Lateral control method, device and vehicle for autonomous driving vehicle

Abstract

The present application discloses a lateral control method, device, and vehicle for an autonomous vehicle, wherein the method includes: obtaining the actual coordinates and current heading angle of the vehicle to obtain the current posture and position information of the target point; calculating the distance deviation, distance deviation change rate, heading angle deviation, and angle deviation change rate from the target point at the current moment based on the current posture and position information, and calculating the state matrix; using a vehicle dynamics model to determine a first model parameter matrix and a second model parameter matrix, and simultaneously selecting a first weighting matrix and a second weighting matrix to determine an optimal matrix according to a linear quadratic regulator (LQR) algorithm, and controlling the vehicle's steering actuator to execute a steering control quantity obtained by multiplying the optimal matrix and the state matrix. In this way, the stability and comfort of vehicle tracking on complex roads with fast curvature and speed changes are ensured, and the control accuracy and adaptability of the LQR controller are improved.

Description

Technical Field

[0001] The present application relates to the field of vehicle technology, and in particular to a lateral control method, device, and vehicle for an autonomous driving vehicle. Background Art

[0002] Lateral control performs tracking control based on the path, curvature and other information output by the upper-level motion planning to reduce tracking errors while ensuring vehicle stability and comfort. Depending on the vehicle model used in lateral control, it can be divided into two types: (1) model-free lateral control methods; (2) model-based lateral control methods. Among them, model-based lateral control methods can be further divided into: lateral control methods based on vehicle kinematic models and lateral control methods based on vehicle dynamic models.

[0003] The mainstream PID (Proportion Integral Differential) control algorithm is a model-free lateral control algorithm that takes the vehicle's current path tracking deviation as input and performs proportional, integral, and differential control on the tracking deviation to obtain the steering control variable.

[0004] However, since this algorithm does not take into account the characteristics of the vehicle itself, it has poor robustness to external interference and cannot meet the effective control of the vehicle during high-speed driving, which needs to be urgently solved. Summary of the Invention

[0005] The present application provides a lateral control method, device and vehicle for an autonomous driving vehicle to ensure the stability and comfort of vehicle tracking on complex roads with rapid changes in curvature and speed, and to improve the control accuracy and adaptability of the LQR controller.

[0006] A first embodiment of the present application provides a lateral control method for an autonomous driving vehicle, comprising the following steps:

[0007] Get the actual coordinates and current heading angle of the vehicle to obtain the current posture and location information of the target point;

[0008] Calculate the distance deviation, distance deviation change rate, heading angle deviation and angle deviation change rate from the target point at the current moment according to the current posture and position information, and calculate the state matrix; and

[0009] A first model parameter matrix and a second model parameter matrix are determined using a vehicle dynamics model, and a first weighting matrix and a second weighting matrix are simultaneously selected to determine an optimal matrix according to a linear quadratic regulator (LQR) algorithm, and to control a steering actuator of the vehicle to execute a steering control amount obtained by multiplying the optimal matrix and the state matrix.

[0010] Optionally, it also includes:

[0011] The vehicle dynamics model is determined based on the front wheel cornering stiffness, rear wheel cornering stiffness, distance from the front axle to the vehicle center of gravity, distance from the rear axle to the vehicle center of gravity, z-axis moment of inertia of the vehicle and the vehicle mass.

[0012] Optionally, the calculating of the distance deviation, distance deviation change rate, heading angle deviation, and angle deviation change rate from the target point at the current moment according to the current posture and position information, and the calculating of the state matrix, includes:

[0013] Determine the type of current road;

[0014] If the type is a straight line type, the target point is the point on the trajectory closest to the current position;

[0015] If the type is a curve type, when the actual speed of the vehicle is greater than a preset threshold, the target point is a point at a preview distance, otherwise it is a point at a distance determined by the road curvature.

[0016] Optionally, the calculation formula for determining the distance based on the road curvature is:

[0017] L=kV+lmin,

[0018] Wherein, k is the linear change ratio of speed, V is the actual speed of the vehicle, and lmin is the minimum setting value of the preview distance.

