A vehicle lateral control method and device on road cross slope, terminal and medium

Abstract

The application discloses a vehicle lateral control method and device on a road transverse slope, a terminal and a medium, and belongs to the field of vehicle lateral control. The method is as follows: determining a current vehicle motion state based on vehicle performance parameters; when the current vehicle motion state meets a preset motion condition, obtaining a sensor zero offset value and a current yaw rate based on the vehicle performance parameters; obtaining a corrected yaw rate based on the current yaw rate and the sensor zero offset value; obtaining road transverse slope angle related data based on the corrected yaw rate and the vehicle performance parameters; obtaining a multi-step vehicle predicted state based on the vehicle performance parameters, a current time state estimation value, a multi-step vehicle control range and the road transverse slope angle related data; obtaining a multi-step target vehicle control amount based on the multi-step vehicle predicted state and a preset target control amount prediction algorithm, so as to perform lateral control on the target vehicle based on the multi-step target vehicle control amount, thereby improving the reliability of vehicle lateral control on the road transverse slope.

Description

Technical Field

[0001] This invention relates to the field of vehicle lateral control, and more particularly to a method, device, terminal, and medium for vehicle lateral control on a road cross slope. Background Technology

[0002] Lateral control is one of the core functions of autonomous driving. Its core objective is to enable the vehicle to accurately track the desired driving trajectory (such as the lane center line) to ensure driving safety and comfort. Model predictive control (MPC) has become the mainstream lateral control algorithm because it can effectively handle multi-constraint and multi-objective optimization problems and has a certain robustness to system model uncertainties.

[0003] However, existing lateral control methods based on MPC are mostly based on the assumption of "horizontal road" and do not fully consider the cross slope interference that is common on actual roads (such as road camber in urban roads, one-way cross slope in mountainous roads, and superelevation / reverse superelevation on curves): the cross slope will cause the vehicle's gravity to generate a lateral component force, change the normal load of the front and rear axles and the tire lateral stiffness, resulting in the tire generating an additional lateral slip angle and causing the vehicle to naturally veer off course; at the same time, the yaw rate sensor has a zero bias error, and the zero drift under dynamic conditions coupled with the cross slope interference will cause the state estimation of the MPC prediction model to be distorted, reducing the optimization accuracy. Summary of the Invention

[0004] This invention provides a method, device, terminal, and medium for lateral vehicle control on road cross slopes, which can solve the above-mentioned problems in the prior art and improve the reliability of lateral vehicle control on road cross slopes.

[0005] This invention provides a method for lateral vehicle control on a road cross slope, comprising: Obtain the vehicle performance parameters, current state estimate, and multi-step vehicle control range of the target vehicle; The current vehicle motion state is determined based on the vehicle performance parameters. When the current vehicle motion state meets the preset motion conditions, the sensor zero bias value and the current yaw rate are obtained based on the vehicle performance parameters. The corrected yaw rate is obtained based on the current yaw rate and the sensor zero bias value; Based on the corrected yaw rate and the vehicle performance parameters, relevant data on the road cross slope angle are obtained; The multi-step vehicle predicted state is obtained based on the vehicle performance parameters, the current state estimate, the multi-step vehicle control range, and the road cross slope angle related data. Based on the multi-step vehicle prediction state and the preset target control quantity prediction algorithm, a multi-step target vehicle control quantity is obtained, and the target vehicle is laterally controlled based on the multi-step target vehicle control quantity.

[0006] In the above scheme, the current vehicle motion state is determined based on vehicle performance parameters. When the preset motion conditions are met, the sensor zero bias value and the current yaw rate are obtained, and then the corrected yaw rate is obtained, which can effectively eliminate the interference of sensor zero bias on subsequent calculations. Then, based on the corrected yaw rate and vehicle performance parameters, relevant data on the road cross slope angle are obtained, making the estimation of the road cross slope more accurate. Finally, a multi-step vehicle prediction state is constructed, and based on the multi-step vehicle prediction state and the preset target control quantity prediction algorithm, a multi-step target vehicle control quantity is obtained, thereby realizing the lateral control of the target vehicle. This makes the lateral control process incorporate the influence of the road cross slope angle, improving the accuracy and stability of vehicle lateral control. Especially under the condition of road cross slope, it can effectively suppress vehicle deviation and significantly improve the reliability of vehicle lateral control on road cross slope.

[0007] Further, the step of determining the current vehicle motion state based on the vehicle performance parameters, and when the current vehicle motion state meets preset motion conditions, obtaining the sensor zero bias value and the current yaw rate based on the vehicle performance parameters, includes: The vehicle historical parameter set corresponding to the vehicle performance parameters is obtained based on a preset set of time sampling points. The current vehicle motion state is determined based on the vehicle historical parameter set and preset parameter thresholds; When the current vehicle motion state meets the preset motion conditions, the sensor zero bias value and the current yaw rate are obtained based on the vehicle performance parameters.

[0008] In the above scheme, the vehicle historical parameter set corresponding to the vehicle performance parameters is obtained based on the preset time sampling point set, and the current vehicle motion state is determined according to the vehicle historical parameter set and the preset parameter threshold. Only when the current vehicle motion state meets the preset motion conditions, the sensor zero bias value and the current yaw rate are obtained based on the vehicle performance parameters. This avoids erroneous updating of the zero bias value in non-straight driving state, thereby ensuring the accuracy and reliability of sensor zero bias value estimation.

