Vehicle side slip angle detection method, device and equipment and storage medium

Abstract

The invention relates to the technical field of intelligent automobiles, in particular to a vehicle side slip angle detection method, device and equipment and a computer storage medium. The method comprises the steps of obtaining a first side slip angle estimated value according to a vehicle dynamics model; according to the chassis acceleration information, through an integral discount factor and a zero clearing mechanism, a second side slip angle estimation value of the vehicle is obtained through direct integration of the acceleration; by introducing the projection of the difference value between the ideal yaw velocity and the actual vehicle yaw velocity, the distortion degree of the current vehicle dynamic model is evaluated, the weight coefficients of different estimated values are obtained, and the self-adaptive weight fusion of the first and second estimated values of the side slip angle is realized. According to the method, the estimation precision of the side slip angle can be effectively improved, state quantity support is provided for the active safety technology and function of the vehicle, the method is developed based on an existing vehicle-mounted sensor, the algorithm design is fully analyzed, the algorithm is low in calculation cost and easy to implement in loading, and the method has high engineering application value.

Description

Technical Field

[0001] The present invention relates to the field of intelligent automobile technology, and in particular to a vehicle center of mass sideslip angle detection method, device, equipment and computer storage medium. Background Art

[0002] With the development of vehicle intelligent technology, the vehicle chassis module composition is increasing, the coupling between subsystems is deepening, and the vehicle control performance has been greatly improved. In order to improve vehicle safety performance and control capabilities, the vehicle's perception and observation of chassis information is increasing.

[0003] The estimation of the center of mass slip angle is of great significance in vehicle dynamics control and safe operation. Accurate estimation of the center of mass slip angle is the basis for realizing vehicle dynamics control. The center of mass slip angle reflects the posture change and lateral stability of the vehicle when turning, and is also a key state quantity for the lateral motion control of the vehicle. Due to the limitations of on-board sensor technology and installation cost considerations, the center of mass slip angle is often not directly obtained through sensor measurement. Therefore, how to develop a real-time and accurate center of mass slip angle estimation algorithm is a major challenge before us.

[0004] As an important part of vehicle state observation technology, the research on the estimation of the center of mass side slip angle of intelligent vehicles has been carried out in many related studies and has made certain progress. For example, patent CN117400952A proposes a method and device for estimating the center of mass side slip angle of a vehicle to solve the problem that the existing technology cannot accurately observe the change of the center of mass side slip angle of the vehicle in real time during the steering process. The algorithm mainly uses the acceleration information of the existing chassis inertial sensor, activates the integral reset state flag, and performs integral reset processing on the center of mass side slip angle integrator, and determines that the current center of mass side slip angle original value of the vehicle is zero, thereby reducing the problem of error accumulation in the center of mass side slip angle estimation algorithm. Patent CN116513211A provides a method for estimating the center of mass side slip angle based on a single-track nonlinear dynamic model of a vehicle. According to the nonlinear single-track vehicle dynamics model, the algorithm establishes a time-delay nonlinear adaptive observer, determines the gain of the time-delay nonlinear adaptive observer, and finally obtains the current working area of ​​the entire tire and the corresponding center of mass side slip angle. Patent CN107901913B proposes a vehicle center of mass sideslip angle method, device, and controller involving multi-source information fusion, which obtains a center of mass sideslip angle estimate by obtaining the distance from the camera preview point to the right lane line, the measured values ​​of lateral acceleration and yaw angular velocity, and the self-aligning torque at the kingpin, and fuses and weights the center of mass sideslip angle estimate based on the dynamic model to improve the final center of mass sideslip angle estimation accuracy.

[0005] Summarizing the existing methods for estimating the sideslip angle of the center of mass of a vehicle, it can be found that the direct integration method based solely on sensor acceleration information faces the problem of error accumulation and amplification during long-term operation; the center of mass sideslip angle estimation algorithm based solely on the vehicle dynamics equation faces the problem of inaccurate dynamics model during use. Using only one type of sensor information or model cannot achieve accurate estimation of the sideslip angle of the center of mass. At the same time, adding hardware sensor devices such as cameras and ultrasonic radars is not a common sensor for current vehicles. Adding this device makes the vehicle hardware cost too high, and there is still a long way to go for application.

