WO2021247016A1

WO2021247016A1 - Detecting a condition of a road surface

Info

Publication number: WO2021247016A1
Application number: PCT/US2020/035944
Authority: WO
Inventors: Samuel K. Strahan; Jonathan Barr; Nevin R. MOLYNEAUX
Original assignee: Sensata Technologies, Inc.
Priority date: 2020-06-03
Filing date: 2020-06-03
Publication date: 2021-12-09

Abstract

Methods, systems, apparatuses, and computer program products for detecting a condition of a road surface are disclosed. In a particular embodiment, detecting a condition of a road surface includes a tire monitoring sensor generating accelerometric data and identifying one or more characteristics of the accelerometric data. In this embodiment, the tire monitoring sensor uses the one or more characteristics to determine a road condition value indicating a condition of a road surface associated with the tire.

Description

DETECTING A CONDITION OF A ROAD SURFACE

BACKGROUND

[0001] Vehicles use a tire pressure monitoring system to identify under-inflated tires and notify an operator of potentially unsafe operating modes. A tire pressure monitoring system uses a sensor such as a tire mounted sensor or valve mounted sensor to determine the tire pressure of a given tire. As the wheel position of a given sensor may change due to tire changing or rotation, the tire pressure monitoring system may use autolocation solutions such as Phase Auto Location (PAL) or Wireless Auto Location (WAL) to determine a wheel position for a given sensor. Such autolocation systems use accelerometric data generated by the sensor to identify the wheel position. Hazards or other irregularities in road surface conditions can introduce noise into the accelerometric data.

SUMMARY OF INVENTION

[0002] Methods, systems, apparatuses, and computer program products for detecting a condition of a road surface using the accelerometric data are disclosed. In a particular embodiment, detecting a condition of a road surface includes a tire monitoring sensor generating accelerometric data and identifying one or more characteristics of the accelerometric data. In this embodiment, the tire monitoring sensor uses the one or more characteristics to determine a road condition value indicating a condition of a road surface associated with a tire.

[0003] In another embodiment, detecting a condition of a road surface includes a vehicle control system (VCS) receiving accelerometric data generated by a tire monitoring sensor and identifying one or more characteristics of the accelerometric data. In this embodiment, the VCS uses the one or more characteristics to determine a road condition value indicating a condition of a road surface associated with the tire monitoring sensor.

[0004] An indication of the condition of the road may be useful for configuring or modifying various parameters and settings of a vehicle. For example, the tire monitoring sensor may provide the road condition value to a vehicle control system (VCS) that adjusts the Electronic Stability Control system or suspension mode of the vehicle to match the condition of the road as indicated by the road condition value. Embodiments of the present disclosure also have the added benefit of utilizing the accelerometric data from tire monitoring sensors for detecting a condition of the road without having to add additional sensors.

[0005] The foregoing and other objects, features and advantages of the invention will be apparent from the following more particular descriptions of exemplary embodiments of the invention as illustrated in the accompanying drawings wherein like reference numbers generally represent like parts of exemplary embodiments of the invention.

BRIEF DESCRIPTION OF DRAWINGS

[0006] FIG. 1 sets forth an isometric diagram of a system for detecting a condition of a road surface in accordance with the present disclosure;

[0007] FIG. 2 sets forth a top view of the system of FIG. 1;

[0008] FIG. 3 illustrates accelerometric data that may be measured using a system in accordance with the present disclosure;

[0009] FIG. 4 illustrates a block diagram of an exemplary vehicle control system (VCS) in accordance with the present disclosure;

[0010] FIG. 5 illustrates another block diagram of an exemplary Telematics Control Unit (TCU) in accordance with the present disclosure;

[0011] FIG. 6 illustrates a block diagram of an exemplary tire monitoring sensor (TMS) in accordance with the present disclosure;

[0012] FIG. 7 illustrates a comparison of accelerometric waveforms before and after the use of bandpass filters;

[0013] FIG. 8 illustrates a comparison of accelerometric waveforms before and after filtering centripetal acceleration;

[0014] FIG. 9 illustrates example energy distributions of accelerometric data under varying road conditions;

[0015] FIG. 10 is a flowchart to illustrate an implementation of a method for detecting a condition of a road surface;

[0016] FIG. 11 is a flowchart to illustrate another implementation of a method for detecting a condition of a road surface;

[0017] FIG. 12 is a flowchart to illustrate another implementation of a method for detecting a condition of a road surface;

[0018] FIG. 13 is a flowchart to illustrate another implementation of a method for detecting a condition of a road surface;

[0019] FIG. 14 is a flowchart to illustrate another implementation of a method for detecting a condition of a road surface;

[0020] FIG. 15 is a flowchart to illustrate another implementation of a method for detecting a condition of a road surface;

[0021] FIG. 16 is a flowchart to illustrate another implementation of a method for detecting a condition of a road surface; [0022] FIG. 17 is a flowchart to illustrate another implementation of a method for detecting a condition of a road surface;

[0023] FIG. 18 is a flowchart to illustrate another implementation of a method for detecting a condition of a road surface;

[0024] FIG. 19 is a flowchart to illustrate another implementation of a method for detecting a condition of a road surface; and

[0025] FIG. 20 is a flowchart to illustrate another implementation of a method for detecting a condition of a road surface.

DESCRIPTION OF EMBODIMENTS

[0026] The terminology used herein for the purpose of describing particular examples is not intended to be limiting for further examples. Whenever a singular form such as “a”, “an” and “the” is used and using only a single element is neither explicitly or implicitly defined as being mandatory, further examples may also use plural elements to implement the same functionality. Likewise, when a functionality is subsequently described as being implemented using multiple elements, further examples may implement the same functionality using a single element or processing entity. It will be further understood that the terms “comprises”, “comprising”, “includes” and/or “including”, when used, specify the presence of the stated features, integers, steps, operations, processes, acts, elements and/or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, processes, acts, elements, components and/or any group thereof. [0027] It will be understood that when an element is referred to as being “connected” or “coupled” to another element, the elements may be directly connected or coupled or via one or more intervening elements. If two elements A and B are combined using an “or”, this is to be understood to disclose all possible combinations, i.e. only A, only B as well as A and B. An alternative wording for the same combinations is “at least one of A and B”. The same applies for combinations of more than two elements.

