TIRE WEAR ESTIMATE

Abstract

Noise factors of a vehicle are detected for each instance. These noise factors include tire pressure, load, and tire rotational speed. The respective radii of the vehicle tire are determined for each instance. These radii are then grouped based on discrete sets of noise factors. A transitional state of vehicle tire health is estimated by extrapolating the respective grouped radii.

Description

AREA OF TECHNOLOGY

[0001] This disclosure relates to a method for estimating tire wear in vehicles. GENERAL STATE OF THE ART

[0002] Vehicles typically include multiple wheels, each consisting of a rim and a tire. Tires wear down under normal use and are eventually replaced when worn. Vehicle maintenance, such as wheel alignment, wheel balancing, routine tire rotation, and suspension maintenance, can contribute to even tire wear, thus extending tire life. SUMMARY

[0003] A system includes a computer comprising a processor and memory. The memory stores instructions executable by the processor to detect noise factors of a vehicle tire for each instance. These noise factors include tire pressure, load, and tire rotational speed. The instructions further include instructions for determining the respective radii of the vehicle tire for each instance. The instructions also include instructions for grouping the respective radii based on the respective discrete sets of noise factors. Finally, the instructions include instructions for estimating a transitional instance of a tire's health state based on extrapolating the respective grouped radii.

[0004] The instructions may also include instructions for determining the respective radii based on the respective vehicle speeds and the respective angular velocities of the vehicle tires. Furthermore, the instructions may include instructions for determining the respective radii based on the detected noise factors.

[0005] The instructions may also include instructions to operate a human-machine interface to output a message specifying the remaining service life of the vehicle tire in response to determining the remaining service life based on the estimated transition point.

[0006] The respective discrete sets of noise factors can be defined by respective minimum thresholds and respective maximum thresholds for the respective noise factors.

[0007] The transitional instance can be estimated using a probabilistic model.

[0008] The transition instance can be estimated using a deterministic model.

[0009] The transitional instance can specify a future distance traveled or a future point in time at which the tire's condition changes from healthy to unhealthy. A vehicle tire may be considered healthy based on the fact that its wear is less than a wear threshold, and unhealthy based on the fact that its wear is greater than or equal to the wear threshold.

[0010] Noise factors can be detected via vehicle sensor data.

[0011] One method involves detecting noise factors of a vehicle tire for each instance. These noise factors include tire pressure, load, and tire rotational speed. The method further involves determining the respective radii of the vehicle tire for each instance. The method also includes grouping the respective radii based on their respective discrete sets of noise factors. Finally, the method involves estimating a transitional state of health for the vehicle tire based on extrapolating the respective grouped radii.

[0012] The method can further include determining the respective radii based on the respective vehicle speeds and the respective angular velocities of the vehicle tires. The method can also include determining the respective radii based on the detected noise factors.

[0013] The procedure may also involve activating a human-machine interface to output a message indicating the remaining service life of the vehicle tire in response to determining the remaining service life based on the estimated transition point.

[0014] The respective discrete sets of noise factors can be defined by respective minimum thresholds and respective maximum thresholds for the respective noise factors.

[0015] The transitional instance can be estimated using a probabilistic model.

[0016] The transition instance can be estimated using a deterministic model.

[0017] The transitional instance can specify a future distance traveled or a future point in time at which the tire's condition changes from healthy to unhealthy. A vehicle tire may be considered healthy based on the fact that its wear is less than a wear threshold, and unhealthy based on the fact that its wear is greater than or equal to the wear threshold.

[0018] Noise factors can be detected via vehicle sensor data.

[0019] Furthermore, this document discloses a computing device programmed to execute any of the preceding process steps. Even further, this document discloses a computer program product comprising a computer-readable medium that stores instructions executable by a computer processor to execute any of the preceding process steps.

[0020] During normal vehicle driving, the tire rubber wears down. An unloaded radius of the tire can be estimated based on the tire's effective rolling radius. This unloaded radius can then be used to estimate the degree of tire wear, which in turn can be used to estimate the remaining service life of the tire. However, data containing noise factors (which may depend on current driving conditions such as the environment around the vehicle, a load in the vehicle, the vehicle's speed, etc.) can affect the estimate of the unloaded radius. Data containing these noise factors may be continuous data over certain areas, which can increase the discrepancy between the estimated remaining service life and the actual remaining service life of the vehicle tire.

[0021] As described in this paper, a computer can determine respective radii for each instance and group these radii based on discrete sets of noise factors. The computer can then estimate a transitional state of health for the tire based on these grouped radii. Analyzing the radii based on discrete sets of noise factors allows the computer to account for the noise factors when estimating the tire's radius over time, which can reduce the discrepancy between the estimated remaining service life and the actual remaining service life of the tire. Respective unloaded radii r ulThe radii used in this disclosure are exemplary for estimating the transitional state of health for vehicle tire 200. It is understood, however, that other radii (e.g., effective rolling radii r) may be used. e ) can also be used to estimate the transitional instance of the health status for the vehicle tire 200. BRIEF DESCRIPTION OF THE DRAWINGS Fig. Figure 1 is a block diagram illustrating an example vehicle control system. Fig. Figure 2 is a block diagram illustrating a side view of an example vehicle tire. Fig. Figure 3 is an exemplary curve illustrating an unloaded radius of the vehicle tire over time for a given discrete set of noise factors. Fig. Figure 4 is an exemplary flowchart of an exemplary process for estimating a transition point of the vehicle tire. DETAILED DESCRIPTION

