Vehicle transverse kinetic parameter identification method and device, electronic equipment and storage medium

Abstract

The invention discloses a vehicle transverse kinetic parameter identification method and device, electronic equipment and a storage medium, and the method comprises the steps that driving track data and controller instruction information of a vehicle under different working conditions are collected, and the different working conditions comprise different curvature paths and / or different driving speeds; the controller instruction information at least comprises a steering wheel steering instruction; according to the driving track data of the vehicle under the different working conditions and the controller instruction information, a vehicle transverse dynamic model is established, the vehicle transverse dynamic model comprises a parameter identification object, and the parameter identification object is related to the vehicle driving track; and identifying the parameter identification object according to the vehicle transverse dynamics model. On one hand, the complexity of parameter identification of the vehicle transverse dynamics model is reduced, and on the other hand, the accuracy of the vehicle transverse dynamics model is ensured.

Description

Technical Field

[0001] The present application relates to the field of autonomous driving simulation technology, and in particular to a vehicle lateral dynamics parameter identification method, device, electronic device, and storage medium. Background Art

[0002] In autonomous driving, accurate vehicle dynamics models play an important role in the control accuracy and offline simulation verification of the underlying controller, especially for control algorithms such as model predictive control that require a long time to predict the future state of the vehicle.

[0003] The lateral dynamics model of a vehicle in the related art usually obtains a set of default parameters provided by the manufacturer, but these default parameters are often unable to adapt to all working conditions. Summary of the invention

[0004] The embodiments of the present application provide a vehicle lateral dynamics parameter identification method, device, electronic device, and storage medium to reduce the complexity of vehicle lateral dynamics model parameter identification while ensuring the accuracy of the vehicle lateral dynamics model.

[0005] The present application embodiment adopts the following technical solutions:

[0006] In a first aspect, an embodiment of the present application provides a method for identifying vehicle lateral dynamics parameters, wherein the method comprises:

[0007] Collecting driving trajectory data and controller command information of the vehicle under different working conditions, wherein the different working conditions include different curvature paths and / or different driving speeds, and the controller command information includes at least a steering wheel steering command;

[0008] Establishing a vehicle lateral dynamics model according to the vehicle driving trajectory data under the different working conditions and the controller instruction information, wherein the vehicle lateral dynamics model includes a parameter identification object, and the parameter identification object is related to the vehicle driving trajectory;

[0009] The parameter identification object is identified according to the vehicle lateral dynamics model.

[0010] In some embodiments, the vehicle lateral dynamics model is established according to the vehicle driving trajectory data under the different working conditions and the controller instruction information, including:

[0011] Determine the front wheel cornering stiffness C f , rear wheel cornering stiffness C r and transmission ratio, as parameter identification objects to be identified, the transmission ratio being used as a proportional coefficient between the steering wheel angle and the front wheel angle;

[0012] Obtain the vehicle steering wheel steering command and the vehicle front wheel turning angle, and calculate the proportional relationship between the vehicle steering wheel angle and the vehicle front wheel turning angle;

[0013] Based on the proportional relationship between the vehicle steering wheel angle and the vehicle front wheel turning angle, a modeling function for the transmission ratio in the parameter identification object to be identified is determined.

[0014] In some embodiments, the determining of a modeling function for the transmission ratio in the parameter identification object to be identified includes:

[0015] Based on the proportional relationship between the vehicle steering wheel angle and the vehicle front wheel turning angle, a nonlinear function is determined as a modeling function;

[0016] The nonlinear function is used to

[0017] It indicates that when the steering wheel angle is smaller, the corresponding transmission ratio is larger;

[0018] Indicates that when the steering wheel angle increases, the corresponding transmission ratio tends to a constant value; and

[0019] When the nonlinear function approaches 1, the corresponding transmission ratio is a fixed transmission ratio.

[0020] In some embodiments, the identifying the parameter identification object according to the vehicle lateral dynamics model includes:

[0021] Determine the parameter identification object list and initial value to be identified, the parameter list includes the front wheel cornering stiffness C f , rear wheel cornering stiffness C r and gear ratio;

[0022] Based on the preset error model, the deviation between the output trajectory corresponding to the identification model and the true value of the manually collected trajectory is compared;

[0023] Based on the error, the Twiddle algorithm is used to perform parameter identification on the vehicle lateral dynamics model to obtain the front wheel cornering stiffness C to be identified in the vehicle lateral dynamics model. f , rear wheel cornering stiffness C r and gear ratio.

[0024] In some embodiments, the Twiddle algorithm is used to perform parameter identification on the vehicle lateral dynamics model to obtain the front wheel cornering stiffness C to be identified in the vehicle lateral dynamics model. f , rear wheel cornering stiffness C r and gear ratios, including:

[0025] Obtaining the error caused by the parameter identification object to be currently identified and the error caused by the parameter identification object after parameter adjustment;

[0026] By comparing the error caused by the current parameter identification object to be identified and the error caused by the parameter identification object after parameter adjustment, the next adjustment direction of the parameter identification object to be identified is determined;

[0027] If the next adjustment direction of the parameter identification object to be identified is less than a predetermined threshold, the result after the current Twiddle algorithm adjustment is used as the result of parameter identification to obtain the front wheel cornering stiffness C to be identified in the vehicle lateral dynamics model. f , rear wheel cornering stiffness C r and gear ratio.