[0028] Accordingly, while further examples are capable of various modifications and alternative forms, some particular examples thereof are shown in the figures and will subsequently be described in detail. However, this detailed description does not limit further examples to the particular forms described. Further examples may cover all modifications, equivalents, and alternatives falling within the scope of the disclosure. Like numbers refer to like or similar elements throughout the description of the figures, which may be implemented identically or in modified form when compared to one another while providing for the same or a similar functionality. [0029] Exemplary methods, apparatuses, systems, and computer program products for detecting a condition of a road surface in accordance with the present disclosure are described with reference to the accompanying drawings, beginning with FIG. 1. FIG. 1 sets forth an isometric diagram of a system (100) for detecting a condition of a road surface in accordance with the present disclosure. FIG. 2 sets forth a top view of the system of FIG. 1. The system (100) of FIG. 1 and FIG. 2 includes a vehicle (101) equipped with tires (103) that include tire monitoring sensors (105) (hereafter, “TMS”). A tire monitoring sensor may be any type of sensor that is configured for monitoring parameters associated with a tire. Examples of a tire monitoring sensor include but are not limited to a tire mounted sensor, a valve-stem mounted sensor, and other sensors as will occur to those of skill in the art.

[0030] The vehicle (101) further includes a vehicle control system (VCS) (107) that controls various components and systems within a vehicle. In a particular embodiment, the VCS (107) includes one or more electronic control units (ECUs) that are configured to control one or more vehicle subsystems. Commonly referred to as the vehicle’s “computers”, an ECU may be a central control unit or may refer collectively to one or more vehicle subsystem control units, such as an Engine Control Module (ECM), a Powertrain Control Module (PCM), a Transmission Control Module (TCM), a Central Timing Module (CTM), a General Electronic Module (GEM), or a Suspension Control Module (SCM). In an embodiment according to the present disclosure, the VCS (107) includes a BCM that includes an Antilock Braking System (ABS) and an Electronic Stability Program (ESP). Alternatively, the VCS (107) may comprise a Telematics Control Unit (TCU) independent of vehicle-based sensors (e.g., an aftermarket system). The vehicle (101) also includes a dashboard display screen (140) for displaying messages from components of the vehicle. For example, the VCS (107) may send a Tow tire pressure’ message to a component connected to the dashboard display screen (140). In this example, in response to receiving the Tow tire pressure’ message, the component may turn on a Tow tire pressure’ indicator that is displayed on the dashboard display screen (140).

[0031] Each TMS (105) may be equipped with a wireless transceiver for bidirectional wireless communication with the VCS (107), as will be described in more detail below. The VCS (107) may be similarly equipped with a wireless transceiver for bidirectional wireless communication with each of the TMSs (105), as will be described in more detail below. The bidirectional wireless communication may be realized by low power communication technology such as Bluetooth Low Energy or other low power bidirectional communication technology that is intended to conserve the amount of energy consumed. Alternatively, each TMS (105) may include a unidirectional transmitter configured to transmit signals to the VCS (107), and the VCS (107) may be equipped with a receiver to receive the transmitted signals. [0032] Each vehicle system may include sensors (113) used to measure and communicate vehicle operating conditions. For example, the ABS may include wheel speed sensors on the wheelbase used to measure wheel speed. The ESP subsystem may include yaw rate sensors configured to measure the yaw-induced acceleration of the vehicle when the vehicle is maneuvering a curve. Readings from such sensors (113) may be provided to the VCS (107), which may provide parameters based on these readings to the TMS (105).

[0033] The vehicle (101) may further include a transceiver (109) communicatively coupled to the VCS (107) for cellular terrestrial communication, satellite communication, or both.

[0034] In a particular embodiment, the TMS (105) is configured to detect a road condition by generating accelerometric data; identifying one or more characteristics of the accelerometric data; based on the one or more characteristics, determining a road condition value that indicates a condition of a road surface associated with the tire; and providing the road condition value to another device (e.g., the VCS (107)) of the tire pressure monitoring system, another component of the vehicle, or a mobile device.

[0035] In a particular embodiment, the TMS (105) may be configured to transmit the accelerometric data or the one or more characteristics of the accelerometric data to another device (e.g., the VCS (107)) of the tire pressure monitoring system; another component of the vehicle; or a mobile device. In this example, the other device may analyze, apply filters, and otherwise process the accelerometric data or characteristics of the data to determine a condition of the road.

[0036] In a particular embodiment, the VCS (107) is configured to detect a road condition by receiving accelerometric data generated by a TMS (105); identifying one or more characteristics of the accelerometric data; and based on the one or more characteristics, determining a road condition value indicating a condition of a road surface associated with the TMS (105). In another embodiment, the VCS may be configured to receive the road condition value from the TMS (105). As will be explained further, the VCS (107) may use the road condition value to modify or cause to be modified other components or settings of the vehicle. For example, the VCS (107) may adjust the Electronic Stability Control system or suspension mode of the vehicle based on the road condition value.

[0037] The arrangement of devices making up the exemplary system illustrated in FIG. 1 and FIG. 1 are for explanation, not for limitation. Data processing systems useful according to various embodiments of the present disclosure may include additional devices and networks, not shown in FIG. 1 and FIG. 2, as will occur to those of skill in the art. Networks in such data processing systems may support many data communications protocols, including for example TCP (Transmission Control Protocol), IP (Internet Protocol), Bluetooth protocol, Near Field Communication, Controller Area Network (CAN) protocol, Local Interconnect Network (LIN) protocol, FlexRay protocol, and others as will occur to those of skill in the art. Various embodiments of the present disclosure may be implemented on a variety of hardware platforms in addition to those illustrated in FIGS. 1 and 2.

