Steering parameter online identification method, system and equipment and storage medium

Abstract

The invention relates to the field of automatic driving control, and provides a steering parameter online identification method, system and device and a storage medium, and the steering parameter online identification method comprises the steps: obtaining a front wheel steering angle calculation value at a current moment; acquiring a steering wheel turning angle at the current moment; on the basis of the front wheel steering angle calculation value at the current moment and the steering wheel steering angle at the current moment, steering parameters at the current moment are identified online by adopting a recursive least square method, and the steering parameters comprise steering zero offset and a steering transmission ratio; and outputting the steering parameter at the current moment. According to the method, accurate online identification of the steering zero offset and the steering transmission ratio is achieved at the same time, and the accuracy and stability of vehicle steering control are improved.

Description

Background Art

[0002] Steering zero offset (steering wheel angle offset), also known as steering zero offset, is defined as the angle of the actual wheel angle offset from the 0-degree position when the vehicle executes a 0-degree steering command. The reason for this is that the wheels and steering wheels cannot be consistent due to mechanical aging or lack of calibration after repair. Due to the existence of steering zero offset, when the steering wheel does not turn, the wheels cannot travel in a straight line, but have a steering angle, resulting in the actual driving route being a circular arc with a large radius, which seriously affects the performance of autonomous driving control.

[0003] Steering zero offset may appear in two ways, divided into fixed and temporary: fixed offsets usually come from deformation of certain axles or pillars of the chassis, tire wear, uneven tire pressure, brake drag, etc. These offsets will always exist until they are cleared after maintenance or calibration; temporary offsets have multiple sources, such as uneven load distribution affecting steering, or crosswinds or cross slopes causing the vehicle to continuously make small turns to stay in a straight line.

[0004] The steering ratio is defined as the ratio between the steering wheel angle and the wheel angle. For an autonomous driving system, the steering ratio is generally set to a fixed steering ratio parameter. This parameter value corresponds to the structure of the chassis. The autonomous driving controller will output the steering wheel angle based on the steering ratio parameter and the desired wheel angle. However, in practice, the steering wheel angle and wheel angle of the vehicle are often nonlinear, and the actual steering ratio does not match the fixed steering ratio parameter, resulting in the actual wheel not turning at the angle expected by the controller. As a result, there will be problems such as understeering or oversteering, which may even affect the stability and comfort of the vehicle in severe cases.

[0005] Errors in steering zero offset and steering gear ratio will lead to unsatisfactory vehicle steering effects, such as excessive control errors when turning, or vehicle deviation when driving in a straight line.

[0006] At present, a commonly used method for handling vehicle steering zero offset and steering transmission ratio errors is calibration, which eliminates this steering zero offset by repairing the machinery, and requires frequent maintenance. This method is relatively costly for autonomous driving fleets, and if the time for calibration cannot be identified, the vehicle may be driven with steering zero offset, resulting in reduced control effect of autonomous driving. Another method is to perform offline identification of steering zero offset and steering ratio. However, since there are many and unstable factors that cause steering zero offset and transmission ratio errors, offline identification cannot meet the timely response to steering zero offset and steering transmission ratio errors.

[0007] The following processing methods have also appeared in the prior art. (1) A method of using a statistical method to perform real-time calibration of the steering wheel angle sensor. The zero offset is obtained by counting the mode of the motion state over a period of time, and the mode of the zero offset selected at each moment is weighted statistically. This method mainly uses two assumptions: 1. The most common turning state of the vehicle is straight driving; 2. The mode of the steering wheel angle is the correct zero offset. This method is mainly aimed at manually driven vehicles. For autonomous driving, this assumption and statistical method are not reliable enough. (2) The method of using Kalman filtering. Kalman filtering is used to process the zero offset of the vehicle body angular velocity and the front wheel angle measured by the gyroscope, mainly for the processing of sensor data. This method requires measuring the front wheel angle, which is not applicable to many vehicles that do not have a sensor for measuring the front wheel angle. (3) Using the more common sensor data, the steering zero offset is obtained through the ordinary least squares (OLS) identification method, and the influence of the sideslip angle is considered at the same time, but the deviation caused by the steering ratio error is not considered. In addition, OLS does not consider the proportion of data points, and the influence of each point on the output identification result is the same. (4) Steering wheel steering zero offset online identification method. Using the iterative ordinary least squares method, the steering ratio is taken as a certain parameter. This method does not consider the proportion of data points, nor does it explain the identification method of the convergence state. (5) The least squares method is used to iteratively identify the vehicle steering zero offset, and the iterative weight parameter is considered. This method takes the steering zero offset as a known measurement data and iteratively identifies the steering ratio. The above three methods (3), (4), and (5) cannot handle the steering zero offset and steering ratio errors at the same time. At the same time, the iterative process requires accumulation, and the convergence speed and accuracy are not high.

