Filtering method, device, vehicle and medium for wheel speed signals

Abstract

Embodiments of the present application provide a wheel speed signal filtering method and device, a vehicle and a medium. The method comprises: acquiring wheel speed signals of a current collection period and a plurality of continuous collection periods before the current collection period; determining a wheel speed signal jitter degree according to the wheel speed signals of the plurality of collection periods; determining a filtering signal weight of the current collection period according to the jitter degree; and filtering the wheel speed signal of the current collection period according to the filtering signal weight to obtain a filtered wheel speed signal. The method dynamically adjusts the filtering strength of the wheel speed signal, balances the stability and accuracy of the wheel speed signal output to the vehicle control system, and improves the reliability of the decision of the control system.

Description

Technical Field

[0001] This application relates to signal processing, and more particularly to a method, apparatus, vehicle, and medium for filtering wheel speed signals. Background Technology

[0002] The accuracy of wheel speed signals directly affects vehicle safety and control performance. Because wheel sensors are installed near the wheels, they are susceptible to interference from road bumps and mechanical vibrations, which can introduce high-frequency noise into the signal. Therefore, low-pass filtering is usually used to attenuate the high-frequency components in the wheel speed signal to obtain a stable wheel speed value.

[0003] Currently, fixed filtering parameters are typically used to filter wheel speed signals in real time. However, the vibration level of wheel speed signals from a moving vehicle is dynamically changing. When the signal is in a smooth state, using fixed parameters for filtering will lead to severe signal distortion. Summary of the Invention

[0004] This application provides a method, device, vehicle, and medium for filtering wheel speed signals, which dynamically adjusts the filtering intensity of the wheel speed signals to balance the stability and accuracy of the wheel speed signals output to the vehicle control system, thereby improving the reliability of the control system's decision-making.

[0005] In a first aspect, embodiments of this application provide a method for filtering wheel speed signals, comprising:

[0006] Acquire the wheel speed signals of the vehicle in the current acquisition cycle and in multiple consecutive acquisition cycles preceding the current acquisition cycle;

[0007] The degree of jitter in the wheel speed signal is determined based on the wheel speed signal from multiple acquisition cycles;

[0008] Based on the degree of jitter, the weight of the filtered signal in the current acquisition period is determined;

[0009] Based on the weights of the filtered signal, the wheel speed signal of the current acquisition period is filtered to obtain the filtered wheel speed signal.

[0010] The weight of the filtered signal refers to the proportion of the filtered signal in the filtered wheel speed signal.

[0011] In one possible implementation, determining the jitter level of the wheel speed signal based on wheel speed signals from multiple acquisition cycles includes:

[0012] Based on the wheel speed signals from multiple acquisition cycles, obtain the first change in the wheel speed signal for each acquisition cycle and the second change in the overall wheel speed signal over the multiple acquisition cycles;

[0013] The degree of jitter in the wheel speed signal is determined based on multiple first changes and the second changes.

[0014] In one possible implementation, determining the jitter level of the wheel speed signal based on a plurality of first changes and a second change includes:

[0015] The third change is obtained by summing the absolute values ​​of the plurality of first changes;

[0016] The degree of jitter in the wheel speed signal is obtained based on the ratio of the second change to the third change.

[0017] In one possible implementation, determining the jitter level of the wheel speed signal based on wheel speed signals from multiple acquisition cycles includes:

[0018] The wheel speed signals from multiple acquisition cycles are input into a pre-acquired jitter detection model for jitter detection to obtain the jitter level of the wheel speed signals;

[0019] The jitter detection model is obtained by training a long short-term memory network based on the wheel speed signal and the corresponding jitter level collected in a continuous cycle.

[0020] In one possible implementation, filtering the wheel speed signal of the current acquisition period according to the weight of the filtered signal to obtain the filtered wheel speed signal includes:

[0021] The target filtered signal is obtained by multiplying the weight of the filtered signal by the filtered signal, and then superimposed on the wheel speed signal of the current acquisition period to obtain the filtered wheel speed signal.

[0022] In one possible implementation, the method of multiplying the weights of the filtered signal by the filtered signal to obtain the target filtered signal, and then superimposing it onto the wheel speed signal of the current acquisition cycle, further includes:

[0023] Based on the vehicle's longitudinal acceleration and wheel speed, determine whether the vehicle is in a slipping or locked-up condition.

[0024] If the vehicle is in a skidding or locked-up condition, the weight of the filtered signal is reset to 0.

[0025] In one possible implementation, determining whether the vehicle is in a slipping or locked-up condition based on the vehicle's longitudinal acceleration and wheel speed includes:

[0026] Calculate the wheel acceleration of the vehicle based on the wheel speed;

[0027] If the absolute value of the difference between the wheel acceleration and the longitudinal acceleration of the vehicle is greater than the preset slippage or lockup determination threshold, then the vehicle is determined to be in a slippage or lockup condition.

[0028] Secondly, embodiments of this application provide a filtering device for wheel speed signals, comprising:

[0029] The acquisition module is used to acquire the wheel speed signals of the vehicle in the current acquisition cycle and in multiple consecutive acquisition cycles preceding the current acquisition cycle.