[0038] FIG. 3 illustrates a reference diagram of a tire (103) in accordance with the present disclosure. As used in this disclosure, the z-axis of the tire (103) is the direction of radial force during rotation, the y-axis of the tire is the direction of lateral force during rotation, and the x-axis of the tire (103) is the direction of tangential force during rotation. The angular speed of rotation, in radians, is represented by co, and is also referred to herein as wheel speed. The measured acceleration for the indicated forces may include, for example, centripetal acceleration.

[0039] For further explanation, FIG. 4 sets forth a diagram of an exemplary vehicle control system (VCS) (400) for detecting a condition of a road surface according to embodiments of the present disclosure. The VCS (400) includes a controller (401) coupled to a memory (403). The controller (401) may be configured to obtain sensor readings related to vehicle operating conditions, as well as data from sources external to the vehicle. The controller (401) may include or implement a microcontroller, an Application Specific Integrated Circuit (ASIC), a digital signal processor (DSP), a programmable logic array (PLA) such as a field programmable gate array (FPGA), or other data computation unit in accordance with the present disclosure. The sensor readings and data, as well as accelerometric data received from the TMS and data derived from the accelerometric data, may be stored in the memory (403). The memory (403) may be volatile memory, or non-volatile memory such as flash memory.

[0040] For wireless communication with a TMS, the VCS (400) includes a TMS transceiver (405) coupled to the controller (401). In one embodiment, the TMS transceiver (405) is a Bluetooth Low Energy transmitter-receiver. In other embodiments, the TMS transceiver (405) may be other types of low power bidirectional communication technology that is intended to conserve energy consumed in the TMS. It is understood that, in some embodiments, the TMS transceiver (405) may be replaced with a unidirectional TMS receiver configured to receive wireless signals from a TMS. [0041] The VCS (400) may further include a transceiver (407) for cellular terrestrial communication, satellite communication, or both. The transceiver (407) may be used to communicatively couple the VCS (400) to an external network (not shown) to transmit, for example, indications (e.g., road condition values) of road conditions determined from acceleration data generated by a TMS.

[0042] The VCS (400) may further comprise a controller area network (CAN) interface (409) for communicatively coupling vehicle sensors and devices to the controller (401). For example, the CAN interface (409) couples a wheel speed sensor (411), a yaw rate sensor (413), an inclination sensor (415), and other sensors (417), to the controller (401). The wheel speed sensor (411) provides a signal indicating the rotational angular speed of the wheel, e.g., in radians per second. The yaw rate sensor (413) may be used to measure the yaw-induced acceleration of the vehicle, for example, when the vehicle is maneuvering a curve, which will influence the magnitude of loading on each tire. The yaw rate sensor (413) may also provide information on the shear forces on the tire where it contacts the road. The inclination sensor (415) may detect longitudinal and/or transverse inclination of the vehicle. The wheel speed sensor (411), the yaw rate sensor (413), and the inclination sensor (415) transmit respective readings to the controller (401).

[0043] In the example of FIG. 4, the memory (403) includes a road condition detector (499) that includes computer program instructions that when executed by the controller (401) cause the VCS (400) to receive accelerometric data generated by a tire monitoring sensor (TMS); identify one or more characteristics of the accelerometric data; and based on the one or more characteristics, determine a road condition value indicating a condition of a road surface associated with the TMS. In another embodiment, the road condition detector (499) includes computer program instructions that when executed by the controller (401) cause the VCS (400) to receive the road condition value from the TMS. In a further embodiment, the road condition detector (499) may include core logic to perform hardware controlled measurements. As will be explained further, the VCS may use the road condition value to modify or cause to be modified other components or settings of the vehicle. For example, the VCS (400) may adjust the Electronic Stability Control system or suspension mode of the vehicle based on the road condition value.

[0044] For further explanation, FIG. 5 sets forth a diagram of an embodiment of a Telematics Control Unit (TCU) (500) (e.g., an aftermarket system not directly coupled to vehicle-based sensors). The TCU (500) of FIG. 5 includes a controller (501), memory (503), and TMS transceiver (505) configured to perform similar functions as described above with respect to the VCS (400) FIG. 4. The TCU (500) also includes a Global Positioning System (GPS) receiver (557) configured to communicate with one or more GPS satellites in order to determine a vehicle location, speed, direction of movement, etc. The TCU (500) also includes an inertial measurement unit (IMU) (559) configured to measure a vehicle’s specific force, angular rate, and/or orientation using a combination of accelerometers, gyroscopes, and/or magnetometers. The TCU (500) also includes an on-board diagnostics (OBD) interface (561) for coupling the TCU (500) to one or more on-board diagnostic devices of a vehicle. The TCU (500) may receive power via a power interface (563) couplable to a vehicle power bus.

[0045] In a particular embodiment, the TCU (500) may be configured like the VCS (400) of FIG. 4 to detect a condition of a road surface, even though the TCU (500) may not have access to other vehicle subsystems through the CAN interface (409) of the VCS (400). In a particular embodiment, the memory (503) includes a road condition detector (599) that includes computer program instructions that when executed by the controller (501) cause the TCU (500) to receive accelerometric data generated by a tire monitoring sensor (TMS); identify one or more characteristics of the accelerometric data; and based on the one or more characteristics, determine a road condition value indicating a condition of a road surface associated with the TMS.

[0046] In another embodiment, the road condition detector (599) includes computer program instructions that when executed by the controller (501) cause the TCU (500) to receive the road condition value from the TMS. As will be explained further, the TCU (500) may use the road condition value to modify or cause to be modified other components or settings of the vehicle. For example, the TCU (500) may adjust the Electronic Stability Control system or suspension mode of the vehicle based on the road condition value.

[0047] For further explanation, FIG. 6 sets forth a diagram of an exemplary TMS (600) for detecting a condition of a road surface according to embodiments of the present disclosure. The TMS (600) includes a processor (601). The processor (601) may include or implement a microcontroller, an Application Specific Integrated Circuit (ASIC), a digital signal processor (DSP), a programmable logic array (PLA) such as a field programmable gate array (FPGA), or other data computation unit in accordance with the present disclosure.

