Lateral control method and control device for automatic driving of a vehicle, vehicle

Abstract

The application discloses a kind of vehicle automatic driving lateral control method and control device, vehicle, the method comprises: obtaining the current operating state information of vehicle, wherein the current state information of vehicle includes: current lateral acceleration and current yaw angular acceleration;According to current lateral acceleration and current yaw angular acceleration, determine the planning point information of next time;According to current lateral acceleration, current yaw angular acceleration and the planning point information of next time, determine the error information of vehicle, and according to the error information of vehicle, the front wheel angle of vehicle is adjusted, so that the operating state information of vehicle satisfies preset condition.The lateral control method of the application can improve the control accuracy when vehicle is automatically driven, so that the automatic driving vehicle is closer to the planning trajectory when steering.

Description

Technical Field

[0001] This invention relates to the field of autonomous driving technology, and in particular to a lateral control method for autonomous driving, a lateral control device for autonomous driving, a vehicle, and a computer-readable storage medium. Background Technology

[0002] Lateral control is one of the three core technologies of autonomous driving. Based on the target path information from the upper-level decision-making and planning system, it outputs corresponding steering control commands to control the vehicle to travel along the target path. The lateral control method is the core of the entire motion control system, and its quality not only affects the tracking accuracy of the intelligent vehicle on the target path, but also affects the stability and comfort of the entire vehicle.

[0003] In existing lateral control technologies, there are two methods: those with predictive models and those without. For methods without predictive models, the lag in the physical system causes the control algorithm to fail to be applied to the controller in a timely manner, resulting in poor tracking performance and significant abrupt changes in the control input. For control methods with predictive models, there are certain errors in estimating the vehicle's position, inaccurate judgment of the aiming point, and biased prediction results. Summary of the Invention

[0004] This invention aims to at least partially solve one of the technical problems in related technologies. Therefore, the first objective of this invention is to propose a lateral control method for autonomous driving of vehicles, which can improve the control accuracy during autonomous driving and enable the autonomous vehicle to more closely follow the planned trajectory when turning.

[0005] The second objective of this invention is to provide a lateral control device for autonomous driving of vehicles.

[0006] The third objective of this invention is to provide a vehicle.

[0007] The fourth objective of this invention is to provide a computer-readable storage medium.

[0008] To achieve the above objectives, a first aspect of the present invention provides a lateral control method for autonomous driving of a vehicle, comprising: acquiring current operating state information of the vehicle, wherein the current operating state information of the vehicle includes: current lateral acceleration and current yaw angle acceleration; determining planning point information for the next moment based on the current lateral acceleration and current yaw angle acceleration; determining error information of the vehicle based on the current lateral acceleration, current yaw angle acceleration and planning point information for the next moment; and adjusting the front wheel steering angle of the vehicle based on the error information of the vehicle so that the operating state information of the vehicle meets preset conditions.

[0009] According to an embodiment of the present invention, a lateral control method for autonomous driving of a vehicle acquires the current operating state information of the vehicle, wherein the current operating state information includes: current lateral acceleration and current yaw angle acceleration; determines the planning point information for the next moment based on the current lateral acceleration and current yaw angle acceleration; determines the vehicle's error information based on the current lateral acceleration, current yaw angle acceleration, and the planning point information for the next moment; and adjusts the front wheel steering angle of the vehicle based on the vehicle's error information to ensure that the vehicle's operating state information meets preset conditions. Therefore, this method can improve the control accuracy of autonomous driving, enabling the autonomous vehicle to more closely approximate the planned trajectory when turning.

[0010] In addition, the lateral control method for autonomous driving of vehicles according to the above embodiments of the present invention may also have the following additional technical features:

[0011] According to one embodiment of the present invention, determining the planning point information for the next moment based on the current lateral acceleration and the current yaw angle acceleration includes: obtaining the planning point information for the next moment using a pre-aiming model based on the current lateral acceleration and the current yaw angle acceleration, wherein the planning point information includes: the longitudinal coordinate of the planning point of the vehicle at the next moment, the lateral coordinate of the planning point of the vehicle at the next moment, and the yaw angle of the planning point of the vehicle at the next moment.

[0012] According to one embodiment of the present invention, the pre-aiming model is expressed by the following formula:

[0013]

[0014]

[0015]

[0016] Where, x p The y-coordinate represents the longitudinal coordinate of the vehicle's next planned point. p This represents the lateral coordinate of the vehicle's next planned point. Let x represent the yaw angle of the vehicle at the next planned point, x represent the vehicle's current longitudinal coordinate, y represent the vehicle's current lateral coordinate, and v represent the yaw angle. x v represents the longitudinal velocity of the vehicle at the current moment. y t represents the lateral velocity of the vehicle at the current moment. a Indicates the length of the prediction period. a represents the yaw angle of the vehicle at the current moment. x a represents the vehicle's current longitudinal acceleration. y This indicates the vehicle's current lateral acceleration. This indicates the vehicle's current yaw rate. This indicates the vehicle's current yaw rate acceleration.