[0006] In summary, the vehicle center of mass sideslip angle detection method in the prior art has the problems of low accuracy and high cost. Summary of the invention

[0007] Therefore, the technical problem to be solved by the present invention is to overcome the problems of low accuracy and high cost in the vehicle center of mass sideslip angle detection method in the prior art.

[0008] In order to solve the above technical problems, the present invention provides a method for detecting a vehicle center of mass sideslip angle, comprising:

[0009] Based on the vehicle dynamics model, the first center of mass sideslip angle estimation value of the target vehicle is obtained in real time;

[0010] Based on the vehicle kinematic information, the estimated value of the second center of mass sideslip angle of the target vehicle is obtained in real time;

[0011] Based on the actual yaw rate and the ideal yaw rate of the target vehicle, the first center of mass sideslip angle estimation value and the second center of mass sideslip angle estimation value are adaptively weighted fused to obtain the center of mass sideslip angle of the target vehicle.

[0012] Preferably, the step of acquiring the first center of mass sideslip angle estimation value of the target vehicle in real time based on the vehicle dynamics model comprises:

[0013] Based on vehicle dynamics parameters, construct a vehicle dynamics mathematical model;

[0014] Based on the vehicle dynamics mathematical model, the system state of the target vehicle is predicted in real time to obtain an estimated value of the first center of mass sideslip angle of the target vehicle.

[0015] Preferably, constructing a vehicle dynamics mathematical model based on vehicle dynamics parameters comprises:

[0016] A three-degree-of-freedom vehicle dynamics mathematical model is constructed based on vehicle dynamics parameters, wherein the vehicle dynamics parameters include the vehicle yaw angular velocity, the vehicle lateral velocity, the vehicle longitudinal velocity, the distance between the vehicle center of mass and the front axle, the distance between the vehicle center of mass and the rear axle, the vehicle curb mass, the vehicle's yaw moment of inertia, the vehicle's front axle lateral force, and the vehicle's rear axle lateral force in the vehicle body coordinate system.

[0017] Preferably, the step of acquiring a second center of mass sideslip angle estimation value of the target vehicle in real time based on vehicle kinematic information comprises:

[0018] Obtain acceleration data from vehicle chassis sensors in real time;

[0019] By directly integrating the acceleration data, an estimated value of a second center of mass sideslip angle of the target vehicle is obtained in real time.

[0020] Preferably, the step of directly integrating the acceleration data to obtain a second center of mass sideslip angle estimation value of the target vehicle in real time comprises:

[0021] Obtaining in real time the average value of the steering wheel angle of the target vehicle within a preset time period before the current moment;

[0022] If the average value is not greater than the preset steering wheel angle threshold, the estimated value of the second center of mass sideslip angle of the current target vehicle is set to 0;

[0023] If the average value is greater than a preset steering wheel angle threshold, an integral discount factor is introduced, and the acceleration data before the current moment is directly integrated to obtain an estimated value of the second center of mass sideslip angle of the current target vehicle.

[0024] Preferably, the step of adaptively weighting and fusing the first center of mass sideslip angle estimation value and the second center of mass sideslip angle estimation value based on the actual yaw angular velocity and the ideal yaw angular velocity of the target vehicle to obtain the center of mass sideslip angle of the target vehicle comprises:

[0025] The difference between the actual yaw rate and the ideal yaw rate of the target vehicle is projected onto the ideal yaw rate to obtain the target variable;

[0026] Setting a weight coefficient for the first center of mass sideslip angle estimation value and the second center of mass sideslip angle estimation value based on the target variable;

[0027] The first center of mass sideslip angle estimation value and the second center of mass sideslip angle estimation value are adaptively weighted fused based on the weight coefficient to obtain the center of mass sideslip angle of the target vehicle.

