A complex road environment-oriented automatic driving vehicle lateral control method

Abstract

This invention discloses a lateral control method for autonomous vehicles in complex road environments. The method includes: constructing an error state-space equation based on a pre-built linear two-degree-of-freedom vehicle model and a preset reference path; calculating the vehicle's state variables based on the reference path; determining the feedback front wheel steering angle based on linear quadratic regulator theory, the error state-space equation, and the state variables; determining the curvature feedforward steering angle and the cross-hill feedforward steering angle based on the feedback front wheel steering angle, the curvature feedforward angle, and the cross-hill feedforward angle; and controlling the vehicle to steer using the target front wheel steering angle. This invention can determine a suitable target front wheel steering angle for vehicles traveling on complex roads and control the vehicle to steer using the target front wheel steering angle, enabling the vehicle to accurately track a predetermined reference path while safely navigating complex roads.

Description

Technical Field

[0001] This invention relates to the field of vehicle lateral control technology, and in particular to a lateral control method for autonomous vehicles in complex road environments. Background Technology

[0002] With the development of autonomous driving technology, academia and industry have placed higher demands on vehicle control precision, requiring vehicles to follow paths with more accurate control rates. Existing lateral control methods are mostly based on structured, standardized roads.

[0003] However, in reality, due to the need for safe passing on curves and planning constraints, many unstructured roads exist with poor conditions: large road curvature leads to significant lateral errors for vehicles, especially heavy-duty vehicles. Due to their large size, when the radius of curvature is too small, vehicles struggle to accurately track their routes, easily creating hazards. Furthermore, due to variations in road construction and maintenance, some sections have significant cross slopes, causing vehicles to tilt noticeably. The vehicle's weight tends to cause it to descend along the cross slope, potentially posing a danger if it is close to the shoulder or other vehicles. Therefore, a more precise lateral control method is urgently needed.

[0004] The above content is only used to help understand 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 objective of this invention is to provide a lateral control method for autonomous vehicles in complex road environments, aiming to solve the technical problem that existing vehicles face difficulties in steering and are prone to danger when encountering complex roads.

[0006] To achieve the above objectives, the present invention provides a lateral control method for autonomous vehicles in complex road environments, the method comprising the following steps:

[0007] Force analysis is performed on a vehicle traveling on a complex road to obtain the vehicle's parameter information, and a linear two-degree-of-freedom vehicle model is constructed based on the parameter information.

[0008] Based on the linear two-degree-of-freedom vehicle model and the preset reference path, an error state space equation is constructed, and the vehicle's state variables are calculated based on the reference path.

[0009] Based on the linear quadratic regulator theory, the error state space equation, and the state variables, the feedback front wheel steering angle is determined.

[0010] Based on the feedback front wheel steering angle and the error state space equation, the curvature feedforward steering angle and the cross slope feedforward steering angle are determined. Based on the feedback front wheel steering angle, the curvature feedforward steering angle, and the cross slope feedforward steering angle, the target front wheel steering angle of the vehicle is determined, and the vehicle is controlled to steer using the target front wheel steering angle.

[0011] Furthermore, the parameter information includes first vehicle parameters and second vehicle parameters. The step of performing force analysis on a vehicle traveling on complex roads to obtain the vehicle's parameter information, and constructing a linear two-degree-of-freedom vehicle model based on the parameter information, includes:

[0012] Obtain third vehicle parameters, wherein the third vehicle parameters include a first distance from the vehicle's center of gravity to the front axle of the vehicle, a second distance from the vehicle's center of gravity to the rear axle of the vehicle, the vehicle's mass, and the vehicle's moment of inertia;

[0013] Force analysis is performed on the tires of the vehicle to obtain the second vehicle parameters corresponding to the vehicle. The second vehicle parameters include the longitudinal velocity of the vehicle, the lateral velocity of the vehicle, the yaw rate of the vehicle, the yaw acceleration of the vehicle, the front wheel steering angle, and the component of the vehicle's gravity along the cross slope.

[0014] Force analysis is performed based on the third vehicle parameters and the second vehicle parameters to obtain the first vehicle parameters corresponding to the vehicle, wherein the first vehicle parameters include the first lateral force of the front wheel and the second lateral force of the rear wheel.

[0015] Based on the parameter information and the third vehicle parameters, a linear two-degree-of-freedom vehicle model is constructed.

[0016] Further, the step of performing force analysis based on the third vehicle parameters and the second vehicle parameters to obtain the first vehicle parameters corresponding to the vehicle includes:

[0017] The sideslip angle of the front wheels is determined based on the vehicle's center of gravity sideslip angle, the front wheel steering angle, the first distance, the longitudinal velocity, and the yaw rate.

[0018] The rear wheel slip angle is determined based on the vehicle's center of gravity sideslip angle, the second distance, the longitudinal velocity, and the yaw rate.

[0019] The first lateral force of the front wheel is determined based on the lateral stiffness and lateral angle of the front wheel.

[0020] The second lateral force of the rear wheel is determined based on the lateral stiffness and lateral angle of the rear wheel.

[0021] Furthermore, the step of constructing the error state space equation based on the linear two-degree-of-freedom vehicle model and the preset reference path includes:

[0022] Based on the first included angle and the second included angle, the equation corresponding to the heading angle error is determined, wherein the first included angle is the angle between the vehicle's velocity direction and the X-axis in the preset vehicle coordinate system, and the second included angle is the angle between the tangent of the path projection point closest to the vehicle's center of mass and the X-axis.

[0023] The distance between the vehicle's center of mass and the projection point of the reference path is taken as the lateral error, and the equations corresponding to the rate of change of the lateral error and the rate of change of the heading angle error are determined.

[0024] Based on the vehicle's longitudinal velocity and the second included angle, determine the equations corresponding to the acceleration of the lateral error and the acceleration of the heading angle error;

[0025] The equations corresponding to the heading angle error, the rate of change of the heading angle error, the rate of change of the lateral error, the acceleration of the lateral error, and the acceleration of the heading angle error are substituted into the linear two-degree-of-freedom vehicle model to construct the error state space equations.

[0026] Further, the state quantities include: lateral error, rate of change of lateral error, heading angle error, and rate of change of heading angle error. The step of calculating the vehicle's state quantities based on the reference path includes:

[0027] On the reference path, determine the path projection point that is closest to the center of gravity of the vehicle, and calculate the lateral error and the rate of change of the lateral error based on the center of gravity and the path projection point.

