Lateral control method, device, vehicle, medium and product

Abstract

The application relates to the technical field of automobiles, in particular to a lateral control method and device, a vehicle, a medium and a product, wherein the method comprises the following steps: acquiring vehicle body posture data, a driver hand torque and front road condition image information; extracting a lane center line of a target lane in the front road condition image information; calculating an offset distance between a vehicle body center line and the lane center line, calculating a target auxiliary torque required by an electric power steering system according to the offset distance, the vehicle body posture data and the driver hand torque; and assisting the vehicle in lateral control based on the target auxiliary torque. Thus, the target resultant torque required for lane keeping cannot be calculated in the related art, the driver hand torque and the auxiliary torque cannot be reasonably distributed, the flexibility of lane keeping assistance and the safety of the vehicle driven by the driver are reduced, human-machine conflicts are increased, and the driving comfort of the driver is reduced.

Description

Technical Field

[0001] This application relates to the field of automotive technology, and in particular to a lateral control method, device, vehicle, medium, and product. Background Technology

[0002] Lane keeping assist technology uses lateral vehicle control to ensure the vehicle stays within the center line of the lane, or automatically corrects deviations when the driver unintentionally strays from the lane. As a key function of advanced driver assistance systems (ADAS), lane keeping assist technology can significantly reduce the incidence of traffic accidents and alleviate driver workload. Its research is of great significance to the development of intelligent and connected vehicles.

[0003] In related technologies, due to the differences and changes in driver behavior, human-machine conflicts are obvious in human-machine co-driving. Related technologies usually cannot calculate the target resultant torque required for lane keeping in a timely manner and cannot reasonably distribute the driver's hand torque and auxiliary torque, thereby reducing the flexibility of lane keeping assistance, the safety of the driver's vehicle, and the driver's driving comfort. Summary of the Invention

[0004] This application provides a lateral control method, device, vehicle, medium, and product to solve the problems in related technologies, such as the inability to calculate the target resultant torque required for lane keeping and the inability to reasonably distribute the driver's hand torque and auxiliary torque, thereby reducing the flexibility of lane keeping assistance, the safety of the driver, and the driving comfort of the driver.

[0005] The first aspect of this application provides a lateral control method, comprising the following steps: acquiring vehicle body posture data, driver hand torque, and forward road condition image information; extracting the lane centerline of the target lane from the forward road condition image information; calculating the offset distance between the vehicle body centerline and the lane centerline; calculating the target auxiliary torque required by the electric power steering system based on the offset distance, vehicle body posture data, and driver hand torque; and assisting the vehicle in lateral control based on the target auxiliary torque.

[0006] Optionally, in one embodiment of this application, calculating the target auxiliary torque required by the electric power steering system based on the offset distance and vehicle posture data includes: identifying the vehicle's lateral position and yaw angle in the vehicle posture data; calculating the target resultant torque required for the vehicle to travel along the lane centerline of the target lane based on the vehicle's lateral position and yaw angle; calculating the weighting coefficients of the driver's hand torque and the target auxiliary torque based on the offset distance; and calculating the target auxiliary torque based on the driver's hand torque, the weighting coefficients of the driver's hand torque and the target auxiliary torque, and the target resultant torque.

[0007] Optionally, in one embodiment of this application, calculating the target resultant torque required for the vehicle to travel on the center line of the target lane based on the vehicle's lateral position and body yaw angle includes: constructing an MPC optimization problem based on the vehicle's lateral position and body yaw angle; and optimizing the target resultant torque required for the vehicle to travel on the center line of the target lane based on the MPC optimization problem.

[0008] Optionally, in one embodiment of this application, the weighting coefficient of the target auxiliary torque is: Where λ is the weighting coefficient of the target auxiliary torque; e is the natural constant; Δy is the offset distance between the vehicle centerline and the lane centerline; Y is the threshold parameter that the driver is allowed to adjust; and the weighting coefficient of the driver's hand torque is (1-λ).

[0009] Optionally, in one embodiment of this application, the formula for calculating the target auxiliary torque is:

[0010] T co =λT d +(1-λ)T a

[0011] in, λ is the weighting coefficient of the target assist torque; e is the natural constant; Δy is the offset between the vehicle centerline and the lane centerline; Y is the threshold parameter that the driver can adjust; T co To meet the target resultant torque for lane keeping; T d The driver's hand torque read from the steering wheel torque sensor; T a The auxiliary torque required for the electric power steering system.