[0022] With reference to Fig. Figure 1-3 includes an exemplary vehicle control system 100 and a vehicle 105. A vehicle computer 110 in the vehicle 105 receives data from sensors 115. The vehicle computer 110 is programmed to detect noise factors of a vehicle tire 200 for each instance. The noise factors include tire pressure, load, and tire rotational speed. The vehicle computer 110 is further programmed to determine the respective radii of the vehicle tire 200 for each instance. The vehicle computer 110 is also programmed to group the respective radii based on respective discrete sets of noise factors. The vehicle computer 110 is further programmed to estimate a transitional instance of a health state for the vehicle tire 200 based on extrapolating the respective grouped radii.

[0023] Now, with reference to Fig. 1. Vehicle 105 can be any suitable type of wheeled object. For example, the vehicle can be a bicycle, a motorcycle, a unicycle, a motor vehicle (e.g., a passenger or commercial vehicle, such as a sedan, a coupé, a truck, an off-road vehicle, a crossover vehicle, a van, a minivan, a taxi, a bus, etc.), a tractor, a steamroller, etc. Vehicle 105 can, for example, be an autonomous vehicle. In other words, Vehicle 105 can operate autonomously, so that Vehicle 105 can be driven without the constant attention of a driver; that is, Vehicle 105 can drive itself without human input.

[0024] Vehicle 105 includes one or more wheels. Each wheel can, for example, include a rim and a vehicle tire 200 (see Fig. 2) The vehicle tires 200 contact a driving surface, such as a road, and the vehicle tires 200 transmit motion from a propulsion system of the vehicle 105 to the driving surface. The rim connects the vehicle tire 200 to the propulsion system and transmits the motion from the propulsion system to the vehicle tire 200. The vehicle tire 200 can be made of rubber. The vehicle tire 200 can be pneumatic, i.e., inflated with gas, such as air, nitrogen, etc. Alternatively, one or more wheels may not have a tire, so that the rim is configured to transmit the motion from a propulsion system to the driving surface.

[0025] The vehicle 105 includes the vehicle computer 110, sensors 115, actuators 120 to operate various vehicle components 125, and a vehicle communication module 130. The communication module 130 enables the vehicle computer 110 to communicate with a remote server computer 140 and / or other vehicles (e.g., via a messaging or broadcast protocol, such as dedicated short range communications (DSRC), a cellular and / or other protocol that can support vehicle-to-vehicle, vehicle-to-infrastructure, vehicle-to-cloud, or the like, and / or via a packet network 135).

[0026] The vehicle computer 110 includes a processor and memory. The memory comprises one or more forms of computer-readable media and stores instructions executable by the vehicle computer 110 for performing various operations, including those disclosed in this document. The vehicle computer 110 may further include two or more computing devices that are operated together to perform operations of the vehicle 105, including those described in this document. Furthermore, the vehicle computer 110 may be a general-purpose computer with a processor and memory as described above, and / or may include an electronic control unit (ECU) or electronic control or the like for a specific function or set of functions, and / or may include a dedicated electronic circuit that incorporates an ASIC manufactured for a specific operation (e.g.,an ASIC for processing sensor data and / or communicating sensor data). In another example, the vehicle computer 110 might include an FPGA (field-programmable gate array), which is an integrated circuit manufactured to be user-configurable. Typically, a hardware description language, such as VHDL (Very High Speed ​​Integrated Circuit Hardware Description Language), is used in electronic design automation to describe digital systems and mixed-signal systems, such as FPGAs and ASICs. For example, an ASIC is manufactured based on VHDL programming provided prior to manufacturing, whereas logical components within an FPGA may be configured based on VHDL programming (e.g.,(stored in a working memory that is electrically connected to the FPGA circuit). In some examples, a combination of processor(s), ASIC(s) and / or FPGA circuits may be included in the vehicle computer 110.

[0027] The vehicle computer 110 can include programming to operate one or more of the drive, steering, transmission, climate control, interior and / or exterior lighting, horn, doors, etc. of the vehicle 105, as well as to determine if and when the vehicle computer 110 should control such operations instead of a human driver.

[0028] The vehicle computer 110 can include more than one processor (e.g., contained in electronic control units (ECUs) or the like, which are included in the vehicle 105) for monitoring and / or controlling various vehicle components 125 (e.g., a transmission control unit, a steering control unit, etc.) or be communicatively coupled to them. The vehicle computer 110 is generally arranged for communication within a vehicle communication network, which may include a bus in the vehicle 105, such as a Controller Area Network (CAN) or the like, and / or other wired and / or wireless mechanisms.

