A method for estimating vehicle state and the vehicle

Abstract

This application provides a method for estimating vehicle state and a vehicle, relating to the field of vehicle control technology. The method relies on the assumption that the vehicle body is a rigid body and the corresponding physical model to determine a first vertical velocity of the vehicle body at each wheel end. A second vertical velocity of the vehicle body at each wheel end is then determined using a prediction model. This prediction model is a neural network model that can learn the mapping relationship between the vehicle's driving state and the vertical motion state at the wheel ends. It can fit the flexible deformation characteristics and local vibration characteristics of the vehicle body during driving, and does not rely on the aforementioned assumptions, thus avoiding errors caused by a non-rigid vehicle body. Furthermore, based on the first and second vertical velocities of the vehicle body, a target vertical velocity of the vehicle body is determined. This integrates the basic calculation results of the physical model with the accurate prediction results of the neural network model, eliminating the problem of large estimation errors caused by a non-rigid vehicle body, and accurately estimating the vertical velocity of the vehicle body at the wheel ends.

Description

Technical Field

[0001] This application relates to the field of vehicle control technology, and more specifically, to a method for estimating vehicle state and a vehicle in the field of vehicle control technology. Background Technology

[0002] The vertical velocity of a vehicle (such as the vertical velocity of the vehicle body at the wheel wells) is a key parameter characterizing the vertical motion state of the vehicle. It directly reflects the vibration amplitude and impact intensity between the vehicle body and the road surface, as well as the working state of the suspension system. It is an important basis for realizing active suspension control, vehicle posture adjustment, and optimization of driving comfort. Determining the accurate vertical velocity is of paramount importance for improving the smoothness, handling stability, and ride experience of the vehicle.

[0003] In related technologies, the vertical acceleration measured by the inertial measurement unit (IMU) in the vehicle is usually integrated, and then combined with angular velocity compensation to determine the above-mentioned vehicle body vertical velocity.

[0004] The aforementioned solutions assume the vehicle body is rigid, calculating the vertical velocity of the vehicle body by compensating for the vehicle's angular velocity. However, in reality, the vehicle body is not rigid and its stiffness distribution is uneven. When a vehicle travels on high-frequency excitation surfaces such as Belgian roads and speed bumps, the vehicle body still experiences significant local deformation and vibration, causing the vertical acceleration measured by the IMU to include local vibration noise. In this case, even using the aforementioned integration method combined with angular velocity compensation to obtain the vehicle body's vertical velocity still contains a large error and cannot accurately reflect the vehicle body's true vertical motion state.

[0005] Therefore, there is an urgent need for a method to estimate the vehicle state in order to accurately estimate the vertical velocity of the vehicle body. Summary of the Invention

[0006] This application provides a method for estimating vehicle state and a vehicle, which can accurately estimate the vertical velocity of the vehicle body at each wheel end.

[0007] Firstly, a method for estimating vehicle state is provided. This method includes: determining a first vertical velocity at each wheel end of the vehicle based on a first vertical velocity at the vehicle's center of mass at the current moment, the vehicle's geometric position parameters, and attitude parameters; inputting the vehicle's driving parameters, road condition parameters, the first vertical velocity, and the attitude parameters into a prediction model to predict a second vertical velocity at each wheel end, the prediction model reflecting the mapping relationship between the vehicle's driving state parameters and the vertical motion state parameters at the wheel ends; and determining a target vertical velocity at each wheel end based on the first and second vertical velocities, the target vertical velocity being used to control the vehicle.

[0008] In the above technical solution, the first vertical velocity of the vehicle body at each wheel end is determined based on the first vertical velocity at the vehicle's center of gravity, the vehicle's geometric position parameters, and attitude parameters. This relies on the assumption that the vehicle body is a rigid body and the corresponding physical model to determine the reference vertical velocity of the vehicle body at each wheel end, i.e., the first vertical velocity of the vehicle body. Subsequently, the driving parameters, road condition parameters, the first vertical velocity, and attitude parameters are input into the prediction model, which is obtained after training multiple sets of training samples and is a neural network model. This prediction model can learn the nonlinear mapping relationship between the vehicle's driving state and the vertical motion state at the wheel ends without relying on the above assumption. This prediction model can adaptively fit the flexible deformation characteristics and local vibration characteristics of the vehicle body during driving, avoiding errors caused by non-rigid body bodies, and can accurately predict the second vertical velocity of the vehicle body. Furthermore, the target vertical velocity of the vehicle body is determined based on the first and second vertical velocities of the vehicle body. This combines the advantages of the basic calculation results of the physical model and the accurate prediction results of the neural network model, effectively eliminating the problem of large estimation errors caused by the fact that the car body is not a rigid body, accurately reflecting the true vertical motion state of the car body at each wheel end, and accurately estimating the vertical velocity of the car body at each wheel end.

[0009] In conjunction with the first aspect, in some possible implementations, determining the target vehicle vertical velocity at each wheel end based on the first vehicle vertical velocity and the second vehicle vertical velocity includes: determining the driving parameters and the road condition parameters, and the target similarity with each sample driving parameter and corresponding sample road condition parameter in a plurality of sample driving parameters; determining a first weighting coefficient based on the plurality of target similarities, the first weighting coefficient being used to reflect the contribution of the second vehicle vertical velocity in determining the target vehicle vertical velocity; and performing a weighted fusion of the first vehicle vertical velocity and the second vehicle vertical velocity based on the second weighting coefficient and the first weighting coefficient to obtain the target vehicle vertical velocity at each wheel end, the second weighting coefficient being the difference between a preset weighting coefficient and the first weighting coefficient, the second weighting coefficient being used to reflect the contribution of the first vehicle vertical velocity in determining the target vehicle vertical velocity.

[0010] In the above technical solution, the current driving parameters and road condition parameters are matched in real time with the target similarity of historical samples (driving parameters of each sample and corresponding road condition parameters). Based on multiple target similarities, a first weighting coefficient is determined. This determines the degree of trust in the neural network model when determining the first vehicle body vertical speed based on the similarity between the actual working conditions and the working conditions corresponding to the neural network model. This avoids the situation where the neural network model over-relies on the second vehicle body vertical speed when it has not been trained to the working conditions corresponding to the current driving parameters and road condition parameters. Furthermore, based on the second weighting coefficient and the first weighting coefficient, the first vehicle body vertical speed and the second vehicle body vertical speed are weighted and fused to obtain the target vehicle body vertical speed at each wheel end. This can fully utilize the accuracy of the basic calculation results of the physical model and the accurate prediction results of the neural network model. The above solution can dynamically allocate weighting coefficients based on actual working conditions, effectively suppressing errors caused by the flexible deformation and local vibration of the vehicle body, and improving the reliability of the target vehicle body vertical speed and the adaptability to the driving environment.

[0011] In combination with the first aspect and the above implementation methods, in some possible implementation methods, a first weighting coefficient is determined based on multiple target similarities, including: when the maximum similarity among the multiple target similarities is greater than or equal to a preset similarity, the first weighting coefficient is determined as a first preset coefficient; when the maximum similarity is less than the preset similarity, the first weighting coefficient is determined as a second preset coefficient, and the second preset coefficient is less than the first preset coefficient.

[0012] In the above technical solution, a first weighting coefficient is determined based on the relationship between the maximum similarity and the preset similarity. When the maximum similarity exceeds the preset similarity, a larger first preset coefficient is used. This fully leverages the predictive model's accurate prediction advantage under similar operating conditions. When the maximum similarity is less than the preset similarity, a smaller second preset coefficient is used. This reduces the impact of the uncertainty of the predictive model when predicting the vertical velocity of the second vehicle body under unfamiliar operating conditions on the vertical velocity of the target vehicle body. This solution can reasonably allocate the weighting coefficients of the prediction results (vertical velocity of the second vehicle body) according to the similarity of operating conditions, avoiding the problem of insufficient adaptability caused by fixed weighting coefficients, and providing a reliable basis for subsequent weighted fusion.

[0013] Combining the first aspect and the above implementation methods, in some possible implementations, the geometric position parameters include a first distance between the center of gravity and the front axle, a second distance between the center of gravity and the rear axle, and a third distance between the left and right wheels. Based on the first vertical velocity at the vehicle's center of gravity at the current moment, the vehicle's geometric position parameters, and attitude operation parameters, the first vertical velocity of the vehicle body at each wheel end is determined, including: based on the pitch angular velocity, roll angular velocity, the first distance, the second distance, and the third distance in the attitude operation parameters, a second vertical velocity, a third vertical velocity, and a fourth vertical velocity are determined. The second vertical velocity is the vertical velocity component generated by the vehicle body pitch motion at the front wheels, and the third vertical velocity is the vertical velocity component generated by the vehicle body pitch motion at the front wheels. The three vertical velocities are the vertical velocity components generated by the vehicle's pitch motion at the rear wheels, and the fourth vertical velocity is the vertical velocity component generated by the vehicle's roll motion between the left and right wheels. Based on the first, second, third, and fourth vertical velocities, the third vertical velocity of the vehicle at each wheel end is determined, and the third vertical velocity of the vehicle at each wheel end is determined as the first vertical velocity of the vehicle at each wheel end; or, the product of the third vertical velocity of the vehicle at each wheel end and the corresponding compensation coefficient is determined as the first vertical velocity of the vehicle at each wheel end. The compensation coefficient is used to compensate for the degree of influence of the corresponding road conditions on the vertical velocity of the vehicle at the wheel end.

[0014] In the above technical solution, based on the pitch angular velocity, roll angular velocity, first distance, second distance, and third distance in the attitude operation parameters, a second vertical velocity, a third vertical velocity, and a fourth vertical velocity (these three vertical velocities) are determined. This can determine the vertical velocity components generated by the vehicle's pitch motion at the front and rear wheels and the vertical velocity components generated by the vehicle's roll motion between the left and right wheels, thus refining the various velocity components. Furthermore, by combining the first vertical velocity at the center of gravity with the above three vertical velocities, the first vertical velocity at the center of gravity can be converted into the third vehicle vertical velocity at the wheel ends, thereby obtaining the first vehicle vertical velocity. A compensation coefficient is then introduced to offset the influence of road conditions on the first vehicle vertical velocity. The above solution determines the first vehicle vertical velocity in two ways. The first method does not consider the compensation coefficient, which allows for a rapid determination of the first vehicle vertical velocity. The second method considers the compensation coefficient, which can take into account both the vehicle's attitude motion characteristics and the influence of road conditions, determining a precise first vehicle vertical velocity, providing a reliable basic input for the subsequent determination of the target vehicle vertical velocity.

[0015] In conjunction with the first aspect and the above-described implementation, in some possible implementations, determining the third vehicle body vertical velocity at each wheel end based on the first vertical velocity, the second vertical velocity, the third vertical velocity, and the fourth vertical velocity includes: determining a first vertical velocity difference between the first vertical velocity and the second vertical velocity, and determining the sum of the first vertical velocity difference and the fourth vertical velocity as the third vehicle body vertical velocity at the left front wheel end; determining the difference between the first vertical velocity difference and the fourth vertical velocity as the third vehicle body vertical velocity at the right front wheel end; determining the sum of the first vertical velocity and the third vertical velocity to obtain a first total vertical velocity, and determining the sum of the first total vertical velocity and the fourth vertical velocity as the third vehicle body vertical velocity at the left rear wheel end; and determining the difference between the first total vertical velocity and the fourth vertical velocity as the third vehicle body vertical velocity at the right rear wheel end.

[0016] In the above technical solution, the basic vertical velocities at the front and rear wheel ends are obtained by adding and subtracting the first vertical velocity and the pitch component. Then, the positive and negative differences in the roll component are superimposed to accurately distinguish the motion direction of the left and right wheels. Based on the decomposition principle of vehicle body spatial motion, this solution maps the effects of the center of mass translation and pitch / roll motion to each wheel end according to their geometric positions, correcting the errors caused by using only the first vertical velocity. In other words, the above solution vector-synthesizes the first vertical velocity (center of mass vertical velocity) and the velocity components generated by pitch / roll according to the position of each wheel. This ensures that the third vertical velocity at each wheel end closely matches the actual vehicle body attitude changes, providing reliable data input for the subsequent determination of the target vertical velocity at each wheel end.