[0012] Optionally, in one embodiment of this application, before assisting the vehicle in lateral control based on the target assist torque, the method further includes: the level of assistance required by the driver; and adjusting the target assist torque according to the level of assistance.

[0013] A second aspect of this application provides a lateral control device, comprising: an acquisition module for acquiring vehicle body posture data, driver hand torque, and forward road condition image information; an extraction module for extracting the lane centerline of a target lane from the forward road condition image information; a calculation module for calculating the offset distance between the vehicle body centerline and the lane centerline, and calculating the target auxiliary torque required by the electric power steering system based on the offset distance, vehicle body posture data, and driver hand torque; and a control module for assisting the vehicle in lateral control based on the target auxiliary torque.

[0014] Optionally, in one embodiment of this application, the calculation module is further configured to: identify the vehicle's lateral position and yaw angle in the vehicle posture data; calculate the target resultant torque required for the vehicle to travel on the lane centerline of the target lane based on the vehicle's lateral position and yaw angle; calculate the weighting coefficients of the driver's hand torque and the target auxiliary torque based on the offset distance; and calculate the target auxiliary torque based on the driver's hand torque, the weighting coefficients of the driver's hand torque and the target auxiliary torque, and the target resultant torque.

[0015] Optionally, in one embodiment of this application, the calculation module is further configured to construct an MPC optimization problem based on the vehicle's lateral position and body yaw angle; and to optimize and determine the target resultant torque required for the vehicle to travel on the lane centerline of the target lane based on the MPC optimization problem.

[0016] Optionally, in one embodiment of this application, the calculation module is further configured to determine the weighting coefficient of the target auxiliary torque as follows: Where λ is the weighting coefficient of the target auxiliary torque; e is the natural constant; Δy is the offset distance between the vehicle centerline and the lane centerline; Y is the threshold parameter that the driver is allowed to adjust; and the weighting coefficient of the driver's hand torque is (1-λ).

[0017] Optionally, in one embodiment of this application, the calculation module is further configured to calculate the target auxiliary torque using the following formula:

[0018] T co =λT d +(1-λ)T a

[0019] in, λ is the weighting coefficient of the target assist torque; e is the natural constant; Δy is the offset between the vehicle centerline and the lane centerline; Y is the threshold parameter that the driver can adjust; T co To meet the target resultant torque for lane keeping; T d The driver's hand torque read from the steering wheel torque sensor; T a The auxiliary torque required for the electric power steering system.

[0020] Optionally, in one embodiment of this application, before assisting the vehicle in lateral control based on the target assist torque, the control module is further configured to determine the level of assistance required by the driver and adjust the target assist torque according to the level of assistance.

[0021] A third aspect of this application provides a vehicle, including: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the lateral control method described above.

[0022] A fourth aspect of this application provides a computer-readable storage medium having a computer program or instructions stored thereon, which, when executed, implement the lateral control method described above.

[0023] A fifth aspect of this application provides a computer program product, including a computer program or instructions, which, when executed, implement the lateral control method described above.

[0024] Therefore, this application has the following beneficial effects:

[0025] The lateral control method proposed in this application, based on the acquired vehicle lateral position and body yaw angle, calculates the target resultant torque required for lane keeping in real time using the MPC controller. It also acquires the driver's hand torque and the offset between the vehicle's centerline and the lane centerline in real time. Weighting coefficients are used to rationally allocate the target auxiliary torque required for lane keeping provided by the electric power steering system, thereby improving the driver's experience, reducing human-machine conflict, ensuring driver safety, and increasing the efficiency of lateral position correction. This solves the problems in related technologies where the target resultant torque required for lane keeping is often uncalculated, and the driver's hand torque and auxiliary torque cannot be rationally allocated, thus reducing the flexibility of lane keeping assistance and driver safety, increasing human-machine conflict, and reducing driver comfort.