[0017] According to one embodiment of the present invention, adjusting the front wheel steering angle of a vehicle based on vehicle error information includes: determining the feedforward front wheel steering angle and the feedback front wheel steering angle of the vehicle based on the vehicle error information; and determining the front wheel steering angle adjustment value of the vehicle based on the feedforward front wheel steering angle and the feedback front wheel steering angle to adjust the front wheel steering angle of the vehicle.

[0018] According to an embodiment of the present invention, after determining the front wheel steering angle adjustment value of the vehicle, the above-mentioned lateral control method for autonomous driving of the vehicle further includes: performing simulation based on the front wheel steering angle adjustment value of the vehicle to determine the actual front wheel steering angle of the vehicle; and obtaining the current operating state information of the vehicle using a two-degree-of-freedom dynamic model based on the actual front wheel steering angle of the vehicle.

[0019] According to one embodiment of the present invention, determining the feedforward front wheel angle and the feedback front wheel angle of a vehicle based on the vehicle's error information includes: determining the feedback front wheel angle of the vehicle using a linear quadratic regulator (LQR) feedback control model based on the vehicle's error information; and determining the feedforward front wheel angle of the vehicle using a feedforward control model based on the vehicle's error information.

[0020] According to an embodiment of the present invention, the above-described lateral control method for autonomous driving of vehicles further includes: when the vehicle's operating state information does not meet preset conditions, adjusting the prediction time length in the aiming model and the parameters of the LQR feedback control model so that the vehicle's error information is within a preset range.

[0021] According to one embodiment of the present invention, determining the vehicle error information based on the current lateral acceleration, the current yaw acceleration, and the planning point information at the next moment includes: establishing a corresponding error model based on the vehicle's two-degree-of-freedom dynamics model; and using the current lateral acceleration, the current yaw acceleration, and the planning point information at the next moment as inputs to the error model to obtain the vehicle error information.

[0022] To achieve the above objectives, a second aspect of the present invention provides a lateral control device for autonomous driving of a vehicle, comprising: an acquisition module for acquiring current operating state information of the vehicle, wherein the current operating state information of the vehicle includes: current lateral acceleration and current yaw angle acceleration; a first determination module for determining planning point information for the next moment based on the current lateral acceleration and the current yaw angle acceleration; and a second determination module for determining error information of the vehicle based on the current lateral acceleration, the current yaw angle acceleration, and the planning point information for the next moment, and adjusting the front wheel steering angle of the vehicle based on the lateral position error and the heading angle error of the vehicle, so as to make the operating state information of the vehicle meet preset conditions.

[0023] According to an embodiment of the present invention, a lateral control device for autonomous driving of a vehicle includes an acquisition module that acquires the current operating state information of the vehicle; a first determining module that determines the planning point information for the next moment based on the current lateral acceleration and the current yaw angle acceleration; and a second determining module that determines the vehicle's error information based on the current lateral acceleration, the current yaw angle acceleration, and the planning point information for the next moment. The device also adjusts the front wheel steering angle of the vehicle based on the lateral position error and the heading angle error to ensure that the vehicle's operating state information meets preset conditions. Therefore, this device can improve the control accuracy during autonomous driving, enabling the autonomous vehicle to more closely approximate the planned trajectory when turning.

[0024] To achieve the above objectives, a third aspect of the present invention provides a vehicle, including a memory, a processor, and a lateral control program for autonomous driving of the vehicle stored in the memory and executable on the processor. When the processor executes the lateral control program for autonomous driving of the vehicle, it implements the above-described lateral control method for autonomous driving of the vehicle.

[0025] According to the embodiments of the present invention, by executing the above-described lateral control method for autonomous driving, the control accuracy of the vehicle during autonomous driving can be improved, enabling the autonomous vehicle to approach the planned trajectory more closely when turning.

[0026] To achieve the above objectives, a fourth aspect of the present invention provides a computer-readable storage medium storing a lateral control program for autonomous driving of a vehicle, which, when executed by a processor, implements the aforementioned lateral control method for autonomous driving of a vehicle.

[0027] According to the computer-readable storage medium of the present invention, by executing the above-described lateral control method for autonomous driving of a vehicle, the control accuracy of the vehicle during autonomous driving can be improved, making the autonomous vehicle closer to the planned trajectory when turning.