[0048] The TMS (600) of FIG. 6 also includes a memory (603) coupled to the processor (601). The memory (603) may also store accelerometric data (625), including a raw digital signal sampled from the accelerometer (607) by the analog-to-digital converter (ADC) (611) and filtered using one or more bandpass filters (613). The memory (603) may also store FFT or Goertzel algorithm configurations (627). The memory (603) may also store one or more acceleration characteristics (615) derived from the accelerometric data (625). The acceleration characteristics (615) may be used to determine a particular road condition associated with a tire (e.g., a road condition experienced by an in-motion vehicle including a tire equipped with the TMS (600)).

[0049] The acceleration characteristics (615) may include an energy distribution for the accelerometric data (625). The energy distribution includes, for a given sampling of accelerometric data, a distribution indicating, for a plurality of bandpass filters (613), a variance measurement within the filter band of the corresponding bandpass filters (613). Example energy distributions are shown in the graph (900) of FIG. 9. The graph (900) shows a variance measurement, indicated by the Y-axis, relative to each of a plurality of filter bands of the X-axis. Line (902) shows the energy distribution for a tire traveling on a smooth or even road condition. For smooth roads, a strong fundamental frequency is present with very low variance in higher and lower frequency bands. In contrast, line (904) shows the energy distribution for a tire traveling on a rough road. Line (904) shows a higher variance in bands outside the main fundamental band. Moreover, while line (902) shows a single peak, line (904) shows multiple peaks.

[0050] The acceleration characteristics (615) may also include one or more attributes of an energy distribution of the accelerometric data (625). For example, the acceleration characteristics (615) may include, for a given energy distribution of a sampling of accelerometric data (625), a number of peaks in the energy distribution, an integral of the energy distribution (e.g., an area under the curve), etc.

[0051] In the example of FIG. 6, the memory (603) includes a road condition detector (699) that includes computer program instructions for detecting a condition of a road surface. In a particular embodiment, the road condition detector (699) includes computer program instructions that when executed by the processor (601) cause the TMS (600) to generate accelerometric data (625); identify one or more characteristics (615) of the accelerometric data (625); based on the one or more characteristics (615), determine a road condition value that indicates a condition of a road surface associated with the tire; and provide the road condition value to another device (e.g., the VCS (400), the TCU (500)) of the tire pressure monitoring system; another component of the vehicle; and a mobile device.

[0052] In a particular embodiment, the road condition detector (699) may be configured to transmit the accelerometric data (625) or the one or more characteristics (615) of the accelerometric data (625) to another device (e.g., the VCS (400), the TCU (500)) of the tire pressure monitoring system; another component of the vehicle; and a mobile device). In this example, the other device may analyze, apply filters, and otherwise process the accelerometric data or characteristics of the data to determine a condition of the road.

[0053] For wireless communication with the VCS (400), the TMS (600) of FIG. 6 includes a transceiver (605) coupled to the processor (601). In one embodiment, the transceiver (605) is a Bluetooth Low Energy transmitter-receiver. In other embodiments, the transceiver (605) may be other types of low energy bidirectional communication technology that is intended to conserve energy consumed in the TMS (600). The TMS (300) transmits data, such as accelerometric data (625), acceleration characteristics (615), and/or an indication of road conditions, to the VCS (400) via the transceiver (605). In an alternative embodiment, the TMS (600) may include a unidirectional transmitter instead of the transceiver (605).

[0054] The accelerometer (607) of FIG. 6 may also be an acceleration sensor, an accelerometric device, a shock sensor, a force sensor, a microelectromechanical systems (MEMs) sensor, or other device that is similarly responsive to acceleration magnitude and/or to changes in acceleration. For example, an accelerometer senses acceleration in a radial plane and outputs a signal proportional to the sensed acceleration. In an embodiment, the accelerometer (607) is configurable with an accelerometer range, a wheel speed parameter, or other vehicle parameter(s) provided by the VCS (400). In some embodiments, the configuration for the accelerometer (607) may be embedded.

[0055] The TMS (600) of FIG. 6 also includes an analog to digital converter (ADC) (611) that receives the signals from the accelerometer (607) and samples them according to a sampling rate. The ADC (611) converts the raw analog signals received from the accelerometer (607) into a raw digital signal that is suitable for digital signal processing. The sample rate of the ADC (611) may be configured via wheel speed from the wheel speed sensor or another vehicle-provided parameter from a vehicle sensor. The sample rate of the ADC (611) may also be embedded.

[0056] The TMS (600) of FIG. 6 also includes a plurality of bandpass filters (613) each configured to attenuate (e.g., reject) frequencies of accelerometric data (625) from the ADC (611) outside of a given filter band. FIG. 7 illustrates a comparison of accelerometric data (625) before and after passing through the bandpass filters (613). Graph (702) shows unfiltered accelerometric data (625) while graph (704) shows the accelerometric data (625) after being passed through a particular bandpass filter (613). Although the bandpass filters (613) are shown as dedicated hardware components of the TMS (600), it is understood that bandpass filtering may also be performed using software processing (e.g., by the processor (601)).

[0057] The TMS (600) may also implement dedicated hardware components (not shown) or logic implemented by the processor (601) to filter centripetal acceleration from accelerometric data (625). For example, dedicated hardware components may accept, as input, data from the ADC (611) and provide output to the bandpass filters (613). As seen in graph (802) of FIG. 8, an accelerating or decelerating vehicle results in a change of centripetal acceleration being experienced by the accelerometer (607). The resulting signal from the ADC (611) shows a slope in the accelerometric data (625). This is in contrast to accelerometric data (625) from a vehicle operating at a steady speed as shown in graph (702) of FIG. 7. The changing centripetal acceleration may be filtered (e.g., compensated for) by calculating the slope of the accelerometric data (625) signal and subtracting the slope to produce an input to the bandpass filters (613). The resulting filtered data as output by the bandpass filters is shown as graph (804) of FIG. 8.

[0058] The TMS (600) of FIG. 6 also includes a battery (609) connected to a power bus (not shown) to power the transceiver (605), the processor (601), the ADC (611), the bandpass filters (613), the accelerometer (607), and the memory (603).