[0028] The heading angle error and the rate of change of the heading angle error are calculated based on the first included angle and the second included angle.

[0029] Furthermore, the step of determining the feedback front wheel steering angle based on the linear quadratic regulator theory, the error state-space equation, and the state variables includes:

[0030] Based on the linear quadratic regulator theory, the error state space equation, the predefined weight matrix, and the weights corresponding to the steering wheel angles of the vehicle, the Riccati equation is determined. The weight matrix includes the weights corresponding to the lateral error, the weights corresponding to the rate of change of the lateral error, the weights corresponding to the heading angle error, and the weights corresponding to the rate of change of the heading angle error.

[0031] The Riccati equation is iterated to obtain the output of the iterated Riccati equation, and the feedback gain rate is determined based on the output.

[0032] The front wheel angle is determined based on the state variables and the feedback gain rate.

[0033] Further, the step of determining the curvature feedforward angle and the cross slope feedforward angle based on the feedback front wheel steering angle and the error state space equation, and determining the target front wheel steering angle of the vehicle based on the feedback front wheel steering angle, the curvature feedforward angle, and the cross slope feedforward angle, and controlling the vehicle to steer using the target front wheel steering angle includes:

[0034] Based on the error state space equation and the feedback front wheel steering angle, calculate the curvature feedforward steering angle and the cross slope feedforward steering angle;

[0035] The curvature feedforward angle, the cross slope feedforward angle, and the feedback front wheel angle are added together to obtain the target front wheel angle of the vehicle, and the vehicle is controlled to steer using the target front wheel angle.

[0036] Furthermore, to achieve the above objectives, the present invention also provides a lateral control device for autonomous vehicles in complex road environments, the lateral control device for autonomous vehicles in complex road environments comprising:

[0037] The construction module is used to perform force analysis on a vehicle traveling on a complex road to obtain the vehicle's parameter information, and to construct a linear two-degree-of-freedom vehicle model based on the parameter information.

[0038] The calculation module is used to construct the error state space equation based on the linear two-degree-of-freedom vehicle model and the preset reference path, and to calculate the state variables of the vehicle based on the reference path.

[0039] The determination module is used to determine the feedback front wheel angle based on the linear quadratic regulator theory, the error state space equation, and the state variables.

[0040] The control module is used to determine the curvature feedforward angle and the cross slope feedforward angle based on the feedback front wheel steering angle and the error state space equation, and to determine the target front wheel steering angle of the vehicle based on the feedback front wheel steering angle, the curvature feedforward angle and the cross slope feedforward angle, and to control the vehicle to steer using the target front wheel steering angle.

[0041] Furthermore, to achieve the above objectives, the present invention also provides a lateral control device for autonomous vehicles in complex road environments. The lateral control device for autonomous vehicles in complex road environments includes: a memory, a processor, and a lateral control program for autonomous vehicles in complex road environments stored in the memory and executable on the processor. When the lateral control program for autonomous vehicles in complex road environments is executed by the processor, it implements the steps of the aforementioned lateral control method for autonomous vehicles in complex road environments.

[0042] Furthermore, to achieve the above objectives, the present invention also provides a computer-readable storage medium storing a lateral control program for an autonomous vehicle in a complex road environment, wherein the lateral control program for an autonomous vehicle in a complex road environment, when executed by a processor, implements the steps of the aforementioned lateral control method for an autonomous vehicle in a complex road environment.

[0043] This invention analyzes the forces acting on a vehicle traveling on complex roads to obtain its parameter information. Based on this information, a linear two-degree-of-freedom vehicle model is constructed. Then, based on this model and a preset reference path, an error state-space equation is built. The vehicle's state variables are calculated using the reference path. Following this, the feedback front wheel steering angle is determined using linear quadratic regulator theory, the error state-space equation, and the state variables. Based on this feedback front wheel steering angle and the error state-space equation, the curvature feedforward steering angle and cross-hill feedforward steering angle are determined. Finally, the target front wheel steering angle is determined, and the vehicle is controlled to steer using this target angle. This invention can determine a suitable target front wheel steering angle for a vehicle traveling on complex roads and control the vehicle to steer using this target angle, enabling the vehicle to accurately follow a predetermined reference path while safely navigating complex roads. Attached Figure Description

[0044] Figure 1 This is a schematic diagram of the structure of the lateral control device for autonomous vehicles in complex road environments within the hardware operating environment involved in the embodiments of the present invention;

[0045] Figure 2 This is a flowchart illustrating the first embodiment of the lateral control method for autonomous vehicles in complex road environments according to the present invention.

[0046] Figure 3 This is a functional module diagram of an embodiment of the lateral control device for autonomous vehicles in complex road environments according to the present invention.

[0047] Figure 4This is a vehicle analysis diagram of an embodiment of the lateral control device for autonomous vehicles in complex road environments according to the present invention.

[0048] Figure 5 This is a vehicle analysis diagram of an embodiment of the lateral control device for autonomous vehicles in complex road environments according to the present invention.

[0049] Figure 6 This is a vehicle analysis diagram of an embodiment of the lateral control device for autonomous vehicles in complex road environments according to the present invention.

[0050] Figure 7 This is a vehicle analysis diagram of an embodiment of the lateral control device for autonomous vehicles in complex road environments according to the present invention.

[0051] The realization of the objective, functional features and advantages of the present invention will be further explained in conjunction with the embodiments and with reference to the accompanying drawings. Detailed Implementation

[0052] It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.

[0053] like Figure 1 As shown, Figure 1 This is a schematic diagram of the structure of the lateral control device for autonomous vehicles in complex road environments within the hardware operating environment involved in the embodiments of the present invention.

[0054] The lateral control device for autonomous vehicles in complex road environments according to embodiments of the present invention can be a PC, an industrial control computer, an autonomous driving domain controller, or other terminal devices.

