Vehicle Lateral Control Method, Device, Equipment and Storage Medium

Abstract

The present invention belongs to the technical field of vehicle control, and discloses a vehicle lateral control method, device, equipment and storage medium. The method includes: obtaining vehicle driving information; calculating a near-point front wheel control amount according to the vehicle driving information and a preset control algorithm; calculating a far-point front wheel control amount according to the vehicle driving information and a target curvature model; calculating an error correction steering wheel angle according to the vehicle driving information; calculating a target front wheel angle according to the near-point front wheel control amount, the far-point front wheel control amount and the error correction steering wheel angle; and performing lateral control on the vehicle according to the target front wheel angle. By the above method, it can not only solve the deficiencies of traditional preview models but also solve the system overshoot influence brought by non-preview LQR controllers at the curvature mutation, and can improve the tracking accuracy, driving stability and riding comfort.

Description

Technical Field

[0001] The present invention relates to the technical field of vehicle control, and particularly to a vehicle lateral control method, device, equipment and storage medium. Background Art

[0002] Motion control is one of the core technologies in the field of driverless research, which can be specifically divided into the lateral motion control and longitudinal speed control of vehicles. Among them, lateral motion control analyzes and calculates based on the real-time position, vehicle speed, navigation and other state information of the vehicle and the state information of the desired trajectory to obtain the corresponding wheel angles so that the vehicle accurately tracks the desired path. Therefore, the vehicle lateral motion control problem can also be understood as a path tracking problem.

[0003] Since a vehicle is a non-linear system, there are control problems such as strong coupling and parameter uncertainty. Therefore, for such a complex system as a vehicle, lateral path tracking control has always been a research hotspot in the field of automotive control. Although the existing lateral control algorithms can effectively achieve the trajectory tracking of intelligent vehicles, they also have some disadvantages: Model Predictive Control (MPC) requires the use of a relatively accurate dynamic system model and has a large amount of calculation, so it is relatively difficult to implement. Sliding Mode Control (SMC) requires parameter adjustment, and the selection of parameters will affect the control effect, and at the same time, high-frequency oscillations will be introduced, affecting driving comfort. Feedback control algorithms (such as PID) require adjustment of control parameters and cannot adapt to the non-linearity of the system. When the system has large non-linearity, the control effect of the PID algorithm is not good. Non-linear control algorithms are difficult to implement, require analysis and modeling of specific problems, and at the same time require a large amount of training data. In the traditional lateral preview tracking model, most use the lateral deviation of the preview point as the reference quantity, often ignoring the influence brought by the heading deviation of the preview point, and the applicable scenarios are limited.

[0004] The above content is only used to assist in understanding the technical solution of the present invention, and does not represent an admission that the above content is prior art. Summary of the Invention

[0005] The main purpose of the present invention is to provide a vehicle lateral control method, device, equipment and storage medium, aiming to solve the technical problems of complex calculation, limited scenarios and poor effects in the existing vehicle lateral control.

[0006] To achieve the above purpose, the present invention provides a vehicle lateral control method, and the method includes the following steps:

[0007] Obtain vehicle driving information;

[0008] Calculate the control quantity of the front wheel at the near point according to the vehicle driving information and a preset control algorithm;

[0009] Calculate the front wheel control amount at the far point according to the vehicle driving information and the target curvature model;

[0010] Calculate the error correction steering wheel angle according to the vehicle driving information;

[0011] Calculate the target front wheel angle according to the front wheel control amount at the near point, the front wheel control amount at the far point, and the error correction steering wheel angle;

[0012] Perform lateral control on the vehicle according to the target front wheel angle.

[0013] Optionally, the calculation of the front wheel control amount at the near point according to the vehicle driving information and the preset control algorithm includes:

[0014] Determine the force balance equation according to the vehicle driving information and the vehicle two-degree-of-freedom model;

[0015] Determine the force equation in the Y-axis direction of the tire according to the vehicle driving information;

[0016] Determine the lateral dynamics differential equation according to the force balance equation and the force equation in the Y-axis direction of the tire;

[0017] Calculate the front wheel control amount at the near point according to the lateral dynamics differential equation and the preset control algorithm.

[0018] Optionally, the calculation of the front wheel control amount at the near point according to the lateral dynamics differential equation and the preset control algorithm includes:

[0019] Determine the front wheel angle formula according to the vehicle state equation and the preset control algorithm;

[0020] Introduce the front wheel angle formula into the angle step to obtain the front wheel control amount at the near point.

[0021] Optionally, the calculation of the front wheel control amount at the far point according to the vehicle driving information and the target curvature model includes:

[0022] Determine the vehicle driving displacement equation according to the vehicle driving information and the target curvature model;

[0023] Obtain the front wheel control amount at the far point according to the vehicle driving displacement equation combined with the target error principle.

