阅读说明：本技术 车辆轮速矫正方法、装置、电子设备和计算机可读介质 (Vehicle wheel speed correction method, device, electronic equipment and computer readable medium ) 是由 马雨菲 孙磊 倪凯 于 2021-11-22 设计创作，主要内容包括：本公开的实施例公开了车辆轮速矫正方法、装置、电子设备和计算机可读介质。该方法的一具体实施方式包括：获取当前车辆的第一轮速数据组,其中,上述第一轮速数据组中的第一轮速数据包括第一轮速信号；基于上述第一轮速数据组中各个第一轮速数据包括的轮速信号,对预设的静态噪声方差进行调整,以生成动态噪声方差；将上述动态噪声方差添加至预设的噪声方差序列,得到添加后噪声方差序列；基于上述添加后噪声方差序列,生成目标轮速值,其中,上述目标轮速值为矫正后的轮速值。该实施方式可以提高车辆轮速矫正的准确度。(Embodiments of the present disclosure disclose a vehicle wheel speed correction method, apparatus, electronic device, and computer readable medium. One embodiment of the method comprises: acquiring a first wheel speed data set of a current vehicle, wherein first wheel speed data in the first wheel speed data set comprises a first wheel speed signal; adjusting a preset static noise variance based on the wheel speed signals included in each first wheel speed data in the first wheel speed data group to generate a dynamic noise variance; adding the dynamic noise variance to a preset noise variance sequence to obtain an added noise variance sequence; and generating a target wheel speed value based on the added noise variance sequence, wherein the target wheel speed value is a corrected wheel speed value. This embodiment can improve the accuracy of the wheel speed correction of the vehicle.)

1. A vehicle wheel speed correction method, comprising:

acquiring a first wheel speed data set of a current vehicle, wherein first wheel speed data in the first wheel speed data set comprises a first wheel speed signal;

adjusting a preset static noise variance based on the wheel speed signals included in each first wheel speed data in the first wheel speed data group to generate a dynamic noise variance;

adding the dynamic noise variance to a preset noise variance sequence to obtain an added noise variance sequence;

and generating a target wheel speed value based on the added noise variance sequence, wherein the target wheel speed value is a corrected wheel speed value.

2. The method of claim 1, wherein the method further comprises:

and sending the target wheel speed value to a vehicle control terminal of the current vehicle so as to control the vehicle.

3. The method of claim 1, wherein the static noise variance is generated by:

acquiring a second wheel speed data set and a high precision data set, wherein the second wheel speed data in the second wheel speed data set comprises a second wheel speed signal and a second wheel speed signal timestamp, and the high precision data in the high precision data set comprises a high precision speed signal and a speed signal timestamp;

generating an interpolation data set based on the second wheel speed data set and the high precision data set according to a second wheel speed signal time stamp included in second wheel speed data in the second wheel speed data set and a speed signal time stamp included in high precision data in the high precision data set;

performing polynomial fitting on each interpolation data in the interpolation data group to obtain a fitting polynomial;

adjusting second wheel speed signals included in each second wheel speed data in the second wheel speed data set by using the fitting polynomial to generate an adjusted wheel speed signal set;

generating a static noise variance based on the adjusted set of wheel speed signals.

4. The method of claim 3, wherein generating a static noise variance based on the adjusted set of wheel speed signals comprises:

acquiring the wheel speed signal sampling frequency of the current vehicle;

generating a signal decomposition data set based on the adjusted wheel speed signal set;

decomposing each adjusted wheel speed signal in the adjusted wheel speed signal group into a wavelet domain according to the wheel speed signal sampling frequency and the signal decomposition data group to generate at least one layer of decomposition speed signals;

performing signal reconstruction on each layer of decomposition speed signals meeting a preset wheel speed noise condition in the at least one layer of decomposition speed signals to generate a first reconstruction signal group to obtain a first reconstruction signal group set;

performing signal superposition on first reconstruction signals in each first reconstruction signal group in the first reconstruction signal group set to obtain a first superposed signal set;

determining a variance of each first superimposed signal of the set of first superimposed signals as the static noise variance.

5. The method of claim 4, wherein the adjusting the preset static noise variance based on the wheel speed signal included in each first wheel speed data in the first wheel speed data set to generate the dynamic noise variance comprises:

decomposing a wheel speed signal included in each first wheel speed data in the first wheel speed data set into a wavelet domain based on the wheel speed signal sampling frequency to generate at least one layer of decomposed wheel speed signals;

performing signal reconstruction on the decomposed wheel speed signals meeting the preset wheel speed noise condition in the at least one layer of decomposed wheel speed signals to generate reconstructed second reconstructed signals, so as to obtain a second reconstructed signal set;

performing signal superposition on each second reconstruction signal group in the second reconstruction signal group set to obtain a second superposed signal set;

generating a dynamic noise variance based on the second set of superimposed signals and the static noise variance.

