Automated detection of tire sensor position

Abstract

Aspects of tire sensor auto-positioning are described. A sensor position detection device receives a tire sensor value from a tire sensor device. Tire sensor values for tire footprint length, longitudinal acceleration, and lateral acceleration are measured or calculated. The sensor position detection device generates a longitudinal acceleration coefficient and a lateral acceleration coefficient using these values, and assigns the tire sensor device to a particular tire position of the vehicle based on a sign of the longitudinal acceleration coefficient and a sign of the lateral acceleration coefficient.

Description

Technical Field

[0001] The field relates to automated detection of tire sensor positions. Background Art

[0002] As tires age, they include various conditions that are beneficial to monitor and estimate. These conditions include tire wear, tire pressure, and tire mismatch for dual tires. Tire wear plays an important role in vehicle factors such as safety, reliability, and performance. As tires wear, the tread loses material and directly affects such vehicle factors. Tires can include sensors or groups of sensors that can detect parameters associated with the tire. Therefore, it is desirable to identify tire sensor locations, for example, to associate a tire with a wheel or wheel position.

[0003] One method of identifying tire sensor locations may be to manually identify the positioning and data entry of this information for each tire and sensor. However, this can be time consuming and may also be subject to user error. Additionally, the tire and wheel may rotate or move, requiring another manual assessment of the tire alignment. Therefore, there is a need in the art for a method of accurately and reliably identifying tire alignment. Summary of the invention

[0004] The present invention provides the following technical solutions:

[0005] 1. A system comprising:

[0006] at least one computing device, the at least one computing device comprising at least one processor; and

[0007] at least one memory, the at least one memory comprising instructions that, when executed, cause the at least one computing device to at least:

[0008] receiving, by the sensor position detection device, a plurality of tire sensor values ​​from the tire sensor device, wherein the plurality of tire sensor values ​​are measured or calculated for a respective one of: a tire footprint length parameter, a longitudinal acceleration parameter, and a lateral acceleration parameter;

[0009] processing the plurality of tire sensor values ​​of the tire footprint length parameter, the longitudinal acceleration parameter, and the lateral acceleration parameter by the sensor position detection device, wherein the sensor position detection device generates at least a longitudinal acceleration coefficient and a lateral acceleration coefficient; and

[0010] The tire sensor device is assigned by the sensor position detection device to a particular tire location associated with the vehicle based at least in part on the sign of the longitudinal acceleration coefficient and the sign of the lateral acceleration coefficient.

[0011] 2. The system of claim 1, wherein the instructions, when executed, cause the at least one computing device to at least:

[0012] A tire footprint length model is identified, the tire footprint length model defining a relationship between at least: a tire footprint length, a nominal footprint length, the longitudinal acceleration coefficient, the longitudinal acceleration, the lateral acceleration coefficient, and the lateral acceleration.

[0013] 3. The system according to claim 1, wherein the tire sensor values ​​are processed in real time by the sensor position detection device.

[0014] 4. The system of claim 3, wherein the sensor position detection device processes the tire sensor values ​​in real time based at least in part on a recursive least squares fitting process.

[0015] 5. The system of claim 1 , wherein the tire sensor device is assigned to a tire location based at least in part on storing a unique tire sensor device identifier of the tire sensor device in association with data indicative of the tire location.

[0016] 6. The system of claim 1 wherein the sensor position detection device processes the plurality of tire sensor values ​​to further identify a nominal tire footprint length.

[0017] 7. The system of claim 1, wherein the instructions, when executed, cause the at least one computing device to at least:

[0018] The specific tire location is transmitted to at least one of a server environment, a client device, or any combination thereof.

[0019] 8. A method comprising:

[0020] receiving, by the sensor position detection device, a plurality of tire sensor values ​​from the tire sensor device, wherein the plurality of tire sensor values ​​are measured or calculated for a respective one of: a tire footprint length parameter, a longitudinal acceleration parameter, and a lateral acceleration parameter;

[0021] processing the plurality of tire sensor values ​​of the tire footprint length parameter, the longitudinal acceleration parameter, and the lateral acceleration parameter by the sensor position detection device, wherein the sensor position detection device generates at least a longitudinal acceleration coefficient and a lateral acceleration coefficient; and

[0022] The tire sensor device is assigned by the sensor position detection device to a particular tire location associated with the vehicle based at least in part on the sign of the longitudinal acceleration coefficient and the sign of the lateral acceleration coefficient.

[0023] 9. The method according to claim 8, further comprising:

[0024] A tire footprint length model is identified that defines a relationship between at least the tire footprint length, the nominal footprint length, the longitudinal acceleration coefficient, the longitudinal acceleration, the lateral acceleration coefficient, and the lateral acceleration.

[0025] 10. The method according to claim 8, wherein the tire sensor values ​​are processed in real time by the sensor position detection device.

[0026] 11. The method of claim 10, wherein the sensor position detection device processes the tire sensor values ​​in real time based at least in part on a recursive least squares fitting process.

[0027] 12. The method of claim 8, wherein the tire sensor device is assigned to a tire location based at least in part on storing a unique tire sensor device identifier of the tire sensor device in association with data indicative of the tire location.

[0028] 13. The method of claim 8, wherein the sensor position detection device processes the plurality of tire sensor values ​​to further identify a nominal tire footprint length.

[0029] 14. The system according to claim 1, further comprising:

[0030] The specific tire location is transmitted to at least one of a server environment, a client device, or any combination thereof.

[0031] 15. A non-transitory computer-readable medium comprising instructions executable by at least one computing device, the instructions, when executed by the at least one computing device, causing the at least one computing device to at least:

[0032] receiving, by the sensor position detection device, a plurality of tire sensor values ​​from the tire sensor device, wherein the plurality of tire sensor values ​​are measured or calculated for a respective one of: a tire footprint length parameter, a longitudinal acceleration parameter, and a lateral acceleration parameter;

[0033] processing the plurality of tire sensor values ​​of the tire footprint length parameter, the longitudinal acceleration parameter, and the lateral acceleration parameter by the sensor position detection device, wherein the sensor position detection device generates at least a longitudinal acceleration coefficient and a lateral acceleration coefficient; and

[0034] The tire sensor device is assigned by the sensor position detection device to a particular tire location associated with the vehicle based at least in part on the sign of the longitudinal acceleration coefficient and the sign of the lateral acceleration coefficient.