[0024] Optionally, the obtaining of the front wheel control amount at the far point according to the vehicle driving displacement equation combined with the target error principle includes:

[0025] Obtain the ideal lateral acceleration according to the vehicle driving displacement equation combined with the target error principle;

[0026] Determine the ideal steering wheel angle according to the ideal lateral acceleration;

[0027] Determine the far-point front-wheel control quantity according to the ideal steering wheel angle;

[0028] Combine the ideal steering wheel angle with a hysteresis module to obtain the far-point front-wheel control quantity.

[0029] Optionally, calculating the error correction steering wheel angle according to the vehicle driving information includes:

[0030] Determine the preview time, steering system transmission ratio, vehicle wheelbase, and current vehicle speed according to the vehicle driving information;

[0031] Determine the ideal yaw rate according to the preview time;

[0032] Determine the error correction steering wheel angle according to the ideal yaw rate, the steering system transmission ratio, the vehicle wheelbase, and the current vehicle speed.

[0033] Optionally, calculating the target front-wheel angle according to the near-point front-wheel control quantity, the far-point front-wheel control quantity, and the error correction steering wheel angle includes:

[0034] Obtain the first weight, the second weight, and the third weight corresponding to the near-point front-wheel control quantity, the far-point front-wheel control quantity, and the error correction steering wheel angle respectively;

[0035] Calculate the target front-wheel angle according to the near-point front-wheel control quantity, the far-point front-wheel control quantity, the error correction steering wheel angle, the first weight, the second weight, and the third weight.

[0036] In addition, to achieve the above object, the present invention also proposes a vehicle lateral control device, and the vehicle lateral control device includes:

[0037] An information acquisition module for acquiring vehicle driving information;

[0038] A near-point control quantity calculation module for calculating a near-point front-wheel control quantity according to the vehicle driving information and a preset control algorithm;

[0039] A far-point control quantity calculation module for calculating a far-point front-wheel control quantity according to the vehicle driving information and a target curvature model;

[0040] An error correction calculation module for calculating an error correction steering wheel angle according to the vehicle driving information;

[0041] An angle calculation module for calculating a target front-wheel angle according to the near-point front-wheel control quantity, the far-point front-wheel control quantity, and the error correction steering wheel angle;

[0042] A vehicle control module for performing lateral control of the vehicle according to the target front-wheel angle.

[0043] In addition, to achieve the above object, the present invention further provides a vehicle lateral control device, where the vehicle lateral control device includes: a memory, a processor, and a vehicle lateral control program stored on the memory and executable on the processor, and the vehicle lateral control program is configured to implement the steps of the vehicle lateral control method as described above.

[0044] In addition, to achieve the above object, the present invention further provides a storage medium, where a vehicle lateral control program is stored on the storage medium, and when the vehicle lateral control program is executed by a processor, the steps of the vehicle lateral control method as described above are implemented.

[0045] The present invention obtains vehicle driving information; calculates a near-point front-wheel control amount according to the vehicle driving information and a preset control algorithm; calculates a far-point front-wheel control amount according to the vehicle driving information and a target curvature model; calculates an error-corrected steering wheel angle according to the vehicle driving information; calculates a target front-wheel angle according to the near-point front-wheel control amount, the far-point front-wheel control amount, and the error-corrected steering wheel angle; and performs lateral control on the vehicle according to the target front-wheel angle. In this way, it is realized to calculate the near-point front-wheel control amount and the far-point front-wheel control amount of the driver's preview point respectively according to the vehicle driving information, and then combine the calculation amount of error correction to finally obtain the target front-wheel angle for vehicle lateral control, so that it can not only solve the deficiencies of the traditional preview model but also solve the system overshoot influence brought by the non-preview LQR controller at the curvature mutation, and can improve the tracking accuracy, driving stability, and riding comfort. BRIEF DESCRIPTION OF THE DRAWINGS

[0046] Figure 1 is a schematic structural diagram of a vehicle lateral control device in a hardware operating environment related to the embodiment solution of the present invention;

[0047] Figure 2 is a schematic flowchart of the first embodiment of the vehicle lateral control method of the present invention;

[0048] Figure 3 is a schematic diagram of vehicle error information in an embodiment of the vehicle lateral control method of the present invention;

[0049] Figure 4 is a schematic diagram of the complete lateral control process in an embodiment of the vehicle lateral control method of the present invention;

[0050] Figure 5 is a schematic flowchart of the second embodiment of the vehicle lateral control method of the present invention;

[0051] Figure 6 is a schematic diagram of a target curvature model in an embodiment of the vehicle lateral control method of the present invention;

[0052] Figure 7 This is a structural block diagram of the first embodiment of the vehicle lateral control device of the present invention.

[0053] The realization of the object of the present invention, functional features and advantages will be further described in conjunction with the embodiments with reference to the accompanying drawings. Specific embodiments

[0054] It should be understood that the specific embodiments described herein are only used to explain the present invention and are not used to limit the present invention.