[0019] Optionally, the calculation formula of the state matrix is:

[0020]

[0021] Among them, e1 is the distance deviation, e2 is the distance deviation change rate, e3 is the heading angle deviation, and e4 is the angle deviation change rate.

[0022] A second embodiment of the present application provides a lateral control device for an autonomous driving vehicle, comprising:

[0023] The acquisition module is used to obtain the actual coordinates and current heading angle of the vehicle, and obtain the current posture and position information of the target point;

[0024] a calculation module, configured to calculate the distance deviation, distance deviation change rate, heading angle deviation, and angle deviation change rate from the target point at the current moment, and calculate a state matrix based on the current posture and position information; and

[0025] A control module is used to determine a first model parameter matrix and a second model parameter matrix using a vehicle dynamics model, and simultaneously select a first weighting matrix and a second weighting matrix to determine an optimal matrix according to a linear quadratic regulator (LQR) algorithm, and control a steering actuator of the vehicle to execute a steering control amount obtained by multiplying the optimal matrix and the state matrix.

[0026] Optionally, it also includes:

[0027] A determination module is configured to determine the vehicle dynamics model based on the vehicle's front wheel cornering stiffness, rear wheel cornering stiffness, distance from the front axle to the vehicle's center of gravity, distance from the rear axle to the vehicle's center of gravity, the vehicle's z-axis moment of inertia, and the vehicle's mass. Optionally, the calculation module includes:

[0028] A judgment unit, used to judge the type of the current road;

[0029] a first determining unit, configured to determine, if the type is a straight road type, that the target point is the point on the track closest to the current position;

[0030] The second determining unit is configured to determine, if the road type is a curve type, that the target point is a point at a preview distance when the actual speed of the vehicle is greater than a preset threshold, and otherwise determine the target point at a distance determined by the road curvature.

[0031] Optionally, the calculation formula for determining the distance based on the road curvature is:

[0032] L=kV+lmin,

[0033] Wherein, k is the linear change ratio of speed, V is the actual speed of the vehicle, and lmin is the minimum setting value of the preview distance.

[0034] Optionally, the calculation formula of the state matrix is:

[0035]

[0036] Among them, e1 is the distance deviation, e2 is the distance deviation change rate, e3 is the heading angle deviation, and e4 is the angle deviation change rate.

[0037] A third aspect of the present application provides a vehicle comprising the aforementioned lateral control device for the autonomous driving vehicle.

[0038] In this way, the actual coordinates and current heading angle of the vehicle can be obtained, and the current posture and position information of the target point can be obtained. The distance deviation, distance deviation change rate, heading angle deviation and angle deviation change rate of the current moment from the target point can be calculated based on the current posture and position information. The state matrix is calculated, and the first model parameter matrix and the second model parameter matrix are determined using the vehicle dynamics model. At the same time, the first weighting matrix and the second weighting matrix are selected to determine the optimal matrix according to the linear quadratic regulator LQR algorithm, and control the steering actuator of the vehicle to execute the steering control amount obtained by multiplying the optimal matrix and the state matrix, thereby ensuring the stability and comfort of vehicle tracking on complex roads with fast curvature and speed changes, and realizing the improvement of the control accuracy and adaptability of the LQR controller.

[0039] Additional aspects and advantages of the present application will be given in part in the description below, and in part will become apparent from the description below, or will be learned through practice of the present application. BRIEF DESCRIPTION OF THE DRAWINGS

[0040] The above and / or additional aspects and advantages of the present application will become apparent and easily understood from the following description of the embodiments in conjunction with the accompanying drawings, in which:

[0041] Figure 1 This is a flowchart of a lateral control method for an autonomous driving vehicle provided according to an embodiment of the present application;

[0042] Figure 2 This is an example diagram of LQR lateral and longitudinal errors according to one embodiment of the present application;

[0043] Figure 3 is a flowchart of an LQR algorithm according to one embodiment of the present application;

[0044] Figure 4 Schematic diagram of the tracking effect of a curve at different speeds before optimization according to one embodiment of the present application;

[0045] Figure 5 Schematic diagram of the tracking effect of a curve at different speeds after optimization according to one embodiment of the present application;

[0046] Figure 6 This is a block diagram of an example of a lateral control device for an autonomous driving vehicle according to an embodiment of the present application. DETAILED DESCRIPTION

[0047] The following describes in detail embodiments of the present application, examples of which are shown in the accompanying drawings, wherein the same or similar reference numerals throughout represent the same or similar elements or elements having the same or similar functions. The embodiments described below with reference to the accompanying drawings are exemplary and are intended to be used to explain the present application, and should not be construed as limiting the present application.

