Road condition monitoring system

Abstract

The present invention relates to a road condition monitoring system. A road condition monitoring system for a vehicle is provided. The vehicle is supported by at least one tire and includes a central communication system. The system includes a processor in electronic communication with the central communication system. An identifier is in electronic communication with the processor, receives vehicle condition data from the central communication system, and identifies instances of free rolling of the at least one tire. A slip estimator is in electronic communication with the processor, receives speed data from the central communication system, and determines slip characteristics of the at least one tire during the instances of free rolling. A classifier is in electronic communication with the processor, receives the slip characteristics of the at least one tire, and identifies road surface conditions from the slip characteristics.

Description

Technical Field

[0001] This invention generally relates to vehicle and tire monitoring systems. More particularly, this invention relates to systems for measuring and collecting vehicle and tire data. This invention relates to a system that uses vehicle and tire data to determine local tire slippage and estimate road conditions in real time. Background Technology

[0002] The conditions of the road on which a vehicle travels affect the performance of the vehicle and the tires that support it. Therefore, it is beneficial to determine the road conditions while the vehicle is in motion. If the road conditions can be determined, they can be incorporated into the vehicle control system to improve the vehicle's handling and performance. However, it is difficult to achieve accurate and reliable detection of the road conditions used by such a system.

[0003] In existing technology, it is generally accepted that tires experience almost no slippage under free-rolling or cruising conditions. Slippage is the relative motion between the tire and the road surface. When a vehicle supported by tires is driven at a constant speed on a straight road, it experiences free-rolling or cruising conditions, causing the tire to experience little or no force from acceleration, deceleration, or cornering.

[0004] However, it has been established that tires do experience quantifiable localized slippage during free-rolling or cruising conditions. It has also been established that quantifying this localized slippage during free-rolling or cruising conditions allows for the estimation of road surface conditions, which can then be incorporated into the vehicle control system. As examples, vehicle control systems may include braking control systems, suspension control systems, steering control systems, etc. Such vehicle control systems can be employed in any vehicle, including driver-operated vehicles, driver-assisted vehicles, and autonomous vehicles.

[0005] The localized slippage of a tire during free-rolling or cruising conditions can be quantified by measuring slip using a high-frequency accelerometer mounted in the tire. However, a high-frequency accelerometer is an expensive, specialized sensor mounted in the tire. Therefore, directly measuring the localized slippage of the tire during free-rolling or cruising conditions is not an economically feasible way to quantify slippage.

[0006] Therefore, there is a need in the art for a system that can economically and accurately estimate tire slippage during free rolling or cruising conditions, which in turn enables real-time estimation of road conditions. Summary of the Invention

[0007] According to an exemplary embodiment of the present invention, a road condition monitoring system for a vehicle is provided. The vehicle is supported by at least one tire and includes a central communication system. The system includes a processor that communicates electronically with the central communication system. An identifier that communicates electronically with the processor receives vehicle condition data from the central communication system and identifies the free-rolling condition (instance) of the at least one tire. A slip estimator that communicates electronically with the processor receives speed data from the central communication system and determines the slip characteristics of the at least one tire during the free-rolling condition. A classifier that communicates electronically with the processor receives the slip characteristics of the at least one tire and identifies road surface conditions based on these slip characteristics.

[0008] This invention also provides the following technical solutions:

[0009] 1. A road condition monitoring system for a vehicle, the vehicle being supported by at least one tire and including a central communication system, the system comprising:

[0010] A processor that communicates electronically with the central communication system;

[0011] An identifier that communicates electronically with the processor receives vehicle status data from the central communication system and identifies the free rolling condition of the at least one tire;

[0012] A slip estimator that communicates electronically with the processor, the slip estimator receiving speed data from the central communication system and determining the slip characteristics of the at least one tire during the free-rolling condition; and

[0013] A classifier that communicates electronically with the processor receives the slip characteristics of the at least one tire and identifies road conditions based on the slip characteristics.

