CN116968752B - Method, device, system and storage medium for determining yaw rate - Google Patents
Method, device, system and storage medium for determining yaw rate Download PDFInfo
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B60—VEHICLES IN GENERAL
- B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
- B60W40/00—Estimation or calculation of non-directly measurable driving parameters for road vehicle drive control systems not related to the control of a particular sub unit, e.g. by using mathematical models
- B60W40/10—Estimation or calculation of non-directly measurable driving parameters for road vehicle drive control systems not related to the control of a particular sub unit, e.g. by using mathematical models related to vehicle motion
Abstract
The present application relates to the field of vehicle control, and more particularly to a method for determining yaw rate, an apparatus for determining yaw rate, a system for determining yaw rate, and a computer-readable storage medium. One aspect of the present application provides a method for determining a yaw rate, the method comprising the steps of: acquiring a measured value of a yaw rate of the vehicle and selectively performing a correction operation on the measured value of the yaw rate to generate a correction value of the yaw rate; acquiring a predicted value of yaw rate and generating a first fusion result based on temporal fusion of the predicted value of yaw rate and a correction value of yaw rate; and determining the yaw rate according to a second fusion result generated by frequency domain fusion of the first fusion result and the correction value of the yaw rate.
Description
Technical Field
The present application relates to the field of vehicle control, and more particularly to a method for determining yaw rate, an apparatus for determining yaw rate, a system for determining yaw rate, and a computer-readable storage medium.
Background
Yaw rate is a parameter describing the angular velocity of the vehicle rotating about a vertical axis in the vehicle body coordinate system, which is an important index for evaluating the operation stability, active safety, and riding comfort of the vehicle when making a lateral motion. For example, in advanced assisted driving systems, vehicle yaw rate may be input as a basic attitude parameter to enable functions such as environmental awareness, vehicle positioning, host and peripheral vehicle trajectory prediction, behavior decision, motion planning, and motion control. The yaw rate is used as a basis for realizing a plurality of functions (such as automatic parking, automatic emergency braking, front collision early warning, lane departure early warning, and the like) in advanced auxiliary driving and unmanned driving, and the accuracy thereof can greatly influence the safety of the corresponding functions. Therefore, it is desirable to obtain a yaw rate with high accuracy.
At present, the yaw rate of the vehicle can be generally obtained through a sensor, but the high-precision sensor has higher cost, and the low-cost sensor has higher observation noise of the yaw rate due to lower precision, and has the problems of calibration error, observation time delay and the like.
Disclosure of Invention
To solve or at least alleviate one or more of the above problems, the following solutions are provided.
According to a first aspect of the present application, there is provided a method for determining a yaw rate, the method comprising the steps of: acquiring a measured value of a yaw rate of the vehicle and selectively performing a correction operation on the measured value of the yaw rate to generate a correction value of the yaw rate; acquiring a predicted value of yaw rate and generating a first fusion result based on temporal fusion of the predicted value of yaw rate and a correction value of yaw rate; and determining the yaw rate according to a second fusion result generated by frequency domain fusion of the first fusion result and the correction value of the yaw rate.
The method for determining a yaw rate according to an embodiment of the present application, wherein performing a correction operation on the measured value of the yaw rate to generate a correction value of the yaw rate includes: and removing a zero drift error in the yaw rate measurement to generate a correction value for the yaw rate.
The method for determining yaw rate according to an embodiment of the present application or any of the above embodiments, wherein selectively performing a correction operation on the measured value of yaw rate to generate a correction value of yaw rate includes: acquiring an updated measurement error value in response to the current vehicle speed being less than a vehicle speed threshold; and performing a correction operation on the measured value of the yaw rate using the updated measurement error value to generate a correction value of the yaw rate in response to the current vehicle speed being equal to or greater than the vehicle speed threshold.
The method for determining yaw rate according to an embodiment of the present application or any of the above embodiments, wherein obtaining an updated measurement error value in response to the current vehicle speed being less than a vehicle speed threshold comprises: acquiring a measurement of yaw rate in response to the current vehicle speed being less than a vehicle speed threshold; and taking a weighted average result of the yaw rate measured value and the historical measurement error value as the updated measurement error value.