[0048] The following describes the lateral control method, device and vehicle of the autonomous driving vehicle according to the embodiments of the present application with reference to the accompanying drawings.

[0049] Before introducing the lateral control method of the autonomous driving vehicle according to the embodiment of the present application, a brief introduction to the processing method in the related art is given.

[0050] In related technologies, the LQR algorithm uses a two-degree-of-freedom dynamic model to design a lateral controller. The advantage of the LQR algorithm is that, by effectively combining it with steering feedforward, it can effectively resolve the steady-state tracking error during some curved driving. When driving on medium-speed curves, its steady-state error approaches zero, thereby greatly improving tracking performance.

[0051] However, tracking performance is significantly reduced in situations with high curvature and high speeds, and the system is highly dependent on the environment and parameter selection. This means that it cannot adapt well to tracking under new conditions when the environment suddenly changes. Furthermore, LQR parameter adjustment is complex, requiring not only the vehicle's own model parameters but also the adjustment of the QR matrix of the LQR objective function. Inaccurate selection of the QR matrix can significantly reduce the tracking performance of the LQR algorithm, leading to control failure. Furthermore, the current LQR algorithm generally uses a fixed QR matrix, resulting in poor system adaptability, which urgently needs to be addressed.

[0052] Therefore, the present application provides a lateral control method for an autonomous driving vehicle, in which the actual coordinates and current heading angle of the vehicle can be obtained, the current posture and position information of the target point can be obtained, and the distance deviation, distance deviation change rate, heading angle deviation and angle deviation change rate from the target point at the current moment are calculated based on the current posture and position information, and the state matrix is calculated. The first model parameter matrix and the second model parameter matrix are determined using the vehicle dynamics model, and the first weighted matrix and the second weighted matrix are selected at the same time to determine the optimal matrix according to the linear quadratic regulator LQR algorithm, and control the steering actuator of the vehicle to execute the steering control amount obtained by multiplying the optimal matrix and the state matrix, thereby ensuring the stability and comfort of vehicle tracking on complex roads with rapid changes in curvature and speed, and realizing the improvement of the control accuracy and adaptability of the LQR controller.

[0053] Specifically, Figure 1 A schematic flow chart of a lateral control method for an autonomous driving vehicle provided in an embodiment of the present application.

[0054] like Figure 1 As shown, the lateral control method of the autonomous driving vehicle includes the following steps:

[0055] In step S101, the actual coordinates and current heading angle of the vehicle are obtained to obtain the current posture and position information of the target point.

[0056] It is understandable that the method of obtaining the actual coordinates and current heading angle of the vehicle, and obtaining the current posture and position information of the target point based on the actual coordinates and current heading angle can adopt the processing method in the relevant technology. To avoid redundancy, it will not be described in detail here.

[0057] In step S102, the distance deviation, distance deviation change rate, heading angle deviation and angle deviation change rate from the target point at the current moment are calculated based on the current posture and position information, and the state matrix is calculated.

[0058] Optionally, in some embodiments, the distance deviation, distance deviation change rate, heading angle deviation and angle deviation change rate from the target point at the current moment are calculated based on the current posture and position information, and the state matrix is calculated, including: judging the type of the current road; if the type is a straight road type, the target point is the point on the trajectory closest to the current position; if the type is a curve type, when the actual speed of the vehicle is greater than a preset threshold, the target point is the point at the preview distance, otherwise it is the point at a distance determined by the road curvature.

[0059] The preset threshold may be a threshold set in advance by the user, a threshold obtained through a limited number of experiments, or a threshold obtained through a limited number of computer simulations. Preferably, the preset threshold is 60 km / h.