[0014] 2. The road condition monitoring system according to technical solution 1, wherein the slip characteristics include the estimated magnitude of slip of the at least one tire during the free rolling condition.

[0015] 3. The road condition monitoring system according to technical solution 1, further comprising a slip adjuster that estimates the relative slip change of the at least one tire during the free-rolling condition, wherein the slip characteristics include the relative slip change of the at least one tire.

[0016] 4. The road condition monitoring system according to technical solution 3, wherein the classifier identifies the road surface condition based on the magnitude of the relative change in slip of the at least one tire.

[0017] 5. The road condition monitoring system according to technical solution 3, wherein the slip adjuster includes a reference slip calculator that receives vehicle atmospheric condition data from the central communication system and estimates a reference slip value for the at least one tire during the free roll condition.

[0018] 6. The road condition monitoring system according to technical solution 5, wherein the atmospheric condition data includes ambient temperature and relative humidity.

[0019] 7. The road condition monitoring system according to technical solution 5 further includes a dry road condition estimator, which analyzes the atmospheric condition data to determine whether the road on which the vehicle travels is dry by generating a probability of road dryness.

[0020] 8. The road condition monitoring system according to technical solution 5, wherein the reference slip calculator receives vehicle speed and tire-related data to estimate the reference slip value of the at least one tire during the free-rolling condition.

[0021] 9. The road condition monitoring system according to technical solution 8, wherein the tire-related data includes at least one of the following: the pressure of the at least one tire, the load on the at least one tire, the wear condition of the at least one tire, and the position of the at least one tire on the vehicle.

[0022] 10. The road condition monitoring system according to technical solution 5, wherein the slip adjuster includes a relative slip calculator, the relative slip calculator receiving the reference slip value and receiving a slip estimate from the slip estimator, the relative slip calculator determining the relative slip change of the at least one tire based on the reference slip value and the slip estimate.

[0023] 11. The road condition monitoring system according to technical solution 1, wherein the classifier uses multinomial logistic regression classification to identify the road surface condition.

[0024] 12. The road condition monitoring system according to technical solution 1, wherein the road surface condition identified by the classifier is based on the road surface type, including at least one of dry surface, wet surface, snow-covered surface and icy surface.

[0025] 13. The road condition monitoring system according to technical solution 1, wherein the vehicle condition data received by the identifier includes vehicle reference speed, vehicle longitudinal acceleration, steering wheel angle, brake pedal command and accelerator pedal position.

[0026] 14. The road condition monitoring system according to technical solution 13, wherein the identifier includes a threshold-based event classifier, including at least one of a linear classifier, a nonlinear classifier, or a Bayesian statistical classifier.

[0027] 15. The road condition monitoring system according to technical solution 14, wherein the vehicle condition data is classified according to each corresponding vehicle condition, wherein a predetermined threshold is set for each vehicle condition, and when the predetermined threshold is met, the free rolling condition of the at least one tire is identified.

[0028] 16. The road condition monitoring system according to technical solution 1, wherein the speed data received by the slip estimator includes vehicle reference speed and wheel speed.

[0029] 17. The road condition monitoring system according to technical solution 16, wherein the slip estimator estimates the slip of the at least one tire as a percentage difference between the vehicle reference speed and the wheel speed.

[0030] 18. The road condition monitoring system according to technical solution 17, wherein the slip estimator employs a recursive least squares algorithm with a forgetting factor.

[0031] 19. The road condition monitoring system according to technical solution 1, wherein the road surface condition identified by the classifier is output from the road condition monitoring system to at least one of the vehicle control system and the discrete estimation system.