[0035] 16. The non-transitory computer-readable medium of claim 15, wherein the instructions, when executed, cause the at least one computing device to at least:

[0036] A tire footprint length model is identified, the tire footprint length model defining a relationship between at least: a tire footprint length, a nominal footprint length, the longitudinal acceleration coefficient, the longitudinal acceleration, the lateral acceleration coefficient, and the lateral acceleration.

[0037] 17. The non-transitory computer readable medium of claim 15, wherein the tire sensor values ​​are processed in real time by the sensor position detection device.

[0038] 18. The non-transitory computer-readable medium of claim 17, wherein the sensor position detection device processes the tire sensor values ​​in real time based at least in part on a recursive least squares fitting process.

[0039] 19. The non-transitory computer-readable medium of claim 15, wherein the tire sensor device is assigned to a tire location based at least in part on storing a unique tire sensor device identifier of the tire sensor device in association with data indicative of the tire location.

[0040] 20. The non-transitory computer-readable medium of claim 15, wherein the sensor position detection device processes the plurality of tire sensor values ​​to further identify a nominal tire footprint length. BRIEF DESCRIPTION OF THE DRAWINGS

[0041] Various aspects of the present disclosure may be better understood with reference to the following drawings. It should be noted that the elements in the drawings are not necessarily drawn to scale, but rather emphasis is placed on clearly illustrating the principles of the present embodiments. In the drawings, similar reference numerals designate similar or corresponding but not necessarily identical elements throughout the several views.

[0042] Figure 1 An example of a tire sensor position detection system according to various aspects of an embodiment of the present disclosure is illustrated.

[0043] Figure 2 Examples of tire footprint length models according to various aspects of embodiments of the present disclosure are illustrated.

[0044] Figures 3 to 6 Graphs illustrating aggregate data of tire footprint length as a function of longitudinal acceleration and lateral acceleration according to various aspects of an embodiment of the present disclosure.

[0045] Figure 7An example of the operation of a tire sensor allocation component using examples of aggregate tire sensor data is illustrated in accordance with various aspects of an embodiment of the present disclosure.

[0046] Figure 8 Components and functions performed by a tire sensor distribution assembly according to various aspects of an embodiment of the present disclosure are illustrated.

[0047] Fig. 9 A set of graphs are illustrated showing footprint length model coefficients calculated using real-time recursive least squares fitting compared to least squares fitting using batch mode data, according to various aspects of embodiments of the present disclosure.

[0048] Fig.10 A computing device for one or more of the components of a tire sensor position detection system is illustrated in accordance with various aspects of embodiments of the present disclosure. DETAILED DESCRIPTION

[0049] As outlined above, tire monitoring plays an important role in vehicle factors such as safety, reliability, and performance. A tire may include a sensor or sensor group that can detect parameters associated with the tire. Therefore, it is desirable to identify tire sensor locations, for example, to associate a tire with a wheel or wheel position. However, existing methods can be time consuming and can also be subject to user error. Therefore, there is a need in the art for a method of accurately and reliably identifying tire location.

[0050] The present disclosure describes a mechanism for automatically positioning a tire sensor to its wheel position using a tire contact patch or footprint length, for example, on a four-wheel position vehicle such as a coupe, sedan, hatchback, van, truck, sport utility vehicle, etc. The present disclosure is also capable of automatically positioning a tire sensor in real time using data collected during standard driving. In some cases, a model based on footprint or (tire contact patch) length can further implement, for example, automatically positioning a tire sensor device based at least in part on the relationship between footprint length and the sign of an acceleration coefficient rather than identifying a specific steering direction and speed. Automatic positioning of a tire sensor device can refer to receiving tire sensor data from a tire sensor device, processing data, and assigning a tire sensor device to a tire position without user input or interaction. Compared to a system that, for example, uses an onboard global positioning system (GPS) device to identify a specific turning direction and then filters the data to a reduced set, the present mechanism can operate using all data. Relative to other technologies, among other features, the ability to use all data points enables the present invention to identify tire sensor positioning faster and also to use data captured in a natural driving scenario. Tire positioning can be accurately transmitted to a vehicle control system. In a footprint length model as described herein, the coefficients evaluated for the sign value may include a lateral acceleration coefficient for lateral acceleration and a longitudinal acceleration coefficient for longitudinal acceleration.

[0051] Figure 1 A tire sensor position detection system 100 is shown. The tire sensor position detection system 100 may include a vehicle 101, tire sensor devices 103a-d (collectively referred to as "a plurality of tire sensor devices 103", and individually referred to as "a tire sensor device 103"), a sensor position detection device 106 (such as a gateway or another device deployed to the vehicle 101), a server environment 109, and a client device 112. The tire sensor devices 103, the sensor position detection device 106, and the server environment 109 may communicate with each other via one or more networks. In some examples, the sensor position detection device 106 may include an edge device or a networking device that may generate, facilitate, or maintain a network to which the tire sensor device 103 is connected.

[0052] Networks may include local area networks (LANs), wide area networks (WANs), and other networks. These networks may include wired or wireless components or a combination thereof. Wired networks may include Ethernet networks, cable networks, fiber optic networks, and telephone networks such as dial-up, digital subscriber line (DSL), and integrated services digital network (ISDN) networks. Wireless networks may include cellular networks, satellite networks, Institute of Electrical and Electronics Engineers (IEEE) 1002.11 wireless networks (i.e., ), Networks, microwave transmission networks, and other networks that rely on radio broadcasting. A network may also include a combination of two or more networks. Examples of networks may include the Internet, an intranet, an extranet, a virtual private network (VPN), and similar networks.

[0053] Vehicle 101 may include a coupe, sedan, hatchback, van, truck, SUV and other types of vehicles. Generally, vehicle 101 may include any vehicle having four wheel or tire positions, including a left front position, a right front position, a left rear position and a right rear position.