[0030] The first determining module is used to determine the jitter level of the wheel speed signal based on the wheel speed signal from multiple acquisition cycles;

[0031] The second determining module is used to determine the weight of the filtered signal in the current acquisition period based on the degree of jitter.

[0032] The filtering module is used to filter the wheel speed signal of the current acquisition period according to the weight of the filtered signal to obtain the filtered wheel speed signal.

[0033] The weight of the filtered signal refers to the proportion of the filtered signal in the filtered wheel speed signal.

[0034] In one possible implementation, the first determining module includes:

[0035] The acquisition unit is used to acquire, based on the wheel speed signals of multiple acquisition cycles, a first change in the wheel speed signal of each acquisition cycle and a second change in the overall wheel speed signal over the multiple acquisition cycles.

[0036] The determining unit is used to determine the degree of jitter of the wheel speed signal based on a plurality of first changes and a second change.

[0037] In one possible implementation, the determining unit is specifically used for:

[0038] The third change is obtained by summing the absolute values ​​of the plurality of first changes;

[0039] The degree of jitter in the wheel speed signal is obtained based on the ratio of the second change to the third change.

[0040] In one possible implementation, the first determining module is specifically used for:

[0041] The wheel speed signals from multiple acquisition cycles are input into a pre-acquired jitter detection model for jitter detection to obtain the jitter level of the wheel speed signals;

[0042] The jitter detection model is obtained by training a long short-term memory network based on the wheel speed signal and the corresponding jitter level collected in a continuous cycle.

[0043] In one possible implementation, the filtering module is specifically used for:

[0044] The target filtered signal is obtained by multiplying the weight of the filtered signal by the filtered signal, and then superimposed on the wheel speed signal of the current acquisition period to obtain the filtered wheel speed signal.

[0045] In one possible implementation, the device further includes:

[0046] The third determining module is used to determine whether the vehicle is in a slipping or locked-up condition based on the vehicle's longitudinal acceleration and wheel speed.

[0047] The adjustment module is used to reset the weight of the filtered signal to 0 if the vehicle is in a slipping or locked-up condition.

[0048] In one possible implementation, the third determining module is specifically used for:

[0049] Calculate the wheel acceleration of the vehicle based on the wheel speed;

[0050] If the absolute value of the difference between the wheel acceleration and the longitudinal acceleration of the vehicle is greater than the preset slippage or lockup determination threshold, then the vehicle is determined to be in a slippage or lockup condition.

[0051] Thirdly, embodiments of this application provide an electronic device, including: a memory and a processor;

[0052] The memory stores computer-executed instructions;

[0053] The processor executes computer execution instructions stored in the memory, causing the processor to perform the first aspect and / or various possible implementations of the first aspect as described above.

[0054] Fourthly, embodiments of this application provide a vehicle, including a vehicle body and the electronic equipment described in the third aspect.

[0055] Fifthly, embodiments of this application provide a computer-readable storage medium storing computer-executable instructions, which, when executed by a processor, are used to implement the first aspect and / or various possible implementations of the first aspect.

[0056] In a sixth aspect, embodiments of this application provide a computer program product, including a computer program that, when executed by a processor, implements the first aspect and / or various possible implementations of the first aspect.

[0057] The wheel speed signal filtering method, device, vehicle, and medium provided in this application embodiment determine the jitter level of the wheel speed signal in real time based on the raw wheel speed signal recently collected by the vehicle. Then, based on the jitter level of the wheel speed signal, the weight ratio of the filtered signal to the filtered wheel speed signal in the current collection cycle is dynamically adjusted, thereby adjusting the filtering intensity of the wheel speed signal. This achieves a balance between the stability and accuracy of the wheel speed signal output to the vehicle control system, thereby improving the reliability of the control system's decision-making. Attached Figure Description

[0058] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with this application and, together with the description, serve to explain the principles of this application.

[0059] Figure 1 A flowchart illustrating a wheel speed signal filtering method provided in Embodiment 1 of this application;

[0060] Figure 2 This is a schematic diagram of wheel speed signals collected in multiple acquisition cycles, provided in Embodiment 2 of this application;

[0061] Figure 3 This is a flowchart illustrating a wheel speed signal filtering method provided in Embodiment 4 of this application;

[0062] Figure 4 This is a schematic diagram of the structure of a wheel speed signal filtering device provided in Embodiment 5 of this application;

[0063] Figure 5 This is a schematic diagram of the structure of a wheel speed signal filtering device provided in Embodiment Six of this application;

[0064] Figure 6 A schematic diagram of the structure of the electronic device provided in this application.

[0065] The accompanying drawings illustrate specific embodiments of this application, which will be described in more detail below. These drawings and descriptions are not intended to limit the scope of the concept in any way, but rather to illustrate the concept of this application to those skilled in the art through reference to particular embodiments. Detailed Implementation

[0066] Exemplary embodiments will now be described in detail, examples of which are illustrated in the accompanying drawings. When the following description relates to the drawings, unless otherwise indicated, the same numbers in different drawings denote the same or similar elements. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with this application. Rather, they are merely examples of apparatuses and methods consistent with some aspects of this application as detailed in the appended claims.