[0026] Additional aspects and advantages of this application will be set forth in part in the description which follows, and in part will be obvious from the description, or may be learned by practice of this application. Attached Figure Description

[0027] The above and / or additional aspects and advantages of this application will become apparent and readily understood from the following description of the embodiments taken in conjunction with the accompanying drawings, wherein:

[0028] Figure 1 This is a flowchart illustrating a lateral control method according to an embodiment of this application;

[0029] Figure 2 This is a flowchart of a lateral control method provided according to an embodiment of this application;

[0030] Figure 3 This is a schematic diagram of a lateral control method according to an embodiment of this application;

[0031] Figure 4 This is a block diagram of a lateral control device according to an embodiment of this application;

[0032] Figure 5 This is a structural schematic diagram of a vehicle according to an embodiment of this application. Detailed Implementation

[0033] The embodiments of this application are described in detail below. Examples of the embodiments are shown in the accompanying drawings, wherein the same or similar reference numerals denote the same or similar elements or elements having the same or similar functions throughout. The embodiments described below with reference to the accompanying drawings are exemplary and intended to explain this application, and should not be construed as limiting this application.

[0034] The following description, with reference to the accompanying drawings, describes a lateral control method, apparatus, vehicle, medium, and product according to embodiments of this application. Addressing the problems mentioned in the background art, such as the inability to calculate the target resultant torque required for lane keeping and the inability to reasonably allocate driver's hand torque and auxiliary torque, thereby reducing the flexibility of lane keeping assistance and the safety of the driver, increasing human-machine conflict, and reducing driver comfort, this application provides a lateral control method. In this method, vehicle body posture data, driver's hand torque, and forward road condition image information are acquired; the lane centerline of the target lane in the forward road condition image information is extracted; the offset distance between the vehicle body centerline and the lane centerline is calculated; based on the offset distance, vehicle posture data, and driver's hand torque, the target auxiliary torque required by the electric power steering system is calculated; and the vehicle is laterally controlled based on the target auxiliary torque. This solves the problems in related technologies, such as the inability to calculate the target resultant torque required for lane keeping and the inability to reasonably allocate driver's hand torque and auxiliary torque, thereby reducing the flexibility of lane keeping assistance and the safety of the driver, increasing human-machine conflict, and reducing driver comfort.

[0035] Specifically, Figure 1 This is a flowchart illustrating a lateral control method provided in an embodiment of this application.

[0036] like Figure 1 As shown, the lateral control method includes the following steps:

[0037] In step S101, vehicle body posture data, driver hand torque, and road condition image information ahead are acquired.

[0038] Understandably, vehicle attitude data includes the vehicle's lateral position, yaw angle, longitudinal acceleration, lateral acceleration, tire slip angle, etc.; the road condition image information ahead includes road surface smoothness, road object information, and the vehicle's relative position on the lane lines, etc.

[0039] It should be noted that, in this embodiment, vehicle posture data can be obtained by reading the bus of the vehicle's controller area network; driver's hand torque can be obtained by the steering wheel torque sensor; and forward road condition image information can be obtained by the forward-facing camera, without specific limitations.

[0040] In step S102, the lane centerline of the target lane is extracted from the road condition image information ahead.

[0041] It is understandable that, such as Figure 2 As shown, the lane center line can be obtained by parsing the lane lines extracted from the road condition image ahead. The parsing of lane lines can be achieved by sensors. The type of sensor is not specifically limited here, and those skilled in the art can set it according to the actual situation.

[0042] Therefore, in the embodiments of this application, the relevant parameters of the lane centerline can be accurately obtained, which facilitates the calculation of the offset distance between the vehicle body centerline and the lane centerline in the following embodiments.

[0043] In step S103, the offset distance between the vehicle's centerline and the lane centerline is calculated, and the target auxiliary torque required by the electric power steering system is calculated based on the offset distance, vehicle attitude data, and driver's hand torque.

[0044] Therefore, in this embodiment, the target auxiliary torque required by the electric power steering system is calculated by offset distance, vehicle posture data and driver's hand torque, thereby achieving accurate and rapid calculation of the target auxiliary torque. The target auxiliary torque can be adjusted in a timely manner according to real-time changes in road conditions and driver's driving behavior, thereby achieving high-efficiency and low-cost vehicle lateral position correction.

[0045] Understandably, the vehicle's centerline can be obtained using sensors inside the vehicle.