[0055] like Figure 1 As shown, the lateral control device for autonomous vehicles in complex road environments may include: a processor 1001, such as a CPU; a network interface 1004; a user interface 1003; a memory 1005; and a communication bus 1002. The communication bus 1002 is used to establish communication between these components. The user interface 1003 may include a display screen or an input unit such as a keyboard; optionally, the user interface 1003 may also include a standard wired interface or a wireless interface. The network interface 1004 may optionally include a standard wired interface or a wireless interface (such as a Wi-Fi interface). The memory 1005 may be high-speed RAM or non-volatile memory, such as a disk drive. Optionally, the memory 1005 may also be a storage device independent of the aforementioned processor 1001.

[0056] Optionally, the lateral control device for autonomous vehicles in complex road environments may also include cameras, RF (Radio Frequency) circuits, sensors, audio circuits, WiFi modules, etc. Among these, sensors include light sensors, motion sensors, and other sensors. Specifically, light sensors may include ambient light sensors and proximity sensors. The ambient light sensor can adjust the brightness of the display screen according to the ambient light level, and the proximity sensor can turn off the display screen and / or backlight when the lateral control device is moved near the ear. As a type of motion sensor, a gravity acceleration sensor can detect the magnitude of acceleration in various directions (generally three axes). When stationary, it can detect the magnitude and direction of gravity, and can be used for applications that identify the posture of the lateral control device in complex road environments (such as landscape / portrait switching, related games, magnetometer posture calibration), vibration recognition functions (such as pedometers, tapping), etc. Of course, the lateral control device for autonomous vehicles in complex road environments may also be equipped with other sensors such as gyroscopes, barometers, hygrometers, thermometers, and infrared sensors, which will not be elaborated here.

[0057] Those skilled in the art will understand that Figure 1 The terminal structure shown does not constitute a limitation on the terminal and may include more or fewer components than shown, or combine certain components, or have different component arrangements.

[0058] like Figure 1 As shown, the memory 1005, which serves as a computer storage medium, may include an operating system, a network communication module, a user interface module, and a lateral control program for autonomous vehicles operating in complex road environments.

[0059] exist Figure 1 In the terminal shown, the network interface 1004 is mainly used to connect to the backend server and communicate with the backend server; the user interface 1003 is mainly used to connect to the client (user terminal) and communicate with the client; and the processor 1001 can be used to call the lateral control program for autonomous vehicles in complex road environments stored in the memory 1005.

[0060] In this embodiment, the lateral control device for autonomous vehicles in complex road environments includes: a memory 1005, a processor 1001, and a lateral control program for autonomous vehicles in complex road environments stored in the memory 1005 and executable on the processor 1001. When the processor 1001 calls the lateral control program for autonomous vehicles in complex road environments stored in the memory 1005, it executes the steps of the lateral control methods for autonomous vehicles in complex road environments in the following embodiments.

[0061] This invention also provides a lateral control method for autonomous vehicles in complex road environments, referring to... Figure 2 , Figure 2 This is a flowchart illustrating the first embodiment of the method of the present invention.

[0062] In this embodiment, the lateral control method for autonomous vehicles in complex road environments includes the following steps:

[0063] Step S101: Perform force analysis on the vehicle traveling on the complex road to obtain the parameter information of the vehicle, and construct a linear two-degree-of-freedom vehicle model based on the parameter information.

[0064] In this embodiment, firstly, a force analysis is performed on a vehicle traveling on a complex road to obtain parameter information related to the vehicle's state. Complex roads refer to road sections with high curvature and large cross slopes. Specifically, for example, Figure 4 As shown, the parameter information includes g, m, and I. z v x v y , and These are, respectively, gravitational acceleration, vehicle mass, vehicle moment of inertia, vehicle longitudinal velocity, vehicle lateral velocity, vehicle yaw rate, vehicle yaw acceleration, and the first lateral force F on the front wheels. yf And the second lateral force F of the rear wheel yr , l f It refers to the distance from the vehicle's center of gravity to the front axle. r This refers to the distance from the vehicle's center of gravity to the rear axle, δ. f Indicates the front wheel steering angle, such as Figure 5 As shown, G y It is the component of the vehicle's weight along the cross slope, and its calculation method is as follows: G y =mgsin(φ) Then, based on the obtained parameter information, a linear two-degree-of-freedom vehicle model is constructed. Specifically, the parameter information can be used to obtain equation (1).

[0065] Then, the first lateral force of the front wheel and the second lateral force of the rear wheel are calculated using equation (2), F. yf =2C f α f F yr =2C r αr ， (2) In the formula, C f and C r The lateral stiffness of the front and rear wheels, α, are respectively. r and α f These represent the slip angles of the front and rear wheels, respectively.

[0066] α r and α f It can be calculated using equation (3). (3) where δ f Indicates the front wheel steering angle, such as Figure 4 As shown, substituting equations (2) and (3) into equation (1) yields equation (4). Equation (4) is the linear two-degree-of-freedom vehicle model.

[0067] Further, in one embodiment, step S101 includes:

[0068] Step a, obtain the third vehicle parameters, wherein the third vehicle parameters include the first distance from the vehicle's center of gravity to the front axle of the vehicle, the second distance from the vehicle's center of gravity to the rear axle of the vehicle, the vehicle's mass, and the vehicle's moment of inertia;

[0069] Step b: Perform force analysis on the tires of the vehicle to obtain the second vehicle parameters corresponding to the vehicle. The second vehicle parameters include the longitudinal velocity of the vehicle, the lateral velocity of the vehicle, the yaw rate of the vehicle, the yaw acceleration of the vehicle, the front wheel steering angle, and the component of the vehicle's weight along the cross slope.

[0070] Step c: Perform force analysis based on the third vehicle parameters and the second vehicle parameters to obtain the first vehicle parameters corresponding to the vehicle, wherein the first vehicle parameters include the first lateral force of the front wheel and the second lateral force of the rear wheel;

[0071] Step d: Construct a linear two-degree-of-freedom vehicle model based on the parameter information and the third vehicle parameters.

[0072] In this embodiment, firstly, third vehicle parameters are obtained, including the first distance from the vehicle's center of mass to the front axle, the second distance from the vehicle's center of mass to the rear axle, the vehicle's mass, and the vehicle's moment of inertia. Force analysis is performed on the vehicle's tires to obtain corresponding second vehicle parameters, including the vehicle's longitudinal velocity, lateral velocity, yaw rate, front wheel angle, and yaw acceleration. Then, force analysis is performed based on the third and second vehicle parameters to obtain corresponding first vehicle parameters. Finally, a linear two-degree-of-freedom vehicle model is constructed based on the parameter information and the third vehicle parameters, as shown in equation (4).