[0009] Furthermore, the vehicle performance parameters include the vehicle's lateral acceleration, and the process of obtaining road cross slope angle-related data based on the corrected yaw rate and the vehicle performance parameters includes: The lateral inertial acceleration of the vehicle is obtained based on the corrected yaw rate; Based on the difference between the vehicle's lateral acceleration and its lateral inertial acceleration, relevant data on the road cross slope angle are obtained.

[0010] In the above scheme, the vehicle performance parameters include the vehicle's lateral acceleration. The vehicle's lateral inertial acceleration is obtained based on the corrected yaw rate. Then, the road cross slope angle data is obtained based on the difference between the vehicle's lateral acceleration and the vehicle's lateral inertial acceleration. This realizes the separation of the gravity lateral acceleration from the total lateral acceleration, thereby accurately calculating the road cross slope angle and providing accurate cross slope disturbance information for subsequent lateral control.

[0011] Furthermore, the step of obtaining the multi-step vehicle predicted state based on the vehicle performance parameters, the current state estimate, the multi-step vehicle control range, and road cross slope angle related data includes: The cross slope interference term is obtained based on the road cross slope angle related data and the vehicle performance parameters; The state transition matrix and control input matrix are obtained based on the vehicle performance parameters. Based on the state transition matrix, the current state estimate, the control input matrix, the multi-step vehicle control range, and the cross slope disturbance term, the multi-step vehicle predicted state is obtained.

[0012] In the above scheme, the cross slope disturbance term is obtained based on the road cross slope angle related data and vehicle performance parameters, and the state transition matrix and control input matrix are obtained based on the vehicle performance parameters. Then, the multi-step vehicle prediction state is obtained based on the state transition matrix, the current state estimate, the control input matrix, the multi-step vehicle control range, and the cross slope disturbance term. This allows the prediction model to fully consider the continuous disturbance of the road cross slope, thereby improving the accuracy of the multi-step vehicle prediction state.

[0013] Furthermore, the vehicle performance parameters also include the controller operating cycle, and the process of obtaining the cross slope disturbance term based on the road cross slope angle correlation data and the vehicle performance parameters includes: An initial cross slope interference term is constructed based on the road cross slope angle correlation data; The cross slope disturbance term is obtained by multiplying the initial cross slope disturbance term and the controller operating cycle.

[0014] In the above scheme, the vehicle performance parameters also include the controller operating cycle. The initial cross slope disturbance term is constructed based on the road cross slope angle related data, and the cross slope disturbance term is obtained by multiplying the initial cross slope disturbance term and the controller operating cycle. This realizes the discretization processing of continuous cross slope disturbance, enabling the discrete prediction model to accurately adapt to the discrete operating cycle of the controller and ensuring the numerical stability of the predicted state recursion.

[0015] Furthermore, the vehicle performance parameters also include front wheel lateral stiffness, rear wheel lateral stiffness, vehicle curb weight, longitudinal velocity, distance from center of gravity to front axle, distance from center of gravity to rear axle, and vehicle moment of inertia. The process of obtaining the state transition matrix and control input matrix based on the vehicle performance parameters includes: The state transition matrix is ​​obtained based on the front wheel lateral stiffness, rear wheel lateral stiffness, vehicle curb weight, longitudinal velocity, distance from center of gravity to front axle, and distance from center of gravity to rear axle. The control input matrix is ​​obtained based on the front wheel lateral stiffness, vehicle curb weight, distance from the center of gravity to the front axle, and vehicle rotational inertia.

[0016] In the above scheme, the vehicle performance parameters include front wheel lateral stiffness, rear wheel lateral stiffness, vehicle curb weight, longitudinal velocity, distance from center of gravity to front axle, distance from center of gravity to rear axle, and vehicle moment of inertia. By obtaining the state transition matrix and control input matrix based on these parameters, the matrix construction is based entirely on the real dynamic characteristics of the target vehicle, thereby improving the physical accuracy of vehicle motion prediction and vehicle adaptability.

[0017] Further, the step of obtaining a multi-step target vehicle control quantity based on the multi-step vehicle prediction state and the preset target control quantity prediction algorithm, and then performing lateral control on the target vehicle based on the multi-step target vehicle control quantity, includes: The tracking deviation is obtained based on the multi-step vehicle prediction state and the preset expected state trajectory. The state tracking error cost is obtained based on the tracking deviation and the preset state weight matrix; The smoothness cost of the control quantity is obtained based on the multi-step vehicle control quantity range and the preset weight penalty matrix; Based on preset vehicle constraints, the state tracking error cost, and the control quantity smoothness cost, multi-step target vehicle control quantities are selected from the multi-step vehicle control quantity range. Lateral control of the target vehicle is performed based on the multi-step target vehicle control quantity.

[0018] In the above scheme, the tracking deviation is obtained based on the multi-step vehicle predicted state and the preset expected state trajectory. The state tracking error cost is obtained based on the tracking deviation and the preset state weight matrix. The control quantity smoothness cost is obtained based on the multi-step vehicle control quantity range and the preset weight penalty matrix. Then, based on the preset vehicle constraints, the state tracking error cost, and the control quantity smoothness cost, the multi-step target vehicle control quantity is selected from the multi-step vehicle control quantity range. Finally, the target vehicle is laterally controlled based on the multi-step target vehicle control quantity. This achieves multi-objective optimization of trajectory tracking accuracy and control smoothness, while satisfying the vehicle's physical constraints and improving the stability of lateral control and ride comfort.