[0046] Optionally, in one embodiment of this application, calculating the target auxiliary torque required by the electric power steering system based on the offset distance and vehicle posture data includes: identifying the vehicle's lateral position and yaw angle in the vehicle posture data; calculating the target resultant torque required for the vehicle to travel along the lane centerline of the target lane based on the vehicle's lateral position and yaw angle; calculating the weighting coefficients of the driver's hand torque and the target auxiliary torque based on the offset distance; and calculating the target auxiliary torque based on the driver's hand torque, the weighting coefficients of the driver's hand torque and the target auxiliary torque, and the target resultant torque.

[0047] Optionally, in one embodiment of this application, the formula for calculating the target auxiliary torque is:

[0048] T co =λT d +(1-λ)T a

[0049] in, λ is the weighting coefficient of the target assist torque; e is the natural constant; Δy is the offset between the vehicle centerline and the lane centerline; Y is the threshold parameter that the driver can adjust; T co To meet the target resultant torque for lane keeping; T d The driver's hand torque read from the steering wheel torque sensor; T a The auxiliary torque required for the electric power steering system.

[0050] Understandably, the target assist torque can be the assist torque provided by the electric power steering system required for lane keeping; the target resultant torque can be the driver's hand torque and the target assist torque required for lane keeping.

[0051] Therefore, in this embodiment of the application, by calculating the target resultant torque and the driver's hand torque obtained by the steering wheel torque sensor, the target auxiliary torque required for lane keeping can be calculated, thereby improving vehicle driving safety, reducing human-machine conflict, and improving the driver's driving experience.

[0052] Optionally, in one embodiment of this application, the weighting coefficient of the target auxiliary torque is: Where λ is the weighting coefficient of the target auxiliary torque; e is the natural constant; Δy is the offset distance between the vehicle centerline and the lane centerline; Y is the threshold parameter that the driver is allowed to adjust; and the weighting coefficient of the driver's hand torque is (1-λ).

[0053] It is understandable that when λ = 1, Δy = 0, at which point the lane is on the center line and no compensation control of the target auxiliary torque is needed; when λ = 0, Δy is very large, at which point the target auxiliary torque plays a dominant role; Y can be set according to the actual situation, and no specific limitation is made here.

[0054] Therefore, in this embodiment of the application, by using the weighting coefficient of the target assist torque and the weighting coefficient of the driver's hand torque, the driver torque and assist torque required for lane keeping can be allocated in a timely and reasonable manner according to changes in driver behavior and road conditions. This yields the target assist torque required for lane keeping provided by the electric power steering system, thereby improving the accuracy of lateral control in human-machine co-driving, enhancing the driver's driving experience, and reducing human-machine conflict.

[0055] Optionally, in one embodiment of this application, calculating the target resultant torque required for the vehicle to travel on the center line of the target lane based on the vehicle's lateral position and body yaw angle includes: constructing an MPC optimization problem based on the vehicle's lateral position and body yaw angle; and optimizing the target resultant torque required for the vehicle to travel on the center line of the target lane based on the MPC optimization problem.

[0056] It is understandable that a lane keeping assist system can include a perception layer and a control layer. The perception layer is the sensor's analysis of lane line information to obtain the vehicle's lateral position and body yaw angle. The control layer mainly uses MPC optimization to solve the problem and adjust the vehicle's lateral position or enable the vehicle to follow the trajectory.

[0057] It should be noted that the embodiments of this application can use an MPC controller to solve the MPC optimization problem. The type of MPC controller is not specifically limited here, and those skilled in the art can set it according to the actual situation.

[0058] Specifically, such as Figure 2 As shown, the target resultant moment of the target lane centerline is calculated using the MPC controller. The specific steps are as follows:

[0059] (1) Construct the state variable ξ(t) using the following formula.

[0060]

[0061] Where ξ(t) is the state variable; y is the abscissa of the vehicle's center of mass in the vehicle coordinate system; and θ is the vehicle yaw angle. It is the first derivative of the x-coordinate of the vehicle's center of mass in the vehicle coordinate system; Let be the first derivative of the vehicle body yaw angle.

[0062] (2) Select the system control quantity using the following formula.

[0063] u(t) = [T co ]

[0064] Where u(t) is the system control variable; T co To meet the target resultant torque for lane keeping.

[0065] (3) Construct the system state-space equation using the following formula.