[0067] To facilitate understanding of the technical content of this solution, the background technology is described in detail below:

[0068] With the continuous development of automotive technology, automobiles are equipped with a large number of Electronic Control Units (ECUs), such as Anti-lock Braking System (ABS), Electronic Stability Program (ESP), and Traction Control System (TCS). These systems all require accurate wheel speed signals to achieve effective vehicle control. Inaccurate wheel speed signals may cause safety systems such as ABS and ESP to malfunction or misoperate. Therefore, accurate wheel speed signals play a crucial role in vehicle braking, steering, and driving stability.

[0069] During vehicle operation, mechanical vibrations occur due to uneven road surfaces and the operation of the vehicle's own mechanical components. Since wheel speed sensors are mounted near the wheels, these vibrations are transmitted to the sensors, resulting in vibration noise mixed in with the wheel speed signal. Furthermore, because the actual wheel speed signal consists primarily of low-frequency components, low-pass filtering is typically used to attenuate the high-frequency components and obtain a stable wheel speed value.

[0070] Current wheel speed signal filtering methods typically use fixed filtering parameters to filter the acquired wheel speed signals, which cannot dynamically adjust the filtering intensity according to the signal vibration state, resulting in distortion of the smooth signal or insufficient stability of the jitter signal.

[0071] In view of this, the inventors considered that adding filtering to a signal would inevitably lead to varying degrees of signal distortion during their research. Therefore, they discovered that by analyzing the trend of signal changes and quantifying the degree of signal jitter, the filtering intensity could be dynamically adjusted. That is, the filtering effect should be reduced for smooth signals with extremely small vibration amplitudes, and the filtering effect should be increased for signals with large vibration amplitudes to optimize the filtering process. This ensures that the processed signal can balance signal stability and accuracy, thereby improving the reliability of the control system's decision-making.

[0072] It should be noted that the wheel speed signal filtering method provided in this solution can be applied to various electronic devices in the vehicle field. For example, after obtaining the filtered wheel speed signal using this filtering method in the vehicle's ECU, the filtered wheel speed signal can be output to the vehicle's ABS, TCS, and ESP control systems for various aspects of vehicle control; alternatively, the above method can also be directly implemented in the vehicle's ABS, TCS, and ESP control systems. This application does not impose specific restrictions on the entities that can implement this solution.

[0073] The technical solution of this application and how the technical solution of this application solves the above-mentioned technical problems are described in detail below with specific embodiments. These specific embodiments can be combined with each other, and the same or similar concepts or processes may not be described again in some embodiments. The embodiments of this application will now be described with reference to the accompanying drawings.

[0074] Figure 1 This is a flowchart illustrating a wheel speed signal filtering method provided in Embodiment 1 of this application, as shown below. Figure 1 As shown, the method includes:

[0075] S101. Acquire the wheel speed signal of the vehicle in the current acquisition cycle and the wheel speed signals of multiple consecutive acquisition cycles preceding the current acquisition cycle.

[0076] In this step, in order to quantify the recent jitter of the original wheel speed signal in real time, it is necessary to acquire the wheel speed signal collected by the vehicle in the current acquisition cycle, as well as the wheel speed signal collected in multiple consecutive acquisition cycles between the current acquisition cycle, to obtain the original wheel speed signal for multiple acquisition cycles.

[0077] Specifically, the number of consecutive acquisition cycles selected can be determined based on the actual application of this solution. This application does not impose specific restrictions on this, such as 4, 5, 8, or other numbers greater than 1. In practical applications, the vehicle's wheel speed sensors will collect the vehicle's wheel speed according to a fixed acquisition cycle, thereby obtaining the original wheel speed signal corresponding to each acquisition cycle.

[0078] S102. Determine the jitter level of the wheel speed signal based on the wheel speed signal from multiple acquisition cycles.

[0079] In this step, since the wheel speed signals acquired over multiple acquisition cycles can characterize the recent trend of the original wheel speed signal, they can be directly used to quantify the jitter of the original wheel speed signal.

[0080] Specifically, the trend of wheel speed signal changes over multiple acquisition cycles can be analyzed to quantify the degree of signal jitter.

[0081] Optionally, based on the wheel speed signal of the current acquisition cycle and the change in wheel speed signal of the previous (or more) acquisition cycles, the number of acquisition cycles used to determine the jitter level of the wheel speed signal can be dynamically adjusted using a sliding window.

[0082] For example, the change in wheel speed signal in the current acquisition period compared to the previous period can be calculated. If the change is greater than a preset fluctuation threshold, the length of the window is shortened to reduce the number of acquisition periods used to determine the change in wheel speed signal jitter. If the change is less than or equal to the preset fluctuation threshold, the length of the window is extended to increase the number of acquisition periods used to determine the change in wheel speed signal jitter.

[0083] This method addresses the lag issue of fixed-period computation through a sliding window mechanism. Specifically, in high-speed jitter scenarios, a shorter window can capture signal abrupt changes more quickly. By identifying jitter in a smaller number of sampling cycles, the response speed for determining the degree of jitter can be improved, avoiding misadjustment of filter strength due to delay. In addition, dynamically adjusting the window length can reduce redundant computation and improve the energy efficiency of the jitter detection process.

[0084] S103. Determine the weight of the filtered signal in the current acquisition cycle based on the degree of jitter.

[0085] The weight of the filtered signal refers to the proportion of the filtered signal in the filtered wheel speed signal.

