in some implementations, the central server 130 and / or fleet maintenance monitor client device 140 can distinguish between signals provided from tires 101 and associated vehicle and / or tire sensors on multiple vehicles. In other words, in various implementations, sensor output values ​​may be associated with a specific tire 101, a specific vehicle, and / or a specific tire-vehicle system for the purpose of onboard or remote / downstream data storage and for specific implementations of calculations disclosed herein. The onboard processor 104 may communicate directly with the hosting server 130, such as... Figure 1 As shown, or alternatively, the driver's mobile device or the computing device installed on the truck may be configured to receive data output from the on-board unit and process / transmit it to the hosting server 130 and / or the fleet management server / device 140.

[0036] Signals received from specific vehicle and / or tire sensors 112, 114, 116, 118 may be stored in on-board memory 106 or in an equivalent data storage network functionally linked to on-board processor 104 for selective retrieval as needed for computations according to the methods disclosed herein. As used herein, "data storage network" generally refers to individual, centralized or distributed logical and / or physical entities configured to store data and enable selective retrieval of data from them, and may include (e.g., but not limited to) memory, lookup tables, files, registers, databases, etc. In some embodiments, raw data signals from various sensors 112, 114, 116, 118 may be transmitted substantially in real time from the vehicle to server 130. Alternatively, particularly considering the inherent inefficiencies in continuous data transmission of high-frequency data, the data may, for example, be compiled, encoded, and / or aggregated for more efficient (e.g., based on periodic time or alternatively based on defined events) transmission from the vehicle to remote server 130 via a suitable communication network.

[0037] Once vehicle and / or tire data is transmitted to hosting server 130 via a communication network, it can be stored, for example, in a database 132 associated with it. Server 130 may include or be otherwise associated with one or more algorithmic models 134 disclosed herein for selectively retrieving and processing vehicle and / or tire data as appropriate input. Model 134 may be implemented at least in part via an execution processor, thereby enabling selective retrieval of vehicle and / or tire data and also enabling electronic communication to input any additional data or algorithms from databases, lookup tables, etc., stored in association with server 130.

[0038] The system 100 may include additional distributed programming logic (such as residing, for example, on a fleet management server or other user computing device 140), or a user interface residing in the vehicle or associated with its driver (not shown) for real-time notifications (e.g., via visual and / or audio indicators), wherein the fleet management device 140 is functionally linked to the on-board device 102 via a communication network in some embodiments. System programming information may be provided, for example, by the driver in the vehicle or from the fleet manager.

[0039] Now it can be described as follows: Figure 2 The exemplary method 200 shown is used to predict tire wear based on relevant physical parameters of a given tire. In a specific embodiment below, a physical-based vibration index is developed and implemented in the context of acoustic imprint analysis.

[0040] Method 200 begins by collecting signals corresponding to a mechanical response (e.g., displacement, velocity, and / or acceleration of the rolling tire) (step 210). As previously described, the data collection stage can implement conventional data acquisition devices, such as accelerometers, which are mounted on the tire and generate signals corresponding to the dynamic mechanical response. The data acquisition devices can continuously collect data as the tire rolls on different roads and surfaces.

[0041] As described above, the signals collected from the data acquisition device can be processed locally, for example, at an electronic control unit residing in the vehicle, or remotely, for example, at a cloud server node. The system can also pre-store or otherwise make accessible information corresponding to each of a plurality of tires, the types of which are similar to, or even include, the tire in question. This information can, for example, include initial wear values ​​and / or expected wear rates for the tire type in question, either of which can be used as a baseline or comparison point for subsequent and empirically developed wear conditions or wear rates.

[0042] In this embodiment, the dynamic response of particular interest related to the body can be measured at and near the imprint (e.g., at its trailing edge). Therefore, accelerometer data are analyzed when the mounted device is in the imprint or when the device is in the trailing edge region. As used herein, the term "imprint region" can (unless otherwise specifically stated) describe the imprint itself and also include its leading and trailing edges. Thus, certain characteristics related to stiffness variations generated according to the techniques disclosed herein can, for example, include responses measured just after the trailing edge of the imprint, but are described for illustrative purposes as occurring within the imprint region.

[0043] For this purpose, for example, time-frequency short-time Fourier transform (STFT) analysis can be used to deconvolve the signal into a spectrum that includes its time, frequency, and amplitude components (step 220).