[0019] Another embodiment of the present invention provides a vehicle lateral control device on a road cross slope, comprising: The basic data acquisition module is used to acquire the vehicle performance parameters, current state estimate, and multi-step vehicle control range of the target vehicle. The initial data acquisition module is used to determine the current vehicle motion state based on the vehicle performance parameters. When the current vehicle motion state meets the preset motion conditions, the sensor zero bias value and the current yaw rate are obtained based on the vehicle performance parameters. The corrected yaw rate acquisition module is used to obtain the corrected yaw rate based on the current yaw rate and the sensor zero bias value; The road cross slope angle related data acquisition module is used to obtain road cross slope angle related data based on the corrected yaw rate and the vehicle performance parameters; The multi-step vehicle prediction state acquisition module is used to obtain the multi-step vehicle prediction state based on the vehicle performance parameters, the current state estimate, the multi-step vehicle control range, and road cross slope angle related data. The multi-step target vehicle control quantity acquisition module is used to obtain the multi-step target vehicle control quantity based on the multi-step vehicle prediction state and the preset target control quantity prediction algorithm, so as to perform lateral control on the target vehicle based on the multi-step target vehicle control quantity.

[0020] Another embodiment of the present invention provides a terminal device, including: a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor, wherein when the processor executes the computer program, it implements the steps of a vehicle lateral control method on a road cross slope as described in the present invention.

[0021] Another embodiment of the present invention also provides a computer-readable storage medium item, including: a stored computer program, which, when the computer program is executed, controls the device where the computer-readable storage medium is located to perform steps such as the vehicle lateral control method on a road cross slope of the present invention. Attached Figure Description

[0022] To more clearly illustrate the technical solution of this application, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained from these drawings without creative effort.

[0023] Figure 1 This is a flowchart illustrating a method for lateral vehicle control on a road cross slope according to an embodiment of the present invention; Figure 2This is a schematic diagram of a vehicle lateral control device on a road cross slope provided in an embodiment of the present invention. Detailed Implementation

[0024] To make the objectives, technical solutions, and advantages of this application clearer, the technical solutions of this application will be clearly and completely described below with reference to the accompanying drawings of the embodiments. Obviously, the described embodiments are only some embodiments of this application, not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0025] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application pertains; the terminology used herein is for the purpose of describing particular embodiments only and is not intended to limit the application; the terms “comprising” and “having”, and any variations thereof, in the specification, claims, and foregoing description of the drawings are intended to cover non-exclusive inclusion.

[0026] In the description of the embodiments of this application, technical terms such as "first" and "second" are used only to distinguish different objects and should not be construed as indicating or implying relative importance or implicitly specifying the number, specific order, or primary and secondary relationship of the indicated technical features. In the description of the embodiments of this application, "multiple" means two or more, unless otherwise explicitly defined.

[0027] In this document, the term "embodiment" means that a particular feature, structure, or characteristic described in connection with an embodiment may be included in at least one embodiment of this application. The appearance of this phrase in various places throughout the specification does not necessarily refer to the same embodiment, nor is it a separate or alternative embodiment mutually exclusive with other embodiments. It will be explicitly and implicitly understood by those skilled in the art that the embodiments described herein can be combined with other embodiments.

[0028] In the description of the embodiments in this application, the term "and / or" 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. Additionally, the character " / " in this document generally indicates that the preceding and following related objects have an "or" relationship.

[0029] In the description of the embodiments of this application, the term "multiple" refers to two or more (including two), similarly, "multiple sets" refers to two or more (including two sets), and "multiple pieces" refers to two or more (including two pieces).

[0030] In the description of the embodiments of this application, unless otherwise expressly specified and limited, technical terms such as "installation," "connection," "joining," and "fixing" should be interpreted broadly. For example, they can refer to a fixed connection, a detachable connection, or an integral part; they can refer to a mechanical connection or an electrical connection; they can refer to a direct connection or an indirect connection through an intermediate medium; they can refer to the internal communication of two components or the interaction between two components. For those skilled in the art, the specific meaning of the above terms in the embodiments of this application can be understood according to the specific circumstances.

[0031] See Figure 1 To address the aforementioned problems in the prior art and improve the reliability of vehicle lateral control on road cross slopes, an embodiment of the present invention provides a vehicle lateral control method on road cross slopes, comprising: Step S1: Obtain the vehicle performance parameters, current state estimate, and multi-step vehicle control range of the target vehicle; Step S2: Determine the current vehicle motion state based on the vehicle performance parameters. When the current vehicle motion state meets the preset motion conditions, obtain the sensor zero bias value and the current yaw rate based on the vehicle performance parameters. Step S3: Obtain the corrected yaw rate based on the current yaw rate and the sensor zero bias value; Step S4: Obtain road cross slope angle related data based on the corrected yaw rate and the vehicle performance parameters; Step S5: Based on the vehicle performance parameters, the current state estimate, the range of multi-step vehicle control quantities, and the road cross slope angle related data, obtain the multi-step vehicle predicted state; Step S6: Based on the multi-step vehicle prediction state and the preset target control quantity prediction algorithm, obtain the multi-step target vehicle control quantity, and perform lateral control on the target vehicle based on the multi-step target vehicle control quantity.