[0032] 20. The road condition monitoring system according to technical solution 19, wherein the discrete estimation system includes a tire grip estimation system. Attached Figure Description

[0033] The invention will be described by way of example and with reference to the accompanying drawings, in which:

[0034] Figure 1 This is a perspective view of an exemplary vehicle and tires employing the road condition monitoring system of the present invention;

[0035] Figure 2 yes Figure 1 The diagram shows a plan view of the vehicle, with some parts of the vehicle indicated by dashed lines;

[0036] Figure 3 yes Figure 1 The schematic diagram of the vehicle shown illustrates data transmission to a cloud-based server and to a user device.

[0037] Figure 4 It is a graphical representation of the tire force curve in the free rolling zone;

[0038] Figure 5 This is a first schematic diagram of an exemplary embodiment of the system for estimating road conditions according to the present invention;

[0039] Figure 6 This is a second schematic diagram of an exemplary embodiment of the system for estimating road conditions of the present invention, which shows an estimation of a reference value for tire slippage;

[0040] Figure 7 It comes from Figure 6 A graphical representation of temperature versus relative humidity; and

[0041] Figure 8 This is a third schematic diagram of an exemplary embodiment of the system for estimating road conditions according to the present invention.

[0042] Throughout the accompanying diagram, similar numbers refer to similar parts.

[0043] definition

[0044] "Axial" and "axially" refer to a line or direction parallel to the axis of rotation of the tire.

[0045] "CAN bus" or "CAN bus system" is an abbreviation for Controller Area Network system, a vehicle bus standard designed to allow microcontrollers and devices to communicate with each other within a vehicle without a host computer. CAN bus is a message-based protocol specifically designed for vehicle applications.

[0046] "Circumferential" refers to a line or direction that extends along the circumference of the annular tread surface perpendicular to the axial direction.

[0047] "Center plane (CP)" refers to the plane that is perpendicular to the tire's axis of rotation and passes through the center of the tread.

[0048] "Traces" refers to the contact area or contact zone created by the flat surface of a tire tread when the tire rotates or rolls.

[0049] "Inner side" refers to the side of the tire that is closest to the vehicle when the tire is mounted on the wheel and the wheel is mounted on the vehicle.

[0050] "Lateral" refers to the axial direction.

[0051] "Lateral edge" refers to a line, measured under normal load and tire inflation, that is tangent to the outermost tread contact patch or imprint of the tire along its axial direction, and that is parallel to the equatorial center plane.

[0052] "Net contact area" refers to the total area of ​​the tread elements that contact the ground between the lateral edges of the tire tread, divided by the total area of ​​the entire tread between the lateral edges.

[0053] "Outer side" refers to the side of the tire furthest from the vehicle when the tire is mounted on the wheel and the wheel is mounted on the vehicle.

[0054] "Radial" and "radially" refer to directions that are radially toward or away from the axis of rotation of the tire.

[0055] "Rib" refers to a circumferentially extending rubber strip on the tread, which is defined by at least one circumferential groove and a second such groove or lateral edge, and is not separated laterally by full-depth grooves.

[0056] "Slippage" is the relative motion between the tire and the road surface.

[0057] The slip angle is the angle between the vehicle's direction of travel and the direction the front wheels are pointing. It is a measurement of the deviation between the plane of tire rotation and the tire's direction of travel.

[0058] "Tread element" or "traction element" refers to a rib or block element defined by the shape of adjacent tread grooves.

[0059] "Tread width" refers to the tread arc length of a tire, measured between the lateral edges of the tread. Detailed Implementation

[0060] An exemplary embodiment of the road condition monitoring system of the present invention is as follows: Figures 1 to 8 The middle is instructed to 10. Turn. Figure 1 The vehicle 12 is supported by tires 14. Although the vehicle 12 is depicted as a passenger car, the invention is not so limited. The principles of the invention can be applied to other vehicle categories, such as commercial trucks, where the vehicle can be supported by more than Figure 1 The tires shown have more or less tire support.

[0061] 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 14 includes a pair of sidewalls 18 extending to a circumferential tread 20. A liner 22 is disposed on the inner surface of the tire 14 and, when the tire is mounted on the wheel 16, forms an inner cavity 24 filled with a pressurized fluid (such as air).