[0044] Based on the principle that tire wear leads to harder tread blocks, various acoustic imprinting models 230 can be developed and implemented to identify and implement defined features in the analyzed STFT spectrum that indicate stiffness changes due to wear. These features can be predefined and retrievably stored for system implementation. Furthermore, or alternatively, systems as disclosed herein can be configured, for example, to automatically define and classify specific time-domain and / or frequency-domain characteristics of the measured energy into groups or categories, which can then be correlated with or otherwise identified relative to a common condition (i.e., stiffness). Those skilled in the art will understand that various machine learning techniques known in the art can be modified as needed to implement the steps disclosed herein, for example, classifying signal characteristics associated with stiffness features. These graphical features can be extracted as tire wear indicators (step 250) by comparing the corresponding spectra of a worn tire and a similar type or associated new (unworn) tire using acoustic imprinting models (step 240).

[0045] As described above, signal transformation can preferably be performed to provide time-frequency domain analysis. (Refer to...) Figure 3A (Indicating "new" tires) and Figure 3B (Representing a "worn" tire), temporal effects can be identified through comparison of the corresponding spectrum to extract acoustic imprint features based on dynamic physics. Further reference... Figure 4A (Indicating "new" tires) and Figure 4B (Representing a "worn" tire), the rear resonance effect can also be identified via a comparison of the corresponding spectrum. One example of a feature or "tire wear indicator" can appear in the deconvolution response as an increase in the high-frequency energy content at the wear tire's mark, for example, in the mark portion of the spectrum relative to that of a new tire. In another example, a relatively high damping effect can be extracted from the deconvolution response immediately following the mark at the trailing edge. In yet another example, a relatively high energy effect can be extracted from the deconvolution response in the tread pass-through band at the wear tire's mark. It is understood that various additional and / or alternative features can be identified as relating to stiffness variations of a given tire or tire type, and thus can be developed and implemented as part of or otherwise within the scope of tire wear models as disclosed herein.

[0046] In one implementation, the selection of the relevant models and / or associated indicators to be identified and extracted can be predetermined, for example, based on the type of tire.

[0047] In step 260, the effects of one or more extractions as disclosed herein can be implemented in a model / algorithm used to estimate the current tire wear condition. Wear model 134 can be implemented at the vehicle for processing via an onboard system, or tire data and / or vehicle data can be processed to provide representative data to a hosted server for remote wear estimation.

[0048] An output signal corresponding to the estimated tire wear condition may optionally be generated to the display unit for displaying a marker to the user (step 271). The marker may, for example, relate to an estimate of the remaining tire tread, an estimated remaining life as a duration or percentage, etc. The marker may be in the form of an alert, for example, essentially color-coded to indicate tire wear condition, with or without a specific alphanumeric indicator on the user interface.

[0049] The output signal corresponding to the estimated tire wear condition can optionally be generated to, for example, a vehicle-associated control unit (step 272) so that certain vehicle operations can be corrected or the change in tire wear can be taken into account in other ways.

[0050] In one implementation, method 200 may involve providing a tire wear estimate as input to a model for predicting wear values ​​at one or more future time points, wherein such predicted values ​​can be compared with corresponding thresholds. For example, a feedback signal corresponding to the predicted tire wear condition (e.g., predicted tread depth at a given distance, time, etc.) may be provided via an interface to an onboard device 102 associated with the vehicle itself, or to a mobile device associated with a user, such as one integrated with a user interface configured to provide alerts or notifications / suggestions that a tire should or will soon need replacement. Within the scope of this disclosure and based on the predicted tire wear (including, for example, tire rotation, alignment, inflation, etc.), other tire-related threshold events may be predicted and implemented for alerts and / or interventions. System 100 may generate such alerts and / or intervention suggestions based on comparisons of various thresholds, threshold groups, and / or non-threshold algorithms with respect to predetermined parameters.

[0051] Tire wear condition (e.g., tread depth) may be provided as input to a traction model, for example, along with certain vehicle data, which may be configured to provide an estimated traction condition or one or more traction characteristics for the corresponding tire. The traction model may include a “digital twin” virtual representation of a physical component, process, or system, wherein digital and physical data are paired and combined with a learning system such as, for example, an artificial neural network. Real vehicle data and / or tire data from a specific tire, vehicle, or tire-vehicle system may be provided throughout the lifecycle of the corresponding asset to generate a virtual representation of the vehicle tire for estimating tire traction, wherein subsequent comparisons of the estimated tire traction with the corresponding measured or determined actual tire traction may preferably be implemented as feedback to a machine learning algorithm executed at the server level.

[0052] In various implementations, the traction model can utilize an associated combination of results from previous tests (including, for example, stopping distance test results, tire traction test results, etc.) collected on many tire-vehicle systems, as well as values ​​of input parameters (e.g., tire tread, inflation pressure, road surface characteristics, vehicle speed and acceleration, slip rate and angle, normal force, braking pressure, and load), wherein tire traction output can be effectively predicted for a given set of current vehicle and tire data inputs.

