Patent Description:
The condition of the road on which a vehicle is traveling affects performance of the vehicle and the tires that support the vehicle. As a result, it is beneficial to determine the condition of the road as the vehicle travels. If the road condition can be determined, the condition may be employed in a vehicle control system to improve handling and performance of the vehicle. However, accurate and reliable detection of road conditions for use by such systems has been difficult to achieve.

In the prior art, there has been a common belief that a tire under a free rolling or cruising condition experiences little to no slip. Slip is the relative motion between the tire and the road surface. A free rolling or cruising condition is experienced when the vehicle supported by the tire is driven at a constant speed on a straight road, so that the tire experiences low or no forces from acceleration, deceleration, or cornering.

However, it has been determined that a tire does experience quantifiable local slip during free rolling or cruising conditions. It has also been determined that quantification of local slip of the tire during free rolling or cruising conditions may enable the condition of the road surface to be estimated, which may then be employed in vehicle control systems. By way of example, a vehicle control system may include brake control systems, suspension control systems, steering control systems, and the like. Such vehicle control systems may be employed on any vehicle, including driver-operated vehicles, driver-assisted vehicles, and autonomous vehicles.

Quantification of local slip of the tire during free rolling or cruising conditions may be accomplished by measuring slip with a high frequency accelerometer mounted in the tire. However, a high frequency accelerometer is a specialized sensor that is costly to install in a tire. Therefore, direct measurement of local slip of the tire during free rolling or cruising conditions is not an economically viable manner of quantifying slip.

As a result, there is a need in the art for a system that accurately and reliably estimates the slip of a tire during free rolling or cruising conditions in an economical manner, which in turn enables estimation of a road condition in real time.

<CIT> describes a method and system for estimating the potential friction between a vehicle tire and a rolling surface.

According to a preferred aspect of the 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 in electronic communication with the central communication system. An identifier that is in electronic communication with the processor receives vehicle condition data from the central communication system and identifies a free rolling instance of the at least one tire. A slip estimator that 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 free rolling instance. A classifier that is in electronic communication with the processor receives the slip characteristics of the at least one tire and identifies a road surface condition from the slip characteristics.

"CAN bus" or "CAN bus system" is an abbreviation for 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 host computer. CAN bus is a message-based protocol, designed specifically for vehicle applications.

"Equatorial centerplane (CP)" means the plane perpendicular to the tire's axis of rotation and passing through the center of the tread.

"Footprint" means the contact patch or area of contact created by the tire tread with a flat surface as the tire rotates or rolls.

"Lateral edges" means a line tangent to the axially outermost tread contact patch or footprint of the tire as measured under normal load and tire inflation, the lines being parallel to the equatorial centerplane.

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

"Rib" means a circumferentially extending strip of rubber on the tread which is defined by at least one circumferential groove and either a second such groove or a lateral edge, the strip being laterally undivided by full-depth grooves.

"Slip" is the relative motion between the tire and the road surface.

"Slip angle" is the angle between a vehicle's direction of travel and the direction in which the front wheels are pointing. Slip angle is a measurement of the deviation between the plane of tire rotation and the direction of travel of a tire.

"Tread element" or "traction element" means a rib or a block element defined by a shape having adjacent grooves.

"Tread Arc Width" means the arc length of the tread of the tire as measured between the lateral edges of the tread.

An exemplary embodiment of the road condition monitoring system <NUM> of the present invention is shown in <FIG>. Turning to <FIG>, a vehicle <NUM> is supported by tires <NUM>. While the vehicle <NUM> is depicted as a passenger car, the invention is not to be so restricted. The principles of the invention find application in other vehicle categories such as commercial trucks in which vehicles may be supported by more or fewer tires than shown in <FIG>.

The tires <NUM> are of conventional construction, and each tire is mounted on a respective wheel <NUM> as known to those skilled in the art. Each tire <NUM> includes a pair of sidewalls <NUM> that extend to a circumferential tread <NUM>. An innerliner <NUM> is disposed on the inner surface of the tire <NUM>, and when the tire is mounted on the wheel <NUM>, an internal cavity <NUM> is formed, which is filled with a pressurized fluid, such as air.

