CN118144798A - Yaw rate self-learning correction method, device, vehicle and storage medium - Google Patents
Yaw rate self-learning correction method, device, vehicle and storage medium Download PDFInfo
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Abstract
The application relates to the technical field of auxiliary driving, in particular to a yaw rate self-learning correction method, a device, a vehicle and a storage medium. The method comprises the following steps: judging whether the vehicle meets the yaw rate deviation self-learning activation condition; if the vehicle meets the yaw rate deviation self-learning activation condition, acquiring the current yaw rate of the vehicle; and determining a current yaw rate deviation value according to the current yaw rate, and performing self-learning correction on the true yaw rate value according to the current yaw rate deviation value. Therefore, in the running process of the vehicle, the real-time self-learning correction is carried out on the yaw rate true value of the vehicle according to the yaw rate deviation value, the problem that the yaw rate data is filtered by using a traditional filter mode, so that the vehicle control system is not timely in response to emergency and the later maintenance period is long is solved, the requirement on human intervention is reduced, and the stability of the transverse auxiliary driving function is improved.
Description
Technical Field
The application relates to the technical field of auxiliary driving, in particular to a yaw rate self-learning correction method, a device, a vehicle and a storage medium.
Background
The application of assisted driving in daily life is becoming more and more popular, the accuracy of the sensor input data is critical to the performance of automatic driving performance, and in real applications, such as yaw rate sensors, may be affected by various factors, such as temperature, aging, electrical interference, production errors, etc., resulting in noise or offset in data reading, and if these inaccurate data are directly used for vehicle control, the driving safety and comfort may be affected.
In the related art, the yaw-rate sensor data is subjected to a filtering process using a filter to reduce noise or deviation.
However, the filter may cause that the system cannot respond to the real situation of sudden change in time, and real vehicle debugging is required to determine a proper filter coefficient, so that the maintenance period is too long, and the problem needs to be solved.
Disclosure of Invention
The application provides a self-learning correction method, a device, a vehicle and a storage medium of yaw rate, which are used for solving the problems that a traditional filter mode is used for filtering yaw rate data, so that a vehicle control system faces the problems of untimely response of emergency and long later maintenance period, reducing the requirement for human intervention and improving the stability of a transverse auxiliary driving function.
In order to achieve the above object, an embodiment of a first aspect of the present application provides a method for self-learning modification of yaw rate, including the steps of:
judging whether the vehicle meets the yaw rate deviation self-learning activation condition;
If the vehicle meets the yaw rate deviation self-learning activation condition, acquiring the current yaw rate of the vehicle;
And determining a current yaw rate deviation value according to the current yaw rate, and performing self-learning correction on the yaw rate true value according to the current yaw rate deviation value.
According to one embodiment of the present application, the determining whether the vehicle satisfies the yaw-rate offset self-learning activation condition includes:
Acquiring the current speed and the current steering wheel rotation speed of the vehicle;
judging whether the current yaw rate is smaller than a first preset threshold value, whether the current vehicle speed is smaller than a second preset threshold value and whether the current steering wheel turning rate is smaller than a third preset threshold value;
If the current yaw rate is smaller than the first preset threshold value, the current vehicle speed is smaller than the second preset threshold value, and whether the current steering wheel angular velocity is smaller than the third preset threshold value, judging that the vehicle meets the yaw rate deviation self-learning activation condition, otherwise, judging that the vehicle does not meet the yaw rate deviation self-learning activation condition.
According to one embodiment of the present application, the determining a current yaw-rate deviation value according to the current yaw-rate includes:
determining the current yaw rate deviation value according to the current yaw rate based on a preset self-learning algorithm, wherein the preset self-learning algorithm is as follows:
Learnt _offset_ YawRate =last Time (Learnt _offset_ YawRate) + [ yaw_rate-last Time (Learnt _offset_ YawRate) ]× YawComp _gain×sample_time;
Wherein Learnt _offset_ YawRate is the current yaw-rate deviation value, yaw_rate is the current yaw-rate, yawComp _gain is a filter coefficient, and sample_time is a step-size coefficient.