[0028] Additional aspects and advantages of the invention 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 the invention. Attached Figure Description

[0029] Figure 1 A flowchart of a lateral control method for autonomous driving of a vehicle according to an embodiment of the present invention;

[0030] Figure 2 This is a schematic diagram of a two-dimensional vehicle dynamics model according to an embodiment of the present invention;

[0031] Figure 3 A block diagram of a vehicle lateral control system according to an embodiment of the present invention;

[0032] Figure 4This is a block diagram of a vehicle lateral control system with a pre-aiming model according to an embodiment of the present invention;

[0033] Figure 5 This is a block diagram of a lateral control device for autonomous driving of a vehicle according to an embodiment of the present invention;

[0034] Figure 6 This is a block diagram of a vehicle according to an embodiment of the present invention. Detailed Implementation

[0035] Embodiments of the present invention are described in detail below, examples of which are illustrated 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 the present invention, and should not be construed as limiting the present invention.

[0036] The following description, with reference to the accompanying drawings, outlines an embodiment of the lateral control method for autonomous driving of vehicles, a lateral control device for autonomous driving of vehicles, a vehicle, and a computer-readable storage medium.

[0037] In related technologies, lateral control for autonomous driving mainly includes two control methods: predictive model-less control and linear predictive model control. The predictive model-less control method uses information about the current vehicle location as the control input for the next moment. Due to physical limitations, the system response lags behind the application time of the current control input, and the shortening of the controllable time requires a larger input to compensate, leading to significant control input jumps during algorithm execution. The linear predictive model control method uses the current vehicle speed and yaw rate as the solution signal for the aiming point. Based on this signal, the corresponding position and yaw angle of the planned path where the aiming point is located are obtained, and then the required steering wheel angle and throttle / brake control inputs are deduced. However, this model has slightly lower prediction accuracy and cannot correctly respond to vehicle acceleration conditions, resulting in some deviation in the prediction results.

[0038] To address the issues of poor control accuracy and inability to respond correctly to vehicle acceleration conditions using linear prediction models, this invention proposes a lateral control method for autonomous driving. This method introduces vehicle linear acceleration and yaw rate acceleration signals as supplementary signals to more accurately control the actual position vector, velocity vector, and angular velocity vector of the aiming point. This can effectively improve the control accuracy of autonomous driving, enabling the autonomous vehicle to approach the planned trajectory more closely when turning.

[0039] Figure 1 This is a flowchart of a lateral control method for autonomous driving of a vehicle according to an embodiment of the present invention.

[0040] like Figure 1As shown, the lateral control method for autonomous driving of a vehicle according to an embodiment of the present invention may include the following steps:

[0041] S1, obtain the current operating status information of the vehicle, including the current lateral acceleration and the current yaw acceleration.

[0042] In an embodiment of the present invention, the vehicle is equipped with an ADAS (Advanced Driving Assistance System). During the autonomous driving process, the ADAS system can monitor the vehicle's operating status information in real time, such as the current wheel speed, longitudinal and lateral acceleration, yaw rate, yaw acceleration, throttle opening, and brake pedal information, and transmit the vehicle's operating status information to the vehicle's autonomous driving control system via the vehicle bus signal.

[0043] S2, determine the planning point information for the next moment based on the current lateral acceleration and the current yaw acceleration.

[0044] According to one embodiment of the present invention, determining the planning point information for the next moment based on the current lateral acceleration and the current yaw angle acceleration includes: obtaining the planning point information for the next moment using a pre-aiming model based on the current lateral acceleration and the current yaw angle acceleration, wherein the planning point information includes: the longitudinal coordinate of the planning point of the vehicle at the next moment, the lateral coordinate of the planning point of the vehicle at the next moment, and the yaw angle of the planning point of the vehicle at the next moment.

[0045] Furthermore, according to one embodiment of the present invention, the pre-aiming model is expressed by the following formula:

[0046]

[0047]

[0048]

[0049] Where, x p The y-coordinate represents the longitudinal coordinate of the vehicle's next planned point. p This represents the lateral coordinate of the vehicle's next planned point. Let x represent the yaw angle of the vehicle at the next planned point, x represent the vehicle's current longitudinal coordinate, y represent the vehicle's current lateral coordinate, and v represent the yaw angle. x v represents the longitudinal velocity of the vehicle at the current moment. y t represents the lateral velocity of the vehicle at the current moment. a Indicates the length of the prediction period. a represents the yaw angle of the vehicle at the current moment. x a represents the vehicle's current longitudinal acceleration.y This indicates the vehicle's current lateral acceleration. This indicates the vehicle's current yaw rate. This indicates the vehicle's current yaw rate acceleration.