[0062] Sensor unit 26 is preferably mounted to each tire 14, preferably by attaching it to the inner liner 22 (by means such as adhesive). Sensor unit 26 measures certain characteristics of the tire 14, including tire pressure and temperature. For this purpose, sensor unit 26 preferably includes a pressure sensor and a temperature sensor, and may have any known configuration, such as a tire pressure management system (TPMS) sensor, and will be referred to as a TPMS sensor unit or TPMS sensor. TPMS sensor unit 26 preferably also includes electronic memory capacity for storing identification (ID) information (referred to as tire ID information) for each tire 14. It will be understood that TPMS sensor unit 26 may be a single unit, or may include more than one unit, and the sensor unit may be mounted on a structure of the tire 14 other than the airtight layer 22.

[0063] Tire ID information may include or be associated with specific data for each tire 14, including: the tire's position on the vehicle 12; tire size, such as rim size, width, and outer diameter; tire type, such as all-weather, summer, winter, off-road, etc.; tire segment, which indicates the specific production line to which the tire belongs; predetermined traction or weather parameters, such as the three-peak snowflake (3PSF) marking for winter tires; Department of Transport (DOT) code; wet grip index, which is a predetermined value based on standardized testing; tire model; manufacturing location; manufacturing date; tread code, which includes compound identification or is associated with it; mold code, which includes tread structure identification or is associated with it; tire footprint shape factor (FSF); mold design drop; tire belt / buffer layer angle; and / or tread material. Tire ID information 32 may also be associated with: service history or other information used to identify specific characteristics and parameters of each tire 14; and tire mechanical characteristics, such as cornering parameters, spring stiffness, load-inflation relationship, etc.

[0064] refer to Figure 2 The vehicle 12 includes a central communication system 28 that enables electronic communication with the TPMS sensor units 26 and 30 mounted on the vehicle, and can be a wired or wireless system. For example, reference should be made to a CAN bus system 28; it should be understood that this reference includes any central electronic communication system for a vehicle, whether it is physically integrated into the vehicle 12 or cloud-based. Various aspects of the road condition monitoring system 10 preferably execute on a processor 32 accessible via the vehicle's CAN bus 28. The CAN bus 28 enables the processor 32 and its accompanying memory to receive data input from the sensors 26 and 30 and interface with other electronic components, as will be described in more detail below.

[0065] As mentioned above, it is beneficial to determine the conditions of the road on which vehicle 12 travels. If the road conditions can be determined, they can be used in the vehicle control system via the vehicle CAN bus 28 to improve the handling and performance of vehicle 12. It has been determined that quantifying the slippage of tire 14 during free-rolling or cruising conditions allows for the estimation of road surface conditions. However, quantifying this by directly measuring the slippage of tire 14 during free-rolling or cruising conditions is not economically feasible.

[0066] The road condition monitoring system 10 uses an indirect technique to determine road conditions, employing standard vehicle sensors 26 and 30 to capture localized slip behavior of the tire 14 during free-rolling or cruising conditions. For convenience, references to free-rolling conditions, free-rolling states, and free-rolling situations will be used below, and it should be understood that these references include cruising conditions, cruising states, and cruising situations, respectively. Figure 4 A tire force curve 34 is shown plotting tire grip 36 against tire slip ratio 38, and a free roll zone 40 is shown. The free roll zone 40 exists when the tire 14 is in a free roll condition or in a free roll state.

[0067] refer to Figure 5 The road condition monitoring system 10 includes a reader 42 that identifies when the tire 14 is in a free-rolling state. The reader 42 receives data from the CAN bus system 28. Figure 2 ) Receives vehicle status data 44. The identifier 42 can be stored on a local onboard processor 32 or remotely via the internet or a cloud-based processor 46. Figure 3 The vehicle status data 44 preferably includes a vehicle reference speed 48, vehicle longitudinal acceleration 50, steering wheel angle 52, brake pedal command 54, and / or gas pedal position 56, which may be derived from a Global Positioning System (GPS). The vehicle status data 44 is electronically transmitted from the CAN bus system 28 to the processor 32 or 46 and input into the identifier 42.