[0032] In the above scheme, the current vehicle motion state is determined based on vehicle performance parameters. When the preset motion conditions are met, the sensor zero bias value and the current yaw rate are obtained, and then the corrected yaw rate is obtained, which can effectively eliminate the interference of sensor zero bias on subsequent calculations. Then, based on the corrected yaw rate and vehicle performance parameters, relevant data on the road cross slope angle are obtained, making the estimation of the road cross slope more accurate. Finally, a multi-step vehicle prediction state is constructed, and based on the multi-step vehicle prediction state and the preset target control quantity prediction algorithm, a multi-step target vehicle control quantity is obtained, thereby realizing the lateral control of the target vehicle. This makes the lateral control process incorporate the influence of the road cross slope angle, improving the accuracy and stability of vehicle lateral control. Especially under the condition of road cross slope, it can effectively suppress vehicle deviation and significantly improve the reliability of vehicle lateral control on road cross slope.

[0033] It's important to note that the lateral acceleration sensor and the yaw rate sensor operate on different principles. The lateral acceleration sensor is similar to a spring-slider structure, allowing it to measure the lateral acceleration of a vehicle moving in a circle on a road with a cross slope, under the influence of centrifugal force and the component of gravity along the cross slope. The yaw rate sensor, on the other hand, only measures the angular velocity of the vehicle's circular motion, allowing for the equivalent calculation of the centrifugal force. Therefore, the road cross slope angle can be calculated using the lateral acceleration and yaw rate. However, the non-zero output value of the yaw rate sensor when there is no rotation (i.e., the actual yaw rate = 0) represents a systematic error inherent in sensors such as gyroscopes. This error can lead to inaccuracies in subsequent road cross slope angle estimations, reducing model accuracy and even causing model failure. Therefore, it is necessary to estimate the accurate yaw rate sensor zero bias online (i.e., to correct the sensor zero bias value). This method mainly uses the straight-line driving method to calculate the zero bias value of the sensor. When the vehicle is traveling in a straight line at a constant speed, the theoretical actual yaw rate is 0. At this time, the difference between the measured value obtained by the yaw rate sensor (that is, the current yaw rate) and 0 is the zero bias value of the sensor.

[0034] Therefore, specifically, the vehicle performance parameters include, but are not limited to, the sensor zero bias and the current yaw rate. The preset motion conditions are set as linear motion. Based on the vehicle performance parameters, the current vehicle motion state is determined. When the current vehicle motion state meets the preset motion conditions, the current yaw rate obtained from the yaw rate sensor is acquired, and the difference between this and 0 is used to obtain the sensor zero bias. It is understood that the vehicle performance parameters include the sensor zero bias and the current yaw rate.

[0035] Then, based on the current yaw rate and the sensor's zero bias value, the corrected yaw rate is obtained: ;in, To correct the yaw rate, The current yaw rate, This is the zero bias value of the sensor.

[0036] Finally, based on the corrected yaw rate and the vehicle performance parameters, road cross slope angle related data are obtained. Based on the vehicle performance parameters, the current state estimate, the multi-step vehicle control range, and the road cross slope angle related data, multi-step vehicle predicted state is obtained. Based on the multi-step vehicle predicted state and the preset target control quantity prediction algorithm, multi-step target vehicle control quantity is obtained, and the target vehicle is laterally controlled based on the multi-step target vehicle control quantity.

[0037] In another embodiment, determining the current vehicle motion state based on the vehicle performance parameters, and when the current vehicle motion state meets preset motion conditions, obtaining the sensor zero bias value and the current yaw rate based on the vehicle performance parameters, includes: The vehicle historical parameter set corresponding to the vehicle performance parameters is obtained based on a preset set of time sampling points. The current vehicle motion state is determined based on the vehicle historical parameter set and preset parameter thresholds; When the current vehicle motion state meets the preset motion conditions, the sensor zero bias value and the current yaw rate are obtained based on the vehicle performance parameters.

[0038] It should be noted that vehicle performance parameters include steering wheel angle, steering wheel rate of change, yaw rate, yaw acceleration, and vehicle speed. The system obtains a set of historical vehicle parameters corresponding to these performance parameters from a preset set of time sampling points prior to the current moment. This historical parameter set includes the steering wheel angle, steering wheel rate of change, yaw rate, absolute value of yaw acceleration, and vehicle speed for each time sampling point. The preset parameter thresholds include the respective thresholds for steering wheel angle, steering wheel rate of change, yaw rate, absolute value of yaw acceleration, and vehicle speed. When the absolute values ​​of all historical vehicle parameters in the preset time sampling set, except for vehicle speed, are less than their corresponding thresholds, and the absolute value of vehicle speed is greater than its corresponding threshold, it is considered that they have maintained linear motion for a period of time. Therefore, the current vehicle motion state is considered to meet the preset motion conditions, i.e., the current vehicle motion state is linear. At this point, the vehicle performance parameters for the current moment are obtained. These vehicle performance parameters include the sensor zero bias and the current yaw rate.

[0039] In another embodiment, the vehicle performance parameters include the vehicle lateral acceleration, and the process of obtaining road cross slope angle-related data based on the corrected yaw rate and the vehicle performance parameters includes: The lateral inertial acceleration of the vehicle is obtained based on the corrected yaw rate; Based on the difference between the vehicle's lateral acceleration and its lateral inertial acceleration, relevant data on the road cross slope angle are obtained.

[0040] It should be noted that the vehicle's lateral inertial acceleration is first obtained based on the corrected yaw rate: ;in, Let be the lateral inertial acceleration of the vehicle. Here, the lateral inertial acceleration of the vehicle is characterized assuming that the vehicle is on a level road surface with no cross slope. This refers to the longitudinal speed of the vehicle.

