Method for estimating tire grip

Abstract

A method for estimating the grip of a tire supporting a vehicle includes: generating a first set of data from a sensor unit mounted on the tire; generating a second set of data from a sensor unit mounted on the tire and from data obtained from the vehicle; and generating a third set of data from data obtained from the vehicle and the Internet. A grip estimation module is provided. The first set of data, the second set of data, and the third set of data are received in the grip estimation module. The grip estimation module uses the first set of data, the second set of data, and the third set of data to calculate a friction probability distribution, and the friction probability distribution is input into at least one vehicle system.

Description

Technical Field

[0001] The present invention generally relates to tire monitoring. More particularly, the present invention relates to systems and methods for sensing specific tire characteristics to predict or estimate certain conditions of a tire. Specifically, the present invention relates to a method for estimating tire grip to improve the accuracy of systems that rely on tire performance. Background Art

[0002] Multiple tires support a vehicle and transfer driving and braking forces from the vehicle to the road surface. It is often beneficial to sense tire characteristics in real time during vehicle operation and use those characteristics to estimate the condition of each tire.

[0003] Methods have been developed for sensing tire characteristics and then estimating the condition of a tire, which are referred to as tire estimation methods. Such methods take into account specific tire characteristics such as tire inflation pressure, tire temperature, tread depth, and tire footprint length, as well as road conditions, to estimate tire conditions such as tire wear state and / or tire load. The estimated tire conditions can then be used to determine whether a tire needs to be replaced, and / or the estimated tire conditions can be input into a vehicle control system to improve the accuracy of such a system.

[0004] It has been found that, in addition to tire wear state and tire load, an estimate of real-time tire grip as a condition can be useful as an input to a vehicle control system.

[0005] In the prior art, the peak grip level of a tire has been measured through laboratory or track testing. For example, in such tests, the ability of a tire to provide traction on different types of road surfaces has been measured. Such surfaces can include paved, unpaved, dry, wet, snow-covered, icy, etc. The resulting test measurements are often used in tire design and / or performance calculations.

[0006] As mentioned above, it has been found that it is useful to obtain an estimate of tire grip in real time during vehicle operation rather than only under test conditions. Such real-time estimates can be beneficial for complex vehicle control systems. However, the development of real-time estimates of tire grip in the prior art has been limited to reactive estimates that are generated in response to measurements of tire slip during traction or braking conditions.

[0007] While methods for generating passive real-time estimates of tire grip can be useful, it would be advantageous to develop proactive rather than passive methods for generating real-time estimates of tire grip. Proactive methods can generate accurate real-time estimates of tire grip without inducing slip by accelerating or braking to excite the tire. Such methods can be employed with other vehicle systems to improve the operation of such systems.

[0008] As a result, there is a need in the art for a method that provides a proactive estimate of peak tire grip levels in real time during vehicle operation. SUMMARY OF THE INVENTION

[0009] In accordance with one aspect of an exemplary embodiment of the present invention, a method for estimating the grip of a tire supporting a vehicle is provided. The method includes the steps of: generating a first set of data from a sensor unit mounted on the tire; generating a second set of data from the sensor unit mounted on the tire and from data obtained from the vehicle; and generating a third set of data from data obtained from the vehicle and the Internet. A grip estimation module is provided, and the first set of data, the second set of data, and the third set of data are received in the grip estimation module. The grip estimation module is utilized to calculate a friction probability distribution using the first set of data, the second set of data, and the third set of data. The friction probability distribution is input into at least one vehicle system.

[0010] Solution 1. A method for estimating the grip of a tire supporting a vehicle, the method comprising the steps of:

[0011] generating a first set of data from a sensor unit mounted on the tire;

[0012] generating a second set of data from the sensor unit mounted on the tire and from data obtained from the vehicle;

[0013] generating a third set of data from data obtained from the vehicle and the Internet;

[0014] providing a grip estimation module;

[0015] receiving the first set of data, the second set of data, and the third set of data in the grip estimation module;

[0016] utilizing the grip estimation module to calculate a friction probability distribution using the first set of data, the second set of data, and the third set of data; and

[0017] inputting the friction probability distribution into at least one vehicle system.