[0028] Preferably, the calculation formula for setting the weight coefficient for the first center of mass sideslip angle estimation value and the second center of mass sideslip angle estimation value based on the target variable is:

[0029]

[0030] Among them, γ e,k is the actual yaw rate error, γ d,k is the ideal yaw rate, γ + eis the target variable, ω limit is a non-negative weight upper limit, satisfying 0<ω limit <1, θ>0 is the sensitivity factor of the fusion coefficient, and the function exp(·) is an exponential function with the natural number e as the base.

[0031] The present invention also provides a vehicle center of mass side slip angle detection device, comprising:

[0032] A dynamics model estimation module, used to obtain a first center of mass sideslip angle estimation value of the target vehicle in real time based on a vehicle dynamics model;

[0033] A kinematic model estimation module, used to obtain a second center of mass sideslip angle estimation value of the target vehicle in real time based on the vehicle kinematic information;

[0034] The adaptive fusion module is used to adaptively weight the first center of mass sideslip angle estimation value and the second center of mass sideslip angle estimation value based on the actual yaw angular velocity and the ideal yaw angular velocity of the target vehicle to obtain the center of mass sideslip angle of the target vehicle.

[0035] The present invention also provides a vehicle center of mass side slip angle detection device, comprising:

[0036] Memory for storing computer programs;

[0037] A processor is used to implement the above-mentioned steps of a vehicle center of mass sideslip angle detection method when executing the computer program.

[0038] The present invention also provides a computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, the steps of the above-mentioned vehicle center of mass sideslip angle detection method are implemented.

[0039] The above technical solution of the present invention has the following advantages compared with the prior art:

[0040] The vehicle center of mass sideslip angle detection method of the present invention obtains a first center of mass sideslip angle estimation value according to the vehicle dynamics model; obtains a second center of mass sideslip angle estimation value of the vehicle by directly integrating the acceleration according to the chassis acceleration information through the integral discount factor and the zeroing mechanism; evaluates the distortion degree of the current vehicle dynamics model by introducing the projection of the difference between the ideal yaw rate and the actual vehicle yaw rate, obtains the weight coefficients of different estimation values, and realizes the adaptive weighted fusion of the first and second center of mass sideslip angle estimation values. The present invention solves the problem of low estimation accuracy of the center of mass sideslip angle estimation algorithm of a single model and sensor information, can effectively improve the center of mass sideslip angle estimation accuracy, and provide state quantity support for vehicle active safety technology and functions. The present method is developed based on existing vehicle-mounted sensors, does not require the introduction of additional vehicle-mounted sensors, and the algorithm design is fully analytical, so that the algorithm has low calculation cost and is easy to install and implement, and has great engineering application value. BRIEF DESCRIPTION OF THE DRAWINGS

[0041] In order to make the content of the present invention more clearly understood, the present invention is further described in detail below according to specific embodiments of the present invention in conjunction with the accompanying drawings, wherein:

[0042] Figure 1 It is a flow chart of the implementation of a vehicle center of mass sideslip angle detection method provided by the present invention. DETAILED DESCRIPTION

[0043] The core of the present invention is to provide a vehicle center of mass sideslip angle detection method, device, equipment and computer storage medium, which effectively improves the accuracy of the vehicle center of mass sideslip angle detection method and reduces the cost.

[0044] In order to enable those skilled in the art to better understand the scheme of the present invention, the present invention is further described in detail below in conjunction with the accompanying drawings and specific implementation methods. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present invention.

[0045] Please refer to Figure 1 , Figure 1 This is a flow chart of a vehicle center of mass sideslip angle detection method provided by the present invention; the specific operation steps are as follows:

[0046] S101: Based on the vehicle dynamics model, obtaining a first center of mass sideslip angle estimation value of the target vehicle in real time;

[0047] S102: based on the vehicle kinematic information, obtaining a second center of mass sideslip angle estimation value of the target vehicle in real time;

[0048] S103: Based on the actual yaw rate and the ideal yaw rate of the target vehicle, adaptively weightedly fuse the first center of mass sideslip angle estimation value and the second center of mass sideslip angle estimation value to obtain the center of mass sideslip angle of the target vehicle.