[0054] Tire sensor device 103 may refer to a device including one or more sensors, one or more sensors respectively identifying values ​​corresponding to a plurality of different parameters. Tire sensor device 103 may include one or more sensors, including motion sensors, environmental sensors, and position sensors. For example, tire sensor device 103 may include a tire pressure monitoring system (TPMS) device that measures the values ​​of pressure, temperature, acceleration, and other types of parameters (such as tire footprint length). Tire sensor device 103 may also include a computing device and a memory and a network communication component. Typically, tire sensor device 103 may communicate wirelessly with edge or sensor position detection device 106. Tire sensor device 103 may generally be connected, integrated, or attached to a tire (or wheel) of vehicle 101. In various examples, tire sensor device 103 may be embedded in a tire, attached to a valve stem of a tire, or otherwise attached to a tire or corresponding wheel. Tire sensor device 103 may also provide information to a user interface display or light of vehicle 101 and / or client device 112. Tire-specific information may include temperature, pressure, leak warning, overfill warning, temperature warning, tire mileage, remaining tire life, and any other data. As a result, positioning of tire sensor device 103 may enable information to be displayed in association with a specific tire location.

[0055] In some examples, a tire may include multiple separate tire sensor devices 103, such as one tire sensor device 103 at least partially on the interior of the tire or tire cavity, which may have a set of sensors capable of measuring conditions within the tire; and a second tire sensor device 103 at least partially on the exterior of the tire, which may have a set of sensors capable of measuring conditions outside the tire. This may be used for comparison of conditions or parameters between the interior and exterior of the tire. This is particularly useful for comparison of environmental factors such as temperature, external air pressure, internal tire pressure, light, humidity, etc. However, any of force, vibration or acceleration and position sensor parameters, as well as any type of parameters, may be compared for verification and comparison purposes.

[0056] The motion sensor can detect motion parameters, such as acceleration and rotational forces along one or more axles. For example, the motion sensor can include an accelerometer or other force, vibration or acceleration detection device. The tire sensor device 103 and / or the sensor position detection device 106 can calculate the footprint length of the tire based on the acceleration or force data of the tire, when this part of the tire contacts the driving surface, and again when this part of the tire is out of contact with the driving surface, as the tire deforms near the sensor (which can be in the sidewall near the tire tread area or the tread area), the footprint length has a uniquely identifiable waveform. In some examples, the vehicle rate or speed can also be used for this calculation of footprint length. The vehicle speed can be measured using the tire sensor device 103, the sensor position detection device 106 or the GPS of the vehicle. The speed can also be received from other. This can include controller area network (CAN) bus data based on speedometer readings or other information.

[0057] Environmental sensors may detect environmental parameters such as temperature, external air pressure, internal tire pressure, light, humidity, etc. For example, environmental sensors may include a barometer, a photometer, and a thermometer.

[0058] Position sensors can detect location and orientation parameters. These sensors can identify global or geographic location and orientation. Position sensors can include global positioning systems (GPS), magnetometers, etc.

[0059] The sensor position detection device 106 may include or have access to a sensor set separate from the tire sensor device 103. This may include a CAN bus based sensor set for the vehicle 101, or another sensor device having one or more sensors corresponding to the entire vehicle 101 rather than a specific tire. The entire vehicle 101 sensor device may also include separate motion sensors, position sensors, environmental sensors, etc.

[0060] Because tire sensor device 103 may be attached to a tire of vehicle 101, the various parameters detected may be tire-specific for a particular tire. These tire-specific values ​​of tire sensor device 103 may be different from the overall or overall state of vehicle 101. Thus, when overall vehicle information is needed, a sensor separate from tire sensor device 103 may be used.

[0061] The server environment 109 may include, for example, a hardware computing device or any other system that provides computing power. Alternatively, the server environment 109 may include one or more computing devices arranged in, for example, one or more server groups, computer groups, computing clusters, or other arrangements. The server environment 109 may include grid computing resources or any other distributed computing arrangements. The computing devices may be located in a single facility, or may be distributed between many different geographic locations. Various applications may be executed on the server environment 109. For example, tire, wheel, and / or vehicle management services 120 may be executed by the server environment 109. Other applications, services, processes, systems, engines, or functions not discussed in detail herein may also be executed or implemented by the server environment 109.

[0062] The server environment 109 may include or operate as one or more virtualized computer instances. For convenience, the server environment 109 is referred to herein as a singular. Although the server environment 109 is referred to in the singular, it should be understood that multiple server environments 109 may be employed in various arrangements as described above. Since the server environment 109 communicates directly or indirectly with the sensor position detection device 106 and the tire sensor device 103, the server environment 109 may be described as a remote server environment 109 or a cloud server environment 109.

[0063] The server environment 109 may include a data repository. The data repository may include a memory of the server environment 109, a mass storage resource of the server environment 109, or any other storage resource on which data may be stored by the server environment 109. In some examples, the data repository may include the memory of various hosts. In some examples, the data repository may include one or more relational databases, object-oriented databases, hierarchical databases, hash tables, or similar key-value data repositories, as well as other data storage applications or data structures. For example, the data stored in the data repository may be associated with the operations of the various services or functional entities described below.

[0064] The vehicle management service 120 can register and track tire sensor devices 103. A group of tire sensor devices 103 can be registered and pre-associated with a specific enterprise and one or more vehicles 101. Each of the vehicles 101 can be associated with a configuration that can be selected by a user through an interface of the vehicle management service 120. The vehicle configuration can indicate information about the vehicle 101. The vehicle configuration can specify tire position parameters. The tire position parameters can also be considered as wheel position parameters. Each tire position parameter can correspond to a tire position where a tire and one or more tire sensor devices 103 can be assigned, for example, using a unique identifier of the tire sensor device 103. The tire position parameters can include a left front position parameter, a right front position parameter, a left rear position parameter, and a right rear position parameter. The value of each tire position parameter can include a unique identifier for the tire sensor device 103, indicating the specific tire sensor device 103 located at the associated tire position.

[0065] The client device 112 may include a processor-based system, such as a computer system. Such a computer system may be embodied as a personal computer (e.g., a desktop computer, a laptop computer, or a similar device), a mobile computing device (e.g., a personal digital assistant, a cellular phone, a smart phone, a web pad, a tablet computer system, a music player, a portable game console, an e-book reader, and a similar device), a media playback device (e.g., a media streaming device, The client device may include a video player, a digital video disc (DVD) player, a set-top box, and similar devices), a video game console, or other device with similar capabilities. The client device may include one or more displays, such as a liquid crystal display (LCD), a gas plasma-based flat panel display, an organic light emitting diode (OLED) display, an electrophoretic ink ("E-ink") display, a projector, or other type of display device.