[0068] The recognizer 42 preferably includes a threshold-based event classifier 60 that analyzes the vehicle condition data 44. For example, the event classifier 60 can be a linear classifier, a nonlinear classifier, or a Bayesian statistical classifier. The vehicle condition data 44 is input into corresponding categories, and a predetermined threshold is set for each vehicle condition. When the predetermined threshold is met, the tire 14 is classified as being in a free-rolling state. For example, when the vehicle reference speed 48 is constant, the vehicle longitudinal acceleration 50 is minimum, the steering wheel angle 52 is constant, the brake pedal command 54 is minimum or zero, and the accelerator pedal position 56 is constant, the tire 14 is classified as being in a free-rolling state, which further identifies free-rolling condition 62.

[0069] When free-roll condition 62 is identified by identifier 42, road condition monitoring system 10 determines the slip characteristics of tire 14 during this free-roll condition. For example, slip estimator 64 may be used to estimate the slip of tire 14 during free-roll condition 62. Slip estimator 64 may be stored on a local on-board processor 32 or on a remote internet or cloud-based processor 46. Slip estimator 64 communicates electronically with CAN bus system 28 and receives speed data 66 from CAN bus system. Speed ​​data 66 preferably includes a vehicle reference speed 48 in kilometers per hour (kph) or miles per hour (mph) from a Global Positioning System (GPS), and a wheel speed 68 in kph or mph, which is the speed of the wheel 16 on which tire 14 is mounted. Slip estimator 64 estimates tire slip 70 as the percentage difference between wheel speed 68 and vehicle speed 48.

[0070] .

[0071] Preferably, the slip estimator 64 employs a recursive least squares algorithm with a forgetting factor, which is a recursive application of least squares regression, allowing each new data point to be considered. The forgetting factor assigns smaller weights to older data points, thereby ensuring that the tire slip estimate 70 is based on the latest data and is therefore a real-time estimate.

[0072] When the slip characteristics of tire 14 are as determined by the slip estimator 64, a classifier 72, which communicates electronically with the slip estimator, receives the tire slip estimate and identifies road surface condition 74. The classifier 72 may be stored on a local on-board processor 32 or on a remote internet or cloud-based processor 46. In this case, the classifier 72 identifies road surface condition 74 based on the estimated magnitude of tire slip 70. Preferably, the classifier 72 employs a multinomial logistic regression classification method (such as softmax regression) to identify road surface condition 74. Multinomial logistic regression classification methods are preferred because they are based on their ability to predict the probabilities of different outcomes of a categorically distributed dependent variable given a set of independent variables.

[0073] The road surface condition 74 identified by classifier 72 is preferably output according to road surface type. For example, road surface condition 74 may include dry surface 76, wet surface 78, snow-covered surface 80, icy surface 82, and / or variations and combinations thereof. Road surface condition 74 can then be output from road condition monitoring system 10 and input to vehicle control system and / or other discrete estimation system 84 via CAN bus system 28. For example, road surface condition 74 may be input to tire grip estimation system employing the technology shown and described in U.S. Patent Application Publication No. 2021 / 0229670, owned by the same assignee as this application (Goodyear Tire & Rubber Company) and incorporated herein in its entirety.

[0074] It has been found that the tire slip estimate 70 from the slip estimator 64 may not be as accurate as expected due to variations in the radius of the tire 14. This variation may stem from tire 14 expansion, aging, and / or wear during vehicle 12 operation. When the tire slip estimate 70 is not as accurate as expected, the accuracy of the classifier 72 in identifying road condition 74 may be less than expected.