[0017] In combination with the first aspect and the above implementation methods, in some possible implementation methods, the method for determining the compensation coefficient corresponding to the third vehicle body vertical velocity at each wheel end includes: determining the road surface excitation frequency of the driving surface corresponding to each wheel to obtain multiple road surface excitation frequencies; taking the road surface excitation frequency of the driving surface corresponding to any wheel among the multiple road surface excitation frequencies as the target excitation frequency, determining the candidate excitation frequency matching the target excitation frequency from multiple sample road surface excitation frequencies; determining the sample compensation coefficient corresponding to the candidate excitation frequency among the multiple sample road surface excitation frequencies as the compensation coefficient corresponding to the third vehicle body vertical velocity at the wheel end, so as to obtain the compensation coefficient corresponding to the third vehicle body vertical velocity at each wheel end.

[0018] In the above technical solution, the road surface excitation frequency corresponding to each wheel is determined, and the road surface excitation frequency of a single wheel is taken as the target excitation frequency. Candidate excitation frequencies matching the target excitation frequency are selected from multiple sample road surface excitation frequencies, and the corresponding sample compensation coefficient is directly assigned to that wheel. This enables precise matching of the compensation coefficient. This solution matches the compensation coefficient based on the road surface excitation frequency, fully considering the differences in the impact of different road surface excitation frequencies on the vertical velocity of the vehicle body at the wheel end, ensuring a high degree of consistency between the compensation coefficient and the actual road surface.

[0019] In conjunction with the first aspect and the above implementation methods, in some possible implementation methods, the method for determining the first vertical velocity at the vehicle's center of gravity at the current moment includes: determining whether the vehicle is in a stable driving state based on the attitude operation parameters; if the vehicle is in a stable driving state, determining the first vertical velocity based on the average of the second rate of change in relative height between multiple wheels and the vehicle body at the current moment; if the vehicle is not in a stable driving state, determining the first vertical velocity based on the integral value of the fifth vertical velocity and the first vertical acceleration at the vehicle's center of gravity at the previous moment over the current preset time period, wherein the first vertical acceleration is determined based on the acceleration measured by the inertial measurement unit.

[0020] In the above technical solution, the vehicle's stability is determined based on accelerations in multiple mutually perpendicular directions, the first rate of change of acceleration, and attitude parameters. This accurately distinguishes different driving conditions and precisely determines the vehicle's driving state. When the vehicle is in a stable driving state, the first vertical velocity is determined based on the average of the rates of change of relative height between multiple wheels and the vehicle body. This eliminates the need for cyclic calculations and complex integration calculations, allowing for rapid determination of the first vertical velocity under stable driving conditions. When the vehicle is not in a stable driving state, the first vertical velocity is determined based on the integral value of the fifth vertical velocity and the first vertical acceleration at the vehicle's center of mass at the previous moment over the current preset time period. That is, using the fifth vertical velocity at the previous moment as a benchmark avoids abrupt changes in vertical velocity caused by measurement errors of a single vertical acceleration. The integral value of the first vertical acceleration reflects the changes in the vehicle's current vertical motion state in real time, promptly capturing the vehicle's dynamic response and compensating for the inability of historical vertical velocities to reflect real-time motion. Therefore, using the integral value of the fifth vertical velocity and the first vertical acceleration reduces the determination error of the first vertical velocity.

[0021] In conjunction with the first aspect and the above-described implementations, in some possible implementations, the attitude operation parameters include roll rate and pitch rate. The first vertical speed is determined based on the integral values ​​of the fifth vertical speed and the first vertical acceleration at the vehicle's center of gravity at the previous moment over the current preset time period. This includes: determining the first vertical speed as the sum of the fifth vertical speed and the integral value; or, determining the first speed adjustment amount as the product of the roll rate, the fourth distance, and the lateral compensation coefficient, where the fourth distance is the lateral offset distance of the inertial measurement unit relative to the center of gravity, and the lateral compensation coefficient is used to compensate for the influence of the fourth distance when determining the first vertical speed; determining the second speed adjustment amount as the product of the pitch rate, the fifth distance, and the longitudinal compensation coefficient, where the fifth distance is the longitudinal offset distance of the inertial measurement unit relative to the center of gravity, and the longitudinal compensation coefficient is used to compensate for the influence of the fifth distance when determining the first vertical speed; and determining the first vertical speed as the sum of the fifth vertical speed, the integral value, the first speed adjustment amount, and the second speed adjustment amount.

[0022] In the above technical solution, the first vertical velocity at the center of gravity is determined in two ways. First, the first vertical velocity is determined directly by summing the fifth vertical velocity at the vehicle's center of gravity from the previous moment and the integral value of the first vertical acceleration over the current preset time period. This formula can quickly determine the fifth vertical velocity at the vehicle's center of gravity and ensure the accuracy of the basic vertical velocity. Second, the first vertical velocity is determined based on the fifth vertical velocity, the aforementioned integral value, the lateral adjustment (first velocity adjustment), and the longitudinal adjustment (second velocity adjustment). The second method can fully account for the error caused by the installation offset of the inertial measurement unit (IMU), and through targeted compensation, the measurement deviation caused by the IMU can be corrected. Therefore, both methods can determine a relatively accurate first vertical velocity, providing a precise basis for the subsequent determination of the target vehicle's vertical velocity.

[0023] In conjunction with the first aspect and the above implementation methods, in some possible implementation methods, the method for determining the lateral compensation coefficient and the longitudinal compensation coefficient includes: obtaining the pitch angular velocity, the corresponding roll angular velocity, the corresponding vertical acceleration, the corresponding fourth distance, the corresponding fifth distance, and the corresponding vertical velocity at the center of mass of each sample vehicle under a preset driving condition, among multiple sample pitch angular velocities. The preset driving condition includes roll motion and pitch motion, and each sample vehicle corresponds to a single attitude motion. When the sample vehicle is in a roll motion condition, based on the roll angular velocity, the longitudinal compensation coefficient is determined by... Based on the corresponding vertical acceleration of the sample, the corresponding fourth distance, and the first compensation coefficient, the first predicted vertical velocity at the centroid is determined. When the error between the first predicted vertical velocity and the corresponding sample vertical velocity is minimized, the lateral compensation coefficient is determined as the first compensation coefficient. When the sample vehicle is in a pitch motion condition, based on the pitch angular velocity of each sample, the corresponding vertical acceleration of the sample, the corresponding fifth distance, and the second compensation coefficient, the second predicted vertical velocity at the centroid is determined. When the error between the second predicted vertical velocity and the corresponding sample vertical velocity is minimized, the longitudinal compensation coefficient is determined as the second compensation coefficient.

[0024] The aforementioned technical solution acquires multiple sets of parameters for the sample vehicle under specific operating conditions, providing solid data support for determining the lateral and longitudinal compensation coefficients. Fitting calculations are performed separately for roll and pitch motion conditions, with the principle of minimizing error. This allows for accurate fitting and error reduction, fully considering the differences in different motion postures, and accurately determining the lateral and longitudinal compensation coefficients. This provides reliable parameter support for the accurate determination of the subsequent first vertical velocity, effectively improving the accuracy of determining the target vehicle's vertical velocity.

[0025] In conjunction with the first aspect and the above implementation methods, in some possible implementation methods, the method for determining the first vertical acceleration includes: obtaining the initial vertical acceleration, zero-bias compensation amount, and noise level threshold measured by the inertial measurement unit at the current moment, wherein the zero-bias compensation amount is the acceleration set to compensate for the inherent bias of the inertial measurement unit, and the noise level threshold is the acceleration used to filter out measurement noise; and based on the zero-bias compensation amount and the noise level threshold, performing zero-bias removal processing and noise reduction processing on the initial vertical acceleration to obtain the first vertical acceleration.

[0026] In the above technical solution, after obtaining the initial vertical acceleration measured by the inertial measurement unit (IMU), the initial vertical acceleration is de-biased based on the zero-bias compensation amount. This effectively eliminates the acceleration error caused by the inherent bias of the IMU. Based on a noise level threshold, the initial vertical acceleration is denoised, which avoids the influence of noise interference on the acceleration, ensuring that the processed first vertical acceleration is accurate and reliable. This solution avoids measurement errors caused by the IMU's own bias and external interference, improves the accuracy of the first vertical acceleration measurement, and provides high-quality input for the subsequent determination of the first vertical velocity.

[0027] In combination with the first aspect and the above implementation, in some possible implementations, based on the zero-bias compensation amount and the noise level threshold, the initial vertical acceleration is subjected to zero-bias removal processing and noise reduction processing to obtain the first vertical acceleration, including: when the first vertical acceleration difference between the initial vertical acceleration and the zero-bias compensation amount is greater than a preset acceleration difference, the second vertical acceleration difference between the first vertical acceleration difference and the noise level threshold is determined as the first vertical acceleration; when the first vertical acceleration difference is less than or equal to the preset acceleration difference, the first vertical acceleration is determined as the first preset vertical acceleration.

[0028] In the above technical solution, when the difference between the initial vertical acceleration and the zero-bias compensation is greater than a preset acceleration difference, noise is further eliminated by subtracting a noise level threshold from the first vertical acceleration difference to obtain an accurate first vertical acceleration. When the first vertical acceleration difference is not greater than the preset acceleration difference, the first preset vertical acceleration is directly used to avoid noise interference. This solution, combining zero-bias compensation and noise filtering, effectively eliminates the influence of inherent sensor bias and random noise, ensuring the authenticity of the vertical acceleration. This provides high-precision input for the accurate determination of the vehicle's vertical velocity, improving the accuracy and stability of vehicle condition assessment.

[0029] Secondly, a vehicle state estimation device is provided, comprising: a first determining module, configured to determine a first vertical velocity of the vehicle body at each wheel end based on a first vertical velocity at the vehicle's center of mass at the current moment, the vehicle's geometric position parameters, and attitude operation parameters; a predicting module, configured to input the vehicle's driving parameters, road condition parameters, the first vertical velocity, and the attitude operation parameters into a predictive model to predict a second vertical velocity of the vehicle body at each wheel end, the predictive model reflecting the mapping relationship between the vehicle's driving state parameters and the vertical motion state parameters at the wheel ends; and a second determining module, configured to determine a target vertical velocity of the vehicle body at each wheel end based on the first vertical velocity and the second vertical velocity, the target vertical velocity being used to control the vehicle.

[0030] In conjunction with the second aspect, in some possible implementations, the second determining module is specifically used to: determine the target similarity between the driving parameters and the road surface condition parameters and each sample driving parameter and corresponding sample road surface condition parameter in a plurality of sample driving parameters; determine a first weighting coefficient based on the plurality of target similarities, the first weighting coefficient being used to reflect the contribution of the second vehicle vertical velocity in determining the target vehicle vertical velocity; and perform weighted fusion of the first vehicle vertical velocity and the second vehicle vertical velocity based on the second weighting coefficient and the first weighting coefficient to obtain the target vehicle vertical velocity at each wheel end, wherein the second weighting coefficient is the difference between the preset weighting coefficient and the first weighting coefficient, and the second weighting coefficient being used to reflect the contribution of the first vehicle vertical velocity in determining the target vehicle vertical velocity.

[0031] In conjunction with the second aspect and the above implementation methods, in some possible implementation methods, the second determining module is further used to: determine a first weighting coefficient based on multiple target similarities, including: when the maximum similarity among the multiple target similarities is greater than or equal to a preset similarity, determining the first weighting coefficient as a first preset coefficient; when the maximum similarity is less than the preset similarity, determining the first weighting coefficient as a second preset coefficient, wherein the second preset coefficient is less than the first preset coefficient.

[0032] In conjunction with the second aspect and the above implementation methods, in some possible implementations, the geometric position parameters include a first distance between the center of gravity and the front axle, a second distance between the center of gravity and the rear axle, and a third distance between the left and right wheels. The first determining module is specifically used to: determine a second vertical velocity, a third vertical velocity, and a fourth vertical velocity based on the pitch angular velocity, roll angular velocity, the first distance, the second distance, and the third distance in the attitude operation parameters. The second vertical velocity is the vertical velocity component generated by the vehicle's pitch motion at the front wheels, and the third vertical velocity is the vertical velocity component generated by the vehicle's pitch motion at the rear wheels. The fourth vertical velocity is the vertical velocity component generated by the vehicle's roll motion between the left and right wheels. Based on the first, second, third, and fourth vertical velocities, the third vertical velocity of the vehicle at each wheel end is determined, and the third vertical velocity of the vehicle at each wheel end is determined as the first vertical velocity of the vehicle at each wheel end; or, the product of the third vertical velocity of the vehicle at each wheel end and the corresponding compensation coefficient is determined as the first vertical velocity of the vehicle at each wheel end, where the compensation coefficient is used to compensate for the influence of the corresponding road conditions on the vertical velocity of the vehicle at the wheel end.