[0008] It should be noted that the information disclosed in the above background technology section is only used to enhance the understanding of the background of the present application, and therefore may include information that does not constitute prior art known to ordinary technicians in the field. Summary of the invention

[0009] In response to the problems in the prior art, the purpose of this application is to provide a method, system, device and storage medium for online identification of steering parameters, so as to achieve accurate online identification of steering zero bias and steering transmission ratio at the same time, and improve the accuracy and stability of steering control of autonomous driving system.

[0010] The present application provides a method for online identification of steering parameters, comprising the following steps:

[0011] Get the calculated value of the front wheel angle at the current moment;

[0012] Get the steering wheel angle at the current moment;

[0013] Based on the calculated value of the front wheel angle at the current moment and the steering wheel angle at the current moment, a recursive least square method is used to online identify the steering parameters at the current moment, wherein the steering parameters include a steering zero offset and a steering transmission ratio;

[0014] Output the steering parameter at the current moment.

[0015] In some embodiments, the step of obtaining the calculated value of the front wheel turning angle at the current moment comprises the following steps:

[0016] Get the vehicle's yaw rate and longitudinal acceleration at the current moment;

[0017] Based on the vehicle yaw rate and the vehicle longitudinal acceleration, a vehicle kinematics model is used to calculate a front wheel turning angle calculation value at a current moment.

[0018] In some embodiments, the steering parameters are identified online using a recursive least square method with a forgetting factor.

[0019] In some embodiments, when the recursive least squares method is used to online identify the steering parameters at the current moment, the identification is performed based on the functional relationship between the front wheel angle, the steering wheel angle and the steering parameters, wherein the steering wheel angle is the input, the front wheel angle is the output, and the steering parameter is the identification quantity.

[0020] In some embodiments, based on the calculated value of the front wheel angle at the current moment and the steering wheel angle at the current moment, the steering parameter is identified online using a recursive least squares method, comprising the following steps:

[0021] Obtaining an estimated value of the front wheel angle at the current moment based on the steering parameter at the previous moment and the steering wheel angle at the current moment;

[0022] Calculating a residual based on the calculated value of the front wheel turning angle at the current moment and the estimated value of the front wheel turning angle;

[0023] The steering parameter at the previous moment is updated based on the residual and the filter gain to obtain the steering parameter at the current moment.

[0024] In some embodiments, after the steering parameter at the current moment is identified online using the recursive least squares method based on the calculated value of the front wheel angle at the current moment and the steering wheel angle at the current moment, the following steps are further included:

[0025] Calculate the identification confidence based on the residuals of a preset number of historical moments;

[0026] Determine whether to output the steering parameter at the current moment according to the recognition confidence.

[0027] In some embodiments, the calculation of the recognition confidence based on the residuals of a preset number of historical moments comprises the following steps:

[0028] Based on the residuals of a preset number of historical moments, the standardized root mean square error and standardized mean of the residuals are calculated;

[0029] The identification confidence is calculated based on the standardized root mean square error and the standardized mean of the residuals.

[0030] In some embodiments, based on the calculated value of the front wheel angle at the current moment and the steering wheel angle at the current moment, before using the recursive least squares method to online identify the steering parameter at the current moment, the following steps are also included:

[0031] Determining whether the vehicle state quantities at the current moment all meet the preset identification conditions, wherein the vehicle state quantities include at least one of the vehicle speed, the yaw rate change, and the front wheel turning angle;

[0032] If all of them are satisfied, then based on the calculated value of the front wheel turning angle at the current moment and the steering wheel turning angle at the current moment, the steering parameter at the current moment is identified online using the recursive least square method.

[0033] In some embodiments, the steering parameters at the current moment include the following steps:

[0034] Performing a limiting process on the identified steering parameter based on a preset steering parameter limit value;

[0035] The identified steering parameters are smoothed using the exponentially weighted moving average method, and the smoothed steering parameters are output.

[0036] The embodiment of the present application further provides a steering parameter identification system for implementing the steering parameter online identification method, the system comprising:

[0037] The data acquisition module is used to obtain the calculated value of the front wheel angle at the current moment and the steering wheel angle at the current moment;

[0038] a parameter identification module, for online identifying the steering parameters at the current moment by using a recursive least square method based on the calculated value of the front wheel angle at the current moment and the steering wheel angle at the current moment, wherein the steering parameters include a steering zero offset and a steering transmission ratio;

[0039] The parameter output module is used to output the steering parameter at the current moment.

[0040] The present application also provides a steering parameter identification device, including:

[0041] processor;

[0042] a memory storing executable instructions of the processor;

[0043] Wherein, the processor is configured to perform the steps of the steering parameter online identification method by executing the executable instructions.

[0044] An embodiment of the present application also provides a computer-readable storage medium for storing a program, and when the program is executed by a processor, the steps of the steering parameter online identification method are implemented.