[0050] Specifically, by substituting the current lateral acceleration and current yaw acceleration obtained in step S1, along with the vehicle's longitudinal and lateral coordinates, longitudinal and lateral velocities, longitudinal and lateral accelerations, and yaw angle at the current moment into the above formula, the planning point information (pre-aiming point information) for the next moment can be obtained through calculation, that is, the longitudinal coordinate x of the vehicle's planning point at the next moment. p Horizontal coordinate y p and yaw angle

[0051] S3 determines the vehicle's error information based on the current lateral acceleration, current yaw acceleration, and the planning point information at the next moment, and adjusts the front wheel steering angle of the vehicle according to the vehicle's error information so that the vehicle's operating status information meets the preset conditions.

[0052] According to one embodiment of the present invention, determining the vehicle error information based on the current lateral acceleration, the current yaw acceleration, and the planning point information at the next moment includes: establishing a corresponding error model based on the vehicle's two-degree-of-freedom dynamics model; and using the current lateral acceleration, the current yaw acceleration, and the planning point information at the next moment as inputs to the error model to obtain the vehicle error information.

[0053] Specifically, the two-degree-of-freedom vehicle dynamics model neglects the role of the vehicle suspension, simplifies the vehicle to two wheels, assumes linear tire lateral slip characteristics, ignores longitudinal drive or resistance, and assumes constant longitudinal speed. The final linear two-degree-of-freedom model of the vehicle can be expressed as follows: Figure 2 As shown. In Figure 2 In the diagram, CG represents the vehicle's center of gravity, MP represents the instantaneous center of motion, and F represents the center of gravity of the vehicle. yf The lateral force on the front axle; F yr For the lateral force on the rear axle, α f The front axle sideslip angle, α r The rear axle sideslip angle, v f v is the speed of the front wheels. r v is the rear wheel speed, and v is the vehicle's center of gravity speed. For the yaw angle, l f l is the distance from the center of mass to the front axle. r δ is the distance from the center of gravity to the rear axle, l is the wheelbase, β is the sideslip angle of the center of gravity, and δ is the actual front wheel steering angle.

[0054] according to Figure 2 The following system of equations can be obtained:

[0055] may =F yf +F yr

[0056]

[0057] By deriving the above system of equations, we can obtain the expression for the two-degree-of-freedom dynamic model as follows:

[0058]

[0059] in, For the vehicle's lateral acceleration, Let C be the yaw acceleration of the vehicle, m be the mass of the vehicle, and C be the yaw acceleration of the vehicle. αf For the front axle lateral stiffness, C αr For rear axle lateral stiffness, I Z Let Z be the moment of inertia of the vehicle body about the Z-axis.

[0060] Furthermore, by deriving the above dynamic model, the corresponding error model can be obtained, which can be expressed by the following formula:

[0061]

[0062] Among them, e n This refers to the lateral position error; For heading angle error, For lateral velocity error, For lateral acceleration error, For heading angular velocity error, For heading angular acceleration error, The directional angular velocity is represented by the sum of the sideslip angle and the yaw angle. Since the sideslip angle is very small, almost zero, the directional angle is approximated by the yaw angle, and the directional angular velocity is considered to be the yaw angular velocity.

[0063] By using the current lateral acceleration and current yaw acceleration obtained in step S1, and the planning point information for the next moment obtained in step S2, as inputs to the error model, the vehicle's error information can be obtained. This error information can be represented by the error matrix e. e express.

[0064] According to one embodiment of the present invention, adjusting the front wheel steering angle of a vehicle based on vehicle error information includes: determining the feedforward front wheel steering angle and the feedback front wheel steering angle of the vehicle based on the vehicle error information; and determining the front wheel steering angle adjustment value of the vehicle based on the feedforward front wheel steering angle and the feedback front wheel steering angle to adjust the front wheel steering angle of the vehicle.

[0065] According to one embodiment of the present invention, determining the feedforward front wheel angle and the feedback front wheel angle of a vehicle based on the vehicle's error information includes: determining the feedback front wheel angle of the vehicle using a linear quadratic regulator (LQR) feedback control model based on the vehicle's error information; and determining the feedforward front wheel angle of the vehicle using a feedforward control model based on the vehicle's error information.

[0066] Specifically, such as Figure 3 As shown, based on the relationships between the data, the matrix equation of the error model can be expressed as:

[0067]

[0068] in, The derivative of the error matrix, e e This indicates vehicle error information; u is the control variable, representing the adjustment value of the vehicle's front wheel steering angle.

[0069] By comparing the matrix equation of the error model with the formula of the error model, the expression for the coefficient matrix A can be obtained as follows:

[0070]

[0071] The expression for the coefficient matrix B is:

[0072]

[0073] Continue to refer to Figure 3 After the vehicle error information is processed using a linear quadratic regulator (LQR) feedback control model, the front wheel steering angle u is fed back. k It can be expressed as u k =-We e , where W is the feedback matrix.

