Method, system, and storage medium for identifying tire contact coefficient and contact force with road surface

Abstract

The application provides a method, system and storage medium for identifying a tire contact coefficient and contact force, comprising the steps of: identifying the slip ratio and side slip angle during vehicle driving, obtaining the acceleration of tire movement through an acceleration sensor, and obtaining information reflecting the tire force condition through the acceleration of the tire; and a model building step: building a tire force identification model through transfer learning to identify the tire force. The application has the beneficial effect that, through the assistance of a deep learning algorithm, the model can learn under different road conditions, vehicle types and driving modes through transfer learning to cope with various situations.

Description

Technical Field

[0001] This invention relates to the field of data processing technology, and in particular to a method, system, and storage medium for identifying the contact coefficient and contact force between a tire and the road surface. Background Technology

[0002] Given the social and economic impacts of road safety, research on estimating friction levels is favored by researchers. For vehicle safety control systems that measure tire forces, valuable measurement parameters include tire longitudinal force, tire lateral force, tire vertical force, and the tire-road friction coefficient; the key is to achieve the measurement of tire forces.

[0003] In the field of road surface inspection, many traditional testing methods still exist: The British Pendulum Test (BPT) is a static device that needs to be set up at a specific test location on the road surface. Its specific method of use involves releasing a smooth rubber slider mounted on a pendulum arm from a horizontal position. When the rubber contacts the road surface, the slider reaches a fixed velocity. The swing height (or angle) of the pendulum after contact with the road surface depends on the degree of kinetic energy dissipated by the slider during contact. By measuring this swing height (or angle) after contact, the coefficient of friction, known as the British Pendulum Number (BPN), can be derived.

[0004] In current tire condition monitoring research, scholars have experimented with different types of sensors, which can be categorized into acoustic, magnetic, optical, piezoelectric, and accelerometer types. These different sensor solutions differ, and each has its own advantages and disadvantages in practical applications.

[0005] Indirect estimation of tire-road friction using optical instruments is quite common. Texture data is collected from a laser profilometer, which can also collect other data, such as roughness. This method is highly valuable because multiple types of information can be collected using a single system.

[0006] Based on a tire load algorithm established by Liang Guanqun et al. using a three-dimensional finite element model of tires, Wang Yan et al. built an intelligent tire testing system, collecting data through experiments and predicting tire vertical force. The method of estimating tire force by identifying the contact patch length using tire radial acceleration has been widely applied.

[0007] Lee et al. developed an algorithm that estimates road surface type in real time based on a deep neural network (DNN) of signals from an intelligent tire system. The authors proposed an intelligent tire system with an accelerometer to measure the response to tire-road interaction during contact under various road conditions.

[0008] In summary, current tire condition sensing solutions using optical sensors (such as laser profilometers) are costly, and their measurement effectiveness and practical validity are still under discussion. Most other sensors cannot be directly mounted on the tires for monitoring; instead, they rely on onboard sensors combined with vehicle dynamics models to indirectly establish condition sensing algorithms, which suffers from significant errors and time lags in application. Currently, there is no robust solution or model for intelligent tire monitoring to assess tire stress and measure the tire-road contact coefficient. Summary of the Invention

[0009] This invention provides a method for identifying the tire-road contact coefficient and contact force, comprising the following steps:

[0010] Identification steps: Identify the slip ratio and sideslip angle during vehicle movement, obtain the acceleration of the tire during movement through the acceleration sensor, and obtain information reflecting the force on the tire through the tire acceleration;

[0011] Model building steps: A tire force recognition model is built using transfer learning to achieve tire force recognition.

[0012] As a further improvement of the present invention, in the identification step... S represents the slip ratio, V t V represents the tire's rotational speed. r Indicates the vehicle's speed.

[0013] As a further improvement of the present invention, before the model building step, the forces received by the tire during movement are measured and collected using the MTS Flat-Trac test platform, including the longitudinal force F of the tire. x Lateral force F y and vertical force F z And control relevant driving parameter values, including vehicle speed V. r Tire rotation speed V t , tire slip ratio s and tire slip angle α, where the vehicle speed V r Tire rotation speed V t Vertical force F z The tire slip angle α is obtained by active control of the test platform, and the longitudinal force F x and lateral force F y The tire slip ratio s, measured by the testing platform, is obtained from the vehicle speed V. r and tire rotation speed V t Indirectly, that is, when the driving speed is constant, the change in slip ratio is achieved by controlling the rotation speed of the tires.