[0033] In conjunction with the second aspect and the above implementation methods, in some possible implementation methods, the first determining module is further configured to: determine the first vertical speed difference between the first vertical speed and the second vertical speed, and determine the sum of the first vertical speed difference and the fourth vertical speed as the third vehicle body vertical speed at the left front wheel end; determine the difference between the first vertical speed difference and the fourth vertical speed as the third vehicle body vertical speed at the right front wheel end; determine the sum of the first vertical speed and the third vertical speed to obtain a first total vertical speed, and determine the sum of the first total vertical speed and the fourth vertical speed as the third vehicle body vertical speed at the left rear wheel end; determine the difference between the first total vertical speed and the fourth vertical speed as the third vehicle body vertical speed at the right rear wheel end.

[0034] In conjunction with the second aspect and the above implementation methods, in some possible implementation methods, the first determining module is further configured to: determine the road surface excitation frequency of the driving surface corresponding to each wheel, thereby obtaining multiple road surface excitation frequencies; take the road surface excitation frequency of the driving surface corresponding to any wheel among the multiple road surface excitation frequencies as the target excitation frequency, and determine a candidate excitation frequency that matches the target excitation frequency from multiple sample road surface excitation frequencies; determine the sample compensation coefficient corresponding to the candidate excitation frequency among the multiple sample road surface excitation frequencies as the compensation coefficient corresponding to the third vehicle body vertical velocity at the wheel end, so as to obtain the compensation coefficient corresponding to the third vehicle body vertical velocity at each wheel end.

[0035] In conjunction with the second aspect and the above implementation methods, in some possible implementation methods, the first determining module is further configured to: determine whether the vehicle is in a stable driving state based on the attitude operating parameters; if the vehicle is in a stable driving state, determine the first vertical velocity based on the average value of the second rate of change in relative height between multiple wheels and the vehicle body at the current moment; if the vehicle is not in a stable driving state, determine the first vertical velocity based on the integral value of the fifth vertical velocity and the first vertical acceleration at the vehicle's center of mass at the previous moment over the current preset time period, wherein the first vertical acceleration is determined based on the acceleration measured by the inertial measurement unit.

[0036] In conjunction with the second aspect and the above implementation methods, in some possible implementation methods, the attitude operation parameters include roll rate and pitch rate. The first determining module is further configured to: determine the first vertical rate by summing the fifth vertical rate and the integral value; or, determine the first speed adjustment amount by multiplying the roll rate, the fourth distance, and the lateral compensation coefficient, where the fourth distance is the lateral offset distance of the inertial measurement unit relative to the center of mass, and the lateral compensation coefficient is used to compensate for the influence of the fourth distance when determining the first vertical rate; determine the second speed adjustment amount by multiplying the pitch rate, the fifth distance, and the longitudinal compensation coefficient, where the fifth distance is the longitudinal offset distance of the inertial measurement unit relative to the center of mass, and the longitudinal compensation coefficient is used to compensate for the influence of the fifth distance when determining the first vertical rate; and determine the first vertical rate by summing the fifth vertical rate, the integral value, the first speed adjustment amount, and the second speed adjustment amount.

[0037] In conjunction with the second aspect and the above-described implementation, in some possible implementations, the device further includes: an acquisition module, configured to acquire, among multiple sample pitch angular velocities of each sample vehicle under a preset driving condition, the sample pitch angular velocity, the corresponding sample roll angular velocity, the corresponding sample vertical acceleration, the corresponding fourth distance, the corresponding fifth distance, and the sample vertical velocity at the center of mass, wherein the preset driving condition includes a roll motion condition and a pitch motion condition, and the sample vehicle corresponds to a single attitude motion; the first determining module is further configured to: when the sample vehicle is in a roll motion condition, based on the various sample roll angular velocities... The first predicted vertical velocity at the centroid is determined based on the corresponding sample vertical acceleration, the corresponding fourth distance, and the first compensation coefficient. When the error between the first predicted vertical velocity and the corresponding sample vertical velocity is minimized, the lateral compensation coefficient is determined as the first compensation coefficient. When the sample vehicle is in pitch motion, the second predicted vertical velocity at the centroid is determined based on the pitch angular velocity of each sample, the corresponding sample vertical acceleration, the corresponding fifth distance, and the second compensation coefficient. When the error between the second predicted vertical velocity and the corresponding sample vertical velocity is minimized, the longitudinal compensation coefficient is determined as the second compensation coefficient.

[0038] In conjunction with the second aspect and the above implementation methods, in some possible implementation methods, the acquisition module is further configured to acquire the initial vertical acceleration, zero-bias compensation amount, and noise level threshold measured by the inertial measurement unit at the current moment. The zero-bias compensation amount is the acceleration set to compensate for the inherent deviation of the inertial measurement unit, and the noise level threshold is the acceleration used to filter out measurement noise. The first determination module is further configured to perform zero-bias removal processing and noise reduction processing on the initial vertical acceleration based on the zero-bias compensation amount and the noise level threshold to obtain the first vertical acceleration.

[0039] In conjunction with the second aspect and the above implementation methods, in some possible implementation methods, the first determining module is further configured to: determine the first vertical acceleration as the second vertical acceleration difference between the first vertical acceleration difference and the noise level threshold when the first vertical acceleration difference between the initial vertical acceleration and the zero bias compensation amount is greater than the preset acceleration difference; and determine the first vertical acceleration as the first preset vertical acceleration when the first vertical acceleration difference is less than or equal to the preset acceleration difference.

[0040] Thirdly, a vehicle is provided, including a memory and a processor. The memory is used to store executable program code, and the processor is used to call and run the executable program code from the memory, causing the vehicle to perform the methods described in the first aspect or any possible implementation thereof. Attached Figure Description

[0041] Figure 1 This is a scenario diagram for estimating vehicle state provided in an embodiment of this application; Figure 2 This is a schematic flowchart illustrating a vehicle state estimation method provided in an embodiment of this application; Figure 3 This is a schematic block diagram illustrating an embodiment of the present application for estimating vertical velocity; Figure 4 This is a schematic diagram of the structure of a vehicle state estimation device provided in an embodiment of this application; Figure 5 This is a schematic diagram of the structure of a controller provided in an embodiment of this application; Figure 6 This is a schematic diagram of the structure of a vehicle provided in an embodiment of this application. Detailed Implementation

[0042] The technical solutions in this application will be clearly and thoroughly described below with reference to the accompanying drawings. In the description of the embodiments of this application, unless otherwise stated, " / " means "or," for example, A / B can mean A or B. "And / or" in the text is merely a description of the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A existing alone, A and B existing simultaneously, and B existing alone. Furthermore, in the description of the embodiments of this application, "multiple" refers to two or more than two.

[0043] Hereinafter, the terms "first" and "second" are used for descriptive purposes only and should not be construed as implying or suggesting relative importance or implicitly indicating the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of that feature.

[0044] The vertical velocity of a vehicle refers to the overall speed of the vehicle body in the vertical direction. For example, the speed at which the vehicle body rises, sinks, or bumps. Vertical velocity is a key parameter characterizing the vertical motion state of a vehicle, directly reflecting the vibration amplitude, impact intensity, and suspension system operating state between the vehicle body and the road surface. Vertical velocity is an important basis for achieving active suspension control and vehicle attitude adjustment. The aforementioned vertical velocities include the vertical velocity at the vehicle's center of gravity and the vertical velocity at each wheel end. The vertical velocity at the center of gravity refers to the speed of the center of gravity in the vertical direction. The center of gravity is the vehicle's equivalent center of gravity, and the vertical velocity at the center of gravity does not consider the effects of lateral tilt, pitch, or local deformation. The vertical velocity at each wheel end refers to the vertical velocity of the vehicle body directly above the wheel.

[0045] The vertical velocity at the center of gravity is used to determine whether the vehicle body is bumpy or bouncy, and is used to determine the basic damping force of the suspension system. The shock absorbers located at the four corners of the vehicle in this suspension system typically adjust their damping force based on the vertical velocity of the vehicle body at their corresponding wheel ends to counteract road impacts and ensure vehicle comfort and grip. Therefore, determining the accurate vertical velocity is essential.

[0046] In related technologies, such as Figure 1 As shown, typically, when vehicle A is moving, the vertical acceleration is measured by an inertial measurement unit (IMU) installed near the center of gravity. The vertical acceleration is integrated to obtain a reference vertical velocity. The reference vertical velocity is then compensated based on the current angular velocity (pitch angular velocity and roll angular velocity) to obtain the aforementioned vehicle body vertical velocity.

[0047] The aforementioned solutions assume the vehicle body is rigid and calculate the vertical velocity of the vehicle body using angular velocity compensation. However, in reality, the vehicle body is not rigid and its stiffness distribution is uneven. The calculated angular velocity is not accurate, as it is affected by local vibration noise caused by uneven stiffness distribution. Therefore, the vertical velocity of the vehicle body obtained through angular velocity compensation calculation still contains errors. Especially when the vehicle travels on high-frequency excitation surfaces such as Belgian roads and speed bumps, the vehicle will experience significant local deformation and vibration, leading to more severe local vibration noise. Therefore, the error in the vertical velocity of the vehicle body corresponding to such high-frequency excitation surfaces is even greater in the aforementioned solutions.

[0048] To address the aforementioned issues, this application proposes a vehicle state estimation method. This method relies on the vehicle body being a rigid body and its corresponding physical model to determine the baseline vertical velocity of the vehicle body at each wheel end, i.e., the first vertical velocity. Then, based on the nonlinear mapping relationship between the vehicle's driving state and the vertical motion state at the wheel ends learned by a prediction model (neural network model), the flexible deformation characteristics and local vibration characteristics of the vehicle body during driving are fitted to predict the vertical velocity of the vehicle body at each wheel end, i.e., the second vertical velocity. By combining the first vertical velocity obtained from the physical model and the second vertical velocity obtained from the neural network model, the advantages of both the basic calculation results of the physical model and the accurate prediction results of the neural network model can be combined. This effectively eliminates the estimation error caused by the vehicle body not being a rigid body, thus accurately estimating the vertical velocity of the vehicle body. This allows the method to achieve precise vehicle control based on this vertical velocity. Specific implementation steps are as follows. Figure 2 .

[0049] Figure 2This is a schematic flowchart illustrating a method for estimating vehicle state provided in an embodiment of this application.

[0050] It should be understood that the vehicle state estimation method provided in this application embodiment can be applied to, for example, Figure 1 The vehicle shown (e.g., vehicle A) is specifically described using this estimation method applied to the vehicle controller within the vehicle.

[0051] For example, such as Figure 2 As shown, the method 200 includes the following steps 201 to 203.

[0052] Step 201: Based on the first vertical velocity at the vehicle's center of mass at the current moment, the vehicle's geometric position parameters, and attitude parameters, determine the first vertical velocity of the vehicle body at each wheel end.

[0053] It should be understood that in step 201 above, the first vertical velocity is the vertical velocity at the vehicle's center of mass at the current moment, specifically referring to the velocity of the center of mass in the vertical direction at the current moment. Geometric position parameters are inherent, static geometric dimensions and positional relationships of the vehicle that do not change with the driving state. Attitude parameters are dynamic attitude parameters of the vehicle that change in real time with the driving state.

[0054] In this application, the geometric position parameters include a first distance between the center of gravity and the front axle, a second distance between the center of gravity and the rear axle, and a third distance between the left and right wheels. The attitude operation parameters include pitch rate and roll rate. Pitch rate refers to the angular velocity of the vehicle's head-up / head-down rotation (rotation about the longitudinal axis), reflecting the rate of pitch of the vehicle body forward and backward. Roll rate refers to the angular velocity of the vehicle's left and right roll rotation (rotation about the lateral axis), reflecting the rate of roll of the vehicle body left and right. The attitude operation parameters also include triaxial acceleration and the rate of change of acceleration along each axis. The triaxial acceleration refers to the linear acceleration of the vehicle in three mutually perpendicular directions (X-axis, Y-axis, and Z-axis) measured by a triaxial accelerometer in the vehicle.

[0055] In addition, the first vertical velocity of the vehicle body at each wheel end is the vertical velocity of the vehicle body at each wheel end at the current moment. The vertical velocity of the vehicle body at the wheel end refers to the vertical velocity of the vehicle body position directly above the wheel in the vertical direction. It can be the vertical velocity of the vehicle body at the left front wheel end, the right front wheel end, the left rear wheel end, or the right rear wheel end.