[0066] The vehicle management service 120 can provide a user interface for managing and tracking various information about the vehicle 101. The client device 112 can access the user interface through a private or public network such as the Internet. The vehicle 101 can be associated with a specific enterprise such as a company and service, an individual contractor or an employee that operates and uses the vehicle 101. The vehicle 101 can be registered in association with a specific enterprise. The administrator of the enterprise and the administrator of the vehicle management service 120 can log in and view the user interface, through which the vehicle 101 and its associated components, such as wheels, tires and brakes, can be managed. The vehicle management service 120 can also include a distributed application or a set of applications, which includes an executable file executed using the sensor position detection device 106. Any of the functions described for the vehicle management service 120 can be performed using a server environment 109 away from the vehicle 101, a sensor position detection device 106 local to the vehicle 101, or both.

[0067] The tire sensor devices 103 deployed and associated with a particular vehicle may not initially be associated with tire position parameters. The sensor position detection device 106 may identify the tire sensor positions and transmit them to the server environment 109. Alternatively, the server environment 109 may receive sensor data from the vehicle 101, and the vehicle management service 120 may identify the tire sensor positions, store the results, and return the positions to the vehicle 101. The components of the tire sensor position detection system 100 may work together to identify the positions of the various tire sensor devices 103 and ultimately utilize tire position parameters local to the vehicle 101 and the server environment 109.

[0068] In the example shown, the sensor position detection device 106 may identify tire sensor device 103a and associate its unique sensor identifier with the left front tire position parameter. The sensor position detection device 106 may identify tire sensor device 103b and associate its unique sensor identifier with the left rear tire position parameter. The sensor position detection device 106 may identify tire sensor device 103c and associate its unique sensor identifier with the right front tire position parameter. The sensor position detection device 106 may identify tire sensor device 103d and associate its unique sensor identifier with the right rear tire position parameter.

[0069] To automatically associate the tire sensor devices 103 with the appropriate tire position parameters, the sensor position detection device 106 may utilize lateral force data, longitudinal force data, and tire contact patch length or footprint length data that are detected and / or calculated using the tire sensor devices 103. In some examples, the tire contact patch length or footprint length data is calculated specifically for each tire sensor device 103, while the lateral force (acceleration) data and longitudinal force data may be obtained for the vehicle 101 as a whole using CAN bus sensors or sensors of the sensor position detection device 106.

[0070] Figure 2 A tire footprint length model 203 is shown along with tire location specific coefficient sign values ​​206 for the longitudinal acceleration coefficient and the lateral acceleration coefficient. The tire footprint length model 203 can be represented using equation (1):

[0071] FPLp00)+p10×AX_G+p01×AY_G(1

[0072] In equation (1), "FPL" may refer to the footprint length or tire contact patch length for a particular tire. The term "p00" may refer to an intercept value, which may represent the FPL under cruising or nominal conditions, where the longitudinal acceleration "AX_G" and the lateral acceleration "AY_G" are below a threshold or zero. The term "p00" may refer to a coefficient parameter, as it may be a coefficient to be multiplied by a nominal value of 1. The longitudinal acceleration coefficient term "p10" may refer to a slope value indicating the sensitivity of the footprint length to the longitudinal acceleration "AX_G", where X may refer to a horizontal X-axis along the direction of travel of the vehicle 101. In the example using coefficient sign value 206, the longitudinal acceleration "AX_G" is positive when accelerating toward the front of the vehicle 101 and is negative when decelerating or accelerating toward the rear of the vehicle 101.

[0073] The lateral acceleration coefficient term "p01" may refer to a slope value indicating the sensitivity of the footprint length to the lateral acceleration "AY_G", where Y may refer to the horizontal Y axis perpendicular to the travel of the vehicle 101, and G may indicate a measurement in multiples of gravity. Other measures of acceleration may also be used. Likewise, using the coefficient sign value 206, the longitudinal acceleration "AY_G" is positive when accelerating (or turning) to the right of the direction of travel and negative when accelerating (or turning) to the left.

[0074] The following example scenarios may describe why the sensor position detection device 106 may use the coefficient sign value 206 shown to identify the tire position. In scenario 1, the vehicle is braking. In this situation, the longitudinal acceleration "AX_G" is negative (-). For the front tires, as the weight is transferred to the front under braking, the footprint length "FPL" will increase. As a result, for the front tires, the longitudinal acceleration coefficient term "p10" will be negative, so that multiplication with the negative longitudinal acceleration causes the FPL to increase according to the tire footprint length model 203. For the rear tires, as the weight is transferred from the rear under braking, the footprint length "FPL" will decrease. Therefore, for the rear tires, the longitudinal acceleration coefficient term "p10" will be positive, so that multiplication with the negative longitudinal acceleration causes the FPL to decrease for the rear tires according to the tire footprint length model 203.

[0075] In scenario 2, the vehicle is accelerating forward, so the longitudinal acceleration "AX_G" is positive (+). For the front tires, as the weight transfers from the front to the rear under forward acceleration, the footprint length "FPL" will decrease. As a result, for the front tires, the longitudinal acceleration coefficient term "p10" will be negative again, so that multiplication with the positive longitudinal acceleration causes the FPL to decrease according to the tire footprint length model 203. For the rear tires, as the weight transfers to the rear under forward acceleration, the footprint length "FPL" will increase. Therefore, for the rear tires, the longitudinal acceleration coefficient term "p10" will be positive again, so that multiplication with the positive longitudinal acceleration causes the FPL to increase for the rear tires according to the tire footprint length model 203. Scenarios 1 and 2 show that whether accelerating or decelerating, the longitudinal acceleration coefficient term "p10" will remain negative for the front tires and will remain positive for the rear tires. Therefore, the sensor position detection device 106 can reliably use the acceleration direction agnostic tire footprint length model 203 to identify front and rear tire positions using everyday sensor data without filtering or classifying the data for specific scenarios such as forward acceleration or deceleration.

[0076] In scenario 3, the vehicle is turning left. In this case, the lateral acceleration "AY_G" is negative (-). For the right tire, as the weight is transferred to the right in a left turn, the footprint length "FPL" will increase. As a result, for the right tire, the lateral acceleration coefficient term "p01" will be negative, so that multiplication with the negative lateral acceleration causes the FPL to increase according to the tire footprint length model 203. For the left tire, as the weight is transferred to the right, the footprint length "FPL" will decrease. Therefore, for the left tire, the longitudinal acceleration coefficient term "p01" will be positive, so that multiplication with the negative longitudinal acceleration causes the FPL to decrease for the left tire according to the tire footprint length model 203.