According to one embodiment of the present application, after determining whether the vehicle satisfies the yaw-rate offset self-learning activation condition, it further includes:
if the vehicle does not meet the yaw rate deviation self-learning activation condition, the yaw rate true value cannot be self-learning corrected.
According to an embodiment of the present application, before determining the current yaw-rate deviation value according to the current yaw-rate based on the preset self-learning algorithm, the method further includes:
Judging whether the true value of the current yaw rate meets a preset correction condition or not;
and if the current yaw rate true value does not meet the preset correction condition, carrying out self-learning correction on the yaw rate true value based on the preset self-learning algorithm, otherwise, not needing to carry out self-learning correction on the yaw rate true value.
According to the self-learning modification method of the yaw rate, when the vehicle meets the yaw rate deviation self-learning activation condition, the current yaw rate of the vehicle is obtained, the current yaw rate deviation value is determined according to the current yaw rate, and the self-learning modification is carried out on the true yaw rate value according to the current yaw rate deviation value. Therefore, in the running process of the vehicle, the real-time self-learning correction is carried out on the yaw rate true value of the vehicle according to the yaw rate deviation value, the problem that the yaw rate data is filtered by using a traditional filter mode, so that the vehicle control system is not timely in response to emergency and the later maintenance period is long is solved, the requirement on human intervention is reduced, and the stability of the transverse auxiliary driving function is improved.
To achieve the above object, a second aspect of the present application provides a self-learning correction device for yaw rate, including:
the judging module is used for judging whether the vehicle meets the yaw rate deviation self-learning activation condition or not;
An acquisition module configured to acquire a current yaw rate of the vehicle when the vehicle satisfies the yaw rate offset self-learning activation condition;
and the correction module is used for determining a current yaw rate deviation value according to the current yaw rate and carrying out self-learning correction on the yaw rate true value according to the current yaw rate deviation value.
According to an embodiment of the present application, the judging module is specifically configured to:
Acquiring the current speed and the current steering wheel rotation speed of the vehicle;
judging whether the current yaw rate is smaller than a first preset threshold value, whether the current vehicle speed is smaller than a second preset threshold value and whether the current steering wheel turning rate is smaller than a third preset threshold value;
If the current yaw rate is smaller than the first preset threshold value, the current vehicle speed is smaller than the second preset threshold value, and whether the current steering wheel angular velocity is smaller than the third preset threshold value, judging that the vehicle meets the yaw rate deviation self-learning activation condition, otherwise, judging that the vehicle does not meet the yaw rate deviation self-learning activation condition.
According to one embodiment of the present application, the correction module is specifically configured to:
determining the current yaw rate deviation value according to the current yaw rate based on a preset self-learning algorithm, wherein the preset self-learning algorithm is as follows:
Learnt _offset_ YawRate =last Time (Learnt _offset_ YawRate) + [ yaw_rate-last Time (Learnt _offset_ YawRate) ]× YawComp _gain×sample_time;
Wherein Learnt _offset_ YawRate is the current yaw-rate deviation value, yaw_rate is the current yaw-rate, yawComp _gain is a filter coefficient, and sample_time is a step-size coefficient.
According to an embodiment of the present application, after determining whether the vehicle satisfies the yaw-rate offset self-learning activation condition, the determination module is further configured to:
if the vehicle does not meet the yaw rate deviation self-learning activation condition, the yaw rate true value cannot be self-learning corrected.
According to one embodiment of the present application, before determining the current yaw-rate deviation value according to the current yaw-rate based on the preset self-learning algorithm, the correction module is further configured to:
Judging whether the true value of the current yaw rate meets a preset correction condition or not;
and if the current yaw rate true value does not meet the preset correction condition, carrying out self-learning correction on the yaw rate true value based on the preset self-learning algorithm, otherwise, not needing to carry out self-learning correction on the yaw rate true value.