[0056] The following describes the specific process of "determining the first vertical velocity of the vehicle body at each wheel end based on the first vertical velocity at the vehicle's center of mass at the current moment, the vehicle's geometric position parameters, and attitude parameters".

[0057] In one possible implementation, the geometric position parameters include a first distance between the center of gravity and the front axle, a second distance between the center of gravity and the rear axle, and a third distance between the left and right wheels. Step 201 includes: determining a second vertical velocity, a third vertical velocity, and a fourth vertical velocity based on the pitch angular velocity, roll angular velocity, the first distance, the second distance, and the third distance in the attitude operation parameters. The second vertical velocity is the vertical velocity component generated by the vehicle's pitch motion at the front wheels, the third vertical velocity is the vertical velocity component generated by the vehicle's pitch motion at the rear wheels, and the fourth vertical velocity is... The vertical velocity component generated by the vehicle's roll motion between the left and right wheels; based on the first vertical velocity, the second vertical velocity, the third vertical velocity, and the fourth vertical velocity, the third vertical velocity of the vehicle at each wheel end is determined, and the third vertical velocity of the vehicle at each wheel end is determined as the first vertical velocity of the vehicle at each wheel end; or, the product between the third vertical velocity of the vehicle at each wheel end and the corresponding compensation coefficient is determined as the first vertical velocity of the vehicle at each wheel end, where the compensation coefficient is used to compensate for the degree of influence of the corresponding road conditions on the vertical velocity of the vehicle at the wheel end.

[0058] It should be understood that the principle behind determining the first vertical velocity of the vehicle body at each wheel end in the above scheme, based on the first vertical velocity at the vehicle's center of gravity, the vertical velocity component generated by the vehicle's pitch motion at the front wheels, the vertical velocity component generated by the vehicle's pitch motion at the rear wheels, and the vertical velocity component generated by the vehicle's roll motion between the left and right wheels, is to decompose the vertical motion of the vehicle body. When the vehicle is moving, the vertical motion can be decomposed into the translation of the center of gravity, the pitch motion of the vehicle body, and the roll motion of the vehicle body. The pitch angular velocity, acting on the distance from the center of gravity to the front and rear axles, will generate vertical velocity components proportional to the distance at the front and rear wheels, respectively, namely the second and third vertical velocities. The roll angular velocity, acting on the fourth distance (i.e., the product of the third distance and the third preset coefficient, specifically half the wheelbase), will generate opposing vertical velocity components between the left and right wheels, namely the fourth vertical velocity. By superimposing the first vertical velocity at the center of mass with the aforementioned vertical velocity components, the true vertical velocity of the vehicle body at each wheel end (the first vertical velocity of the vehicle body) can be obtained, which can fully reflect the spatial motion relationship of the vehicle body.

[0059] It should also be understood that, in the above scheme, the vertical velocity of the third vehicle body at each wheel end and the corresponding compensation coefficient can be the compensation coefficient corresponding to the left front wheel. The compensation coefficient corresponding to the right front wheel The compensation coefficient corresponding to the left rear wheel Compensation coefficient corresponding to the right rear wheel .

[0060] In the above technical solution, based on the pitch angular velocity, roll angular velocity, first distance, second distance, and third distance in the attitude operation parameters, a second vertical velocity, a third vertical velocity, and a fourth vertical velocity (these three vertical velocities) are determined. This can determine the vertical velocity components generated by the vehicle's pitch motion at the front and rear wheels and the vertical velocity components generated by the vehicle's roll motion between the left and right wheels, thus refining the various velocity components. Furthermore, by combining the first vertical velocity at the center of gravity with the above three vertical velocities, the first vertical velocity at the center of gravity can be converted into the third vehicle vertical velocity at the wheel ends, thereby obtaining the first vehicle vertical velocity. A compensation coefficient is then introduced to offset the influence of road conditions on the first vehicle vertical velocity. The above solution determines the first vehicle vertical velocity in two ways. The first method does not consider the compensation coefficient, which allows for a rapid determination of the first vehicle vertical velocity. The second method considers the compensation coefficient, which can take into account both the vehicle's attitude motion characteristics and the influence of road conditions, determining a precise first vehicle vertical velocity, providing a reliable basic input for the subsequent determination of the target vehicle vertical velocity.

[0061] In some embodiments, determining a second vertical velocity, a third vertical velocity, and a fourth vertical velocity based on the pitch angular velocity, roll angular velocity, the first distance, the second distance, and the third distance in the attitude operation parameters includes: determining the product of the pitch angular velocity and the first distance as the second vertical velocity; determining the product of the pitch angular velocity and the second distance as the third vertical velocity; and determining the fourth vertical velocity based on the product of the roll angular velocity and the fourth distance, wherein the fourth distance is the product of the third distance and a third preset coefficient.

[0062] It should be understood that the aforementioned third preset coefficient is .

[0063] In some embodiments, determining a third vehicle body vertical velocity at each wheel end based on the first vertical velocity, the second vertical velocity, the third vertical velocity, and the fourth vertical velocity includes: determining a first vertical velocity difference between the first vertical velocity and the second vertical velocity, and determining the sum of the first vertical velocity difference and the fourth vertical velocity as the third vehicle body vertical velocity at the left front wheel end; determining the difference between the first vertical velocity difference and the fourth vertical velocity as the third vehicle body vertical velocity at the right front wheel end; determining the sum of the first vertical velocity and the third vertical velocity to obtain a first total vertical velocity, and determining the sum of the first total vertical velocity and the fourth vertical velocity as the third vehicle body vertical velocity at the left rear wheel end; and determining the difference between the first total vertical velocity and the fourth vertical velocity as the third vehicle body vertical velocity at the right rear wheel end.

[0064] In the above technical solution, the basic vertical velocities at the front and rear wheel ends are obtained by adding and subtracting the first vertical velocity and the pitch component. Then, the positive and negative differences in the roll component are superimposed to accurately distinguish the motion direction of the left and right wheels. Based on the decomposition principle of vehicle body spatial motion, this solution maps the effects of the center of mass translation and pitch / roll motion to each wheel end according to their geometric positions, correcting the errors caused by using only the first vertical velocity. In other words, the above solution vector-synthesizes the first vertical velocity (center of mass vertical velocity) and the velocity components generated by pitch / roll according to the position of each wheel. This ensures that the third vertical velocity at each wheel end closely matches the actual vehicle body attitude changes, providing reliable data input for the subsequent determination of the target vertical velocity at each wheel end.

[0065] In some embodiments, determining a first vertical velocity difference between the first vertical velocity and the second vertical velocity, and determining the sum of the first vertical velocity difference and the fourth vertical velocity as the third vehicle body vertical velocity at the left front wheel end, includes: determining the third vehicle body vertical velocity at the left front wheel end based on the following formula (1); and determining the difference between the first vertical velocity difference and the fourth vertical velocity as the third vehicle body vertical velocity at the right front wheel end, including: determining the third vehicle body vertical velocity at the right front wheel end based on the following formula (2). The degree; and, determining the sum of the first vertical velocity and the third vertical velocity to obtain the first total vertical velocity, and determining the sum of the first total vertical velocity and the fourth vertical velocity as the third body vertical velocity at the left rear wheel end, including: determining the third body vertical velocity at the left rear wheel end based on the following formula (3); and, determining the difference between the first total vertical velocity and the fourth vertical velocity as the third body vertical velocity at the right rear wheel end, including: determining the third body vertical velocity at the right rear wheel end based on the following formula (4); (1) (2) (3) (4) in, The vertical velocity of the third vehicle body at the left front wheel tip. This is the first vertical velocity. The pitch angular velocity, The first distance, The second distance, The roll rate is angular velocity. The third distance, This is the third preset coefficient. The fourth distance, This is the second vertical velocity. This is the third vertical velocity. This is the fourth vertical velocity. The vertical velocity of the third vehicle body at the right front wheel tip. The vertical velocity of the third vehicle body at the left rear wheel tip. The vertical velocity of the third vehicle body at the right rear wheel tip.

[0066] In other words, the first method is to measure the vertical speed of the third vehicle body at the end of the left front wheel. The first vertical velocity of the vehicle body is determined at the end of the left front wheel; the third vertical velocity of the vehicle body is determined at the end of the right front wheel. The first vertical velocity of the vehicle body is determined at the right front wheel tip; the third vertical velocity of the vehicle body is determined at the left rear wheel tip. The first vertical velocity of the vehicle body at the left rear wheel end is determined; the third vertical velocity of the vehicle body at the right rear wheel end is determined. The first method is to determine the vertical velocity of the vehicle body at the right rear wheel tip. The second method is to determine the vertical velocity of the vehicle body at the left front wheel tip. Compensation coefficient corresponding to the left front wheel The product of the two values ​​is determined as the first vertical velocity of the vehicle body at the left front wheel end; the third vertical velocity of the vehicle body at the right front wheel end is determined as... Compensation coefficient corresponding to the right front wheel The product of these two values ​​is determined as the first vertical velocity of the vehicle body at the right front wheel end; the third vertical velocity of the vehicle body at the left rear wheel end is determined as... Compensation coefficient corresponding to the left rear wheel The product of the two values ​​is determined as the first vertical velocity of the vehicle body at the left rear wheel end; the third vertical velocity of the vehicle body at the right rear wheel end is determined as... Compensation coefficient corresponding to the right rear wheel The product of these two values ​​is determined as the first vertical velocity of the vehicle body at the right rear wheel end.

[0067] The compensation coefficient corresponding to the third vertical velocity of the vehicle body at each wheel end is determined by the following implementation method, specifically described by the compensation coefficient corresponding to the third vertical velocity of the vehicle body at any wheel end.

[0068] In one possible implementation, the method for determining the compensation coefficient corresponding to the third vehicle body vertical velocity at each wheel end includes: determining the road surface excitation frequency of the driving surface corresponding to each wheel to obtain multiple road surface excitation frequencies; taking the road surface excitation frequency of the driving surface corresponding to any wheel among the multiple road surface excitation frequencies as the target excitation frequency, determining the candidate excitation frequency matching the target excitation frequency from multiple sample road surface excitation frequencies; and determining the sample compensation coefficient corresponding to the candidate excitation frequency among the multiple sample road surface excitation frequencies as the compensation coefficient corresponding to the third vehicle body vertical velocity at the wheel end, so as to obtain the compensation coefficient corresponding to the third vehicle body vertical velocity at each wheel end.

[0069] It should be understood that in the above scheme, the road surface excitation frequency refers to the input frequency after the roughness of the driving road surface has been converted. This input frequency refers to the vibration frequency input to the suspension system, which is used to determine the vibration response characteristics of the suspension system and the vehicle body. This can be understood from two perspectives: in a spatial perspective, the spatial wavelength of the roughness is... The vehicle is traveling at its full speed. When driving, the wheels will... The frequency is subjected to the force of up-and-down bumps. This is the road surface excitation frequency. Different wheels travel on different road surfaces, and the corresponding frequency varies. They are different. From a temporal perspective, when a vehicle is in motion, the suspension system and body vibrate periodically with the undulations of the road surface. The repetition frequency of this periodic vibration is the road excitation frequency. From a perceptual perspective, different road surfaces (such as smooth asphalt roads, gravel roads, Belgian roads, and speed bumps) have different levels of unevenness and density, resulting in different road excitation frequencies. The rougher the road surface, the more severe the bumps, and the higher the road excitation frequency.

[0070] It should also be understood that each sample road surface excitation frequency corresponds to a sample compensation coefficient, and the sample compensation coefficient monotonically increases with the sample road surface excitation frequency. That is, the higher the sample road surface excitation frequency, the larger the sample compensation coefficient. Furthermore, different road surfaces cause differences in IMU angular velocity noise, which affects the accuracy of determining the first vertical velocity of the vehicle body at each wheel end. Therefore, it is necessary to dynamically calibrate the compensation coefficient based on the road surface excitation frequency to counteract noise interference under different road surfaces. In other words, the compensation coefficient is used to compensate for the impact of IMU angular velocity noise caused by different road surfaces on the vehicle body's vertical velocity.

[0071] Furthermore, in the above scheme, the matching of candidate excitation frequencies that match the target excitation frequency can be that the candidate excitation frequency is the same as the target excitation frequency, or that the frequency deviation between the candidate excitation frequency and the target excitation frequency is less than a preset deviation, i.e., the frequency deviation is very small. Optionally, the preset deviation is 2Hz.