[0066]

[0067] in, ξ(t) is the first derivative of the state variable; ξ(t) is the state variable; u(t) is the system control variable.

[0068] (4) The discrete linear time-varying state space equation is obtained by linearizing and discretizing using the following formula.

[0069] ξ(k+1)=A(k)ξ(k)+B(k)u(k)

[0070] Where ξ(k+1) is the state variable of the linear time-varying system at time k+1; A(k) is the coefficient matrix; ξ(k) is the state variable of the linear time-varying system at time k; B(k) is the coefficient matrix; and u(k) is the control variable of the linear time-varying system.

[0071] Therefore, in this embodiment of the application, the time domain is discretized by changing t to k, so that the following embodiments can be transformed into operations under each small step size.

[0072] (5) Use the following formula to design the prediction equation and construct new state variables.

[0073]

[0074] in, Let ξ(t|k) be the state variables of the linear time-varying system at time t; let u(t-1|k) be the state variables of the linear time-varying system at time t; and let u(t-1|k) be the system control variables at time t-1.

[0075] (6) The new state-space expression is obtained using the following formula.

[0076]

[0077] in, The matrix represents the state variables of the linear time-varying system at time t+1. ξ is the coefficient matrix; ξ(k) is the state variable of the linear time-varying system at time k; ξ is the coefficient matrix; Δu(k) is the control increment; ψ(t|k) is the output quantity; ξ(t|k) is the linear time-varying system state quantity at time t; It is a coefficient matrix.

[0078] (7) The following equation is used to obtain the output equation of the linear time-varying system in the prediction time domain.

[0079] Y(k)=η k ξ(k)+Θ k ΔU(k)

[0080] Where Y(k) is the output of the linear time-varying system in the prediction time domain; η k Θ k All are coefficient matrices; ξ(k) is the state variable of the linear time-varying system at time k; ΔU(k) ​​is the control variable in the prediction time domain.

[0081] in, ΔU(k)=[Δu(k|k)…Δu(k+N c |k)] T

[0082]

[0083] Where Nc represents the control time domain and Np represents the prediction time domain; It is a coefficient matrix; It is a coefficient matrix; Here is the coefficient matrix; Δu(k|k) is the control increment at time k; Δu(k+N) is the coefficient matrix. c |k) represents the control increment at time k in the control time domain; ΔU(k) ​​represents the control quantity in the prediction time domain.

[0084] (8) Design the objective function using the following formula.

[0085]

[0086] Where, ψ ref ρ is the system reference output; Q is the output weight matrix; R is the control increment weight matrix; ρ is the relaxation factor weight coefficient; ε is the relaxation factor; i is the step size; Nc is the control time domain; Np is the prediction time domain.

[0087] (9) Construct the constraints as follows.

[0088]

[0089] Among them, a x The lateral acceleration of the vehicle; a y denoted as vehicle longitudinal acceleration; μ as road adhesion coefficient; g as gravitational acceleration; α as tire slip angle; and β as center of mass slip angle.

[0090] (10) Using the above constraints, the quadratic programming of the following formula is used to solve the MPC optimization problem.

[0091] ΔU(k)=[Δu(k), Δu(k+1),…, Δu(k+N c )] T

[0092] Where ΔU(k) ​​is the control quantity in the prediction time domain; Δu(k) is the control increment at time k; Δu(k) is the control increment at time k+1; Δu(k+N) is the control increment at time k+1; c ) is the control increment when the control time domain is added to k.

[0093] Therefore, in this embodiment of the application, in order to facilitate the iterative solution of the objective function in the host computer, the MPC optimization problem is transformed into a quadratic programming problem. By solving the constrained optimization problem, a set of incremental sequences can be obtained: taking the first element of the sequence in this formula as the actual control increment, the target resultant torque that satisfies lane keeping can be obtained. Furthermore, the MPC controller can be embedded in the vehicle electronic equipment. Therefore, the storage and retrieval of the MPC controller program are convenient, and its functions are easy to implement in the joint development of vehicle software and hardware.

[0094] In step S104, the vehicle is assisted in lateral control based on the target auxiliary torque.

[0095] Optionally, in one embodiment of this application, before assisting the vehicle in lateral control based on the target assist torque, the method further includes: the level of assistance required by the driver; and adjusting the target assist torque according to the level of assistance.