6. The method of claim 5, wherein generating a target wheel speed value based on the post-addition noise variance sequence comprises:

performing curve fitting on each added noise variance in the added noise variance sequence to obtain a wheel speed noise variance curve equation;

inputting the current wheel speed value of the current vehicle into the wheel speed noise variance curve equation to generate a current noise variance value;

and generating a target wheel speed value by using the current noise variance value and the second superposed signal set.

7. The method of claim 1, wherein each noise variance in the sequence of noise variances corresponds to a same interval period.

8. A wheel speed correction device for a vehicle, comprising:

an acquisition unit configured to acquire a first wheel speed data set of a current vehicle, wherein first wheel speed data in the first wheel speed data set includes a first wheel speed signal;

an adjusting unit configured to adjust a preset static noise variance based on a wheel speed signal included in each first wheel speed data in the first wheel speed data group to generate a dynamic noise variance;

the adding unit is configured to add the dynamic noise variance to a preset noise variance sequence to obtain an added noise variance sequence;

a generating unit configured to generate a target wheel speed value based on the post-addition noise variance sequence, wherein the target wheel speed value is a corrected wheel speed value.

9. An electronic device, comprising:

one or more processors;

a storage device having one or more programs stored thereon,

when executed by the one or more processors, cause the one or more processors to implement the method of any one of claims 1-7.

10. A computer-readable medium, on which a computer program is stored, wherein the program, when executed by a processor, implements the method of any one of claims 1-7.

Technical Field

The embodiment of the disclosure relates to the technical field of computers, in particular to a method and a device for correcting wheel speed of a vehicle, electronic equipment and a computer readable medium.

Background

Vehicle wheel speed, is an important data for safe driving of a vehicle. At present, when performing wheel speed correction on a vehicle, the following methods are generally adopted: and setting a fixed noise variance to correct the wheel speed so as to reduce the influence of noise on the wheel speed data.

However, when the wheel speed correction of the vehicle is performed in the above manner, there are often the following technical problems:

the noise signal of the wheel speed sensor has the characteristics of strong randomness and large influence by road surface unevenness, so that the fixed noise variance cannot be applied to all driving environments, the accuracy of wheel speed correction is reduced, and the driving safety is further reduced.

Disclosure of Invention

This summary is provided to introduce a selection of concepts in a simplified form that are further described below in the detailed description. This summary is not intended to identify key features or essential features of the claimed subject matter, nor is it intended to be used to limit the scope of the claimed subject matter.

Some embodiments of the present disclosure provide a method, an apparatus, an electronic device and a computer readable medium for correcting a wheel speed of a vehicle, so as to solve the technical problems mentioned in the background section above.

In a first aspect, some embodiments of the present disclosure provide a method for correcting wheel speed of a vehicle, the method comprising: acquiring a first wheel speed data set of a current vehicle, wherein first wheel speed data in the first wheel speed data set comprises a first wheel speed signal; adjusting a preset static noise variance based on the wheel speed signals included in each first wheel speed data in the first wheel speed data group to generate a dynamic noise variance; adding the dynamic noise variance to a preset noise variance sequence to obtain an added noise variance sequence; and generating a target wheel speed value based on the added noise variance sequence, wherein the target wheel speed value is a corrected wheel speed value.

In a second aspect, some embodiments of the present disclosure provide a vehicle wheel speed correction device, comprising: an acquisition unit configured to acquire a first wheel speed data set of a current vehicle, wherein first wheel speed data in the first wheel speed data set includes a first wheel speed signal; an adjusting unit configured to adjust a preset static noise variance based on a wheel speed signal included in each of the first wheel speed data in the first wheel speed data group to generate a dynamic noise variance; the adding unit is configured to add the dynamic noise variance to a preset noise variance sequence to obtain an added noise variance sequence; a generating unit configured to generate a target wheel speed value based on the post-addition noise variance sequence, wherein the target wheel speed value is a corrected wheel speed value.

In a third aspect, some embodiments of the present disclosure provide an electronic device, comprising: one or more processors; a storage device having one or more programs stored thereon, which when executed by one or more processors, cause the one or more processors to implement the method described in any of the implementations of the first aspect.

In a fourth aspect, some embodiments of the present disclosure provide a computer readable medium on which a computer program is stored, wherein the program, when executed by a processor, implements the method described in any of the implementations of the first aspect.