[0041] Then, based on the difference between the vehicle's lateral acceleration and its lateral inertial acceleration, relevant data on the road cross slope angle are obtained. Specifically, because when a vehicle travels on a cross slope road, the lateral forces satisfy a balance relationship, and the vehicle's lateral acceleration... It consists of two parts: ,in For the cross slope angle of the road, g is the acceleration due to gravity. It is data related to the cross slope angle of the road, which is used to characterize the acceleration corresponding to the lateral component of gravity, generated by the cross slope and moving downwards along the slope.

[0042] The estimated road cross slope angle is essentially obtained from measurements. Separate from And then solve .

[0043] Therefore: ; It is understandable that the vehicle's lateral acceleration is directly measured by the vehicle's lateral acceleration sensor and is included in the vehicle's performance parameters.

[0044] In another embodiment, obtaining the multi-step vehicle predicted state based on the vehicle performance parameters, the current state estimate, the multi-step vehicle control range, and road cross slope angle related data includes: The cross slope interference term is obtained based on the road cross slope angle related data and the vehicle performance parameters; The state transition matrix and control input matrix are obtained based on the vehicle performance parameters. Based on the state transition matrix, the current state estimate, the control input matrix, the multi-step vehicle control range, and the cross slope disturbance term, the multi-step vehicle predicted state is obtained.

[0045] In another embodiment, the vehicle performance parameters further include the controller operating cycle, and the step of obtaining the cross slope disturbance term based on the road cross slope angle correlation data and the vehicle performance parameters includes: An initial cross slope interference term is constructed based on the road cross slope angle correlation data; The cross slope disturbance term is obtained by multiplying the initial cross slope disturbance term and the controller operating cycle.

[0046] In another embodiment, the vehicle performance parameters further include front wheel lateral stiffness, rear wheel lateral stiffness, vehicle curb weight, longitudinal velocity, distance from center of gravity to front axle, distance from center of gravity to rear axle, and vehicle moment of inertia. The process of obtaining the state transition matrix and control input matrix based on the vehicle performance parameters includes: The state transition matrix is ​​obtained based on the front wheel lateral stiffness, rear wheel lateral stiffness, vehicle curb weight, longitudinal velocity, distance from center of gravity to front axle, and distance from center of gravity to rear axle. The control input matrix is ​​obtained based on the front wheel lateral stiffness, vehicle curb weight, distance from the center of gravity to the front axle, and vehicle rotational inertia.

[0047] It should be noted that, firstly, a state-space model for the lateral and yaw motion is established based on the two-degree-of-freedom dynamic model, resulting in the following equation: ; ; ; in, Let be the rate of change of the state vector in the state-space model for lateral and yaw motion. The target vehicle's state vector contains the vehicle's lateral position, lateral velocity, yaw angle, and yaw rate during the trajectory tracking process. The control quantity specifically refers to the control quantity of the vehicle's front wheel steering angle. Here is the state transition matrix. To control the input matrix, For the vehicle's curb weight, The distance from the center of gravity to the front axle. The distance from the center of gravity to the rear axle. For the front wheel lateral stiffness, L represents the rear wheel lateral stiffness, and L represents the vehicle's moment of inertia. This refers to the longitudinal vehicle speed within the vehicle speed range. It can be seen that, based on the aforementioned front wheel lateral stiffness... Rear wheel lateral stiffness Vehicle curb weight (m) and longitudinal speed The state transition matrix is ​​obtained by taking the distance 'a' from the centroid to the front axis and the distance 'b' from the centroid to the rear axis. Based on the aforementioned front wheel lateral stiffness The control input matrix is ​​obtained from the vehicle's curb weight m, the distance a from the center of gravity to the front axle, and the vehicle's moment of inertia L. .

[0048] On roads with a cross slope angle, the target vehicle will be additionally affected by the gravitational component of the sliding force. Therefore, it is necessary to add an initial cross slope disturbance term to the state space model of lateral motion and yaw motion mentioned above, resulting in the following final version of the state space model: ; ; In the formula, It is the rate of change of the state vector in the final state-space model. The initial disturbance term for the cross slope. The road cross slope angle (i.e., the angle between the road surface and the horizontal plane) is obviously based on the aforementioned road cross slope angle data. Constructing the initial disturbance term of the cross slope .

[0049] The second step is to establish a discretized state-space model: The final state-space model described above is then discretized: The final state-space model described above is discretized using the Euler method to obtain the discretized state-space equations: ; ; ; ; In the formula, x(k) represents the state vector of the target vehicle at the current time k; u(k) is the control quantity of the target vehicle at the current time k, that is, the front wheel steering angle control quantity of the vehicle at the current time k; x(k+1) is the state vector (predicted value) of the target vehicle at the next time k+1. To and 4x4 identity matrices of the same dimension For the controller's operating cycle, therefore based on the initial cross slope disturbance term and the controller's operating cycle The product of these terms yields the cross slope interference term. ; dt represents the rate of change of the function with respect to time t. In calculus, dt is the symbolic representation of the derivative; A is the target state transition matrix, which is derived from the state transition matrix. Composition, B is the target control input matrix, which is composed of the control input matrix. constitute.

[0050] Once a discretized state-space model is established, it can be used for future state prediction.

[0051] Next, the prediction model is established, specifically: Based on the prediction model, MPC predicts the future state of the vehicle in the prediction time domain and in the control time domain. The front wheel steering angle sequence is optimized internally, and dynamic control is achieved through the rolling time domain.