[0029] The vehicle computer 110 can transmit messages to various devices in the vehicle 105 via the vehicle 105 network and / or receive messages (e.g., CAN messages) from the various devices (e.g., sensors 115, an actuator 120, ECUs, etc.). Alternatively or additionally, in cases where the vehicle computer 110 actually comprises a multitude of devices, the vehicle communication network can be used for communication between devices that are referred to in this disclosure as the vehicle computer 110. Furthermore, as mentioned below, various controllers and / or sensors 115 can provide data to the vehicle computer 110 via the vehicle communication network.

[0030] The sensors 115 of the vehicle 105 can include a variety of devices known to provide data to the vehicle computer 110. For example, the sensors 115 can include optical distance and speed measurement sensor(s) (Light Detection and Ranging sensor(s) - LIDAR sensor(s)) 115, etc., which is / are located on the top of the vehicle 105, behind a windshield of the vehicle 105, around the vehicle 105, etc., and which provides relative locations, sizes, and shapes of objects surrounding the vehicle 105. As another example, one or more radar sensors 115, mounted on the bumpers of the vehicle 105, can provide data to provide locations of objects, other vehicles, etc., relative to the location of the vehicle 105. The sensors 115 can also alternatively or additionally include, for example, (one) camera sensor(s) 115 (e.g.Front view, side view, etc.), which provides / provide images of an area surrounding the vehicle 105. In the context of this disclosure, an object is a physical (i.e., material) item that has mass and can be represented by physical phenomena (e.g., light or other electromagnetic waves or sound, etc.) detectable by sensors 115. Thus, the vehicle 105, as well as other items including those discussed below, fall under the definition of "object" in this document.

[0031] The vehicle computer 110 is programmed to receive data from one or more sensors 115 essentially continuously, periodically, and / or on command from a remote server computer 140, etc. The data may, for example, include the location of the vehicle 105. Location data specifies a point or points on a surface and may be in a known format (e.g., geocoordinates, such as latitude and longitude coordinates, obtained via a navigation system, such as those known to use the Global Positioning System (GPS)). Additionally or alternatively, the data may include the speed of the vehicle 105. The vehicle speed may include longitudinal and / or lateral components. Longitudinal speed data specifies the distance between points over a time period that the vehicle requires to travel from one point to another on the surface.Lateral velocity data specifies a distance perpendicular to a line extending from one point to another over a time period that the vehicle takes to travel along the road surface. The velocity data can be obtained via the navigation system in separate instances. That is, specific velocities can be obtained in corresponding instances. The instances specify discrete moments at which the vehicle computer 110 analyzes the condition (i.e., the degree of wear) of the vehicle tire 200. The instances can be defined by a distance (e.g., miles, kilometers, etc.) traveled by the vehicle 105 and / or a time period (e.g., milliseconds, seconds, minutes, etc.).

[0032] As another example, the data can include the angular velocity of the vehicle's wheel 105. Angular velocity data indicates the distance of rotation about an axis of rotation of the wheel over time. The wheel's angular velocity data can be obtained in the respective instances via a wheel speed sensor 115. That is, specific angular velocities can be obtained in the corresponding instances. However, due to the latency in the communication between the navigation system and the vehicle computer 110, the speed data may be obtained by the vehicle computer 110 with a delay. In this situation, the vehicle computer 110 can introduce a time delay (e.g., according to known data processing techniques) to synchronize the speed data and the angular velocity data (i.e.,to shift the angular velocity data in time in order to compensate for the latency in the communication between the navigation system and the vehicle computer 110).

[0033] As another example, the data could include tire pressure data for vehicle 200. For instance, vehicle 105 might include a sensor for monitoring the air pressure of vehicle tires 200 (i.e., a tire pressure sensor - TPS). The TPS outputs data indicating the pressure level of the tires 200. The TPS uses pressure sensors mounted either inside or on the outer surface of each tire. Pressure sensors mounted inside the tires communicate using short-range wireless signals. The tire pressure data can be obtained from the respective devices. That is, specific pressures can be obtained from the relevant devices.

[0034] As another example, the data can include load data. Load data specifies the weight carried by the wheels of vehicle 105. This load data can be obtained via a weight sensor coupled to the wheel or its axle carrier. The weight sensor can communicate using short-range wireless signals or via the vehicle network. The wheel's load data can be obtained in the respective instances. That is, specific loads can be obtained in the corresponding instances.

[0035] The actuators 120 of the vehicle 105 are implemented via circuits, chips, or other electronic and / or mechanical components that can actuate various vehicle subsystems according to suitable control signals, as is known. The actuators 120 can be used to control components 125 that include the drive and steering of a vehicle 105.

[0036] In the context of this disclosure, a vehicle component 125 is one or more hardware components designed to perform a mechanical or electromechanical function or operation – such as moving the vehicle 105, slowing down or stopping the vehicle 105, steering the vehicle 105, etc. Non-limiting examples of components 125 include a drive component (which may include, for example, an internal combustion engine and / or an electric motor, etc.), a transmission component, a steering component (which may include, for example, one or more of a steering wheel, a steering rack, etc.), a suspension component (which may include, for example, one or more of a damper, such as a shock absorber or a strut, a bushing, a spring, a control arm, a ball joint, a linkage, etc.), a parking aid component, an adaptive cruise control component, etc.