The method for determining a yaw rate according to an embodiment of the present application or any one of the above embodiments, wherein performing a correction operation on the measured value of the yaw rate with the updated measurement error value to generate a correction value of the yaw rate in response to the current vehicle speed being equal to or greater than the vehicle speed threshold value includes: performing a correction operation on the measured value of the yaw rate by subtracting the updated measurement error value from the measured value of the yaw rate in response to the current vehicle speed being equal to or greater than the vehicle speed threshold; and taking the difference between the measured value of the yaw rate and the updated measured error value as a correction value of the yaw rate.
The method for determining a yaw rate according to an embodiment of the present application or any of the above embodiments, wherein obtaining the predicted value of the yaw rate includes: at least one or more of a front wheel rotation angle value, a rear wheel rotation angle value, a side slope gravity component value and a side wind resistance value are input into a linear prediction model to obtain a predicted value of the yaw rate.
The method for determining yaw rate according to an embodiment of the present application or any of the above embodiments, wherein generating a first fusion result based on a temporal fusion of the predicted value of yaw rate and the correction value of yaw rate comprises: and performing a weighted average operation on the estimated value and the observed value in a time domain by using a Kalman filtering algorithm to generate the first fusion result, wherein the estimated value of the yaw rate is used as an estimated value and the corrected value of the yaw rate is used as an observed value.
The method for determining a yaw rate according to an embodiment of the present application or any of the above embodiments, wherein determining the yaw rate from a second fusion result generated by frequency domain fusion of the first fusion result and a correction value of the yaw rate comprises: performing a complementary filtering operation on the first fusion result and the correction value of the yaw rate to generate the second fusion result; and taking the second fusion result as the yaw rate.
The method for determining yaw rate according to an embodiment of the present application or any one of the above embodiments, wherein performing a complementary filtering operation on the first fusion result and a correction value of the yaw rate to generate the second fusion result comprises: processing the first fusion result with a first filter to generate a first filtered result; processing the correction value of the yaw rate with a second filter to generate a second filtered result; and combining the first filtering result and the second filtering result to generate the second fusion result.
The method for determining yaw rate according to an embodiment of the present application or any of the above embodiments, wherein a sum of a frequency response of the first filter and a frequency response of the second filter is one.
According to a second aspect of the present application, there is provided a system for determining yaw rate, the system comprising: a memory; a processor coupled to the memory; and a computer program stored on the memory and running on the processor, the running of the computer program causing the steps of the method for determining yaw rate according to the first aspect of the present application to be performed.
According to a third aspect of the present application, there is provided a computer storage medium comprising instructions which, when executed, perform the steps of the method for determining yaw rate according to the first aspect of the present application.
The scheme for determining the yaw rate according to one or more embodiments of the present application can reduce or eliminate the observation error through the correction operation on the measured value of the yaw rate, obtain the first fusion result through the time domain fusion of the predicted value of the yaw rate and the correction value of the yaw rate to provide the observation time delay compensation of the yaw rate under the transient condition, and improve the accuracy of the yaw rate under the steady condition through the frequency domain fusion of the first fusion result and the correction value of the yaw rate, thereby improving the accuracy and the response speed of the transient response and reducing the error and the offset of the steady response. Thus, the approach for determining yaw rate according to one or more embodiments of the present application enables a high accuracy yaw rate to be obtained at low cost, thereby providing a robust and reliable input for vehicle dynamics control.
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The foregoing and/or other aspects and advantages of the present application will become more apparent and more readily appreciated from the following description of the various aspects taken in conjunction with the accompanying drawings in which like or similar elements are designated with the same reference numerals. The drawings include:
FIG. 1 illustrates a flow diagram of a method for determining yaw rate in accordance with one or more embodiments of the present application.
FIG. 2 illustrates a flow diagram of a method for selectively correcting a measure of yaw rate in accordance with one or more embodiments of the present application.
Fig. 3 illustrates a schematic block diagram of frequency domain fusion in accordance with one or more embodiments of the present application.
Fig. 4 shows a block diagram of an apparatus for determining yaw rate in accordance with one or more embodiments of the present application.
FIG. 5 illustrates a block diagram of a system for determining yaw rate in accordance with one or more embodiments of the present application.