[0077] In scenario 4, the vehicle is turning right, so the lateral acceleration "AY_G" is positive (+). For the right tire, as the weight shifts to the left under a right turn, the footprint length "FPL" will decrease. As a result, for the right tire, the lateral acceleration coefficient term "p01" will be negative again, so that multiplication with the positive lateral acceleration causes the FPL to decrease according to the tire footprint length model 203. For the left tire, as the weight shifts to the left during a right turn, the footprint length "FPL" will increase. Therefore, for the left tire, the longitudinal acceleration coefficient term "p01" will be positive again, so that multiplication with the positive longitudinal acceleration causes the FPL to increase for the left tire according to the tire footprint length model 203. Scenarios 3 and 4 show that regardless of whether turning left or right, the lateral acceleration coefficient term "p01" will remain negative for the right tire and will remain positive for the left tire. The tire footprint length model 203 can be acceleration and turn direction agnostic. Therefore, the sensor position detection device 106 can reliably identify left and right tire positions using the tire footprint length model 203 without filtering or classifying the sensor data into specific scenarios such as left turns or right turns.

[0078] Figure 3 1 shows a graph providing one example of time-stamped real-world sensor data aggregated over time from a tire sensor device 103 attached to the left front tire of a vehicle 101. The time-stamped sensor data may include acceleration magnitude and a contact patch length or footprint length calculated using the data.

[0079] The slope of the contact patch length with respect to the longitudinal acceleration "Ax(G)" is negative (-) or slopes downward, such that the footprint or contact patch length decreases as the longitudinal acceleration increases. The longitudinal acceleration coefficient term "p10" represents this slope. The sensor position detection device 106 can identify that the acceleration coefficient term "p10" is negative, and can determine that the tire sensor device 103 that generated the data is a front tire. In some examples, the sensor position detection device 106 can mark or assign the tire sensor device 103 to indicate that it is a front tire independently of the left or right designation. However, in other examples, the sensor position detection device 106 can temporarily store the data until a specific location or position is identified.

[0080] The slope of the contact patch length with respect to the lateral acceleration "Ay(G)" is positive (+) or slopes upward, so that the footprint or contact patch length increases as the lateral acceleration increases. The lateral acceleration coefficient term "p01" represents this slope. The sensor position detection device 106 can identify that the acceleration coefficient term "p01" is positive, and can determine that the tire sensor device 103 that generated the data is a left tire. In some examples, the sensor position detection device 106 can mark or assign the tire sensor device 103 to indicate that it is a left tire independently of the front or rear designation. However, in other examples, the sensor position detection device 106 can temporarily store the data until a specific location or position is identified. In this example, the aggregate acceleration data of the tire sensor device 103 (including the contact patch length data based at least in part on the acceleration data) indicates that the tire sensor device 103 is a left front tire.

[0081] Figure 4 A graph is shown that provides one example of time-stamped real-world sensor data aggregated over time from a tire sensor device 103 attached to the right front tire of a vehicle 101. The time-stamped sensor data may include acceleration magnitude and a contact patch length or footprint length calculated using the data.

[0082] The slope of the contact patch length with respect to the longitudinal acceleration "Ax(G)" is negative (-) or slopes downward, such that the footprint or contact patch length decreases as the longitudinal acceleration increases. The longitudinal acceleration coefficient term "p10" represents this slope. The sensor position detection device 106 can identify that the acceleration coefficient term "p10" is negative, and can determine that the tire sensor device 103 that generated the data is a front tire. In some examples, the sensor position detection device 106 can mark or assign the tire sensor device 103 to indicate that it is a front tire independently of the left or right designation. However, in other examples, the sensor position detection device 106 can temporarily store the data until a specific location or position is identified.

[0083] The slope of the contact patch length with respect to the lateral acceleration "Ay(G)" is negative (-) or slopes downward, so that the footprint or contact patch length decreases as the lateral acceleration increases in the positive direction (right). The lateral acceleration coefficient term "p01" represents this slope. The sensor position detection device 106 can identify that the acceleration coefficient term "p01" is negative, and can determine that the tire sensor device 103 that generated the data is a right-side tire. In some examples, the sensor position detection device 106 can mark or assign the tire sensor device 103 to indicate that it is a right-side tire independently of the front or rear designation. However, in other examples, the sensor position detection device 106 can temporarily store the data until a specific location or position is identified. In this example, the aggregate acceleration data of the tire sensor device 103 (including the contact patch length data based at least in part on the acceleration data) indicates that the tire sensor device 103 is a right front tire.

[0084] Figure 5 A graph is shown that provides one example of time-stamped real-world sensor data aggregated over time from a tire sensor device 103 attached to the left rear tire of a vehicle 101. The time-stamped sensor data may include acceleration magnitude and a contact patch length or footprint length calculated using the data.

[0085] The slope of the contact patch length with respect to the longitudinal acceleration "Ax(G)" is positive (+) or slopes upward, such that the footprint or contact patch length increases as the longitudinal acceleration increases. The longitudinal acceleration coefficient term "p10" represents this slope. The sensor position detection device 106 can identify that the acceleration coefficient term "p10" is positive, and can determine that the tire sensor device 103 that generated the data is a rear tire. In some examples, the sensor position detection device 106 can mark or assign the tire sensor device 103 to indicate that it is a rear tire independently of the left or rear designation. However, in other examples, the sensor position detection device 106 can temporarily store the data until a specific location or position is identified.

[0086] The slope of the contact patch length with respect to the lateral acceleration "Ay(G)" is positive (+) or slopes upward, so that the footprint or contact patch length increases as the lateral acceleration increases. The lateral acceleration coefficient term "p01" represents this slope. The sensor position detection device 106 can identify that the acceleration coefficient term "p01" is positive, and can determine that the tire sensor device 103 that generated the data is a left tire. In some examples, the sensor position detection device 106 can mark or assign the tire sensor device 103 to indicate that it is a left tire independently of the front or rear designation. However, in other examples, the sensor position detection device 106 can temporarily store the data until a specific location or position is identified. In this example, the aggregate acceleration data of the tire sensor device 103 (including the contact patch length data based at least in part on the acceleration data) indicates that the tire sensor device 103 is a left rear tire.

[0087] Figure 6 A graph is shown that provides one example of time-stamped real-world sensor data aggregated over time from a tire sensor device 103 attached to the right rear tire of a vehicle 101. The time-stamped sensor data may include acceleration magnitude and a contact patch length or footprint length calculated using the data.