[0055] Refer to Figure 1 , Figure 1 This is a schematic structural diagram of a vehicle lateral control device for the hardware operating environment involved in the embodiment solution of the present invention.

[0056] As Figure 1 shown, the vehicle lateral control device may include: a processor 1001, such as a central processing unit (CPU), a communication bus 1002, a user interface 1003, a network interface 1004, and a memory 1005. Among them, the communication bus 1002 is used to realize the connection and communication between these components. The user interface 1003 may include a display screen (Display) and an input unit such as a keyboard (Keyboard). Optionally, the user interface 1003 may further include a standard wired interface and a wireless interface. The network interface 1004 may optionally include a standard wired interface and a wireless interface (such as a wireless-fidelity (Wi-Fi) interface). The memory 1005 may be a high-speed random access memory (Random Access Memory, RAM) or a stable non-volatile memory (Non-Volatile Memory, NVM), such as a disk memory. Optionally, the memory 1005 may also be a storage device independent of the aforementioned processor 1001.

[0057] Those skilled in the art can understand that Figure 1 the structure shown in

[0058] As Figure 1 shown, the memory 1005, as a storage medium, may include an operating system, a network communication module, a user interface module, and a vehicle lateral control program.

[0059] In Figure 1In the vehicle lateral control device shown, the network interface 1004 is mainly used for data communication with a network server; the user interface 1003 is mainly used for data interaction with a user; the processor 1001 and the memory 1005 in the vehicle lateral control device of the present invention can be arranged in the vehicle lateral control device, and the vehicle lateral control device calls the vehicle lateral control program stored in the memory 1005 through the processor 1001 and executes the vehicle lateral control method provided by the embodiments of the present invention.

[0060] The embodiments of the present invention provide a vehicle lateral control method. Refer to Figure 2 , Figure 2 which is a schematic flowchart of the first embodiment of a vehicle lateral control method of the present invention.

[0061] In this embodiment, the vehicle lateral control method includes the following steps:

[0062] Step S10: Obtain vehicle driving information.

[0063] It should be noted that the execution subject of this embodiment is a vehicle controller, specifically a vehicle-mounted computer control system installed on the vehicle, which can be an in-vehicle computer or other devices capable of implementing this function.

[0064] It should be understood that although existing lateral control algorithms can effectively achieve the trajectory tracking of intelligent vehicles, they also have some disadvantages: Model Predictive Control (MPC) requires the use of a relatively accurate dynamic system model and has a large amount of computation, so it is relatively difficult to implement. Sliding Mode Control (SMC) requires parameter adjustment, and the selection of parameters will affect the control effect. At the same time, it will introduce high-frequency oscillations, affecting driving comfort. Feedback control algorithms (such as PID) require adjustment of control parameters and cannot adapt to the nonlinearity of the system. When there is significant nonlinearity in the system, the control effect of the PID algorithm is not good. Nonlinear control algorithms are relatively difficult to implement, requiring analysis and modeling of specific problems and a large amount of training data. In traditional lateral preview tracking models, most use the lateral deviation of the preview point as a reference quantity, often ignoring the influence brought by the heading deviation of the preview point, and the applicable scenarios are limited. Based on the vehicle lateral dynamics model, a preview error model is derived through derivation. Based on this, a preview LQR lateral controller is designed, which solves the problem that the non-preview LQR controller cannot control the vehicle steering in time when the curvature of the reference path suddenly changes, resulting in a large error, and a feedforward controller is added to reduce the influence of road curvature on the tracking performance. The present invention provides a preview-based LQR intelligent vehicle lateral control method according to the problems existing in the prior art. In this algorithm, the visual information required for the driver's steering control includes two preview points, far and near. For the far point, the "optimal curvature model" is used to solve an optimal trajectory curvature. For the near point, LQR is used to solve the front wheel control quantity, and then an error correction module is added. Finally, the front wheel steering angles obtained by the three modules are weighted and summed. This method can not only solve the deficiencies of traditional preview models but also solve the system overshoot effect brought by non-preview LQR controllers at the curvature mutation, and can improve the tracking accuracy, driving stability, and riding comfort.