[0037] The vehicle 105 also includes a human-machine interface (MMS) 118. The MMS 118 includes user input devices such as buttons, keys, switches, pedals, levers, touchscreens, and / or microphones, etc. The input devices can include sensors 115 to detect user input and provide user input data to the vehicle computer 110. That is, the vehicle computer 110 can be programmed to receive user input from the MMS 118. The occupant can provide user input via the MMS 118 (e.g., by selecting a virtual button on a touchscreen display, by providing voice commands, etc.).For example, a touchscreen display included in an MMS 118 can include sensors 115 to detect that an occupant has selected a virtual button on the touchscreen display, for example to select or deselect an operation, whereby the input can be received in the vehicle computer 110 and used to determine the selection of the user input.

[0038] The MMS 118 typically also includes output devices, such as displays (including touchscreen displays), speakers, and / or lights, etc., which output signals or data to the occupant. The HMI 118 is connected to the vehicle communication network and can send and / or receive messages to and from the vehicle computer 110 and other vehicle subsystems.

[0039] Additionally, the vehicle computer 110 can be configured to communicate with devices outside the vehicle 105 via a vehicle-to-vehicle communication module 130 or an interface (e.g., through vehicle-to-vehicle (V2V) or vehicle-to-infrastructure (V2X) wireless communication (cellular and / or short-range radio communication, etc.) with another vehicle and / or with a remote server computer 140 (typically via direct radio frequency communication)). The communication module 130 could include one or more mechanisms, such as a transceiver, through which the computers of vehicles can communicate using any desired combination of wireless (e.g., cellular, wireless, satellite, microwave, and radio frequency) communication mechanisms and any desired network topology (or topologies if a variety of communication mechanisms are used).Examples of communication provided via the Communication Module 130 include cellular, Bluetooth, IEEE 802.11, Dedicated Short Range Communication (DSRC), Cellular V2X (CV2X), and / or wide area networks (WANs), including the Internet, which provide data communication services. The term "V2X" is used herein to refer to communication that can take place from vehicle to vehicle (V2V) and / or from vehicle to infrastructure (V2I) and that can be provided by the Communication Module 130 according to any suitable short range communication mechanism (e.g., DSRC, cellular, or the like).

[0040] The network 135 represents one or more mechanisms by which a vehicle computer 110 can communicate with remote computing devices (e.g., the remote server computer 140, another vehicle computer, etc.). Accordingly, the network 135 can be one or more of various wired or wireless communication mechanisms, including any desired combination of wired (e.g., cable and fiber optic) and / or wireless (e.g., cellular, wireless, satellite, microwave, and radio frequency) communication mechanisms and any desired network topology (or network topologies if multiple communication mechanisms are used). Example communication networks include wireless communication networks (e.g., using Bluetooth®, Bluetooth® Low Energy (BLE), IEEE 802.11, vehicle-to-vehicle (V2V) communication), such as dedicated short-range communication (DSRC), etc.), local area networks (LAN) and / or wide area networks (WAN) that include the Internet and provide data communication services.

[0041] In some examples, the remote server computer 140 can be a conventional computing device (i.e., one that includes one or more processors and one or more memories) programmed to perform operations as disclosed in this document. Furthermore, the remote server computer 140 can be accessed via the network 135 (e.g., the Internet, a mobile network, and / or another wide area network).

[0042] The vehicle computer 110 is programmed to estimate a transitional state of the vehicle tire 200's health. This transitional state indicates a future distance traveled (e.g., miles, kilometers, etc.) and / or a future point in time (e.g., days, weeks, etc.) at which the tire's health will change from healthy to unhealthy. In this context, a vehicle tire 200 is healthy if its wear is less than a wear threshold, and unhealthy if its wear is greater than or equal to the wear threshold. The wear threshold specifies the maximum level of wear on the vehicle tire 200 below which it meets design and performance specifications. The wear threshold can be stored (e.g., in the memory of the vehicle computer 110).The wear threshold can be determined empirically (e.g., based on tests and / or simulation to determine performance capabilities for different types of tires that are subject to different levels of wear).

[0043] The vehicle computer 110 can determine the remaining service life of the vehicle tire 200 based on the transition instance. For example, the vehicle computer 110 can determine the remaining service life based on a difference between the actual distance traveled by the vehicle tire 200 and the estimated distance traveled specified by the transition instance. In this situation, the vehicle computer 110 can display the remaining service life of the vehicle tire 200 as a distance to be traveled (e.g., miles, kilometers, etc.). Additionally or alternatively, the vehicle computer 110 can then divide the difference by an average distance traveled (e.g., determined based on historical data from sensors 115) over a given period of time (e.g., miles per day) to determine the remaining service life of the vehicle tire 200.In this situation, the vehicle computer 110 can display the remaining service life as a time period.