[0072] In the above technical solution, the road surface excitation frequency corresponding to each wheel is determined, and the road surface excitation frequency of a single wheel is taken as the target excitation frequency. Candidate excitation frequencies matching the target excitation frequency are selected from multiple sample road surface excitation frequencies, and the corresponding sample compensation coefficient is directly assigned to that wheel. This enables precise matching of the compensation coefficient. This solution matches the compensation coefficient based on the road surface excitation frequency, fully considering the differences in the impact of different road surface excitation frequencies on the vertical velocity of the vehicle body at the wheel end, ensuring a high degree of consistency between the compensation coefficient and the actual road surface.

[0073] The first vertical velocity at the vehicle's center of gravity at the current moment is determined using the following method.

[0074] In one possible implementation, the method for determining the first vertical velocity at the vehicle's center of gravity at the current moment includes: determining whether the vehicle is in a stable driving state based on the attitude operating parameters; if the vehicle is in a stable driving state, determining the first vertical velocity based on the average of the second rate of change in relative height between multiple wheels and the vehicle body at the current moment; if the vehicle is not in a stable driving state, determining the first vertical velocity based on the integral value of the fifth vertical velocity and the first vertical acceleration at the vehicle's center of gravity at the previous moment over the current preset time period, wherein the first vertical acceleration is determined based on the acceleration measured by the inertial measurement unit.

[0075] It should be understood that the above scheme includes the vehicle's acceleration in multiple mutually perpendicular directions and the first rate of change of acceleration. These multiple mutually perpendicular directions include the X direction (longitudinal direction), Y direction (lateral direction), and Z direction (vertical direction). Therefore, the acceleration in these multiple mutually perpendicular directions includes longitudinal acceleration, lateral acceleration, and vertical acceleration. A smooth driving state refers to a state in which the vehicle maintains a stable speed, a stable body posture without significant bumps or swaying, and smooth and controllable driving operation.

[0076] It should also be understood that, in the above scheme, the second rate of change in relative height between each wheel and the vehicle body can be measured by a height sensor at the corresponding location. Furthermore, the vehicle periodically determines the vertical velocity at its center of gravity; the vertical velocity at the vehicle's center of gravity at the current moment is the first vertical velocity, and the vertical velocity at the vehicle's center of gravity at the moment before the current moment (i.e., the previous moment) is the fifth vertical velocity. Additionally, in this application, the aforementioned inertial measurement unit is installed near the center of gravity.

[0077] In the above technical solution, the vehicle's stability is determined based on accelerations in multiple mutually perpendicular directions, the first rate of change of acceleration, and attitude parameters. This accurately distinguishes different driving conditions and precisely determines the vehicle's driving state. When the vehicle is in a stable driving state, the first vertical velocity is determined based on the average of the rates of change of relative height between multiple wheels and the vehicle body. This eliminates the need for cyclic calculations and complex integration calculations, allowing for rapid determination of the first vertical velocity under stable driving conditions. When the vehicle is not in a stable driving state, the first vertical velocity is determined based on the integral value of the fifth vertical velocity and the first vertical acceleration at the vehicle's center of mass at the previous moment over the current preset time period. That is, using the fifth vertical velocity at the previous moment as a benchmark avoids abrupt changes in vertical velocity caused by measurement errors of a single vertical acceleration. The integral value of the first vertical acceleration reflects the changes in the vehicle's current vertical motion state in real time, promptly capturing the vehicle's dynamic response and compensating for the inability of historical vertical velocities to reflect real-time motion. Therefore, using the integral value of the fifth vertical velocity and the first vertical acceleration reduces the determination error of the first vertical velocity.

[0078] In some embodiments, the attitude operating parameters further include the vehicle's acceleration in multiple mutually perpendicular directions and a first rate of change of acceleration. The multiple mutually perpendicular directions include the longitudinal direction, the lateral direction, and the vertical direction. Based on these attitude operating parameters, determining whether the vehicle is in a stable driving state includes: the longitudinal acceleration being less than or equal to a preset longitudinal acceleration, the lateral acceleration being less than or equal to a preset lateral acceleration, the vertical acceleration being less than or equal to a preset vertical acceleration, and the first rate of change of longitudinal acceleration being less than or equal to a first preset rate of change, the first rate of change of lateral acceleration being less than or equal to a second preset rate of change, the first rate of change of vertical acceleration being less than or equal to a third preset rate of change, and the pitch rate... If the pitch angle is less than or equal to a preset pitch rate and the roll rate is less than or equal to a preset roll rate, the vehicle is determined to be in a stable driving state. If the longitudinal acceleration is greater than a preset longitudinal acceleration, and / or the lateral acceleration is greater than a preset lateral acceleration, and / or the vertical acceleration is greater than a preset vertical acceleration, and / or the first rate of change of longitudinal acceleration is greater than a first preset rate of change, and / or the first rate of change of lateral acceleration is greater than a second preset rate of change, and / or the first rate of change of vertical acceleration is greater than a third preset rate of change, and / or the pitch rate is greater than a preset pitch rate of change, and / or the roll rate is greater than a preset roll rate, the vehicle is determined to be not in a stable driving state.

[0079] In one possible implementation, the attitude operating parameters include roll rate and pitch rate. The first vertical speed is determined based on the integral values ​​of the fifth vertical speed and the first vertical acceleration at the vehicle's center of gravity at the previous moment over the current preset time period. This includes: determining the first vertical speed as the sum of the fifth vertical speed and the integral value; or, determining the first speed adjustment amount as the product of the roll rate, the fourth distance, and the lateral compensation coefficient, where the fourth distance is the lateral offset distance of the inertial measurement unit relative to the center of gravity, and the lateral compensation coefficient is used to compensate for the influence of the fourth distance when determining the first vertical speed; determining the second speed adjustment amount as the product of the pitch rate, the fifth distance, and the longitudinal compensation coefficient, where the fifth distance is the longitudinal offset distance of the inertial measurement unit relative to the center of gravity, and the longitudinal compensation coefficient is used to compensate for the influence of the fifth distance when determining the first vertical speed; and determining the first vertical speed as the sum of the fifth vertical speed, the integral value, the first speed adjustment amount, and the second speed adjustment amount.

[0080] It should be understood that the time interval of the above integral value is from the previous moment to the current moment. This integral value is actually the vertical velocity obtained by integrating the first vertical acceleration. This integral value is used to indicate the change in vertical velocity of the inertial measurement unit's own position. The above-mentioned first and second velocity adjustment amounts are considered as attitude correction terms. The inertial measurement unit is not installed at the center of gravity. When the vehicle rolls / pitches, a vertical velocity difference will occur between its own position and the center of gravity. The roll angular velocity will cause the inertial measurement unit to generate a first velocity adjustment amount, and the pitch angular velocity will cause the inertial measurement unit to generate a second velocity adjustment amount. After the above two attitude correction terms, the change in vertical velocity of the inertial measurement unit's own position can be converted into the change in vertical velocity at the center of gravity.

[0081] It should be noted that the above scheme determines the vertical velocity at the center of mass at the current moment, which is the first vertical velocity at the vehicle's center of mass, based on the vertical velocity at the previous moment (i.e., the fifth vertical velocity) plus the change in vertical velocity at the center of mass at the current moment. The fifth vertical velocity can be considered the initial vertical velocity before the change (at the previous moment), and the change in vertical velocity at the center of mass at the current moment can be considered the change in vertical velocity over the time interval from the previous moment to the current moment. The first velocity adjustment is a lateral attitude correction term used to compensate for the lateral offset of the inertial measurement unit relative to the center of mass. The resulting velocity measurement error is addressed by a second velocity adjustment term, which is a longitudinal attitude correction term used to compensate for the longitudinal offset of the inertial measurement unit relative to the center of mass. The aforementioned method for determining the first vertical velocity at the vehicle's center of gravity at the current moment not only ensures the continuity of vertical velocity calculation but also eliminates the measurement error caused by the IMU's installation position offset through attitude correction terms, resulting in a more accurate first vertical velocity.

[0082] It should also be noted that this lateral compensation coefficient is used to correct for the differences between the assumption that the vehicle body is a rigid body and the actual flexible deformation of the vehicle body, measurement errors of the inertial measurement unit, and other non-ideal factors, calibrating the product of the roll rate and the fourth distance as the true vertical velocity compensation amount. This longitudinal compensation coefficient is also used to correct for the differences of the above-mentioned non-ideal factors, but calibrates the product of the pitch rate and the fifth distance as the true vertical velocity compensation amount.

[0083] In the above technical solution, the first vertical velocity at the center of gravity is determined in two ways. First, the first vertical velocity is determined directly by summing the fifth vertical velocity at the vehicle's center of gravity from the previous moment and the integral value of the first vertical acceleration over the current preset time period. This formula can quickly determine the fifth vertical velocity at the vehicle's center of gravity and ensure the accuracy of the basic vertical velocity. Second, the first vertical velocity is determined based on the fifth vertical velocity, the aforementioned integral value, the lateral adjustment (first velocity adjustment), and the longitudinal adjustment (second velocity adjustment). The second method can fully account for the error caused by the installation offset of the inertial measurement unit (IMU), and through targeted compensation, the measurement deviation caused by the IMU can be corrected. Therefore, both methods can determine a relatively accurate first vertical velocity, providing a precise basis for the subsequent determination of the target vehicle's vertical velocity.

[0084] In one possible implementation, the method for determining the lateral compensation coefficient and the longitudinal compensation coefficient includes: acquiring the pitch angular velocity, the corresponding roll angular velocity, the corresponding vertical acceleration, the corresponding fourth distance, the corresponding fifth distance, and the corresponding vertical velocity at the center of mass of each sample vehicle under a preset driving condition, among multiple sample pitch angular velocities. The preset driving condition includes roll motion and pitch motion, and each sample vehicle corresponds to a single attitude motion. When the sample vehicle is in roll motion, based on the roll angular velocity and the corresponding vertical acceleration... The first predicted vertical velocity at the centroid is determined based on the degree, the corresponding fourth distance, and the first compensation coefficient. When the error between the first predicted vertical velocity and the corresponding sample vertical velocity is minimized, the lateral compensation coefficient is determined as the first compensation coefficient. When the sample vehicle is in pitch motion, the second predicted vertical velocity at the centroid is determined based on the pitch angular velocity of each sample, the corresponding sample vertical acceleration, the corresponding fifth distance, and the second compensation coefficient. When the error between the second predicted vertical velocity and the corresponding sample vertical velocity is minimized, the longitudinal compensation coefficient is determined as the second compensation coefficient.

[0085] It should be understood that in the above scheme, the single attitude motion corresponding to the sample vehicle means that the sample vehicle is either in a roll motion condition or in a pitch motion condition. The error value between the first predicted vertical velocity and the corresponding sample vertical velocity refers to the total error value between multiple first predicted vertical velocities and the one-to-one corresponding sample vertical velocities, as shown in the following formula (5).

[0086] Among them, the roll motion condition refers to the condition corresponding to the vehicle tilting left and right around its longitudinal axis. For example, the left side of the vehicle body rolls down and the right side rises. This roll motion condition often occurs in driving scenarios such as turning the vehicle or one wheel running over a bump or depression in the road surface. The pitch motion condition refers to the condition corresponding to the vehicle swaying back and forth around its lateral axis. Specifically, the front of the vehicle sinking and the rear of the vehicle rising can be regarded as pitching down, and the front of the vehicle rising and the rear of the vehicle sinking can be regarded as pitching up. This pitch motion condition often occurs in driving scenarios such as braking, accelerating, and driving uphill or downhill. The difference between the above two motion conditions is that the axes of rotation are different. The pitch motion condition is a back-and-forth sway, changing the height of the front and rear of the vehicle; the roll motion condition is a left and right tilt, changing the height of the left and right sides of the vehicle body.

[0087] The aforementioned technical solution acquires multiple sets of parameters for the sample vehicle under specific operating conditions, providing solid data support for determining the lateral and longitudinal compensation coefficients. Fitting calculations are performed separately for roll and pitch motion conditions, with the principle of minimizing error. This allows for accurate fitting and error reduction, fully considering the differences in different motion postures, and accurately determining the lateral and longitudinal compensation coefficients. This provides reliable parameter support for the accurate determination of the subsequent first vertical velocity, effectively improving the accuracy of determining the target vehicle's vertical velocity.

[0088] The specific formula (5) described above is as follows; (5) in, This is the total error value. Based on a certain The sample roll angular velocity, the corresponding sample vertical acceleration, the corresponding fourth distance, and the first compensation coefficient are used to determine the first predicted vertical velocity at the centroid. For the corresponding The vertical velocity of the sample. From 1 to , This includes the number of sample pitch angular velocities, sample roll angular velocities, sample vertical accelerations, or sample vertical velocities at the center of mass. Furthermore, the error value between the second predicted vertical velocity and the corresponding sample vertical velocity is calculated similarly and will not be elaborated upon here.