[0073] Specifically, the third vehicle parameters include m as the vehicle's mass, I... z The vehicle's moment of inertia, l f It refers to the distance from the vehicle's center of gravity to the front axle.r This refers to the distance from the vehicle's center of gravity to the rear axle. The second vehicle parameter includes v. x v y , and The vehicle's longitudinal velocity, lateral velocity, yaw rate, yaw acceleration, and front wheel steering angle δ are respectively measured. f The first vehicle parameter includes the first lateral force F of the front wheels. yf And the second lateral force F of the rear wheel yr The component of the vehicle's weight along the cross slope, G y .

[0074] Further, in one embodiment, step c includes:

[0075] Step c1: Determine the sideslip angle of the front wheels based on the vehicle's center of gravity sideslip angle, the front wheel steering angle, the first distance, the longitudinal velocity, and the yaw rate.

[0076] Step c2: Determine the rear wheel slip angle based on the vehicle's center of gravity sideslip angle, the second distance, longitudinal velocity, and yaw rate.

[0077] Step c3: Determine the first lateral force of the front wheel based on the lateral stiffness and lateral angle of the front wheel;

[0078] Step c4: Determine the second lateral force of the rear wheel based on the lateral stiffness and lateral angle of the rear wheel.

[0079] In this embodiment, the vehicle parameter information also includes: the first lateral force of the front wheel and the second lateral force of the rear wheel. Then, the sideslip angle of the front wheel can be determined based on the vehicle's center of gravity sideslip angle, front wheel turning angle, first distance, longitudinal velocity, and yaw rate. The sideslip angle of the rear wheel can be determined based on the vehicle's center of gravity sideslip angle, second distance, longitudinal velocity, and yaw rate, specifically as shown in equation (3). After obtaining the sideslip angles of the front wheel and the rear wheel, the first lateral force of the front wheel is determined based on the side stiffness of the front wheel and the sideslip angle of the front wheel. The second lateral force of the rear wheel is determined based on the side stiffness of the rear wheel and the sideslip angle of the rear wheel, specifically as shown in equation (2).

[0080] Step S102: Based on the linear two-degree-of-freedom vehicle model and the preset reference path, construct the error state space equation, and calculate the vehicle's state variables based on the reference path.

[0081] In this embodiment, firstly, the error state space equation is constructed using the pre-built linear two-degree-of-freedom vehicle model and a preset reference path. After constructing the error state space equation, the vehicle's state variables are calculated using the reference path. These state variables include lateral error, the rate of change of lateral error, heading angle error, and the rate of change of heading angle error.

[0082] Furthermore, in one embodiment, the step of constructing the error state space equation based on a linear two-degree-of-freedom vehicle model and a preset reference path includes:

[0083] Step e: Determine the equation corresponding to the heading angle error based on the first included angle and the second included angle, wherein the first included angle is the angle between the vehicle's velocity direction and the X-axis in the preset vehicle coordinate system, and the second included angle is the angle between the tangent of the path projection point closest to the vehicle's center of mass and the X-axis.

[0084] Step f: The distance between the vehicle's center of mass and the projection point of the reference path is taken as the lateral error, and the equations corresponding to the rate of change of the lateral error and the rate of change of the heading angle error are determined.

[0085] Step g: Based on the longitudinal velocity of the vehicle and the second included angle, determine the equations corresponding to the acceleration of the lateral error and the acceleration of the heading angle error.

[0086] Step h involves substituting the equations corresponding to the heading angle error, the rate of change of the heading angle error, the rate of change of the lateral error, the acceleration of the lateral error, and the acceleration of the heading angle error into the linear two-degree-of-freedom vehicle model to construct the error state space equations.

[0087] In this embodiment, the first included angle is the angle between the vehicle's velocity direction and a preset X-axis, and the second included angle is the angle between the tangent of the vehicle's center of mass at the projection point of the reference path and the X-axis, such as... Figure 6 As shown, the first included angle ψ is the angle between the vehicle's velocity direction and the X-axis of the vehicle coordinate system. Specifically, counterclockwise can be considered positive and clockwise negative. The second included angle ψ ref Let the angle between the tangent line to the projection point of the vehicle's center of gravity onto the reference path and the aforementioned X-axis be defined, with counterclockwise as positive. Let the lateral error be e. y This is the distance between the vehicle's center of gravity and the path projection point of the reference path; specifically, it can be positive if the vehicle is on the left side of the path. Heading angle error e ψ The heading angle is the difference between the vehicle's heading angle and the heading angle of the reference path point; that is, the first included angle is the vehicle's heading angle, and the second included angle is the reference path point's heading angle, i.e., the heading angle error e. ψ e can be calculated using equation (5) ψ=ψ-ψ ref (5).

[0088] Next, the equations corresponding to the rate of change of the lateral error and the rate of change of the heading angle error are determined. Specifically,

[0089] Then, based on the vehicle's longitudinal velocity and the second included angle, an analysis is performed to determine the equations corresponding to the acceleration of the lateral error and the acceleration of the heading angle error. Specifically, it is assumed that the vehicle's longitudinal velocity remains constant, i.e., v x If constant, then Simultaneously, assume a reference yaw acceleration. We can obtain: Finally, the equations corresponding to the heading angle error, the rate of change of the heading angle error, the rate of change of the lateral error, the acceleration of the lateral error, and the acceleration of the heading angle error are substituted into the linear two-degree-of-freedom vehicle model to construct the error state space equation. Specifically, by substituting equations (5), (6), and (7) into equation (4), the error state space equation shown in equation (8) can be obtained.

[0090]

[0091] To facilitate recording and expression, And denoted as equation (9),

[0092] The error state space equation is then denoted as equation (10):

[0093] Further, in one embodiment, the step of calculating the vehicle's state variables based on the reference path includes:

[0094] Step i: Determine the path projection point on the reference path that is closest to the center of gravity of the vehicle, and calculate the lateral error and the rate of change of the lateral error based on the center of gravity and the path projection point.

[0095] Step j: Calculate the heading angle error and the rate of change of the heading angle error based on the first included angle and the second included angle.