[0088] The slope of the contact patch length with respect to the longitudinal acceleration "Ax(G)" is positive (+) or slopes upward, so that the footprint or contact patch length increases as the longitudinal acceleration increases. In this example, the slope is not a steep slope, but is still positive. The longitudinal acceleration coefficient term "p10" represents this slope. The sensor position detection device 106 can identify that the acceleration coefficient term "p10" is positive, and can determine that the tire sensor device 103 that generated the data is a rear tire. In some examples, the sensor position detection device 106 can mark or assign the tire sensor device 103 to indicate that it is a rear tire independently of the left or rear designation. However, in other examples, the sensor position detection device 106 can temporarily store the data until a specific location or position is identified.

[0089] The slope of the contact patch length with respect to the lateral acceleration "Ay(G)" is negative (-) or slopes downward, so that the footprint or contact patch length decreases as the lateral acceleration increases in the positive direction (right). The lateral acceleration coefficient term "p01" represents this slope. The sensor position detection device 106 can identify that the acceleration coefficient term "p01" is negative, and can determine that the tire sensor device 103 that generated the data is a right-side tire. In some examples, the sensor position detection device 106 can mark or assign the tire sensor device 103 to indicate that it is a right-side tire independently of the front or rear designation. However, in other examples, the sensor position detection device 106 can temporarily store the data until a specific location or position is identified. In this example, the aggregate acceleration data of the tire sensor device 103 (including the contact patch length data based at least in part on the acceleration data) indicates that the tire sensor device 103 is a right front tire.

[0090] Figure 7 The tire sensor distribution assembly 703 of the sensor position detection device 106 is shown using the tire sensor distribution assembly 703 from Figures 3 to 6 1. In this example, the data may correspond to a small sport utility vehicle. However, as discussed above, the tire sensor allocation component 703 may perform similar operations on any four-wheeled vehicle 101.

[0091] Tire sensor distribution component 703 can receive time-stamped sensor data including footprint length, longitudinal acceleration, and lateral acceleration to generate data points indicating at least this information. This can achieve three-dimensional information, thereby achieving Figures 3 to 6 . However, the data may be processed without the plot. Tire sensor allocation component 703 may process the data separately for each tire sensor device 103. Tire sensor allocation component 703 may identify coefficient values ​​within a predetermined confidence value, which in this example may be 95%. Any predetermined confidence value may be used.

[0092] Tire sensor assignment component 703 may receive time-stamped sensor data of tire sensor device 103a to identify nominal footprint length “p00”, longitudinal acceleration coefficient term “p10”, and lateral acceleration coefficient term “p01”. Tire sensor assignment component 703 may use longitudinal acceleration coefficient term “p10” and lateral acceleration coefficient term “p01” to assign a sensor identifier of tire sensor device 103a to a particular tire position. Since p10 is negative and p01 is positive, tire sensor assignment component 703 may mark or assign tire sensor device 103a to the left front position.

[0093] Tire sensor assignment component 703 may receive time-stamped sensor data for tire sensor device 103b. Since p10 is positive and p01 is positive, tire sensor assignment component 703 may mark or assign tire sensor device 103b to the left rear position. Tire sensor assignment component 703 may receive time-stamped sensor data for tire sensor device 103c. Since p10 is negative and p01 is negative, tire sensor assignment component 703 may mark or assign tire sensor device 103c to the right front position. Tire sensor assignment component 703 may receive time-stamped sensor data for tire sensor device 103d. Since p10 is positive and p01 is negative, tire sensor assignment component 703 may mark or assign tire sensor device 103d to the right front position.

[0094] Figure 8 Additional details of the tire sensor allocation component 703 are shown. The tire sensor allocation component 703 may include an outlier filter component 806, a coefficient evaluation component 812, and a sensor location identification component 815. Although these functions are described as being performed by specific subcomponents of the tire sensor allocation component 703, another component of the sensor location detection device 106 or a component of the server environment 109 may additionally or alternatively perform all or a portion of these functions. Various components may refer to logical code components, physical devices that execute code, or flowchart blocks executed by the tire sensor allocation component 703.

[0095] The outlier filter component 806 can receive the sensor data and filter it to remove outliers. In some examples, the outlier filter component 806 can include a quartile filter component. The quartile filter component uses the 1.5 quartile value or another value to filter outliers from data that is closer to the median or mean. The sensor data can include data that is aggregated and marked in association with the sensor identifier of the specific tire sensor device 103. The sensor data can include time-stamped data measured by the tire sensor device 103 and other on-board sensors of the vehicle 101, such as those sensors attached to the CAN bus. The sensor data can include longitudinal acceleration data of the tire (or vehicle 101), lateral acceleration data of the tire (or vehicle 101), and footprint length of the tire. In some examples, the footprint length can be a measurement value identified and provided by the tire sensor device 103, and there is no CAN bus or tire-agnostic data for the vehicle 101.

[0096] The coefficient evaluation component 812 can use the data filtered for outliers to identify tire-specific values ​​of footprint length model parameters, including the nominal footprint length "p00", the longitudinal acceleration coefficient "p10", and the lateral acceleration coefficient term "p01". The coefficient evaluation component 812 can transmit or provide the longitudinal acceleration coefficient "p10" and the lateral acceleration coefficient term "p01" to the sensor location identification component 815. The coefficient evaluation component 812 can include least squares fitting, recursive least squares fitting with a forgetting factor, or another type of fitting model. The forgetting factor can refer to a factor or weight in which older values ​​in the history of values ​​of the parameter in question are weighted less than newer values. Recursive least squares fitting can implement real-time embedded applications of the system through the sensor position detection device 106 in a vehicle environment rather than in a server environment or a laboratory environment. Least squares fitting can be suitable for large-scale analysis, such as at the server level.

[0097] The sensor location identification component 815 can use the longitudinal acceleration coefficient "p10" and the lateral acceleration coefficient term "p01" to identify the tire position or location. The sensor location identification component 815 can use the longitudinal acceleration coefficient "p10" to identify whether the sensor data indicates that the tire sensor device 103 is a front tire or a rear tire. If the longitudinal acceleration coefficient "p10" is negative, the sensor location identification component 815 can mark or assign the tire sensor device 103 to the front tire. If the longitudinal acceleration coefficient "p10" is positive, the sensor location identification component 815 can mark or assign the tire sensor device 103 to the rear tire.