[0018] Solution 2. The method for estimating the grip of a tire supporting a vehicle according to Solution 1, wherein the first set of data includes the measured tire pressure, the measured tire temperature, and the measured tire footprint length.

[0019] Solution 3. The method for estimating the grip of a tire supporting a vehicle according to Solution 1, wherein the first set of data includes tire identification information.

[0020] Solution 4. The method for estimating the grip of a tire supporting a vehicle according to Solution 3, wherein the tire identification information includes at least one of the following: tire size, tire type, tire segment, traction parameter, weather parameter, Department of Transportation code, and wet grip index.

[0021] Solution 5. The method for estimating the grip of a tire supporting a vehicle according to Solution 1, wherein the second set of data includes the current tire load and the current tire wear state.

[0022] Solution 6. The method for estimating the grip of a tire supporting a vehicle according to Solution 5, further comprising the step of calculating the current tire load and the current tire wear state using a sensor fusion module.

[0023] Solution 7. The method for estimating the grip of a tire supporting a vehicle according to Solution 6, wherein:

[0024] the data from the sensor unit mounted on the tire includes tire pressure, tire temperature, tire footprint length, and tire identification information, each of which is input into the sensor fusion module; and

[0025] the data obtained from the vehicle includes vehicle system data from the vehicle CAN bus, and the vehicle system data is input into the sensor fusion module.

[0026] Solution 8. The method for estimating the grip of a tire supporting a vehicle according to Solution 1, wherein the third set of data includes the output from a road surface condition classification module.

[0027] Solution 9. The method for estimating the grip of a tire supporting a vehicle according to Solution 8, wherein:

[0028] the data obtained from the vehicle includes vehicle system data from the vehicle CAN bus, and the vehicle system data is input into the road surface condition classification module; and

[0029] the data from the Internet includes weather data, and the weather data is input into the road surface condition classification module.

[0030] Solution 10. The method for estimating the grip of a tire supporting a vehicle according to Solution 8, wherein:

[0031] The data from the Internet is obtained from a web application programming interface.

[0032] Solution 11. The method for estimating the grip of a tire supporting a vehicle according to Solution 8, wherein the output from the road surface condition classification module includes at least one of the following: road condition, relative humidity, ambient air temperature, rough unevenness index, and road topology.

[0033] Solution 12. The method for estimating the grip of a tire supporting a vehicle according to Solution 1, wherein the grip estimation module is stored on a processor installed on the vehicle.

[0034] Solution 13. The method for estimating the grip of a tire supporting a vehicle according to Solution 1, wherein the grip estimation module is stored on a cloud-based processor.

[0035] Solution 14. The method for estimating the grip of a tire supporting a vehicle according to Solution 1, wherein the grip estimation module includes a pre-trained statistical model.

[0036] Solution 15. The method for estimating the grip of a tire supporting a vehicle according to Solution 1, wherein the friction probability distribution indicates the average expected value of the grip level of the tire, the variance of the grip level of the tire on the road surface, and the type of distribution.

[0037] Solution 16. The method for estimating the grip of a tire supporting a vehicle according to Solution 1, wherein the at least one vehicle system includes at least one of the following: autonomous emergency braking system, curved road speed limit warning system, anti-lock braking system, road friction estimation system, electronic stability system, traction control system, and adaptive cruise control system. Description of the Drawings

[0038] The present invention will be described by way of example and with reference to the accompanying drawings, in which:

[0039] Figure 1 is a schematic perspective view of a vehicle including a tire, which adopts an exemplary embodiment of the method for estimating tire grip of the present invention;

[0040] Figure 2 is a schematic diagram of the steps and system of an exemplary embodiment of the method for estimating tire grip of the present invention; and

[0041] Figure 3 It is a graph of the tire-road-friction probability distribution generated by an exemplary embodiment of the method for estimating tire grip according to the present invention.

[0042] Throughout the drawings, like numerals refer to like parts.