[0096] In this embodiment, it should be noted that the target parameters include: lateral error, rate of change of lateral error, heading angle error, and rate of change of heading angle error. The path projection point closest to the vehicle's center of gravity is determined on the reference path. Based on the center of gravity and the path projection point, the lateral error and its rate of change are calculated. Based on the first included angle and the second included angle, the heading angle error and its rate of change are calculated.

[0097] Specifically, such as Figure 7 As shown, the projection point matching the vehicle's position in the reference path is calculated. The calculation method involves traversing the reference path points and finding the point closest to the vehicle's centroid, which is the path projection point. The vehicle's centroid is denoted as P. ego (x ego ,y ego ,ψ ego The path projection point is P. proj (x proj ,y proj ,ψ proj ).

[0098] Let ΔX and ΔY be the differences between the vehicle's center of mass and the projection point of the reference path in the vehicle coordinate system, respectively. Their calculation method is as follows:

[0099]

[0100] If we define the lateral error as positive when a vehicle is on the left side of the road and negative when it is on the right side of the road, then the lateral error can be calculated by the following formula.

[0101] e y =ΔYcos(ψ) proj )-ΔXsin(ψ proj (12)

[0102] The rate of change of the lateral error at time k+1 The calculation method is as follows:

[0103]

[0104] Defining the heading angle error as positive when the vehicle is facing outwards and negative when it is facing inwards, the heading angle error can be calculated using the following formula. Figure 7 For example, the vehicle's heading angle ψ ego Greater than the road direction angle ψ proj Therefore, the heading angle error is positive. The heading angle error is determined based on the difference between the first included angle and the second included angle. Figure 7 As shown, the first included angle is the vehicle's heading angle ψ. ego The second included angle is the road direction angle ψ. proj .

[0105]

[0106] Rate of change of heading angle error at time k+1 The calculation method is as follows:

[0107]

[0108] The front wheel steering angle can be accurately obtained by using four state change quantities: lateral error, rate of change of lateral error, heading angle error, and rate of change of heading angle error.

[0109] Step S103: Determine the feedback front wheel angle based on the linear quadratic regulator theory, the error state space equation, and the state variables;

[0110] In this embodiment, firstly, the Riccati equation is determined by the linear quadratic regulator theory, the error state space equation, and the state variables. Then, the feedback gain rate is determined by the Riccati equation, and the feedback front wheel angle is determined based on the feedback gain rate and the state variables.

[0111] Furthermore, in one embodiment, step S103 further includes:

[0112] Step k: Determine the Riccati equation based on the linear quadratic regulator theory, the error state space equation, the predefined weight matrix, and the weights corresponding to the steering wheel angles of the vehicle. The weight matrix includes the weights corresponding to the lateral error, the weights corresponding to the rate of change of the lateral error, the weights corresponding to the heading angle error, and the weights corresponding to the rate of change of the heading angle error.

[0113] Step 1: Iterate the Riccati equation to obtain the output of the iterated Riccati equation, and determine the feedback gain rate based on the output.

[0114] Step m: Determine the front wheel angle based on the state variables and the feedback gain rate.

[0115] In this embodiment, the Riccati equation is determined based on the error state space equation, a predefined weight matrix, and the weights corresponding to the steering angles of the vehicle's steering wheels. The weight matrix includes weights corresponding to lateral errors, the rate of change of lateral errors, the heading angle error, and the rate of change of the heading angle error. Specifically, the predefined weight matrix Q and the weights R corresponding to the steering angles of the vehicle's steering wheels are represented as follows:

[0116]

[0117] R = r

[0118] Where q1, q2, q3, and q4 are the weights corresponding to the lateral error, the rate of change of the lateral error, the heading angle error, and the rate of change of the heading angle error, respectively. The larger the value, the greater the importance attached to it, and the larger the final value will be, thus the higher the degree of control of the system over this state variable. R is the input to the state-space equation, that is, the weight corresponding to the steering wheel angle of the vehicle. The larger the weight R, the greater the restriction on the steering wheel angle, that is, the smaller the steering angle.

[0119] Next, the Riccati equation is iterated to obtain the output of the iterated Riccati equation. Based on the output, the feedback gain ratio is determined. Specifically, the Riccati equation is:

[0120] Among them, A d =e AT ≈I+AT, where I is the identity matrix. C d =CT and T are the sampling periods. The Riccati equation is iterated to obtain P. t+1 and P t+1 Substitute into the formula From this, the feedback gain ratio K can be obtained. The negative value of the product of the state variable and the feedback gain ratio is taken as the front wheel rotation angle, specifically, it can be expressed as δ. fb =-KX.

[0121] Step S104: Based on the feedback front wheel steering angle and the error state space equation, determine the curvature feedforward steering angle and the cross slope feedforward steering angle, and based on the feedback front wheel steering angle, the curvature feedforward steering angle and the cross slope feedforward steering angle, determine the target front wheel steering angle of the vehicle, and control the vehicle to steer using the target front wheel steering angle.

[0122] In this embodiment, the curvature feedforward angle and cross slope feedforward angle are obtained through the error state-space equation and the feedback front wheel steering angle. The target front wheel steering angle is determined by the feedback front wheel steering angle, curvature feedforward angle, and cross slope feedforward angle. Specifically, the feedback front wheel steering angle is δ. fb Curvature feedforward rotation angle δ ff1 and cross slope feedforward rotation angle δ ff2 The target front wheel steering angle is δ target δ target The calculation method is: δ target =δ fb +δ ff1 +δ ff2 .

[0123] It should also be noted that, for safety reasons, after obtaining the target front wheel steering angle, a limit on the rate of change of the target front wheel steering angle needs to be imposed. Let the front wheel steering angle at time k be denoted as δ. k δ k This is the target front wheel steering angle, and the change amplitude of the front wheel steering angle in adjacent cycles is Δδ. Therefore, the corrected front wheel steering angle at this moment is:

[0124]

[0125] For safety reasons, the steering angle of the target's front wheels needs to be limited. Let the maximum front wheel steering angle be δ. max The minimum front wheel steering angle is δmin Therefore, the corrected front wheel steering angle at this moment is:

[0126]

[0127] Furthermore, in one embodiment, step S104 further includes:

[0128] Step n: Calculate the curvature feedforward angle and the cross slope feedforward angle based on the error state space equation and the feedback front wheel steering angle;

[0129] Step o involves adding the curvature feedforward angle, the cross slope feedforward angle, and the feedback front wheel angle to obtain the target front wheel angle of the vehicle, and then controlling the vehicle to steer using the target front wheel angle.