According to the self-learning correction device for the yaw rate, when the vehicle meets the yaw rate deviation self-learning activation condition, the current yaw rate of the vehicle is obtained, the current yaw rate deviation value is determined according to the current yaw rate, and the yaw rate true value is subjected to self-learning correction according to the current yaw rate deviation value. Therefore, in the running process of the vehicle, the real-time self-learning correction is carried out on the yaw rate true value of the vehicle according to the yaw rate deviation value, the problem that the yaw rate data is filtered by using a traditional filter mode, so that the vehicle control system is not timely in response to emergency and the later maintenance period is long is solved, the requirement on human intervention is reduced, and the stability of the transverse auxiliary driving function is improved.
To achieve the above object, an embodiment of a third aspect of the present application provides a vehicle, including: the system comprises a memory, a processor and a computer program stored in the memory and capable of running on the processor, wherein the processor executes the program to realize the self-learning modification method of the yaw rate.
To achieve the above object, a fourth aspect of the present application provides a computer-readable storage medium having stored thereon a computer program for execution by a processor for implementing the self-learning modification method of yaw rate as described in the above embodiments.
Additional aspects and advantages of the application will be set forth in part in the description which follows and, in part, will be obvious from the description, or may be learned by practice of the application.
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The foregoing and/or additional aspects and advantages of the application will become apparent and readily appreciated from the following description of the embodiments, taken in conjunction with the accompanying drawings, in which:
fig. 1 is a flowchart of a method for self-learning modification of yaw rate according to an embodiment of the present application;
FIG. 2 is a schematic diagram of determining whether a yaw rate shift self-learning activation condition is satisfied according to one embodiment of the present application;
FIG. 3 is a schematic illustration of determining yaw rate bias values according to one embodiment of the application;
Fig. 4 is a block schematic diagram of a self-learning correction device for yaw rate according to an embodiment of the present application;
Fig. 5 is a schematic structural diagram of a vehicle according to an embodiment of the present application.
Detailed Description
Embodiments of the present application are described in detail below, examples of which are illustrated in the accompanying drawings, wherein like or similar reference numerals refer to like or similar elements or elements having like or similar functions throughout. The embodiments described below by referring to the drawings are illustrative and intended to explain the present application and should not be construed as limiting the application.
The self-learning modification method of the yaw rate, the device, the vehicle and the storage medium according to the embodiment of the present application will be described below with reference to the accompanying drawings, and first the self-learning modification method of the yaw rate according to the embodiment of the present application will be described with reference to the accompanying drawings.
Fig. 1 is a flowchart of a method of self-learning correction of yaw rate according to an embodiment of the present application.
As shown in fig. 1, the self-learning correction method of the yaw rate includes the steps of:
in step S101, it is determined whether the vehicle satisfies the yaw-rate offset self-learning activation condition.
It will be appreciated that when the vehicle activates the lane keeping function or the lateral deviation correcting function, a corresponding requested steering wheel angle can be calculated, and the requested steering wheel angle is calculated based on the yaw rate of the vehicle, so that in the case of an error in the yaw rate, the calculated requested steering wheel angle is inaccurate, thereby affecting the performance of automatic lateral driving control of the vehicle. Therefore, in order to improve the automatic driving transverse control experience of the vehicle and improve the driving safety of the vehicle, the embodiment of the application can correct the deviation of the yaw rate sensor data in real time by judging whether the vehicle meets the yaw rate deviation self-learning activation condition in real time and introducing the yaw rate deviation self-learning function in the running process of the vehicle.
For ease of understanding, how to determine whether the vehicle satisfies the yaw-rate offset self-learning activation condition is described in detail below.
As one possible implementation, in some embodiments, determining whether the vehicle satisfies the yaw-rate offset self-learning activation condition includes: acquiring the current speed and the current steering wheel rotation speed of the vehicle; judging whether the current yaw rate is smaller than a first preset threshold value, whether the current vehicle speed is smaller than a second preset threshold value and whether the current steering wheel rotation angle speed is smaller than a third preset threshold value; if the current yaw rate is smaller than the first preset threshold value, the current vehicle speed is smaller than the second preset threshold value, and whether the current steering wheel angular velocity is smaller than the third preset threshold value, judging that the vehicle meets the yaw rate deviation self-learning activation condition, otherwise, judging that the vehicle does not meet the yaw rate deviation self-learning activation condition.