[0043] Definition

[0044] "ANN" or "artificial neural network" is an adaptive tool for non-linear statistical data modeling, which changes its structure based on external or internal information flowing through the network during the learning phase. The ANN neural network is a non-linear statistical data modeling tool for modeling the complex relationship between input and output or finding patterns in data.

[0045] "Axial" and "axially" mean a line or direction parallel to the axis of rotation of the tire.

[0046] "CAN bus" or "CAN bus system" is an abbreviation for the Controller Area Network system, which is a vehicle bus standard designed to allow microcontrollers and devices to communicate with each other within a vehicle without a main computer. The CAN bus is a message-based protocol, which is specifically designed for vehicle applications.

[0047] "Circumferential" means a line or direction extending along the perimeter of the surface of the annular tread perpendicular to the axial direction.

[0048] "Equatorial center plane (CP)" means a plane perpendicular to the axis of rotation of the tire and passing through the center of the tread.

[0049] "Footprint" means the ground contact surface or contact area produced by a tire tread with a flat surface when the tire rotates or rolls.

[0050] "Inner side" means the side of the tire closest to the vehicle when the tire is mounted on a wheel and the wheel is mounted on a vehicle.

[0051] "Lateral" means the axial direction.

[0052] "Lateral edge" means a line tangent to the axially outermost tread ground contact surface or footprint of the tire as measured under normal load and tire inflation, and these lines are parallel to the equatorial center plane.

[0053] "Net contact area" means the total area of the ground contact tread elements between the lateral edges around the entire circumference of the tire tread divided by the total area of the entire tread between the lateral edges.

[0054] "Outer side" means the side of the tire farthest from the vehicle when the tire is mounted on a wheel and the wheel is mounted on a vehicle.

[0055] "Radial" and "radially" mean in a direction radially toward or away from the axis of rotation of the tire.

[0056] "Rib" means a circumferentially extending rubber strip on the tread defined by at least one circumferential groove and a second such groove or lateral edges, the strip not being separated by full-depth grooves laterally.

[0057] "Tread element" or "traction element" means a rib or block element defined by the shape of having adjacent grooves.

[0058] "Tread arc width" means the arc length of the tire tread as measured between the lateral edges of the tread. DETAILED DESCRIPTION

[0059] REFERENCE Figures 1 to 3 , an exemplary embodiment of a method for estimating tire grip according to the present invention is indicated at 10.

[0060] SPECIAL REFERENCE Figure 1 , the method 10 for estimating tire grip estimates the grip of each tire 12 supporting the vehicle 14. It will be understood that the vehicle 14 can be any vehicle type and is shown by way of example as a passenger vehicle. The tire 12 has a conventional construction and each tire is mounted on a corresponding wheel 16, as known to those skilled in the art. Each tire 12 includes a pair of sidewalls 18 extending to a circumferential tread 20, which wears due to road wear over time. A liner 22 is disposed on the inner surface of the tire 12 and forms an inner cavity 24 when the tire is mounted on the wheel 16, the inner cavity being filled with a pressurized fluid such as air.

[0061] A sensor unit 26 is mounted to each tire 12, such as by attachment to the liner 22 (by means such as an adhesive), and measures certain characteristics of the tire, such as tire pressure 28 ( Figure 2 ) and temperature 30. For this reason, the sensor unit 26 preferably includes a pressure sensor and a temperature sensor and can have any known configuration, such as a tire pressure management system (TPMS) sensor. The sensor unit 26 preferably also includes an electronic memory capacity for storing identification (ID) information of each tire 12 (referred to as tire ID information 32). The sensor unit 26 preferably also measures the contact patch length 34 of the tire 12. It will be understood that the sensor unit 26 can be a single unit or can include more than one unit, and the sensor unit can be mounted on a structure of the tire 12 other than the liner 22.

[0062] Turning to Figure 2, Method 10 for estimating tire grip includes directly measuring tire parameters using sensor unit 26. As mentioned above, the tire parameters include the pressure inside cavity 24 (which is referred to as tire inflation pressure 28), the temperature inside the cavity (which is referred to as tire temperature 30), the length of the footprint of tire 12 along the tire equatorial center plane (which is referred to as footprint length 34), and tire ID information 32.