[0044] To estimate the transition point, it is assumed that the vehicle tire 200 operates under free-rolling conditions (i.e., is not subject to longitudinal and / or lateral slip). A vehicle tire 200 has at least three different radius lengths under free-rolling conditions (see Fig. 2): an unloaded radius r ul , a loaded radius r l and an effective rolling radius r e An unloaded radius r ul The radius of the vehicle tire 200 (i.e., a distance from the center C of the wheel to an outer circumferential surface of the vehicle tire 200) is when the vehicle tire 200 is not in contact with the ground surface. A loaded radius r lis the distance between the center C of the wheel and a point where the vehicle tire touches the ground. An effective rolling radius r e is a distance from the center C of the wheel to an instantaneous center of rotation C i (i.e., a point on vehicle tire 200 that has no speed in the respective instance) of vehicle tire 200. The vehicle computer 110 estimates the transition instance based on the effective rolling radius r. e and the unloaded radius r ul ·

[0045] The vehicle computer 110 is programmed to determine the effective rolling radius r eto determine the vehicle tire 200 based on the data from the sensors 115. For example, the vehicle computer 110 can obtain the speed of the vehicle 105 (e.g., for the respective instances) and the angular velocity of the wheel (e.g., for the respective instances), as discussed above. The vehicle computer 110 can then determine the effective rolling radius r e Determine according to the following: re=vω where v is the vehicle speed and ω is the angular velocity of the wheel. Specific effective rolling radii r e can be determined in the appropriate instances.

[0046] The vehicle computer 110 is programmed to detect noise factors for the respective instances based on data from the sensors 115. This means that specific noise factors can be obtained in the corresponding instances. The noise factors include tire pressure, load, and tire rotational speed. The vehicle computer 110 can, for example, determine the tire pressure based on the pressure data, as discussed above. The vehicle computer 110 can, for example, determine the load based on the load data, as discussed above. The vehicle computer 110 can, for example, determine the tire speed based on the angular velocity data, as discussed above.

[0047] When detecting noise factors, the vehicle computer 110 is programmed to determine the respective unloaded radii r. ul for the respective instances based on the respective effective rolling radii r eand to determine the respective detected noise factors for the respective instances. Specific unloaded radii r ul can be determined in the appropriate instances. The unloaded radii r ul can be determined according to the following: rul=re+ρ0(D tan−1(Bρρ0)+Eρρ0) where ρ0 is an expected deformation of a vehicle tire 200 subjected to a nominal load, ρ is an actual deformation of the vehicle tire 200 (e.g., determined according to known calculation methods for a given tire pressure, load, and type of vehicle tire 200) for the respective instance, D is a parameter based on the load and varies when the load varies, B is a parameter based on a downward force on the vehicle tire 200 along a tire characteristic curve (i.e., a graphical representation of a relationship between various tire properties, such as slip angle, lateral force, coefficient of friction, slip velocity, etc.), and E is a parameter based on the tire stiffness and varies with the tire pressure.B, D, and E can, for example, be derived from a lookup table or similar that assigns the respective values ​​of B, D, and E to the respective discrete sets of noise factors. The lookup table can be stored (e.g., in the memory of the vehicle computer 110). The lookup table can be determined empirically (e.g., based on tests and / or simulation to determine different values ​​of the respective parameters B, D, and E for different values ​​of the respective noise factors).

[0048] The vehicle computer 110 can then determine the respective unloaded radii r ul group based on respective discrete sets of noise factors. That is, the respective uncontaminated radii r ulNoise factors, which are determined (e.g., according to Equation 2) based on respective detected noise factors lying within a common discrete set of noise factors, are grouped together. The respective discrete sets of noise factors are defined by their respective minimum and maximum thresholds. These minimum and maximum thresholds define respective environments (or bins) for each set of noise factors, related across respective ranges (i.e., differences between maxima and minima) of the respective noise factors. The respective minimum and maximum thresholds can be determined empirically (e.g., by...).based on tests and / or simulation to determine a number of neighborhoods that can be analyzed given the available computing resources of the vehicle computer 110).

[0049] Fig. Figure 3 is a hypothetical representation of data showing an exemplary course of 300 of respective grouped unloaded radii r. ul The time course t (i.e., from one instance to another) is illustrated for each discrete set of noise factors. The vehicle computer 110 can generate respective curves 300 for each of the respective discrete sets of noise factors, which graphically represent a change in the respective grouped unloaded radii r. ul The vehicle computer 110 represents the time course t. It can then calculate future uncontaminated radii r for the respective discrete sets of noise factors. ulp (in Fig. 3 (shown in dashed lines) predict. To predict the future unloaded radii r ulp To predict the respective discrete sets of noise factors, the vehicle computer 110 can generate a regression line L for the respective grouped unloaded radii r. ul (e.g., according to known calculation methods (e.g., linear least squares, linear regression, random sample consensus (RANSAC), etc.)). The regression line L is a line through points 305 (which represent the respective unloaded radii r). ul (represented in the respective instances), minimizing the respective distances between the respective points 305 and the line L. The vehicle computer 110 can then extrapolate the regression line L (e.g., using known extrapolation methods such as linear extrapolation) to determine the future unloaded radii r. ulpto predict for the respective discrete set of noise factors in future time instances.