The first preset threshold, the second preset threshold and the third preset threshold may be preset by a person skilled in the art, may be obtained through limited experiments, or may be obtained through limited computer simulation, which is not specifically described herein.
Specifically, the current speed of the vehicle and the current steering wheel angular velocity may be obtained by related technical means, for example, the current speed of the vehicle may be obtained by a wheel sensor, a vehicle-mounted speed meter, or the like, the current steering wheel angular velocity may be directly measured by the steering wheel angular velocity sensor, as shown in fig. 2, after the current speed of the vehicle and the current steering wheel angular velocity are obtained, whether the current yaw rate is smaller than a first preset threshold value and whether the current speed is smaller than a second preset threshold value and whether the current steering wheel angular velocity is smaller than a third preset threshold value may be determined, if the current yaw rate is smaller than the first preset threshold value (the self-learning correction is avoided during fast turning), and the current speed is smaller than the second preset threshold value (the vehicle is indicated to be in a static state or near static state), and the current steering wheel angular velocity is smaller than the third preset threshold value (the steering wheel is indicated to be in slow turning, the excessive turning may cause the yaw rate change of the vehicle), the yaw rate offset self-learning activation condition is determined to be satisfied if the yaw rate, otherwise, and the yaw rate offset self-learning activation condition is not satisfied if any value is not satisfied.
In step S102, if the vehicle satisfies the yaw rate deviation self-learning activation condition, the current yaw rate of the vehicle is acquired.
Specifically, after the vehicle satisfies the Yaw rate offset self-learning activation condition, the Yaw rate of the vehicle may be directly measured by a Yaw-rate sensor in the vehicle dynamics control system.
In step S103, a current yaw rate deviation value is determined according to the current yaw rate, and a self-learning correction is performed on the yaw rate true value according to the current yaw rate deviation value.
Specifically, after the vehicle meets the yaw rate deviation self-learning activation condition, the current yaw rate deviation value can be determined according to the current yaw rate, and the yaw rate true value is subjected to self-learning modification according to the current yaw rate deviation value, so that the vehicle is in a state that the theoretical yaw rate true value is 0, and the automatic modification of the yaw rate deviation is realized.
How the current yaw-rate deviation value is determined from the current yaw-rate is described in detail below.
As one possible implementation, in some embodiments, determining the current yaw-rate deviation value from the current yaw-rate includes: based on a preset self-learning algorithm, determining a current yaw rate deviation value according to the current yaw rate, wherein the preset self-learning algorithm is as follows:
Learnt _offset_ YawRate =last Time (Learnt _offset_ YawRate) + [ yaw_rate-last Time (Learnt _offset_ YawRate) ]× YawComp _gain×sample_time;
Wherein Learnt _offset_ YawRate is the current yaw-rate deviation value, yaw_rate is the current yaw-rate, yawComp _gain is the filter coefficient, and sample_time is the step-size coefficient.
Specifically, as shown in fig. 3, based on a preset self-learning algorithm, a current yaw rate deviation value may be calculated, and a self-learning correction may be performed on the yaw rate true value. The algorithm can be regarded as a discrete-time low-pass filter, wherein the filtering coefficient YawComp _gain determines the dependency of the new value of the yaw rate on the past value, and the smaller the coefficient is, the greater the dependency of the new value on the old value is, so that the retention of the low-frequency signal and the suppression of the high-frequency signal are realized, the effect of low-pass filtering is achieved, and the accuracy of compensation is ensured. The algorithm realizes the smoothing processing of the self-learning yaw rate offset value, thereby filtering out possible short-time sudden noise or errors and providing a more stable and accurate yaw rate offset reference value.