[0053] In one implementation, the output from the traction model can be incorporated into an active safety system. As used herein, the term "active safety system" preferably encompasses systems commonly known to those skilled in the art, including but not limited to examples such as collision avoidance systems, advanced driver assistance systems (ADAS), and anti-lock braking systems (ABS), which can be configured to utilize traction model output information to achieve optimal performance. For example, collision avoidance systems are typically configured to take evasive actions, such as automatically engaging the brakes of the primary vehicle to avoid or mitigate a potential collision with a target vehicle, and enhanced information regarding tire traction and, consequently, the braking capability of the tire-vehicle system is highly desirable.

[0054] In another implementation, the ride-sharing autonomous fleet can use output data from the traction model to disable or otherwise selectively exempt vehicles with low tread depth during severe weather, or potentially limit the maximum speed of such vehicles.

[0055] In various implementations, the method may also involve comparing the current wear value with a threshold to determine whether (or when) the tire requires intervention, such as, for example, replacement. The method may alternatively or further include predicting wear values ​​at one or more future points in time, wherein such predicted values ​​can be compared with respective thresholds. For example, in… Figure 1As indicated in the diagram, the feedback signal corresponding to the predicted tire wear condition (e.g., the predicted tread depth at a given distance, time, etc.) can be provided via interface 120 to an onboard device 102 associated with the vehicle itself, or to a mobile device 140 associated with a user, such as being integrated with a user interface configured to provide an alert or notification / recommendation that a tire should or will soon need to be replaced.

[0056] As another example, an autonomous vehicle fleet may include many vehicles with different minimum tire wear states (e.g., tread depth) values, where the fleet management system can be configured to disable the deployment of vehicles that have fallen below a minimum threshold. The fleet management system may also implement different minimum tread state values ​​corresponding to wheel positions. The system may be configured accordingly to operate based on the minimum tire tread value of each of a plurality of tires associated with a vehicle, or, in an implementation, the aggregated tread state of the plurality of tires may be calculated for comparison with a minimum threshold.

[0057] In various implementations, the method may also include data streaming even in the absence of a detected threshold violation, wherein estimated and / or predicted wear values ​​can be displayed in real time on a local user interface and / or (e.g., a remote display associated with a fleet management server).

[0058] Throughout the specification and claims, unless the context otherwise requires, the following terms have at least the meaning explicitly associated herein. The meanings indicated below are not necessarily limiting of the terms, but rather provide illustrative examples only. The meanings of “an,” “a,” and “the” may include plural references, and the meaning of “in…” may include “in…” and “on…”. As used herein, the phrase “in one embodiment” does not necessarily refer to the same embodiment, although it may refer to the same embodiment.

[0059] The various exemplary logic blocks, modules, and algorithmic steps described in conjunction with the embodiments disclosed herein can be implemented as electronic hardware, computer software, or a combination of both. To clearly illustrate this interchangeability between hardware and software, the various exemplary components, blocks, modules, and steps have been generally described above in terms of their functionality. Whether such functionality is implemented as hardware or software depends on the specific application and the design constraints imposed on the system as a whole. The described functionality may be implemented differently for each specific application, but such implementation decisions should not be construed as departing from the scope of this disclosure.

[0060] The various exemplary logic blocks and modules described in connection with the embodiments disclosed herein can be implemented or executed by a machine such as a general-purpose processor, a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA) or other programmable logic device, discrete gate or transistor logic, discrete hardware components, or any combination thereof designed to perform the functions described herein. A general-purpose processor may be a microprocessor, but in alternative embodiments, the processor may be a controller, a microcontroller, or a state machine, a combination thereof, etc. The processor may also be implemented as a combination of computing devices, such as a combination of a DSP and a microprocessor, a combination of multiple microprocessors, a combination of one or more microprocessors combined with a DSP core, or any other combination of such configurations.

[0061] The steps of the methods, processes, or algorithms described in conjunction with the embodiments disclosed herein may be directly embodied in hardware, in a software module executed by a processor, or a combination of both. The software module may reside in RAM memory, flash memory, ROM memory, EPROM memory, EEPROM memory, registers, hard disk, removable disk, CD-ROM, or any other form of computer-readable medium known in the art. An exemplary computer-readable medium may be coupled to a processor such that the processor can read information from and write information to the memory / storage medium. Alternatively, the medium may be integrated into the processor. The processor and medium may reside in an ASIC. The ASIC may reside in a user terminal. Alternatively, the processor and medium may reside as discrete components in the user terminal.