The above embodiments of the present disclosure have the following advantages: through the vehicle wheel speed correction method of some embodiments of this disclosure, can improve the accuracy of correcting to wheel speed, and then can improve driving safety. Specifically, the reason why the accuracy of the wheel speed correction is reduced is that: the noise signal of the wheel speed sensor has the characteristics of strong randomness and large influence by road surface unevenness, so that the fixed noise variance cannot be applied to all driving environments. Based on this, the vehicle wheel speed correction method according to some embodiments of the present disclosure generates the dynamic noise variance by adjusting the preset static noise variance, so that the dynamic noise variance may be applicable to all driving environments even when the noise signal has the influence of strong randomness, road unevenness, and the like. Therefore, the accuracy of the wheel speed correction of the vehicle can be improved, and the obtained target wheel speed value is more accurate. Further, driving safety can be improved.

Drawings

The above and other features, advantages and aspects of various embodiments of the present disclosure will become more apparent by referring to the following detailed description when taken in conjunction with the accompanying drawings. Throughout the drawings, the same or similar reference numbers refer to the same or similar elements. It should be understood that the drawings are schematic and that elements and elements are not necessarily drawn to scale.

FIG. 1 is a schematic diagram of an application scenario of a wheel speed correction method of a vehicle according to some embodiments of the present disclosure;

FIG. 2 is a flow chart of some embodiments of a vehicle wheel speed correction method according to the present disclosure;

FIG. 3 is a flow chart of further embodiments of a method of wheel speed correction for a vehicle according to the present disclosure;

FIG. 4 is a schematic structural diagram of some embodiments of a vehicle wheel speed correction device according to the present disclosure;

FIG. 5 is a schematic structural diagram of an electronic device suitable for use in implementing some embodiments of the present disclosure.

Detailed Description

Embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. While certain embodiments of the present disclosure are shown in the drawings, it is to be understood that the disclosure may be embodied in various forms and should not be construed as limited to the embodiments set forth herein. Rather, these embodiments are provided for a more thorough and complete understanding of the present disclosure. It should be understood that the drawings and embodiments of the disclosure are for illustration purposes only and are not intended to limit the scope of the disclosure.

It should be noted that, for convenience of description, only the portions related to the related invention are shown in the drawings. The embodiments and features of the embodiments in the present disclosure may be combined with each other without conflict.

It should be noted that the terms "first", "second", and the like in the present disclosure are only used for distinguishing different devices, modules or units, and are not used for limiting the order or interdependence relationship of the functions performed by the devices, modules or units.

It is noted that references to "a", "an", and "the" modifications in this disclosure are intended to be illustrative rather than limiting, and that those skilled in the art will recognize that "one or more" may be used unless the context clearly dictates otherwise.

The names of messages or information exchanged between devices in the embodiments of the present disclosure are for illustrative purposes only, and are not intended to limit the scope of the messages or information.

The present disclosure will be described in detail below with reference to the accompanying drawings in conjunction with embodiments.

Fig. 1 is a schematic diagram of an application scenario of a wheel speed correction method of a vehicle according to some embodiments of the present disclosure.

In the application scenario of fig. 1, first, the computing device 101 may obtain a first wheel speed data set 102 of the current vehicle, wherein the first wheel speed data in the first wheel speed data set 102 includes a first wheel speed signal. Next, the computing device 101 may adjust the preset static noise variance 103 based on the wheel speed signals included in the respective first wheel speed data in the first wheel speed data set 102 described above to generate the dynamic noise variance 104. The computing device 101 may then add the above-described dynamic noise variance 104 to a preset noise variance sequence 105, resulting in an added noise variance sequence 106. Finally, the computing device 101 may generate a target wheel speed value 107 based on the post-addition noise variance sequence 106, wherein the target wheel speed value 107 is a corrected wheel speed value.

The computing device 101 may be hardware or software. When the computing device is hardware, it may be implemented as a distributed cluster composed of multiple servers or terminal devices, or may be implemented as a single server or a single terminal device. When the computing device is embodied as software, it may be installed in the hardware devices enumerated above. It may be implemented, for example, as multiple software or software modules to provide distributed services, or as a single software or software module. And is not particularly limited herein.

It should be understood that the number of computing devices in FIG. 1 is merely illustrative. There may be any number of computing devices, as implementation needs dictate.

With continued reference to FIG. 2, a flow 200 of some embodiments of a vehicle wheel speed correction method according to the present disclosure is shown. The flow 200 of the method for correcting the wheel speed of the vehicle comprises the following steps:

in step 201, a first wheel speed data set of a current vehicle is obtained.

In some embodiments, the executing entity (such as the computing device 101 shown in fig. 1) of the vehicle wheel speed correction method may acquire the first wheel speed data set of the current vehicle in a wired manner or a wireless manner. The first wheel speed data set may be wheel speed data within a preset time interval (e.g., 0.2 seconds). The first wheel speed data in the first wheel speed data set may include a first wheel speed signal. The first wheel speed signal may include, but is not limited to, at least one of: wheel speed values and time stamps. The wheel speed value may be used to characterize the speed of the wheel in the direction of vehicle travel.