[0096] It is understandable that when 0 < λ < 1, the auxiliary torque Ta depends on the level of assistance required by the driver, i.e. the deviation of the vehicle from the center line of the lane. Through the intervention of the auxiliary torque, lateral stability control under human-machine co-driving is achieved.

[0097] It should be noted that the required level of driver assistance can be set according to the driver's actual situation, and no specific restrictions are made here.

[0098] Therefore, in this embodiment, the target assist torque can be modified according to the driver's needs, which improves the driver's driving experience and reduces human-machine conflict. Its control process can be independently integrated into the autonomous driving controller, which is easy to jointly develop with the vehicle controller and has broad practical value.

[0099] To enable those skilled in the art to further understand the lateral control method of the embodiments of this application, this application implements the lateral control method of the embodiments of this application using a lane line perception module, a target resultant torque calculation module, and a human-machine co-driving torque distribution module. The following describes the implementation of the lateral control method in conjunction with... Figure 3 To elaborate.

[0100] like Figure 3 As shown, the lane line perception module is used to collect road condition image information, analyze the extracted lane lines, and calculate the offset distance between the vehicle's centerline and the lane centerline; the target resultant torque calculation module is used to calculate the target resultant torque required for lane keeping through the MPC controller; the human-machine co-driving torque allocation module is used to allocate the driver's hand torque and auxiliary torque through an exponential weighting coefficient λ to obtain the target auxiliary torque required by the electric power steering system. According to the lateral control method proposed in this application embodiment, by acquiring the vehicle's lateral position and body yaw angle, the target resultant torque required for lane keeping is calculated in real time by the MPC controller, and the driver's hand torque and the offset distance between the vehicle's centerline and the lane centerline are acquired in real time. The target auxiliary torque required for lane keeping required by the electric power steering system is reasonably allocated through a weighting coefficient, thereby improving the driver's driving experience, reducing human-machine conflict, ensuring the driver's driving safety, and improving the efficiency of vehicle lateral position correction.

[0101] Next, the lateral control device proposed according to the embodiments of this application is described with reference to the accompanying drawings.

[0102] Figure 4 This is a block diagram of a lateral control device according to an embodiment of this application.

[0103] like Figure 4 As shown, the horizontal control device 40 includes: an acquisition module 401, an extraction module 402, a calculation module 404, and a control module 404.

[0104] The system includes: an acquisition module 401 for acquiring vehicle body posture data, driver hand torque, and forward road condition image information; an extraction module 402 for extracting the lane centerline of the target lane from the forward road condition image information; a calculation module 404 for calculating the offset distance between the vehicle body centerline and the lane centerline, and calculating the target auxiliary torque required by the electric power steering system based on the offset distance, vehicle posture data, and driver hand torque; and a control module 404 for assisting the vehicle in lateral control based on the target auxiliary torque.

[0105] Optionally, in one embodiment of this application, the calculation module 404 is further configured to: identify the vehicle's lateral position and yaw angle in the vehicle posture data; calculate the target resultant torque required for the vehicle to travel on the lane centerline of the target lane based on the vehicle's lateral position and yaw angle; calculate the weighting coefficients of the driver's hand torque and the target auxiliary torque based on the offset distance; and calculate the target auxiliary torque based on the driver's hand torque, the weighting coefficients of the driver's hand torque and the target auxiliary torque, and the target resultant torque.

[0106] Optionally, in one embodiment of this application, the calculation module 404 is further configured to construct an MPC optimization problem based on the vehicle's lateral position and body yaw angle; and to optimize and determine the target resultant torque required for the vehicle to travel on the lane centerline of the target lane based on the MPC optimization problem.

[0107] Optionally, in one embodiment of this application, the calculation module 404 is further configured to set the weighting coefficient of the target auxiliary torque as follows: Where λ is the weighting coefficient of the target auxiliary torque; e is the natural constant; Δy is the offset distance between the vehicle centerline and the lane centerline; Y is the threshold parameter that the driver is allowed to adjust; and the weighting coefficient of the driver's hand torque is (1-λ).