[0130] In this embodiment, the predefined curvature feedforward angle and the predefined cross slope feedforward angle are calculated based on the error state space equation and the feedback front wheel angle.

[0131] Specifically, for road sections with large curvature, a predefined curvature feedforward turning angle δ is introduced. ff1 Ignoring the influence of road cross slope, δ fb With δ fb Substituting -KX into the error state space equation (10), we get the following equation:

[0132]

[0133] For a fixed-circle motion, when a steady state is reached, let Therefore, we can conclude that:

[0134] right By solving the matrix, we can obtain:

[0135] Where K1 is the first term of the feedback gain ratio K, and K3 is the third term of the feedback gain ratio K.

[0136] To ensure that the lateral error of the autonomous vehicle is zero on a curve with high curvature, let X ss1 The first row of the matrix is ​​0, from which the curvature feedforward rotation angle can be calculated:

[0137] Where ρ is the road curvature, that is: This step will greatly improve the accuracy of vehicle tracking at road ends with high curvature.

[0138] For road sections with large cross slopes, a cross slope feedforward turning angle δ is introduced. ff2 Ignoring the effect of curvature, δ ff2 With δ fbSubstituting -KX into the error state-space equation, we get the following equation:

[0139]

[0140] For a road section with a fixed cross slope, when a steady state is reached, let Then we can obtain X ss2 =(A-BK) -1 [Bδ ff2 +D sin(φ)]

[0141] For X ss2 =(A-BK) -1 [Bδ ff2 Solve the matrix using +D sin(φ)].

[0142]

[0143] Where K1 is the first term of the feedback gain ratio K, and K3 is the third term of the feedback gain ratio K.

[0144] Next, to ensure the autonomous vehicle maintains a lateral error of 0 on the steep cross slope, let X... ss2 The first term is 0, that is:

[0145]

[0146] We can solve for:

[0147]

[0148] This step will greatly improve the accuracy of vehicle tracking at the end of a steep cross slope.

[0149] By analyzing vehicles traveling on the road, corner compensation is applied to sections with large curvature and large cross slopes, enabling autonomous vehicles to accurately track their tracks even on rough roads, resulting in higher control precision and better control performance.

[0150] This embodiment proposes a lateral control method for autonomous vehicles in complex road environments. It analyzes the forces acting on the vehicle on complex roads to obtain its parameter information. Based on this parameter information, a linear two-degree-of-freedom vehicle model is constructed. Then, based on the linear two-degree-of-freedom vehicle model and a preset reference path, an error state-space equation is constructed. The vehicle's state variables are calculated based on the reference path. Then, based on linear quadratic regulator theory, the error state-space equation, and the state variables, the feedback front wheel steering angle is determined. Based on the feedback front wheel steering angle and the error state-space equation, the curvature feedforward steering angle and cross-hill feedforward steering angle are determined. Finally, based on the feedback front wheel steering angle, curvature feedforward angle, and cross-hill feedforward angle, the target front wheel steering angle of the vehicle is determined, and the vehicle is controlled to steer using the target front wheel steering angle. This method can determine a suitable target front wheel steering angle for vehicles traveling on complex roads and control the vehicle to steer using the target front wheel steering angle, enabling the vehicle to accurately track the predetermined reference path while safely navigating complex roads.

[0151] The present invention also provides a lateral control device for autonomous vehicles in complex road environments, referring to... Figure 3 The lateral control device for autonomous vehicles in complex road environments includes:

[0152] The construction module 10 is used to perform force analysis on a vehicle traveling on a complex road to obtain the vehicle's parameter information, and to construct a linear two-degree-of-freedom vehicle model based on the parameter information.

[0153] The calculation module 20 is used to construct the error state space equation based on the linear two-degree-of-freedom vehicle model and the preset reference path, and to calculate the state variables of the vehicle based on the reference path.

[0154] The determination module 30 is used to determine the feedback front wheel angle based on the linear quadratic regulator theory, the error state space equation, and the state quantity.

[0155] The control module 40 is used to determine the curvature feedforward angle and the cross slope feedforward angle based on the feedback front wheel steering angle and the error state space equation, and to determine the target front wheel steering angle of the vehicle based on the feedback front wheel steering angle, the curvature feedforward angle and the cross slope feedforward angle, and to control the vehicle to steer using the target front wheel steering angle.

[0156] Furthermore, module 10 is also used for:

[0157] Obtain third vehicle parameters, wherein the third vehicle parameters include a first distance from the vehicle's center of gravity to the front axle of the vehicle, a second distance from the vehicle's center of gravity to the rear axle of the vehicle, the vehicle's mass, and the vehicle's moment of inertia;

[0158] Force analysis is performed on the tires of the vehicle to obtain the second vehicle parameters corresponding to the vehicle. The second vehicle parameters include the longitudinal velocity of the vehicle, the lateral velocity of the vehicle, the yaw rate of the vehicle, the yaw acceleration of the vehicle, the front wheel steering angle, and the component of the vehicle's gravity along the cross slope.

[0159] Force analysis is performed based on the third vehicle parameters and the second vehicle parameters to obtain the first vehicle parameters corresponding to the vehicle, wherein the first vehicle parameters include the first lateral force of the front wheel and the second lateral force of the rear wheel.

[0160] Based on the parameter information and the third vehicle parameters, a linear two-degree-of-freedom vehicle model is constructed.

[0161] Furthermore, module 10 is also used for:

[0162] The sideslip angle of the front wheels is determined based on the vehicle's center of gravity sideslip angle, the front wheel steering angle, the first distance, the longitudinal velocity, and the yaw rate.

[0163] The rear wheel slip angle is determined based on the vehicle's center of gravity sideslip angle, the second distance, the longitudinal velocity, and the yaw rate.

[0164] The first lateral force of the front wheel is determined based on the lateral stiffness and lateral angle of the front wheel.

[0165] The second lateral force of the rear wheel is determined based on the lateral stiffness and lateral angle of the rear wheel.