[0060] Specifically, if Figure 2 As shown, Vx is the longitudinal speed of the vehicle, Vy is the lateral speed of the vehicle, ψ is the yaw rate of the vehicle, and ratio is the ratio of the vehicle steering wheel angle to the front wheel deflection angle, which is given by Figure 2 It can be seen that the calculation of the horizontal and vertical errors is obtained by comparing the current time point with the target point, so the following will explain in detail how to obtain the target point.

[0061] Specifically, the road type can generally include straight road type and curved road type. If the road type is a straight road type, the target point is selected as the point on the trajectory closest to the current position to ensure the accuracy of straight-line tracking; if the road type is a curved road type, it can be determined based on the actual speed of the vehicle. For example, when the actual speed of the vehicle is greater than a preset threshold, the speed threshold (i.e., the preset threshold) is adjusted according to the maximum speed limit of the trajectory section, usually selected as the speed limit, and the preview distance L = lmax of the target point is selected; for another example, when the actual speed of the vehicle is less than the preset threshold, the road curvature determines the distance, and the calculation formula can be:

[0062] L=kV+lmin.

[0063] Where k is the linear change ratio of speed, V is the actual speed of the vehicle, and lmin is the minimum setting value of the preview distance.

[0064] It should be noted that lmin is selected as twice the vehicle's minimum turning radius, lmax = lmin / 2 + v*v / 2a, where a is the vehicle's set comfortable deceleration, typically set to 3, and k = α*kp. kp is the curvature of the point on the trajectory closest to the current position. α is an adjustable coefficient that can be adjusted based on the actual tracking situation. If the curve is entered early and the position deviation is large, α can be reduced; otherwise, it can be increased. Then, the preview distance is obtained by substituting the above formula into the target distance. Preview aiming is performed along the vehicle's forward direction to obtain the corresponding target point. If the preview distance is greater than the distance to the endpoint, the distance to the endpoint is selected as the preview distance.

[0065] Furthermore, in some embodiments, e1 is the displacement from the current moment to the target point, and the state matrix state is calculated by the following formula:

[0066] e3 = θ - θ1;

[0067] e2=Vx*e3+Vy;

[0068]

[0069] get

[0070] Where R is the curvature radius of the target trajectory point, e1 is the range deviation, e2 is the rate of change of the range deviation, e3 is the heading angle deviation, and e4 is the rate of change of the angle deviation.

[0071] In step S103, a first model parameter matrix and a second model parameter matrix are determined using a vehicle dynamics model, and a first weighting matrix and a second weighting matrix are simultaneously selected to determine an optimal matrix according to a linear quadratic regulator (LQR) algorithm, and to control a steering actuator of the vehicle to execute a steering control amount obtained by multiplying the optimal matrix and the state matrix.

[0072] Among them, such as Figure 3 As shown, Figure 3 The flowchart of the LQR algorithm mainly includes the following steps:

[0073] S301, sensing environment and vehicle information.

[0074] The perceived environment and vehicle information include: vehicle coordinates and heading angle, and tracking trajectory target point coordinates and heading angle.

[0075] S302, data processing.

[0076] After the data is processed, the processed data is sent to step S309.

[0077] S303, judging the curvature of a straight road or a curve. If it is a straight road, execute step S304; if it is a curve, execute step S305.

[0078] S304: The target point is the point on the trajectory closest to the current position, and the process jumps to step S308.

[0079] S305, determining whether the actual vehicle speed is greater than a preset threshold, if yes, executing step S306, otherwise executing step S307.

[0080] S306: The target point is selected as the preview distance L=lmax, and the process jumps to step S308.

[0081] S307: The target point is selected as the preview distance L=kV+lmin.

[0082] S308, state quantity, and jump to execute step S310.

[0083] Among them, the distance deviation e1, distance deviation change rate e2, heading deviation e3, and angle deviation change rate e4 between the vehicle and the target point at the current moment are calculated according to the real-time posture and target point position information, thereby obtaining the state matrix state.

[0084] S309, QR weight matrix selector.