[0028] In some embodiments, the method further comprises:

[0029] By using the Twiddle algorithm to identify the front wheel cornering stiffness C f , rear wheel cornering stiffness C r The transmission ratio and the error after parameter adjustment are continuously adjusted until the accuracy of the vehicle lateral dynamics model meets a preset threshold.

[0030] In some embodiments, the collecting of driving trajectory data and controller instruction information of the vehicle under different working conditions, wherein the different working conditions include different curvature paths and / or different driving speeds, and the controller instruction information includes at least a steering wheel steering instruction, includes:

[0031] Combining the different curvature paths and the different driving speeds as different working conditions;

[0032] For each working condition, the driving trajectory is divided into sections at uniform time intervals according to the same curvature sign;

[0033] When each working condition corresponds to a fixed vehicle lateral dynamics parameter, the trajectories divided under different working conditions are stored by category;

[0034] If there is a curvature sign jump in the driving trajectory within the uniform time interval, it is discarded.

[0035] In a second aspect, an embodiment of the present application further provides a vehicle lateral dynamics parameter identification device, wherein the device comprises:

[0036] A collection module, used to collect the driving trajectory data of the vehicle under different working conditions and controller command information, wherein the different working conditions include different curvature paths and / or different driving speeds, and the controller command information at least includes a steering wheel steering command;

[0037] A modeling module, for establishing a vehicle lateral dynamics model according to the vehicle driving trajectory data under the different working conditions and the controller instruction information, wherein the vehicle lateral dynamics model includes a parameter identification object, and the parameter identification object is related to the vehicle driving trajectory;

[0038] The parameter identification module is used to identify the parameter identification object according to the vehicle lateral dynamics model.

[0039] In a third aspect, an embodiment of the present application further provides an electronic device, comprising: a processor; and a memory arranged to store computer executable instructions, wherein the executable instructions, when executed, cause the processor to perform the above method.

[0040] In a fourth aspect, an embodiment of the present application further provides a computer-readable storage medium, which stores one or more programs. When the one or more programs are executed by an electronic device including multiple application programs, the electronic device executes the above method.

[0041] At least one of the above technical solutions adopted in the embodiments of the present application can achieve the following beneficial effects: collecting the driving trajectory data and controller instruction information of the vehicle under different working conditions, wherein the different working conditions include different curvature paths and / or different driving speeds, and the controller instruction information at least includes steering wheel steering instructions, so as to provide data guarantee for the parameter identification of the vehicle lateral dynamics model. According to the driving trajectory data of the vehicle under the different working conditions and the controller instruction information, a vehicle lateral dynamics model is established, wherein the vehicle lateral dynamics model includes a parameter identification object, and the parameter identification object is related to the vehicle driving trajectory. The parameter identification object is modeled by nonlinear modeling. Finally, according to the vehicle lateral dynamics model, the parameter identification object is identified to complete the parameter identification. BRIEF DESCRIPTION OF THE DRAWINGS

[0042] The drawings described herein are used to provide a further understanding of the present application and constitute a part of the present application. The illustrative embodiments of the present application and their descriptions are used to explain the present application and do not constitute an improper limitation on the present application. In the drawings:

[0043] Figure 1 A schematic diagram of the flow chart of the vehicle lateral dynamics parameter identification method in an embodiment of the present application;

[0044] Figure 2 This is a schematic diagram of the overall process of identifying vehicle lateral dynamics parameters in an embodiment of the present application;

[0045] Figure 3 This is a schematic diagram of nonlinear modeling of transmission ratio in an embodiment of the present application;

[0046] Figure 4This is a schematic diagram of the structure of a vehicle lateral dynamics parameter identification device in an embodiment of the present application;

[0047] Figure 5 It is a schematic diagram of the structure of an electronic device in an embodiment of the present application. DETAILED DESCRIPTION

[0048] In order to make the purpose, technical solution and advantages of the present application clearer, the technical solution of the present application will be clearly and completely described below in combination with the specific embodiments of the present application and the corresponding drawings. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present application.

[0049] The technical solutions provided by various embodiments of the present application are described in detail below in conjunction with the accompanying drawings.

[0050] The present application embodiment provides a method for identifying vehicle lateral dynamics parameters, such as Figure 1 As shown, a schematic flow chart of a vehicle lateral dynamics parameter identification method in an embodiment of the present application is provided, and the method at least includes the following steps S110 to S130:

[0051] Step S110, collecting driving trajectory data and controller command information of the vehicle under different working conditions, wherein the different working conditions include different curvature paths and / or different driving speeds, and the controller command information at least includes a steering wheel steering command.