[0166] Furthermore, the computing module 20 is also used for:

[0167] Based on the first included angle and the second included angle, the equation corresponding to the heading angle error is determined, wherein the first included angle is the angle between the vehicle's velocity direction and the X-axis in the preset vehicle coordinate system, and the second included angle is the angle between the tangent of the path projection point closest to the vehicle's center of mass and the X-axis.

[0168] The distance between the vehicle's center of mass and the projection point of the reference path is taken as the lateral error, and the equations corresponding to the rate of change of the lateral error and the rate of change of the heading angle error are determined.

[0169] Based on the vehicle's longitudinal velocity and the second included angle, determine the equations corresponding to the acceleration of the lateral error and the acceleration of the heading angle error;

[0170] The equations corresponding to the heading angle error, the rate of change of the heading angle error, the rate of change of the lateral error, the acceleration of the lateral error, and the acceleration of the heading angle error are substituted into the linear two-degree-of-freedom vehicle model to construct the error state space equations.

[0171] Furthermore, the computing module 20 is also used for:

[0172] On the reference path, determine the path projection point that is closest to the center of gravity of the vehicle, and calculate the lateral error and the rate of change of the lateral error based on the center of gravity and the path projection point.

[0173] The heading angle error and the rate of change of the heading angle error are calculated based on the first included angle and the second included angle.

[0174] Furthermore, module 30 is also used for:

[0175] Based on the linear quadratic regulator theory, the error state space equation, the predefined weight matrix, and the weights corresponding to the steering wheel angles of the vehicle, the Riccati equation is determined. The weight matrix includes the weights corresponding to the lateral error, the weights corresponding to the rate of change of the lateral error, the weights corresponding to the heading angle error, and the weights corresponding to the rate of change of the heading angle error.

[0176] The Riccati equation is iterated to obtain the output of the iterated Riccati equation, and the feedback gain rate is determined based on the output.

[0177] The front wheel angle is determined based on the state variables and the feedback gain rate.

[0178] Furthermore, the control module 40 is also used for:

[0179] Based on the error state space equation and the feedback front wheel steering angle, calculate the curvature feedforward steering angle and the cross slope feedforward steering angle;

[0180] The curvature feedforward angle, the cross slope feedforward angle, and the feedback front wheel angle are added together to obtain the target front wheel angle of the vehicle, and the vehicle is controlled to steer using the target front wheel angle.

[0181] The methods executed by the above-mentioned program units can be referred to in the various embodiments of the lateral control method for autonomous vehicles in complex road environments of the present invention, which will not be repeated here.

[0182] Furthermore, this invention also proposes a lateral control device for autonomous vehicles in complex road environments. The lateral control device for autonomous vehicles in complex road environments includes: a memory, a processor, and a lateral control program for autonomous vehicles in complex road environments stored in the memory and executable on the processor. When the lateral control program for autonomous vehicles in complex road environments is executed by the processor, it implements the steps of the lateral control method for autonomous vehicles in complex road environments as described above.

[0183] Furthermore, this embodiment of the invention also proposes a computer-readable storage medium storing a lateral control program for an autonomous vehicle in a complex road environment. When the lateral control program for an autonomous vehicle in a complex road environment is executed by a processor, it implements the steps of the lateral control method for an autonomous vehicle in a complex road environment as described above.

[0184] It should be noted that, in this document, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or system that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or system. Unless otherwise specified, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or system that includes that element.

[0185] The sequence numbers of the above embodiments of the present invention are for descriptive purposes only and do not represent the superiority or inferiority of the embodiments.

[0186] Through the above description of the embodiments, those skilled in the art can clearly understand that the methods of the above embodiments can be implemented by means of software plus necessary general-purpose hardware platforms. Of course, they can also be implemented by hardware, but in many cases the former is a better implementation method. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product is stored in a storage medium (such as ROM / RAM, magnetic disk, optical disk) as described above, and includes several instructions to cause a terminal device (which may be a mobile phone, computer, server, air conditioner, or network device, etc.) to execute the methods described in the various embodiments of the present invention.

[0187] The above are merely preferred embodiments of the present invention and do not limit the scope of the patent. Any equivalent structural or procedural transformations made based on the description and drawings of the present invention, or direct or indirect applications in other related technical fields, are similarly included within the scope of patent protection of the present invention.

Claims

1. A lateral control method for autonomous vehicles in complex road environments, characterized in that, The lateral control method for autonomous vehicles in complex road environments includes the following steps: Force analysis is performed on a vehicle traveling on a complex road to obtain the vehicle's parameter information, and a linear two-degree-of-freedom vehicle model is constructed based on the parameter information. Based on the linear two-degree-of-freedom vehicle model and the preset reference path, an error state space equation is constructed, and the vehicle's state variables are calculated based on the reference path. Based on the linear quadratic regulator theory, the error state space equation, and the state variables, the feedback front wheel steering angle is determined. Based on the feedback front wheel steering angle and the error state space equation, the curvature feedforward steering angle and the cross slope feedforward steering angle are determined. Then, based on the feedback front wheel steering angle, the curvature feedforward steering angle, and the cross slope feedforward steering angle, the target front wheel steering angle of the vehicle is determined, and the vehicle is controlled to steer using the target front wheel steering angle. Wherein, the curvature feedforward steering angle is: , For road curvature, It refers to the distance from the vehicle's center of gravity to the front axle. It refers to the distance from the vehicle's center of gravity to the rear axle. and These are the vehicle's mass and its longitudinal speed, respectively. and These are the lateral stiffness of the front and rear wheels, respectively. It is the feedback gain rate. The third item; the cross slope feedforward rotation angle is: g is the acceleration due to gravity.

2. The lateral control method for autonomous vehicles in complex road environments as described in claim 1, characterized in that, The parameter information includes first vehicle parameters and second vehicle parameters. The step of performing force analysis on a vehicle traveling on complex roads to obtain the vehicle's parameter information, and constructing a linear two-degree-of-freedom vehicle model based on the parameter information, includes: Obtain third vehicle parameters, wherein the third vehicle parameters include a first distance from the vehicle's center of gravity to the front axle of the vehicle, a second distance from the vehicle's center of gravity to the rear axle of the vehicle, the vehicle's mass, and the vehicle's moment of inertia; Force analysis is performed on the tires of the vehicle to obtain the second vehicle parameters corresponding to the vehicle. The second vehicle parameters include the longitudinal velocity of the vehicle, the lateral velocity of the vehicle, the yaw rate of the vehicle, the yaw acceleration of the vehicle, the front wheel steering angle, and the component of the vehicle's gravity along the cross slope. Force analysis is performed based on the third vehicle parameters and the second vehicle parameters to obtain the first vehicle parameters corresponding to the vehicle, wherein the first vehicle parameters include the first lateral force of the front wheel and the second lateral force of the rear wheel. Based on the parameter information and the third vehicle parameters, a linear two-degree-of-freedom vehicle model is constructed.