It will be appreciated that in order to ensure that the yaw rate can fluctuate within a safe range while ensuring the stability and reliability of the preset self-learning algorithm, the embodiment of the present application sets a limit interval for the current yaw rate deviation value, i.e., [ -3/180×pi,3/180×pi ]. After calculating the current yaw rate deviation value based on a preset self-learning algorithm, judging whether the current yaw rate deviation value is in a limiting interval, and if so, directly carrying out self-learning modification on the yaw rate true value according to the current yaw rate deviation value; if the current yaw rate deviation value is not in the limiting interval, further judging whether the current yaw rate deviation value is larger than the upper limit value 3/180×pi of the limiting interval or smaller than the lower limit value-3/180×pi of the limiting interval, if the current yaw rate deviation value is larger than the upper limit value of the limiting interval, taking the upper limit value 3/180×pi as a new current yaw rate deviation value to perform self-learning modification on the yaw rate true value, and if the current yaw rate deviation value is smaller than the lower limit value of the limiting interval, taking the lower limit value-3/180×pi as a new current yaw rate deviation value to perform self-learning modification on the yaw rate true value, so that abrupt change is avoided, and the output stability of control torque is ensured.
It should be noted that, when the difference between the current yaw rate and the yaw rate offset value learned from the last time is large, the filter can adjust the difference, but the adjustment range is limited due to the effect of the coefficient, so as to avoid excessive reaction to short-time abrupt errors or noise; when the difference between the current yaw rate and the last self-learned yaw rate offset value is small or almost no, the preset self-learning algorithm hardly makes too large adjustment on the current yaw rate offset value, thereby ensuring the stability of output.
Therefore, through algorithm design, the yaw rate data of the sensor can be automatically corrected to eliminate deviation caused by long-time use, environmental change or other external factors; through continuous learning and adjustment, the algorithm can ensure that the yaw rate input corresponding to the system in activating the transverse auxiliary driving function is accurate, thereby reducing the error in calculating the steering wheel request angle and improving the stability of the transverse auxiliary driving function; the preset self-learning algorithm reduces the need for external calibration or human intervention, thereby reducing maintenance costs and the occurrence of human error.
Further, in some embodiments, after determining whether the vehicle satisfies the yaw-rate offset self-learning activation condition, further comprising: if the vehicle does not satisfy the yaw rate deviation self-learning activation condition, the yaw rate true value cannot be self-learning corrected.
That is, if the vehicle does not satisfy the yaw rate deviation self-learning activation condition, that is, the current yaw rate is greater than or equal to the first preset threshold, or the current vehicle speed is greater than or equal to the second preset threshold, or the current steering wheel rotational speed is greater than or equal to the third preset threshold, the yaw rate true value cannot be self-learned.
Further, in some embodiments, before determining the current yaw-rate deviation value according to the current yaw-rate based on the preset self-learning algorithm, the method further includes: judging whether the true value of the current yaw rate meets a preset correction condition or not; if the current yaw rate true value meets the preset correction condition, carrying out self-learning correction on the yaw rate true value based on a preset self-learning algorithm, otherwise, not needing to carry out self-learning correction on the yaw rate true value.
It is understood that when the current yaw rate is less than the first preset threshold, the current vehicle speed is less than the second preset threshold, and the current steering wheel rotation angle speed is less than the third preset threshold, it indicates that the yaw rate true value of the vehicle should be 0 in theory, so before determining the current yaw rate deviation value according to the current yaw rate based on the preset self-learning algorithm, it may be determined whether the current yaw rate true value satisfies the preset modification condition, if the current yaw rate true value satisfies the preset modification condition (i.e., the current yaw rate true value is not 0), it may perform the self-learning modification on the yaw rate true value based on the preset self-learning algorithm, and if the current yaw rate true value does not satisfy the preset modification condition (i.e., the current yaw rate true value is 0), it may not be performed the self-learning modification on the yaw rate true value.