[0085] That is, the embodiment of the present application can determine the model parameter matrices A and B based on the above-mentioned dynamic model parameters, and simultaneously select the weighting matrices Q and R (selection of QR weights).

[0086] The state matrix can be obtained in this way, and the state matrix and QR weight matrix selector are input to the LQR controller.

[0087] S310, LQR controller.

[0088] S311, the vehicle turns to the actuator and jumps to step S301.

[0089] Thus, based on the controller parameters determined above, the steering control amount of the autonomous vehicle is calculated and transmitted to the steering actuator for execution.

[0090] Optionally, in some embodiments, it also includes: determining the vehicle dynamics model based on the vehicle's front wheel lateral stiffness, rear wheel lateral stiffness, the distance from the front axle to the vehicle's center of gravity, the distance from the rear axle to the vehicle's center of gravity, the vehicle's z-axis moment of inertia and the vehicle's mass.

[0091] That is to say, the parameters of the vehicle dynamics model mainly include: front wheel cornering stiffness Cf, vehicle dynamics model Cr, distance lf from the front axle to the vehicle center of gravity, distance lr from the rear axle to the vehicle center of gravity, vehicle z-axis moment of inertia Iz and vehicle mass m.

[0092] It should be noted that the parameters of the above vehicle dynamics model can be obtained by querying the basic information of the vehicle or by re-measuring. Specifically, those skilled in the art can handle it according to the actual situation, and no specific limitation is made here.

[0093] Furthermore, the calculation formulas for determining the first model parameter matrix matrix_a_ and the second model parameter matrix matrix_b_ using the vehicle dynamics model can be shown as follows:

[0094]

[0095]

[0096] Among them, Cf and Cr are the cornering stiffness of the front and rear wheels, lf and lr are the distances from the front and rear axles to the center of gravity, Iz is the moment of inertia of the vehicle about the z-axis, and m is the mass of the whole vehicle.

[0097] Furthermore, select the first weighting matrix Q and the second weighting matrix R, for the first weighting matrix Q, select the diagonal matrix matrix_q_ = diag[q1, q2, q3, q4], where the four parameters q1, q2, q3, and q4 respectively correspond to the four variables of the state matrix state, and the selection of q1 and q3 is the key to LQR control; the second weighting matrix R selects the identity matrix matrix_r_ = [1]; from the above Figure 3 flowchart, it can be seen that the tracking system makes a selection by perceiving the environmental information:

[0098] (1) First, judge the road curvature radius R with R1 and R2. R1 and R2 are the boundary conditions for distinguishing straight roads and curved roads, small curvature and large curvature respectively;

[0099] (2) When R < R1, it is determined that the tracking curve is a straight line, and q3 = kq * q1 is selected, where kq = 0.1 * V;

[0100] (3) When R1 < R < R2, it is determined that the tracking trajectory is a small-curvature curved road, and q3 = kq * q1 is selected, where kq = V;

[0101] (4) When R < R2, it is determined that the tracking trajectory is a large-curvature curved road, and q3 = kq * q1 is selected, where kq = 10 * V;

[0102] (5) According to the above formula, only the initial value of q1 needs to be determined by the real vehicle's single straight-line tracking to obtain the first weighting matrix Q.

[0103] Furthermore, determine the optimal matrix matrix_k according to the numerical iterative solution of the Riccati equation, which is obtained by the following formula;

[0104]

[0105]

[0106] Among them, max_num_iterationa is the maximum number of iterations, which is selected as 150, matrix_a_T and matrix_b_T are the transposed matrices of matrix_a_ and matrix_b_ respectively, matrix_p is the process iteration matrix, and the initial value is the Q matrix.