[0014] As a further improvement of the present invention, in the identification step, a deep learning algorithm is used to establish a relationship model between tire acceleration characteristics and tire-road contact force.

[0015] As a further improvement of the present invention, in the identification step, the relationship model is constructed by combining CNN and LSTM or GRU networks to build a deep learning model.

[0016] As a further improvement of the present invention, the relational model includes a slip ratio model and a sideslip angle model. The slip ratio model and sideslip angle model each include an input layer, a CNN layer, a convolutional attention module, an LSTM or GRU layer, a Dense layer, and an output layer, connected sequentially. The input layer receives input data and transmits the received data to the CNN layer. The input data of the slip ratio model includes the tire's longitudinal acceleration and radial acceleration, as well as sub-signals reconstructed from these accelerations using wavelet packet decomposition. The input data of the sideslip angle model includes longitudinal acceleration, lateral acceleration, radial acceleration, and sub-signals reconstructed from the lateral acceleration using wavelet packet decomposition. The CNN layer uses multi-scale convolutional kernels to transform the original data into a more compact and informative representation while extracting different scale features of the multi-scale acceleration signal. The convolutional attention module decomposes and reconstructs the signal in different frequency domains using wavelet packet decomposition, and then distributes the decomposed signals as inputs to different channels to achieve cross-frequency domain feature extraction. The LSTM or GRU layer performs temporal modeling on the extracted features, learning the relationships and dynamic changes in the sequence. The Dense layer and the output layer summarize the above information and output the final slip rate and side slip angle monitoring results.

[0017] As a further improvement of the present invention, in the identification step, the slip ratio model and the sideslip angle model are trained by experimental data, so that the slip ratio model can identify the slip ratio of the tire during vehicle driving, and the sideslip angle model can identify the sideslip angle of the tire during vehicle driving.

[0018] In the model building process, the trained slip ratio model and sideslip angle model are integrated into the tire force recognition model through transfer learning. The specific steps are as follows: Select the trained slip ratio model and sideslip angle model; replace the input layers of the slip ratio model and sideslip angle model with new fully connected layers, ensuring the number of nodes in the replaced fully connected layers matches the original input shape of the slip ratio model and sideslip angle model; replace the output layers of the slip ratio model and sideslip angle model with new fully connected layers; finally, freeze the remaining network structures and trained model parameters of the slip ratio model and sideslip angle model. At this point, the slip ratio model and sideslip angle model are respectively referred to as the "slip ratio module" and "tire sideslip angle module" in the tire longitudinal force recognition model. The "slip angle module" in the tire force recognition model provides information on tire deformation and slippage for tire force identification. For the tire force recognition model, after introducing the slip ratio module and the slip angle module, the tire force task is decomposed into two learning paths: commonality learning and feature learning. Commonality learning refers to the path of the slip ratio module or the slip angle module; feature learning is performed directly through the input for extracting other force-related information. For the connection between commonality learning and feature learning, the feature learning connection is fully connected to the input; the commonality learning connection uses a custom connection layer. After the tire force recognition model is built, it is retrained using data to achieve tire force recognition.

[0019] The present invention also provides a computer-readable storage medium storing a computer program configured to implement the steps of the method described herein when invoked by a processor.

[0020] The present invention also provides a system for identifying the contact coefficient and contact force between a tire and the road surface, comprising: an acceleration sensor, a memory, a processor, and a computer program stored in the memory, the computer program being configured to implement the steps of the method described in the present invention when invoked by the processor, the acceleration sensor being mounted on the central axis of the tire liner.

[0021] As a further improvement of the present invention, the system also includes a high-speed slip ring and a signal acquisition device. The high-speed slip ring device is installed at the center of the car wheel rim. The wires of the acceleration sensor are connected to the high-speed slip ring, and then the wires are led out from the high-speed slip ring to the signal acquisition device to realize the transmission of acceleration signals from inside the tire to outside the tire.