[0045] It is to be understood that the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the present disclosure.

[0046] The steering parameter online identification method, system, device and storage medium of the present application have the following beneficial effects:

[0047] In the steering parameter online identification method of the present application, the relevant calculated values ​​at the current moment are first obtained, and then based on the calculated values, the recursive least squares method is used to online identify the steering zero offset and the steering transmission ratio, thereby realizing accurate online identification of the steering zero offset and the steering transmission ratio at the same time. The online identification method has stronger real-time performance and stronger robustness than the existing calibration-based method and offline identification method; due to the simultaneous real-time identification of the steering zero offset and the steering transmission ratio, the influence of the two can be estimated more accurately and independently, and the error will not be mistakenly attributed to one of the quantities, nor will overfitting of one of the quantities be generated. After the identified steering parameters are output to the automatic driving system, the control module of the automatic driving system will use the steering zero offset and the steering transmission ratio obtained by the identification to compensate the output steering control quantity, and the steering zero offset compensation and the steering transmission ratio compensation will be considered in the steering command to improve the control accuracy and control stability. Therefore, by adopting the steering parameter online identification method, it is helpful for the automatic driving system to generate more accurate steering commands and improve the stability and accuracy of vehicle control. BRIEF DESCRIPTION OF THE DRAWINGS

[0048] Other features, objects and advantages of the present application will become more apparent by reading the detailed description of non-limiting embodiments made with reference to the following drawings.

[0049] Figure 1 is a flow chart of a method for online identification of steering parameters according to an embodiment of the present application;

[0050] Figure 2 is a flow chart of identifying steering parameters according to an embodiment of the present application;

[0051] Figure 3 is a flow chart of a method for online identification of steering parameters of a specific example of an embodiment of the present application;

[0052] Figure 4 is a structural schematic diagram of a steering parameter identification system according to an embodiment of the present application;

[0053] Figure 5 is a structural schematic diagram of a steering parameter identification device according to an embodiment of the present application;

[0054] Figure 6 It is a schematic diagram of the structure of a computer-readable storage medium according to an embodiment of the present application. DETAILED DESCRIPTION

[0055] Example embodiments will now be described more fully with reference to the accompanying drawings. However, example embodiments can be implemented in a variety of forms and should not be construed as limited to the examples set forth herein; rather, these embodiments are provided so that the disclosure will be more comprehensive and complete and to fully convey the concepts of the example embodiments to those skilled in the art. The described features, structures, or characteristics may be combined in any suitable manner in one or more embodiments.

[0056] In addition, the accompanying drawings are only schematic illustrations of the present disclosure and are not necessarily drawn to scale. The same reference numerals in the figures represent the same or similar parts, and thus their repeated descriptions will be omitted. Some of the block diagrams shown in the accompanying drawings are functional entities and do not necessarily correspond to physically or logically independent entities. These functional entities can be implemented in software form, or in one or more hardware modules or integrated circuits, or in different networks and / or processor devices and / or microcontroller devices. Although "first" or "second" etc. are used in this specification to represent certain features, they are only used to represent the function and are not used as a limitation on the number and importance of specific features.

[0057] The flowcharts shown in the accompanying drawings are only exemplary and do not necessarily include all the steps. For example, some steps may be decomposed, while some steps may be combined or partially combined. Therefore, the actual execution order may be changed according to the actual situation.

[0058] There are several key difficulties in solving the problems of steering zero offset and steering transmission ratio error: First, there are relatively few measurement values ​​that can be used, and it is necessary to solve how to estimate the steering zero offset and steering transmission ratio in the absence of wheel steering measurement values; second, the coupling of the two estimated quantities. If they are not fully estimated, the steering error caused by the steering zero offset may be misjudged as the steering error caused by the steering transmission ratio error, or vice versa. Therefore, it is necessary to estimate both quantities at the same time; third, the selection of data points to be added to the estimate. For example, large errors may occur when the speed is low; fourth, the judgment of whether the online identification has converged.

[0059] like Figure 1As shown, the embodiment of the present application provides a method for online identification of steering parameters, comprising the following steps:

[0060] S100: Obtaining the calculated value of the front wheel turning angle at the current moment;

[0061] S200: Obtaining the current steering wheel angle;

[0062] The steering wheel angle may be obtained by a steering wheel angle sensor;

[0063] S300: Based on the calculated value of the front wheel angle at the current moment and the steering wheel angle at the current moment, online identifying the steering parameters at the current moment by using a recursive least square method, wherein the steering parameters include a steering zero offset and a steering transmission ratio;

[0064] S400: Outputting the steering parameter at the current moment;

[0065] The output steering parameter here is output to the controller of the autonomous driving system, so that the controller of the autonomous driving system can generate a more accurate steering instruction based on the steering zero offset and steering transmission ratio at the current moment.