[0074] The feedback matrix W can be expressed by the following formula:

[0075] W = (R + B) T PB) -1 B T PA

[0076] Where R is the control weight matrix, and the value of the control weight matrix is ​​different when the vehicle is at different turning radii.

[0077] Calculate matrix P according to the Riccati equation, the expression is:

[0078] P = A T PA-A T PB(R+B T PB) -1 B T PA+Q

[0079] Where matrix Q is the state weight matrix, expressed as:

[0080]

[0081] Wherein, q1, q2, q3 and q4 represent the weight values ​​of lateral position error, lateral velocity error, yaw angle error and yaw angular velocity error, respectively.

[0082] Substituting the control weight matrix R, state weight matrix Q, matrix P, coefficient matrix A, and coefficient matrix B into the formula: W = (R + B) T PB) -1 B T PA can be used to obtain the feedback matrix W, and then the feedback front wheel steering angle u can be obtained. k .

[0083] Furthermore, the feedforward front wheel steering angle δ can be calculated based on the feedback matrix W. f The specific process is as follows:

[0084] Let the third row of the 4×4 feedback matrix W be denoted as W(3). A feedforward control model can be established, which can be expressed by the following formula:

[0085]

[0086] Let e n =0, so the feedforward front wheel steering angle δ can be obtained. f for:

[0087]

[0088] Feedforward front wheel steering angle δ f Feedback on front wheel steering angle u k Summing these values ​​yields the front wheel steering angle adjustment value, u. The front wheel steering angle is then adjusted based on this value until the error between the vehicle's operating status information and the planned point at the next moment is zero, enabling the autonomous vehicle to follow the planned path.

[0089] According to an embodiment of the present invention, the above-described lateral control method for autonomous driving of vehicles further includes: when the vehicle's operating state information does not meet preset conditions, adjusting the prediction time length in the aiming model and the parameters of the LQR feedback control model so that the vehicle's error information is within a preset range.

[0090] In other words, when the error between the current operating status information of an autonomous vehicle and the planned point information at the next moment is not zero, the error information of the vehicle can be kept within a preset range by adjusting the prediction time length in the preview model or adjusting the parameters of the LQR feedback control model, such as the sampling period, so that the autonomous vehicle can travel according to the planned path.

[0091] According to one embodiment of the present invention, after determining the front wheel steering angle adjustment value of the vehicle, the method further includes: performing simulation based on the front wheel steering angle adjustment value of the vehicle to determine the actual front wheel steering angle of the vehicle; and obtaining the current operating state information of the vehicle using a two-degree-of-freedom dynamic model based on the actual front wheel steering angle of the vehicle.

[0092] Specifically, such as Figure 4 As shown, the pre-aiming model plans the driving path of the autonomous vehicle based on the current vehicle operating state and the pre-aiming time. The vehicle control system (i.e., the LQR feedback control model and the feedforward control model) obtains the front wheel steering angle adjustment value u based on the error information. The Carsim simulation software simulates the front wheel steering angle adjustment value u, and the actual front wheel steering angle δ of the vehicle can be obtained after simulation. Substituting the actual front wheel steering angle δ of the vehicle into the following two-degree-of-freedom dynamic model formula:

[0093]

[0094] The driving state information of an autonomous vehicle after planning its route using the pre-aiming model can be obtained, namely the current lateral acceleration and the current yaw acceleration. The current lateral acceleration and the current yaw acceleration are used as feedback inputs to the pre-aiming model, and this process is repeated to compensate for the error information until the error information is zero. The vehicle is then controlled to drive according to the driving state information when the error is zero.

[0095] In summary, the lateral control method of the present invention uses the vehicle's two-degree-of-freedom system as the error model, LQR control as the closed-loop feedback, and feedforward control to compensate for steady-state errors. Furthermore, it utilizes lateral acceleration and yaw acceleration as control parameters for the aiming model to improve the inherent hysteresis problem of the physical system.

[0096] In summary, the lateral control method for autonomous driving of vehicles according to embodiments of the present invention acquires the current operating state information of the vehicle, wherein the current operating state information of the vehicle includes: current lateral acceleration and current yaw angle acceleration; determines the planning point information for the next moment based on the current lateral acceleration and current yaw angle acceleration; determines the vehicle's error information based on the current lateral acceleration, current yaw angle acceleration, and the planning point information for the next moment; and adjusts the front wheel steering angle of the vehicle based on the vehicle's error information to ensure that the vehicle's operating state information meets preset conditions. Therefore, this method can improve the control accuracy of autonomous driving, enabling the autonomous vehicle to more closely approximate the planned trajectory when turning.

[0097] Corresponding to the above embodiments, the present invention also proposes a lateral control device for autonomous driving of vehicles.

[0098] Figure 5This is a block diagram of a lateral control device for autonomous driving of a vehicle according to an embodiment of the present invention.