[0107] Furthermore, the front wheel angle is obtained by multiplying the optimal matrix matrix_k and the state matrix state, and finally multiplied by the ratio and output to the actuator to achieve tracking;

[0108] The simulation comparison of the curve with an initial deviation of 0.5m (simulating sudden environmental changes) was verified. Figure 4 and Figure 5 As shown, Figure 4 Schematic diagram of tracking error without target point and parameter adaptation, where line 1 is the case of V = 20km / h, line 2 is the case of V = 30km / h, line 3 is the case of V = 40km / h, line 4 is the case of V = 50km / h, and line 5 is the case of V = 60km / h. Figure 5 Figure 6 is a schematic diagram of tracking error after adding target points and adaptive optimization of QR matrix. Line 6 is the case of V=20km / h, line 7 is the case of V=30km / h, line 8 is the case of V=40km / h, line 9 is the case of V=50km / h, and line 10 is the case of V=60km / h. Obviously, the optimized system can produce smaller fluctuations after interference and can recover stability faster, thereby improving the comfort and stability of the system.

[0109] Therefore, based on LQR control, adaptive preview selection control is added to ensure the stability and comfort of vehicle tracking on complex roads with rapid changes in curvature and speed; at the same time, an adaptive formula is summarized to determine the QR matrix selection to solve the problem of QR matrix selection, and finally the effect is improved through simulation comparison.

[0110] According to the lateral control method of an autonomous driving vehicle proposed in an embodiment of the present application, the actual coordinates and current heading angle of the vehicle can be obtained, the current posture and position information of the target point can be obtained, and the distance deviation, distance deviation change rate, heading angle deviation and angle deviation change rate from the target point at the current moment can be calculated based on the current posture and position information. The state matrix is calculated, and the first model parameter matrix and the second model parameter matrix are determined using the vehicle dynamics model, and the first weighted matrix and the second weighted matrix are selected at the same time to determine the optimal matrix according to the linear quadratic regulator LQR algorithm, and control the vehicle's steering actuator to execute the steering control amount obtained by multiplying the optimal matrix and the state matrix, thereby ensuring the stability and comfort of vehicle tracking on complex roads with fast curvature and speed changes, and realizing improved control accuracy and adaptability of the LQR controller.

[0111] Next, the lateral control device for an autonomous driving vehicle proposed in accordance with an embodiment of the present application will be described with reference to the accompanying drawings.

[0112] Figure 6 It is a block diagram of a lateral control device for an autonomous driving vehicle according to an embodiment of the present application.

[0113] like Figure 6 As shown, the lateral control device 10 of the autonomous driving vehicle includes: an acquisition module 100, a calculation module 200 and a control module 300.

[0114] The acquisition module 100 is used to obtain the actual coordinates and current heading angle of the vehicle, and obtain the current posture and position information of the target point;

[0115] The calculation module 200 is used to calculate the distance deviation, distance deviation change rate, heading angle deviation and angle deviation change rate from the target point at the current moment according to the current posture and position information, and calculate the state matrix; and

[0116] The control module 300 is used to determine a first model parameter matrix and a second model parameter matrix using a vehicle dynamics model, and simultaneously select a first weighting matrix and a second weighting matrix to determine an optimal matrix according to a linear quadratic regulator (LQR) algorithm, and control the vehicle's steering actuator to execute a steering control amount obtained by multiplying the optimal matrix and the state matrix.

[0117] Optionally, in some embodiments, the aforementioned lateral control device 10 for the autonomous driving vehicle further includes:

[0118] The determination module is used to determine the vehicle dynamics model based on the vehicle's front wheel cornering stiffness, rear wheel cornering stiffness, the distance from the front axle to the vehicle's center of gravity, the distance from the rear axle to the vehicle's center of gravity, the vehicle's z-axis moment of inertia and the vehicle's mass.

[0119] Optionally, in some embodiments, the calculation module 200 includes:

[0120] A judgment unit, used to judge the type of the current road;

[0121] a first determining unit, configured to determine, if the type is a straight track type, that the target point is the point on the track closest to the current position;

[0122] The second determining unit is configured to determine, if the road type is a curve type, that the target point is a point at a preview distance when the actual speed of the vehicle is greater than a preset threshold, otherwise the target point is a point at a distance determined by the road curvature.

[0123] Optionally, in some embodiments, the calculation formula for determining the distance based on the road curvature is:

[0124] L=kV+lmin,

[0125] Where k is the linear change ratio of speed, V is the actual speed of the vehicle, and lmin is the minimum setting value of the preview distance.