A sensor unit <NUM> preferably is mounted to each tire <NUM>, preferably by attachment to the innerliner <NUM> by means such as an adhesive. The sensor unit <NUM> measures certain characteristics of the tire <NUM>, including tire pressure and temperature. For this reason, the sensor unit <NUM> preferably includes a pressure sensor and a temperature sensor, and may be of any known configuration, such as a tire pressure management system (TPMS) sensor, and will be referred to as a TPMS sensor unit or a TPMS sensor. The TPMS sensor unit <NUM> preferably also includes electronic memory capacity for storing identification (ID) information for each tire <NUM>, known as tire ID information. It is to be understood that the TPMS sensor unit <NUM> 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 <NUM> other than the innerliner <NUM>.

The tire ID information may include or be correlated to specific data for each tire <NUM>, including: a position of the tire on the vehicle <NUM>; tire size, such as rim size, width, and outer diameter; tire type, such as all weather, summer, winter, off-the-road, and the like; tire segment, which is the specific product line to which the tire belongs; predetermined traction or weather parameters, such as a three-peak snowflake indicator for winter tires (3PSF); Department of Transportation (DOT) code; wet grip index, which is a predetermined value based on a standardized test; tire model; manufacturing location; manufacturing date; treadcap code, which includes or correlates to a compound identification; a mold code that includes or correlates to a tread structure identification; a tire footprint shape factor (FSF); a mold design drop; a tire belt/breaker angle; and/or an overlay material. The tire ID information <NUM> may also correlate to a service history or other information to identify specific features and parameters of each tire <NUM>, as well as mechanical characteristics of the tire, such as cornering parameters, spring rate, load-inflation relationship, and the like.

Referring to <FIG>, the vehicle <NUM> includes a central communication system <NUM> that enables electronic communication with the TPMS sensor units <NUM> and sensors <NUM> that are mounted on the vehicle, and may be a wired or a wireless system. By way of example, reference shall be made to a CAN bus system <NUM>, with the understanding that such reference includes any central electronic communication system for a vehicle, whether it is physically incorporated into the vehicle <NUM> or is cloud-based. Aspects of the road condition monitoring system <NUM> preferably are executed on a processor <NUM> that is accessible through the vehicle CAN bus <NUM>. The CAN bus <NUM> enables the processor <NUM>, and accompanying memory, to receive input of data from the sensors <NUM> and <NUM> and to interface with other electronic components, as will be described in greater detail below.

As mentioned above, it is beneficial to determine the condition of the road over which the vehicle <NUM> travels. If the road condition can be determined, the condition may be employed in a vehicle control system through the vehicle CAN bus <NUM> to improve handling and performance of the vehicle <NUM>. It has been determined that quantification of slip of the tire <NUM> during free rolling or cruising conditions may enable the condition of the road surface to be estimated. However, quantification by direct measurement of slip of the tire <NUM> during free rolling or cruising conditions is not economically viable.

The road condition monitoring system <NUM> determines a road condition using an indirect technique, which employs standard vehicle sensors <NUM> and <NUM> to capture local slip behavior of the tire <NUM> during free rolling or cruising conditions. For the purpose of convenience, reference hereinafter shall be made to free rolling conditions, free rolling state, and free rolling instance, with the understanding that such reference includes cruising conditions, cruising state, and cruising instance, respectively. <FIG> shows a tire force curve <NUM>, which plots tire grip <NUM> versus tire slip ratio <NUM>, and shows a free rolling zone <NUM>. The free rolling zone <NUM> exists when the tire <NUM> is in free rolling conditions or a free rolling state.

With reference to <FIG>, the road condition monitoring system <NUM> includes an identifier <NUM> that identifies when the tire <NUM> is in a free rolling state. The identifier <NUM> receives vehicle condition data <NUM> from the CAN bus system <NUM> (<FIG>). The identifier <NUM> may be stored on the local vehicle-mounted processor <NUM> or on a remote Internet or cloud-based processor <NUM> (<FIG>), as will be described below. The vehicle condition data <NUM> includes a vehicle reference speed <NUM> that may be from a global positioning system (GPS), a vehicle longitudinal acceleration <NUM>, a steering wheel angle <NUM>, a brake pedal command <NUM>, and/or a gas pedal position <NUM>. The vehicle condition data <NUM> is electronically communicated from the CAN bus system <NUM> to the processor <NUM> or <NUM>, and is input into the identifier <NUM>.