[0099] like Figure 5 As shown, the lateral control device for vehicle autonomous driving according to an embodiment of the present invention includes: an acquisition module 10, a first determination module 20, and a second determination module 30.

[0100] The acquisition module 10 acquires the vehicle's current operating status information, including the current lateral acceleration and current yaw angle acceleration. The first determination module 20 determines the planning point information for the next moment based on the current lateral acceleration and current yaw angle acceleration. The second determination module 30 determines the vehicle's error information based on the current lateral acceleration, current yaw angle acceleration, and the planning point information for the next moment, and adjusts the front wheel steering angle of the vehicle based on the vehicle's lateral position error and heading angle error to ensure that the vehicle's operating status information meets preset conditions.

[0101] According to an embodiment of the present invention, the first determining module 20 determines the planning point information for the next moment based on the current lateral acceleration and the current yaw angle acceleration. Specifically, it is used to obtain the planning point information for the next moment using a pre-aiming model based on the current lateral acceleration and the current yaw angle acceleration. The planning point information includes: the longitudinal coordinate of the planning point of the vehicle at the next moment, the lateral coordinate of the planning point of the vehicle at the next moment, and the yaw angle of the planning point of the vehicle at the next moment.

[0102] According to one embodiment of the present invention, the pre-aiming model is expressed by the following formula:

[0103]

[0104]

[0105]

[0106] Where, x p The y-coordinate represents the longitudinal coordinate of the vehicle's next planned point. p This represents the lateral coordinate of the vehicle's next planned point. Let x represent the yaw angle of the vehicle at the next planned point, x represent the vehicle's current longitudinal coordinate, y represent the vehicle's current lateral coordinate, and v represent the yaw angle. x v represents the longitudinal velocity of the vehicle at the current moment. y t represents the lateral velocity of the vehicle at the current moment. a Indicates the length of the prediction period. a represents the yaw angle of the vehicle at the current moment. x a represents the vehicle's current longitudinal acceleration. yThis indicates the vehicle's current lateral acceleration. This indicates the vehicle's current yaw rate. This indicates the vehicle's current yaw rate acceleration.

[0107] According to one embodiment of the present invention, the second determining module 30 adjusts the front wheel steering angle of the vehicle based on the vehicle's error information. Specifically, it is used to determine the feedforward front wheel steering angle and the feedback front wheel steering angle of the vehicle based on the vehicle's error information; and to determine the front wheel steering angle adjustment value of the vehicle based on the feedforward front wheel steering angle and the feedback front wheel steering angle, so as to adjust the front wheel steering angle of the vehicle.

[0108] According to one embodiment of the present invention, after determining the front wheel steering angle adjustment value of the vehicle, the second determining module 30 is further configured to perform simulation based on the front wheel steering angle adjustment value of the vehicle to determine the actual front wheel steering angle of the vehicle; and to obtain the current operating state information of the vehicle using a two-degree-of-freedom dynamic model based on the actual front wheel steering angle of the vehicle.

[0109] According to one embodiment of the present invention, the second determining module 30 determines the feedforward front wheel angle and the feedback front wheel angle of the vehicle based on the vehicle's error information. Specifically, it is used to determine the feedback front wheel angle of the vehicle using a linear quadratic regulator (LQR) feedback control model based on the vehicle's error information; and to determine the feedforward front wheel angle of the vehicle using a feedforward control model based on the vehicle's error information.

[0110] According to one embodiment of the present invention, the second determining module 30 is further configured to adjust the prediction time length in the aiming model and the parameters of the LQR feedback control model when the vehicle's operating status information does not meet the preset conditions, so as to make the vehicle's error information within the preset range.

[0111] According to one embodiment of the present invention, the second determining module 30 determines the vehicle error information based on the current lateral acceleration, the current yaw angle acceleration and the planning point information at the next moment. Specifically, it is used to establish a corresponding error model based on the two-degree-of-freedom dynamic model of the vehicle; and to use the current lateral acceleration, the current yaw angle acceleration and the planning point information at the next moment as inputs to the error model to obtain the vehicle error information.

[0112] It should be noted that for details not disclosed in the lateral control device for autonomous driving of vehicles in this embodiment of the invention, please refer to the details disclosed in the lateral control method for autonomous driving of vehicles in this embodiment of the invention, which will not be repeated here.

[0113] According to an embodiment of the present invention, a lateral control device for autonomous driving of a vehicle includes an acquisition module that acquires the current operating state information of the vehicle; a first determining module that determines the planning point information for the next moment based on the current lateral acceleration and the current yaw angle acceleration; and a second determining module that determines the vehicle's error information based on the current lateral acceleration, the current yaw angle acceleration, and the planning point information for the next moment. The device also adjusts the front wheel steering angle of the vehicle based on the lateral position error and the heading angle error to ensure that the vehicle's operating state information meets preset conditions. Therefore, this device can improve the control accuracy during autonomous driving, enabling the autonomous vehicle to more closely approximate the planned trajectory when turning.