[0098] The sensor location identification component 815 can use the lateral acceleration coefficient "p01" to identify whether the sensor data indicates that the tire sensor device 103 is a left tire or a right tire. If the lateral acceleration coefficient "p01" is negative, the sensor location identification component 815 can mark or assign the tire sensor device 103 to the right tire. If the lateral acceleration coefficient "p01" is positive, the sensor location identification component 815 can mark or assign the tire sensor device 103 to the left tire. Therefore, the sensor location identification component 815 can assign or mark the tire sensor device 103 in association with the left front position, the right front position, the left rear position, or the right rear position. Figure 8 The code or pseudo-code provided in provides a non-limiting example of the process of the sensor location identification component 815.

[0099] This process may be repeated or performed in parallel or partially concurrently to identify the locations of all tire sensor devices 103. In some cases, such as dual tire axles for large trucks, more than one tire sensor device 103 may be assigned to the same location. Inside versus outside may be determined using temperature readings, where a hotter temperature may correspond to an inside tire and a cooler temperature may correspond to an outside tire compared between two tires with the same location assignment. All of the discussed data, including all received sensor data, intermediate data such as coefficients and model parameter values, and location tags or indicia may be stored locally to at least one device of the vehicle 101. The data may also be transmitted to the vehicle management service 120. Moving forward, the locally and remotely stored sensor data for a particular tire sensor device 103 may be stored in association with a vehicle identifier for the vehicle 101, an enterprise identifier for the enterprise that owns or operates the vehicle 101, and tire locations including front-to-rear and left-to-right indicators.

[0100] Sensor position detection device 106 may also dynamically provide updated positions of tire sensor device 103, with or without manual indication that the tire or tire sensor device 103 should be re-evaluated. However, sensor position detection device 106 may receive manual indication that the tire or tire sensor device 103 is to be re-evaluated or that the tire has been rotated, and may clear its aggregate data and restart the process.

[0101] Fig. 9 A set of graphs are shown showing the footprint length model coefficients calculated using real-time recursive least squares fitting compared to least squares fitting using batch mode data. In each scenario, the least squares fit batch mode calculation is a flat line because it is identified as a single value using a batch of predetermined data. It can be seen that the real-time recursive least squares fit converges to the least squares fit batch mode value after considering many samples. However, in the context of the present mechanism, the sign value of each coefficient is identified with a very small number of samples. In real-time computing operations using processing through real-time recursive least squares fitting, where data is received as the data is detected by the sensor device, the system can quickly identify the tire position. In this context, real-time reception can indicate that the data is received within milliseconds or centiseconds from being measured or calculated. Real-time computing operations such as real-time recursive least squares fitting actions can be considered real-time when the processing is completed within milliseconds or centiseconds after receipt.

[0102] Fig.10An example computing device 1000 for any of the computing devices of the tire sensor position detection system 100 is illustrated. This may include a computing device 1000 corresponding to a vehicle 101, a sensor position detection device 106, a server environment 109, and a user device accessing a vehicle management service 120. The computing device 1000 includes, for example, at least one processing system having a processor 1002 and a memory 1004, both of which are electrically and communicatively coupled to a local interface 1008. The local interface 1008 may be embodied as a data bus / control bus with accompanying addresses or other addressing, control and / or command lines for data communication and addressing between the processor 1002, the memory 1004, and executable instructions 1012. The executable instructions 1012 of the computing device 800 may include a vehicle management service 120, a tire position classification process, a rule-based and / or threshold-based tire position classification process, and the like.

[0103] In various embodiments, the memory 1004 stores data 1006 in a data repository and other software or executable code components that can be executed by the processor 1002. The data repository 1006 may include data related to the operation of the tire sensor position detection system 100, as well as other data. Among other things, the executable code components of the various computing devices 800 may include components associated with any of the functions described for the tire sensor position detection system 100, and an operating system executed by the processor 1002. Where any of the components discussed herein are implemented in software, any of a variety of programming languages ​​may be employed, such as, for example, C, C++, C#, Objective C, Perl, PHP, VISUAL RUBY, or other programming languages.

[0104] The memory 1004 stores software for execution by the processor 1002. In this regard, the term "executable" or "for execution" refers to a form of software, whether in source, object, machine, or other form, that can ultimately be run or executed by the processor 1002. Examples of executable programs include, for example, a compiled program that can be translated into a machine code format and loaded into a random access portion of the memory 1004 and executed by the processor 1002, source code that can be expressed in an object code format and loaded into a random access portion of the memory 1004 and executed by the processor 1002, or source code that can be interpreted by another executable program to generate instructions in a random access portion of the memory 1004 and executed by the processor 1002, etc.

[0105] In various embodiments, the memory 1004 may include both volatile and non-volatile memory and data storage components. A volatile component is a component that does not retain data values ​​when power is lost. A non-volatile component is a component that retains data when power is lost. Therefore, the memory 1004 may include a random access memory (RAM), a read-only memory (ROM), a magnetic or other hard drive, a solid state, a semiconductor, a universal serial bus (USB) flash drive, a memory card, an optical disk (e.g., a compact disk (CD) or a digital versatile disk (DVD)), a floppy disk, a tape, or any combination thereof. In addition, the RAM may include, for example, a static random access memory (SRAM), a dynamic random access memory (DRAM), or a magnetic random access memory (MRAM) and / or other similar memory devices. The ROM may include, for example, a programmable read-only memory (PROM), an erasable programmable read-only memory (EPROM), an electrically erasable programmable read-only memory (EEPROM), or other similar memory devices. The executable program may be stored in any part or component of the memory 1004.

[0106] Processor 1002 may be embodied as one or more microprocessors, one or more discrete logic circuits having logic gates for implementing various logic functions, an application specific integrated circuit (ASIC) having appropriate logic gates, and / or a programmable logic device (e.g., a field programmable gate array (FPGA) and a complex programmable logic device (CPLD)).

[0107] If embodied in software, executable instructions 1012 may represent one or more modules or groups of code including program instructions for implementing the specified logical functions discussed herein. Program instructions may be embodied in the form of source code or machine code, where source code includes human-readable statements written in a programming language and machine code includes machine instructions that can be recognized by a suitable execution system, such as a processor in a computer system or other system. Thus, processor 1002 may be directed to implement certain processes, such as those shown in the flowcharts described herein, by executing program instructions. In the context of the present disclosure, a non-transitory computer-readable medium may be any tangible medium that may contain, store, or maintain any logic, application, software, or executable code component described herein that is used by or in conjunction with an instruction execution system.