[0050] The vehicle computer 110 can generate respective discrete transition instances (i.e., respective transition instances for the respective discrete sets of noise factors) based on the respective predicted future unloaded radii r. ulp Determine. For example, the vehicle computer 110 can determine the respective predicted future unloaded radii r. ulp for the respective discrete set of noise factors with a threshold value r ult Compare for the unloaded radius. The unloaded radius threshold r ult Specifies a radius at which the wear of the vehicle tire reaches the wear threshold value. The unloaded radius threshold value r ultcan be determined empirically (e.g., based on tests and / or simulations to determine different radii at which different types of vehicle tires reach corresponding wear thresholds) or can be set based on vehicle performance requirements. The threshold r ult The unloaded radius can be stored (e.g., in a memory location of the vehicle computer 110). When identifying the respective predicted future unloaded radius r ulp , which defines the threshold of the unloaded radius r ult first reached or exceeded, for each of the respective discrete sets of noise factors, the vehicle computer 110 determines that the respective discrete transition instance is an identical future instance corresponding to the respective predicted future uncontaminated radius r ulp is assigned to the one who first reaches the threshold value r ultfor the unloaded radius reached or exceeded.

[0051] The vehicle computer 110 can estimate the transition instance based on the respective discrete transition instances. For example, the vehicle computer 110 can use a probabilistic model to estimate the transition instance. In this situation, the vehicle computer 110 can determine an earliest discrete transition instance (i.e., a transition instance predicted to occur in a future instance that is closest to a current instance (e.g., in terms of distance and / or time)) from the respective discrete transition instances (e.g., based on a minimum function) and a latest discrete transition instance (i.e., a transition instance predicted to occur in a future instance that is furthest away from a current instance (e.g., in terms of distance and / or time)) from the respective discrete transition instances (e.g., based on a maximum function).The vehicle computer 110 can then estimate that the transition instance will lie between the earliest discrete transition instance and the latest discrete transition instance.

[0052] As another example, the vehicle computer 110 can use a deterministic model to estimate the transition instance. In this situation, the vehicle computer 110 can estimate a number of points 305 (i.e., unloaded radii r). ul The vehicle computer 110 counts the points 300 in each of the respective discrete sets of noise factors and selects the curve 300 with the largest number of points 305. The vehicle computer 110 can then estimate that the transition instance is the discrete transition instance of the selected curve 300.

[0053] The vehicle computer 110 can be programmed to output the remaining service life of the vehicle tire 200 to a user. The vehicle computer 110 can determine the remaining service life of the vehicle tire 200 based on the estimated transition instance, as discussed above. For example, the vehicle computer 110 can actuate the HMI 118 to output a message specifying the remaining service life of the vehicle tire 200. In the situation where the probabilistic model is used, the message can specify a range (defined, for example, by respective remaining service lives determined based on the earliest discrete transition instance and the latest discrete transition instance, respectively) for the remaining service life of the vehicle tire 200.In the situation where the deterministic model is used, the message can specify a remaining usage time, which is determined based on the discrete transition instance of the selected history 300. The message can be, for example, a visual message (e.g., text displayed on a screen on a dashboard, an infotainment system, and / or a user device (e.g., the user's mobile phone)). As another example, the message can be an audible message (e.g., output via speakers in the vehicle 105 or the user device).

[0054] Fig.Figure 4 is a representation of an exemplary process 400 for determining the remaining service life of a vehicle tire 200. The process 400 begins at a block 405. The process 400 can be executed by a vehicle computer 110 contained in a vehicle 105, which executes program instructions stored in a memory therein.

[0055] In block 405, the vehicle computer determines 110 respective effective rolling radii r. e of the vehicle tire 200 for respective instances. For example, the vehicle computer 110 can obtain speed data for the respective instances (e.g., via a navigation system) and angular velocity data of the vehicle tire 200 for the respective instances (e.g., via sensors 115), as discussed above. The vehicle computer 110 can then determine the respective effective rolling radii r e Calculate using the preceding equation 1. Process 400 transitions to block 410.

[0056] In block 410, the vehicle computer detects 110 noise factors for the respective instances. These noise factors include tire pressure, load, and tire rotational speed. The vehicle computer 110 can determine the respective noise factors based on data from sensors 115, as discussed above. Process 400 then proceeds to block 415.

[0057] In block 415, the vehicle computer determines 110 respective unloaded radii r. ul of the vehicle tire 200 for the respective instances. The respective unloaded radii r ul The values ​​of the vehicle tire 200 can be determined according to the preceding equation 2. Process 400 transitions to a block 420.

[0058] In block 420, the vehicle computer 110 groups the respective unloaded radii r. ulbased on respective discrete sets of noise factors, as discussed above. The vehicle computer 110 can then generate respective profiles 300, which represent a change in the respective grouped unloaded radii r. ul Over time t, the respective discrete sets of noise factors are generated, as discussed above. Process 400 transitions to block 425.

[0059] For block 425, the vehicle computer 110 estimates respective discrete transition instances based on the respective trajectories 300. For example, the vehicle computer 110 can estimate the grouped unloaded radii r ul extrapolate via a regression line L to determine future unloaded radii r ulp to predict future instances, as discussed above. The vehicle computer 110 can then predict the future unloaded radii r. ulp with an unloaded radius threshold value r ultcompare, as discussed above. The vehicle computer 110 determines that the respective discrete transition instance is an identical future instance corresponding to the respective predicted future uncontaminated radius r. ulp is assigned to the one who first reaches the threshold value r ult for the uncontaminated radius, as discussed above. Process 400 transitions to Block 430.