[0114] Corresponding to the above embodiments, the present invention also proposes a vehicle.

[0115] Figure 6 This is a block diagram of a vehicle according to an embodiment of the present invention.

[0116] like Figure 6 As shown, the vehicle 200 of this embodiment includes a memory 210, a processor 220, and a lateral control program for autonomous driving stored in the memory 210 and executable on the processor 220. When the processor 220 executes the lateral control program for autonomous driving, it implements the aforementioned lateral control method for autonomous driving.

[0117] According to the embodiments of the present invention, by executing the above-described lateral control method for autonomous driving, the control accuracy of the vehicle during autonomous driving can be improved, enabling the autonomous vehicle to approach the planned trajectory more closely when turning.

[0118] Corresponding to the above embodiments, the present invention also proposes a computer-readable storage medium.

[0119] The computer-readable storage medium of this invention stores a lateral control program for autonomous driving of a vehicle, which, when executed by a processor, implements the aforementioned lateral control method for autonomous driving of a vehicle.

[0120] According to the computer-readable storage medium of the present invention, by executing the above-described lateral control method for autonomous driving of a vehicle, the control accuracy of the vehicle during autonomous driving can be improved, making the autonomous vehicle closer to the planned trajectory when turning.

[0121] It should be noted that the logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as a sequenced list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by, or in conjunction with, an instruction execution system, apparatus, or device (such as a computer-based system, a processor-included system, or other system that can fetch and execute instructions from, an instruction execution system, apparatus, or device). For the purposes of this specification, "computer-readable medium" can be any means that can contain, store, communicate, propagate, or transmit programs for use by, or in conjunction with, an instruction execution system, apparatus, or device. More specific examples (a non-exhaustive list) of computer-readable media include: an electrical connection having one or more wires (electronic device), a portable computer disk drive (magnetic device), random access memory (RAM), read-only memory (ROM), erasable and editable read-only memory (EPROM or flash memory), fiber optic devices, and portable optical disc read-only memory (CDROM). Alternatively, the computer-readable medium may be paper or other suitable media on which the program can be printed, since the program can be obtained electronically, for example, by optically scanning the paper or other medium, followed by editing, interpreting, or otherwise processing as necessary, and then stored in a computer memory.

[0122] It should be understood that various parts of the present invention can be implemented in hardware, software, firmware, or a combination thereof. In the above embodiments, multiple steps or methods can be implemented in 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 one or a combination of the following techniques known in the art: 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 (PGAs), field-programmable gate arrays (FPGAs), etc.

[0123] In the description of this specification, 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 the invention. 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.

[0124] 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 indicated technical features. Thus, a feature defined as "first" or "second" may explicitly or implicitly include at least one of that feature. In the description of this invention, "a plurality of" means at least two, such as two, three, etc., unless otherwise explicitly specified.

[0125] In this invention, unless otherwise explicitly specified and limited, the terms "installation," "connection," "linking," and "fixing," etc., should be interpreted broadly. For example, they can refer to a fixed connection, a detachable connection, or an integral part; they can refer to a mechanical connection or an electrical connection; they can refer to a direct connection or an indirect connection through an intermediate medium; they can refer to the internal communication of two components or the interaction between two components, unless otherwise explicitly limited. Those skilled in the art can understand the specific meaning of the above terms in this invention according to the specific circumstances.

[0126] Although embodiments of the present invention have been shown and described above, it is understood that the above embodiments are exemplary and should not be construed as limiting the present invention. Those skilled in the art can make changes, modifications, substitutions and variations to the above embodiments within the scope of the present invention.

Claims

1. A lateral control method for autonomous driving of a vehicle, characterized in that, include: Obtain the current operating status information of the vehicle, wherein the current status information of the vehicle includes: current lateral acceleration and current yaw angle acceleration; The planning point information for the next moment is determined based on the current lateral acceleration and the current yaw acceleration, including: The planning point information for the next moment is obtained using a pre-aiming model based on the current lateral acceleration and the current yaw acceleration. The vehicle's error information is determined based on the current lateral acceleration, the current yaw acceleration, and the planning point information at the next moment. The front wheel steering angle of the vehicle is adjusted based on the vehicle's error information so that the vehicle's operating status information meets preset conditions. When the vehicle's operating status information does not meet the preset conditions, the prediction time length in the pre-aiming model is adjusted so that the vehicle's error information is within the preset range. The planning point information includes: the longitudinal coordinates of the planning point of the vehicle at the next moment, the lateral coordinates of the planning point of the vehicle at the next moment, and the yaw angle of the planning point of the vehicle at the next moment; Adjusting the front wheel steering angle of the vehicle based on the vehicle's error information includes: The feedforward front wheel angle and feedback front wheel angle of the vehicle are determined based on the vehicle's error information; The front wheel angle adjustment value of the vehicle is determined based on the feedforward front wheel angle and the feedback front wheel angle, so as to adjust the front wheel angle of the vehicle.