[0063] Tire ID information 32 may include or be associated with specific data for each tire 12, and the data includes: tire size, such as rim size, width, and outer diameter; tire type, such as all-weather, summer, winter, off-road, etc.; tire segment, which is the specific production line to which the tire belongs; predetermined traction or weather parameters, such as the three-peak snowflake mark (3PSF) for winter tires; Department of Transportation (DOT) code; wet grip index, which is a predetermined value based on a standardized test; tire model; manufacturing location; manufacturing date; tread running surface code, which includes or is associated with a compound identification code; mold code, which includes or is associated with a tread structure identification code; tire footprint shape factor (FSF); mold design drop; tire belt / carcass ply angle; and / or cover material. Tire ID information 32 may also be associated with maintenance history or other information to identify specific characteristics and parameters of each tire 12 and the mechanical characteristics of the tire (such as, turning parameters, spring stiffness, load-inflation relationship, etc.).

[0064] Method 10 for estimating tire grip includes providing a transmission device 52 for sending the measured characteristics of tire pressure 28, tire temperature 30, and footprint length 34 from sensor unit 26 and tire ID information 32 to a grip estimation module 50. The grip estimation module 50 will be described in more detail below, and the grip estimation module 50 may be stored on a local processor installed in the vehicle or on a remote processor 54. The transmission device 52 may include an antenna for wireless transmission or a wire for wired transmission.

[0065] Method 10 also includes providing a transmission device 36 for sending the measured characteristics of tire pressure 28, tire temperature 30, and footprint length 34 from sensor unit 26 and tire ID information 32 to a sensor fusion module 44 stored in a memory 38. The processor 38 may be integrated into the sensor unit 26 or may be a remote processor, which may be installed on vehicle 14 or be internet- or cloud-based. The transmission device 36 may include an antenna for wireless transmission, or electrical contacts or a wire for wired transmission.

[0066] Method 10 further includes providing a transmission device 42 for sending data from vehicle CAN bus 40 to a sensor fusion module 44 stored on processor 38. The transmission device 42, which can be an antenna for wireless transmission or a wire for wired transmission, enables communication between processor 38 and vehicle CAN bus 40. Such communication with CAN bus 40 enables communication between processor 38 and other vehicle systems.

[0067] Thus, the fusion module 44 on processor 38 receives data of tire pressure 28, tire temperature 30, tire footprint length 34, and tire ID information 32 from sensor unit 26 and receives vehicle system data from CAN bus 40. The sensor fusion module 44 dissects, organizes, and processes the received data. More particularly, the sensor fusion module 44 analyzes road condition data and other data from CAN bus 40 and correlates it with data of tire parameters of tire pressure 28, tire temperature 30, tire footprint length 34, and tire ID information 32.

[0068] The sensor fusion module 44 uses the correlated data to calculate the current tire load 46 and / or the current tire wear state 48. The calculation of the tire load 46 can be performed according to any known method. Exemplary methods for calculating the tire load 46 are shown and described in U.S. Patent Nos. 9,120,356 and 10,245,906, both of which are owned by The Goodyear Tire & Rubber Company, the same assignee as this application, and are incorporated herein by reference in their entirety. Similarly, the calculation of the tire wear state 48 can be performed according to any known method. An exemplary method for calculating the tire wear state 48 is shown and described in U.S. Published Patent Application No. 2019 / 0001757, which is owned by The Goodyear Tire & Rubber Company, the same assignee as this application, and is incorporated herein by reference in its entirety.

[0069] A transmission device 56 is provided for sending the current tire load 46 and / or the current tire wear state 48 from the sensor fusion module 44 to a grip estimation module 50. The grip estimation module 50 will be described in more detail below and the grip estimation module 50 can be stored on processor 54. The transmission device 56 can include an antenna for wireless transmission or a wire for wired transmission.