Detailed Description
The present application is described more fully hereinafter with reference to the accompanying drawings, in which exemplary embodiments of the application are shown. This application may, however, be embodied in different forms and should not be construed as limited to the embodiments set forth herein. The above-described embodiments are provided to fully complete the disclosure herein so as to more fully convey the scope of the application to those skilled in the art.
In this specification, terms such as "comprising" and "including" mean that there are other elements and steps not directly or explicitly recited in the description and claims, nor do the subject matter of the present application exclude the presence of other elements and steps.
Unless specifically stated otherwise, terms such as "first" and "second" do not denote a sequential order of elements in terms of time, space, size, etc., but rather are merely used to distinguish one element from another.
Hereinafter, various exemplary embodiments according to the present application will be described in detail with reference to the accompanying drawings.
FIG. 1 illustrates a flow diagram of a method for determining yaw rate in accordance with one or more embodiments of the present application.
As shown in fig. 1, in step S101, a measured value of the yaw rate of the vehicle is acquired and a correction operation is selectively performed on the measured value of the yaw rate to generate a correction value of the yaw rate.
Alternatively, in step S101, a measured value of the yaw rate of the vehicle may be acquired using an inertial measurement unit (Inertial Measurement Unit, IMU). For example, a tuning fork gyroscope, a laser gyroscope, a fiber optic gyroscope, a liquid-float gyroscope, or the like may be disposed near the vehicle centroid position for obtaining a measurement of the yaw rate of the vehicle.
Alternatively, in step S101, after the measured value of the yaw rate is acquired, a correction operation may be selectively performed on the measured value of the yaw rate to generate a correction value of the yaw rate. Alternatively, the correction operation may include removing a zero drift error in the measured value of the yaw rate. Optionally, the correction operation may also include digital filtering processes such as piecewise averaging, spiking, denoising, and the like. It should be noted that, when the vehicle is just started, the vehicle is still in a stationary state or is close to a stationary state, and the measured value of the yaw rate acquired by the inertial measurement unit should be theoretically 0 at this time, whereas the measured value of the yaw rate acquired at this time in actual operation is usually not 0, which is a zero drift, and thus a zero drift error will be introduced into the measured value.
Alternatively, in step S101, a correction operation may be selectively performed on the obtained measured value of the yaw rate based on a comparison of the current vehicle speed and the vehicle speed threshold value to generate a correction value of the yaw rate. Optionally, when the current vehicle speed is less than the vehicle speed threshold, an updated measurement error value may be obtained at least according to the measurement value of the current yaw rate; when the current vehicle speed is equal to or greater than the vehicle speed threshold value, a correction operation may be performed on the measured value of the current yaw rate using the updated measurement error value to generate a correction value of the yaw rate. For example, the vehicle speed threshold may be set to be around 1 km/h.
In one embodiment, when the current vehicle speed is less than the vehicle speed threshold, a measurement of the current yaw rate may be obtained and a weighted average of the measurement of the current yaw rate and the historical measurement error value may be stored as an updated measurement error value. For example, the updated measurement error value at the previous time may be selected as the history measurement error value, or the average of the updated measurement error values at the previous times may be selected as the history measurement error value. For example, a weighted average operation may be performed on the current yaw rate measurement value and the historical measurement error value using a low pass filter to obtain an updated measurement error value. In performing the weighted average operation, a weight a may be assigned to the measured value of the current yaw rate and a weight b may be assigned to the historical measurement error value to perform the weighted average operation on both to obtain an updated measurement error value. As an example, the weight a may be set to 0.01 and the weight b may be set to 0.99.
In one embodiment, when the current vehicle speed is equal to or greater than the vehicle speed threshold value, a measured value of the current yaw rate and a stored updated measurement error value may be acquired, and a correction operation is performed on the measured value of the current yaw rate by subtracting the updated measurement error value from the measured value of the current yaw rate to take a difference between the measured value of the current yaw rate and the updated measurement error value as the correction value of the yaw rate.
Methods for selectively correcting the measured value of the yaw rate according to one or more embodiments of the present application will be further described below in conjunction with fig. 2.
In step S103, a predicted value of the yaw rate is acquired and a first fusion result is generated based on time-domain fusion of the predicted value of the yaw rate and the correction value of the yaw rate.