[0066] In the steering parameter online identification method of the present application, firstly, the relevant calculated values ​​at the current moment are obtained through steps S100 and S200, and then the steering zero offset and the steering transmission ratio are identified online based on the calculated values ​​through step S300 by using the recursive least squares method, thereby realizing accurate online identification of the steering zero offset and the steering transmission ratio at the same time, and outputting the identified steering zero offset and the steering transmission ratio through step S400. The online identification method of the present application has stronger real-time performance and stronger robustness than the existing calibration-based method and offline identification method; because the steering zero offset and the steering transmission ratio are identified in real time at the same time, the influence of the two can be estimated more accurately and independently, and the error will not be mistakenly attributed to one of the quantities, nor will overfitting for one of the quantities be generated. After the identified steering parameters are output to the automatic driving system, the control module of the automatic driving system will use the steering zero offset and the steering transmission ratio obtained by the identification to compensate the output steering control quantity, and the steering zero offset compensation and the steering transmission ratio compensation will be considered in the steering instruction obtained to improve the control accuracy and control stability. Therefore, by adopting this steering parameter online identification method, it helps the autonomous driving system to generate more accurate steering instructions and improve the stability and accuracy of vehicle control.

[0067] In this embodiment, before step S100, the step of initializing the steering zero offset and the steering transmission ratio is also included. The initial value of the steering transmission ratio generally uses the steering transmission ratio parameter given when the chassis leaves the factory as the initial value, and the initial value of the steering zero offset is set to 0, that is, there is no steering zero offset.

[0068] In this embodiment, the vehicle kinematics model is used to obtain the calculated value of the front wheel turning angle at the current moment, which can solve the problem that the front wheel turning angle cannot be directly measured. Specifically, the step S100: obtaining the estimated value of the front wheel turning angle at the current moment includes the following steps:

[0069] Get the vehicle's yaw rate and longitudinal acceleration at the current moment;

[0070] Based on the vehicle yaw rate and the vehicle longitudinal acceleration, a vehicle kinematics model is used to calculate a front wheel turning angle calculation value at a current moment.

[0071] The vehicle motion model is, for example, an Ackerman model, which is used to describe a kinematic model in which the paths of the inner and outer wheels of a vehicle are different when turning. The following formula can be used to obtain the calculated value of the front wheel turning angle:

[0072]

[0073] Among them, δ f is the calculated value of the front wheel turning angle, L is the vehicle wheelbase, ω is the vehicle yaw rate, which can be obtained by the yaw rate sensor in the vehicle odometer, v x is the longitudinal acceleration of the vehicle, which can be obtained by differential calculation of the vehicle speed. The front wheel steering angle can be calculated by the above formula (1), which solves the problem that the front wheel steering angle measurement value cannot be directly obtained when there is no steering angle sensor installed on the front wheel. There is no need to install an additional steering angle sensor on the front wheel.

[0074] In this embodiment, when the recursive least squares method is used to online identify the steering parameters at the current moment, the identification is performed based on the functional relationship between the front wheel angle, the steering wheel angle and the steering parameter, wherein the steering wheel angle is the input, the front wheel angle is the output, and the steering parameter is the identification value.

[0075] In this embodiment, the functional relationship between the front wheel angle, the steering wheel angle and the steering parameter is expressed by a steering model, and the steering model specifically adopts the following formula:

[0076]

[0077] Where θ represents the steering wheel angle, i gear represents the steering gear ratio, δ 0 This formula (2) represents the response of the front wheel angle to the steering wheel.

[0078] Combining formula (1) and formula (2), we can get:

[0079]

[0080] Among them, the vehicle wheelbase L is a known parameter, the vehicle angular velocity ω, the vehicle longitudinal acceleration v x , the steering wheel angle θ are all real-time measurements, and the steering gear ratio i gear and steering bias δ 0 is the steering parameter that needs to be identified.

[0081] In this embodiment, in step S300, the steering parameter is identified online using a recursive least square method with a forgetting factor. The recursive least square method with a forgetting factor (RLS-FF) is an adaptive filter algorithm that recursively searches for coefficients that minimize a weighted linear least squares cost function associated with an input signal, i.e., identifies a coefficient matrix. Compared with the ordinary iterative least square method, RLS-FF can suppress the influence of noise on parameter estimation through the forgetting factor, thereby improving the robustness of the algorithm. At the same time, the least square method (RLS) generally has a better convergence speed and accuracy, so the steering parameter online identification method can obtain better robustness.