The identifier <NUM> preferably includes a threshold based event classifier <NUM>, which analyzes the vehicle condition data <NUM>. For example, the event classifier <NUM> may be a linear classifier, nonlinear classifier, or a Bayesian statistical classifier. The vehicle condition data <NUM> are input into respective categories, and predetermined thresholds have been set for each vehicle condition. When the predetermined thresholds are met, the tire <NUM> is classified as being in a free rolling state. For example, when the vehicle reference speed <NUM> is constant, the vehicle longitudinal acceleration <NUM> is minimal, the steering wheel angle <NUM> is constant, the brake pedal command <NUM> is minimal or zero, and the gas pedal position <NUM> is constant, the tire <NUM> is classified as being in a free rolling state, which in turn identifies a free rolling instance <NUM>.

When a free rolling instance <NUM> is identified by the identifier <NUM>, the road condition monitoring system <NUM> determines slip characteristics of the tire <NUM> during the free rolling instance. For example, a slip estimator <NUM> may be employed to estimate the slip of the tire <NUM> during the free rolling instance <NUM>. The slip estimator <NUM> may be stored on the local vehicle-mounted processor <NUM> or on the remote Internet or cloud-based processor <NUM>. The slip estimator <NUM> is in electronic communication with the CAN bus system <NUM> and receives speed data <NUM> from the CAN bus system. The speed data <NUM> includes the vehicle reference speed <NUM> that may be from a global positioning system (GPS) in kilometers per hour (kph) or miles per hour (mph), and a wheel speed <NUM>, which is the speed of the wheel <NUM> on which the tire <NUM> is mounted, in kph or mph. The slip estimator <NUM> estimates tire slip <NUM> as a percentage difference between the wheel speed <NUM> and the vehicle speed <NUM>: <MAT>.

Preferably, the slip estimator <NUM> employs a recursive least squares algorithm with a forgetting factor, which is a recursive application of a least squares regression algorithm that enables each new data point to be taken into account. The forgetting factor provides less weight to older data points, thereby ensuring that the tire slip estimation <NUM> is based on the most recent data and thus is a real time estimation.

When the slip characteristic of the tire <NUM> is the tire slip estimation <NUM> as determined by the slip estimator <NUM>, a classifier <NUM> that is in electronic communication with the slip estimator receives the tire slip estimation and identifies a road surface condition <NUM>. The classifier <NUM> may be stored on the local vehicle-mounted processor <NUM> or on the remote Internet or cloud-based processor <NUM>. In such a case, the classifier <NUM> identifies the road surface condition <NUM> based on the magnitude of the estimated tire slip <NUM>. Preferably, the classifier <NUM> employs a multinomial logistic regression classification methodology, such as a softmax regression, to identify the road surface condition <NUM>. The multinomial logistic regression classification methodology is preferred based on its capability to predict the probabilities of different outcomes of a categorically distributed dependent variable when given a set of independent variables.

The road surface condition <NUM> identified by the classifier <NUM> preferably is output by road surface type. For example, the road surface condition <NUM> may include a dry surface <NUM>, a wet surface <NUM>, a snow-covered surface <NUM>, an icy surface <NUM>, and/or variations and combinations thereof. The road surface condition <NUM> may then be output from the road condition monitoring system <NUM> and input through the CAN bus system <NUM> into vehicle control systems and/or into other discrete estimation systems <NUM>. For example, the road surface condition <NUM> may be input into a tire grip estimation system that employs a technique shown and described in <CIT>.

It has been discovered that the tire slip estimation <NUM> from the slip estimator <NUM> may not be as consistently accurate as desired due to variation in the radius of the tire <NUM>. Such variation may stem from expansion of the tire <NUM> during operation of the vehicle <NUM>, aging of the tire, and/or wear of the tire. When the tire slip estimation <NUM> is not as consistently accurate as desired, the identification of the road surface condition <NUM> by the classifier <NUM> may be less accurate than is desired.