[0052] First, define For the time domain of prediction, it represents the number of steps in the future prediction; To control the time domain, the number of control input steps is used to characterize the optimization. Based on the above discretized state-space equations, the state prediction equations in the prediction time domain are recursively obtained: (1) The one-step prediction formula is: ; Where x(k|k) represents the best estimate of the target vehicle state at time k; u(k|k) represents the control input calculated at time k and applied to time k; and x(k+1|k) represents the state at time k based on the estimated current state (i.e., the current state estimate) x(k|k), the current control u(k|k), and the cross slope disturbance term. The predicted vehicle state at the next time step k+1. It is understood that, in this embodiment, to distinguish between the notation conventions of state observation and prediction recursion, x(k|k) is used to represent the best estimate of the target vehicle state at the current time k (i.e., x(k)), and the two have the same meaning.

[0053] Similarly, in the embodiments of this application, in order to unify the symbolic expression of control quantities in optimization solution and prediction recursion, u(k|k) is used to represent the control input calculated at the current time k and applied to the current time k (i.e., u(k)), and the two have the same meaning.

[0054] (2) The step prediction formula is: + ; in, exist In the step prediction formula, for i = 0, 1, ..., Each step of the prediction process takes the cross slope interference into account, enabling accurate prediction of the vehicle's future path even in scenarios with cross slope interference. It is the i-th vehicle prediction state in the multi-step vehicle prediction state. This indicates that at the current time k, based on the estimated current state (i.e., the estimated state value at the current time) x(k|k), the current control (i.e., one step of the vehicle control range in the multi-step vehicle control range) u(k|k), and the cross slope disturbance term... Predicted Time k+ The vehicle's status. ) represents the current time k with respect to the future k-th... Step (i.e., time) The control input prediction value.

[0055] Therefore, based on the state transition matrix The target state transition matrix A and the estimated state value at the current time are constructed. The control input matrix The constructed target control input matrix, the multi-step vehicle control range and the cross slope interference term The multi-step vehicle prediction state is obtained. .

[0056] In another embodiment, the step of obtaining a multi-step target vehicle control quantity based on the multi-step vehicle prediction state and the preset target control quantity prediction algorithm, and then performing lateral control on the target vehicle based on the multi-step target vehicle control quantity, includes: The tracking deviation is obtained based on the multi-step vehicle prediction state and the preset expected state trajectory. The state tracking error cost is obtained based on the tracking deviation and the preset state weight matrix; The smoothness cost of the control quantity is obtained based on the multi-step vehicle control quantity range and the preset weight penalty matrix; Based on preset vehicle constraints, the state tracking error cost, and the control quantity smoothness cost, multi-step target vehicle control quantities are selected from the multi-step vehicle control quantity range. Lateral control of the target vehicle is performed based on the multi-step target vehicle control quantity.

[0057] It should be noted that after obtaining the multi-step vehicle prediction state, a preset target control quantity prediction algorithm is used to solve for the optimal multi-step target vehicle control quantity. The specific process of the preset target control quantity prediction algorithm is as follows: (1) The tracking deviation is obtained based on the multi-step vehicle predicted state and the preset expected state trajectory: Let the preset desired state trajectory be It contains the expected state value at each time point in the future prediction time domain. (i=1,2,…, The multi-step vehicle prediction state. With the preset expected state trajectory The difference is calculated to obtain the tracking bias E: ; in, These are the representative parameters of the multi-step vehicle prediction state, i.e. = Therefore, the equation for calculating the tracking deviation is: ; Where Φ, Γ, and Ξ are prediction matrices constructed based on the target state transition matrix, the target control input matrix, and the cross slope disturbance term, x(k|k) is the estimated state value at the current time, and U corresponds to... , This is the cross slope interference term.

[0058] (2) The state tracking error cost is obtained based on the tracking deviation and the preset state weight matrix: Define a preset state weight matrix Q, which is used to weight the tracking errors of different state components. Then, the state tracking error cost... for: ; in, for The expected state at any given moment, all Summarized ,all Summarized .

[0059] Expressed in matrix form ,for: ; in Used to measure the deviation between the predicted state and the desired state, and to compensate for the offset caused by cross slope disturbance), E is i ( A matrix consisting of i=0,1,... .

[0060] Substituting the equation for the tracking deviation into the equation and expanding it, we obtain a quadratic expression for U: ; Expanding the above equation, we get: ; Combining like terms, we get: ; The matrix form of the tracking cost is: ; Where C is a constant term, representing a term independent of U. Since it does not affect the determination of the control quantity U, it can be ignored. Therefore, the constant term C is used as a substitute here. To account for road cross slope interference, an interference term is introduced into the calculation of the state tracking error cost. Therefore, the impact of road cross slope on the lateral movement of the vehicle is reflected in the aforementioned state tracking error cost. The above formula allows for accurate prediction of the vehicle's future path in scenarios with road cross slope interference. It also allows for the calculation of the deviation value from the expected path, resulting in a tracking error cost function that takes into account road cross slope interference. This leads to a more accurate tracking cost function and a more precise solution in scenarios with road cross slope, accurately reflecting the correction effect of road cross slope on the front wheel steering angle optimization.

[0061] (3) The smoothness cost of the control quantity is obtained based on the range of multi-step vehicle control quantity and the preset weight penalty matrix: Define a preset weight penalty matrix R, representing the weight penalty matrix of the control quantity, i.e., the penalty for the front wheel steering angle control quantity. Then, the control quantity smoothness cost... for: ; Where U is the control sequence within the range of multi-step vehicle control (U = ), To control the time domain.