Alternatively, in step S103, at least one or more of the front wheel rotation angle value, the rear wheel rotation angle value, the side slope gravity component value, and the side wind resistance value may be input to the linear prediction model to obtain a predicted value of the yaw rate.
In one embodiment, the front wheel steering angle value may be determined according to the steering wheel angle and the vehicle speed, and the rear wheel steering angle value may be determined according to a relationship between the front wheel steering angle value and the rear wheel steering angle value. For example, the steering wheel angle may be obtained by measuring and calculating the steering wheel rotation angle using a steering wheel angle sensor when the driver rotates the steering wheel, or may be obtained by a steering wheel signal input from an intelligent driving system. For example, the vehicle speed may be obtained by measuring and calculating the rotational speed of the tire by a vehicle speed sensor. In one embodiment, the front wheel steering angle value may be measured using a front wheel steering angle sensor, and the rear wheel steering angle value may be determined based on a relationship between the front wheel steering angle value and the rear wheel steering angle value. When the vehicle is on a slope, the gravity of the vehicle can be decomposed into two components, namely, a gravity component along the slope surface and a gravity component perpendicular to the slope, and the side slope gravity component value can be understood as the gravity component along the slope surface.
In one embodiment, the front wheel steering angle value, the rear wheel steering angle value, and the side slope gravity component value may be input to a linear prediction model to obtain a predicted value of the yaw rate. Alternatively, the linear prediction model may be constructed by the following equation (1) and equation (2):
=/>+/>formula (1)
A c =
B c =
=/>+/>Formula (2)
In the case of the formula (1),representing the vehicle centroid slip angle->Representing predicted yaw rate, +.>Indicating the cornering altitude of the front wheel, +.>Indicating the cornering height of the rear wheel +.>Indicating the mass of the whole car>Longitudinal speed representing the centre of mass of the vehicle->Represents the distance of the rear axis to the centroid, +.>Representing the distance of the front axis to the centroid, +.>Representing the moment of inertia of the whole vehicle about the z-axis through the centroid, < >>Indicating the front wheel angle->Indicating the angle of the rear wheel->Representing the weight of the side slope and the matrixA c AndB c the first matrix of the two terms on the right side of the equal sign in equation (1) is defined separately. The equation (1) is a linear prediction model in continuous time, and the equation (2) is a linear prediction model in discrete time. In formula (2), ∈>Represents the centroid slip angle of the vehicle at the current moment,predictive value representing the yaw rate at the present moment,/->Representing the vehicle centroid slip angle at the last moment, < >>Representing the final fusion result of the yaw rate at the previous moment,/->Indicating the front wheel angle at the last moment, < >>Indicating the rear wheel angle at the last moment +.>Representing the weight component of the side slope at the last moment, < + >>And->Respectively as a matrixA c AndB c is a time-discrete representation of (a).
By the above formula (2), the front wheel steering angle at least based on the previous time can be obtainedRear wheel steering angle->And side slope gravity component->To obtain the predictive value +.>. The predicted value of the yaw rate is obtained based on the front wheel rotation angle value and the rear wheel rotation angle value, so that the response phase of the yaw rate can be advanced under the transient working condition, the observation time delay of the yaw rate is eliminated or lightened, and the accurate and rapid transient response is realized.
Alternatively, in step S103, after the predicted value of the yaw rate is acquired, a weighted average operation may be performed on the estimated value and the observed value in the time domain using a kalman filter algorithm with the predicted value of the yaw rate as an estimated value and the corrected value of the yaw rate generated in step S101 as an observed value to generate the first fusion result. In one embodiment, a predicted value of the yaw rate at the current time may be taken as an estimated value and a correction value of the yaw rate at the previous time may be taken as an observed value, and a weighted average operation may be performed on the estimated value and the observed value in the time domain using a kalman filter algorithm to generate an optimal estimate of the yaw rate at the current time as a first fusion result.
In step S105, the yaw rate is determined from the second fusion result generated by frequency-domain fusion of the first fusion result, which can be obtained with reference to step S103 described above, and the correction value of the yaw rate, which can be obtained with reference to step S101 described above.