[0060] At block 430, the vehicle computer 110 estimates a transition instance based on the respective discrete transition instances. For example, the vehicle computer 110 can use a probabilistic model to estimate the transition instance, as discussed above. As another example, the vehicle computer 110 can use a deterministic model to estimate the transition instance, as discussed above. Process 400 transitions to block 435.

[0061] In block 435, the vehicle computer 110 outputs the remaining service life of the vehicle tire 200. The vehicle computer 110 can, for example, activate an HMI 118 to issue an audible and / or visual message to a user indicating the remaining service life of the vehicle tire 200, as discussed above. The vehicle computer 110 can determine the remaining service life of the vehicle tire 200 based on the transition instance, as discussed above. Process 400 transitions to block 440.

[0062] At block 440, the vehicle computer 110 determines whether to continue with process 400. For example, the vehicle computer 110 may decide not to continue if the vehicle 105 is in an OFF state. Conversely, the vehicle computer 110 may decide to continue while the vehicle 105 is in an ON state. If the vehicle computer 110 decides to continue, process 400 returns to block 405. Otherwise, process 400 terminates.

[0063] In general, the described computing systems and / or devices can use any of a range of computer operating systems, including, but not limited to, versions and / or variants of Ford's Sync® application, AppLink / Smart Device Link Middleware, Microsoft Automotive®, Microsoft Windows®, Unix (e.g., the Solaris® operating system, distributed by Oracle Corporation in Redwood Shores, California), AIX UNIX, distributed by International Business Machines in Armonk, New York, Linux, Mac OSX and iOS, distributed by Apple Inc. in Cupertino, California, BlackBerry OS, distributed by Blackberry, Ltd. in Waterloo, Canada, and Android, developed by Google, Inc. and the Open Handset Alliance, or the QNX® CAR infotainment platform, offered by QNX Software Systems.Examples of computing devices include, but are not limited to, an early onboard computer, a computer workstation, a server, a desktop, notebook, laptop or handheld computer, or any other computing system and / or any other computing device.

[0064] Computers and computing devices generally contain computer-executable instructions, which can be executed by one or more computing devices, such as those listed above. Computer-executable instructions can be compiled or interpreted by computer programs created using a variety of programming languages ​​and / or technologies, including, but not limited to, Java™, C, C++, Matlab, Simulink, Stateflow, Visual Basic, JavaScript, Perl, HTML, and others, either alone or in combination. Some of these applications can be compiled and executed on a virtual machine, such as the Java Virtual Machine, the Dalvik Virtual Machine, or similar. Generally, a processor (e.g., a microprocessor) receives instructions (e.g., from memory, a computer-readable medium, etc.).) and executes these instructions, thereby carrying out one or more processes, including one or more of the processes described herein. Such instructions and other data can be stored and transmitted using a variety of computer-readable media. A file in a computing device is generally a collection of data stored on a computer-readable medium, such as a storage medium, random-access memory, etc.

[0065] Storage can include a computer-readable medium (also called a processor-readable medium), which is any non-transient (e.g., tangible) medium involved in providing data (e.g., instructions) that can be read by a computer (e.g., by a computer's processor). Such a medium can take many forms, including, without limitation, non-volatile and volatile media. Non-volatile media can include, for example, optical disks or magnetic disks and other permanent storage devices. Volatile media can include, for example, dynamic random access memory (DRAM), which is typically main memory.Such instructions can be transmitted through one or more transmission media, including coaxial cables, copper wire, and fiber optics, including the wires that comprise a system bus coupled to a processor of an ECU. Common forms of computer-readable media include, for example, RAM, a PROM, an EPROM, a FLASH EEPROM, any other memory chip or cartridge, or any other medium from which a computer can read.

[0066] Databases, data repositories, or other data storage systems described in this document can include various mechanisms for storing, accessing, and retrieving different types of data, including a hierarchical database, a set of files in a file system, an application database in a user-defined format, a relational database management system (RDBMS), and so on. Each such data storage system is generally contained within a computing device that employs a computer operating system, such as one of those mentioned above, and is accessed in one or more of a variety of ways over a network. A file system can be accessed by a computer operating system and can contain files stored in various formats.An RDBMS generally uses the Structured Query Language (SQL) in addition to a language for creating, storing, editing and executing stored procedures, such as the PL / SQL language mentioned above.

[0067] In some examples, system elements may be implemented as computer-readable instructions (e.g., software) on one or more computing devices (e.g., servers, personal computers, etc.) stored on computer-readable media associated with them (e.g., disks, memory, etc.). A computer program product may include such instructions stored on computer-readable media for performing the functions described herein.

[0068] With regard to the media, processes, systems, procedures, heuristics, etc., described herein, it is understood that, even if the steps of such processes, etc., have been described as being carried out in a specific sequence, such processes may nevertheless be implemented in such a way that the described steps are carried out in a sequence that differs from the sequence described herein. It is further understood that certain steps may be carried out simultaneously, other steps may be added, or certain steps described herein may be omitted. In other words, the descriptions of processes in this document serve the purpose of illustrating certain embodiments and should in no way be interpreted as limiting the patent claims.