In step 202, a preset static noise variance is adjusted based on the wheel speed signals included in each first wheel speed data in the first wheel speed data set to generate a dynamic noise variance.

In some embodiments, the executing body may adjust a preset static noise variance based on the wheel speed signal included in each of the first wheel speed data in the first wheel speed data set to generate a dynamic noise variance. The static noise variance can be used to characterize the noise variance when the vehicle moves at a constant speed on a standard road (e.g., dry, flat road), and can be used as an initial value for noise variance adjustment. The preset static noise variance may be adjusted to generate a dynamic noise variance by:

in a first step, a variance value of wheel speed values included in each first wheel speed data in the first wheel speed data set is determined.

And secondly, determining the ratio of the variance value to a variance threshold value as an adjustment coefficient of the static noise variance.

And thirdly, determining the product of the adjusting coefficient and the static noise variance as the dynamic noise variance.

And 203, adding the dynamic noise variance to a preset noise variance sequence to obtain an added noise variance sequence.

In some embodiments, the execution body may add the dynamic noise variance to a preset noise variance sequence to obtain an added noise variance sequence. Wherein, the above-mentioned post-addition noise variance sequence may correspond to a preset observation period, for example, 50 wheel speed data.

In some optional implementations of some embodiments, each noise variance in the noise variance sequence may correspond to the same interval period.

In practice, the observation period may be a time length of an observation sequence window. The observation sequence window may be used to acquire first wheel speed data. The observation sequence window is moved a distance (e.g., 10 wheel speed data) once per observation direction (e.g., each time stamp may correspond to one wheel speed data as time increases). Then, the first wheel speed data set may be wheel speed data corresponding to consecutive time stamps observed in the observation sequence window closest to the current time.

As an example, the observation sequence window may observe wheel speed data within 2 seconds, e.g., including 50 wheel speed data. Wherein the step size of the observation sequence window may be 10 wheel speed data within 0.4 seconds. For the 10 wheel speed data observed in each step, the corresponding noise variance is determined and added to the last bit in the noise variance sequence. Then, 5 noise variances may be included in the preset noise variance sequence. Since the observation sequence window is a fixed observation length, the first bit in the noise variance sequence can be removed after moving one step, so that the wheel speed data in the observation sequence window can be corresponded.

And step 204, generating a target wheel speed value based on the added noise variance sequence.

In some embodiments, the execution body may generate the target wheel speed value based on the post-addition noise variance sequence. Wherein, the target wheel speed value is a corrected wheel speed value. The target wheel speed value may be generated by:

firstly, determining the average value of each added noise variance in the added noise variance sequence as a target noise variance value.

And secondly, taking the target noise variance value as the uncertainty of wheel speed measurement, and using the uncertainty in the variance setting of the wheel speed sensor during multi-sensor fusion. Thus, a corrected wheel speed value can be obtained.

Optionally, the executing body may further send the target wheel speed value to a vehicle control terminal of the current vehicle to perform vehicle control. Wherein, the vehicle control terminal may include but is not limited to at least one of the following: vehicle dynamic control system, vehicle body electronic stability system and/or brake anti-lock system, etc. In particular, the information sent to the vehicle dynamics control system may be used to more accurately control the speed of the vehicle. The transmission to the body electronic stability system can be used to more accurately stabilize the body. The pressure sent to the brake anti-lock system can be used for automatically adjusting the pressure of the brake pump, and the condition that the individual wheels are locked to cause driving risks due to the fact that the system is triggered due to the fact that the wheel speed value is inaccurate is avoided. Thus, driving safety can be improved.

The above embodiments of the present disclosure have the following advantages: through the vehicle wheel speed correction method of some embodiments of this disclosure, can improve the accuracy of correcting to wheel speed, and then can improve driving safety. Specifically, the reason why the accuracy of the wheel speed correction is reduced is that: the noise signal of the wheel speed sensor has the characteristics of strong randomness and large influence by road surface unevenness, so that the fixed noise variance cannot be applied to all driving environments. Based on this, the vehicle wheel speed correction method according to some embodiments of the present disclosure generates the dynamic noise variance by adjusting the preset static noise variance, so that the dynamic noise variance may be applicable to all driving environments even when the noise signal has the influence of strong randomness, road unevenness, and the like. Therefore, the accuracy of the wheel speed correction of the vehicle can be improved, and the obtained target wheel speed value is more accurate. Further, driving safety can be improved.