[0086] In this step, the weight of the filtered signal used for filtering in the current acquisition period needs to be determined based on the jitter level of the quantized signal.

[0087] It should be understood that the weight of the filtered signal represents the filtering strength of the original wheel speed signal. Specifically, if the original wheel speed signal has a large degree of jitter, the determined filtering weight should be larger to increase the filtering strength of the original wheel speed signal, thereby ensuring the stability of the processed wheel speed signal; if the original wheel speed signal has a small degree of jitter, the determined filtering weight should be smaller to reduce the filtering strength of the original wheel speed signal, thereby avoiding severe distortion of the smoothed signal and ensuring the accuracy of the processed wheel speed signal.

[0088] In other words, the determined filtering weights are inversely proportional to the jitter level of the original wheel speed signals acquired based on historical data.

[0089] The filtered signal refers to the signal obtained by filtering the original wheel speed signal using a specific filtering algorithm from the previous acquisition cycle. The filtering algorithm is a first-order inertial filtering algorithm.

[0090] It should be understood that if a first-order inertial filtering algorithm is selected, then from the time domain perspective, the update formula of the first-order inertial filtering algorithm is as follows:

[0091]

[0092] In the formula, This represents the filtered wheel speed signal for the current acquisition period. This is the currently acquired filtered signal. This is the raw wheel speed signal for the current acquisition cycle. These are the filter coefficients. ,in The time constant selected for the filtering algorithm This is the acquisition period for the wheel speed signal.

[0093] Therefore, it can be seen that in this scheme, It can be used to represent the weights of a filtered signal.

[0094] S104. Filter the wheel speed signal of the current acquisition cycle according to the weight of the filtered signal.

[0095] In this step, the filtering intensity of the wheel speed signal in the current acquisition period will be adjusted according to the weight of the acquired filtered signal, and then the wheel speed signal in the current acquisition period will be filtered using the adjusted filtering intensity.

[0096] The wheel speed signal filtering method provided in this application determines the jitter level of the wheel speed signal in real time based on the raw wheel speed signal recently collected by the vehicle. Then, based on the jitter level of the wheel speed signal, it dynamically adjusts the weight ratio of the filtered signal to the filtered wheel speed signal in the current collection cycle, thereby adjusting the filtering intensity of the wheel speed signal. This achieves a balance between the stability and accuracy of the wheel speed signal output to the vehicle control system, thereby improving the reliability of the control system's decision-making.

[0097] Furthermore, in one possible implementation, step S104 in the above embodiments can be implemented using the following method:

[0098] The target filtered signal is obtained by multiplying the weight of the filtered signal by the filtered signal, and then superimposed on the wheel speed signal of the current acquisition cycle to obtain the filtered wheel speed signal.

[0099] In other words, the filtering method can be represented by the following formula:

[0100]

[0101] In the formula, This represents the filtered wheel speed signal; Represents the weights of the filtered signal; This represents the original wheel speed signal; Indicates the filtered signal; This is the target filtered signal.

[0102] The method provided in this implementation fuses the filtered signal and the original wheel speed signal in a weighted average manner. This allows the processed filtered signal to suppress the defects caused by interference noise to a certain extent, ensuring the stability of the output wheel speed signal, while also ensuring that the original wheel speed signal is not distorted and retaining the accuracy of the output wheel speed signal, thus achieving the effect of balancing the stability and accuracy of the wheel speed signal.

[0103] Furthermore, Embodiment 2 of this application provides a filtering method for wheel speed signals. Based on the above embodiments, this embodiment provides a specific method for determining the degree of jitter in wheel speed signals, including:

[0104] Step 2.1: Based on the wheel speed signals from multiple acquisition cycles, obtain the first change in the wheel speed signal for each acquisition cycle and the second overall change in the wheel speed signal over multiple acquisition cycles.

[0105] The first change in the wheel speed signal in each acquisition cycle refers to the change in the original wheel speed signal of that acquisition cycle compared to the original wheel speed signal of the previous cycle.

[0106] The second change in the total over the multiple acquisition cycles refers to the change in the original wheel speed signal of the last acquisition cycle (i.e., the current acquisition cycle) compared to the change in the original wheel speed signal of the first acquisition cycle.

[0107] Step 2.2: Determine the degree of jitter in the wheel speed signal based on multiple first and second changes.

[0108] In this step, multiple first variables reflect the amplitude of wheel speed signal changes between two adjacent acquisition cycles, providing a direct view of the local fluctuations in wheel speed within the smallest time unit. The second variable reflects the overall trend of wheel speed signal changes over these multiple acquisition cycles. Therefore, based on these multiple first and second variables, the random local fluctuations of the original wheel speed signal deviating from the overall trend of wheel speed can be identified, thus revealing the degree of jitter in the wheel speed signal.

[0109] In one possible implementation, step 2.2 can be achieved using steps 2.2.1 and 2.2.2 as follows:

[0110] Step 2.2.1: Calculate the sum of the absolute values ​​of multiple first changes to obtain the third change.

[0111] Step 2.2.2: Based on the ratio of the second change to the third change, obtain the jitter level of the wheel speed signal.