[0089] The first vertical acceleration is determined using the following implementation method.

[0090] In one possible implementation, the method for determining the first vertical acceleration includes: acquiring the initial vertical acceleration, zero-bias compensation amount, and noise level threshold measured by the inertial measurement unit at the current moment, wherein the zero-bias compensation amount is the acceleration set to compensate for the inherent bias of the inertial measurement unit, and the noise level threshold is the acceleration used to filter out measurement noise; and based on the zero-bias compensation amount and the noise level threshold, performing zero-bias removal processing and noise reduction processing on the initial vertical acceleration to obtain the first vertical acceleration.

[0091] It should be understood that in the above scheme, the zero-bias compensation is used to address the inaccuracy of the vertical acceleration reference and the long-term drift of the inertial measurement unit, ensuring that the vertical acceleration is zero when the vehicle is stationary. The noise level threshold is used to address the issues of vertical acceleration jitter and noise interference, making the vertical acceleration more accurate and preventing its error from being amplified during integration.

[0092] In the above technical solution, after obtaining the initial vertical acceleration measured by the inertial measurement unit (IMU), the initial vertical acceleration is de-biased based on the zero-bias compensation amount. This effectively eliminates the acceleration error caused by the inherent bias of the IMU. Based on a noise level threshold, the initial vertical acceleration is denoised, which avoids the influence of noise interference on the acceleration, ensuring that the processed first vertical acceleration is accurate and reliable. This solution avoids measurement errors caused by the IMU's own bias and external interference, improves the accuracy of the first vertical acceleration measurement, and provides high-quality input for the subsequent determination of the first vertical velocity.

[0093] In some embodiments, the method for determining the zero-bias compensation amount includes: acquiring multiple initial vertical accelerations measured by an inertial measurement unit at multiple times before the current time after the vehicle is powered on; determining the average value of the multiple initial vertical accelerations to obtain an average vertical acceleration; determining a minimum vertical acceleration from the average vertical acceleration and the maximum initial vertical acceleration among the multiple initial vertical accelerations; and determining the minimum vertical acceleration as the zero-bias compensation amount.

[0094] In some embodiments, determining a minimum vertical acceleration from the average vertical acceleration and the maximum initial vertical acceleration among a plurality of initial vertical accelerations includes: determining the minimum vertical acceleration based on the following formula (6); (6) in, This is the minimum vertical acceleration. The average vertical acceleration, To determine the sign of the average vertical acceleration, This represents the absolute value of the average vertical acceleration. This represents the maximum initial vertical acceleration.

[0095] In some embodiments, the method for determining the noise level threshold includes: determining the difference between each initial vertical acceleration and the minimum vertical acceleration among a plurality of initial vertical accelerations to obtain a plurality of third vertical acceleration differences; and determining the largest third vertical acceleration difference among the plurality of third vertical acceleration differences as the noise level threshold.

[0096] In some embodiments, the initial vertical acceleration is subjected to de-biasing and denoising processing based on the zero-bias compensation amount and the noise level threshold to obtain the first vertical acceleration, including: if the first vertical acceleration difference between the initial vertical acceleration and the zero-bias compensation amount is greater than a preset acceleration difference, the second vertical acceleration difference between the first vertical acceleration difference and the noise level threshold is determined as the first vertical acceleration; if the first vertical acceleration difference is less than or equal to the preset acceleration difference, the first vertical acceleration is determined as a first preset vertical acceleration.

[0097] Optionally, the first preset vertical acceleration is 0.

[0098] It should be understood that the aforementioned bias compensation amount and noise level threshold are not fixed and can be updated. If the interval between the current moment and the last time the bias compensation amount was calculated exceeds a preset duration, and the initial vertical acceleration at the current moment is less than or equal to the maximum initial vertical acceleration, the bias compensation amount and noise level threshold are updated. If the interval does not exceed the preset duration and / or the initial vertical acceleration at the current moment is greater than the maximum initial vertical acceleration, the determined bias compensation amount and noise level threshold are used.

[0099] It should also be understood that before determining the second vertical acceleration difference between the first vertical acceleration difference and the noise level threshold as the first vertical acceleration, the second vertical acceleration difference can be high-pass processed to remove the measurement error caused by the ramp to the inertial measurement unit and avoid the initial vertical acceleration measured from including gravitational acceleration.

[0100] In the above technical solution, when the difference between the initial vertical acceleration and the zero-bias compensation is greater than a preset acceleration difference, noise is further eliminated by subtracting a noise level threshold from the first vertical acceleration difference to obtain an accurate first vertical acceleration. When the first vertical acceleration difference is not greater than the preset acceleration difference, the first preset vertical acceleration is directly used to avoid noise interference. This solution, combining zero-bias compensation and noise filtering, effectively eliminates the influence of inherent sensor bias and random noise, ensuring the authenticity of the vertical acceleration. This provides high-precision input for the accurate determination of the vehicle's vertical velocity, improving the accuracy and stability of vehicle condition assessment.

[0101] Step 202: Input the vehicle's driving parameters, road condition parameters, first vertical velocity, and attitude operation parameters into the prediction model to predict the second vertical velocity of the vehicle body at each wheel end. The prediction model is used to reflect the mapping relationship between the vehicle's driving state parameters and the vertical motion state parameters at the wheel ends.

[0102] It should be understood that in step 202 above, the driving parameters are used to reflect the vehicle's own state parameters when driving, including the vehicle's overall speed, pedal opening, gear position, etc. The road condition parameters are used to reflect the road environment conditions when the vehicle is driving, including the road surface excitation frequency, as well as congestion level, weather type, road surface humidity, etc.

[0103] The prediction model in step 202 is trained based on each sample driving parameter, the corresponding sample road surface condition parameter, the corresponding sample first vertical velocity, the corresponding sample attitude operation parameter, and the corresponding sample second vehicle body vertical velocity at each wheel end.

[0104] In some embodiments, the method for determining the prediction model includes: acquiring each sample driving parameter, corresponding sample road surface condition parameter, corresponding sample first vertical velocity, corresponding sample attitude operation parameter, and corresponding sample second vehicle body vertical velocity at each wheel end from multiple sample driving parameters; inputting each sample driving parameter, corresponding sample road surface condition parameter, corresponding sample first vertical velocity, and corresponding sample attitude operation parameter into an initial prediction model; analyzing each sample driving parameter, corresponding sample road surface condition parameter, corresponding sample first vertical velocity, and corresponding sample attitude operation parameter by the initial prediction model to obtain the predicted second vehicle body vertical velocity at each wheel end in each group of vehicle body vertical velocities; adjusting the model parameters in the initial prediction model based on the first deviation between the predicted second vehicle body vertical velocity at each wheel end in each group of vehicle body vertical velocities and the corresponding sample second vehicle body vertical velocity at each wheel end, until the first deviation between the latest predicted second vehicle body vertical velocity at each wheel end and the corresponding sample second vehicle body vertical velocity at each wheel end is less than a first preset deviation, and determining the adjusted initial prediction model as the prediction model.

[0105] It should be understood that in the above scheme, a sample driving parameter, a sample road surface condition parameter, a sample first vertical velocity, and a sample attitude operation parameter correspond to a set of vehicle vertical velocities. This set of vehicle vertical velocities includes sample second vehicle vertical velocities at each of the multiple wheel ends.

[0106] Step 203: Based on the first vehicle vertical velocity and the second vehicle vertical velocity, determine the target vehicle vertical velocity at each wheel end. The target vehicle vertical velocity is used to control the vehicle.

[0107] It should be understood that in step 203 above, the first vehicle body vertical velocity is determined using a model-driven approach. When determining the second vehicle body vertical velocity, it does not rely on the assumption that the vehicle body is a rigid body, nor does it involve multiple formulas. Instead, it learns from a large amount of historical real-vehicle data (vehicle driving parameters, road condition parameters, the first vertical velocity, and attitude parameters, and the vehicle body vertical velocity at each wheel end) and establishes a nonlinear mapping relationship (reflected by a prediction model). In other words, the second vehicle body vertical velocity is determined using a data-driven approach. This nonlinear mapping relationship can automatically fit the speed estimation errors caused by non-ideal factors such as vehicle body flexible deformation, uneven stiffness, and differences in installation position. Therefore, it can theoretically avoid speed estimation errors caused by the non-rigidity of the vehicle body.

[0108] It should be noted that after controlling the vehicle's vertical speed by the target vehicle body, the vehicle's vibration response can be suppressed, which can improve ride comfort and wheel ground contact, as shown in the following embodiment.

[0109] In some embodiments, the method 200 further includes: determining the average value of the target vehicle vertical velocity at multiple wheel ends to obtain an average vertical velocity; determining the vertical velocity difference between the target vehicle vertical velocity at each wheel end and the average vertical velocity to obtain multiple second vertical velocity differences; determining the target damping force of the shock absorber in the suspension system corresponding to each wheel based on each of the multiple second vertical velocity differences; and controlling the shock absorber corresponding to each wheel to output the corresponding target damping force.

[0110] The following describes the specific process of "determining the target vertical velocity of the vehicle body at each wheel end based on the first vertical velocity of the vehicle body and the second vertical velocity of the vehicle body," that is, the specific implementation method of integrating the first vertical velocity of the vehicle body and the second vertical velocity of the vehicle body.

[0111] In one possible implementation, step 203, determining the target vehicle vertical velocity at each wheel end based on the first vehicle vertical velocity and the second vehicle vertical velocity, includes: determining the driving parameters and the road condition parameters, and the target similarity with each sample driving parameter and corresponding sample road condition parameter in a plurality of sample driving parameters; determining a first weighting coefficient based on the plurality of target similarities, the first weighting coefficient being used to reflect the contribution of the second vehicle vertical velocity in determining the target vehicle vertical velocity; and performing a weighted fusion of the first vehicle vertical velocity and the second vehicle vertical velocity based on the second weighting coefficient and the first weighting coefficient to obtain the target vehicle vertical velocity at each wheel end, the second weighting coefficient being the difference between the preset weighting coefficient and the first weighting coefficient, the second weighting coefficient being used to reflect the contribution of the first vehicle vertical velocity in determining the target vehicle vertical velocity.

[0112] It should be understood that in the above scheme, determining the target similarity between the driving parameter and the road condition parameter and each of the multiple sample driving parameters and their corresponding sample road condition parameters means that there is a target similarity between a driving parameter and a road condition parameter and a sample driving parameter and their corresponding sample road condition parameter, resulting in multiple target similarities. This target similarity is used to reflect the degree of similarity between a driving parameter and a road condition parameter (considered as the first parameter) and their corresponding sample driving parameter and their corresponding sample road condition parameter (considered as the second parameter).

[0113] It should also be understood that in the above scheme, the first weighting coefficient can be regarded as the degree of confidence in the second vehicle vertical velocity when determining the target vehicle vertical velocity, and the second weighting coefficient can be regarded as the degree of confidence in the first vehicle vertical velocity when determining the target vehicle vertical velocity.

[0114] In the above technical solution, the current driving parameters and road condition parameters are matched in real time with the target similarity of historical samples (driving parameters of each sample and corresponding road condition parameters). Based on multiple target similarities, a first weighting coefficient is determined. This determines the degree of trust in the neural network model when determining the first vehicle body vertical speed based on the similarity between the actual working conditions and the working conditions corresponding to the neural network model. This avoids the situation where the neural network model over-relies on the second vehicle body vertical speed when it has not been trained to the working conditions corresponding to the current driving parameters and road condition parameters. Furthermore, based on the second weighting coefficient and the first weighting coefficient, the first vehicle body vertical speed and the second vehicle body vertical speed are weighted and fused to obtain the target vehicle body vertical speed at each wheel end. This can fully utilize the accuracy of the basic calculation results of the physical model and the accurate prediction results of the neural network model. The above solution can dynamically allocate weighting coefficients based on actual working conditions, effectively suppressing errors caused by the flexible deformation and local vibration of the vehicle body, and improving the reliability of the target vehicle body vertical speed and the adaptability to the driving environment.