[0059] For further explanation, FIG. 10 sets forth a flow chart illustrating an exemplary method for detecting a condition of a road surface according to embodiments of the present disclosure that includes generating (1002), by a tire monitoring sensor (1000) associated with a wheel, accelerometric data (625). The tire monitoring sensor (1000) may include, for example, a Tire Mounted Sensor (TMS), a Valve Mounted Sensor (VMS), or other sensors as can be appreciated. For example, the tire monitoring sensor (1000) may include an accelerometer (607) such as an acceleration sensor, an accelerometric device, a shock sensor, a force sensor, a microelectromechanical systems (MEMs) sensor, or other device that is similarly responsive to acceleration magnitude and/or to changes in acceleration. For example, an accelerometer senses acceleration in a radial plane and outputs a signal proportional to the sensed acceleration. An ADC (611) may sample the electric pulse signals and generate a digital signal suitable for digital signal processing. The digital signal (611) may be further filtered by applying one or more bandpass filters (613) and/or by filtering centripetal acceleration. The digital signal (611) may then be stored as accelerometric data (625) (e.g., in memory (603)).

[0060] The method of FIG. 10 also includes identifying (1004), by the tire monitoring sensor (1000), one or more characteristics of the accelerometric data (625). The one or more characteristics of the accelerometric data (625) may include an energy distribution for the accelerometric data (625). The energy distribution includes, for a given sampling of accelerometric data, a distribution indicating, for a plurality of bandpass filters (613), a variance measurement within the filter band of the corresponding bandpass filters (613). The one or more characteristics of the accelerometric data (625) may also include one or more attributes of the energy distribution or based on the energy distribution. For example, the one or more attributes may include a peak value in the energy distribution, a number of peaks in the energy distribution, an integral of the energy distribution (e.g., an area under the energy distribution), etc.

[0061] The method of FIG. 10 also includes determining (1006), by the tire monitoring sensor (1000), based on the one or more characteristics, a road condition value that indicates a condition of a road surface. Determining (1006) the road condition value may include comparing the one or more characteristics to one or more thresholds. For example, assuming that the one or more characteristics include a number of peaks in an energy distribution of accelerometric data (625), the road condition may be determined to be a “smooth” or normal road condition where the number of peaks falls below the threshold, while the road condition may be determined to be a “rough” road condition where the number of peaks meets or exceeds the threshold. Determining (1006) the road condition value may include determining a range or “bin” into which the one or more characteristics are included. For example, assuming that the one or more characteristics include an integral of an energy distribution of the accelerometric data (625), the road condition may be determined to be a first road condition where the integral falls within a first range, a second road condition where the integral falls within a second range, and a third road condition where the integral falls within a third range, etc.

[0062] The thresholds and/or ranges to which the one or more characteristics are compared may be based on predefined values. The predefined values may be based on a particular vehicle type, a particular make and/or model of a tire, etc. The thresholds and/or ranges may also be based on reference data (e.g., reference accelerometric data, a reference energy distribution, etc.). For example, the tire monitoring sensor (1000) may store accelerometric data (625) indicated as corresponding to smooth and/or rough road conditions. The accelerometric data (625) of the vehicle in motion may then be compared to the reference data to determine the road condition. For example, a number of peaks in of an energy distribution for accelerometric data (625) may be compared to a number of peaks in a reference energy distribution for a smooth road condition. As another example, a cross- correlation between the measured signal and the reference signal may be used. Where the number of peaks is equal or falling within a predefined threshold, the road condition may be determined as being a smooth road condition. Where the number of peaks in the energy distribution is greater than (e.g., by a threshold amount) the reference energy distribution, the road condition may be determined as a rough road condition.

[0063] Where a plurality of characteristics is used to determine a road condition, the road condition may be determined by calculating degrees of similarity, confidence scores, or other values relative to the threshold or reference values. For example, where a majority, but not all, characteristics indicate a particular road condition, the overall road condition may be determined to be the particular road condition.

[0064] Where a plurality of characteristics is used to determine a road condition, the road condition may be determined by calculating degrees of similarity, confidence scores, or other values relative to the threshold or reference values. For example, where a majority, but not all, characteristics indicate a particular road condition, the overall road condition may be determined to be the particular road condition.

[0065] The VCS (400) may receive updates from remote computing devices for various data used in determining the road condition. Such updates may be sent to sensors (1000) or used to modify sensors (1000) or the VCS (1000). For example, for embodiments where the determination of road conditions is based on a reference signal, updates to the reference signal may be received. Updates to the reference signal may also be generated by the VCS (400) and/or sensors (1000) based on measured signals. Moreover, updates to thresholds or algorithms used in determining road conditions may also be received.

[0066] Detecting a condition of a road surface provides for a determination of road conditions using sensors equipped with accelerometers. For example, existing TMSs may already include accelerometers for use in Phase Auto Location systems or other wheel autolocation systems. Thus, noise found in accelerometric data can be leveraged in order to identify current road conditions.

[0067] For further explanation, FIG. 11 sets forth a flow chart illustrating an exemplary method for detecting a condition of a road surface according to embodiments of the present disclosure. Like the method of FIG. 10, the method of FIG. 11 also includes generating (1002) accelerometric data (625); identifying (1004) one or more characteristics of the accelerometric data (625); and determining (1006), based on the one or more characteristics, a road condition value that indicates a condition of a road surface. [0068] The method of FIG. 11 differs from FIG. 10 in that the method of FIG. 11 also includes filtering (1102) (e.g., compensating for) centripetal acceleration from the accelerometric data (625). Accelerometric data (625) for a wheel that is accelerating or decelerating includes an overall slope (see, e.g., graph (802) of FIG. 8). The centripetal acceleration may be filtered by calculating the slope of the accelerometric data (625) signal and subtracting the slope. The resulting signal may be further filtered or processed (e.g., using bandpass filters (613), etc.).

[0069] For further explanation, FIG. 12 sets forth a flow chart illustrating an exemplary method for detecting a condition of a road surface according to embodiments of the present disclosure. Like the method of FIG. 10, the method of FIG. 12 also includes generating (1002) accelerometric data (625); identifying (1004) one or more characteristics of the accelerometric data (625); and determining (1006), based on the one or more characteristics, a road condition value that indicates a condition of a road surface.