[0022] The beneficial effects of this invention are: with the support of deep learning algorithms, the model of this invention can learn under different road conditions, vehicle types and driving modes through transfer learning to cope with various situations. Attached Figure Description

[0023] Figure 1 This is a diagram illustrating tire slippage;

[0024] Figure 2 This is a diagram of the sideslip angle;

[0025] Figure 3 This is a slip ratio model diagram;

[0026] Figure 4 This is a model diagram of the sideslip angle;

[0027] Figure 5 This is a diagram of the tire force recognition model. Detailed Implementation

[0028] Tires are the only part of a car that directly contacts the ground, providing reliable and timely information about contact dynamics. Identifying tire forces can significantly improve vehicle safety, handling stability, fuel economy, and ride comfort. By understanding the interaction forces between the tire and the road surface and the parameters of the tire-contact surface, road conditions can be inferred. Combining tire force monitoring values ​​with the tire-road contact coefficient and transmitting the processed data to traffic and highway authorities can improve the quality of traffic management and road maintenance, and increase the efficiency of road quality and performance testing.

[0029] To identify the coefficient of friction and tire forces between the tire and the road surface during vehicle operation, this invention provides a method for identifying the tire-road contact coefficient and contact force. This invention employs a deep learning algorithm, driven by data, to first identify the slip ratio and sideslip angle during vehicle operation, and finally uses transfer learning to build a tire force identification model to achieve tire force recognition.

[0030] The tire brush model is a commonly used model for analyzing tire mechanics. Analysis of the tire brush model reveals that slip ratio and sideslip angle are two key physical quantities controlling the forces acting on the tire, and they are closely related to the tire's longitudinal force and lateral force: Longitudinal force and slip ratio: F x =μ x (s) F z (μ x (s) is the longitudinal friction coefficient, a function of the slip ratio; lateral force and sideslip angle: F y =μ y (α)F z (μ y (α) is the lateral friction coefficient, a function of the sideslip angle. The model is constructed using a research approach based on acceleration-slip ratio-longitudinal force and acceleration-slip angle-lateral force. The slip ratio and sideslip angle are introduced below.

[0031] Regarding longitudinal forces on the tire, under normal circumstances, the tire will not completely slip, but the wheel often slips while rolling. However, the slip zone of the tire is not uniform across the entire tire-road contact surface; the adhesion zone and the slip zone are not strictly defined. Slippage, often resembling brush hairs, usually occurs even within the adhesion zone, making it difficult to quantify the size of the slip zone to describe the slippage situation. Nevertheless, the deformation and slippage of the tire in the contact zone cause the theoretical rolling distance of the tire per unit time to be greater than the actual distance traveled by the vehicle, resulting in a tire rotational linear velocity V. t With respect to the actual speed V of the vehicle r This difference leads to the concept of tire slip ratio, which describes the size of the slip zone relative to the contact zone. When describing tire slippage, it is described as a function of the ratio of the average relative speed between the tire and the ground to the vehicle speed; this ratio parameter is called the wheel slip ratio.

[0032] The definition of slip ratio aims to reflect the tire speed V t With respect to the actual speed V of the vehicle r The differences between them. For example Figure 1 The figure shows a tire rolling on the ground. The ideal distance the tire travels without slipping is denoted as d. F The actual distance traveled by the vehicle is denoted as d. A When the tires drive the slip, d F >d A When the tires brake and slip, d F <d A The slip ratio should ideally be the difference d between the two. F -d A The ideal travel distance d of the tire F The ratio is used to characterize tire slippage:

[0033]

[0034] But d F -d A The value is relatively small, and because tire motion is not as easily measured as that of a rigid body, it is difficult to determine tire slippage by accurately measuring the difference in actual travel distance. To obtain the instantaneous value of s, the tire speed should be monitored in real time over a very short period of time.

[0035]

[0036] Therefore, in most studies, the slip ratio is often defined as follows:

[0037]

[0038] During a vehicle turn, the front wheels begin to steer first under the driver's control. At this time, if... Figure 2 As shown, the vertical force F z Acting on the axle of the rolling tire, the tire plane of the vehicle's front wheel forms an angle α with the tire's trajectory on the road surface; this angle is called the slip angle. Due to the slip angle, a relative displacement occurs between the wheel and the ground in a direction perpendicular to the wheel plane. This causes the wheel to deform and slip under the influence of gravity and friction with the ground, thus generating a lateral force F on the wheel and tire. y It provides the power for steering the vehicle.