[0115] In some embodiments, the driving parameters include the vehicle speed, and the road condition parameters include the road excitation frequency of the driving road surface. Determining the target similarity between the driving parameters and the road condition parameters and each of the plurality of sample driving parameters and their corresponding sample road condition parameters includes: using any sample driving parameter as a reference sample driving parameter, using the corresponding sample road condition parameter as a reference sample road condition parameter, determining a first similarity between the vehicle speed and the reference sample vehicle speed, and determining a second similarity between the road excitation frequency and the reference sample road excitation frequency; and determining the average of the first similarity and the second similarity as the target similarity between the vehicle speed and the road excitation frequency and the reference sample vehicle speed and their corresponding reference sample road excitation frequency, to obtain the target similarity between the driving parameters and the road condition parameters and each of the plurality of sample driving parameters and their corresponding sample road condition parameters.

[0116] In one possible implementation, determining a first weighting coefficient based on multiple target similarities includes: if the maximum similarity among the multiple target similarities is greater than or equal to a preset similarity, determining the first weighting coefficient as a first preset coefficient; if the maximum similarity is less than the preset similarity, determining the first weighting coefficient as a second preset coefficient, wherein the second preset coefficient is less than the first preset coefficient.

[0117] It should be understood that in the above scheme, the deviation between the first preset coefficient and the fourth preset coefficient is the first deviation coefficient, the fourth preset coefficient is 1, and the first deviation coefficient is moderate. Optionally, the first deviation coefficient is 0.5. That is, optionally, the first preset coefficient is 0.5. Optionally, the second preset coefficient is 0.1.

[0118] It's important to note that the prediction model is a neural network model. While it can be trained with a large amount of training data, real-world road conditions are complex and diverse, and the training data cannot cover them all. This limits the range of road conditions the prediction model can cover; in other words, many road conditions may not include the actual road conditions mentioned above. Therefore, when the maximum similarity is greater than or equal to the preset similarity, the above scheme does not set the first preset coefficient to be close to the fourth preset coefficient, but rather to a preset coefficient that deviates slightly from the fourth preset coefficient, such as 0.5. That is, it does not overly rely on the prediction model.

[0119] In the above technical solution, a first weighting coefficient is determined based on the relationship between the maximum similarity and the preset similarity. When the maximum similarity exceeds the preset similarity, a larger first preset coefficient is used. This fully leverages the predictive model's accurate prediction advantage under similar operating conditions. When the maximum similarity is less than the preset similarity, a smaller second preset coefficient is used. This reduces the impact of the uncertainty of the predictive model when predicting the vertical velocity of the second vehicle body under unfamiliar operating conditions on the vertical velocity of the target vehicle body. This solution can reasonably allocate the weighting coefficients of the prediction results (vertical velocity of the second vehicle body) according to the similarity of operating conditions, avoiding the problem of insufficient adaptability caused by fixed weighting coefficients, and providing a reliable basis for subsequent weighted fusion.

[0120] Figure 3 This is a schematic block diagram illustrating an embodiment of the present application for estimating vertical velocity.

[0121] For example, such as Figure 3 As shown, based on the vehicle's attitude parameters at the current moment, it is determined whether the vehicle is in a stable driving state. If the vehicle is not in a stable driving state, the first vertical velocity is determined based on the integral values ​​of the fifth vertical velocity and the first vertical acceleration at the vehicle's center of mass at the previous moment over the current preset time period. If the vehicle is in a stable driving state, the first vertical velocity is determined based on the average value of the second rate of change in relative height between multiple wheels and the vehicle body at the current moment. The first vertical velocity at the vehicle's center of mass, the vehicle's geometric position parameters, and attitude parameters are input into the rigid body kinematics model to obtain the first vertical velocity of the vehicle body at each wheel end at the current moment. The first vertical velocity at the vehicle's center of mass, the vehicle's driving parameters, attitude parameters, and road condition parameters are input into the prediction model to obtain the second vertical velocity of the vehicle body at each wheel end at the current moment. Based on the first vertical velocity of the vehicle body at each wheel end and the second vertical velocity of the vehicle body at each wheel end at the current moment, the target vertical velocity of the vehicle body at each wheel end at the current moment is determined.

[0122] Figure 4This is a schematic diagram of the structure of a vehicle state estimation device provided in an embodiment of this application.

[0123] For example, such as Figure 4 As shown, the device 400 includes: The first determining module 401 is used to determine the first vertical velocity of the vehicle body at each wheel end based on the first vertical velocity at the vehicle's center of mass at the current moment, the vehicle's geometric position parameters, and attitude operation parameters. The prediction module 402 is used to input the vehicle's driving parameters, road condition parameters, first vertical velocity, and attitude operation parameters into the prediction model to predict the second vertical velocity of the vehicle body at each wheel end. The prediction model is used to reflect the mapping relationship between the vehicle's driving state parameters and the vertical motion state parameters at the wheel ends. The second determining module 403 is used to determine the target vertical velocity of the vehicle body at each wheel end based on the first vertical velocity of the vehicle body and the second vertical velocity of the vehicle body. The target vertical velocity of the vehicle body is used to control the vehicle.

[0124] Optionally, the second determining module 403 is specifically used for: determining the driving parameters and the road surface condition parameters, and the target similarity between them and each sample driving parameter and the corresponding sample road surface condition parameter in a plurality of sample driving parameters; determining a first weighting coefficient based on the plurality of target similarities, the first weighting coefficient being used to reflect the contribution of the second vehicle vertical speed in determining the target vehicle vertical speed; and performing weighted fusion of the first vehicle vertical speed and the second vehicle vertical speed based on the second weighting coefficient and the first weighting coefficient to obtain the target vehicle vertical speed at each wheel end, wherein the second weighting coefficient is the difference between the preset weighting coefficient and the first weighting coefficient, and the second weighting coefficient being used to reflect the contribution of the first vehicle vertical speed in determining the target vehicle vertical speed.

[0125] Optionally, the second determining module 403 is further configured to: determine a first weighting coefficient based on multiple target similarities, including: when the maximum similarity among the multiple target similarities is greater than or equal to a preset similarity, determining the first weighting coefficient as a first preset coefficient; when the maximum similarity is less than the preset similarity, determining the first weighting coefficient as a second preset coefficient, wherein the second preset coefficient is less than the first preset coefficient.

[0126] Optionally, the geometric position parameters include a first distance between the center of gravity and the front axle, a second distance between the center of gravity and the rear axle, and a third distance between the left and right wheels. The first determining module 401 is specifically used to: determine a second vertical velocity, a third vertical velocity, and a fourth vertical velocity based on the pitch angular velocity, roll angular velocity, the first distance, the second distance, and the third distance in the attitude operation parameters. The second vertical velocity is the vertical velocity component generated by the vehicle's pitch motion at the front wheels, the third vertical velocity is the vertical velocity component generated by the vehicle's pitch motion at the rear wheels, and the fourth vertical velocity is... The vertical velocity component generated by the vehicle's roll motion between the left and right wheels; based on the first vertical velocity, the second vertical velocity, the third vertical velocity, and the fourth vertical velocity, the third vertical velocity of the vehicle at each wheel end is determined, and the third vertical velocity of the vehicle at each wheel end is determined as the first vertical velocity of the vehicle at each wheel end; or, the product between the third vertical velocity of the vehicle at each wheel end and the corresponding compensation coefficient is determined as the first vertical velocity of the vehicle at each wheel end, where the compensation coefficient is used to compensate for the degree of influence of the corresponding road conditions on the vertical velocity of the vehicle at the wheel end.

[0127] Optionally, the first determining module 401 is further configured to: determine the first vertical speed difference between the first vertical speed and the second vertical speed, and determine the sum of the first vertical speed difference and the fourth vertical speed as the third vehicle body vertical speed at the left front wheel end; determine the difference between the first vertical speed difference and the fourth vertical speed as the third vehicle body vertical speed at the right front wheel end; determine the sum of the first vertical speed and the third vertical speed to obtain a first total vertical speed, and determine the sum of the first total vertical speed and the fourth vertical speed as the third vehicle body vertical speed at the left rear wheel end; and determine the difference between the first total vertical speed and the fourth vertical speed as the third vehicle body vertical speed at the right rear wheel end.

[0128] Optionally, the first determining module 401 is further configured to: determine the road surface excitation frequency corresponding to each wheel, thereby obtaining multiple road surface excitation frequencies; take the road surface excitation frequency corresponding to any wheel among the multiple road surface excitation frequencies as the target excitation frequency, and determine a candidate excitation frequency matching the target excitation frequency from multiple sample road surface excitation frequencies; and determine the sample compensation coefficient corresponding to the candidate excitation frequency among the multiple sample road surface excitation frequencies as the compensation coefficient corresponding to the third vehicle body vertical velocity at the wheel end, so as to obtain the compensation coefficient corresponding to the third vehicle body vertical velocity at each wheel end.

[0129] Optionally, the first determining module 401 is further configured to: determine whether the vehicle is in a stable driving state based on the attitude operating parameters; if the vehicle is in a stable driving state, determine the first vertical velocity based on the average value of the second rate of change in relative height between multiple wheels and the vehicle body at the current moment; if the vehicle is not in a stable driving state, determine the first vertical velocity based on the integral value of the fifth vertical velocity and the first vertical acceleration at the vehicle's center of mass at the previous moment over the current preset time period, wherein the first vertical acceleration is determined based on the acceleration measured by the inertial measurement unit.

[0130] Optionally, the attitude operating parameters include roll rate and pitch rate. The first determining module 401 is further configured to: determine the first vertical rate by summing the fifth vertical rate and the integral value; or, determine the first speed adjustment amount by multiplying the roll rate, the fourth distance, and the lateral compensation coefficient, where the fourth distance is the lateral offset distance of the inertial measurement unit relative to the center of mass, and the lateral compensation coefficient is used to compensate for the influence of the fourth distance when determining the first vertical rate; determine the second speed adjustment amount by multiplying the pitch rate, the fifth distance, and the longitudinal compensation coefficient, where the fifth distance is the longitudinal offset distance of the inertial measurement unit relative to the center of mass, and the longitudinal compensation coefficient is used to compensate for the influence of the fifth distance when determining the first vertical rate; and determine the first vertical rate by summing the fifth vertical rate, the integral value, the first speed adjustment amount, and the second speed adjustment amount.

[0131] Optionally, the device 400 further includes: an acquisition module, configured to acquire, in the case of each sample vehicle under a preset driving condition, a plurality of sample pitch angular velocities, the corresponding sample roll angular velocity, the corresponding sample vertical acceleration, the corresponding fourth distance, the corresponding fifth distance, and the corresponding sample vertical velocity at the center of mass, wherein the preset driving condition includes a roll motion condition and a pitch motion condition, and the sample vehicle corresponds to a single attitude motion; the first determination module 401 is further configured to: when the sample vehicle is in a roll motion condition, determine, based on the sample roll angular velocity, the corresponding sample vertical acceleration, the first determination module 401, and the corresponding sample vertical velocity at the center of mass, respectively. The first predicted vertical velocity at the center of mass is determined by using acceleration, the corresponding fourth distance, and the first compensation coefficient. When the error between the first predicted vertical velocity and the corresponding sample vertical velocity is minimized, the lateral compensation coefficient is determined as the first compensation coefficient. When the sample vehicle is in pitch motion, the second predicted vertical velocity at the center of mass is determined based on the pitch angular velocity of each sample, the corresponding sample vertical acceleration, the corresponding fifth distance, and the second compensation coefficient. When the error between the second predicted vertical velocity and the corresponding sample vertical velocity is minimized, the longitudinal compensation coefficient is determined as the second compensation coefficient.

[0132] Optionally, the acquisition module is further configured to acquire the initial vertical acceleration, zero-bias compensation amount, and noise level threshold measured by the inertial measurement unit at the current moment. The zero-bias compensation amount is the acceleration set to compensate for the inherent deviation of the inertial measurement unit, and the noise level threshold is the acceleration used to filter out measurement noise. The first determination module 401 is further configured to perform zero-bias removal processing and noise reduction processing on the initial vertical acceleration based on the zero-bias compensation amount and the noise level threshold to obtain the first vertical acceleration.

[0133] Optionally, the first determining module 401 is further configured to: determine the first vertical acceleration as the second vertical acceleration difference between the first vertical acceleration difference and the noise level threshold when the first vertical acceleration difference between the initial vertical acceleration and the zero bias compensation amount is greater than the preset acceleration difference; and determine the first vertical acceleration as the first preset vertical acceleration when the first vertical acceleration difference is less than or equal to the preset acceleration difference.

[0134] Figure 5 This is a schematic diagram of the structure of a controller provided in an embodiment of this application.

[0135] For example, such as Figure 5 As shown, the controller 500 includes a storage module 501 and a processing module 502. The storage module 501 stores executable program code 503, and the processing module 502 is used to call and execute the executable program code 503 to perform a vehicle state estimation method.