[0075] Turn Figure 6 and Figure 8 The road condition monitoring system 10 can determine other slip characteristics of the tire 14 to adjust the tire slip estimate 70 and take into account changes in the radius of the tire 14. For example, the tire slip estimate 70 can be corrected by estimating the relative change in slip. The estimation of the relative change in slip of the tire 14 is performed by the slip adjuster 86. The slip adjuster 86 preferably includes a reference slip calculator 88 and a relative slip calculator 90.

[0076] Reference slip calculator 88 estimates a reference slip value 92. Reference slip calculator 88 receives vehicle atmospheric condition data 94 from CAN bus system 28, which preferably includes ambient temperature 96 and relative humidity 98. Dry road condition estimator 100 analyzes atmospheric condition data 94 to determine whether the road on which vehicle 12 travels is dry. For example, additional reference... Figure 7 The received ambient temperature 94 and the received relative humidity 98 can be plotted against each other to form a graph 102. When graph 102 indicates a dry road condition 104, estimator 100 generates a probability 106 of a dry road. When probability 106 is greater than a predetermined probability threshold 108 (e.g., approximately 0.95), the latest tire slip estimate 70 from slip estimator 64 is used as a reference slip value 92. When probability 106 is less than the predetermined probability threshold 108, the previous tire slip estimate 70 used as the reference slip value 92 is retained as the reference slip value.

[0077] Return to Figure 8 The road condition monitoring system 10 preferably also analyzes vehicle speed 48 and tire-related data 110 to account for short-term changes in tire condition that may affect tire slippage, thereby improving the robustness of the system. Tire-related data 110 preferably includes tire pressure 112, load 114 on tire 14, tire wear condition 116, and / or tire position 118 on vehicle 12, as measured by TPMS sensor 26. Tire load 114 may be measured by a load sensor or calculated using load estimation techniques, such as those shown and described in U.S. Patent No. 10,245,906, owned by the same assignee as this application (Goodyear Tire & Rubber Company) and incorporated herein in its entirety. Tire wear condition 116 may be measured by a wear sensor or calculated using wear estimation techniques, such as those shown and described in U.S. Patent Application Publication No. 2021 / 0061022, owned by the same assignee as this application (Goodyear Tire & Rubber Company) and incorporated herein in its entirety. Tire position 118 may be input from a sensor or from the aforementioned tire ID information, or may be calculated by positioning technology, such as the positioning technology shown and described in U.S. Patent Application Serial No. 17 / 151,310, which is owned by the same assignee as this application (Goodyear Tire & Rubber Company) and is incorporated herein in its entirety.

[0078] Vehicle speed 48 and tire-related data 110 are input into a reference slip calculator 88 to improve the estimation of reference slip value 92 by employing a threshold or range in a manner similar to that described above. For example, when vehicle speed 48 is within a predetermined optimal range, the latest tire slip estimate 70 from slip estimator 64 is used as the reference slip value 92. When vehicle speed 48 is outside the predetermined optimal range, a previous tire slip estimate 70 used as the reference slip value 92 is retained. When tire pressure 112 is above a predetermined minimum threshold, the latest tire slip estimate 70 from slip estimator 64 is used as the reference slip value 92. When tire pressure 112 is below the predetermined minimum threshold, a previous tire slip estimate 70 used as the reference slip value 92 is retained.

[0079] When the tire load 114 is below a predetermined minimum threshold, the latest tire slip estimate 70 from the slip estimator 64 is used as the reference slip value 92. When the tire load 114 is above the predetermined minimum threshold, the previous tire slip estimate 70 used as the reference slip value 92 is retained as the reference slip value. When the tire wear condition 116 is below a predetermined minimum threshold, the latest tire slip estimate 70 from the slip estimator 64 is used as the reference slip value 92. When the tire wear condition 116 is above the predetermined minimum threshold, the previous tire slip estimate 70 used as the reference slip value 92 is retained as the reference slip value. If the position 118 of the tire 14 matches the previously indicated position, the latest tire slip estimate 70 from the slip estimator 64 is used as the reference slip value 92. If the position 118 of the tire 14 matches the previously indicated position, the previous tire slip estimate 70 used as the reference slip value 92 is retained as the reference slip value.