[0112] In the steps, the larger the ratio of the second change to the third change (i.e., the closer it is to 1), the less the signal curve changes back and forth, and the smaller the jitter of the wheel speed signal it represents; the smaller the ratio of the second change to the third change, the greater the back and forth change of the signal curve, and the greater the jitter of the wheel speed signal it represents.

[0113] Correspondingly, the degree of jitter in the wheel speed signal can be measured using... Value is used to represent, where The formula for calculating the value is as follows:

[0114]

[0115] in, Indicates the first The raw wheel speed signal acquired in each acquisition cycle (i.e., the current acquisition cycle); Indicates the first The raw wheel speed signal acquired in each acquisition cycle; Indicates the first The raw wheel speed signal acquired in each acquisition cycle (i.e., the current acquisition cycle); Indicates the first The raw wheel speed signal acquired in each acquisition cycle; This indicates the number of consecutive acquisition cycles preceding the current acquisition cycle within a set of acquisition cycles.

[0116] It should be understood that The larger the value, the greater the jitter in the wheel speed signal; The smaller the value, the greater the jitter in the wheel speed signal.

[0117] In one possible implementation, the weights of the filtered signal can be determined using the following formula:

[0118]

[0119] It should be understood that The larger the value, the smaller the jitter of the wheel speed signal, and thus the smaller the weight of the filtered signal. The larger the value, the greater the jitter of the wheel speed signal, and consequently the greater the weight of the filtered signal.

[0120] As a specific example Figure 2 This is a schematic diagram of wheel speed signals acquired in multiple acquisition cycles as provided in Embodiment 2 of this application. The following will be combined with... Figure 2 The first and second changes provided in this embodiment will be described as follows:

[0121] like Figure 2As shown in the figure, segment A represents the overall second change in the wheel speed signal over multiple acquisition cycles. These multiple acquisition cycles include the 10th acquisition cycle and the nine acquisition cycles preceding the 10th. In other words, the second change is the difference between the original wheel speed signal in the 10th acquisition cycle and the original wheel speed signal in the 1st acquisition cycle.

[0122] In the figure, segment B represents the first change in the wheel speed signal during the 5th acquisition cycle. In other words, the first change in the 5th acquisition cycle is equal to the difference between the original wheel speed signal in the 5th acquisition cycle and the original wheel speed signal in the 4th acquisition cycle.

[0123] The methods provided in steps 2.2.1 and 2.2.2 above obtain a third change that reflects the local total fluctuation amplitude of wheel speed by first summing the absolute values ​​of the first changes in wheel speed signals from multiple adjacent acquisition cycles, and then quantifying the jitter of wheel speed signals by the ratio of the second change to the third change. This method makes the wheel speed jitter quantization process rely only on basic mathematical operations, greatly reducing computational overhead and achieving the effect of outputting reliable jitter quantization results in a low-cost and high-real-time manner, thus providing data support for the subsequent weight adjustment of filtered signals.

[0124] The wheel speed signal filtering method provided in this application calculates the first change in the wheel speed signal in each acquisition cycle and the second change in the overall wheel speed signal over multiple acquisition cycles. Based on the multiple first and second changes, the jitter level of the wheel speed signal is determined. By using simple mathematical modeling, the jitter level of the signal can be quantified, enabling the determination of the jitter level of the signal in real time with limited computing power, thus reducing the hardware adaptation threshold of this solution.

[0125] Furthermore, Embodiment 4 of this application provides a filtering method for wheel speed signals. Based on the above embodiments, this embodiment provides another specific method for determining the jitter level of wheel speed signals, including:

[0126] Wheel speed signals from multiple acquisition cycles are input into a pre-acquired jitter detection model for jitter detection to obtain the degree of jitter in the wheel speed signals.

[0127] The jitter detection model is trained on a Long Short-Term Memory (RNN) network based on the continuously collected wheel speed signals and the corresponding jitter levels.

[0128] Alternatively, other temporal models can be used to obtain the jitter detection model, such as temporal convolutional networks.

[0129] It should be understood that training a time series model using historical data enables the trained jitter detection model to identify jitter patterns in the signal.

[0130] Specifically, the jitter detection model can be obtained using the following methods:

[0131] First, a large number of wheel speed signal samples are collected in advance, and the corresponding jitter level is labeled for each sample to obtain a sample set. Each sample includes wheel speed signals from multiple consecutive collection cycles.

[0132] Next, based on the sample set, LSTM is used to model the temporal features of the signal, resulting in a jitter detection model with the input being a sequence of wheel speed signals from multiple consecutive acquisition cycles and the output being the jitter level of the signal.

[0133] The wheel speed signal filtering method provided in this application uses an LSTM model to automatically capture the jitter level of the signal. This method can reduce the dependence on manually defined jitter level, improve the automation level of the algorithm, and achieve the effect of efficiently and accurately outputting the jitter level of the wheel speed signal.

[0134] Furthermore, Figure 3 This is a flowchart illustrating a wheel speed signal filtering method provided in Embodiment 4 of this application, as shown below. Figure 3 As shown, based on the above embodiments, before step S104, the method provided in this embodiment further includes:

[0135] S201. Determine whether the vehicle is slipping or locked based on the vehicle's longitudinal acceleration and wheel speed.

[0136] In this step, it is necessary to obtain the vehicle's longitudinal acceleration and wheel speed to determine the vehicle's actual operating conditions, which will be used to further adjust the weights of the filtered signal.