[0065] In specific implementation, the Linear Quadratic Regulator (LQR) theory is one of the earliest and most mature state-space design methods in modern control theory, and it is a control algorithm based on models and optimization principles. The LQR controller is more suitable for applications such as driverless driving on highways and trajectory tracking in most urban scenarios, without considering the influence of trajectory shape on the control system. Therefore, this method is sensitive to sudden curvature changes, and system overshoot will occur at the sudden curvature change. Thus, a preview algorithm is introduced to mitigate the influence brought by sudden curvature changes. In most traditional lateral preview tracking models, the lateral deviation of the preview point is used as the reference quantity, often ignoring the influence brought by the heading deviation of the preview point. The deficiencies of the classical preview model are manifested in the following two aspects: First, the optimal curvature model is based on the "Ackermann geometric relationship", and the relationship described by the optimal curvature model only holds when the front wheel steering angle is very small; Second, the contribution of direction information to the estimation of the optimal steering angle is missing in the model. Therefore, by using two preview points, the near point and the far point, to provide the lateral distance deviation and the heading angle deviation, the driver preview model is improved. In the present invention, during the process of lateral control of a vehicle using the Linear Quadratic Regulator (LQR) control algorithm, a preview optimal curvature model is introduced, and the optimal front wheel steering angle is obtained by weighting, so as to solve the problem that the system will overshoot at the sudden curvature change, thereby improving the tracking accuracy and driving stability.

[0066] In specific implementation, vehicle driving information refers to the relevant information of the vehicle motion state and static parameters used to calculate the near-point front wheel control amount, the far-point front wheel control amount, and the error correction steering wheel angle, including but not limited to parameters such as vehicle body mass, longitudinal speed at the vehicle center of mass, longitudinal distance from the vehicle center of mass to the front axle, longitudinal distance from the vehicle center of mass to the rear axle, vehicle yaw moment of inertia, vehicle yaw angle, yaw angular acceleration in the z-axis direction, lateral acceleration of the vehicle, lateral force of the front wheel tire, and lateral force of the rear wheel tire. The vehicle driving information also includes visual information that can be obtained by the driver during driving, mainly the field of view within the vehicle's front windshield. The visual information includes the driver's preview point, and the preview point includes a near point and a far point.

[0067] Step S20: Calculate the near-point front wheel control amount according to the vehicle driving information and the preset control algorithm.

[0068] It should be noted that the preset control algorithm refers to the LQR algorithm, which is a control algorithm based on models and optimization principles.

[0069] It should be understood that the near-point front wheel control amount refers to the front wheel control amount calculated by considering the near point of the preview point in the visual information of the driver's driving steering.

[0070] Further, to accurately calculate the control amount of the front wheels at the near point, step S20 includes: determining a force balance equation according to the vehicle driving information and the vehicle two-degree-of-freedom model; determining a force equation in the Y-axis direction of the tire according to the vehicle driving information; determining a lateral dynamics differential equation according to the force balance equation and the force equation in the Y-axis direction of the tire; and calculating the control amount of the front wheels at the near point according to the lateral dynamics differential equation and a preset control algorithm.

[0071] In a specific implementation, first, according to the vehicle two-degree-of-freedom model, the force balance equations along the y-axis and around the z-axis can be obtained:

[0072]

[0073]

[0074] Among them, m: vehicle body mass; V x : longitudinal speed at the vehicle center of mass; l f : longitudinal distance from the vehicle center of mass to the front axle; l r : longitudinal distance from the vehicle center of mass to the rear axle; I z : vehicle yaw moment of inertia; : vehicle yaw angle; : yaw angular acceleration in the z-axis direction; : lateral acceleration of the vehicle; F vf : lateral force of the front tire; F vr : lateral force of the rear tire.

[0075] It should be noted that when the vehicle is driving, the tire is subjected to a lateral force, and the tire will generate a side slip angle. Considering that within the linear region of the tire, the tire lateral force and the side slip angle are approximately in a proportional relationship, the force in the Y-axis direction of the tire can be expressed as:

[0076]

[0077] Among them, among them, C αf : front wheel cornering stiffness; C αr : rear wheel cornering stiffness; : steering wheel angle; : front wheel speed angle; : rear wheel speed angle.

[0078] It should be understood that simplifying and arranging the above formulas can obtain the lateral dynamics differential equation as follows:

[0079]

[0080] Among them, among them, : lateral position error relative to the road surface; : Relative road surface heading angle error; : Desired yaw rate.

[0081] It should be understood that when the lateral dynamics differential equation is obtained, the near-point front wheel control quantity is calculated according to the lateral dynamics differential equation and the preset control algorithm.

[0082] In this way, an accurate lateral dynamics differential equation of the vehicle is calculated through the vehicle two-degree-of-freedom model, making the final calculation of the near-point front wheel control quantity more accurate.

[0083] Furthermore, in order to accurately calculate the near-point front wheel control quantity, the steps of calculating the near-point front wheel control quantity according to the lateral dynamics differential equation and the preset control algorithm include: determining the front wheel steering angle formula according to the vehicle state equation and the preset control algorithm; introducing the front wheel steering angle formula into the steering angle step to obtain the near-point front wheel control quantity.

[0084] In specific implementation, according to the system state equation of the vehicle and using the LQR principle, introducing feedback control, the front wheel steering angle formula can be obtained as:

[0085]

[0086] Among them, the K value can be calculated using matlab software.