[0049] Based on the above embodiments, this embodiment describes step S101 in detail:

[0050] The vehicle dynamics model is a differential equation that describes the laws of vehicle motion and is generally used to analyze the ride comfort and handling stability of a vehicle. The common two-degree-of-freedom vehicle dynamics model is based on the assumption of a single-vehicle model, which only considers the tire side slip characteristics and ignores the longitudinal and lateral coupling relationship of tire forces, the left-right transfer of loads, and nonlinear effects such as suspension motion, road slope, and lateral and longitudinal aerodynamics.

[0051] In some embodiments, based on the vehicle dynamics model, obtaining a first center of mass sideslip angle estimate of the target vehicle in real time includes:

[0052] Based on vehicle dynamics parameters, construct a vehicle dynamics mathematical model;

[0053] Based on the vehicle dynamics mathematical model, the system state of the target vehicle is predicted in real time to obtain an estimated value of the first center of mass sideslip angle of the target vehicle.

[0054] In one embodiment, the vehicle dynamics model is a three-degree-of-freedom vehicle dynamics model, and its specific construction process includes:

[0055] Based on the vehicle dynamics parameters, a three-degree-of-freedom vehicle dynamics mathematical model is constructed, wherein the vehicle dynamics parameters include the vehicle yaw angular velocity, the vehicle lateral velocity, the vehicle longitudinal velocity, the distance between the vehicle center of mass and the front axle, the distance between the vehicle center of mass and the rear axle, the vehicle curb mass, the vehicle's yaw moment of inertia, the vehicle's front axle lateral force, and the vehicle's rear axle lateral force in the vehicle body coordinate system:

[0056]

[0057] Among them, γ,v y and v x They represent the vehicle's yaw rate, lateral velocity, and longitudinal velocity in the vehicle body coordinate system. f and L r Respectively represent the distance between the center of mass of the vehicle and the front and rear axles. m represents the curb weight of the vehicle, I zz Represents the vehicle's yaw moment of inertia. F yf and F yr They represent the lateral forces on the front and rear axles of the vehicle respectively, which come from the lateral motion of the tires.

[0058] In one embodiment, the method for real-time prediction of the target vehicle system state includes, but is not limited to, an unscented Kalman filter algorithm or a nonlinear observer method;

[0059] Among them, the unscented Kalman filter (UKF) is a nonlinear filtering algorithm that approximates the mean and covariance of a nonlinear system through an unscented transformation. Its basic steps are as follows:

[0060] Determine parameters: Select appropriate scaling parameters α, β, κ, etc., and calculate weights Wm and Wc based on these parameters.

[0061] Generate σ points: Centered on the current state estimate, a set of σ points is generated through the square root matrix of the covariance.

[0062] Time update: propagate the σ point through the nonlinear state transfer function and calculate the state prediction value and covariance prediction value.

[0063] Measurement update: propagate the σ point through the nonlinear measurement function, calculate the measurement prediction value and the cross-correlation matrix, and update the state estimate and covariance according to the difference between the actual measurement value and the predicted value. Using the UKF algorithm, the state variables in the vehicle dynamics model (such as the center of mass slip angle) can be used as the quantity to be estimated, and the vehicle speed, steering angle and other information measured by the sensor can be used as input. Combined with the nonlinear relationship of the vehicle dynamics model, the estimated value of the center of mass slip angle can be iteratively updated.

[0064] Among them, the nonlinear observer is a tool for estimating the state of a nonlinear system. There are many design methods, and the common ones are the extended Kalman filter (EKF) and the sliding mode observer. Taking EKF as an example, it realizes state estimation by linearizing the nonlinear system based on the Kalman filter. Its basic principle is:

[0065] Linearization: Taylor expansion is performed on the nonlinear system near the current state estimation point to obtain the linearized state transfer matrix and observation matrix.

[0066] Prediction and update: Use the linearized matrix to perform state prediction and update according to the steps of Kalman filtering. In the vehicle dynamics system, the nonlinear observer can use the vehicle's dynamics model as the system model and the vehicle motion parameters measured by the sensor as the observation information. Through the design of the observer, the state variables such as the vehicle's center of mass side slip angle can be estimated.