[0108] In addition, one or more of the components including software or program instructions described herein may be embodied in a non-transitory computer-readable medium for use by an instruction execution system such as processor 1002 or in conjunction with an instruction execution system. A computer-readable medium may contain, store and / or maintain software or program instructions for execution by an instruction execution system or in conjunction with an instruction execution system. A computer-readable medium may include physical media, such as magnetic, optical, semiconductor and / or other suitable media or drives. In addition, any logic or component described herein may be implemented and structured in various ways. For example, one or more components described may be implemented as modules or components of a single application. In addition, one or more components described herein may be executed in a computing device or by using multiple computing devices.

[0109] A flow chart or process diagram may represent certain methods or processes, functions and operations of the embodiments discussed herein. Each block may represent one or a combination of steps or executions in a process. Alternatively or additionally, each block may represent a module, segment or portion of a code including program instructions for implementing a specified logical function. The program instructions may be embodied in the form of source code or machine code, the source code including human-readable statements written in a programming language, and the machine code including digital instructions that can be recognized by a suitable execution system such as processor 1002. The machine code may be converted from source code, etc. In addition, each block may represent a circuit or multiple interconnected circuits, or be connected to a circuit or multiple interconnected circuits to implement a certain logical function or process step.

[0110] Although the flowchart illustrates a specific order, it should be understood that the order may be different from the depicted order. For example, the execution order of two or more blocks may be disrupted relative to the order shown. In addition, two or more blocks shown in succession may be executed simultaneously or partially simultaneously. In addition, in some embodiments, one or more of the blocks may be skipped or omitted. In addition, for purposes such as enhancing utility, accounting, performance measurement, or providing troubleshooting assistance, any number of counters, state variables, warning semaphores, or messages may be added to the logic flow described herein. Such variations that are understood to be used to implement a process consistent with the concepts described herein are within the scope of the embodiments.

[0111] Although embodiments have been described in detail herein, these descriptions are by way of example. In other words, the embodiments described herein are not limited to the specifically described embodiments. The features of the embodiments described herein are representative, and in alternative embodiments, certain features and elements may be added or omitted. In addition, those skilled in the art may modify aspects of the embodiments described herein without departing from the spirit and scope of the invention as defined by the appended claims, and the scope of the claims should be given the broadest interpretation so as to cover modifications and equivalent structures.

Claims

1. A system comprising: at least one computing device, the at least one computing device comprising at least one processor; and at least one memory, the at least one memory comprising instructions that, when executed, cause the at least one computing device to at least: receiving, by the sensor position detection device, a plurality of tire sensor values ​​from the tire sensor device, wherein the plurality of tire sensor values ​​are measured or calculated for a respective one of: a tire footprint length parameter, a longitudinal acceleration parameter, and a lateral acceleration parameter; processing the plurality of tire sensor values ​​of the tire footprint length parameter, the longitudinal acceleration parameter, and the lateral acceleration parameter by the sensor position detection device, wherein the sensor position detection device generates at least a longitudinal acceleration coefficient and a lateral acceleration coefficient; as well as The tire sensor device is assigned by the sensor position detection device to a particular tire location associated with the vehicle based at least in part on the sign of the longitudinal acceleration coefficient and the sign of the lateral acceleration coefficient.

2. The system according to claim 1, wherein: When executed, the instructions cause the at least one computing device to at least: A tire footprint length model is identified, the tire footprint length model defining a relationship between at least: a tire footprint length, a nominal footprint length, the longitudinal acceleration coefficient, the longitudinal acceleration, the lateral acceleration coefficient, and the lateral acceleration.

3. The system according to claim 1, wherein: The tire sensor values ​​are processed in real time by the sensor position detection device.

4. The system according to claim 3, wherein: The sensor position detection device processes the tire sensor values ​​in real time based at least in part on a recursive least squares fitting process.

5. The system according to claim 1, wherein: The tire sensor device is assigned to a tire location based at least in part on storing a unique tire sensor device identifier of the tire sensor device in association with data indicative of the tire location.

6. The system according to claim 1, wherein: The sensor position detection device processes the plurality of tire sensor values ​​to further identify a nominal tire footprint length.

7. The system according to claim 1, wherein: When executed, the instructions cause the at least one computing device to at least: The specific tire location is transmitted to at least one of a server environment, a client device, or any combination thereof.

8. A method comprising: receiving, by the sensor position detection device, a plurality of tire sensor values ​​from the tire sensor device, wherein the plurality of tire sensor values ​​are measured or calculated for a respective one of: a tire footprint length parameter, a longitudinal acceleration parameter, and a lateral acceleration parameter; processing the plurality of tire sensor values ​​of the tire footprint length parameter, the longitudinal acceleration parameter, and the lateral acceleration parameter by the sensor position detection device, wherein the sensor position detection device generates at least a longitudinal acceleration coefficient and a lateral acceleration coefficient; as well as The tire sensor device is assigned by the sensor position detection device to a particular tire location associated with the vehicle based at least in part on the sign of the longitudinal acceleration coefficient and the sign of the lateral acceleration coefficient.

9. The method according to claim 8, further comprising: A tire footprint length model is identified that defines a relationship between at least the tire footprint length, the nominal footprint length, the longitudinal acceleration coefficient, the longitudinal acceleration, the lateral acceleration coefficient, and the lateral acceleration.

10. A non-transitory computer-readable medium comprising instructions executable by at least one computing device, the instructions, when executed by the at least one computing device, causing the at least one computing device to at least: receiving, by the sensor position detection device, a plurality of tire sensor values ​​from the tire sensor device, wherein the plurality of tire sensor values ​​are measured or calculated for a respective one of: a tire footprint length parameter, a longitudinal acceleration parameter, and a lateral acceleration parameter; processing the plurality of tire sensor values ​​of the tire footprint length parameter, the longitudinal acceleration parameter, and the lateral acceleration parameter by the sensor position detection device, wherein the sensor position detection device generates at least a longitudinal acceleration coefficient and a lateral acceleration coefficient; and The tire sensor device is assigned by the sensor position detection device to a particular tire location associated with the vehicle based at least in part on the sign of the longitudinal acceleration coefficient and the sign of the lateral acceleration coefficient.

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