[0082] The above formula (3) is converted into a formula suitable for RLS-FF as follows:

[0083] y=x T w (4)

[0084] Where x and w are in matrix form, x and y are known data, and w is the parameter to be identified; and:

[0085]

[0086] Based on RLS-FF, formula (4) can be identified to obtain the steering zero bias and steering transmission ratio that need to be identified. The formula of the recursive least square method with forgetting factor is as follows:

[0087]

[0088] w n =w n-1 +e n g n

[0089] The subscript n represents the nth data, n-1 represents the n-1th data, and the superscript ^ in the symbol represents the estimated value of the corresponding quantity. 0 =0, that is, the initial residual is considered to be 0. The initial w 0The value is obtained by using the initial value of the steering zero bias and the steering gear ratio. The recursive least squares method only needs to be based on the current input and the identification value of the previous moment to get the current estimated value, and there is no need to save the previous data. e represents the residual, that is, the error between the calculated value and the estimated value, g is the filter gain, P is the inverse covariance error matrix, P 0 =δI, the initial value of P is equal to the coefficient * unit matrix I. The larger the initial value of P, the faster the initial convergence of RLS-FF and the larger the oscillation. On the contrary, the smaller the initial value of P, the slower the initial convergence of RLS-FF and the smaller the oscillation. The initial value of P can be adjusted according to actual needs. λ is the forgetting factor. Taking 1 means no forgetting. The value is generally between 0.98 and 1. After calculating the above four formulas, w n It is the latest estimate of the coefficient matrix, that is, the steering zero bias and steering transmission ratio at the current moment.

[0090] like Figure 2 As shown, in this embodiment, in step S300, based on the calculated value of the front wheel angle at the current moment and the steering wheel angle at the current moment, the recursive least squares method is used to online identify the steering parameter, including the following steps:

[0091] S310: Obtaining an estimated value of the front wheel angle at the current moment based on the steering parameter at the previous moment and the steering wheel angle at the current moment

[0092] S320: Calculate the residual error e based on the calculated value of the front wheel turning angle and the estimated value of the front wheel turning angle at the current moment n ;

[0093] S330: Based on the residual and the filter gain g n Update the steering parameter at the previous moment to obtain the steering parameter w at the current moment n .

[0094] like Figure 3 As shown, in this embodiment, based on the calculated value of the front wheel angle at the current moment and the steering wheel angle at the current moment, after the recursive least square method is used to online identify the steering parameter at the current moment, the following steps are also included:

[0095] S340: Calculating recognition confidence based on residuals of a preset number of historical moments;

[0096] As mentioned above, the residual is defined as:

[0097]

[0098] In this embodiment, the current recognition confidence is further calculated based on the residual;

[0099] S350: Determine whether to output the steering parameter at the current moment according to the recognition confidence.

[0100] In this embodiment, the step S340: calculating the recognition confidence based on the residuals of a preset number of historical moments, includes the following steps:

[0101] Based on the residuals of a preset number of historical moments, the standardized root mean square error NRMSE and standardized mean NMean of the residuals are calculated; the specific calculation formula is as follows:

[0102]

[0103] Among them, N is a preset number, which means that the past N data are considered;

[0104] Based on the standardized root mean square error and the standardized mean of the residual, the identification confidence is calculated. Specifically, the calculation formula of the identification confidence is as follows:

[0105]

[0106] In this embodiment, the normalized root mean square error (NRMSE) and mean of the residual are used to check the confidence of the identification result, so as to determine whether the identification converges and decide whether to output the identification result. A threshold of the identification confidence is preset. If the identification confidence is higher than the threshold, the steering zero bias and steering gear ratio at the current moment are output. If the identification confidence is lower than or equal to the threshold, the default steering zero bias and steering gear ratio are output. The default steering zero bias and steering gear ratio are the initial value of the steering zero bias and the initial value of the steering gear ratio set during initialization.

[0107] In applications, fluctuations in the measured values ​​of vehicle speed, yaw rate, etc. may lead to errors. Therefore, in this embodiment, the input data for online identification is further filtered. Specifically, Figure 3 As shown, in this embodiment, before the step S300: using the recursive least squares method to online identify the steering parameters at the current moment based on the calculated value of the front wheel angle at the current moment and the steering wheel angle at the current moment, the following steps are also included:

[0108] S210: Determine whether the vehicle state quantities at the current moment all meet the preset identification conditions;

[0109] The vehicle state comprises at least one of a vehicle speed, a yaw rate change and a front wheel turning angle;

[0110] The preset identification condition includes, for example, at least one of the following conditions:

[0111] The vehicle speed is greater than the preset minimum speed threshold. When the vehicle speed is low, the denominator in the vehicle kinematics model (the above formula (1)) is small, and a small jitter will lead to a large deviation in the result. Therefore, the data when the speed is too low is removed.