[0067] Based on the above embodiments, this embodiment describes step S102 in detail:

[0068] In some embodiments, obtaining a second center of mass sideslip angle estimate of the target vehicle in real time based on vehicle kinematic information includes:

[0069] Obtain acceleration data from vehicle chassis sensors in real time;

[0070] By directly integrating the acceleration data, an estimated value of a second center of mass sideslip angle of the target vehicle is obtained in real time.

[0071] In one embodiment, the specific steps of directly integrating the acceleration data include:

[0072] By introducing a discount factor τ>0, the lateral acceleration a at the center of mass at time k is measured using the chassis inertial sensor. y,k , yaw angular velocity γ k and the longitudinal velocity v measured by the positioning unit x,k , the expression of the estimated value of the second center of mass sideslip angle at the k+1th moment can be expressed as:

[0073]

[0074] In one embodiment, considering that there is a certain delay from the driver's steering wheel angle to the lateral movement of the vehicle, and when the vehicle has no steering wheel input for a period of time, the estimated value of the center of mass slip angle should be 0. Therefore, before directly integrating the acceleration data, consider adding a zeroing and triggering mechanism:

[0075] Obtaining in real time the average value of the steering wheel angle of the target vehicle within a preset time period before the current moment;

[0076] If the average value is not greater than the preset steering wheel angle threshold, the estimated value of the second center of mass sideslip angle of the current target vehicle is set to 0;

[0077] If the average value is greater than a preset steering wheel angle threshold, an integral discount factor is introduced, and the acceleration data before the current moment is directly integrated to obtain an estimated value of the second center of mass sideslip angle of the current target vehicle.

[0078] In one embodiment, by designing the dead zone trigger function D(S w ) to reset and trigger the second center of mass sideslip angle estimate:

[0079]

[0080] The function sgn(·) represents a sign function with an amplitude of 1, S w is the average steering wheel angle value over a period of time, T Sw is the steering wheel angle threshold that triggers the center of mass sideslip angle estimation algorithm. The final second center of mass sideslip angle estimation value based on vehicle kinematic information is The expression is

[0081]

[0082] Based on the above embodiments, this embodiment describes step S103 in detail:

[0083] According to the vehicle dynamics model, the calculated value of the steady-state center of mass sideslip angle γ is obtained. rd for:

[0084]

[0085] Among them, δ f Represents the calculated front wheel steering angle input.

[0086] Considering that the vehicle dynamics model is often changing during actual driving, and the vehicle is often in a constantly changing transient process, the true value is closely related to the current driving state, tire state, actuator response, etc., and is often quite different from the ideal reference value. Therefore, when the vehicle state measurement value deviates greatly from the ideal dynamic value, we can assume that the vehicle dynamics model under the current driving condition is quite different from the nominal dynamics model, and the vehicle's current motion condition is relatively intense; otherwise, it is considered that the current driving condition is good, and the vehicle dynamics model is basically consistent with the nominal dynamics model. Therefore, the current reference yaw rate is denoted as γ d,k , which is consistent with the measured actual yaw rate γ k The difference is denoted as γ e,k =γ k -γ d,k .

[0087] In one embodiment, the yaw rate error γ is e,k Towards the reference yaw rate γ d,k Projection, denoted as target variable γ + e This variable characterizes the mismatch degree of the current dynamic model to a certain extent. The weight coefficient of the first center of mass sideslip angle estimate based on the dynamic model is defined as ω UKF , the weight coefficient of the second center of mass sideslip angle estimate based on the direct integration method is defined as ω DI , which satisfies ω DI +ω UKF =1. Then the weight coefficient ω UKF ,ω DI The expression is:

[0088]

[0089] Among them, γ e,k is the actual yaw rate error, γ d,k is the ideal yaw rate, γ + e is the target variable, ω limit is a non-negative weight upper limit, satisfying 0<ωlimit <1, θ>0 is the sensitivity factor of the fusion coefficient, and the function exp(·) is an exponential function with the natural number e as the base.