[0039] If a tire slips while rolling forward on the road, its contact patch will also undergo longitudinal stretching and deflection. Although the tire's surface is perpendicular to the road surface, the direction of wheel movement will form an angle α with the tire surface; this angle is called the slip angle. As the wheel rolls forward, the undeformed tread will undergo lateral deformation similar to longitudinal deformation upon entering the contact patch area. As the tread moves to the rear of the contact patch, its lateral deformation intensifies until it approaches the rear boundary of the tire's contact patch area. Similar to longitudinal slip, the vertical load gradually decreases at the rear of the tire's contact patch, and the maximum lateral friction force provided by it also decreases accordingly.

[0040] In actual driving, it's difficult to obtain tire force directly by using force sensors on the tires. However, information reflecting the tire's force can be obtained through other physical data such as acceleration and pressure. To achieve real-time tire force monitoring, selecting a suitable sensor is crucial. Accelerometers have wide applications in this area. Accelerometers offer advantages such as small size, low cost, and stable output; considering cost and practicality, they are more suitable for developing smart tires compared to other types of sensors. This invention uses a triaxial accelerometer to obtain the tire's acceleration during movement. Based on research on sensor installation positions, the accelerometer is fixed on the central axis of the tire's inner liner.

[0041] Currently, accelerometers in the monitoring field still commonly use wired transmission solutions. However, realizing intelligent tire systems requires overcoming the challenge of transmitting acceleration signals from the rotating tire. To address this, this invention installs a high-speed slip ring device at the center of the vehicle wheel rim. The accelerometer sensor's wires are connected to the high-speed slip ring, and then wires from the slip ring are led out to a signal acquisition device. This method enables the transmission of acceleration signals from inside the tire to the outside.

[0042] Before modeling, this invention requires sufficient types and quantities of data. The data is acquired using the MTS Flat-Trac tire force and torque measurement system from the China Automotive Technology and Research Center (CATARC). The MTS Flat-Trac platform can simulate the vehicle's motion under different conditions, such as acceleration and deceleration, as well as vertical road bumps. This dynamic loading test system can reproduce the vehicle's motion on a straight road with excellent accuracy and repeatability, and can synthesize various highly dynamic road bump events to simulate various working conditions. In the experiments of this invention, the MTS Flat-Trac test platform mainly measures and collects the forces received by the tire during its motion, such as the longitudinal force F of the tire. x Lateral force F y and vertical force F z And control relevant driving parameter values, such as vehicle speed V. r Tire rotation speed V t The tire slip ratio (s) and tire slip angle (α) are among the factors. The vehicle speed (V) is also a factor. r Tire rotation speed V t Vertical force F z The tire slip angle α is obtained by active control of the test platform, and the longitudinal force F x and lateral force F y The tire slip ratio s, measured by the testing platform, is obtained from the vehicle speed V. r and tire speed V t Indirectly, that is, when the driving speed is constant, the change in slip ratio is achieved by controlling the rotation speed of the tires.

[0043] Regarding the choice of model building scheme, while traditional tire models can obtain stable estimates of tire forces, incorrect tire and road parameters can lead to biases in tire force estimation. Tire aging and wear, as well as various complex driving conditions such as weather, all make accurate identification of these parameters difficult. Furthermore, tires are typical nonlinear systems, making it very difficult to directly establish analytical expressions relating these information to tire forces. Neural network models can establish highly complex, nonlinear, and multidimensional relationships between selected input parameters and outputs, thus achieving acceptable accuracy in predicting tire loads across the entire range of tire operating conditions. Therefore, a deep learning algorithm was chosen to establish a model of the relationship between tire acceleration characteristics and tire-road contact force.

[0044] In selecting deep learning algorithms, a combination of CNN and LSTM or GRU networks is used to build the deep learning model, and a self-attention mechanism is introduced to improve feature extraction efficiency. LSTM and GRU networks are derivatives of RNN networks and are commonly used deep learning networks for processing time series data. The combination of CNN and LSTM or GRU is well-suited for feature extraction of tire acceleration. Multi-scale convolutional kernels are employed in the CNN network to extract features at different scales from signals in different frequency domains (acceleration signals with multi-scale characteristics). Combining CNN with LSTM or GRU fully utilizes the local feature extraction capabilities of CNN and the time series processing capabilities of LSTM / GRU. This fusion model can more comprehensively capture the spatiotemporal features of tire acceleration signals.