[0052] For data-driven vehicle dynamics parameter identification, it is necessary to collect vehicle driving trajectories under different working conditions through offline acquisition to provide data guarantee for vehicle lateral dynamics model parameter identification. It can be understood that different working conditions mainly refer to different curvatures or different vehicle speeds, or a combination of different curvatures and different vehicle speeds.

[0053] The controller command information mainly includes the steering wheel steering command steer. In addition, it can also include the accelerator pedal command. At the same time, the segmented trajectory data collected under different working conditions can be stored by category and used as the input for the vehicle lateral dynamics parameter identification.

[0054] like Figure 2 As shown, the steering wheel instructions in the bottom-level control instruction information are obtained by offline acquisition.

[0055] Step S120, establishing a vehicle lateral dynamics model according to the vehicle driving trajectory data under the different working conditions and the controller instruction information, wherein the vehicle lateral dynamics model includes a parameter identification object, and the parameter identification object is related to the vehicle driving trajectory.

[0056] When establishing the vehicle lateral dynamics model, the classic vehicle lateral dynamics model is used for modeling, and the necessary parameter identification objects are selected as the parameter identification objects to be identified. It can be understood that the initial parameter values ​​of the parameter identification objects are provided by the manufacturer. For the classic vehicle lateral dynamics model, there are fewer parameters to be identified and it has better interpretability.

[0057] like Figure 2 As shown, when establishing the vehicle lateral dynamics model, the speed is acquired through offline acquisition, and error correction is performed based on the vehicle driving trajectory output by the vehicle lateral dynamics model.

[0058] The parameter identification object is related to the vehicle's driving trajectory and includes at least: the front wheel cornering stiffness, the rear wheel cornering stiffness and the transmission ratio. Among them, the front wheel cornering stiffness and the rear wheel cornering stiffness are relatively fixed and do not need further modeling. The transmission ratio needs to be modeled as a nonlinear function. The nonlinear modeling method for the transmission ratio simplifies the parameter identification process to a certain extent, and can improve the model accuracy in the small curvature straight driving scenario.

[0059] Step S130: identifying the parameter identification object according to the vehicle lateral dynamics model.

[0060] During parameter identification, the algorithm for identifying the vehicle lateral dynamics parameters based on the vehicle lateral dynamics model and the related algorithm is simple to implement and can achieve higher model accuracy in a shorter time in practical applications.

[0061] like Figure 2 As shown in FIG. 1 , the error is obtained by offline acquisition, and the vehicle lateral dynamics model is optimized based on the Twiddle parameter positive definiteness.

[0062] In some methods, the identification of vehicle lateral dynamics parameters is modeled as a parameter estimation problem, for example, by using an extended Kalman filter, but the design and parameter estimation process of such a method is relatively complicated, or it is modeled as an error estimation problem. The reason is that a noise will be added after the existing lateral dynamics model to represent the uncertainty of the model, but this modeling process puts higher requirements on the subsequent optimization algorithm. Through the above method, the driving trajectory data and controller command information of the vehicle under different working conditions are collected, and the vehicle lateral dynamics model is established according to the driving trajectory data of the vehicle under the different working conditions and the controller command information. The complexity of the identification of the parameters of the vehicle lateral dynamics model can be reduced.

[0063] In other methods, the vehicle dynamics modeling method has gradually developed into data-driven neural network dynamics, and the dynamics modeling has a high accuracy. However, due to the high nonlinearity of neural network dynamics, learning-based rather than traditional optimization methods are often used. However, due to the high demand for data in neural dynamics models, if the amount of data is insufficient, the obtained vehicle dynamics model may not be well generalized in new scenarios. Through the above method, according to the vehicle lateral dynamics model, the parameter identification object is identified, and the accuracy of the dynamics model can be guaranteed.

[0064] In one embodiment of the present application, the vehicle lateral dynamics model is established according to the vehicle driving trajectory data under the different working conditions and the controller instruction information, including: determining the front wheel cornering stiffness C f , rear wheel cornering stiffness C r and transmission ratio, as the parameter identification object to be identified, the transmission ratio is used as the proportional coefficient between the steering wheel angle and the front wheel turning angle; obtaining the vehicle steering wheel steering command and the vehicle front wheel turning angle, and statistically calculating the proportional relationship between the vehicle steering wheel angle and the vehicle front wheel turning angle; based on the proportional relationship between the vehicle steering wheel angle and the vehicle front wheel turning angle, determining the modeling function of the transmission ratio in the parameter identification object to be identified.

[0065] Determine the front wheel cornering stiffness C f , rear wheel cornering stiffness C r and transmission ratio (the proportional coefficient between the steering wheel angle and the front wheel angle) as the parameter identification object to be identified. The transmission ratio is used as the proportional coefficient between the steering wheel angle and the front wheel angle. When modeling the transmission ratio, the proportional relationship between the vehicle steering wheel angle and the vehicle front wheel angle is statistically analyzed, and finally, according to the proportional relationship, the modeling function of the transmission ratio in the parameter identification object to be identified is determined. Preferably, the modeling function is a nonlinear function. Preferably, the modeling function is a coth function, specifically as follows Figure 3 As shown,

[0066] The vehicle lateral dynamics parameter identification method proposed in the above steps uses a classic vehicle lateral dynamics model, which requires fewer parameters to be identified and has better interpretability.