According to the self-learning modification method of the yaw rate, when the vehicle meets the yaw rate deviation self-learning activation condition, the current yaw rate of the vehicle is obtained, the current yaw rate deviation value is determined according to the current yaw rate, and the self-learning modification is carried out on the true yaw rate value according to the current yaw rate deviation value. Therefore, in the running process of the vehicle, the real-time self-learning correction is carried out on the yaw rate true value of the vehicle according to the yaw rate deviation value, the problem that the yaw rate data is filtered by using a traditional filter mode, so that the vehicle control system is not timely in response to emergency and the later maintenance period is long is solved, the requirement on human intervention is reduced, and the stability of the transverse auxiliary driving function is improved.
Next, a self-learning correction device for yaw rate according to an embodiment of the present application will be described with reference to the accompanying drawings.
Fig. 4 is a block diagram schematically showing a self-learning correction device for yaw rate according to an embodiment of the present application.
As shown in fig. 4, the self-learning correction device 10 for yaw rate includes: a judging module 100, an acquiring module 200 and a correcting module 300.
Wherein, the judging module 100 is configured to judge whether the vehicle meets the yaw rate deviation self-learning activation condition;
an obtaining module 200, configured to obtain a current yaw rate of the vehicle when the vehicle meets a yaw rate offset self-learning activation condition;
The correction module 300 is configured to determine a current yaw rate deviation value according to the current yaw rate, and perform self-learning correction on the yaw rate true value according to the current yaw rate deviation value.
According to one embodiment of the present application, the determining module 100 is specifically configured to:
acquiring the current speed and the current steering wheel rotation speed of the vehicle;
judging whether the current yaw rate is smaller than a first preset threshold value, whether the current vehicle speed is smaller than a second preset threshold value and whether the current steering wheel rotation angle speed is smaller than a third preset threshold value;
If the current yaw rate is smaller than the first preset threshold value, the current vehicle speed is smaller than the second preset threshold value, and whether the current steering wheel angular velocity is smaller than the third preset threshold value, judging that the vehicle meets the yaw rate deviation self-learning activation condition, otherwise, judging that the vehicle does not meet the yaw rate deviation self-learning activation condition.
According to one embodiment of the application, the correction module 300 is specifically configured to:
based on a preset self-learning algorithm, determining a current yaw rate deviation value according to the current yaw rate, wherein the preset self-learning algorithm is as follows:
Learnt _offset_ YawRate =last Time (Learnt _offset_ YawRate) + [ yaw_rate-last Time (Learnt _offset_ YawRate) ]× YawComp _gain×sample_time;
Wherein Learnt _offset_ YawRate is the current yaw-rate deviation value, yaw_rate is the current yaw-rate, yawComp _gain is the filter coefficient, and sample_time is the step-size coefficient.
According to one embodiment of the present application, after determining whether the vehicle satisfies the yaw-rate offset self-learning activation condition, the determination module 100 is further configured to:
if the vehicle does not satisfy the yaw rate deviation self-learning activation condition, the yaw rate true value cannot be self-learning corrected.
According to one embodiment of the present application, before determining the current yaw-rate deviation value according to the current yaw-rate based on the preset self-learning algorithm, the modification module 300 is further configured to:
judging whether the true value of the current yaw rate meets a preset correction condition or not;
If the current yaw rate true value does not meet the preset correction condition, carrying out self-learning correction on the yaw rate true value based on a preset self-learning algorithm, otherwise, not needing to carry out self-learning correction on the yaw rate true value.
It should be noted that the explanation of the foregoing embodiment of the self-learning modification method for yaw rate is also applicable to the self-learning modification device for yaw rate of this embodiment, and will not be repeated here.
According to the self-learning correction device for the yaw rate, when the vehicle meets the yaw rate deviation self-learning activation condition, the current yaw rate of the vehicle is obtained, the current yaw rate deviation value is determined according to the current yaw rate, and the yaw rate true value is subjected to self-learning correction according to the current yaw rate deviation value. Therefore, in the running process of the vehicle, the real-time self-learning correction is carried out on the yaw rate true value of the vehicle according to the yaw rate deviation value, the problem that the yaw rate data is filtered by using a traditional filter mode, so that the vehicle control system is not timely in response to emergency and the later maintenance period is long is solved, the requirement on human intervention is reduced, and the stability of the transverse auxiliary driving function is improved.