Alternatively, in step S105, a complementary filtering operation may be performed on the first fusion result and the correction value of the yaw rate to generate a second fusion result, and the second fusion result may be taken as the finally determined yaw rate. Alternatively, the first fused result may be processed with a first filter to generate a first fused result, the correction value of the yaw rate may be processed with a second filter to generate a second fused result, and the first and second fused results may be combined to generate a second fused result. Optionally, the sum of the frequency response of the first filter and the frequency response of the second filter is one. In one embodiment, the first filter may be implemented as a high-pass filter, the second filter may be implemented as a low-pass filter, the high-frequency features in the first fusion result may be extracted using the high-pass filter and the low-frequency features in the correction value of the yaw rate may be extracted using the low-pass filter, and the high-frequency features and the low-frequency features may be added to generate the second fusion result. In one embodiment, the high frequency feature in the first fusion result at the current time may be extracted using a high pass filter and the low frequency feature in the correction value of the yaw rate at the previous time may be extracted using a low pass filter, and the high frequency feature and the low frequency feature may be added to generate the second fusion result as the finally determined yaw rate at the current time.
Through the frequency domain fusion of the first fusion result and the correction value of the yaw rate, steady-state deviation existing in the linear prediction model can be reduced or eliminated, and fusion accuracy under steady-state working conditions is improved.
In one or more embodiments, the various steps described in FIG. 1 may be performed continuously at discrete times after the vehicle is started. In one embodiment, at time k-1, a measure of the current vehicle speed and the current yaw rate may be obtained and the current vehicle speed compared to a vehicle speed threshold. When the current vehicle speed is smaller than the vehicle speed threshold value, the weighted average result of the measured value of the current yaw rate and the historical measured error value can be used as the updated measured error value at the k-1 moment and stored in the memory. In one embodiment, at time k, a measure of the current vehicle speed and the current yaw rate may be obtained and the current vehicle speed is compared to a vehicle speed threshold. When the current vehicle speed is equal to or greater than the vehicle speed threshold value, for example, the updated measurement error value at the time k-1 may be read from the memory to perform a correction operation on the measured value of the yaw rate at the time k, for example, the difference between the measured value of the yaw rate at the time k and the updated measurement error value at the time k-1 may be used as the correction value of the yaw rate at the time k. In one embodiment, at the k time, a predicted value of the yaw rate at the k+1 time may be acquired, and an optimal estimate of the yaw rate at the k+1 time may be generated as the first fusion result based on time-domain fusion of the predicted value of the yaw rate at the k+1 time and the correction value of the yaw rate at the k time. In one embodiment, at the k time, the first fusion result at the k+1 time and the correction value of the yaw rate at the k time may be further subjected to frequency domain fusion to generate the second fusion result at the k+1 time as the determined yaw rate.
According to the method for determining the yaw rate, the observation error can be reduced or eliminated through the correction operation of the measured value of the yaw rate, the first fusion result is obtained through time domain fusion of the predicted value of the yaw rate and the correction value of the yaw rate, so that the observation time delay compensation of the yaw rate under the transient working condition is provided, the accuracy of the yaw rate under the steady working condition is improved through frequency domain fusion of the first fusion result and the correction value of the yaw rate, the accuracy and the response speed of transient response are improved, and the error and the offset of steady response are reduced. Thus, the approach for determining yaw rate according to one or more embodiments of the present application enables a high accuracy yaw rate to be obtained at low cost, thereby providing a robust and reliable input for vehicle dynamics control.
FIG. 2 illustrates a flow diagram of a method for selectively correcting a measure of yaw rate in accordance with one or more embodiments of the present application.
As shown in fig. 2, in step S201, a measured value of the yaw rate of the vehicle is acquired.
In step S203, the current vehicle speed is acquired and it is determined whether the current vehicle speed is less than a vehicle speed threshold. When it is judged that the current vehicle speed is less than the vehicle speed threshold, step S205 is entered; otherwise, step S207 and step S209 are performed.
In step S205, an updated measurement error value is acquired based at least on the measured value of the yaw rate. Alternatively, a weighted average of the measured value of the yaw rate and the historical measurement error value may be used as the updated measurement error value and stored in the memory.
In step S207, an update measurement error value is output in response to the current vehicle speed being equal to or greater than the vehicle speed threshold. Alternatively, the updated measurement error value may be read from the memory and output in response to the current vehicle speed being greater than or equal to the vehicle speed threshold.