Turning to <FIG> and <FIG>, the road condition monitoring system <NUM> may determine other slip characteristics of the tire <NUM> to adjust the tire slip estimation <NUM> and account for variation in the radius of the tire <NUM>. For example, a correction to the tire slip estimation <NUM> may be made by estimating a relative change in slip. The estimation of relative change in slip of the tire <NUM> is performed by a slip adjustor <NUM>. The slip adjustor <NUM> preferably includes a reference slip calculator <NUM> and a relative slip calculator <NUM>.

The reference slip calculator <NUM> estimates a reference slip value <NUM>. The reference slip calculator <NUM> receives vehicle atmospheric condition data <NUM> from the CAN bus system <NUM>, which preferably includes an ambient temperature <NUM> and a relative humidity <NUM>. A dry road condition estimator <NUM> analyzes the atmospheric condition data <NUM> to determine if the road on which the vehicle <NUM> is traveling is dry. For example, with additional reference to <FIG>, the received ambient temperature <NUM> and the received relative humidity <NUM> may be plotted <NUM> against one another. When the plot <NUM> indicates a dry road condition <NUM>, the estimator <NUM> generates a probability <NUM> of road dryness. When the probability <NUM> is greater than a predetermined probability threshold <NUM>, such as about <NUM>, the most recent tire slip estimation <NUM> from the slip estimator <NUM> is used as the reference slip value <NUM>. When the probability <NUM> is less than the predetermined probability threshold <NUM>, a prior tire slip estimation <NUM> that was used as the reference slip value <NUM> is retained as the reference slip value.

Returning to <FIG>, the road condition monitoring system <NUM> preferably also analyzes vehicle speed <NUM> and tire-related data <NUM> to account for short term variations in tire conditions that may affect tire slip, thereby increasing the robustness of the system. The tire-related data <NUM> preferably includes tire pressure <NUM> as measured by the TPMS sensor <NUM>, a load <NUM> on the tire <NUM>, a wear state <NUM> of the tire, and/or a position <NUM> of the tire on the vehicle <NUM>. The tire load <NUM> may be measured by a load sensor, or may be calculated by a load estimation technique, such as the one shown and described in <CIT>. The tire wear state <NUM> may be measured by a wear sensor, or may be calculated by a wear estimation technique, such as the one shown and described in <CIT>. The tire position <NUM> may be input from a sensor or from the above-described tire ID information, or may be calculated by a location technique, such as the one shown and described in <CIT>.

The vehicle speed <NUM> and tire-related data <NUM> are input into the reference slip calculator <NUM> to improve the estimation of the reference slip value <NUM> by employing thresholds or ranges in a manner similar to that as described above. For example, when the vehicle speed <NUM> is inside of a predetermined optimum range, the most recent tire slip estimation <NUM> from the slip estimator <NUM> is used as the reference slip value <NUM>. When the vehicle speed <NUM> is outside of the predetermined optimum range, a prior tire slip estimation <NUM> that was used as the reference slip value <NUM> is retained as the reference slip value. When the tire pressure <NUM> is above a predetermined minimum threshold, the most recent tire slip estimation <NUM> from the slip estimator <NUM> is used as the reference slip value <NUM>. When the tire pressure <NUM> is below the predetermined minimum threshold, a prior tire slip estimation <NUM> that was used as the reference slip value <NUM> is retained as the reference slip value.

When the tire load <NUM> is below a predetermined minimum threshold, the most recent tire slip estimation <NUM> from the slip estimator <NUM> is used as the reference slip value <NUM>. When the tire load <NUM> is above the predetermined minimum threshold, a prior tire slip estimation <NUM> that was used as the reference slip value <NUM> is retained as the reference slip value. When the tire wear state <NUM> is below a predetermined minimum threshold, the most recent tire slip estimation <NUM> from the slip estimator <NUM> is used as the reference slip value <NUM>. When the tire wear state <NUM> is above the predetermined minimum threshold, a prior tire slip estimation <NUM> that was used as the reference slip value <NUM> is retained as the reference slip value. If the position <NUM> of the tire <NUM> is consistent with a previously indicated position, the most recent tire slip estimation <NUM> from the slip estimator <NUM> is used as the reference slip value <NUM>. If the position <NUM> of the tire <NUM> is consistent with the previously indicated position, a prior tire slip estimation <NUM> that was used as the reference slip value <NUM> is retained as the reference slip value.