[0062] Unless otherwise specifically stated or otherwise understood in the context in which they are used, the conditional language used herein (such as "may," "may," "can," "for example," etc.) is generally intended to convey that certain embodiments include certain features, elements, and / or states, while other embodiments do not include certain features, elements, and / or states. Therefore, such conditional language is not generally intended to imply that features, elements, and / or states are necessary in any way for one or more embodiments, or that one or more embodiments need to include logic for determining, with or without author input or prompting, whether such features, elements, and / or states are included in any particular embodiment or whether they will be performed in any particular embodiment.

[0063] While this document generally describes certain preferred embodiments of the invention in relation to tire wear estimation for fleet management systems and more specifically autonomous vehicle fleets or commercial truck applications, the invention is explicitly not limited thereto, and unless otherwise stated, the term “vehicle” as used herein may refer to an automobile, truck, or any of its equivalents that may include one or more tires and thus require accurate estimation or prediction of tire wear, as well as potential disabling, replacement, or intervention (whether self-propelled or otherwise) in the form of, for example, direct vehicle control adjustments.

[0064] Unless otherwise stated, the term “user” as used herein may refer to a driver, passenger, mechanic, technician, fleet manager, or any other person or entity that may be associated, for example, with a device having a user interface for providing the features and steps disclosed herein.

[0065] The preceding detailed description has been provided for purposes of illustration and description. Therefore, although specific embodiments of the new and useful invention have been described, these references are not intended to be construed as limiting the scope of the invention, except as set forth in the following claims.

Claims

1. A tire wear prediction method (200), the method (200) comprising: Signals corresponding to the dynamic mechanical behavior of the tires are collected via at least one data acquisition device mounted on the tires of the motor vehicle (210). Construct a spectrum (220) that includes the time and frequency content of the signal in a graphical manner. Implement at least one model including a predetermined spectrum associated with an unworn version of the tire for comparison with a spectrum graphically constructed of the tire's imprint region; Based on the comparison, one or more graphical features of the spectrum associated with the imprint area of ​​the tire are extracted as a predefined index of stiffness variation (250). Tire wear condition is estimated based on one or more extracted graphical features (260); and Output signals are selectively generated based on the estimated tire wear condition (271, 272). Acoustic imprint analysis is used to compare the predetermined spectrum associated with the unworn version of the tire with the spectrum constructed graphically from the imprinted regions of the tire; The graphical construction of the spectrum includes deconvolution of the signal into its respective time, frequency, and amplitude components.

2. The method (200) according to claim 1, wherein the extracted one or more graphic features include one or more of the following: High-frequency energy features identified at the imprinted region; Signal damping characteristics at the trailing edge of the imprinted region; and The tread at the imprinted region exhibits increased energy characteristics in the frequency band.

3. The method (200) according to claim 1, wherein the signal is deconvolved by short-time Fourier transform analysis.

4. The method (200) of claim 1, wherein the at least one model is selected and retrieved based on the determined tire type for implementation.

5. A system (100) for predicting tire wear, the system (100) comprising: At least one data acquisition device (112, 114, 116, 118) is mounted on a tire of a motor vehicle and configured to generate signals corresponding to the dynamic mechanical behavior of the tire. and A computing device (102, 130, 140) communicatively linked to the at least one data acquisition device to receive generated signals therefrom, wherein the computing device (102, 130, 140) is further configured to guide the execution of the steps of the method (200) according to any one of claims 1 to 4.

6. The system (100) of claim 5, wherein the output signal is provided to the display unit for selectively displaying one or more markers corresponding to the estimated tire wear condition.

7. The system (100) of claim 5, wherein the output signal is provided to the vehicle control unit for automatically intervening in one or more vehicle control attributes based on the estimated tire wear condition.

8. The system (100) of claim 5 further includes one or more data storage media (106, 132) communicatively linked to the computing device (102, 130, 140) and storing thereon the at least one model for selectively retrieving and implementing based on the determined tire type.

9. A computing device (102) for installation on a motor vehicle, the computing device (102) being communicatively linked to at least one data acquisition device (118) when installed on the motor vehicle, and further configured to guide the execution of the steps of the method (200) according to any one of claims 1 to 4.

10. The computing device (102) according to claim 9, wherein the output signal thus selectively generated is provided to a display unit for selectively displaying one or more markers corresponding to the estimated tire wear condition.

11. The computing device (102) of claim 9, wherein the output signal thus selectively generated is provided to the vehicle control unit for automatically intervening in one or more vehicle control attributes based on the estimated tire wear condition.

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