[0090] In summary, the present invention first constructs a vehicle chassis dynamics model, and obtains a first center of mass sideslip angle estimate based on the dynamics model; then, based on the acceleration value measured by the chassis sensor, an integral discount factor and a reset trigger mechanism are designed to obtain a second center of mass sideslip angle estimate based on kinematic information in real time. On this basis, by introducing the projection of the difference between the ideal yaw velocity value and the actual yaw velocity of the vehicle on the ideal yaw velocity component as a reference evaluation index of the current dynamics model mismatch degree, an adaptive weighting coefficient factor is designed to achieve a fusion estimate of the center of mass sideslip angle. The present invention can effectively improve the accuracy of center of mass sideslip angle estimation, and provide state quantity support for vehicle active safety technology and functions. This algorithm is developed based on existing vehicle-mounted sensors, and the algorithm design is fully analytical, so that the algorithm has low computational cost and is easy to install and implement, and has great engineering application value.

[0091] An embodiment of the present invention provides a vehicle center of mass sideslip angle detection device; the specific device may include:

[0092] A dynamics model estimation module, used to obtain a first center of mass sideslip angle estimation value of the target vehicle in real time based on a vehicle dynamics model;

[0093] A kinematic model estimation module, used to obtain a second center of mass sideslip angle estimation value of the target vehicle in real time based on the vehicle kinematic information;

[0094] The adaptive fusion module is used to adaptively weight the first center of mass sideslip angle estimation value and the second center of mass sideslip angle estimation value based on the actual yaw angular velocity and the ideal yaw angular velocity of the target vehicle to obtain the center of mass sideslip angle of the target vehicle.

[0095] The vehicle center of mass sideslip angle detection device of the present embodiment is used to implement the aforementioned vehicle center of mass sideslip angle detection method. Therefore, the specific implementation method of the vehicle center of mass sideslip angle detection device can be seen in the embodiment part of the vehicle center of mass sideslip angle detection method above. For example, the dynamic model estimation module, the kinematic model estimation module, and the adaptive fusion module are respectively used to implement steps S101, S102, and S103 in the aforementioned vehicle center of mass sideslip angle detection method. Therefore, its specific implementation method can refer to the description of the corresponding various parts of the embodiment, which will not be repeated here.

[0096] A specific embodiment of the present invention further provides a vehicle center of mass sideslip angle detection device, comprising: a memory for storing a computer program; a processor for implementing the steps of the above-mentioned vehicle center of mass sideslip angle detection method when executing the computer program.

[0097] A specific embodiment of the present invention further provides a computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, the steps of the above-mentioned vehicle center of mass sideslip angle detection method are implemented.

[0098] Those skilled in the art will appreciate that the embodiments of the present application may be provided as methods, systems, or computer program products. Therefore, the present application may adopt the form of a complete hardware embodiment, a complete software embodiment, or an embodiment in combination with software and hardware. Moreover, the present application may adopt the form of a computer program product implemented in one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) that include computer-usable program code.

[0099] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0100] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0101] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0102] Obviously, the above embodiments are merely examples for the purpose of clear explanation and are not intended to limit the implementation methods. For those skilled in the art, other different forms of changes or modifications can be made based on the above description. It is not necessary and impossible to list all the implementation methods here. The obvious changes or modifications derived therefrom are still within the scope of protection of the present invention.

Claims

1. A vehicle center of mass sideslip angle detection method, characterized in that: include: Based on the vehicle dynamics model, the first center of mass sideslip angle estimation value of the target vehicle is obtained in real time; Based on the vehicle kinematic information, the estimated value of the second center of mass sideslip angle of the target vehicle is obtained in real time; Based on the actual yaw rate and the ideal yaw rate of the target vehicle, the first center of mass sideslip angle estimation value and the second center of mass sideslip angle estimation value are adaptively weighted fused to obtain the center of mass sideslip angle of the target vehicle.

2. The vehicle center of mass sideslip angle detection method according to claim 1, characterized in that: The method of obtaining a first center of mass sideslip angle estimation value of the target vehicle in real time based on the vehicle dynamics model comprises: Based on vehicle dynamics parameters, construct a vehicle dynamics mathematical model; Based on the vehicle dynamics mathematical model, the system state of the target vehicle is predicted in real time to obtain an estimated value of the first center of mass sideslip angle of the target vehicle.