[0137] In one specific implementation, the vehicle's longitudinal acceleration The longitudinal acceleration can be obtained based on the output of the inertial sensor in the Inertial Measurement Unit (IMU). Specifically, it is calculated using the following formula:

[0138]

[0139] In the formula, The longitudinal acceleration is obtained from the IMU inertial sensor. Longitudinal slope is the angle of inclination of the road surface coordinate system relative to the horizontal plane in the direction of vehicle travel (longitudinal).

[0140] In one possible implementation, steps 1.1 and 1.2 can be used to determine whether the vehicle is in a slipping or locked-up condition:

[0141] Step 1.1: Calculate the wheel acceleration of the vehicle based on the wheel speed.

[0142] Specifically, what formula is used to calculate the wheel acceleration of the vehicle in the nth data acquisition cycle? :

[0143]

[0144] In the formula, For the first Wheel speed of the vehicle in each collection cycle; For the first Wheel speed of the vehicle per collection cycle.

[0145] Step 1.2: If the absolute value of the difference between the wheel acceleration and the longitudinal acceleration of the vehicle is greater than the preset slippage or lockup determination threshold, then the vehicle is determined to be in a slippage or lockup condition.

[0146] In this step, when the vehicle is driving normally, the "pure rolling" of the wheels will make the wheel acceleration and the vehicle's longitudinal acceleration highly synchronized. However, the slippage or lock-up condition will break this synchronization, causing a significant deviation between the two. Therefore, the absolute value of the difference between the vehicle's wheel acceleration and longitudinal acceleration can be used to determine whether the vehicle is in a slippage or lock-up condition.

[0147] The preset threshold for determining slippage or lockup can be determined based on the actual application scenario, such as vehicle type and tire characteristics. This application does not impose specific restrictions on this.

[0148] In other words, if Then it is determined that the vehicle is locked or skidding; if If this is the case, then it is determined that the vehicle is not in a locked or skidding condition. This indicates the preset threshold for determining slippage or lockup.

[0149] The method provided in this implementation calculates the acceleration of the wheel itself and compares it with the difference between the wheel and the overall longitudinal acceleration of the vehicle. When the absolute value of the difference exceeds a set threshold, it determines that the vehicle is slipping or locking up. This enables the vehicle to quickly and accurately identify its driving conditions and provides real-time and reliable data support for adjusting the weights of the filtered signal.

[0150] S202. If the vehicle is in a skidding or locked-up condition, the weight of the filter signal is reset to 0.

[0151] It should be understood that when a wheel slips or locks up, the amplitude of the wheel speed signal vibration is small, and the vibration accounts for a very small proportion of the wheel slip. In this case, reducing the weight of the filter value is beneficial to improving the sensitivity of the signal. Therefore, in this step, when it is determined that the vehicle is slipping or locked up, the weight of the filter signal is reset to 0.

[0152] Optionally, in one specific implementation, the weight of the filtered signal determined based on the degree of jitter can be set as the first weight. The weights of the filtered signals, determined based on whether the vehicle is in a slipping or locked-up condition, are set as weights. Finally, based on the first and second weights, the weights of the filtered signal used to filter the wheel speed signal of the current acquisition cycle are jointly determined. ,Right now Among them, when the vehicle is locked or skidding, When the vehicle is not in a locked or skidding condition, .

[0153] The wheel speed signal filtering method provided in this application determines whether the vehicle is in a slipping or locked-up condition based on the vehicle's longitudinal acceleration and wheel speed, and adjusts the weight of the filtered signal when the vehicle is in a slipping or locked-up condition. This fully considers the signal characteristics under the special condition of wheel slippage / lock-up, and does not filter the wheel speed signal under this special condition. This avoids over-filtering that masks the real signal changes when the wheel is slipping or locked, thus preventing insufficient sensitivity of the control system (such as ABS) and enhancing the adaptability of this solution to different operating conditions.

[0154] Figure 4 This is a schematic diagram of the structure of a wheel speed signal filtering device provided in Embodiment 5 of this application, as shown below. Figure 4 As shown, the wheel speed signal filtering device 30 provided in this embodiment includes:

[0155] The acquisition module 301 is used to acquire the wheel speed signals of the vehicle in the current acquisition cycle and in multiple consecutive acquisition cycles before the current acquisition cycle;

[0156] The first determining module 302 is used to determine the jitter level of the wheel speed signal based on the wheel speed signal from multiple acquisition cycles.

[0157] The second determining module 303 is used to determine the weight of the filtered signal in the current acquisition period based on the degree of jitter;

[0158] The filtering module 304 is used to filter the wheel speed signal of the current acquisition period according to the weight of the filtered signal to obtain the filtered wheel speed signal.

[0159] The weight of the filtered signal refers to the proportion of the filtered signal in the filtered wheel speed signal.

[0160] The wheel speed signal filtering device 30 provided in this embodiment can execute the method provided in the above method embodiment. Its implementation principle and technical effect are similar, and will not be described in detail here.

[0161] Figure 5 This is a schematic diagram of the structure of a wheel speed signal filtering device provided in Embodiment Six of this application, as shown below. Figure 5 As shown, based on the above embodiments, the wheel speed signal filtering device 30 provided in this embodiment further includes:

[0162] The third determining module 305 is used to determine whether the vehicle is in a slipping or locked condition based on the vehicle's longitudinal acceleration and wheel speed.