[0087] It should be noted that due to the existence of road curvature, the error will not converge to 0, so feedforward control is introduced for steering angle compensation, and the formula is as follows:

[0088]

[0089] Therefore, the corrected front wheel steering angle is as follows:

[0090]

[0091] In this way, the accurate calculation of the front wheel control quantity of the near point of the preview point is realized, and then the subsequent calculation of the target steering wheel angle is made faster and more scientific.

[0092] Step S30: Calculate the far-point front wheel control quantity according to the vehicle driving information and the target curvature model.

[0093] It should be noted that the target curvature model refers to the preview optimal curvature model, which makes a judgment, rolls and adjusts by simulating the driver's comparison of the deviation between the future preview time point trajectory and the target trajectory based on the current vehicle driving state, and takes into account the lag of the vehicle actuator.

[0094] Step S40: Calculate the error correction steering wheel angle according to the vehicle driving information.

[0095] It should be understood that the error-corrected steering wheel angle refers to the corrected angle of the final steering wheel obtained by taking into account and correcting the error of the far point angle.

[0096] Further, in order to calculate the error-corrected steering wheel angle, step S40 includes: determining the preview time, the steering system transmission ratio, the vehicle wheelbase, and the current vehicle speed according to the vehicle driving information;

[0097] Determining the ideal yaw rate according to the preview time;

[0098] Determining the error-corrected steering wheel angle according to the ideal yaw rate, the steering system transmission ratio, the vehicle wheelbase, and the current vehicle speed.

[0099] In a specific implementation, for the input of road information of the heading angle deviation as Figure 3 shown, the ideal yaw rate can be directly obtained from the formula:

[0100]

[0101] It should be noted that among them, T p is the preview time, and thus the far point angle error-corrected steering wheel angle can be obtained:

[0102]

[0103] where i is the steering system transmission ratio, L is the vehicle wheelbase, and V is the current vehicle speed.

[0104] In this way, by combining the actual steering wheel angle and the ideal error for consideration, the error of the steering wheel angle is corrected, and the accuracy of lateral control is improved.

[0105] Step S50: Calculating the target front wheel angle according to the near point front wheel control amount, the far point front wheel control amount, and the error-corrected steering wheel angle.

[0106] It should be understood that after calculating the near point front wheel control amount, the far point front wheel control amount, and the error-corrected steering wheel angle, a weighted calculation is performed to obtain the final target front wheel angle.

[0107] Further, in order to accurately calculate the target front wheel angle, step S50 includes: respectively obtaining the first weight, the second weight, and the third weight corresponding to the near point front wheel control amount, the far point front wheel control amount, and the error-corrected steering wheel angle; calculating the target front wheel angle according to the near point front wheel control amount, the far point front wheel control amount, the error-corrected steering wheel angle, the first weight, the second weight, and the third weight.

[0108] It should be noted that asFigure 4 As shown, the lateral preview control algorithm adopted in the solution of this embodiment is mainly divided into three parts: for the near point, the LQR control is used to obtain the front wheel control quantity , which is the first part; for the far point, the preview optimal curvature model is used to obtain the front wheel control quantity through correction , which is the second part; then the steering wheel angle is corrected by adding the far point angle error , which is the third part; finally, the three calculated front wheel angles are weighted and summed to obtain the optimal front wheel angle , formula:

[0109]

[0110] wherein, w1, w2, and w3 are , , weight values, which are the first weight, the second weight, and the third weight in sequence, and the specific values can be adjusted according to the actual situation.

[0111] In this way, the calculation of the target front wheel angle is realized by means of weight calculation, making the target front wheel angle more accurate, and considering the preview points and error correction of the near point and the far point, resulting in higher precision of lateral control.

[0112] Step S60: Perform lateral control on the vehicle according to the target front wheel angle.

[0113] It should be understood that after the target front wheel angle is determined, the vehicle-mounted system or in-vehicle computer directly controls the front wheels of the vehicle according to the target front wheel angle, thereby realizing lateral control.

[0114] In this embodiment, the vehicle driving information is obtained; the near point front wheel control quantity is calculated according to the vehicle driving information and the preset control algorithm; the far point front wheel control quantity is calculated according to the vehicle driving information and the target curvature model; the error correction steering wheel angle is calculated according to the vehicle driving information; the target front wheel angle is calculated according to the near point front wheel control quantity, the far point front wheel control quantity, and the error correction steering wheel angle; and lateral control is performed on the vehicle according to the target front wheel angle. In this way, the near point front wheel control quantity and the far point front wheel control quantity of the driver's preview point are respectively calculated according to the vehicle driving information, and then combined with the calculation quantity of error correction, and finally the target front wheel angle for lateral control of the vehicle is obtained, so that both the deficiencies of the traditional preview model and the system overshoot influence brought by the non-preview LQR controller at the curvature mutation can be solved, and the tracking accuracy, driving stability, and riding comfort can be improved.