[0112] The yaw angular velocity change is less than or equal to the angular velocity change threshold; when the yaw angular velocity change is too large, there may be noise influence, therefore, remove the data with too large yaw angular velocity change, the yaw angular velocity change can be obtained by calculating the difference between the yaw angular velocity at the current moment and the yaw angular velocity at the previous moment;

[0113] The calculated front wheel steering angle value obtained by the vehicle kinematic model is within the preset front wheel steering angle range, thereby removing data exceeding the maximum wheel steering angle in physics;

[0114] The preset identification conditions may also include other data filtering conditions, thereby filtering out other data that may cause large identification errors;

[0115] If all conditions are satisfied, then S300: based on the calculated value of the front wheel angle at the current moment and the steering wheel angle at the current moment, a recursive least square method is used to online identify the steering parameter at the current moment;

[0116] If at least one preset identification condition is not met, the current data is ignored, and the default values ​​of the steering zero offset and the steering transmission ratio are output, or the steering zero offset and the steering transmission ratio at the previous moment are output.

[0117] Therefore, the present application sets conditions for filtering the input data for online identification, excludes some data with large errors, and further improves the stability of online identification of steering parameters.

[0118] In this embodiment, based on the output of the steering parameter at the current moment determined by the identification confidence,

[0119] like Figure 3 As shown, the step S400: outputting the steering parameter at the current moment includes the following steps:

[0120] S410: performing a limiting process on the identified steering parameter based on a preset steering parameter limit value;

[0121] Specifically, the value ranges of the steering zero bias and the steering transmission ratio are set respectively. When the identified steering zero bias exceeds the value range of the steering zero bias, the default value of the steering zero bias is output, or the boundary value of the value range closest to the steering zero bias is used as the steering zero bias after limiting; when the identified steering transmission ratio exceeds the value range of the steering transmission ratio, the default value of the steering transmission ratio is output, or the boundary value of the value range closest to the steering transmission ratio is used as the steering transmission ratio after limiting;

[0122] By limiting the steering zero offset and steering transmission ratio, excessive or insufficient steering zero offset and steering transmission ratio are eliminated, thereby improving the steering control stability and control accuracy of the autonomous driving system.

[0123] S420: Smoothing the identified steering parameters using an exponentially weighted moving average (EWMA) method, and outputting the smoothed steering parameters.

[0124] EWMA is a special weighted moving average method. It assigns different weights to the data in the time series, so that the data closer to the present has a larger weight, thereby better reflecting the recent trend of change. The principle of the exponentially weighted moving average method is that it fully considers the impact of the time interval on the data and provides a specific method for setting weights, that is, the weight of each period decays exponentially as the time interval increases. This method not only reduces the amount of data storage during the calculation process, but also considers the different roles played by data in different periods.

[0125] like Figure 4 As shown, the embodiment of the present application also provides a steering parameter identification system for implementing the steering parameter online identification method, the system comprising:

[0126] The data acquisition module M100 is used to obtain the calculated value of the front wheel angle at the current moment and the steering wheel angle at the current moment;

[0127] The parameter identification module M200 is used to identify the steering parameters at the current moment online by using a recursive least square method based on the calculated value of the front wheel angle at the current moment and the steering wheel angle at the current moment, wherein the steering parameters include a steering zero offset and a steering transmission ratio;

[0128] The parameter output module M300 is used to output the steering parameters at the current moment.

[0129] In the steering parameter online identification system of the present application, the relevant calculated values ​​at the current moment are first obtained through the data acquisition module M100, and then the parameter identification module M200 is used to identify the steering zero offset and the steering transmission ratio online based on the calculated values, thereby realizing accurate online identification of the steering zero offset and the steering transmission ratio at the same time, and outputting the identified steering zero offset and the steering transmission ratio through the parameter output module M300. The online identification method adopted by the online identification system of the present application has stronger real-time performance and stronger robustness than the existing calibration-based method and offline identification method; due to the simultaneous real-time identification of the steering zero offset and the steering transmission ratio, the influence of the two can be estimated more accurately and independently, and the error will not be mistakenly attributed to one of the quantities, nor will overfitting for one of the quantities be generated. After the steering parameters obtained by the identification are output to the automatic driving system, the control module of the automatic driving system will use the steering zero offset and the steering transmission ratio obtained by the identification to compensate the output steering control quantity, and the steering zero offset compensation and the steering transmission ratio compensation will be considered in the steering instruction obtained to improve the control accuracy and control stability. Therefore, by adopting the online steering parameter identification method, the autonomous driving system can generate more accurate steering instructions and improve the stability and accuracy of vehicle control.

[0130] An embodiment of the present application also provides a steering parameter identification device, comprising a processor; a memory, in which executable instructions of the processor are stored; wherein the processor is configured to perform the steps of the steering parameter online identification method by executing the executable instructions.

[0131] Those skilled in the art will appreciate that various aspects of the present application may be implemented as a system, method or program product. Therefore, various aspects of the present application may be specifically implemented in the following forms, namely: a complete hardware implementation, a complete software implementation (including firmware, microcode, etc.), or a combination of hardware and software, which may be collectively referred to as "circuit", "module" or "platform" herein.

[0132] Refer to the following Figure 5 The electronic device 600 according to this embodiment of the present application is described. Figure 5 The electronic device 600 shown is merely an example and should not bring any limitation to the functions and scope of use of the embodiments of the present application.