[0069] The revelation has been described in an illustrative manner, and it is understood that the terminology used is intended to be descriptive and not restrictive. In light of the foregoing teachings, many modifications and variations of the present revelation are possible, and the revelation can be implemented differently than specifically described.

[0070] According to the present invention, a system is provided comprising a computer including a processor and a memory, wherein the memory stores instructions that can be executed by the processor to: detect noise factors of a vehicle tire for respective instances, wherein the noise factors include tire pressure, load, and tire rotational speed; determine respective radii of the vehicle tire for the respective instances; group the respective radii based on respective discrete sets of noise factors; and estimate a transitional instance of a health state for the vehicle tire based on extrapolating the respective grouped radii.

[0071] According to one embodiment, the instructions also include instructions for determining the respective radii based on the respective vehicle speeds and the respective angular velocities of the vehicle tire.

[0072] According to one embodiment, the instructions also include instructions for determining the respective radii based on the detected noise factors.

[0073] According to one embodiment, the instructions further include instructions for operating a human-machine interface to output a message specifying a remaining service life of the vehicle tire, in response to determining the remaining service life based on the estimated transition point.

[0074] According to one embodiment, the respective discrete sets of noise factors are defined by respective minimum thresholds and respective maximum thresholds for the respective noise factors.

[0075] According to one embodiment, the transitional instance is estimated using a probabilistic model.

[0076] According to one embodiment, the transition instance is estimated using a deterministic model.

[0077] According to one embodiment, the transitional instance specifies a future distance traveled or a future point in time at which the health status changes from healthy to unhealthy.

[0078] According to one embodiment, the vehicle tire is healthy based on the fact that the wear of the vehicle tire is less than a wear threshold value, and the vehicle tire is not healthy based on the fact that the wear is greater than or equal to the wear threshold value.

[0079] According to one embodiment, the noise factors are detected via vehicle sensor data.

[0080] According to the present invention, a method comprises: detecting noise factors of a vehicle tire for respective instances, wherein the noise factors include a tire pressure, a load, and a tire rotational speed; determining respective radii of the vehicle tire for the respective instances; grouping the respective radii based on respective discrete sets of noise factors; and estimating a transitional instance of a health state for the vehicle tire based on extrapolating the respective grouped radii.

[0081] In one aspect of the invention, the method involves determining the respective radii based on the respective vehicle speeds and the respective angular velocities of the vehicle tire.

[0082] In one aspect of the invention, the method further includes determining the respective radii based on the detected noise factors.

[0083] In one aspect of the invention, the method involves actuating a human-machine interface to output a message indicating a remaining service life of the vehicle tire, in response to determining the remaining service life based on the estimated transition point.

[0084] In one aspect of the invention, the respective discrete sets of noise factors are defined by respective minimum thresholds and respective maximum thresholds for the respective noise factors.

[0085] In one aspect of the invention, the transitional instance is estimated using a probabilistic model.

[0086] In one aspect of the invention, the transitional instance is estimated using a deterministic model.

[0087] In one aspect of the invention, the transitional instance specifies a future distance traveled or a future point in time at which the state of health changes from healthy to unhealthy.

[0088] In one aspect of the invention, the vehicle tire is healthy on the basis that the wear of the vehicle tire is less than a wear threshold value, and the vehicle tire is not healthy on the basis that the wear is greater than or equal to the wear threshold value.

[0089] In one aspect of the invention, noise factors are detected via vehicle sensor data.

Claims

[1] Procedure, encompassing: Detecting noise factors of a vehicle tire for each instance, wherein the noise factors include tire pressure, load, and tire rotational speed; Determining the respective radii of the vehicle tire for the respective instances; Grouping the respective radii based on respective discrete sets of noise factors; and Estimating a transitional instance of a health state for the vehicle tire based on extrapolating the respective grouped radii. [2] Method according to claim 1, further comprising determining the respective radii based on the respective vehicle speeds and the respective angular velocities of the vehicle tire. [3] Method according to claim 2, further comprising determining the respective radii further on the basis of the detected noise factors. [4] Method according to claim 1, further comprising actuating a human-machine interface to output a message indicating a remaining service life of the vehicle tire in response to determining the remaining service life based on the estimated transition point. [5] Method according to claim 1, wherein the respective discrete sets of noise factors are defined by respective minimum thresholds and respective maximum thresholds for the respective noise factors. [6] Method according to claim 1, wherein the transition instance is estimated using a probabilistic model. [7] Method according to claim 1, wherein the transition instance is estimated using a deterministic model. [8] Method according to claim 1, wherein the transitional instance specifies a future distance traveled or a future time at which the health condition changes from healthy to unhealthy. [9] Method according to claim 8, wherein the vehicle tire is healthy on the basis that wear of the vehicle tire is less than a wear threshold value and the vehicle tire is not healthy on the basis that wear is greater than or equal to the wear threshold value. [10] Method according to claim 1, wherein the noise factors are detected via vehicle sensor data. [11] Computer programmed to perform the method according to any one of claims 1-10. [12] Computer program product comprising instructions to execute the method according to any one of claims 1-10. [13] Vehicle comprising a computer programmed to perform the method according to any one of claims 1-10.

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