[0062] (4) Based on the preset vehicle constraints, state tracking error cost, and control quantity smoothness cost, select the multi-step target vehicle control quantity from the multi-step vehicle control quantity range: Adding the state tracking error cost to the control smoothness cost, we construct the total cost function, i.e., the objective function J: ; Consider pre-set vehicle constraints, including but not limited to front wheel steering angle amplitude constraints and front wheel steering angle change rate constraints.

[0063] (5) Lateral control of the target vehicle based on multi-step target vehicle control variables: After obtaining the total cost function, i.e. the objective function, in each control cycle, the objective function is solved using the existing quadratic programming (QP) algorithm to obtain the multi-step target vehicle control quantity. And only the first step of the target vehicle control quantity is taken. As the output of the current cycle This represents the front wheel steering angle control value, which is output to the electric power steering (EPS) system to control the lateral movement of the target vehicle. In the next control cycle, the current state is remeasured or estimated, and the above rolling optimization process is repeated.

[0064] Based on the above method embodiments, corresponding apparatus embodiments are provided; like Figure 2 As shown, one embodiment of the present invention provides a vehicle lateral control device on a road cross slope, comprising: The basic data acquisition module is used to acquire the vehicle performance parameters, current state estimate, and multi-step vehicle control range of the target vehicle. The initial data acquisition module is used to determine the current vehicle motion state based on the vehicle performance parameters. When the current vehicle motion state meets the preset motion conditions, the sensor zero bias value and the current yaw rate are obtained based on the vehicle performance parameters. The corrected yaw rate acquisition module is used to obtain the corrected yaw rate based on the current yaw rate and the sensor zero bias value; The road cross slope angle related data acquisition module is used to obtain road cross slope angle related data based on the corrected yaw rate and the vehicle performance parameters; The multi-step vehicle prediction state acquisition module is used to obtain the multi-step vehicle prediction state based on the vehicle performance parameters, the current state estimate, the multi-step vehicle control range, and road cross slope angle related data. The multi-step target vehicle control quantity acquisition module is used to obtain the multi-step target vehicle control quantity based on the multi-step vehicle prediction state and the preset target control quantity prediction algorithm, so as to perform lateral control on the target vehicle based on the multi-step target vehicle control quantity.

[0065] It is understood that the above-described device embodiments correspond to the method embodiments of the present invention, and can realize the vehicle lateral control method on a road cross slope provided by any of the above-described method embodiments of the present invention.

[0066] It should be noted that the device embodiments described above are merely illustrative, and some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs. Furthermore, in the accompanying drawings of the device embodiments provided by this invention, the connection relationships between modules indicate that they have communication connections, which can specifically be implemented as one or more communication buses or signal lines. Those skilled in the art can understand and implement this without any creative effort.

[0067] Based on the above-described embodiment of a vehicle lateral control method on a road cross slope, another embodiment of the present invention provides a terminal device, which includes a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor. When the processor executes the computer program, it implements a vehicle lateral control method on a road cross slope according to any embodiment of the present invention.

[0068] For example, in this embodiment, the computer program can be divided into one or more modules, which are stored in the memory and executed by the processor to complete the present invention. The one or more modules may be a series of computer program instruction segments capable of performing a specific function, which describe the execution process of the computer program in the terminal device.

[0069] The terminal device may be a desktop computer, laptop, handheld computer, or cloud server, etc. The terminal device may include, but is not limited to, a processor and a memory.

[0070] The processor can be a Central Processing Unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor can be a microprocessor or any conventional processor. The processor is the control center of the terminal device, connecting all parts of the terminal device via various interfaces and lines.

[0071] Based on the above-described method embodiments, another embodiment of the present invention provides a computer-readable storage medium including a stored computer program, wherein, when the computer program is executed, it controls the device where the computer-readable storage medium is located to execute a vehicle lateral control method on a road cross slope as described in any of the above-described method embodiments of the present invention.

[0072] The modules / units integrated in the device / terminal equipment, if implemented as software functional units and sold or used as independent products, can be stored in a computer-readable storage medium. Based on this understanding, all or part of the processes in the above embodiments of the present invention can also be implemented by a computer program instructing related hardware. The computer program can be stored in a computer-readable storage medium, and when executed by a processor, it can implement the steps of the various method embodiments described above. The computer program includes computer program code, which can be in the form of source code, object code, executable files, or certain intermediate forms. The computer-readable medium can include: any entity or device capable of carrying the computer program code, recording media, USB flash drives, portable hard drives, magnetic disks, optical disks, computer memory, read-only memory (ROM), random access memory (RAM), electrical carrier signals, telecommunication signals, and software distribution media, etc.

[0073] The above description represents the preferred embodiments of the present invention. It should be noted that those skilled in the art can make various improvements and modifications without departing from the principles of the present invention, and these improvements and modifications are also considered to be within the scope of protection of the present invention.

Claims

1. A method for lateral vehicle control on a road cross slope, characterized in that, include: Obtain the vehicle performance parameters, current state estimate, and multi-step vehicle control range of the target vehicle; The current vehicle motion state is determined based on the vehicle performance parameters. When the current vehicle motion state meets the preset motion conditions, the sensor zero bias value and the current yaw rate are obtained based on the vehicle performance parameters. The corrected yaw rate is obtained based on the current yaw rate and the sensor zero bias value; Based on the corrected yaw rate and the vehicle performance parameters, relevant data on the road cross slope angle are obtained; The multi-step vehicle predicted state is obtained based on the vehicle performance parameters, the current state estimate, the multi-step vehicle control range, and the road cross slope angle related data. Based on the multi-step vehicle prediction state and the preset target control quantity prediction algorithm, a multi-step target vehicle control quantity is obtained, and the target vehicle is laterally controlled based on the multi-step target vehicle control quantity.