[0070] The method of FIG. 12 differs from FIG. 11 in that the method of FIG. 12 also includes applying (1202), a plurality of bandpass filters (513) to the accelerometric data (525). Each of the bandpass filters (513) may be configured to attenuate (e.g., reject) frequencies of accelerometric data outside of a given filter band.

[0071] For further explanation, FIG. 13 sets forth a flow chart illustrating an exemplary method for detecting a condition of a road surface according to embodiments of the present disclosure. Like the method of FIG. 12, the method of FIG. 13 also includes generating (1002) accelerometric data (625); applying (1202) a plurality of bandpass filters (613) to the accelerometric data (625); identifying (1004) one or more characteristics of the accelerometric data (625); and determining (1006), based on the one or more characteristics, a road condition value that indicates a condition of a road surface.

[0072] The method of FIG. 13 differs from FIG. 12 in that the method of FIG. 13 also includes calculating (1302), for the plurality of bandpass filters (613), an energy distribution. The energy distribution includes, for a given sampling of accelerometric data (625), a distribution indicating, for a plurality of bandpass filters (613), a variance measurement within the filter band of the corresponding bandpass filters (613).

[0073] For further explanation, FIG. 14 sets forth a flow chart illustrating an exemplary method for detecting a condition of a road surface according to embodiments of the present disclosure. Like the method of FIG. 10, the method of FIG. 14 also includes generating (1002) accelerometric data (625); identifying (1004) one or more characteristics of the accelerometric data (625); and determining (1006), based on the one or more characteristics, a road condition value that indicates a condition of a road surface.

[0074] The method of FIG. 14 differs from FIG. 10 in that the method of FIG. 14 also includes providing (1402) an indication of the road condition to a remote computing device. The remote computing device may include, for example, a remotely disposed server, cloud computing environment, etc. The indication of the road condition may also be sent with an indication of a location of the vehicle (e.g., determined using a Global Positioning System (GPS) sensor, one or more cellular connections, etc.). The indication of the road condition may be sent via the VCS. For example, the tire monitoring sensor (1000) provides the indication of the road condition to the VCS for sending to the remote computing device.

Thus, the remotely disposed computing device is provided with an indication of a particular road condition at a particular location. The remotely disposed computing device may then provide the indication of the road condition at the location to other vehicles or services as can be appreciated.

[0075] For further explanation, FIG. 15 sets forth a flow chart illustrating an exemplary method for detecting a condition of a road surface according to embodiments of the present disclosure that includes receiving (1502) from a tire monitoring sensor (1000) associated with a wheel, by a VCS (1501) of a vehicle, accelerometric data. For example, a tire monitoring sensor (1000) may include an accelerometer such as an acceleration sensor, an accelerometric device, a shock sensor, a force sensor, a microelectromechanical systems (MEMs) sensor, or other device that is similarly responsive to acceleration magnitude and/or to changes in acceleration. For example, an accelerometer senses acceleration in a plane and outputs a signal proportional to the sensed acceleration. An ADC may sample the signal and generate a digital signal suitable for digital signal processing. The digital signal may be further filtered by applying one or more bandpass filters and/or by filtering centripetal acceleration. The digital signal may then be stored as accelerometric data (e.g., in memory). The TMS may be configured to transmit the accelerometric data to the VCS and the VCS may be configured to receive the transmitted accelerometric data.

[0076] The method of FIG. 15 also includes identifying (1504), by the VCS (1501), one or more characteristics of the accelerometric data. The one or more characteristics of the accelerometric data may include an energy distribution for the accelerometric data.

[0077] The method of FIG. 15 also includes determining (1506), by the VCS (1501), based on the one or more characteristics, a road condition value that indicates a condition of a road surface. Determining (1506) the road condition value may include comparing the one or more characteristics to one or more thresholds. For example, assuming that the one or more characteristics include a number of peaks in an energy distribution of accelerometric data, the road condition may be determined to be a “smooth” or normal road condition where the number of peaks falls below the threshold, while the road condition may be determined to be a “rough” road condition where the number of peaks meets or exceeds the threshold. Determining (1506) the road condition value may include determining a range or “bin” into which the one or more characteristics are included. For example, assuming that the one or more characteristics include an integral of an energy distribution of the accelerometric data, the road condition may be determined to be a first road condition where the integral falls within a first range, a second road condition where the integral falls within a second range, and a third road condition where the integral falls within a third range, etc.

[0078] For further explanation, FIG. 16 sets forth a flow chart illustrating an exemplary method for detecting a condition of a road surface according to embodiments of the present disclosure. Like the method of FIG. 15, the method of FIG. 16 also includes receiving (1502) from a tire monitoring sensor (1000) associated with a wheel, by a VCS (1501) of a vehicle, accelerometric data; identifying (1504) one or more characteristics of the accelerometric data (625); and determining (1506), based on the one or more characteristics, a road condition value that indicates a condition of a road surface.

[0079] The method of FIG. 16 differs from FIG. 15 in that the method of FIG. 16 also includes filtering (1602) (e.g., compensating for) centripetal acceleration from the accelerometric data. Accelerometric data for a wheel that is accelerating, or decelerating includes an overall slope (see, e.g., graph (802) of FIG. 8). The centripetal acceleration may be filtered by calculating the slope of the accelerometric data signal and subtracting the slope. The resulting signal may be further filtered or processed (e.g., using bandpass filters, etc.). [0080] For further explanation, FIG. 17 sets forth a flow chart illustrating an exemplary method for detecting a condition of a road surface according to embodiments of the present disclosure. Like the method of FIG. 15, the method of FIG. 17 also includes receiving (1502) from sensor (1000) associated with a wheel, by a VCS (1501) of a vehicle, accelerometric data; identifying (1504) one or more characteristics of the accelerometric data (625); and determining (1506), based on the one or more characteristics, a road condition value that indicates a condition of a road surface.

[0081] The method of FIG. 17 differs from FIG. 15 in that the method of FIG. 17 also includes applying (1702) a plurality of bandpass filters to the accelerometric data. Each bandpass filter may be configured to attenuate (e.g., reject) frequencies of accelerometric data outside of a given filter band.