[0070] Method 10 includes providing a transmission device 58 for sending data from vehicle CAN bus 40 to a road surface condition classification module 60, which may be stored on a processor installed locally on the vehicle or on a remote processor 62 based on the cloud or the Internet. The transmission device 58 may include an antenna for wireless transmission or a wire for wired transmission.

[0071] The road surface condition classification module 60 uses vehicle data from CAN bus 40 and data from the Internet to generate an estimate of the condition of the road surface on which vehicle 14 is traveling. Exemplary data from CAN bus 40 employed by the road surface condition classification module 60 includes an image 64 of the road surface (such as from a forward-looking camera installed on the vehicle) and the geographical location 66 of vehicle 14. Exemplary data from the Internet includes geographical data, weather conditions in the geographical area of vehicle 14, road map data, etc. Data from the Internet may be obtained from a web application programming interface (API). Of course, the road surface condition classification module 60 may employ other data from CAN bus system 40 and the Internet.

[0072] An exemplary method for estimating road surface conditions performed by the road surface condition classification module 60 is shown and described in U.S. Patent No. 9,751,533, which is owned by the same assignee as the present application, The Goodyear Tire & Rubber Company, and is incorporated herein by reference in its entirety.

[0073] The road surface condition classification module 60 generates a road condition output 68 including the following: a road condition 70, which may indicate a dry road, a wet road, a snow-covered road, and / or an ice-covered road; a relative humidity 72 of the geographical area in which vehicle 14 is operating; an ambient air temperature 74 of the geographical area in which the vehicle is operating; a rough unevenness index 76, which may be indicated according to the international roughness index (IRI), which is a standard for indicating the road profile; and / or a topology 78 of the road on which the vehicle is operating.

[0074] A transmission device 80 is provided for sending the road condition output 68 of the road surface condition classification module 60 to the grip estimation module 50. The transmission device 80 may include an antenna for wireless transmission or a wire for wired transmission.

[0075] As mentioned above, the grip estimation module 50 may be stored on a local vehicle-mounted processor or on a remote processor 54 based on the Internet or cloud. The grip estimation module 50 receives three independent data sets. The first data set 82 includes the measured characteristics of tire pressure 28, tire temperature 30, and footprint length 34 from the sensor unit 26, as well as tire ID information 32, as described above. The second data set 84 includes the current tire load 46 and the current tire wear state 48 from the sensor fusion module 44, as described above. The third data set 86 includes the road condition output 68 from the road condition classification module 60, as described above. The grip estimation module 50 includes a pre-trained statistical model that models the relationships between the three data sets 82, 84, and 86 and outputs a tire-road friction probability distribution (referred to as the friction probability distribution 88).

[0076] Reference Figure 3 , the friction probability distribution 88 preferably includes a distribution 94 of the normalized probability of tire-road grip 90 over friction 92. The friction probability distribution 88 indicates the average expected value of the grip level of the tire 12 on the road surface, the variance of the grip level of the tire on the road surface, and the type of the distribution.

[0077] Thus, the method 10 for estimating tire grip generates a friction probability distribution 88 that can be input into one or more vehicle systems via the vehicle CAN bus 40. For example, the friction probability distribution 88 can be input into the following: an autonomous emergency braking (AEB) system, a curve speed warning system, an anti-lock braking system (ABS), a road friction estimation system, an electronic stability system, a traction control system, the safety distance calculation of an adaptive cruise control system, etc.

[0078] The method 10 for estimating tire grip is an active method that generates an accurate real-time estimate of the peak level of tire grip during vehicle operation, which is output in the friction probability distribution 88. The method 10 for estimating tire grip generates the friction probability distribution 88 without exciting the tire by causing skidding through acceleration or braking. The friction probability distribution 88 generated by the method 10 for estimating tire grip is preferably employed together with other vehicle systems to improve the operation of such systems.

[0079] It will be understood that, without affecting the overall concept or operation of the present invention, the steps and accompanying structures of the above-described method 10 for estimating tire grip may be altered or rearranged, or components or steps known to those skilled in the art may be omitted or added. For example, without affecting the overall concept or operation of the present invention, electronic communication may be carried out by wired connection or wireless communication. Such wireless communication includes radio frequency (RF) and Bluetooth® communication. Additionally, without affecting the overall concept or operation of the present invention, tire characteristics and tire conditions other than those described above and known to those skilled in the art may be employed.