In step S209, a correction operation is performed on the measured value of the yaw rate using the updated measurement error value to generate a correction value of the yaw rate. Alternatively, the correction operation may be performed on the measured value of the yaw rate by subtracting the updated measured error value from the measured value of the yaw rate, and the difference between the measured value of the yaw rate and the updated measured error value is taken as the correction value of the yaw rate.
In one or more embodiments, the various steps described in FIG. 2 may be performed continuously at discrete times after the vehicle is started. In one embodiment, at time k, a measure of the current vehicle speed and the current yaw rate may be obtained and the current vehicle speed is compared to a vehicle speed threshold. When the current vehicle speed is smaller than the vehicle speed threshold value, the weighted average result of the measured value of the current yaw rate and the historical measured error value can be used as the updated measured error value at the moment k and stored in the memory. In one embodiment, at time k+1, a measure of the current vehicle speed and the current yaw rate may be obtained and the current vehicle speed is compared to a vehicle speed threshold. When the current vehicle speed is equal to or greater than the vehicle speed threshold value, for example, the updated measurement error value at the time k may be read from the memory to perform a correction operation on the measured value of the yaw rate at the time k+1, for example, the difference between the measured value of the yaw rate at the time k+1 and the updated measurement error value at the time t may be used as the correction value of the yaw rate at the time k+1.
Fig. 3 illustrates a schematic block diagram of frequency domain fusion in accordance with one or more embodiments of the present application.
As shown in fig. 3, after the first fusion result and the correction value of the yaw rate are generated, the yaw rate may be determined from the second fusion result generated by frequency-domain fusion of the first fusion result and the correction value of the yaw rate.
Referring to fig. 3, a complementary filtering operation may be performed on the first fusion result and the correction value of the yaw rate using the first filter and the second filter. Specifically, it is possible to process the first fusion result with the first filter to generate a first filter result, process the correction value of the yaw rate with the second filter to generate a second filter result, and sum the first filter result and the second filter result with the adder and take the sum result as the determined yaw rate. Optionally, the sum of the frequency response of the first filter and the frequency response of the second filter is one.
In one embodiment, the first filter may be implemented as a high-pass filter, the second filter may be implemented as a low-pass filter, the high-frequency feature in the first fusion result may be extracted using the high-pass filter and the low-frequency feature in the correction value of the yaw rate may be extracted using the low-pass filter, and the high-frequency feature and the low-frequency feature may be added to generate the determined yaw rate. Illustratively, the cut-off frequencies of the first filter and the second filter may be simultaneously selected to be values in the range of 1-10 Hz.
Fig. 4 shows a block diagram of an apparatus for determining yaw rate in accordance with one or more embodiments of the present application.
As shown in fig. 4, the apparatus 40 for determining a yaw rate includes a measuring unit 410, a correcting unit 420, a time domain fusion unit 430, and a frequency domain fusion unit 440.
The measurement unit 410 is configured to acquire a measured value of the yaw rate of the vehicle.
Alternatively, the measurement unit 410 may be implemented as an inertial measurement unit and disposed near the vehicle centroid position for obtaining a measurement of the yaw rate of the vehicle.
The correction unit 420 is configured to selectively perform a correction operation on the measured value of the yaw rate to generate a correction value of the yaw rate.
Alternatively, the correction unit 420 may be configured to selectively perform a correction operation on the obtained measured value of the yaw rate to generate a correction value of the yaw rate based on a comparison of the current vehicle speed and the vehicle speed threshold. Alternatively, the correction unit 420 may be configured to obtain an updated measurement error value at least from the measurement value of the current yaw rate when the current vehicle speed is less than the vehicle speed threshold; when the current vehicle speed is equal to or greater than the vehicle speed threshold value, the correction unit 420 may be configured to perform a correction operation on the measured value of the current yaw rate using the updated measurement error value to generate a correction value of the yaw rate. In one embodiment, when the current vehicle speed is less than the vehicle speed threshold, the correction unit 420 may be configured to acquire a measured value of the current yaw rate and store a weighted average result of the measured value of the current yaw rate and the historical measurement error value as the updated measurement error value. In one embodiment, when the current vehicle speed is equal to or greater than the vehicle speed threshold value, the correction unit 420 may be configured to acquire a measured value of the current yaw rate and a stored updated measurement error value, and perform a correction operation on the measured value of the current yaw rate by subtracting the updated measurement error value from the measured value of the current yaw rate to take a difference between the measured value of the current yaw rate and the updated measurement error value as the correction value of the yaw rate.