3. The vehicle center of mass sideslip angle detection method according to claim 2, characterized in that: The constructing of the vehicle dynamics mathematical model based on the vehicle dynamics parameters comprises: A three-degree-of-freedom vehicle dynamics mathematical model is constructed based on vehicle dynamics parameters, wherein the vehicle dynamics parameters include the vehicle yaw angular velocity, the vehicle lateral velocity, the vehicle longitudinal velocity, the distance between the vehicle center of mass and the front axle, the distance between the vehicle center of mass and the rear axle, the vehicle curb mass, the vehicle's yaw moment of inertia, the vehicle's front axle lateral force, and the vehicle's rear axle lateral force in the vehicle body coordinate system.

4. The vehicle center of mass sideslip angle detection method according to claim 1, characterized in that: The method of acquiring a second center of mass sideslip angle estimation value of the target vehicle in real time based on vehicle kinematic information comprises: Obtain acceleration data from vehicle chassis sensors in real time; By directly integrating the acceleration data, an estimated value of a second center of mass sideslip angle of the target vehicle is obtained in real time.

5. The vehicle center of mass sideslip angle detection method according to claim 1, characterized in that: The step of directly integrating the acceleration data to obtain a second center of mass sideslip angle estimation value of the target vehicle in real time comprises: Obtaining in real time the average value of the steering wheel angle of the target vehicle within a preset time period before the current moment; If the average value is not greater than the preset steering wheel angle threshold, the estimated value of the second center of mass sideslip angle of the current target vehicle is set to 0; If the average value is greater than a preset steering wheel angle threshold, an integral discount factor is introduced, and the acceleration data before the current moment is directly integrated to obtain an estimated value of the second center of mass sideslip angle of the current target vehicle.

6. The vehicle center of mass sideslip angle detection method according to claim 1, characterized in that: The step of adaptively weighting and fusing the first estimated value of the center of mass sideslip angle and the second estimated value of the center of mass sideslip angle based on the actual yaw rate and the ideal yaw rate of the target vehicle to obtain the center of mass sideslip angle of the target vehicle comprises: The difference between the actual yaw rate and the ideal yaw rate of the target vehicle is projected onto the ideal yaw rate to obtain the target variable; Setting a weight coefficient for the first center of mass sideslip angle estimation value and the second center of mass sideslip angle estimation value based on the target variable; The first center of mass sideslip angle estimation value and the second center of mass sideslip angle estimation value are adaptively weighted fused based on the weight coefficient to obtain the center of mass sideslip angle of the target vehicle.

7. The vehicle center of mass sideslip angle detection method according to claim 6, characterized in that: The calculation formula for setting the weight coefficient for the first center of mass sideslip angle estimation value and the second center of mass sideslip angle estimation value based on the target variable is: Among them, γ e,k is the actual yaw rate error, γ d,k is the ideal yaw rate, γ + e is the target variable, ω limit is a non-negative weight upper limit, satisfying 0<ω limit <1, θ>0 is the sensitivity factor of the fusion coefficient, and the function exp(·) is an exponential function with the natural number e as the base.

8. A vehicle center of mass side slip angle detection device, characterized in that: include: A dynamics model estimation module, used to obtain a first center of mass sideslip angle estimation value of the target vehicle in real time based on a vehicle dynamics model; A kinematic model estimation module, used to obtain a second center of mass sideslip angle estimation value of the target vehicle in real time based on the vehicle kinematic information; The adaptive fusion module is used to adaptively weight the first center of mass sideslip angle estimation value and the second center of mass sideslip angle estimation value based on the actual yaw angular velocity and the ideal yaw angular velocity of the target vehicle to obtain the center of mass sideslip angle of the target vehicle.

9. A vehicle center of mass side slip angle detection device, characterized in that: include: Memory for storing computer programs; A processor is used to implement the steps of a vehicle center of mass sideslip angle detection method as described in any one of claims 1 to 7 when executing the computer program.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the steps of a vehicle center of mass sideslip angle detection method as claimed in any one of claims 1 to 7 are implemented.

Images