[0045] The model incorporates a Convolutional Block Attention Module (CBAM) to improve feature extraction efficiency. During data preprocessing, wavelet packet decomposition reconstructs the signal across different frequency domains, which are then used as inputs and distributed to different channels. The CBAM enables cross-channel (cross-frequency domain) feature extraction, enhancing the model's robustness.

[0046] The accelerometer's sampling frequency is 1000Hz, while according to the Nyquist sampling theorem, the frequency is 500Hz. Through three-layer wavelet packet decomposition, a total of 2... 3 =8 frequency bands, each with a frequency range of 500 / 8 = 62.5Hz. These 8 frequency bands are 0-62.5Hz, 62.5-125Hz, 125-187.5Hz, 187.5-250Hz, 250-312.5Hz, 312.5-375Hz, 375-437.5Hz, and 437.5-500Hz. The 8×3 wavelet coefficients of the three-directional acceleration signals are reconstructed individually to obtain the reconstructed frequency band signals: The reconstructed frequency band signal is used as the time-frequency domain variation feature in the recognition task.

[0047] First, an acceleration-slip ratio model and an acceleration-side slip angle model were built and trained using experimental data. The model framework is as follows: Figure 3 , 4 This enables the model to identify tire slip ratio and sideslip angle during vehicle movement. Then, through model-based transfer learning, the trained slip ratio and sideslip angle identification models are integrated into a new tire force identification model, such as... Figure 5 The specific steps are as follows:

[0048] The trained slip ratio and sideslip angle recognition models are selected. The input layers of both models are replaced with new fully connected layers, maintaining the same number of nodes as the original input shape. The output layers are also replaced with new fully connected layers. Finally, the remaining network structure and trained model parameters are frozen (using pre-trained network weights instead of randomly initialized weights). These are then referred to as the "slip ratio module" in the tire longitudinal force recognition model and the "slip angle module" in the tire lateral force recognition model, respectively, providing information on tire deformation and slippage for tire force recognition. For the tire force recognition model, the introduction of these two "modules" decomposes the tire force task into two learning paths: commonality learning and feature learning. Commonality learning refers to the path of the "slip ratio module" or "slip angle module." The "knowledge" (parameters) learned by these modules during pre-training allows the model to achieve better performance in subsequent tire force monitoring tasks. This part is mainly used for extracting tire deformation and slippage information.

[0049] Feature learning is performed directly through the input. Because the inputs to the slip ratio and sideslip angle recognition models ignore some acceleration signals (such as the time-domain signal of lateral acceleration), but still contain tire force information, this part is mainly used for extracting other force-related information. For the connections between commonality learning and feature learning, feature learning connections are fully connected to the input; commonality learning connections use a custom connection layer, making the module have the same input connections as the original slip ratio or sideslip angle recognition models.

[0050] After the model was built, the tire force recognition model was retrained using the data to achieve tire force recognition. The training employed an adaptive learning rate optimization algorithm (Adam), which dynamically adjusts the learning rate to improve the model's convergence speed and training effectiveness.

[0051] The beneficial effects of this invention include:

[0052] 1. This invention achieves tire-side transmission of acceleration signals while ensuring transmission quality through a simple high-speed slip ring. Furthermore, the monitoring equipment can be adapted and installed to suit different wheel rim types.

[0053] 2. With the support of deep learning algorithms, the model of this invention can learn under different road conditions, vehicle types and driving modes through transfer learning to cope with various situations.

[0054] The above description, in conjunction with specific preferred embodiments, provides a further detailed explanation of the present invention. It should not be construed that the specific implementation of the present invention is limited to these descriptions. For those skilled in the art, various simple deductions or substitutions can be made without departing from the concept of the present invention, and all such modifications and substitutions should be considered within the scope of protection of the present invention.