The time domain fusion unit 430 is configured to acquire a predicted value of the yaw rate and perform a time domain fusion operation on the predicted value of the yaw rate and a correction value of the yaw rate to generate a first fusion result.
Alternatively, the time domain fusion unit 430 may have a linear prediction model that may obtain a predicted value of the yaw rate based at least on the steering wheel angle signal. Alternatively, the time domain fusion unit 430 may have a kalman filter model that may perform optimal estimation on the estimated value and the observed value in the time domain using a kalman filter algorithm with the predicted value of the yaw rate as the estimated value and the corrected value of the yaw rate as the observed value to generate the first fusion result.
The frequency domain fusion unit 440 is configured to perform a frequency domain fusion operation on the first fusion result and the correction value of the yaw rate to generate a second fusion result as the determined yaw rate.
Alternatively, the frequency domain fusion unit 440 may be configured to perform a complementary filtering operation on the first fusion result and the correction value of the yaw rate to generate a second fusion result, and to take the second fusion result as the finally determined yaw rate. In one embodiment, the frequency domain fusion unit 440 may include a high-pass filter, a low-pass filter, and an adder, the high-pass filter may be configured to extract a high-frequency feature in the first fusion result and the low-pass filter may be configured to extract a low-frequency feature in the correction value of the yaw-rate, and the adder may be configured to add the high-frequency feature and the low-frequency feature to generate the determined yaw-rate. Illustratively, the cut-off frequencies of the high-pass filter and the low-pass filter may be simultaneously selected to be values in the range of 1-10 Hz.
FIG. 5 illustrates a block diagram of a system for determining yaw rate in accordance with one or more embodiments of the present application.
As shown in fig. 5, the system 50 for determining yaw rate includes a communication unit 510, a memory 520 (e.g., a nonvolatile memory such as a flash memory, a ROM, a hard disk drive, a magnetic disk, an optical disk), a processor 530, and a computer program 540 stored on the memory 520 and executable on the processor 530.
The communication unit 510 serves as a communication interface configured to establish a communication connection between the task scheduling system 50 and an external device or network (e.g., cloud, mobile terminal, etc.).
The memory 520 stores a computer program 540 executable by the processor 530. Furthermore, it is possible to provide a device for the treatment of a disease. The memory 520 may also store data (e.g., a measured value of the yaw rate, a correction value of the yaw rate, a predicted value of the yaw rate, a first fusion result, etc.) generated when the processor 530 executes the computer program, and data or commands received from the outside via the communication unit 510.
Processor 530 is configured to execute computer program 540 to implement methods for determining yaw rate in accordance with one or more embodiments of the present application.
The system for determining the yaw rate according to one or more embodiments of the present application can reduce or eliminate the observation error through the correction operation of the measured value of the yaw rate, obtain the first fusion result through the time domain fusion of the predicted value of the yaw rate and the correction value of the yaw rate to provide the observation time delay compensation of the yaw rate under the transient condition, and improve the accuracy of the yaw rate under the steady condition through the frequency domain fusion of the first fusion result and the correction value of the yaw rate, thereby improving the accuracy and the response speed of the transient response and reducing the error and the offset of the steady response. Thus, the approach for determining yaw rate according to one or more embodiments of the present application enables a high accuracy yaw rate to be obtained at low cost, thereby providing a robust and reliable input for vehicle dynamics control.
Where applicable, hardware, software, or a combination of hardware and software may be used to implement the various embodiments provided by the present application. Moreover, where applicable, the various hardware components and/or software components set forth herein may be combined into composite components comprising software, hardware, and/or both without departing from the scope of the present application. Where applicable, the various hardware components and/or software components set forth herein may be separated into sub-components comprising software, hardware, or both without departing from the scope of the present application. Further, where applicable, it is contemplated that software components may be implemented as hardware components, and vice versa.
Software (such as program code and/or data) according to the present application may be stored on one or more computer storage media. It is also contemplated that the software identified herein may be implemented using one or more general-purpose or special-purpose computers and/or computer systems that are networked and/or otherwise. Where applicable, the order of the various steps described herein may be changed, combined into composite steps, and/or divided into sub-steps to provide features described herein.