[0115] Refer to Figure 5 , Figure 5Schematic flowchart of the second embodiment of a vehicle lateral control method according to the present invention.

[0116] Based on the above first embodiment, the vehicle lateral control method in this embodiment includes, in the step S30:

[0117] Step S301: Determine the vehicle driving displacement equation according to the vehicle driving information and the target curvature model.

[0118] It should be noted that a driver model is also embedded in this solution. The driver model mainly simulates the operation of a real driver on the power or steering of the vehicle. The driver model can be any deep learning and machine training model, and this embodiment does not limit this. The solution of this embodiment makes a judgment and adjusts iteratively by simulating the driver to compare the deviation between the future preview time point trajectory and the target trajectory based on the current vehicle driving state, and also considers the lag of the vehicle actuator. The preview model and specific practices are as Figure 6 shown.

[0119] It should be understood that when the vehicle is in motion, the vehicle receives the expected path transmitted by the upstream decision-making and planning module, performs polynomial fitting on the path, and the equation of the track center f(t) can be obtained. At a certain driving moment t, the state of the vehicle is as follows:

[0120]

[0121] In specific implementation, the driver previews a distance d forward, and the corresponding preview time can be recorded as T = d / V. At this time, the steering wheel angle corresponds to a track curvature of 1 / R, and the lateral acceleration is , then after T moments, the vehicle driving displacement equation is as follows:

[0122]

[0123] Step S302: Obtain the far-point front wheel control quantity according to the vehicle driving displacement equation in combination with the target error principle.

[0124] It should be noted that according to the target error principle is the minimum error principle. According to the target error principle, the ideal lateral acceleration is first obtained, then the ideal steering wheel angle is determined, and finally the far-point front wheel control quantity is calculated in combination with the hysteresis module.

[0125] Furthermore, in order to obtain the far-point front wheel control quantity, step S302 includes: obtaining the ideal lateral acceleration according to the vehicle driving displacement equation in combination with the target error principle; determining the ideal steering wheel angle according to the ideal lateral acceleration; determining the far-point front wheel control quantity according to the ideal steering wheel angle; combining the ideal steering wheel angle with the hysteresis module to obtain the far-point front wheel control quantity.

[0126] It should be noted that according to the "minimum error principle", it is always desired to select the optimal trajectory curvature 1 / R such that after time T, the lateral position y(t+T) of the vehicle coincides with the expected position f(t+T). Combining the following basic formula:

[0127]

[0128] The ideal lateral acceleration can be obtained as follows:

[0129]

[0130] Then the ideal steering wheel angle is:

[0131]

[0132] Due to the delay in the vehicle's actuator, the actual steering wheel angle is inconsistent with the ideal steering wheel angle, and a hysteresis module needs to be added:

[0133]

[0134] Therefore, the actual steering wheel angle (front wheel control amount at the far point) is as follows:

[0135]

[0136] In this way, the front wheel control amount at the far point is calculated in combination with the hysteresis module, so that the final result can consider the target error principle and improve the comprehensiveness of the calculation.

[0137] In this embodiment, the vehicle travel displacement equation is determined according to the vehicle travel information and the target curvature model; the front wheel control amount at the far point is obtained according to the vehicle travel displacement equation in combination with the target error principle. In this way, the calculation and correction of the front wheel control amount at the far point are realized in combination with the target error principle, and the accuracy and comprehensiveness of the final calculation result are improved.

[0138] In addition, an embodiment of the present invention also provides a storage medium, on which a vehicle lateral control program is stored. When the vehicle lateral control program is executed by a processor, the steps of the vehicle lateral control method described above are implemented.

[0139] Since this storage medium adopts all the technical solutions of the above-mentioned all embodiments, it has at least all the beneficial effects brought by the technical solutions of the above-mentioned embodiments, which will not be elaborated here one by one.

[0140] Referring to Figure 7 , Figure 7 is the structural block diagram of the first embodiment of the vehicle lateral control device of the present invention.

[0141] As shown in Figure 7As shown in the figure, the vehicle lateral control device proposed by the embodiment of the present invention includes:

[0142] An information acquisition module 10, configured to acquire vehicle driving information.

[0143] A near-point control quantity calculation module 20, configured to calculate a near-point front-wheel control quantity according to the vehicle driving information and a preset control algorithm.

[0144] A far-point control quantity calculation module 30, configured to calculate a far-point front-wheel control quantity according to the vehicle driving information and a target curvature model.

[0145] An error correction calculation module 40, configured to calculate an error correction steering wheel angle according to the vehicle driving information.

[0146] A steering angle calculation module 50, configured to calculate a target front-wheel steering angle according to the near-point front-wheel control quantity, the far-point front-wheel control quantity, and the error correction steering wheel angle.

[0147] A vehicle control module 60, configured to perform lateral control on the vehicle according to the target front-wheel steering angle.