[0067] In one embodiment of the present application, the determination of the modeling function of the transmission ratio in the parameter identification object to be identified includes: determining a nonlinear function as a modeling function based on the proportional relationship between the vehicle steering wheel angle and the vehicle front wheel angle; the nonlinear function is used to characterize that when the steering wheel angle is smaller, the corresponding transmission ratio is larger; when the steering wheel angle increases, the corresponding transmission ratio tends to a constant value; and when the nonlinear function tends to 1, the corresponding transmission ratio is a fixed transmission ratio.

[0068] For more details, please refer to Figure 3 , using the first quadrant of the nonlinear function, as the steering wheel angle increases, the transmission ratio tends to a constant value, which conforms to the large curvature turning scenario. The smaller the steering wheel angle, the larger the transmission ratio, which conforms to the dead zone characteristics in the small curvature straight driving scenario, and the identification parameter is a proportional coefficient. When the coth function tends to 1, it is a fixed transmission ratio. Therefore, the modeling function is selected to be consistent with the proportional relationship between the coth function and the vehicle steering wheel angle and the vehicle front wheel angle, so that the coth function can characterize the characteristics of the transmission ratio under different working conditions.

[0069] In one embodiment of the present application, the identifying the parameter identification object according to the vehicle lateral dynamics model includes: determining a list of parameter identification objects to be identified and initial values, wherein the parameter list includes the front wheel cornering stiffness C f , rear wheel cornering stiffness C r and transmission ratio; based on a preset error model, comparing the deviation between the output trajectory corresponding to the identification model and the true value of the manually collected trajectory; based on the error, using the Twiddle algorithm to perform parameter identification on the vehicle lateral dynamics model, and obtaining the front wheel cornering stiffness C to be identified in the vehicle lateral dynamics model f , rear wheel cornering stiffness C r and gear ratio.

[0070] like Figure 2 As shown, determine the parameter list and initial values ​​that need to be adjusted, the parameter list includes the front wheel cornering stiffness C f , rear wheel cornering stiffness C r And the transmission ratio. Based on the preset error model, compare the deviation between the output trajectory corresponding to the identification model and the true value of the manually collected trajectory. Construct the error model required for training. Since it is the vehicle lateral dynamics model parameter identification, in the vehicle lateral dynamics model, specifically, the true value of the offline collected data is used for the vehicle longitudinal trajectory, and the error model uses the sum of the average lateral error and the average angular error. Based on the preset error model, compare the deviation between the output trajectory corresponding to the identification model and the true value of the manually collected trajectory. It can be understood that the identification model is the vehicle lateral dynamics model.

[0071] Then, based on the error, the Twiddle algorithm is used to perform parameter identification on the vehicle lateral dynamics model to obtain the front wheel cornering stiffness C to be identified in the vehicle lateral dynamics model. f , rear wheel cornering stiffness C r and gear ratio.

[0072] In one embodiment of the present application, the Twiddle algorithm is used to perform parameter identification on the vehicle lateral dynamics model to obtain the front wheel cornering stiffness C to be identified in the vehicle lateral dynamics model. f , rear wheel cornering stiffness C r and a transmission ratio, including: obtaining an error caused by a current parameter identification object to be identified and an error caused by a parameter identification object after parameter adjustment; determining a next adjustment direction of the parameter identification object to be identified by comparing the relationship between the error caused by the current parameter identification object to be identified and the error caused by the parameter identification object after parameter adjustment; if the next adjustment direction of the parameter identification object to be identified is less than a predetermined threshold, taking the current result after Twiddle algorithm adjustment as the result of parameter identification, and obtaining the front wheel cornering stiffness C to be identified in the vehicle lateral dynamics model f , rear wheel cornering stiffness C r and gear ratio.

[0073] A simple parameter update rule is defined based on the Twiddle algorithm, that is, the next parameter adjustment direction is determined by comparing the error between the current parameter and the adjusted parameter, so that the trajectory output by the model continuously approaches the true trajectory. The result after twiddle adjustment is taken as the result of parameter identification when the error between the model output trajectory and the true trajectory is less than a certain threshold.

[0074] The transmission ratio nonlinear modeling method proposed in the embodiment of the present application simplifies the parameter identification process to a certain extent and improves the model accuracy in the straight-line driving scenario with small curvature.

[0075] In one embodiment of the present application, the method further comprises: using the Twiddle algorithm to identify the front wheel cornering stiffness C f , rear wheel cornering stiffness C r The transmission ratio and the error after parameter adjustment are continuously adjusted until the accuracy of the vehicle lateral dynamics model meets a preset threshold.