2. The lateral control method for autonomous driving of vehicles according to claim 1, characterized in that, The pre-aiming model is expressed by the following formula: in, This represents the longitudinal coordinate of the planned point of the vehicle at the next moment. This represents the lateral coordinate of the planned point for the vehicle at the next moment. This indicates the yaw angle of the vehicle at the next planned point. This represents the longitudinal coordinate of the vehicle at the current moment. This represents the lateral coordinate of the vehicle at the current moment. This represents the longitudinal speed of the vehicle at the current moment. This represents the lateral velocity of the vehicle at the current moment. Indicates the length of the prediction period. This indicates the yaw angle of the vehicle at the current moment. This indicates the current longitudinal acceleration of the vehicle. This indicates the current lateral acceleration of the vehicle. This indicates the current yaw rate of the vehicle. This indicates the current yaw rate acceleration of the vehicle.

3. The lateral control method for autonomous driving of vehicles according to claim 2, characterized in that, After determining the front wheel steering angle adjustment value of the vehicle, the method further includes: Simulations are performed based on the front wheel steering angle adjustment value of the vehicle to determine the actual front wheel steering angle of the vehicle. The current operating status information of the vehicle is obtained using a two-degree-of-freedom dynamic model based on the actual front wheel steering angle.

4. The lateral control method for autonomous driving of vehicles according to claim 2, characterized in that, Determining the feedforward front wheel steering angle and feedback front wheel steering angle of the vehicle based on the vehicle's error information includes: The feedback front wheel steering angle of the vehicle is determined using a linear quadratic regulator (LQR) feedback control model based on the vehicle's error information. The feedforward front wheel steering angle of the vehicle is determined using a feedforward control model based on the vehicle's error information.

5. The lateral control method for autonomous driving of a vehicle according to claim 4, characterized in that, Also includes: When the vehicle's operating status information does not meet the preset conditions, the parameters of the LQR feedback control model are adjusted so that the vehicle's error information is within the preset range.

6. The lateral control method for autonomous driving of vehicles according to claim 4, characterized in that, The vehicle's error information is determined based on the current lateral acceleration, the current yaw acceleration, and the planning point information for the next moment, including: A corresponding error model is established based on the two-degree-of-freedom dynamic model of the vehicle. The current lateral acceleration, the current yaw acceleration, and the planning point information at the next moment are used as inputs to the error model to obtain the vehicle's error information.

7. A lateral control device for autonomous driving of a vehicle, characterized in that, include: The acquisition module is used to acquire the current operating status information of the vehicle, wherein the current status information of the vehicle includes: current lateral acceleration and current yaw acceleration; The first determining module is used to determine the planning point information for the next moment based on the current lateral acceleration and the current yaw angle acceleration, including: The planning point information for the next moment is obtained using a pre-aiming model based on the current lateral acceleration and the current yaw acceleration. The second determining module is used to determine the error information of the vehicle based on the current lateral acceleration, the current yaw angle acceleration and the planning point information at the next moment, and to adjust the front wheel steering angle of the vehicle based on the lateral position error and the heading angle error of the vehicle so that the vehicle's operating status information meets the preset conditions. When the vehicle's operating status information does not meet the preset conditions, the prediction time length in the pre-aiming model is adjusted so that the vehicle's error information is within the preset range. The planning point information includes: the longitudinal coordinates of the planning point of the vehicle at the next moment, the lateral coordinates of the planning point of the vehicle at the next moment, and the yaw angle of the planning point of the vehicle at the next moment; Adjusting the front wheel steering angle of the vehicle based on the vehicle's error information includes: The feedforward front wheel angle and feedback front wheel angle of the vehicle are determined based on the vehicle's error information; The front wheel angle adjustment value of the vehicle is determined based on the feedforward front wheel angle and the feedback front wheel angle, so as to adjust the front wheel angle of the vehicle.

8. A vehicle, characterized in that, The system includes a memory, a processor, and a lateral control program for autonomous driving of a vehicle stored in the memory and executable on the processor. When the processor executes the lateral control program for autonomous driving of the vehicle, it implements the lateral control method for autonomous driving of a vehicle according to any one of claims 1-6.

9. A computer-readable storage medium, characterized in that, It stores a lateral control program for autonomous driving of a vehicle, which, when executed by a processor, implements the lateral control method for autonomous driving of a vehicle according to any one of claims 1-6.

Images