Referring further to FIG. 3, a flow 300 of another embodiment of a method for correcting wheel speed of a vehicle is shown. The flow 300 of the method for correcting the wheel speed of the vehicle comprises the following steps:

in step 301, a first wheel speed data set of a current vehicle is obtained.

In some embodiments, the specific implementation manner and technical effects of step 301 may refer to step 201 in those embodiments corresponding to fig. 2, and are not described herein again.

Step 302, adjusting a preset static noise variance based on the wheel speed signals included in each first wheel speed data in the first wheel speed data set to generate a dynamic noise variance.

In some embodiments, an executing body (e.g., the computing device 101 shown in fig. 1) of the vehicle wheel speed correction method may decompose the wheel speed signal included in each of the first wheel speed data sets into a wavelet domain based on the wheel speed signal sampling frequency to generate at least one layer of decomposed wheel speed signals. Wherein the static noise variance may be generated by:

in a first step, a second wheel speed data set and a high precision data set are obtained. Wherein the second wheel speed data in the second wheel speed data set may include: the second wheel speed signal and the second wheel speed signal timestamp, and the high accuracy data in the high accuracy data set may include: high precision speed signals and speed signal time stamps. The second wheel speed data set may be wheel speed data acquired in advance, for a preset time period (e.g., for 20 seconds), under a preset condition (e.g., a flat, dry road surface), during a uniform motion of the vehicle (e.g., uniform acceleration, uniform deceleration, and/or uniform motion). The high-precision data set may be wheel speed data measured by a sensor with higher precision, which is additionally installed on the current vehicle, and at the same time as the preset time duration.

And a second step of inserting a second wheel speed signal included in each wheel speed data in the second wheel speed data set into the high-precision data set based on a second wheel speed signal time stamp included in the second wheel speed data set and a speed signal time stamp included in the high-precision data set to generate an interpolation data set. Wherein the frequency is different between the sensor acquiring the second wheel speed data set and the sensor acquiring the high accuracy data set. Therefore, the second wheel speed signal included in each wheel speed data in the above-described second wheel speed data set may be inserted into the high accuracy data set. In the insertion process, the insertion may be performed in order of magnitude of time points within the above-mentioned preset time period corresponding to the timestamps (for example, the time point corresponding to the second wheel speed data with the smallest timestamp is 0 second, and the time point corresponding to the second wheel speed data with the largest timestamp is 20 seconds). Therefore, the respective insertion data in the insertion data group may be arranged in order of magnitude of the time point.

And thirdly, performing polynomial fitting on each interpolation data in the interpolation data group to obtain a fitting polynomial. Wherein the argument of the fitting polynomial may be a time point within the above-mentioned preset time period.

And fourthly, adjusting the second wheel speed signal included in each second wheel speed data in the second wheel speed data set by using the fitting polynomial so as to generate an adjusted wheel speed signal set. First, time points corresponding to each second wheel speed data in the second wheel speed data set may be input to the fitting polynomial to obtain a fitting wheel speed signal set. Then, the second wheel speed signals included in each second wheel speed data in the second wheel speed data set may be replaced with corresponding fitted wheel speed signals in the fitted wheel speed signal set to generate an adjusted wheel speed signal set.

And fifthly, generating static noise variance based on the adjusted wheel speed signal group. Wherein, the variance value of each adjusted wheel speed signal in the set of adjusted wheel speed signals can be determined as the static noise variance.

In some optional implementations of some embodiments, the generating of the static noise variance based on the adjusted set of wheel speed signals may further include:

firstly, acquiring the wheel speed signal sampling frequency of the current vehicle. Wherein, the wheel speed signal sampling frequency may be the sampling frequency of a wheel speed sensor installed in the current vehicle. The wheel speed signal sampling frequency is used to determine the number of wavelet decomposition layers.

And secondly, generating a signal decomposition data set based on the adjusted wheel speed signal set. Wherein, first, polynomial decomposition may be performed on each adjusted wheel speed signal in the above adjusted wheel speed signal set by the following wheel speed signal decomposition formula to determine the highest degree of the polynomial:

。

wherein the content of the first and second substances,and expressing a wheel speed signal decomposition formula.Representing a noise signal.Representing the wheel speed signal (i.e., the valid wheel speed signal after the noise signal is removed).And representing the time point corresponding to the time stamp.Representing the polynomial coefficients.Indicating a serial number.The highest degree is indicated.

Then, a wavelet function with an vanishing distance greater than the above-mentioned highest degree in the preset wavelet function group may be determined as the target wavelet function. In practice, since the vehicle is in a constant motion state, a first-order polynomial is usually used to represent the wheel speed signal. Thus, the target wavelet function may be a DB2 wavelet function with an extinction distance of 2.