Fig. 5 is a schematic structural diagram of a vehicle according to an embodiment of the present invention. The vehicle may include:
Memory 501, processor 502, and a computer program stored on memory 501 and executable on processor 502.
The processor 502 implements the self-learning correction method of the yaw rate provided in the above-described embodiment when executing the program.
Further, the vehicle further includes:
a communication interface 503 for communication between the memory 501 and the processor 502.
Memory 501 for storing a computer program executable on processor 502.
The memory 501 may include high-speed RAM (Random Access Memory ) memory, and may also include non-volatile memory, such as at least one disk memory.
If the memory 501, the processor 502, and the communication interface 503 are implemented independently, the communication interface 503, the memory 501, and the processor 502 may be connected to each other via a bus and perform communication with each other. The bus may be an ISA (Industry Standard Architecture ) bus, a PCI (PERIPHERAL COMPONENT INTERCONNECT, external device interconnect) bus, or EISA (Extended Industry Standard Architecture ) bus, among others. The buses may be divided into address buses, data buses, control buses, etc. For ease of illustration, only one thick line is shown in fig. 5, but not only one bus or one type of bus.
Alternatively, in a specific implementation, if the memory 501, the processor 502, and the communication interface 503 are integrated on a chip, the memory 501, the processor 502, and the communication interface 503 may perform communication with each other through internal interfaces.
The processor 502 may be a CPU (Central Processing Unit ) or an ASIC (Application SPECIFIC INTEGRATED Circuit, application specific integrated Circuit) or one or more integrated circuits configured to implement embodiments of the present invention.
The embodiment of the present invention also provides a computer-readable storage medium having stored thereon a computer program which, when executed by a processor, implements the self-learning correction method of yaw rate as above.
Furthermore, the terms "first," "second," and the like, are used for descriptive purposes only and are not to be construed as indicating or implying a relative importance or implicitly indicating the number of technical features indicated. Thus, a feature defining "a first" or "a second" may explicitly or implicitly include at least one such feature. In the description of the present application, the meaning of "plurality" means at least two, for example, two, three, etc., unless specifically defined otherwise.
In the description of the present specification, a description referring to terms "one embodiment," "some embodiments," "examples," "specific examples," or "some examples," etc., means that a particular feature, structure, material, or characteristic described in connection with the embodiment or example is included in at least one embodiment or example of the present application. In this specification, schematic representations of the above terms are not necessarily directed to the same embodiment or example. Furthermore, the particular features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples. Furthermore, the different embodiments or examples described in this specification and the features of the different embodiments or examples may be combined and combined by those skilled in the art without contradiction.
While embodiments of the present application have been shown and described above, it will be understood that the above embodiments are illustrative and not to be construed as limiting the application, and that variations, modifications, alternatives and variations may be made to the above embodiments by one of ordinary skill in the art within the scope of the application.
Claims (10)
1. The self-learning correction method of the yaw rate is characterized by comprising the following steps of:
judging whether the vehicle meets the yaw rate deviation self-learning activation condition;
If the vehicle meets the yaw rate deviation self-learning activation condition, acquiring the current yaw rate of the vehicle;
And determining a current yaw rate deviation value according to the current yaw rate, and performing self-learning correction on the yaw rate true value according to the current yaw rate deviation value.
2. The method of claim 1, wherein the determining whether the vehicle satisfies a yaw-rate offset self-learning activation condition comprises:
Acquiring the current speed and the current steering wheel rotation speed of the vehicle;
judging whether the current yaw rate is smaller than a first preset threshold value, whether the current vehicle speed is smaller than a second preset threshold value and whether the current steering wheel turning rate is smaller than a third preset threshold value;
If the current yaw rate is smaller than the first preset threshold value, the current vehicle speed is smaller than the second preset threshold value, and whether the current steering wheel angular velocity is smaller than the third preset threshold value, judging that the vehicle meets the yaw rate deviation self-learning activation condition, otherwise, judging that the vehicle does not meet the yaw rate deviation self-learning activation condition.