[0076] The vehicle driving trajectory under different working conditions is sliced ​​according to different driving curvatures and driving speeds, and then the Twiddle algorithm is used for parameter identification. The algorithm for identifying the vehicle lateral dynamics parameters based on the Twiddle algorithm proposed in the embodiment of the present application is simple to implement, and can achieve high model accuracy in a shorter time in practical applications.

[0077] Specifically, for the Twiddle algorithm, please refer to Farag, Wael. "Complex trajectory tracking using PID control for autonomous driving." International Journal of Intelligent Transportation Systems Research 18.2(2020):356-366.

[0078] The above method uses the Twiddle algorithm to adjust the parameters according to the error to correct the direction until the model accuracy meets a certain set threshold.

[0079] In one embodiment of the present application, the driving trajectory data and controller command information of the vehicle under different operating conditions are collected, and the different operating conditions include different curvature paths and / or different driving speeds, and the controller command information includes at least a steering wheel steering instruction, including: combining the different curvature paths and the different driving speeds as different operating conditions; for each operating condition, dividing the driving trajectory according to the same curvature sign at a uniform time interval; when each operating condition corresponds to a fixed vehicle lateral dynamics parameter, the divided trajectories under different operating conditions are stored by category; if there is a curvature sign jump in the driving trajectory within the uniform time interval, it is discarded.

[0080] First, the vehicle driving data and underlying controller command information under different working conditions are collected, and the working conditions are regarded as a combination of different curvature paths and different driving speeds, while covering as many traffic scenarios as possible such as straight driving, lane changing, and turning at intersections.

[0081] Furthermore, for each working condition, the driving trajectory is divided into the same curvature sign, for example, if the curvature is all positive or all negative, the trajectory with curvature sign change is divided at a uniform time interval, and the trajectory is discarded if there is a curvature sign jump within the time interval.

[0082] Finally, assuming that each working condition corresponds to a fixed vehicle lateral dynamics parameter, the segmented trajectories under different working conditions are stored by category and used as input for the vehicle lateral dynamics parameter identification in the subsequent step.

[0083] The present application also provides a vehicle lateral dynamics parameter identification device 400, such as Figure 4 As shown, a schematic diagram of the structure of a vehicle lateral dynamics parameter identification device in an embodiment of the present application is provided, wherein the vehicle lateral dynamics parameter identification device 400 at least includes: a collection module 410, a modeling module 420, and a parameter identification module 430, wherein:

[0084] In one embodiment of the present application, the acquisition module 410 is specifically used to: collect the vehicle's driving trajectory data and controller command information under different operating conditions, the different operating conditions including different curvature paths and / or different driving speeds, and the controller command information at least includes a steering wheel steering command.

[0085] For data-driven vehicle dynamics parameter identification, it is necessary to collect vehicle driving trajectories under different working conditions through offline acquisition to provide data guarantee for vehicle lateral dynamics model parameter identification. It can be understood that different working conditions mainly refer to different curvatures or different vehicle speeds, or a combination of different curvatures and different vehicle speeds.

[0086] The controller command information mainly includes the steering wheel steering command steer. In addition, it can also include the accelerator pedal command. At the same time, the segmented trajectory data collected under different working conditions can be stored by category and used as the input for the vehicle lateral dynamics parameter identification.

[0087] like Figure 2 As shown, the steering wheel instructions in the bottom-level control instruction information are obtained by offline acquisition.

[0088] In one embodiment of the present application, the modeling module 420 is specifically used to establish a vehicle lateral dynamics model based on the vehicle's driving trajectory data under the different working conditions and the controller command information, wherein the vehicle lateral dynamics model includes a parameter identification object, and the parameter identification object is related to the vehicle's driving trajectory.

[0089] When establishing the vehicle lateral dynamics model, the classic vehicle lateral dynamics model is used for modeling, and the necessary parameter identification objects are selected as the parameter identification objects to be identified. It can be understood that the initial parameter values ​​of the parameter identification objects are provided by the manufacturer. For the classic vehicle lateral dynamics model, there are fewer parameters to be identified and it has better interpretability.

[0090] like Figure 2 As shown, when establishing the vehicle lateral dynamics model, the speed is acquired through offline acquisition, and error correction is performed based on the vehicle driving trajectory output by the vehicle lateral dynamics model.

[0091] The parameter identification object is related to the vehicle's driving trajectory and includes at least: the front wheel cornering stiffness, the rear wheel cornering stiffness and the transmission ratio. Among them, the front wheel cornering stiffness and the rear wheel cornering stiffness are relatively fixed and do not need further modeling. The transmission ratio needs to be modeled as a nonlinear function. The nonlinear modeling method for the transmission ratio simplifies the parameter identification process to a certain extent, and can improve the model accuracy in the small curvature straight driving scenario.

[0092] In one embodiment of the present application, the parameter identification module 430 is specifically used to: identify the parameter identification object according to the vehicle lateral dynamics model.

[0093] During parameter identification, the algorithm for identifying the vehicle lateral dynamics parameters based on the vehicle lateral dynamics model and the related algorithm is simple to implement and can achieve higher model accuracy in a shorter time in practical applications.