And thirdly, decomposing each adjusted wheel speed signal in the adjusted wheel speed signal group into a wavelet domain according to the wheel speed signal sampling frequency and the signal decomposition data group to generate at least one layer of decomposition speed signals. Wherein the number of decomposition layers can be first determined by the following formula:

。

wherein the content of the first and second substances,the number of decomposition layers is shown.Representing the wheel speed signal sampling frequency.Representing a preset maximum sampling frequency.

Then, wavelet decomposition may be performed on each adjusted wheel speed signal in the set of adjusted wheel speed signals by using the target wavelet function to obtain a wavelet domain of the number of decomposition layers. The wavelet domain may include at least one layer of decomposed speed signals, i.e., the wheel speed signals of the decomposed layers.

And fourthly, performing signal reconstruction on each layer of decomposed speed signals meeting the preset wheel speed noise condition in the at least one layer of decomposed speed signals to generate a first reconstructed signal group, so as to obtain a first reconstructed signal group set. Wherein the noise has a high frequency characteristic. Thus, the preset wheel speed noise condition may be the first and second layer decomposition speed signals. Therefore, the first layer decomposition velocity signal and the second layer decomposition velocity signal in the at least one layer decomposition velocity signal can be subjected to signal reconstruction, namely, the frequency of the normal signal is restored from the wavelet domain, and a first reconstruction signal set is obtained. Specifically, the first reconstruction signal set may include two first reconstruction signal sets, which respectively correspond to the first layer decomposition velocity signal and the second layer decomposition velocity signal after signal reconstruction.

And fifthly, performing signal superposition on the first reconstruction signals in each first reconstruction signal group in the first reconstruction signal group set to obtain a first superposed signal set. The superposition may be obtained by adding each first reconstructed signal in one first reconstructed signal group and each first reconstructed signal in another first reconstructed signal group according to a corresponding relationship of the timestamps.

And sixthly, determining the variance of each first superposed signal in the first superposed signal set as the static noise variance. Wherein the second wheel speed data set may be wheel speed data during uniform motion (e.g., uniform acceleration motion, uniform deceleration motion, and/or uniform motion) of the vehicle under a predetermined condition (e.g., flat, dry road surface) for a predetermined period of time (e.g., within 20 seconds). The high-precision data set may be wheel speed data measured by a sensor with higher precision, which is additionally installed on the current vehicle, and at the same time as the preset time duration. Therefore, the static noise variance can be used to characterize the noise variance when the vehicle is moving at a constant speed on a standard road surface (e.g., dry, flat road surface), and can be used as an initial value for noise variance adjustment. Thus, the above-described static noise variance can be more accurate than a static noise variance set empirically.

In some optional implementations of some embodiments, the executing body may adjust a preset static noise variance based on the wheel speed signal included in each first wheel speed data in the first wheel speed data set to generate a dynamic noise variance, and may include the following steps:

in a first step, based on the wheel speed signal sampling frequency, the wheel speed signals included in each first wheel speed data in the first wheel speed data set are decomposed into wavelet domain to generate at least one layer of decomposed wheel speed signals. Wherein corresponding wheel speed signal acquisition frequencies may be obtained for different vehicles or sensors.

And secondly, performing signal reconstruction on the decomposed wheel speed signals meeting the preset wheel speed noise condition in the at least one layer of decomposed wheel speed signals to generate reconstructed second reconstructed signals, so as to obtain a second reconstructed signal set.

And thirdly, performing signal superposition on each second reconstruction signal group in the second reconstruction signal group set to obtain a second superposed signal set. Each of the second superimposed signals in the second set of superimposed signals may be used to characterize a noise signal included in each of the first wheel speed data in the first wheel speed data set.

In some embodiments, the specific implementation manner of the first to third steps and the technical effects brought by the implementation manner of the first to third steps may refer to the generation manner of the static noise variance, and are not described herein again.

And fourthly, generating a dynamic noise variance based on the second superposed signal set and the static noise variance. Wherein the variance value of each second superimposed signal in the set of second superimposed signals may be determined as a dynamic noise variance. In addition, the calculation of the weighted average value can be performed by using a preset weight to generate the dynamic noise variance. For example, the preset weight may be 0.5.

And 303, adding the dynamic noise variance to a preset noise variance sequence to obtain an added noise variance sequence.

In some embodiments, the specific implementation manner and technical effects of step 303 may refer to step 203 in those embodiments corresponding to fig. 2, and are not described herein again.

And step 304, performing curve fitting on each added noise variance in the added noise variance sequence to obtain a wheel speed noise variance curve equation.

In some embodiments, the executing entity may perform curve fitting on each post-addition noise variance in the post-addition noise variance sequence to obtain a wheel speed noise variance curve equation. The wheel speed noise variance curve equation can be used to characterize the functional relationship between the wheel speed and the noise.