[0080] Therefore, the reference slip calculator 88 determines and outputs an optimal reference slip value 92. This optimal reference slip value 92, along with the aforementioned slip estimate 70 from the slip estimator 64, is input into the relative slip calculator 90. The relative slip calculator 90 compares the slip estimate 70 from the slip estimator 64 with the optimal reference slip value 92 to determine the relative change in tire slip 120.

[0081] When the slip characteristics of tire 14 are relative changes in tire slip 120, classifier 72 receives the relative changes in tire slip to identify road surface condition 74. In this case, classifier 72 identifies road surface condition 74 based on the magnitude of the relative change in tire slip 120. As described above, classifier 72 preferably employs a multinomial logistic regression classification method (such as softmax regression) to identify road surface condition 74. The road surface condition 74 identified by classifier 72 is preferably output according to road surface type, such as dry surface 76, wet surface 78, snow-covered surface 80, icy surface 82, and / or variations and combinations thereof.

[0082] As described above, road condition 74 can be output from road condition monitoring system 10 and input to vehicle control system and / or other discrete estimation system 84 via CAN bus system 28. For example, road condition 74 can be input to the aforementioned tire grip estimation system. Vehicle control system may include braking control system, suspension control system, steering control system, etc. Such vehicle control system can be used in any vehicle, including driver-operated vehicles, driver-assisted vehicles, and autonomous vehicles.

[0083] refer to Figure 3The road condition monitoring system 10 can store data on a local on-board processor 32 or on a remote internet or cloud-based processor 46, with wireless data transmission 122 between the vehicle 12 and the cloud-based processor. Road conditions 74 can also be wirelessly transmitted 124 from the cloud-based processor 46 to a display device 126 (such as a smartphone) or fleet manager on a user-accessible display of the vehicle 12. Alternatively, road conditions 74 can be wirelessly transmitted 128 from the vehicle's CAN bus 28 to the display device 126.

[0084] In this way, the road condition monitoring system 10 identifies the free-rolling condition of the tire 14 and estimates the tire slippage during the free-rolling condition in an economical, accurate, and reliable manner, which in turn enables real-time estimation of the road surface condition 74. The road condition monitoring system 10 utilizes an indirect technique to determine the road surface condition 74, which employs standard vehicle sensors to capture the local slippage behavior of each tire 14 during the free-rolling condition. Furthermore, the road condition monitoring system 10 is tire-independent and therefore provides a robust and accurate system even when different tires 14 are mounted on the vehicle 12.

[0085] The present invention also includes a method for estimating road surface conditions 74 traversed by vehicle 12. This method includes, as presented above, and... Figures 1 to 8 The steps described are shown in the figure.

[0086] It will be understood that the structure of the road condition estimation system 10 and the steps of the accompanying methods described above may be altered or rearranged, or components or steps known to those skilled in the art may be omitted or added, without affecting the overall concept or operation of the invention. For example, electronic communication may be conducted via a wired connection or wireless communication without affecting the overall concept or operation of the invention. Such wireless communication may include radio frequency (RF) and Bluetooth® communication. Furthermore, vehicle and tire characteristics other than those described above and known to those skilled in the art may be employed without affecting the overall concept or operation of the invention. Moreover, while examples of statistical analysis techniques have been provided above, any applicable techniques known to those skilled in the art may be employed without affecting the overall concept or operation of the invention.

[0087] The invention has been described with reference to preferred embodiments. During the reading and understanding of this description, others will conceive of potential modifications and variations. It will be understood that all such modifications and variations are included within the scope of the invention as set forth in the appended claims or their equivalents.