[0082] For further explanation, FIG. 18 sets forth a flow chart illustrating an exemplary method for detecting a condition of a road surface according to embodiments of the present disclosure. Like the method of FIG. 17, the method of FIG. 18 also includes receiving (1502) from a tire monitoring sensor (1000) associated with a wheel, by a VCS (1501) of a vehicle, accelerometric data; applying (1702) a plurality of bandpass filters (613) to the accelerometric data (625); identifying (1504) one or more characteristics of the accelerometric data (625); and determining (1506), based on the one or more characteristics, a road condition value that indicates a condition of a road surface.

[0083] The method of FIG. 18 differs from FIG. 17 in that the method of FIG. 18 also includes calculating (1802), for the plurality of bandpass filters, an energy distribution. The energy distribution includes, for a given sampling of accelerometric data, a distribution indicating, for a plurality of bandpass filters, a variance measurement within the filter band of the corresponding bandpass filters.

[0084] For further explanation, FIG. 19 sets forth a flow chart illustrating an exemplary method for detecting a condition of a road surface according to embodiments of the present disclosure. Like the method of FIG. 15, the method of FIG. 19 also includes receiving (1502) from a tire monitoring sensor (1000) associated with a wheel, by a VCS (1501) of a vehicle, accelerometric data; identifying (1504) one or more characteristics of the accelerometric data (625); and determining (1506), based on the one or more characteristics, a road condition value that indicates a condition of a road surface.

[0085] The method of FIG. 19 differs from FIG. 15 in that the method of FIG. 19 also includes modifying (1902), based on the determined road condition, one or more attributes of an Electronic Stability Control (ECS) system or a suspension mode of the vehicle. For example, the VCS (1501) may provide a signal indicating the road condition, or a signal based on the road condition, to the ECS system based on the determined road condition. The VCS (1501) may also adjust a variable suspension mode of the vehicle to a mode corresponding to the determined road condition (e.g., a mode for rough roads, a mode for smooth roads, etc.).

[0086] For further explanation, FIG. 20 sets forth a flow chart illustrating an exemplary method for detecting a condition of a road surface according to embodiments of the present disclosure. Like the method of FIG. 15, the method of FIG. 20 also includes receiving (1502) from a tire monitoring sensor (1000) associated with a wheel, by a VCS (1501) of a vehicle, accelerometric data; identifying (1504) one or more characteristics of the accelerometric data (625); and determining (1506), based on the one or more characteristics, a road condition value that indicates a condition of a road surface.

[0087] The method of FIG. 20 differs from FIG. 15 in that the method of FIG. 20 also includes providing (2002), by the VCS, an indication of the road condition to a remote computing device. The remote computing device may include, for example, a remotely disposed server, cloud computing environment, etc. The indication of the road condition may also be sent with an indication of a location of the vehicle (e.g., determined using a Global Positioning System (GPS) sensor, one or more cellular connections, etc.). Thus, the remotely disposed computing device is provided with an indication of a particular road condition at a particular location. The remotely disposed computing device may then provide the indication of the road condition at the location to other vehicles or services as can be appreciated.

[0088] In view of the explanations set forth above, readers will recognize that the benefits of detecting a condition of a road surface according to embodiments of the present disclosure include, but are not limited to:

• Improved performance of a tire pressure monitoring system by providing the ability to determine road conditions using existing tire/wheel/valve-mounted sensors equipped with accelerometers (e.g., for use in Phase Auto Location systems or other wheel placement detection systems).

[0089] Exemplary embodiments of the present invention are described largely in the context of a fully functional computer system for detecting a condition of a road surface. Readers of skill in the art will recognize, however, that the present invention also may be embodied in a computer program product disposed upon computer readable storage media for use with any suitable data processing system. Such computer readable storage media may be any storage medium for machine-readable information, including magnetic media, optical media, or other suitable media. Examples of such media include magnetic disks in hard drives or diskettes, compact disks for optical drives, magnetic tape, and others as will occur to those of skill in the art. Persons skilled in the art will immediately recognize that any computer system having suitable programming means will be capable of executing the steps of the method of the invention as embodied in a computer program product. Persons skilled in the art will recognize also that, although some of the exemplary embodiments described in this specification are oriented to software installed and executing on computer hardware, nevertheless, alternative embodiments implemented as firmware or as hardware are well within the scope of the present invention. [0090] The present invention may be a system, an apparatus, a method, a tire pressure monitoring system, a tire monitoring sensor, a device, a vehicle control system, and/or a computer program product. The computer program product may include a computer readable storage medium (or media) having computer readable program instructions thereon for causing a processor to carry out aspects of the present invention.

[0091] The computer readable storage medium can be a tangible device that can retain and store instructions for use by an instruction execution device. The computer readable storage medium may be, for example, but is not limited to, an electronic storage device, a magnetic storage device, an optical storage device, an electromagnetic storage device, a semiconductor storage device, or any suitable combination of the foregoing. A non-exhaustive list of more specific examples of the computer readable storage medium includes the following: a portable computer diskette, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or Flash memory), a static random access memory (SRAM), a portable compact disc read-only memory (CD- ROM), a digital versatile disk (DVD), a memory stick, a floppy disk, a mechanically encoded device such as punch-cards or raised structures in a groove having instructions recorded thereon, and any suitable combination of the foregoing. A computer readable storage medium, as used herein, is not to be construed as being transitory signals per se , such as radio waves or other freely propagating electromagnetic waves, electromagnetic waves propagating through a waveguide or other transmission media (e.g., light pulses passing through a fiber optic cable), or electrical signals transmitted through a wire.

[0092] Computer readable program instructions described herein can be downloaded to respective computing/processing devices from a computer readable storage medium or to an external computer or external storage device via a network, for example, the Internet, a local area network, a wide area network and/or a wireless network. The network may comprise copper transmission cables, optical transmission fibers, wireless transmission, routers, firewalls, switches, gateway computers and/or edge servers. A network adapter card or network interface in each computing/processing device receives computer readable program instructions from the network and forwards the computer readable program instructions for storage in a computer readable storage medium within the respective computing/processing device.