[0133] like Figure 5 As shown, the electronic device 600 is in the form of a general computing device. The components of the electronic device 600 may include, but are not limited to: at least one processing unit 610, at least one storage unit 620, a bus 630 connecting different system components (including the storage unit 620 and the processing unit 610), a display unit 640, etc.

[0134] The storage unit stores a program code, which can be executed by the processing unit 610, so that the processing unit 610 performs the steps of various exemplary embodiments of the present application described in the above-mentioned steering parameter online identification method section of this specification. For example, the processing unit 610 can perform the following steps: Figure 1 Follow the steps shown in .

[0135] The storage unit 620 may include a readable medium in the form of a volatile storage unit, such as a random access memory unit (RAM) 6201 and / or a cache memory unit 6202 , and may further include a read-only memory unit (ROM) 6203 .

[0136] The storage unit 620 may also include a program / utility 6204 having a set (at least one) of program modules 6205, such program modules 6205 including but not limited to: an operating system, one or more application programs, other program modules and program data, each of which or some combination may include the implementation of a network environment.

[0137] Bus 630 may represent one or more of several types of bus structures, including a memory unit bus or memory unit controller, a peripheral bus, an accelerated graphics port, a processing unit, or a local bus using any of a variety of bus architectures.

[0138] The electronic device 600 may also communicate with one or more external devices 700 (e.g., keyboards, pointing devices, Bluetooth devices, etc.), may also communicate with one or more devices that enable a user to interact with the electronic device 600, and / or communicate with any device that enables the electronic device 600 to communicate with one or more other computing devices (e.g., routers, modems, etc.). Such communication may be performed via an input / output (I / O) interface 650. Furthermore, the electronic device 600 may also communicate with one or more networks (e.g., a local area network (LAN), a wide area network (WAN), and / or a public network, such as the Internet) via a network adapter 660. The network adapter 660 may communicate with other modules of the electronic device 600 via a bus 630. It should be understood that, although not shown in the figure, other hardware and / or software modules may be used in conjunction with the electronic device 600, including but not limited to: microcode, device drivers, redundant processing units, external disk drive arrays, RAID systems, tape drives, and data backup storage systems.

[0139] In the steering parameter identification device, the program in the memory implements the steps of the steering parameter online identification method when executed by the processor. Therefore, the device can also obtain the technical effect of the above-mentioned steering parameter online identification method.

[0140] The embodiment of the present application also provides a computer-readable storage medium for storing a program, and when the program is executed by a processor, the steps of the steering parameter online identification method are implemented. In some possible implementations, various aspects of the present application can also be implemented in the form of a program product, which includes a program code. When the program product is executed on a terminal device, the program code is used to enable the terminal device to execute the steps according to various exemplary implementations of the present application described in the above-mentioned steering parameter online identification method section of this specification.

[0141] refer to Figure 6 As shown, a program product 800 for implementing the above method according to an embodiment of the present application is described, which can adopt a portable compact disk read-only memory (CD-ROM) and include program code, and can be executed on a terminal device, such as a personal computer. However, the program product of the present application is not limited thereto. In this document, a readable storage medium can be any tangible medium containing or storing a program, which can be used by or in combination with an instruction execution system, an apparatus or a device.

[0142] The program product may use any combination of one or more readable media. The readable medium may be a readable signal medium or a readable storage medium. The readable storage medium may be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, device or device, or any combination of the above. More specific examples (non-exhaustive list) of readable storage media include: an electrical connection with one or more wires, a portable disk, 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 disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above.

[0143] The computer readable storage medium may include a data signal propagated in a baseband or as part of a carrier wave, wherein a readable program code is carried. This propagated data signal may take a variety of forms, including but not limited to an electromagnetic signal, an optical signal, or any suitable combination of the above. The readable storage medium may also be any readable medium other than a readable storage medium, which may send, propagate, or transmit a program for use by an instruction execution system, an apparatus, or a device or used in combination with it. The program code contained on the readable storage medium may be transmitted with any appropriate medium, including but not limited to wireless, wired, optical cable, RF, etc., or any suitable combination of the above.

[0144] Program code for performing the operations of the present application may be written in any combination of one or more programming languages, including object-oriented programming languages ​​such as Java, C++, etc., and conventional procedural programming languages ​​such as "C" or similar programming languages. The program code may be executed entirely on the user computing device, partially on the user device, as a separate software package, partially on the user computing device and partially on a remote computing device, or entirely on a remote computing device or server. In the case of a remote computing device, the remote computing device may be connected to the user computing device through any type of network, including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computing device (e.g., using an Internet service provider to connect through the Internet).

[0145] When the program in the computer storage medium is executed by the processor, the steps of the steering parameter online identification method are implemented. Therefore, the computer storage medium can also obtain the technical effect of the above-mentioned steering parameter online identification method.