2. The method for lateral vehicle control on a road cross slope according to claim 1, characterized in that, The process of determining the current vehicle motion state based on the vehicle performance parameters, and when the current vehicle motion state meets preset motion conditions, obtaining the sensor zero bias value and the current yaw rate based on the vehicle performance parameters, includes: The vehicle historical parameter set corresponding to the vehicle performance parameters is obtained based on a preset set of time sampling points. The current vehicle motion state is determined based on the vehicle historical parameter set and preset parameter thresholds; When the current vehicle motion state meets the preset motion conditions, the sensor zero bias value and the current yaw rate are obtained based on the vehicle performance parameters.

3. The method for lateral vehicle control on a road cross slope according to claim 1, characterized in that, The vehicle performance parameters include the vehicle's lateral acceleration. The road cross slope angle related data obtained based on the corrected yaw rate and the vehicle performance parameters includes: The lateral inertial acceleration of the vehicle is obtained based on the corrected yaw rate; Based on the difference between the vehicle's lateral acceleration and its lateral inertial acceleration, relevant data on the road cross slope angle are obtained.

4. The method for lateral vehicle control on a road cross slope according to claim 1, characterized in that, The process of obtaining the multi-step vehicle predicted state based on the vehicle performance parameters, the current state estimate, the multi-step vehicle control range, and road cross slope angle related data includes: The cross slope interference term is obtained based on the road cross slope angle related data and the vehicle performance parameters; The state transition matrix and control input matrix are obtained based on the vehicle performance parameters. Based on the state transition matrix, the current state estimate, the control input matrix, the multi-step vehicle control range, and the cross slope disturbance term, the multi-step vehicle predicted state is obtained.

5. The method for lateral vehicle control on a road cross slope according to claim 4, characterized in that, The vehicle performance parameters also include the controller operating cycle. The method for obtaining the cross slope disturbance term based on the road cross slope angle correlation data and the vehicle performance parameters includes: An initial cross slope interference term is constructed based on the road cross slope angle correlation data; The cross slope disturbance term is obtained by multiplying the initial cross slope disturbance term and the controller operating cycle.

6. The method for lateral vehicle control on a road cross slope according to claim 4, characterized in that, The vehicle performance parameters also include front wheel lateral stiffness, rear wheel lateral stiffness, vehicle curb weight, longitudinal velocity, distance from center of gravity to front axle, distance from center of gravity to rear axle, and vehicle moment of inertia. The process of obtaining the state transition matrix and control input matrix based on these vehicle performance parameters includes: The state transition matrix is ​​obtained based on the front wheel lateral stiffness, rear wheel lateral stiffness, vehicle curb weight, longitudinal velocity, distance from center of gravity to front axle, and distance from center of gravity to rear axle. The control input matrix is ​​obtained based on the front wheel lateral stiffness, vehicle curb weight, distance from the center of gravity to the front axle, and vehicle rotational inertia.

7. The method for lateral vehicle control on a road cross slope according to claim 1, characterized in that, The step of obtaining a multi-step target vehicle control quantity based on the multi-step vehicle prediction state and the preset target control quantity prediction algorithm, and then performing lateral control on the target vehicle based on the multi-step vehicle control quantity, includes: The tracking deviation is obtained based on the multi-step vehicle prediction state and the preset expected state trajectory. The state tracking error cost is obtained based on the tracking deviation and the preset state weight matrix; The smoothness cost of the control quantity is obtained based on the multi-step vehicle control quantity range and the preset weight penalty matrix; Based on preset vehicle constraints, the state tracking error cost, and the control quantity smoothness cost, multi-step target vehicle control quantities are selected from the multi-step vehicle control quantity range. Lateral control of the target vehicle is performed based on the multi-step target vehicle control quantity.

8. A vehicle lateral control device on a road cross slope, characterized in that, include: The basic data acquisition module is used to acquire the vehicle performance parameters, current state estimate, and multi-step vehicle control range of the target vehicle. The initial data acquisition module is used to determine the current vehicle motion state based on the vehicle performance parameters. When the current vehicle motion state meets the preset motion conditions, the sensor zero bias value and the current yaw rate are obtained based on the vehicle performance parameters. The corrected yaw rate acquisition module is used to obtain the corrected yaw rate based on the current yaw rate and the sensor zero bias value; The road cross slope angle related data acquisition module is used to obtain road cross slope angle related data based on the corrected yaw rate and the vehicle performance parameters; The multi-step vehicle prediction state acquisition module is used to obtain the multi-step vehicle prediction state based on the vehicle performance parameters, the current state estimate, the multi-step vehicle control range, and road cross slope angle related data. The multi-step target vehicle control quantity acquisition module is used to obtain the multi-step target vehicle control quantity based on the multi-step vehicle prediction state and the preset target control quantity prediction algorithm, so as to perform lateral control on the target vehicle based on the multi-step target vehicle control quantity.

9. A terminal device, characterized in that, The system includes a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor, wherein, when the processor executes the computer program, it implements a vehicle lateral control method on a road cross slope as described in any one of claims 1-7.

10. A computer-readable storage medium, characterized in that, include: A stored computer program, wherein, when the computer program is executed, it controls the device containing the computer-readable storage medium to perform a vehicle lateral control method on a road cross slope as described in any one of claims 1-7.

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