[0126] Optionally, in some embodiments, the calculation formula of the state matrix is:

[0127]

[0128] Among them, e1 is the distance deviation, e2 is the distance deviation change rate, e3 is the heading angle deviation, and e4 is the angle deviation change rate.

[0129] It should be noted that the above explanation of the embodiment of the lateral control method of the autonomous driving vehicle is also applicable to the lateral control device of the autonomous driving vehicle of this embodiment, and will not be repeated here.

[0130] According to the lateral control device of the autonomous driving vehicle proposed in the embodiment of the present application, the actual coordinates and current heading angle of the vehicle can be obtained, the current posture and position information of the target point can be obtained, and the distance deviation, distance deviation change rate, heading angle deviation and angle deviation change rate from the target point at the current moment can be calculated based on the current posture and position information, and the state matrix can be calculated. The first model parameter matrix and the second model parameter matrix can be determined using the vehicle dynamics model, and the first weighting matrix and the second weighting matrix can be selected at the same time to determine the optimal matrix according to the linear quadratic regulator LQR algorithm, and control the steering actuator of the vehicle to execute the steering control amount obtained by multiplying the optimal matrix and the state matrix, thereby ensuring the stability and comfort of vehicle tracking on complex roads with fast curvature and speed changes, and realizing the improvement of the control accuracy and adaptability of the LQR controller.

[0131] In addition, an embodiment of the present application also proposes a vehicle, which includes the above-mentioned lateral control device of the autonomous driving vehicle.

[0132] According to the vehicle proposed in the embodiment of the present application, the lateral control device of the above-mentioned autonomous driving vehicle can obtain the actual coordinates and current heading angle of the vehicle, obtain the current posture and position information of the target point, and calculate the distance deviation, distance deviation change rate, heading angle deviation and angle deviation change rate from the target point at the current moment based on the current posture and position information, calculate the state matrix, and use the vehicle dynamics model to determine the first model parameter matrix and the second model parameter matrix, and at the same time select the first weighting matrix and the second weighting matrix to determine the optimal matrix according to the linear quadratic regulator LQR algorithm, and control the steering actuator of the vehicle to execute the steering control amount obtained by multiplying the optimal matrix and the state matrix, thereby ensuring the stability and comfort of vehicle tracking on complex roads with fast curvature and speed changes, and realizing the improvement of the control accuracy and adaptability of the LQR controller.

[0133] In the description of this specification, the description with reference to the terms "one embodiment", "some embodiments", "example", "specific example", or "some examples" means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present application. In this specification, the schematic representations of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described can be combined in any one or N embodiments or examples in a suitable manner. In addition, those skilled in the art can combine and combine different embodiments or examples described in this specification and features of different embodiments or examples without contradiction.

[0134] Furthermore, the terms "first" and "second" are used for descriptive purposes only and should not be understood to indicate or imply relative importance or implicitly specify the number of technical features indicated. Thus, a feature specified as "first" or "second" may explicitly or implicitly include at least one such feature. In the description of this application, "N" means at least two, for example, two, three, etc., unless otherwise specifically defined.

[0135] Any process or method description in a flowchart or otherwise described herein may be understood to represent a module, fragment or portion of code comprising one or more executable instructions for implementing the steps of a custom logical function or process, and the scope of the preferred embodiments of the present application includes alternative implementations in which functions may be performed out of the order shown or discussed, including performing functions in a substantially simultaneous manner or in reverse order depending on the functions involved, which should be understood by those skilled in the art to which the embodiments of the present application belong.

[0136] It should be understood that various parts of the present application can be implemented using hardware, software, firmware, or a combination thereof. In the above embodiment, the N steps or methods can be implemented using software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented using hardware, as in another embodiment, any one of the following technologies known in the art or a combination thereof can be used to implement: a discrete logic circuit having a logic gate circuit for implementing a logic function on a data signal, an application-specific integrated circuit having a suitable combination of logic gate circuits, a programmable gate array (PGA), a field programmable gate array (FPGA), etc.

[0137] Those skilled in the art will understand that all or part of the steps in the method of the above embodiment can be completed by instructing related hardware through a program, and the program can be stored in a computer-readable storage medium. When the program is executed, it includes one or a combination of the steps of the method embodiment.