[0136] Figure 6 This is a schematic diagram of the structure of a vehicle provided in an embodiment of this application.

[0137] For example, such as Figure 6 As shown, the vehicle 600 includes a memory 601 and a processor 602. The memory 601 stores executable program code 603, and the processor 602 is used to call and execute the executable program code 603 to perform a vehicle state estimation method.

[0138] Furthermore, embodiments of this application also protect an apparatus that may include a memory and a processor, wherein the memory stores executable program code, and the processor is used to call and execute the executable program code to perform a vehicle state estimation method provided in embodiments of this application.

[0139] This embodiment can divide the device into functional modules based on the above method example. For example, each module can correspond to a separate function, or two or more functions can be integrated into one processing module. The integrated module can be implemented in hardware. It should be noted that the module division in this embodiment is illustrative and only represents one logical functional division. In actual implementation, there may be other division methods.

[0140] When the functional modules are divided according to their respective functions, the device may further include a first determining module, a predicting module, a second determining module, and an acquiring module, etc. It should be noted that all relevant content in the above method embodiments can be referenced to the functional descriptions of the corresponding functional modules, and will not be repeated here.

[0141] It should be understood that the apparatus provided in this embodiment is used to perform the above-described vehicle state estimation method, and therefore can achieve the same effect as the above-described implementation method.

[0142] When using an integrated unit, the device may include a processing module and a storage module. When the device is applied to a vehicle, the processing module can be used to control and manage the vehicle's movements. The storage module can be used to support the vehicle in executing relevant executable program code.

[0143] The processing module may be a processor or a controller, which can implement or execute various exemplary logic blocks, modules, and circuits shown in conjunction with the disclosure of this application. The processor may also be a combination of functions that implement computing capabilities, such as a combination of one or more microprocessors, a combination of digital signal processing (DSP) and a microprocessor, etc., and the storage module may be a memory.

[0144] In addition, the device provided in the embodiments of this application may specifically be a chip, component or module. The chip may include a connected processor and a memory. The memory is used to store instructions. When the processor calls and executes the instructions, the chip can execute a vehicle state estimation method provided in the above embodiments.

[0145] This embodiment also provides a computer-readable storage medium storing executable program code. When the executable program code is run on a computer, the computer performs the aforementioned method steps to implement the vehicle state estimation method provided in the above embodiment.

[0146] This embodiment also provides a computer program product that, when run on a computer, causes the computer to perform the aforementioned related steps to implement a vehicle state estimation method provided in the above embodiment.

[0147] In this embodiment, the device, computer-readable storage medium, computer program product, or chip are all used to execute the corresponding methods provided above. Therefore, the beneficial effects they can achieve can be referred to the beneficial effects in the corresponding methods provided above, and will not be repeated here.

[0148] Through the above description of the embodiments, those skilled in the art will understand that, for the sake of convenience and brevity, only the division of the above functional modules is used as an example. In actual applications, the above functions can be assigned to different functional modules as needed, that is, the internal structure of the device can be divided into different functional modules to complete all or part of the functions described above.

[0149] In the embodiments provided in this application, it should be understood that the disclosed apparatus and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of modules or units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another device, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection between devices or units may be electrical, mechanical, or other forms.

[0150] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

Claims

1. A method for estimating vehicle state, characterized in that, The method includes: Based on the first vertical velocity at the vehicle's center of mass at the current moment, the vehicle's geometric position parameters, and attitude parameters, determine the first vertical velocity of the vehicle body at each wheel end. The vehicle's driving parameters, road condition parameters, first vertical velocity, and attitude operation parameters are input into the prediction model to predict the second vertical velocity of the vehicle body at each wheel end. The prediction model is used to reflect the mapping relationship between the vehicle's driving state parameters and the vertical motion state parameters at the wheel ends. Based on the first vehicle vertical velocity and the second vehicle vertical velocity, a target vehicle vertical velocity is determined at each wheel end, and the target vehicle vertical velocity is used to control the vehicle. The method for determining the first vertical velocity at the vehicle's center of gravity at the current moment includes: Based on the attitude operation parameters, determine whether the vehicle is in a stable driving state; When the vehicle is not in a stable driving state, the first vertical velocity is determined based on the integral value of the fifth vertical velocity and the first vertical acceleration at the vehicle's center of gravity at the previous moment over the current preset time period; The attitude operation parameters include roll rate and pitch rate. Determining the first vertical velocity based on the integral value of the fifth vertical velocity and the first vertical acceleration at the vehicle's center of gravity at the previous moment over the current preset time period includes: The product of the roll rate, the fourth distance, and the lateral compensation coefficient is determined as the first speed adjustment amount, and the product of the pitch rate, the fifth distance, and the longitudinal compensation coefficient is determined as the second speed adjustment amount. The first vertical velocity is determined by the sum of the fifth vertical velocity, the integral value, the first velocity adjustment amount, and the second velocity adjustment amount. The methods for determining the lateral compensation coefficient and the longitudinal compensation coefficient include: The sample vehicles are obtained from multiple sample pitch angular velocities under preset driving conditions. The sample pitch angular velocity, the corresponding sample roll angular velocity, the corresponding sample vertical acceleration, the corresponding fourth distance, the corresponding fifth distance, and the corresponding sample vertical velocity at the center of mass are obtained. The preset driving conditions include roll motion conditions and pitch motion conditions. The sample vehicles correspond to a single attitude motion. When the sample vehicle is in a rolling motion condition, based on the rolling angular velocity of each sample, the corresponding vertical acceleration of the sample, the corresponding fourth distance and the first compensation coefficient, the first predicted vertical velocity at the center of mass is determined, and when the error between the first predicted vertical velocity and the corresponding sample vertical velocity is minimized, the lateral compensation coefficient is determined as the first compensation coefficient. When the sample vehicle is in pitch motion, based on the pitch angular velocity of each sample, the corresponding vertical acceleration of the sample, the corresponding fifth distance and the second compensation coefficient, the second predicted vertical velocity at the centroid is determined, and when the error between the second predicted vertical velocity and the corresponding sample vertical velocity is minimized, the longitudinal compensation coefficient is determined as the second compensation coefficient.

2. The method according to claim 1, characterized in that, The first vertical acceleration is determined based on the acceleration measured by the inertial measurement unit. The fourth distance is the lateral offset distance of the inertial measurement unit relative to the center of mass. The lateral compensation coefficient is used to compensate for the influence of the fourth distance when determining the first vertical velocity. The fifth distance is the longitudinal offset distance of the inertial measurement unit relative to the center of mass. The longitudinal compensation coefficient is used to compensate for the influence of the fifth distance when determining the first vertical velocity.

3. The method according to claim 1, characterized in that, The step of determining the target vertical velocity of the vehicle body at each wheel end based on the first vertical velocity and the second vertical velocity of the vehicle body includes: Determine the target similarity between the driving parameters and the road surface condition parameters and each sample driving parameter and corresponding sample road surface condition parameter in a plurality of sample driving parameters; Based on the similarity of multiple targets, a first weighting coefficient is determined. The first weighting coefficient is used to reflect the contribution of the second vehicle vertical velocity when determining the vertical velocity of the target vehicle. Based on the second weighting coefficient and the first weighting coefficient, the first vehicle vertical velocity and the second vehicle vertical velocity are weighted and fused to obtain the target vehicle vertical velocity at each wheel end. The second weighting coefficient is the difference between the preset weighting coefficient and the first weighting coefficient. The second weighting coefficient is used to reflect the contribution of the first vehicle vertical velocity in determining the target vehicle vertical velocity.

4. The method according to claim 3, characterized in that, The determination of the first weighting coefficient based on multiple target similarities includes: If the maximum similarity among the plurality of target similarities is greater than or equal to a preset similarity, the first weighting coefficient is determined as the first preset coefficient; If the maximum similarity is less than the preset similarity, the first weighting coefficient is determined as the second preset coefficient, and the second preset coefficient is less than the first preset coefficient.

5. The method according to claim 1, characterized in that, The geometric position parameters include a first distance between the center of gravity and the front axle, a second distance between the center of gravity and the rear axle, and a third distance between the left and right wheels. The determination of the first vertical velocity of the vehicle body at each wheel end based on the first vertical velocity at the vehicle's center of gravity at the current moment, the vehicle's geometric position parameters, and attitude parameters includes: Based on the pitch angular velocity, roll angular velocity, first distance, second distance, and third distance in the attitude operation parameters, a second vertical velocity, a third vertical velocity, and a fourth vertical velocity are determined. The second vertical velocity is the vertical velocity component generated by the vehicle's pitch motion at the front wheels, the third vertical velocity is the vertical velocity component generated by the vehicle's pitch motion at the rear wheels, and the fourth vertical velocity is the vertical velocity component generated by the vehicle's roll motion between the left and right wheels. Based on the first vertical velocity, the second vertical velocity, the third vertical velocity, and the fourth vertical velocity, a third vehicle body vertical velocity at each wheel end is determined, and this third vehicle body vertical velocity at each wheel end is defined as the first vehicle body vertical velocity at each wheel end; or, The product of the third vertical velocity of the vehicle body at each wheel end and the corresponding compensation coefficient is determined as the first vertical velocity of the vehicle body at each wheel end. The compensation coefficient is used to compensate for the influence of the corresponding road conditions on the vertical velocity of the vehicle body at the wheel end.

6. The method according to claim 5, characterized in that, The step of determining the third vertical velocity of the vehicle body at each wheel end based on the first vertical velocity, the second vertical velocity, the third vertical velocity, and the fourth vertical velocity includes: Determine the first vertical velocity difference between the first vertical velocity and the second vertical velocity, and sum the first vertical velocity difference with the fourth vertical velocity to determine the third vehicle body vertical velocity at the left front wheel end; The difference between the first vertical velocity difference and the fourth vertical velocity is determined as the third vertical velocity of the vehicle body at the right front wheel end; The sum of the first vertical velocity and the third vertical velocity is determined to obtain the first total vertical velocity, and the sum of the first total vertical velocity and the fourth vertical velocity is determined as the third vertical velocity of the vehicle body at the left rear wheel end. The difference between the first total vertical velocity and the fourth vertical velocity is determined as the third vertical velocity of the vehicle body at the right rear wheel end.

7. The method according to claim 5, characterized in that, The method for determining the compensation coefficient corresponding to the third vertical velocity of the vehicle body at each wheel end includes: Determine the road surface excitation frequency corresponding to each wheel to obtain multiple road surface excitation frequencies; The road surface excitation frequency of any wheel corresponding to the driving road surface among the multiple road surface excitation frequencies is taken as the target excitation frequency, and a candidate excitation frequency matching the target excitation frequency is determined from multiple sample road surface excitation frequencies. The sample compensation coefficient corresponding to the candidate excitation frequency among the multiple sample road surface excitation frequencies is determined as the compensation coefficient corresponding to the third vehicle body vertical velocity at the wheel end, so as to obtain the compensation coefficient corresponding to the third vehicle body vertical velocity at each wheel end.

8. The method according to claim 1, characterized in that, The method further includes: When the vehicle is in a stable driving state, the first vertical velocity is determined based on the average of the second rate of change in relative height between multiple wheels and the vehicle body at the current moment.

9. The method according to claim 1, characterized in that, The method for determining the first vertical acceleration includes: The initial vertical acceleration, zero-bias compensation amount, and noise level threshold measured by the inertial measurement unit at the current moment are obtained. The zero-bias compensation amount is the acceleration set to compensate for the inherent bias of the inertial measurement unit, and the noise level threshold is the acceleration used to filter out measurement noise. Based on the zero-bias compensation amount and the noise level threshold, the initial vertical acceleration is subjected to zero-bias removal processing and noise reduction processing to obtain the first vertical acceleration.

10. The method according to claim 9, characterized in that, The step of performing zero-bias removal and noise reduction processing on the initial vertical acceleration based on the zero-bias compensation amount and the noise level threshold to obtain the first vertical acceleration includes: If the first vertical acceleration difference between the initial vertical acceleration and the zero bias compensation amount is greater than the preset acceleration difference, the second vertical acceleration difference between the first vertical acceleration difference and the noise level threshold is determined as the first vertical acceleration. If the first vertical acceleration difference is less than or equal to the preset acceleration difference, the first vertical acceleration is determined as the first preset vertical acceleration.

11. A vehicle, characterized in that, The vehicles include: Memory, used to store executable program code; A processor for calling and running the executable program code from the memory, causing the vehicle to perform the method as described in any one of claims 1 to 10.

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