The embodiments and examples set forth herein are presented to best explain the embodiments in accordance with the application and its particular application and to thereby enable those skilled in the art to make and use the application. However, those skilled in the art will recognize that the foregoing description and examples have been presented for the purpose of illustration and example only. The description as set forth is not intended to cover various aspects of the application or to limit the application to the precise form disclosed.
Claims (11)
1. A method for determining yaw rate, the method comprising the steps of:
acquiring a measured value of a yaw rate of the vehicle and selectively performing a correction operation on the measured value of the yaw rate to generate a correction value of the yaw rate;
acquiring a predicted value of yaw rate and generating a first fusion result based on temporal fusion of the predicted value of yaw rate and a correction value of yaw rate; and
determining the yaw rate based on a second fusion result generated by frequency-domain fusion of the first fusion result and the correction value of the yaw rate,
wherein selectively performing a correction operation on the measured value of the yaw rate to generate a correction value of the yaw rate includes:
acquiring an updated measurement error value based at least on the measured value of the yaw rate in response to the current vehicle speed being less than the vehicle speed threshold; and
performing a correction operation on the measured value of the yaw rate using the updated measurement error value to generate a correction value of the yaw rate in response to the current vehicle speed being equal to or greater than the vehicle speed threshold.
2. The method of claim 1, wherein performing a correction operation on the measured value of yaw rate to generate a correction value of yaw rate comprises:
and removing a zero drift error in the yaw rate measurement to generate a correction value for the yaw rate.
3. The method of claim 1, wherein obtaining an updated measurement error value in response to the current vehicle speed being less than a vehicle speed threshold comprises:
acquiring a measurement of yaw rate in response to the current vehicle speed being less than a vehicle speed threshold;
and taking a weighted average result of the yaw rate measured value and the historical measurement error value as the updated measurement error value.
4. The method of claim 1, wherein performing a correction operation on the measured value of the yaw rate with the updated measured error value to generate the correction value of the yaw rate in response to the current vehicle speed being equal to or greater than the vehicle speed threshold value comprises:
performing a correction operation on the measured value of the yaw rate by subtracting the updated measurement error value from the measured value of the yaw rate in response to the current vehicle speed being equal to or greater than the vehicle speed threshold; and
and taking the difference value between the measured value of the yaw rate and the updated measured error value as a correction value of the yaw rate.
5. The method of claim 1, wherein obtaining a predicted value of yaw rate comprises:
at least one or more of a front wheel rotation angle value, a rear wheel rotation angle value, a side slope gravity component value and a side wind resistance value are input into a linear prediction model to obtain a predicted value of the yaw rate.
6. The method of claim 1, wherein generating a first fusion result based on a temporal fusion of the predicted value of the yaw rate and the correction value of the yaw rate comprises:
and performing a weighted average operation on the estimated value and the observed value in a time domain by using a Kalman filtering algorithm to generate the first fusion result, wherein the estimated value of the yaw rate is used as an estimated value and the corrected value of the yaw rate is used as an observed value.
7. The method of claim 1, wherein determining the yaw rate from a second fusion result generated from a frequency domain fusion of the first fusion result and a correction value of the yaw rate comprises:
performing a complementary filtering operation on the first fusion result and the correction value of the yaw rate to generate the second fusion result; and
and taking the second fusion result as the yaw rate.
8. The method of claim 7, wherein performing a complementary filtering operation on the first fusion result and a correction value for the yaw rate to generate the second fusion result comprises:
processing the first fusion result with a first filter to generate a first filtered result;
processing the correction value of the yaw rate with a second filter to generate a second filtered result; and
and combining the first filtering result and the second filtering result to generate the second fusion result.
9. The method of claim 8, wherein a sum of a frequency response of the first filter and a frequency response of the second filter is one.
10. A system for determining yaw rate, the system comprising:
a memory;
a processor coupled to the memory; and
computer program stored on the memory and running on the processor, the running of the computer program causing the execution of the method for determining yaw rate according to any one of claims 1-9.
11. A computer-readable storage medium, characterized in that the computer-storage medium comprises instructions that, when executed, perform the method for determining yaw rate according to any one of claims 1-9.
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