[0108] Optionally, in one embodiment of this application, the calculation module 404 is further configured to calculate the target auxiliary torque using the following formula:

[0109] T co =λT d +(1-λ)T a

[0110] in, λ is the weighting coefficient of the target assist torque; e is the natural constant; Δy is the offset between the vehicle centerline and the lane centerline; Y is the threshold parameter that the driver can adjust; T co To meet the target resultant torque for lane keeping; T d The driver's hand torque read from the steering wheel torque sensor; T a The auxiliary torque required for the electric power steering system.

[0111] Optionally, in one embodiment of this application, before assisting the vehicle in lateral control based on the target assist torque, the control module 404 is further used to determine the level of assistance required by the driver and to adjust the target assist torque according to the level of assistance.

[0112] It should be noted that the foregoing explanation of the lateral control method embodiment also applies to the lateral control device of this embodiment, and will not be repeated here.

[0113] According to the lateral control device proposed in this application embodiment, by acquiring the vehicle's lateral position and body yaw angle, the target resultant torque required for lane keeping is calculated in real time by the MPC controller, and the driver's hand torque and the offset distance between the vehicle's body centerline and the lane centerline are acquired in real time. The target auxiliary torque required for lane keeping provided by the electric power steering system is reasonably allocated through weighting coefficients, thereby improving the driver's driving experience, reducing human-machine conflict, ensuring the safety of the driver, and improving the efficiency of vehicle lateral position correction.

[0114] Figure 5 A schematic diagram of the structure of a vehicle provided in an embodiment of this application. The vehicle may include:

[0115] The memory 501, the processor 502, and the computer program stored on the memory 501 and capable of running on the processor 502.

[0116] When the processor 502 executes the program, it implements the lateral control method provided in the above embodiments.

[0117] Furthermore, the vehicle also includes:

[0118] Communication interface 503 is used for communication between memory 501 and processor 502.

[0119] The memory 501 is used to store computer programs that can run on the processor 502.

[0120] The memory 501 may include high-speed RAM (Random Access Memory) memory, and may also include non-volatile memory, such as at least one disk storage.

[0121] If the memory 501, processor 502, and communication interface 503 are implemented independently, then the communication interface 503, memory 501, and processor 502 can be interconnected via a bus to complete communication between them. The bus can be an ISA (Industry Standard Architecture) bus, a PCI (Peripheral Component Interconnect) bus, or an EISA (Extended Industry Standard Architecture) bus, etc. The bus can be divided into address bus, data bus, control bus, etc. For ease of representation, Figure 5 The bus is represented by a single thick line, but this does not mean that there is only one bus or one type of bus.

[0122] Optionally, in a specific implementation, if the memory 501, processor 502, and communication interface 503 are integrated on a single chip, then the memory 501, processor 502, and communication interface 503 can communicate with each other through an internal interface.

[0123] Processor 502 may be a CPU (Central Processing Unit), an ASIC (Application Specific Integrated Circuit), or one or more integrated circuits configured to implement embodiments of this application.

[0124] This application also provides a computer-readable storage medium storing a computer program or instructions thereon, which, when executed, implements the lateral control method described above.

[0125] This application also provides a computer program product, including a computer program or instructions, which, when executed, implement the above-described lateral control method.

[0126] In the description of this specification, the references to terms such as "one embodiment," "some embodiments," "example," "specific example," or "some examples," etc., indicate that a specific feature, structure, material, or characteristic described in connection with that embodiment or example is included in at least one embodiment or example of this application. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples. Moreover, without contradiction, those skilled in the art can combine and integrate the different embodiments or examples described in this specification, as well as the features of different embodiments or examples.

[0127] Furthermore, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include at least one of that feature. In the description of this application, "N" means at least two, such as two, three, etc., unless otherwise explicitly specified.

[0128] Any process or method described in the flowchart or otherwise herein can be understood as representing a module, segment, or portion of code comprising one or N executable instructions for implementing custom logic functions or processes, and the scope of the preferred embodiments of this application includes additional implementations in which functions may be performed not in the order shown or discussed, including substantially simultaneously or in reverse order depending on the functions involved, as should be understood by those skilled in the art to which embodiments of this application pertain.