3. The method of claim 1, wherein said determining a current yaw-rate deviation value from said current yaw-rate comprises:
determining the current yaw rate deviation value according to the current yaw rate based on a preset self-learning algorithm, wherein the preset self-learning algorithm is as follows:
Learnt _offset_ YawRate =last Time (Learnt _offset_ YawRate) + [ yaw_rate-last Time (Learnt _offset_ YawRate) ]× YawComp _gain×sample_time;
Wherein Learnt _offset_ YawRate is the current yaw-rate deviation value, yaw_rate is the current yaw-rate, yawComp _gain is a filter coefficient, and sample_time is a step-size coefficient.
4. The method according to claim 1 or 2, characterized by further comprising, after determining whether the vehicle satisfies the yaw-rate offset self-learning activation condition:
if the vehicle does not meet the yaw rate deviation self-learning activation condition, the yaw rate true value cannot be self-learning corrected.
5. The method of claim 3, further comprising, prior to determining the current yaw-rate deviation value from the current yaw-rate based on the preset self-learning algorithm:
Judging whether the true value of the current yaw rate meets a preset correction condition or not;
and if the current yaw rate true value does not meet the preset correction condition, carrying out self-learning correction on the yaw rate true value based on the preset self-learning algorithm, otherwise, not needing to carry out self-learning correction on the yaw rate true value.
6. A self-learning correction device for yaw rate, comprising:
the judging module is used for judging whether the vehicle meets the yaw rate deviation self-learning activation condition or not;
An acquisition module configured to acquire a current yaw rate of the vehicle when the vehicle satisfies the yaw rate offset self-learning activation condition;
and the correction module is used for determining a current yaw rate deviation value according to the current yaw rate and carrying out self-learning correction on the yaw rate true value according to the current yaw rate deviation value.
7. The apparatus of claim 6, wherein the determining module is specifically configured to:
Acquiring the current speed and the current steering wheel rotation speed of the vehicle;
judging whether the current yaw rate is smaller than a first preset threshold value, whether the current vehicle speed is smaller than a second preset threshold value and whether the current steering wheel turning rate is smaller than a third preset threshold value;
If the current yaw rate is smaller than the first preset threshold value, the current vehicle speed is smaller than the second preset threshold value, and whether the current steering wheel angular velocity is smaller than the third preset threshold value, judging that the vehicle meets the yaw rate deviation self-learning activation condition, otherwise, judging that the vehicle does not meet the yaw rate deviation self-learning activation condition.
8. The apparatus of claim 6, wherein the correction module is specifically configured to:
determining the current yaw rate deviation value according to the current yaw rate based on a preset self-learning algorithm, wherein the preset self-learning algorithm is as follows:
Learnt _offset_ YawRate =last Time (Learnt _offset_ YawRate) + [ yaw_rate-last Time (Learnt _offset_ YawRate) ]× YawComp _gain×sample_time;
Wherein Learnt _offset_ YawRate is the current yaw-rate deviation value, yaw_rate is the current yaw-rate, yawComp _gain is a filter coefficient, and sample_time is a step-size coefficient.
9. A vehicle, characterized by comprising: a memory, a processor and a computer program stored on the memory and executable on the processor, the processor executing the program to implement the method of self-learning modification of yaw rate as claimed in any one of claims 1 to 5.
10. A computer storage medium having stored thereon a computer program, characterized in that the program is executed by a processor for realizing the self-learning modification method of yaw rate according to any one of claims 1 to 5.
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| CN202410337777.0A CN118144798A (en) | 2024-03-22 | 2024-03-22 | Yaw rate self-learning correction method, device, vehicle and storage medium |
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