[0148] In this embodiment, by acquiring vehicle driving information; calculating a near-point front-wheel control quantity according to the vehicle driving information and a preset control algorithm; calculating a far-point front-wheel control quantity according to the vehicle driving information and a target curvature model; calculating an error correction steering wheel angle according to the vehicle driving information; calculating a target front-wheel steering angle according to the near-point front-wheel control quantity, the far-point front-wheel control quantity, and the error correction steering wheel angle; and performing lateral control on the vehicle according to the target front-wheel steering angle. In this way, it is realized to calculate the near-point front-wheel control quantity and the far-point front-wheel control quantity of the driver's preview point respectively according to the vehicle driving information, and then combine the calculated quantity of error correction to finally obtain the target front-wheel steering angle for the lateral control of the vehicle, so as to not only solve the deficiencies of the traditional preview model but also solve the system overshoot influence brought by the non-preview LQR controller at the curvature mutation, and can improve the tracking accuracy, driving stability, and riding comfort.

[0149] In one embodiment, the near-point control quantity calculation module 20 is further configured to determine a force balance equation according to the vehicle driving information and a vehicle two-degree-of-freedom model; determine a tire Y-axis direction force equation according to the vehicle driving information; determine a lateral dynamics differential equation according to the force balance equation and the tire Y-axis direction force equation; and calculate a near-point front-wheel control quantity according to the lateral dynamics differential equation and a preset control algorithm.

[0150] In one embodiment, the near-point control quantity calculation module 20 is further configured to determine a front-wheel steering angle formula according to a vehicle state equation and a preset control algorithm; and introduce a steering angle step size into the front-wheel steering angle formula to obtain a near-point front-wheel control quantity.

[0151] In one embodiment, the far - point control quantity calculation module 30 is further configured to determine a vehicle driving displacement equation according to the vehicle driving information and the target curvature model; and obtain a far - point front - wheel control quantity according to the vehicle driving displacement equation in combination with the target error principle.

[0152] In one embodiment, the far - point control quantity calculation module 30 is further configured to obtain an ideal lateral acceleration according to the vehicle driving displacement equation in combination with the target error principle; determine an ideal steering wheel angle according to the ideal lateral acceleration; determine a far - point front - wheel control quantity according to the ideal steering wheel angle; and obtain a far - point front - wheel control quantity by combining the ideal steering wheel angle with a hysteresis module.

[0153] In one embodiment, the error correction calculation module 40 is further configured to determine a preview time, a steering system transmission ratio, a vehicle wheelbase, and a current vehicle speed according to the vehicle driving information; determine an ideal yaw rate according to the preview time; and determine an error - corrected steering wheel angle according to the ideal yaw rate, the steering system transmission ratio, the vehicle wheelbase, and the current vehicle speed.

[0154] In one embodiment, the steering angle calculation module 50 is further configured to respectively obtain first weights, second weights, and third weights corresponding to the near - point front - wheel control quantity, the far - point front - wheel control quantity, and the error - corrected steering wheel angle; and calculate a target front - wheel steering angle according to the near - point front - wheel control quantity, the far - point front - wheel control quantity, the error - corrected steering wheel angle, the first weight, the second weight, and the third weight.

[0155] It should be understood that the above is only an example for illustration and does not constitute any limitation to the technical solution of the present invention. In specific applications, those skilled in the art can set according to needs, and the present invention does not make any restrictions in this regard.

[0156] It should be noted that the above - described work process is only illustrative and does not limit the protection scope of the present invention. In actual applications, those skilled in the art can select some or all of them according to actual needs to achieve the purpose of the solution of this embodiment, and no restrictions are imposed here.

[0157] In addition, for the technical details not described in detail in this embodiment, reference can be made to the vehicle lateral control method provided in any embodiment of the present invention, which will not be elaborated here.

[0158] In addition, it should be noted that in this text, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or system including a series of elements not only includes those elements, but also includes other elements not expressly listed, or further includes elements inherent in such process, method, article or system. Without further limitation, an element defined by the statement "including one..." does not exclude the existence of additional identical elements in the process, method, article or system including such element.

[0159] The serial numbers of the above-described embodiments of the present invention are only for description and do not represent the superiority or inferiority of the embodiments.

[0160] Through the description of the above embodiments, those skilled in the art can clearly understand that the above-described embodiment methods can be implemented by means of software plus a necessary general hardware platform. Of course, they can also be implemented by hardware, but in many cases the former is a better implementation. Based on such an understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, can be embodied in the form of a software product. The computer software product is stored in a storage medium (such as a Read Only Memory (ROM) / RAM, magnetic disk, optical disk), and includes several instructions for causing a terminal device (which can be a mobile phone, computer, server, or network device, etc.) to execute the methods described in the various embodiments of the present invention.