[0129] It should be understood that various parts of this application can be implemented using hardware, software, firmware, or a combination thereof. In the above embodiments, steps or methods can be implemented using software or firmware stored in memory and executed by a suitable instruction execution system. For example, if implemented in hardware, as in another embodiment, it can be implemented using any of the following techniques known in the art, or a combination thereof: discrete logic circuits having logic gates for implementing logical functions on data signals, application-specific integrated circuits (ASICs) having suitable combinational logic gates, programmable gate arrays (FPGAs), field-programmable gate arrays (FPGAs), etc.

[0130] Those skilled in the art will understand that all or part of the steps of the methods implementing the above embodiments can be implemented by a program instructing related hardware. The program can be stored in a computer-readable storage medium, and when executed, the program includes one or a combination of the steps of the method embodiments.

[0131] Although embodiments of this application have been shown and described above, it is understood that the above embodiments are exemplary and should not be construed as limiting this application. Those skilled in the art can make changes, modifications, substitutions and variations to the above embodiments within the scope of this application.

Claims

1. A lateral control method, characterized in that, Includes the following steps: Acquire vehicle body posture data, driver hand torque, and road condition image information ahead; Extract the lane centerline of the target lane from the road condition image information ahead; Calculate the offset distance between the vehicle's centerline and the lane centerline, and calculate the target auxiliary torque that the electric power steering system needs to provide based on the offset distance, the vehicle's attitude data, and the driver's hand torque; The vehicle is assisted in lateral control based on the target auxiliary torque; The step of calculating the target assist torque required by the electric power steering system based on the offset distance and the vehicle attitude data includes: Identify the vehicle's lateral position and yaw angle from the vehicle posture data; Calculate the target resultant torque required for the vehicle to travel along the center line of the target lane based on the vehicle's lateral position and the vehicle's yaw angle; The weighting coefficients of the driver's hand torque and the target auxiliary torque are calculated based on the offset distance. The target auxiliary torque is then calculated based on the driver's hand torque, the weighting coefficients of the driver's hand torque and the target auxiliary torque, and the target resultant torque. The calculation of the target resultant torque required for the vehicle to travel along the lane centerline of the target lane based on the vehicle's lateral position and body yaw angle includes: Based on the vehicle's lateral position and the vehicle body yaw angle, construct an MPC optimization solution problem; Based on the MPC optimization problem, the target resultant torque required for the vehicle to travel on the center line of the target lane is determined. The weighting coefficient of the target auxiliary torque is ,in, is the weighting coefficient for the target auxiliary torque; e is the natural constant; Δy is the offset between the vehicle centerline and the lane centerline; Y is the threshold parameter that the driver is allowed to adjust. The weighting coefficient for the driver's hand torque is: ; The formula for calculating the target auxiliary torque is: in, , The weighting coefficient for the target auxiliary torque; e is the natural constant; Δy is the offset between the vehicle centerline and the lane centerline; Y is the threshold parameter that the driver can adjust; T co To meet the target resultant torque for lane keeping; The driver's hand torque is read from the steering wheel torque sensor; The auxiliary torque required for the electric power steering system.

2. The lateral control method according to claim 1, characterized in that, Before assisting the vehicle in lateral control based on the target auxiliary torque, the method further includes: The required level of assistance for the driver; The target auxiliary torque is adjusted according to the auxiliary level.

3. A lateral control device, characterized in that, The lateral control device is used to implement the lateral control method as described in any one of claims 1-2, and the lateral control device includes the following steps: The acquisition module is used to acquire vehicle body posture data, driver hand torque, and road condition image information ahead; The extraction module is used to extract the lane centerline of the target lane from the road condition image information ahead; The calculation module is used to calculate the offset distance between the vehicle body centerline and the lane centerline, and calculate the target auxiliary torque that the electric power steering system needs to provide based on the offset distance, the vehicle body posture data and the driver's hand torque. A control module is used to assist the vehicle in lateral control based on the target auxiliary torque.

4. A vehicle, characterized in that, include: A memory, a processor, and a computer program stored in the memory and executable on the processor, the processor executing the program to implement the lateral control method according to any one of claims 1-2.

5. A computer-readable storage medium having a computer program or instructions stored thereon, characterized in that, When the computer program or instructions are executed, they implement the lateral control method according to any one of claims 1-2.

6. A computer program product, comprising a computer program or instructions, characterized in that, When the computer program or instructions are executed, they implement the lateral control method according to any one of claims 1-2.

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