[0093] Computer readable program instructions for carrying out operations of the present invention may be assembler instructions, instruction-set-architecture (ISA) instructions, machine instructions, machine dependent instructions, microcode, firmware instructions, state-setting data, or either source code or object code written in any combination of one or more programming languages, including an object oriented programming language such as Smalltalk, C++ or the like, and conventional procedural programming languages, such as the "C" programming language or similar programming languages. The computer readable program instructions may execute entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer or server. In the latter scenario, the remote computer may be connected to the user's computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or the connection may be made to an external computer (for example, through the Internet using an Internet Service Provider). In some embodiments, electronic circuitry including, for example, programmable logic circuitry, field-programmable gate arrays (FPGA), or programmable logic arrays (PLA) may execute the computer readable program instructions by utilizing state information of the computer readable program instructions to personalize the electronic circuitry, in order to perform aspects of the present invention.

[0094] Aspects of the present invention are described herein with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and/or block diagrams, and combinations of blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer readable program instructions.

[0095] These computer readable program instructions may be provided to a processor of a general purpose computer, special purpose computer, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions/acts specified in the flowchart and/or block diagram block or blocks. These computer readable program instructions may also be stored in a computer readable storage medium that can direct a computer, a programmable data processing apparatus, and/or other devices to function in a particular manner, such that the computer readable storage medium having instructions stored therein comprises an article of manufacture including instructions which implement aspects of the function/act specified in the flowchart and/or block diagram block or blocks.

[0096] The computer readable program instructions may also be loaded onto a computer, other programmable data processing apparatus, or other device to cause a series of operational steps to be performed on the computer, other programmable apparatus or other device to produce a computer implemented process, such that the instructions which execute on the computer, other programmable apparatus, or other device implement the functions/acts specified in the flowchart and/or block diagram block or blocks.

[0097] The flowchart and block diagrams in the Figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of the present invention. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of instructions, which comprises one or more executable instructions for implementing the specified logical function(s). In some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams and/or flowchart illustration, and combinations of blocks in the block diagrams and/or flowchart illustration, can be implemented by special purpose hardware-based systems that perform the specified functions or acts or carry out combinations of special purpose hardware and computer instructions.

[0098] It will be understood from the foregoing description that modifications and changes may be made in various embodiments of the present disclosure without departing from its true spirit. The descriptions in this specification are for purposes of illustration only and are not to be construed in a limiting sense. The scope of the present disclosure is limited only by the language of the following claims.

Claims

CLAIMS What is claimed is:

1. A method of detecting a condition of a road surface, the method comprising: generating, by a tire monitoring sensor associated with a wheel, accelerometric data; identifying, by the tire monitoring sensor, one or more characteristics of the accelerometric data; and based on the one or more characteristics, determining, by the tire monitoring sensor, a road condition value indicating a condition of a road surface associated with the tire.

2. The method of claim 1, further comprising filtering, by the tire monitoring sensor, centripetal acceleration from the accelerometric data.

3. The method of claim 1, further comprising applying, by the tire monitoring sensor, a plurality of bandpass filters to the accelerometric data.

4. The method of claim 3, further comprising calculating, for the plurality of bandpass filters, by the tire monitoring sensor, an energy distribution.

5. The method of claim 4, wherein the one or more characteristics of the accelerometric data comprise an integral for the energy distribution.

6. The method of claim 5, wherein the road condition is determined based on a comparison of the integral to at least one of: one or more thresholds and one or more other integrals, wherein the one or more other integrals correspond to one or more other reference energy distributions.

7. The method of claim 4, wherein the one or more characteristics of the accelerometric data comprise one or more peaks in the energy distribution.

8. The method of claim 7, wherein the road condition is determined based on a comparison of the one or more peaks to at least one of: one or more thresholds and one or more other peaks, wherein the one or more other peaks correspond to one or more reference energy distributions.

9. The method of claim 1, further comprising providing, by the tire monitoring sensor, an indication of the determined road condition to a remote computing device.

10. A tire monitoring sensor for detecting a condition of a road surface, the tire monitoring sensor configured to: generate accelerometric data; identify one or more characteristics of the accelerometric data; and based on the one or more characteristics, determine a road condition value indicating a condition of a road surface associated with the tire.

11. The tire monitoring sensor of claim 10, wherein the tire monitoring sensor is further configured to filter centripetal acceleration from the accelerometric data.

12. The tire monitoring sensor of claim 10, wherein the tire monitoring sensor is further configured to apply a plurality of bandpass filters to the accelerometric data.

13. The tire monitoring sensor of claim 12, wherein the tire monitoring sensor is further configured to calculate, for the plurality of bandpass filters, an energy distribution comprising a plurality of energy values.

14. The tire monitoring sensor of claim 13, wherein the one or more characteristics of the accelerometric data comprise an integral for the energy distribution.

15. A method of detecting a condition of a road surface, the method comprising: receiving, by a vehicle control system (VCS), accelerometric data generated by a tire monitoring sensor; identifying, by the VCS, one or more characteristics of the accelerometric data; and based on the one or more characteristics, determining, by the VCS, a road condition value indicating a condition of a road surface associated with the tire monitoring sensor.

16. The method of claim 15, further comprising based on the determined road condition, modifying, by the VCS, one or more attributes of at least one of: an Electronic Stability Control system and a suspension mode of a vehicle.

17. The method of claim 15, further comprising filtering, by the VCS, centripetal acceleration from the accelerometric data.

18. The method of claim 15, further comprising applying, by the VCS, a plurality of bandpass filters to the accelerometric data.

19. The method of claim 18 further comprising calculating, for the plurality of bandpass filters, by the VCS, an energy distribution.

20. An apparatus for detecting a condition of a road surface, the apparatus including: a processor; a memory coupled the processor, the memory including computer program instructions that when executed by the processor cause the apparatus to: receive accelerometric data generated by a tire monitoring sensor; identify one or more characteristics of the accelerometric data; and based on the one or more characteristics, determine a road condition value indicating a condition of a road surface associated with the tire monitoring sensor.