The reference slip calculator <NUM> thus determines and outputs an optimum reference slip value <NUM>. The optimum reference slip value <NUM> is input into the relative slip calculator <NUM> along with the above-described the slip estimation <NUM> from the slip estimator <NUM>. The relative slip calculator <NUM> compares the slip estimation <NUM> from the slip estimator <NUM> and the optimum reference slip value <NUM> to determine a relative change in tire slip <NUM>.

When the slip characteristic of the tire <NUM> is the relative change in tire slip <NUM>, the classifier <NUM> receives the relative change in tire slip to identify the road surface condition <NUM>. In such a case, the classifier <NUM> identifies the road surface condition <NUM> based on the magnitude of the relative change in tire slip <NUM>. As described above, the classifier <NUM> preferably employs a multinomial logistic regression classification methodology, such as a softmax regression, to identify the road surface condition <NUM>. The road surface condition <NUM> identified by the classifier <NUM> preferably is output according to road surface type, such as a dry surface <NUM>, wet surface <NUM>, snow-covered surface <NUM>, icy surface <NUM>, and/or variations and combinations thereof.

As described above, the road surface condition <NUM> may be output from the road condition monitoring system <NUM> and input through the CAN bus system <NUM> into vehicle control systems and/or into other discrete estimation systems <NUM>. For example, the road surface condition <NUM> may be input into the above-described tire grip estimation system. The vehicle control systems may include brake control systems, suspension control systems, steering control systems, and the like. Such vehicle control systems may be employed on any vehicle, including driver-operated vehicles, driver-assisted vehicles, and autonomous vehicles.

With reference to <FIG>, the road condition monitoring system <NUM> may be stored on a local, vehicle-mounted processor <NUM> or on a remote Internet or cloud-based processor <NUM>, with wireless data transmission <NUM> between the vehicle <NUM> and the cloud-based processor. The road surface condition <NUM> may also be wirelessly transmitted <NUM> from the cloud-based processor <NUM> to a display device <NUM> for a display that is accessible to a user of the vehicle <NUM>, such as a smartphone, or to a fleet manager. Alternatively, the road surface condition <NUM> may be wirelessly transmitted <NUM> from the vehicle CAN bus <NUM> to the display device <NUM>.

In this manner, the road condition monitoring system <NUM> identifies free rolling conditions of a tire <NUM> and accurately and reliably estimates the slip of the tire during the free rolling conditions in an economical manner, which in turn enables estimation of a road surface condition <NUM> in real time. The road condition monitoring system <NUM> determines the road surface condition <NUM> with an indirect technique, which employs standard vehicle sensors to capture local slip behavior of each tire <NUM> during the free rolling conditions. In addition, the road condition monitoring system <NUM> is tire agnostic, and thus provides a robust and accurate system even when different tires <NUM> are mounted on the vehicle <NUM>.

The present invention also includes a method for estimating a road surface condition <NUM> over which a vehicle <NUM> travels. The method includes steps in accordance with the description that is presented above and shown in <FIG>.

Claim 1:
A road condition monitoring system for a vehicle, the vehicle (<NUM>) being supported by at least one tire (<NUM>) and including a central communication system (<NUM>), the system (<NUM>) comprising:
a processor (<NUM>) in electronic communication with the central communication system (<NUM>);
an identifier (<NUM>) in electronic communication with the processor (<NUM>), the identifier (<NUM>) being configured for receiving vehicle condition data from the central communication system (<NUM>) and identifying a free rolling instance of the at least one tire (<NUM>);
a slip estimator (<NUM>) in electronic communication with the processor (<NUM>), the slip estimator (<NUM>) being configured for receiving speed data from the central communication system (<NUM>) and determining slip characteristics of the at least one tire (<NUM>) during the free rolling instance, wherein the speed data received by the slip estimator (<NUM>) includes a vehicle reference speed and a wheel speed, and wherein the slip estimator (<NUM>) is configured to estimate a slip of the at least one tire (<NUM>) as a percentage difference between the vehicle reference speed and the wheel speed; and
a classifier (<NUM>) in electronic communication with the processor (<NUM>), the classifier (<NUM>) being configured for receiving the slip characteristics of the at least one tire (<NUM>) and identifying a road surface condition from the slip characteristics.