Claims

1. A method for identifying the tire-road contact coefficient and contact force, characterized in that, Includes the following steps: Identification steps: Identify the slip ratio and sideslip angle during vehicle movement, obtain the acceleration of the tire during movement through the acceleration sensor, and obtain information reflecting the force on the tire through the tire acceleration; Model building steps: A tire force recognition model is built using transfer learning to achieve tire force recognition; In the identification step, a deep learning algorithm is used to establish a model of the relationship between tire acceleration characteristics and tire-road contact force; The relationship model includes a slip ratio model and a side slip angle model. The slip ratio model and the side slip angle model include an input layer, a CNN layer, a convolutional attention module, an LSTM or GRU layer, a Dense layer, and an output layer connected in sequence. The input layer receives the input data and transmits the received data to the CNN layer. The input data for the slip ratio model includes the tire's longitudinal and radial accelerations, as well as the sub-signals reconstructed from these accelerations by wavelet packet decomposition of the convolutional attention module. The input data for the sideslip angle model includes longitudinal acceleration, lateral acceleration, and radial acceleration. The radial and lateral accelerations are reconstructed from wavelet packet decomposition by the convolutional attention module. The CNN layer employs multi-scale convolutional kernels, transforming the original data into a more compact and informative representation while extracting different scale features of the multi-scale acceleration signal. The convolutional attention module decomposes and reconstructs the signal across different frequency domains using wavelet packet decomposition, then distributes the decomposition as input to different channels, enabling cross-frequency domain feature extraction. The LSTM or GRU layer performs temporal modeling on the extracted features, learning the relationships and dynamic changes within the sequence. The Dense layer and output layer summarize the above information, outputting the final slip rate and sideslip angle monitoring results. In the identification step, the slip ratio model and the sideslip angle model are trained using experimental data, so that the slip ratio model can identify the slip ratio of the tires during vehicle operation, and the sideslip angle model can identify the sideslip angle of the tires during vehicle operation. In the model building step, the trained slip ratio model and sideslip angle model are integrated into the tire force recognition model through transfer learning. The specific operation is as follows: Select the trained slip ratio model and sideslip angle model, and replace the input layer of the slip ratio model and sideslip angle model with a new fully connected layer. After the replacement, the number of nodes of the fully connected layer is consistent with the original input shape of the slip ratio model and sideslip angle model. The output layers of the slip ratio model and the sideslip angle model are replaced with new fully connected layers. Finally, the remaining network structures and trained model parameters of the slip ratio model and the sideslip angle model are frozen. At this point, the slip ratio model and the sideslip angle model are referred to as the slip ratio module in the tire longitudinal force recognition model and the sideslip angle module in the tire lateral force recognition model, respectively, providing information on tire deformation and slippage for tire force recognition. For the tire force recognition model, after introducing the slip ratio module and the sideslip angle module, the tire force recognition task is decomposed into two learning paths: common learning and feature learning. Common learning refers to the path of the slip ratio module or the sideslip angle module; feature learning is performed directly through the input and is used for the extraction of other force-related information. For the connection between common learning and feature learning, the feature learning connection is selected to be fully connected to the input layer. Common learning connections use a custom connection layer; After the tire force recognition model is built, the model is retrained using data to achieve tire force recognition.

2. The method according to claim 1, characterized in that: In the identification step, S represents the slip ratio. Indicates the tire rotation speed. Indicates the vehicle's speed.

3. The method according to claim 2, characterized in that, Before the model building step, the forces acting on the tire during movement, including the longitudinal force of the tire, are measured and collected using the MTS Flat-Trac test platform. Lateral force and vertical force And control relevant driving parameter values, including vehicle speed. Tire rotation speed Tire slip ratio and tire slip angle Among them, vehicle speed Tire rotation speed Vertical force and tire slip angle Longitudinal force obtained through active control of the testing platform and lateral force Tire slip ratio, measured by the testing platform This is based on vehicle speed. and tire rotation speed Indirectly, that is, when the driving speed is constant, the change in slip ratio is achieved by controlling the rotation speed of the tires.

4. The method according to claim 1, characterized in that, In the identification step, the relationship model is built using a combination of CNN and LSTM or CNN and GRU networks to construct a deep learning model.

5. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program configured to implement the steps of the method according to any one of claims 1-4 when invoked by a processor.

6. A system for identifying the tire-road contact coefficient and contact force, characterized in that, include: An acceleration sensor, a memory, a processor, and a computer program stored in the memory, the computer program being configured to implement the steps of the method of any one of claims 1-4 when invoked by the processor, wherein the acceleration sensor is mounted on the central axis of the tire liner.

7. The system according to claim 6, characterized in that, The system also includes a high-speed slip ring and a signal acquisition device. The high-speed slip ring is installed at the center of the car wheel rim. The wires of the acceleration sensor are connected to the high-speed slip ring, and then the wires from the high-speed slip ring are connected to the signal acquisition device to realize the transmission of acceleration signals from inside the tire to outside the tire.

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