[0094] like Figure 2 As shown in FIG. 1 , the error is obtained by offline acquisition, and the vehicle lateral dynamics model is optimized based on the Twiddle parameter positive definiteness.

[0095] In one embodiment of the present application, the modeling module 420 is also used to

[0096] Determine the front wheel cornering stiffness C f , rear wheel cornering stiffness C r and transmission ratio, as parameter identification objects to be identified, the transmission ratio being used as a proportional coefficient between the steering wheel angle and the front wheel angle;

[0097] Obtain the vehicle steering wheel steering command and the vehicle front wheel turning angle, and calculate the proportional relationship between the vehicle steering wheel angle and the vehicle front wheel turning angle;

[0098] Based on the proportional relationship between the vehicle steering wheel angle and the vehicle front wheel turning angle, a modeling function for the transmission ratio in the parameter identification object to be identified is determined.

[0099] In one embodiment of the present application, the modeling module 420 is also used to

[0100] Based on the proportional relationship between the vehicle steering wheel angle and the vehicle front wheel turning angle, a nonlinear function is determined as a modeling function;

[0101] The nonlinear function is used to

[0102] It indicates that when the steering wheel angle is smaller, the corresponding transmission ratio is larger;

[0103] Indicates that when the steering wheel angle increases, the corresponding transmission ratio tends to a constant value; and

[0104] When the nonlinear function approaches 1, the corresponding transmission ratio is a fixed transmission ratio.

[0105] In one embodiment of the present application, the parameter identification module 430 is also used to

[0106] Determine the parameter identification object list and initial value to be identified, the parameter list includes the front wheel cornering stiffness C f , rear wheel cornering stiffness C r and gear ratio;

[0107] Based on the preset error model, the deviation between the output trajectory corresponding to the identification model and the true value of the manually collected trajectory is compared;

[0108] Based on the error, the Twiddle algorithm is used to perform parameter identification on the vehicle lateral dynamics model to obtain the front wheel cornering stiffness C to be identified in the vehicle lateral dynamics model. f , rear wheel cornering stiffness C r and gear ratio.

[0109] In one embodiment of the present application, the parameter identification module 430 is also used to

[0110] Obtaining the error caused by the parameter identification object to be currently identified and the error caused by the parameter identification object after parameter adjustment;

[0111] By comparing the error caused by the current parameter identification object to be identified and the error caused by the parameter identification object after parameter adjustment, the next adjustment direction of the parameter identification object to be identified is determined;

[0112] If the next adjustment direction of the parameter identification object to be identified is less than a predetermined threshold, the result after the current Twiddle algorithm adjustment is used as the result of parameter identification to obtain the front wheel cornering stiffness C to be identified in the vehicle lateral dynamics model. f , rear wheel cornering stiffness C r and gear ratio.

[0113] In one embodiment of the present application, the parameter identification module 430 is also used to

[0114] By using the Twiddle algorithm to identify the front wheel cornering stiffness C f , rear wheel cornering stiffness C r The transmission ratio and the error after parameter adjustment are continuously adjusted until the accuracy of the vehicle lateral dynamics model meets a preset threshold.

[0115] In one embodiment of the present application, the acquisition module 410 is also used to

[0116] Combining the different curvature paths and the different driving speeds as different working conditions;

[0117] For each working condition, the driving trajectory is divided into sections at uniform time intervals according to the same curvature sign;

[0118] When each working condition corresponds to a fixed vehicle lateral dynamics parameter, the trajectories divided under different working conditions are stored by category;

[0119] If there is a curvature sign jump in the driving trajectory within the uniform time interval, it is discarded.

[0120] It can be understood that the above-mentioned vehicle lateral dynamics parameter identification device can implement the various steps of the vehicle lateral dynamics parameter identification method provided in the aforementioned embodiment, and the relevant explanations about the vehicle lateral dynamics parameter identification method are applicable to the vehicle lateral dynamics parameter identification device and will not be repeated here.

[0121] Figure 5 This is a schematic diagram of the structure of an electronic device according to an embodiment of the present application. Figure 5 At the hardware level, the electronic device includes a processor, and optionally also includes an internal bus, a network interface, and a memory. The memory may include a memory, such as a high-speed random access memory (RAM), and may also include a non-volatile memory (non-volatile memory), such as at least one disk storage. Of course, the electronic device may also include hardware required for other services.

[0122] The processor, network interface and memory can be interconnected through an internal bus, which can be an ISA (Industry Standard Architecture) bus, a PCI (Peripheral Component Interconnect) bus or an EISA (Extended Industry Standard Architecture) bus. The bus can be divided into an address bus, a data bus, a control bus, etc. For ease of representation, Figure 5 Only one bidirectional arrow is used in the diagram, but this does not mean that there is only one bus or only one type of bus.

[0123] The memory is used to store the program. Specifically, the program may include a program code, and the program code includes a computer operation instruction. The memory may include a memory and a non-volatile memory, and provides instructions and data to the processor.