3. The lateral control method for autonomous vehicles in complex road environments as described in claim 2, characterized in that, The step of performing force analysis based on the third vehicle parameters and the second vehicle parameters to obtain the first vehicle parameters corresponding to the vehicle includes: The front wheel slip angle is determined based on the vehicle's center of gravity sideslip angle, the front wheel steering angle, the first distance, the longitudinal velocity, and the yaw rate. The rear wheel slip angle is determined based on the vehicle's center of gravity sideslip angle, the second distance, the longitudinal velocity, and the yaw rate. The first lateral force of the front wheel is determined based on the lateral stiffness and lateral angle of the front wheel. The second lateral force of the rear wheel is determined based on the lateral stiffness and lateral angle of the rear wheel.

4. The lateral control method for autonomous vehicles in complex road environments as described in claim 1, characterized in that, The step of constructing the error state space equation based on the linear two-degree-of-freedom vehicle model and the preset reference path includes: Based on the first included angle and the second included angle, the equation corresponding to the heading angle error is determined, wherein the first included angle is the angle between the vehicle's velocity direction and the X-axis in the preset vehicle coordinate system, and the second included angle is the angle between the tangent of the path projection point closest to the vehicle's center of mass and the X-axis. The distance between the vehicle's center of mass and the projection point of the reference path is taken as the lateral error, and the equations corresponding to the rate of change of the lateral error and the rate of change of the heading angle error are determined. Based on the vehicle's longitudinal velocity and the second included angle, determine the equations corresponding to the acceleration of the lateral error and the acceleration of the heading angle error; The equations corresponding to the heading angle error, the rate of change of the heading angle error, the rate of change of the lateral error, the acceleration of the lateral error, and the acceleration of the heading angle error are substituted into the linear two-degree-of-freedom vehicle model to construct the error state space equations.

5. The lateral control method for autonomous vehicles in complex road environments as described in claim 4, characterized in that, The state variables include: lateral error, rate of change of lateral error, heading angle error, and rate of change of heading angle error. The step of calculating the vehicle's state variables based on the reference path includes: On the reference path, determine the path projection point closest to the center of gravity of the vehicle, and calculate the lateral error and the rate of change of the lateral error based on the center of gravity and the path projection point. The heading angle error and the rate of change of the heading angle error are calculated based on the first included angle and the second included angle.

6. The lateral control method for autonomous vehicles in complex road environments as described in claim 5, characterized in that, The step of determining the feedback front wheel steering angle based on the linear quadratic regulator theory, the error state-space equation, and the state variables includes: Based on the linear quadratic regulator theory, the error state space equation, the predefined weight matrix, and the weights corresponding to the steering wheel angles of the vehicle, the Riccati equation is determined. The weight matrix includes the weights corresponding to the lateral error, the weights corresponding to the rate of change of the lateral error, the weights corresponding to the heading angle error, and the weights corresponding to the rate of change of the heading angle error. The Riccati equation is iterated to obtain the output of the iterated Riccati equation, and the feedback gain rate is determined based on the output. The front wheel angle is determined based on the state variables and the feedback gain rate.

7. The lateral control method for autonomous vehicles in complex road environments as described in any one of claims 1 to 6, characterized in that, The steps of determining the curvature feedforward angle and the cross slope feedforward angle based on the feedback front wheel steering angle and the error state space equation, and determining the target front wheel steering angle of the vehicle based on the feedback front wheel steering angle, the curvature feedforward angle, and the cross slope feedforward angle, and controlling the vehicle to steer using the target front wheel steering angle, include: Based on the error state space equation and the feedback front wheel steering angle, calculate the curvature feedforward steering angle and the cross slope feedforward steering angle; The curvature feedforward angle, the cross slope feedforward angle, and the feedback front wheel angle are added together to obtain the target front wheel angle of the vehicle, and the vehicle is controlled to steer using the target front wheel angle.

8. A lateral control device for autonomous vehicles in complex road environments, characterized in that, The lateral control device for autonomous vehicles in complex road environments includes: The construction module is used to perform force analysis on a vehicle traveling on a complex road to obtain the vehicle's parameter information, and to construct a linear two-degree-of-freedom vehicle model based on the parameter information. The calculation module is used to construct the error state space equation based on the linear two-degree-of-freedom vehicle model and the preset reference path, and to calculate the state variables of the vehicle based on the reference path. The determination module is used to determine the feedback front wheel angle based on the linear quadratic regulator theory, the error state space equation, and the state variables. The control module is used to determine the curvature feedforward angle and the cross slope feedforward angle based on the feedback front wheel steering angle and the error state space equation, and to determine the target front wheel steering angle of the vehicle based on the feedback front wheel steering angle, the curvature feedforward angle, and the cross slope feedforward angle, and to control the vehicle to steer using the target front wheel steering angle; wherein, the curvature feedforward angle is: , For road curvature, It refers to the distance from the vehicle's center of gravity to the front axle. It refers to the distance from the vehicle's center of gravity to the rear axle. and These are the vehicle's mass and its longitudinal speed, respectively. and These are the lateral stiffness of the front and rear wheels, respectively. It is the feedback gain rate. The third item; the cross slope feedforward rotation angle is: g is the acceleration due to gravity.

9. A lateral control device for autonomous vehicles in complex road environments, characterized in that, The lateral control device for autonomous vehicles in complex road environments includes: a memory, a processor, and a lateral control program for autonomous vehicles in complex road environments stored in the memory and executable on the processor. When the lateral control program for autonomous vehicles in complex road environments is executed by the processor, it implements the steps of the lateral control method for autonomous vehicles in complex road environments as described in any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a lateral control program for an autonomous vehicle in a complex road environment, which, when executed by a processor, implements the steps of the lateral control method for an autonomous vehicle in a complex road environment as described in any one of claims 1 to 7.

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