Claims

1. A lateral control method for an autonomous driving vehicle, characterized in that: The following steps are involved: Get the actual coordinates and current heading angle of the vehicle to obtain the current posture and location information of the target point; Calculate the distance deviation, distance deviation change rate, heading angle deviation and angle deviation change rate from the target point at the current moment according to the current posture and position information, and calculate the state matrix; as well as Determining a first model parameter matrix and a second model parameter matrix using a vehicle dynamics model, and simultaneously selecting a first weighting matrix and a second weighting matrix to determine an optimal matrix according to a linear quadratic regulator (LQR) algorithm, and controlling a steering actuator of the vehicle to execute a steering control variable obtained by multiplying the optimal matrix and the state matrix; Wherein, obtaining the target point includes: When the current road type is a straight road type, the target point is the point on the trajectory closest to the current position; In the case where the current road type is a curve type, if the actual speed of the vehicle is greater than a preset threshold, the target point is a point at a preview distance, wherein the preview distance L=lmax; otherwise, the target point is a point at a distance determined by the road curvature, wherein the distance determined by the road curvature is L=kV+lmin, wherein lmax=lmin / 2+V*V / 2a, lmin is the minimum setting value of the preview distance, V is the actual speed of the vehicle, a is the comfortable deceleration set for the vehicle, k=α*kp, kp is the curvature of the point on the trajectory closest to the current position, and α is an adjustable coefficient.

2. The method according to claim 1, characterized in that Also includes: The vehicle dynamics model is determined based on the front wheel cornering stiffness, rear wheel cornering stiffness, distance from the front axle to the vehicle center of gravity, distance from the rear axle to the vehicle center of gravity, z-axis moment of inertia of the vehicle and the vehicle mass.

3. The method according to claim 1, characterized in that The calculation formula of the state matrix is: Among them, e1 is the distance deviation, e2 is the distance deviation change rate, e3 is the heading angle deviation, and e4 is the angle deviation change rate.

4. A lateral control device for an autonomous vehicle, characterized in that: include: The acquisition module is used to obtain the actual coordinates and current heading angle of the vehicle, and obtain the current posture and position information of the target point; A calculation module is used to calculate the distance deviation, distance deviation change rate, heading angle deviation and angle deviation change rate from the target point at the current moment according to the current posture and position information, and calculate the state matrix; as well as Determining a first model parameter matrix and a second model parameter matrix using a vehicle dynamics model, and simultaneously selecting a first weighting matrix and a second weighting matrix to determine an optimal matrix according to a linear quadratic regulator (LQR) algorithm, and controlling a steering actuator of the vehicle to execute a steering control variable obtained by multiplying the optimal matrix and the state matrix; The acquiring module acquiring the target point includes: When the current road type is a straight road type, the target point is the point on the trajectory closest to the current position; In the case where the current road type is a curve type, if the actual speed of the vehicle is greater than a preset threshold, the target point is a point at a preview distance, wherein the preview distance L=lmax; otherwise, the target point is a point at a distance determined by the road curvature, wherein the distance determined by the road curvature is L=kV+lmin, wherein lmax=lmin / 2+V*V / 2a, lmin is the minimum setting value of the preview distance, V is the actual speed of the vehicle, a is the comfortable deceleration set for the vehicle, k=α*kp, kp is the curvature of the point on the trajectory closest to the current position, and α is an adjustable coefficient.

5. The device according to claim 4, characterized in that Also includes: A determination module is used to determine the vehicle dynamics model based on the front wheel cornering stiffness, rear wheel cornering stiffness, distance from the front axle to the vehicle center of gravity, distance from the rear axle to the vehicle center of gravity, z-axis moment of inertia of the vehicle and the vehicle mass.

6. The device according to claim 4, characterized in that The calculation formula of the state matrix is: Among them, e1 is the distance deviation, e2 is the distance deviation change rate, e3 is the heading angle deviation, and e4 is the angle deviation change rate.

7. A vehicle, characterized in that: include: A lateral control device for an autonomous driving vehicle as claimed in any one of claims 4 to 6.

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