[0146] The above content is a further detailed description of the present application in combination with specific preferred implementation methods, and it cannot be determined that the specific implementation of the present application is limited to these descriptions. For ordinary technicians in the technical field to which the present application belongs, several simple deductions or substitutions can be made without departing from the concept of the present application, which should be deemed to fall within the scope of protection of the present application.

Claims

1. A method for online identification of steering parameters, characterized in that: The steps include: Get the calculated value of the front wheel angle at the current moment; Get the steering wheel angle at the current moment; Based on the calculated value of the front wheel angle at the current moment and the steering wheel angle at the current moment, a recursive least square method is used to online identify the steering parameters at the current moment, wherein the steering parameters include a steering zero offset and a steering transmission ratio; Output the steering parameter at the current moment.

2. The method for online identification of steering parameters according to claim 1, characterized in that: The step of obtaining the calculated value of the front wheel turning angle at the current moment comprises the following steps: Get the vehicle's yaw rate and longitudinal acceleration at the current moment; Based on the vehicle yaw rate and the vehicle longitudinal acceleration, a vehicle kinematics model is used to calculate a front wheel turning angle calculation value at a current moment.

3. The method for online identification of steering parameters according to claim 1, characterized in that: The steering parameters are identified online using a recursive least square method with a forgetting factor.

4. The method for online identification of steering parameters according to claim 1, characterized in that: When the recursive least squares method is used to online identify the steering parameters at the current moment, the identification is performed based on the functional relationship between the front wheel angle, the steering wheel angle and the steering parameter, wherein the steering wheel angle is the input, the front wheel angle is the output, and the steering parameter is the identification amount.

5. The method for online identification of steering parameters according to claim 4, characterized in that: Based on the calculated value of the front wheel turning angle at the current moment and the steering wheel turning angle at the current moment, a recursive least square method is used to online identify the steering parameter, comprising the following steps: Obtaining an estimated value of the front wheel angle at the current moment based on the steering parameter at the previous moment and the steering wheel angle at the current moment; Calculating a residual based on the calculated value of the front wheel turning angle at the current moment and the estimated value of the front wheel turning angle; The steering parameter at the previous moment is updated based on the residual and the filter gain to obtain the steering parameter at the current moment.

6. The method for online identification of steering parameters according to claim 5, characterized in that: After the steering parameter at the current moment is identified online using the recursive least square method based on the calculated value of the front wheel angle at the current moment and the steering wheel angle at the current moment, the following steps are also included: Calculate the identification confidence based on the residuals of a preset number of historical moments; Determine whether to output the steering parameter at the current moment according to the recognition confidence.

7. The method for online identification of steering parameters according to claim 6, characterized in that: The calculation of the recognition confidence based on the residuals of a preset number of historical moments includes the following steps: Based on the residuals of a preset number of historical moments, the standardized root mean square error and standardized mean of the residuals are calculated; The identification confidence is calculated based on the standardized root mean square error and the standardized mean of the residuals.

8. The method for online identification of steering parameters according to claim 1, characterized in that: Based on the calculated value of the front wheel angle at the current moment and the steering wheel angle at the current moment, before the recursive least square method is used to online identify the steering parameter at the current moment, the following steps are also included: Determining whether the vehicle state quantities at the current moment all meet the preset identification conditions, wherein the vehicle state quantities include at least one of the vehicle speed, the yaw rate change, and the front wheel turning angle; If all of them are satisfied, then based on the calculated value of the front wheel turning angle at the current moment and the steering wheel turning angle at the current moment, the steering parameter at the current moment is identified online using the recursive least square method.

9. The method for online identification of steering parameters according to claim 1, characterized in that: Outputting the steering parameter at the current moment includes the following steps: Performing a limiting process on the identified steering parameter based on a preset steering parameter limit value; The identified steering parameters are smoothed using the exponentially weighted moving average method, and the smoothed steering parameters are output.

10. A steering parameter identification system, characterized in that: For implementing the steering parameter online identification method according to any one of claims 1 to 9, the system comprises: The data acquisition module is used to obtain the calculated value of the front wheel angle at the current moment and the steering wheel angle at the current moment; a parameter identification module, for online identifying the steering parameters at the current moment by using a recursive least square method based on the calculated value of the front wheel angle at the current moment and the steering wheel angle at the current moment, wherein the steering parameters include a steering zero offset and a steering transmission ratio; The parameter output module is used to output the steering parameter at the current moment.

11. A steering parameter identification device, characterized in that: include: processor; a memory storing executable instructions of the processor; The processor is configured to execute the steps of the steering parameter online identification method according to any one of claims 1 to 9 by executing the executable instructions.

12. A computer-readable storage medium for storing a program, characterized in that: When the program is executed by a processor, the steps of the steering parameter online identification method described in any one of claims 1 to 9 are implemented.

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