[0163] The adjustment module 306 is used to reset the weight of the filtered signal to 0 if the vehicle is in a slipping or locked-up condition.

[0164] In one possible implementation, the first determining module 302 includes:

[0165] The acquisition unit is used to acquire, based on the wheel speed signals of multiple acquisition cycles, the first change in the wheel speed signal of each acquisition cycle and the second change in the overall wheel speed signal over multiple acquisition cycles.

[0166] The determining unit is used to determine the degree of jitter in the wheel speed signal based on multiple first and second changes.

[0167] In one possible implementation, the determining unit is specifically used for:

[0168] The third change is obtained by summing the absolute values ​​of multiple first changes.

[0169] The degree of jitter in the wheel speed signal is obtained based on the ratio of the second change to the third change.

[0170] In one possible implementation, the first determining module 302 is specifically used for:

[0171] The wheel speed signals from multiple acquisition cycles are input into a pre-acquired jitter detection model for jitter detection to obtain the jitter level of the wheel speed signals;

[0172] The jitter detection model is obtained by training a long short-term memory network based on the wheel speed signal and the corresponding jitter level collected in a continuous cycle.

[0173] In one possible implementation, the filtering module 304 is specifically used for:

[0174] The target filtered signal is obtained by multiplying the weight of the filtered signal by the filtered signal, and then superimposed on the wheel speed signal of the current acquisition cycle to obtain the filtered wheel speed signal.

[0175] In one possible implementation, the third determining module 305 is specifically used for:

[0176] Calculate the vehicle's wheel acceleration based on the wheel speed;

[0177] If the absolute value of the difference between the vehicle's wheel acceleration and longitudinal acceleration is greater than the preset threshold for determining slippage or lockup, the vehicle is determined to be in a slippage or lockup condition.

[0178] The wheel speed signal filtering device 30 provided in this embodiment can execute the method provided in the above method embodiment. Its implementation principle and technical effect are similar, and will not be described in detail here.

[0179] Figure 6 A schematic diagram of the structure of the electronic device provided in this application. Figure 6 As shown, the electronic device 40 provided in this embodiment includes at least one processor 401 and a memory 402. Optionally, the device 40 further includes a communication component 403. The processor 401, memory 402, and communication component 403 are connected via a bus 404.

[0180] In a specific implementation, at least one processor 401 executes computer execution instructions stored in memory 402, causing at least one processor 401 to perform the above-described method.

[0181] The specific implementation process of processor 401 can be found in the above method embodiments, and its implementation principle and technical effect are similar. It will not be repeated here.

[0182] In the above embodiments, it should be understood that the processor can be a Central Processing Unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), etc. The general-purpose processor can be a microprocessor or any conventional processor. The steps of the method disclosed in this invention can be directly implemented by a hardware processor, or implemented by a combination of hardware and software modules within the processor.

[0183] The memory may include read-only memory and random access memory. The memory may be volatile or non-volatile, or may include both. Non-volatile memory may include read-only memory (ROM), programmable read-only memory (PROM), erasable programmable read-only memory (EPROM), electrically erasable programmable read-only memory (EEPROM), or flash memory. Volatile memory may include random access memory (RAM), which serves as an external cache. Many forms of RAM are available by way of example, but not limitation. Examples include Static Random Access Memory (SRAM), Dynamic Random Access Memory (DRAM), Synchronous DRAM (SDRAM), Double Data Rate SDRAM (DDR SDRAM), Enhanced Synchronous DRAM (ESDRAM), Sync Link DRAM (SLDRAM), and Direct Rambus RAM (DR RAM).

[0184] The bus can be an Industry Standard Architecture (ISA) bus, a Peripheral Component Interconnect (PCI) bus, or an Extended Industry Standard Architecture (EISA) bus, etc. Buses can be categorized as address buses, data buses, control buses, etc. For ease of illustration, the buses shown in the accompanying drawings are not limited to a single bus or a single type of bus.

[0185] This application also provides a vehicle including a vehicle body and electronic equipment for performing the above-described methods.

[0186] This application also provides a computer program product, including a computer program that, when executed, implements the above-described method.

[0187] This application also provides a computer-readable storage medium storing computer-executable instructions, which, when executed, implement the above-described method.

[0188] The aforementioned readable storage medium can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as SRAM, EEPROM, EPROM, PROM, ROM, magnetic storage, flash memory, magnetic disk, or optical disk. The readable storage medium can be any available medium accessible to a general-purpose or special-purpose computer.

[0189] An exemplary readable storage medium is coupled to a processor, enabling the processor to read information from and write information to the readable storage medium. Of course, the readable storage medium can also be a component of the processor. The processor and the readable storage medium can reside within an ASIC. Alternatively, the processor and the readable storage medium can exist as discrete components in a device.

[0190] The division of units is merely a logical functional division; in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be indirect coupling or communication connection through some interfaces, devices, or units, and may be electrical, mechanical, or other forms.

[0191] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0192] In addition, the functional units in the various embodiments of the present invention can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit.