In step 305, the current wheel speed value of the current vehicle is input to a wheel speed noise variance curve equation to generate a current noise variance value.

In some embodiments, the executing entity may input a current wheel speed value of the current vehicle to the wheel speed noise variance curve to generate a current noise variance value. And step 306, generating a target wheel speed value by using the current noise variance value and the second superposed signal set.

In some embodiments, the executing entity may generate the target wheel speed value by using the current noise variance value and the second superimposed signal set. Wherein the target wheel speed value may be generated by:

in the first step, an average value of each second superimposed signal in the second superimposed signal set is determined as a noise signal.

And secondly, determining the difference value between the current wheel speed value and the noise signal as a target wheel speed value. Therefore, the influence of noise on the current wheel speed value can be reduced to a certain extent, and a more accurate wheel speed value, namely a target wheel speed value, is obtained.

Specifically, in the vehicle positioning process of multi-sensor fusion, the current noise variance value may be used as an uncertainty of wheel speed measurement in the variance setting of the wheel speed sensor in multi-sensor fusion to generate a target wheel speed value. For example, the current noise variance value may be used as a weight of the current wheel speed value, and the product of the current noise variance value and the current wheel speed value in the multi-sensor fusion is the target wheel speed value. Since the target wheel speed value is more accurate than the current wheel speed value, the vehicle displacement distance measured from the wheel speed value is also more accurate. Thus, the accuracy of vehicle positioning can be further improved. To improve driving safety.

As can be seen from fig. 3, compared with the description of some embodiments corresponding to fig. 2, the flow 300 of the wheel speed correction method in some embodiments corresponding to fig. 3 embodies the steps of generating the static noise variance and the dynamic noise variance. Firstly, compared with the static noise variance set according to experience, the static noise variance can be better used for representing the noise variance when the vehicle moves at a constant speed on a standard road surface (for example, a dry and flat road surface) and can be used as an initial value of the noise variance adjustment. Therefore, the accuracy of the wheel speed correction is improved. Then, the static noise variance is adjusted in real time to generate the dynamic noise variance, so that the dynamic noise variance can be suitable for the noise signal of the wheel speed sensor and has the characteristics of strong randomness and large influence by road surface unevenness, and the influence is reduced. Thus, the accuracy of the wheel speed correction can be further improved. Further, driving safety is improved.

With further reference to FIG. 4, as an implementation of the methods illustrated in the above figures, the present disclosure provides some embodiments of a vehicle wheel speed correction apparatus, which correspond to those of FIG. 2, and which may be particularly applicable to various electronic devices.

As shown in fig. 4, the wheel speed correction apparatus 400 of some embodiments includes: an acquisition unit 401, an adjustment unit 402, an addition unit 403, and a generation unit 404. The acquiring unit 401 is configured to acquire a first wheel speed data set of a current vehicle, where the first wheel speed data in the first wheel speed data set includes a first wheel speed signal; an adjusting unit 402 configured to adjust a preset static noise variance based on a wheel speed signal included in each first wheel speed data in the first wheel speed data set to generate a dynamic noise variance; an adding unit 403, configured to add the above dynamic noise variance to a preset noise variance sequence, resulting in an added noise variance sequence; a generating unit 404 configured to generate a target wheel speed value based on the post-addition noise variance sequence, wherein the target wheel speed value is a corrected wheel speed value.

It will be understood that the elements described in the apparatus 400 correspond to various steps in the method described with reference to fig. 2. Thus, the operations, features and resulting advantages described above with respect to the method are also applicable to the apparatus 400 and the units included therein, and will not be described herein again.

Referring now to FIG. 5, a block diagram of an electronic device (e.g., computing device 101 of FIG. 1) 500 suitable for use in implementing some embodiments of the present disclosure is shown. The electronic device shown in fig. 5 is only an example, and should not bring any limitation to the functions and the scope of use of the embodiments of the present disclosure.

As shown in fig. 5, electronic device 500 may include a processing means (e.g., central processing unit, graphics processor, etc.) 501 that may perform various appropriate actions and processes in accordance with a program stored in a Read Only Memory (ROM) 502 or a program loaded from a storage means 508 into a Random Access Memory (RAM) 503. In the RAM503, various programs and data necessary for the operation of the electronic apparatus 500 are also stored. The processing device 501, the ROM502, and the RAM503 are connected to each other through a bus 504. An input/output (I/O) interface 505 is also connected to bus 504.