[0161] The above are only the preferred embodiments of the present invention and do not limit the patent scope of the present invention. Any equivalent structure or equivalent process transformation made by using the description and drawings of the present invention, or directly or indirectly applied in other related technical fields, shall be equally included in the patent protection scope of the present invention.

Claims

1. A vehicle lateral control method, characterized in that, The vehicle lateral control method includes: Obtaining vehicle driving information; Determining a force balance equation according to the vehicle driving information and a vehicle two-degree-of-freedom model, where the force balance equation is an equation along the y-axis and around the z-axis obtained according to the vehicle two-degree-of-freedom model; Determining a tire force equation in the Y-axis direction according to the vehicle driving information; Determining a lateral dynamics differential equation according to the force balance equation and the tire force equation in the Y-axis direction; Calculating a near-point front-wheel control amount according to the lateral dynamics differential equation and a preset control algorithm; Calculating a far-point front-wheel control amount according to the vehicle driving information and a target curvature model; Calculating an error correction steering wheel angle according to the vehicle driving information; Calculating a target front-wheel angle according to the near-point front-wheel control amount, the far-point front-wheel control amount, and the error correction steering wheel angle; Performing lateral control on the vehicle according to the target front-wheel angle.

2. The method according to claim 1, characterized in that, The calculating the near-point front-wheel control amount according to the lateral dynamics differential equation and the preset control algorithm includes: Determining a front-wheel angle formula according to a vehicle state equation and the preset control algorithm; Introducing the front-wheel angle formula into a step size of the angle to obtain the near-point front-wheel control amount.

3. The method according to claim 1, characterized in that The calculating the far-point front-wheel control amount according to the vehicle driving information and the target curvature model includes: Determining a vehicle driving displacement equation according to the vehicle driving information and the target curvature model; Obtaining the far-point front-wheel control amount according to the vehicle driving displacement equation in combination with a target error principle.

4. The method according to claim 3, wherein The obtaining the far-point front-wheel control amount according to the vehicle driving displacement equation in combination with the target error principle includes: Obtaining an ideal lateral acceleration according to the vehicle driving displacement equation in combination with the target error principle; Determining an ideal steering wheel angle according to the ideal lateral acceleration; Determining the far-point front-wheel control amount according to the ideal steering wheel angle; Combining the ideal steering wheel angle with a hysteresis module to obtain the far-point front-wheel control amount.

5. The method according to claim 1, characterized in that, The calculating the error correction steering wheel angle according to the vehicle driving information includes: Determining a preview time, a steering system transmission ratio, a vehicle wheelbase, and a current vehicle speed according to the vehicle driving information; Determining an ideal yaw rate according to the preview time; Determining the error correction steering wheel angle according to the ideal yaw rate, the steering system transmission ratio, the vehicle wheelbase, and the current vehicle speed.

6. The method according to any one of claims 1 to 5, characterized in that, The calculating the target front-wheel angle according to the near-point front-wheel control amount, the far-point front-wheel control amount, and the error correction steering wheel angle includes: Respectively obtaining a first weight, a second weight, and a third weight corresponding to the near-point front-wheel control amount, the far-point front-wheel control amount, and the error correction steering wheel angle; Calculating the target front-wheel angle according to the near-point front-wheel control amount, the far-point front-wheel control amount, the error correction steering wheel angle, the first weight, the second weight, and the third weight.

7. A vehicle lateral control device, characterized in that, The vehicle lateral control device includes: An information acquisition module for acquiring vehicle driving information; The near-point control quantity calculation module is used to determine the force balance equation according to the vehicle driving information and the vehicle two-degree-of-freedom model. The force balance equation is the equation along the y-axis and around the z-axis obtained according to the vehicle two-degree-of-freedom model; determine the tire force equation in the Y-axis direction according to the vehicle driving information; determine the lateral dynamics differential equation according to the force balance equation and the tire force equation in the Y-axis direction; calculate the near-point front-wheel control quantity according to the lateral dynamics differential equation and a preset control algorithm; The far-point control quantity calculation module is used to calculate the far-point front-wheel control quantity according to the vehicle driving information and the target curvature model; The error correction calculation module is used to calculate the error correction steering wheel angle according to the vehicle driving information; The steering angle calculation module is used to calculate the target front-wheel steering angle according to the near-point front-wheel control quantity, the far-point front-wheel control quantity and the error correction steering wheel angle; The vehicle control module is used to perform lateral control on the vehicle according to the target front-wheel steering angle.

8. A vehicle lateral control device, characterized in that, The device includes: a memory, a processor, and a vehicle lateral control program stored on the memory and executable on the processor. The vehicle lateral control program is configured to implement the vehicle lateral control method according to any one of claims 1 to 6.

9. A storage medium, characterized in that, The vehicle lateral control program is stored on the storage medium. When the vehicle lateral control program is executed by the processor, it implements the vehicle lateral control method according to any one of claims 1 to 6.

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