[0124] The processor reads the corresponding computer program from the non-volatile memory into the memory and then runs it, forming a vehicle lateral dynamics parameter identification device at the logical level. The processor executes the program stored in the memory and is specifically used to perform the following operations:

[0125] Collecting driving trajectory data and controller command information of the vehicle under different working conditions, wherein the different working conditions include different curvature paths and / or different driving speeds, and the controller command information includes at least a steering wheel steering command;

[0126] Establishing a vehicle lateral dynamics model according to the vehicle driving trajectory data under the different working conditions and the controller instruction information, wherein the vehicle lateral dynamics model includes a parameter identification object, and the parameter identification object is related to the vehicle driving trajectory;

[0127] The parameter identification object is identified according to the vehicle lateral dynamics model.

[0128] The above application Figure 1 The method performed by the vehicle lateral dynamics parameter identification device disclosed in the illustrated embodiment can be applied to a processor or implemented by a processor. The processor may be an integrated circuit chip with signal processing capabilities. In the implementation process, each step of the above method can be completed by an integrated logic circuit of hardware in the processor or an instruction in the form of software. The above processor can be a general-purpose processor, including a central processing unit (CPU), a network processor (NP), etc.; it can also be a digital signal processor (DSP), an application specific integrated circuit (ASIC), a field programmable gate array (FPGA) or other programmable logic devices, discrete gates or transistor logic devices, discrete hardware components. The methods, steps and logic block diagrams disclosed in the embodiments of the present application can be implemented or executed. The general-purpose processor can be a microprocessor or the processor can also be any conventional processor, etc. The steps of the method disclosed in the embodiments of the present application can be directly embodied as a hardware decoding processor for execution, or a combination of hardware and software modules in the decoding processor for execution. The software module can be located in a storage medium mature in the art such as a random access memory, a flash memory, a read-only memory, a programmable read-only memory, or an electrically erasable programmable memory, a register, etc. The storage medium is located in the memory, and the processor reads the information in the memory and completes the steps of the above method in combination with its hardware.

[0129] The electronic device may also perform Figure 1 The method is implemented by the vehicle lateral dynamics parameter identification device in Figure 1 The functions of the illustrated embodiment will not be described in detail in the embodiments of the present application.

[0130] The present application also provides a computer-readable storage medium, which stores one or more programs, wherein the one or more programs include instructions, which, when executed by an electronic device including multiple application programs, enable the electronic device to execute Figure 1 The method performed by the vehicle lateral dynamics parameter identification device in the illustrated embodiment is specifically used to perform:

[0131] Collecting driving trajectory data and controller command information of the vehicle under different working conditions, wherein the different working conditions include different curvature paths and / or different driving speeds, and the controller command information includes at least a steering wheel steering command;

[0132] Establishing a vehicle lateral dynamics model according to the vehicle driving trajectory data under the different working conditions and the controller instruction information, wherein the vehicle lateral dynamics model includes a parameter identification object, and the parameter identification object is related to the vehicle driving trajectory;

[0133] The parameter identification object is identified according to the vehicle lateral dynamics model.

[0134] Those skilled in the art will appreciate that embodiments of the present invention may be provided as methods, systems, or computer program products. Therefore, the present invention may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Moreover, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0135] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0136] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0137] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0138] In a typical configuration, a computing device includes one or more processors (CPU), input / output interfaces, network interfaces, and memory.

[0139] The memory may include non-permanent storage in a computer-readable medium, random access memory (RAM) and / or non-volatile memory in the form of read-only memory (ROM) or flash RAM. The memory is an example of a computer-readable medium.

[0140] Computer readable media include permanent and non-permanent, removable and non-removable media that can be implemented by any method or technology to store information. Information can be computer readable instructions, data structures, program modules or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technology, compact disk read-only memory (CD-ROM), digital versatile disk (DVD) or other optical storage, magnetic cassettes, magnetic tape magnetic disk storage or other magnetic storage devices or any other non-transmission media that can be used to store information that can be accessed by a computing device. As defined herein, computer readable media does not include temporary computer readable media (transitory media), such as modulated data signals and carrier waves.

[0141] It should also be noted that the terms "include", "comprises" or any other variations thereof are intended to cover non-exclusive inclusion, so that a process, method, commodity or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, commodity or device. In the absence of more restrictions, the elements defined by the sentence "comprises a ..." do not exclude the existence of other identical elements in the process, method, commodity or device including the elements.

[0142] Those skilled in the art will appreciate that the embodiments of the present application may be provided as methods, systems or computer program products. Therefore, the present application may adopt the form of a complete hardware embodiment, a complete software embodiment or an embodiment in combination with software and hardware. Moreover, the present application may adopt the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) that contain computer-usable program code.

[0143] The above is only an embodiment of the present application and is not intended to limit the present application. For those skilled in the art, the present application may have various changes and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application should be included in the scope of the claims of the present application.