[0193] If a function is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this invention, or the part that contributes to the prior art, or a part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods of the various embodiments of this invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, ROM, RAM, magnetic disks, or optical disks.

[0194] Those skilled in the art will understand that all or part of the steps of the above-described method embodiments can be implemented by hardware related to program instructions. The aforementioned program can be stored in a computer-readable storage medium. When executed, the program performs the steps of the above-described method embodiments; and the aforementioned storage medium includes various media capable of storing program code, such as ROM, RAM, magnetic disks, or optical disks.

[0195] The above embodiments are merely preferred embodiments provided to fully illustrate the present invention, and the scope of protection of the present invention is not limited thereto. Equivalent substitutions or modifications made by those skilled in the art based on the present invention are all within the scope of protection of the present invention.

[0196] Finally, it should be noted that other embodiments of the invention will readily occur to those skilled in the art upon consideration of the specification and practice of the invention disclosed herein. This invention is intended to cover any variations, uses, or adaptations of the invention that follow the general principles of the invention and include common knowledge or customary techniques in the art not disclosed herein, and is not limited to the precise structures described above and shown in the accompanying drawings, and various modifications and changes can be made without departing from its scope. The scope of the invention is limited only by the appended claims.

Claims

1. A filtering method for wheel speed signals, characterized in that, include: Acquire the wheel speed signals of the vehicle in the current acquisition cycle and in multiple consecutive acquisition cycles preceding the current acquisition cycle; The degree of jitter in the wheel speed signal is determined based on the wheel speed signal from multiple acquisition cycles; Based on the degree of jitter, the weight of the filtered signal in the current acquisition period is determined; Based on the weights of the filtered signal, the wheel speed signal of the current acquisition period is filtered to obtain the filtered wheel speed signal. The weight of the filtered signal refers to the proportion of the filtered signal in the filtered wheel speed signal.

2. The method according to claim 1, characterized in that, The step of determining the jitter level of the wheel speed signal based on wheel speed signals from multiple acquisition cycles includes: Based on the wheel speed signals from multiple acquisition cycles, obtain the first change in the wheel speed signal for each acquisition cycle and the second change in the overall wheel speed signal over the multiple acquisition cycles; The degree of jitter in the wheel speed signal is determined based on multiple first changes and the second changes.

3. The method according to claim 2, characterized in that, Determining the jitter level of the wheel speed signal based on multiple first changes and second changes includes: The third change is obtained by summing the absolute values ​​of the plurality of first changes; The degree of jitter in the wheel speed signal is obtained based on the ratio of the second change to the third change.

4. The method according to claim 1, characterized in that, The step of determining the jitter level of the wheel speed signal based on wheel speed signals from multiple acquisition cycles includes: The wheel speed signals from multiple acquisition cycles are input into a pre-acquired jitter detection model for jitter detection to obtain the jitter level of the wheel speed signals; The jitter detection model is obtained by training a long short-term memory network based on the wheel speed signal and the corresponding jitter level collected in a continuous cycle.

5. The method according to any one of claims 1 to 4, characterized in that, The step of filtering the wheel speed signal of the current acquisition period according to the weight of the filtered signal to obtain the filtered wheel speed signal includes: The target filtered signal is obtained by multiplying the weight of the filtered signal by the filtered signal, and then superimposed on the wheel speed signal of the current acquisition period to obtain the filtered wheel speed signal.

6. The method according to claim 5, characterized in that, The method of multiplying the weights of the filtered signal by the filtered signal to obtain the target filtered signal, and then superimposing it onto the wheel speed signal of the current acquisition cycle, further includes: Based on the vehicle's longitudinal acceleration and wheel speed, determine whether the vehicle is in a slipping or locked-up condition. If the vehicle is in a skidding or locked-up condition, the weight of the filtered signal is reset to 0.

7. The method according to claim 6, characterized in that, The step of determining whether the vehicle is in a slipping or locked-up condition based on the vehicle's longitudinal acceleration and wheel speed includes: Calculate the wheel acceleration of the vehicle based on the wheel speed; If the absolute value of the difference between the wheel acceleration and the longitudinal acceleration of the vehicle is greater than the preset slippage or lockup determination threshold, then the vehicle is determined to be in a slippage or lockup condition.

8. A filtering device for wheel speed signals, characterized in that, include: The acquisition module is used to acquire the wheel speed signals of the vehicle in the current acquisition cycle and in multiple consecutive acquisition cycles preceding the current acquisition cycle. The first determining module is used to determine the jitter level of the wheel speed signal based on the wheel speed signal from multiple acquisition cycles; The second determining module is used to determine the weight of the filtered signal in the current acquisition period based on the degree of jitter. The filtering module is used to filter the wheel speed signal of the current acquisition period according to the weight of the filtered signal to obtain the filtered wheel speed signal. The weight of the filtered signal refers to the proportion of the filtered signal in the filtered wheel speed signal.

9. An electronic device, characterized in that, include: Memory, processor; The memory stores computer-executed instructions; The processor executes computer execution instructions stored in the memory, causing the processor to perform the method as described in any one of claims 1-7.

10. A vehicle, characterized in that, It includes the vehicle body and the electronic equipment as described in claim 9.

11. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer-executable instructions, which, when executed by a processor, are used to implement the method as described in any one of claims 1-7.

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