[0080] The present invention has been described with reference to preferred embodiments. Others will envision potential modifications and variations upon reading and understanding this specification. It will be understood that all such modifications and variations are included within the scope of the present invention as set forth in the appended claims or their equivalents.

Claims

1. A method for estimating the grip of a tire supporting a vehicle, the method comprising the steps of: generating a first set of data from a sensor unit mounted on the tire; generating a second set of data from the sensor unit mounted on the tire and from data obtained from the vehicle; generating a third set of data from data obtained from the vehicle and from the Internet; providing a grip estimation module; receiving the first set of data, the second set of data, and the third set of data in the grip estimation module; using the grip estimation module to calculate a friction probability distribution using the first set of data, the second set of data, and the third set of data; and inputting the friction probability distribution into at least one vehicle system.

2. The method for estimating the grip of a tire supporting a vehicle according to claim 1, wherein, the first set of data includes the measured tire pressure, the measured tire temperature, and the measured tire footprint length.

3. The method for estimating the grip of a tire supporting a vehicle according to claim 1, wherein, the first set of data includes tire identification information.

4. The method for estimating the grip of a tire supporting a vehicle according to claim 3, wherein, the tire identification information includes at least one of the following: tire size, tire type, tire segment, traction parameter, weather parameter, Department of Transportation code, and wet grip index.

5. The method for estimating the grip of a tire supporting a vehicle according to claim 1, wherein, the second set of data includes the current tire load and the current tire wear state.

6. The method for estimating the grip of a tire supporting a vehicle according to claim 5, which further comprises the steps of: using a sensor fusion module to calculate the current tire load and the current tire wear state.

7. The method for estimating the grip of a tire supporting a vehicle according to claim 6, wherein: the data from the sensor unit mounted on the tire includes tire pressure, tire temperature, tire footprint length, and tire identification information, each of which is input into the sensor fusion module; and the data obtained from the vehicle includes vehicle system data from the vehicle CAN bus, and the vehicle system data is input into the sensor fusion module.

8. The method for estimating the grip of a tire supporting a vehicle according to claim 1, wherein, the third set of data includes the output from a road surface condition classification module.

9. The method for estimating the grip of a tire supporting a vehicle according to claim 8, wherein: the data obtained from the vehicle includes vehicle system data from the vehicle CAN bus, and the vehicle system data is input into the road surface condition classification module; and the data from the Internet includes weather data, and the weather data is input into the road surface condition classification module.

10. The method for estimating the grip of a tire supporting a vehicle according to claim 8, wherein: the data from the Internet is obtained from a web application programming interface.

11. The method for estimating the grip of a tire supporting a vehicle according to claim 8, wherein, the output from the road surface condition classification module includes at least one of the following: road condition, relative humidity, ambient air temperature, rough unevenness index, and road topology, wherein the road condition indicates a dry road, a wet road, a snow-covered road, and / or an ice-covered road.

12. The method for estimating the grip of a tire supporting a vehicle according to claim 1, wherein, the grip estimation module is stored on a processor installed in the vehicle.

13. The method for estimating the grip of a tire supporting a vehicle according to claim 1, wherein, the grip estimation module is stored on a cloud-based processor.

14. The method for estimating the grip of a tire supporting a vehicle according to claim 1, wherein, the grip estimation module includes a pre-trained statistical model.

15. The method for estimating the grip of a tire supporting a vehicle according to claim 1, wherein, the friction probability distribution indicates the average expected value of the grip level of the tire, the variance of the grip level of the tire on the road surface, and the type of distribution.

16. The method for estimating the grip of a tire supporting a vehicle according to claim 1, wherein, the at least one vehicle system includes at least one of the following: autonomous emergency braking system, curved road speed limit warning system, anti-lock braking system, road friction estimation system, electronic stability system, traction control system, and adaptive cruise control system.

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