Claims

1. A method for identifying vehicle lateral dynamic parameters, wherein: The method comprises: Collecting driving trajectory data and controller command information of the vehicle under different working conditions, wherein the different working conditions include different curvature paths and / or different driving speeds, and the controller command information includes at least a steering wheel steering command; Establishing a vehicle lateral dynamics model according to the vehicle driving trajectory data under the different working conditions and the controller instruction information, wherein the vehicle lateral dynamics model includes a parameter identification object, and the parameter identification object is related to the vehicle driving trajectory; The parameter identification object is identified according to the vehicle lateral dynamics model.

2. The method of claim 1, wherein: The step of establishing a vehicle lateral dynamics model according to the vehicle driving trajectory data under the different working conditions and the controller instruction information includes: Determine the front wheel cornering stiffness C f , rear wheel cornering stiffness C r and transmission ratio, as parameter identification objects to be identified, the transmission ratio being used as a proportional coefficient between the steering wheel angle and the front wheel angle; Obtain the vehicle steering wheel steering command and the vehicle front wheel turning angle, and calculate the proportional relationship between the vehicle steering wheel angle and the vehicle front wheel turning angle; A modeling function that determines the transmission ratio based on the proportional relationship between the vehicle's steering wheel angle and the vehicle's front wheel angle.

3. The method of claim 2, wherein: The modeling function for determining the transmission ratio comprises: Based on the proportional relationship between the vehicle steering wheel angle and the vehicle front wheel turning angle, a nonlinear function is determined as a modeling function; The nonlinear function is used to It indicates that when the steering wheel angle is smaller, the corresponding transmission ratio is larger; Indicates that when the steering wheel angle increases, the corresponding transmission ratio tends to a constant value; and When the nonlinear function approaches 1, the corresponding transmission ratio is a fixed transmission ratio.

4. The method of claim 1, wherein: The step of identifying the parameter identification object according to the vehicle lateral dynamics model includes: Determine the parameter identification object list and initial value to be identified, the parameter list includes the front wheel cornering stiffness C f , rear wheel cornering stiffness C r and gear ratio; Based on the preset error model, the deviation between the output trajectory corresponding to the identification model and the true value of the manually collected trajectory is compared; Based on the error, the Twiddle algorithm is used to perform parameter identification on the vehicle lateral dynamics model to obtain the front wheel cornering stiffness C to be identified in the vehicle lateral dynamics model. f , rear wheel cornering stiffness C r and gear ratio.

5. The method of claim 4, wherein: The Twiddle algorithm is used to perform parameter identification on the vehicle lateral dynamics model to obtain the front wheel cornering stiffness C to be identified in the vehicle lateral dynamics model. f , rear wheel cornering stiffness C r and gear ratios, including: Obtaining the error caused by the parameter identification object to be currently identified and the error caused by the parameter identification object after parameter adjustment; By comparing the error caused by the current parameter identification object to be identified and the error caused by the parameter identification object after parameter adjustment, the next adjustment direction of the parameter identification object to be identified is determined; If the next adjustment direction of the parameter identification object to be identified is less than a predetermined threshold, the result after the current Twiddle algorithm adjustment is used as the result of parameter identification to obtain the front wheel cornering stiffness C to be identified in the vehicle lateral dynamics model. f , rear wheel cornering stiffness C r and gear ratio.

6. The method of claim 5, wherein: The method further comprises: By using the Twiddle algorithm to identify the front wheel cornering stiffness C f , rear wheel cornering stiffness C r The transmission ratio and the error after parameter adjustment are continuously adjusted until the accuracy of the vehicle lateral dynamics model meets a preset threshold condition.

7. The method of claim 1, wherein: The collecting of driving trajectory data and controller instruction information of the vehicle under different working conditions, wherein the different working conditions include different curvature paths and / or different driving speeds, and the controller instruction information includes at least a steering wheel steering instruction, includes: Combining the different curvature paths and the different driving speeds as different working conditions; For each working condition, the driving trajectory is divided according to the same curvature sign and uniform time interval; Under each working condition, corresponding to a fixed vehicle lateral dynamics parameter, the segmented trajectories under different working conditions are stored by category; If there is a curvature sign jump in the driving trajectory within the uniform time interval, it is discarded.

8. A vehicle lateral dynamics parameter identification device, wherein: The device comprises: A collection module, used to collect the driving trajectory data of the vehicle under different working conditions and controller command information, wherein the different working conditions include different curvature paths and / or different driving speeds, and the controller command information at least includes a steering wheel steering command; A modeling module, for establishing a vehicle lateral dynamics model according to the vehicle driving trajectory data under the different working conditions and the controller instruction information, wherein the vehicle lateral dynamics model includes a parameter identification object, and the parameter identification object is related to the vehicle driving trajectory; The parameter identification module is used to identify the parameter identification object according to the vehicle lateral dynamics model.

9. An electronic device, comprising: processor; as well as A memory arranged to store computer executable instructions, which when executed cause the processor to perform the method of any one of claims 1 to 7.

10. A computer-readable storage medium storing one or more programs, which, when executed by an electronic device including a plurality of application programs, causes the electronic device to execute any one of the methods of claims 1 to 7.

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