Generally, the following devices may be connected to the I/O interface 505: input devices 506 including, for example, a touch screen, touch pad, keyboard, mouse, camera, microphone, accelerometer, gyroscope, etc.; output devices 507 including, for example, a Liquid Crystal Display (LCD), speakers, vibrators, and the like; storage devices 508 including, for example, magnetic tape, hard disk, etc.; and a communication device 509. The communication means 509 may allow the electronic device 500 to communicate with other devices wirelessly or by wire to exchange data. While fig. 5 illustrates an electronic device 500 having various means, it is to be understood that not all illustrated means are required to be implemented or provided. More or fewer devices may alternatively be implemented or provided. Each block shown in fig. 5 may represent one device or may represent multiple devices as desired.

In particular, according to some embodiments of the present disclosure, the processes described above with reference to the flow diagrams may be implemented as computer software programs. For example, some embodiments of the present disclosure include a computer program product comprising a computer program embodied on a computer readable medium, the computer program comprising program code for performing the method illustrated in the flow chart. In some such embodiments, the computer program may be downloaded and installed from a network via the communication means 509, or installed from the storage means 508, or installed from the ROM 502. The computer program, when executed by the processing device 501, performs the above-described functions defined in the methods of some embodiments of the present disclosure.

It should be noted that the computer readable medium described above in some embodiments of the present disclosure may be a computer readable signal medium or a computer readable storage medium or any combination of the two. A computer readable storage medium may be, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination of the foregoing. More specific examples of the computer readable storage medium may include, but are not limited to: an electrical connection having one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing. In some embodiments of the disclosure, a computer readable storage medium may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device. In some embodiments of the present disclosure, however, a computer readable signal medium may include a propagated data signal with computer readable program code embodied therein, for example, in baseband or as part of a carrier wave. Such a propagated data signal may take many forms, including, but not limited to, electro-magnetic, optical, or any suitable combination thereof. A computer readable signal medium may also be any computer readable medium that is not a computer readable storage medium and that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device. Program code embodied on a computer readable medium may be transmitted using any appropriate medium, including but not limited to: electrical wires, optical cables, RF (radio frequency), etc., or any suitable combination of the foregoing.

In some embodiments, the clients, servers may communicate using any currently known or future developed network Protocol, such as HTTP (HyperText Transfer Protocol), and may interconnect with any form or medium of digital data communication (e.g., a communications network). Examples of communication networks include a local area network ("LAN"), a wide area network ("WAN"), the Internet (e.g., the Internet), and peer-to-peer networks (e.g., ad hoc peer-to-peer networks), as well as any currently known or future developed network.

The computer readable medium may be embodied in the apparatus; or may exist separately without being assembled into the electronic device. The computer readable medium carries one or more programs which, when executed by the electronic device, cause the electronic device to: acquiring a first wheel speed data set of a current vehicle, wherein first wheel speed data in the first wheel speed data set comprises a first wheel speed signal; adjusting a preset static noise variance based on the wheel speed signals included in each first wheel speed data in the first wheel speed data group to generate a dynamic noise variance; adding the dynamic noise variance to a preset noise variance sequence to obtain an added noise variance sequence; and generating a target wheel speed value based on the added noise variance sequence, wherein the target wheel speed value is a corrected wheel speed value.

Computer program code for carrying out operations for embodiments of the present disclosure may be written in any combination of one or more programming languages, including an object oriented programming language such as Java, Smalltalk, C + +, and conventional procedural programming languages, such as the "C" programming language or similar programming languages. The program code may execute entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer through any type of network, including a Local Area Network (LAN) or a Wide Area Network (WAN), or the connection may be made to an external computer (for example, through the Internet using an Internet service provider).

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

The units described in some embodiments of the present disclosure may be implemented by software, and may also be implemented by hardware. The described units may also be provided in a processor, and may be described as: a processor includes an acquisition unit, an adjustment unit, an addition unit, and a generation unit. Where the names of these units do not in some cases constitute a limitation on the unit itself, for example, the acquiring unit may also be described as a "unit that acquires the first wheel speed data set".

The functions described herein above may be performed, at least in part, by one or more hardware logic components. For example, without limitation, exemplary types of hardware logic components that may be used include: field Programmable Gate Arrays (FPGAs), Application Specific Integrated Circuits (ASICs), Application Specific Standard Products (ASSPs), systems on a chip (SOCs), Complex Programmable Logic Devices (CPLDs), and the like.

The foregoing description is only exemplary of the preferred embodiments of the disclosure and is illustrative of the principles of the technology employed. It will be appreciated by those skilled in the art that the scope of the invention in the embodiments of the present disclosure is not limited to the specific combination of the above-mentioned features, but also encompasses other embodiments in which any combination of the above-mentioned features or their equivalents is made without departing from the inventive concept as defined above. For example, the above features and (but not limited to) technical features with similar functions disclosed in the embodiments of the present disclosure are mutually replaced to form the technical solution.