Claims

1. A road condition monitoring system for a vehicle, the vehicle being supported by at least one tire and including a central communication system, the system comprising: A processor that communicates electronically with the central communication system; An identifier that communicates electronically with the processor receives vehicle status data from the central communication system and identifies the free rolling condition of at least one tire when the vehicle reference speed is constant, the vehicle longitudinal acceleration is minimum, the steering wheel angle is constant, the brake pedal command is minimum or zero, and the accelerator pedal position is constant. A slip estimator that communicates electronically with the processor receives speed data from the central communication system and determines the slip characteristics of the at least one tire during the free-rolling condition; as well as A classifier that communicates electronically with the processor receives the slip characteristics of the at least one tire and identifies road conditions based on the slip characteristics.

2. The road condition monitoring system according to claim 1, wherein, The slip characteristics include the estimated magnitude of slip of the at least one tire during the free-rolling condition.

3. The road condition monitoring system of claim 1, further comprising a slip adjuster that estimates the relative slip change of the at least one tire during the free-rolling condition, wherein, The slip characteristics include the relative slip changes of the at least one tire.

4. The road condition monitoring system according to claim 3, wherein, The classifier identifies the road surface condition based on the magnitude of the relative change in slip of the at least one tire.

5. The road condition monitoring system according to claim 3, wherein, The slip adjuster includes a reference slip calculator that receives vehicle atmospheric condition data from the central communication system and estimates a reference slip value for the at least one tire during the free-rolling condition.

6. The road condition monitoring system according to claim 5, wherein, The atmospheric condition data includes ambient temperature and relative humidity.

7. The road condition monitoring system of claim 5, further comprising a dry road condition estimator, the dry road condition estimator analyzing the atmospheric condition data to determine whether the road on which the vehicle travels is dry by generating a probability of road dryness.

8. The road condition monitoring system according to claim 5, wherein, The reference slip calculator receives vehicle speed and tire-related data to estimate the reference slip value of the at least one tire during the free-rolling condition.

9. The road condition monitoring system according to claim 8, wherein, The tire-related data includes at least one of the following: the pressure of the at least one tire, the load on the at least one tire, the wear condition of the at least one tire, and the position of the at least one tire on the vehicle.

10. The road condition monitoring system according to claim 5, wherein, The slip adjuster includes a relative slip calculator that receives the reference slip value and a slip estimate from the slip estimator, and the relative slip calculator determines the relative slip change of the at least one tire based on the reference slip value and the slip estimate.

11. The road condition monitoring system according to claim 1, wherein, The classifier uses multinomial logistic regression classification to identify the road surface conditions.

12. The road condition monitoring system according to claim 1, wherein, The road surface condition identified by the classifier is based on the road surface type, including at least one of dry surface, wet surface, snow-covered surface and icy surface.

13. The road condition monitoring system according to claim 1, wherein, The recognizer includes a threshold-based event classifier, including at least one of a linear classifier, a nonlinear classifier, or a Bayesian statistical classifier.

14. The road condition monitoring system according to claim 13, wherein, The vehicle condition data is categorized according to each corresponding vehicle condition, wherein a predetermined threshold is set for each vehicle condition, and when the predetermined threshold is met, the free rolling condition of the at least one tire is identified.

15. The road condition monitoring system according to claim 1, wherein, The speed data received by the slip estimator includes the vehicle reference speed and wheel speed.

16. The road condition monitoring system according to claim 15, wherein, The slip estimator estimates the slip of the at least one tire as a percentage difference between the vehicle reference speed and the wheel speed.

17. The road condition monitoring system according to claim 16, wherein, The slip estimator employs a recursive least squares algorithm with a forgetting factor.

18. The road condition monitoring system according to claim 1, wherein, The road surface conditions identified by the classifier are output from the road condition monitoring system to at least one of the vehicle control system and the discrete estimation system.

19. The road condition monitoring system according to claim 18, wherein, The discrete estimation system includes a tire grip estimation system.

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