Efficient lateral control method and system for autonomous vehicle, and vehicle

Abstract

An efficient lateral control method and system for an autonomous vehicle, and a vehicle, relating to the technical field of vehicle lateral control. The method comprises: during autonomous driving of a vehicle, acquiring real-time position information of the vehicle, and calculating a current state variable on the basis of the real-time position information and a preset reference trajectory; determining a reference speed, a reference heading angle, and a reference curvature; on the basis of an offline LQR control gain table, obtaining control gain intervals respectively corresponding to the reference speed, the reference heading angle, and the reference curvature; performing linear interpolation on the control gain intervals respectively corresponding to the reference speed, the reference heading angle, and the reference curvature, to obtain final control gains respectively corresponding to the reference speed, the reference heading angle, and the reference curvature; and obtaining a control law on the basis of the final control gains respectively corresponding to the reference speed, the reference heading angle and the reference curvature, and the current state variable. The present application can guarantee calculation precision and increases a calculation speed, thereby achieving a better lateral control effect.

Description

Efficient lateral control method, system and vehicle for autonomous vehicle

[0001] The present application claims priority to the Chinese patent application No. 202411373069.9, filed on September 29, 2024, and entitled "Efficient lateral control method, system and vehicle for autonomous vehicle", the entire content of which is incorporated herein by reference. TECHNICAL FIELD

[0002] The present application relates to the field of vehicle lateral control, and in particular to an efficient lateral control method, system and vehicle for autonomous vehicle. BACKGROUND

[0003] The lateral control system is a key function of autonomous vehicle, which can effectively improve the comfort and safety of driving. The linear quadratic regulator (LQR) control algorithm is a widely used method in the control design and analysis of autonomous vehicle. The LQR controller is used to calculate the steering wheel angle to ensure accurate trajectory tracking.

[0004] However, in actual use, the calculation of the LQR control algorithm is completed by Riccati recursion, and the number of iterations is closely related to the stability of the system. A higher number of iterations can make the vehicle quickly track, but it also consumes too much calculation time, which leads to a low frequency of sending control instructions to the chassis for execution, and ultimately the lateral control effect of the vehicle is poor. On the contrary, too few iterations require a long time to achieve trajectory tracking within the error tolerance range, resulting in low tracking accuracy. Therefore, the existing vehicle lateral control method based on LQR control is difficult to achieve good lateral control effect because it iteratively calculates the LQR control law online. SUMMARY

[0005] To solve the above problems of the prior art, the present application provides an efficient lateral control method, system and vehicle for autonomous vehicle. By pre-constructing an offline LQR control gain table, the LQR control gain table can be directly called during the actual driving of the autonomous vehicle, and the LQR control law can be quickly obtained by table lookup, and then the steering wheel angle can be calculated for lateral control. This method can effectively improve the calculation speed while ensuring the calculation accuracy, and ultimately achieve better lateral control effect.

[0006] In a first aspect, the present application provides an efficient lateral control method for autonomous vehicle.

[0007] An efficient lateral control method for autonomous vehicle, comprising:

[0008] In the automatic driving process of the vehicle, real-time position information of the vehicle is acquired, and a current state variable is calculated according to the real-time position information and a preset reference trajectory;

[0009] According to the preset reference trajectory, a reference speed, a reference heading angle and a reference curvature are determined;

[0010] Based on an offline linear quadratic regulator (LQR) control gain table, control gains of left and right endpoints of a discrete interval corresponding to the reference speed, the reference heading angle and the reference curvature in respective working ranges are determined respectively;

[0011] According to the control gains of the left and right endpoints of the discrete interval corresponding to the reference speed, the reference heading angle and the reference curvature respectively, control gain intervals corresponding to the reference speed, the reference heading angle and the reference curvature respectively are obtained;

[0012] The control gain intervals corresponding to the reference speed, the reference heading angle and the reference curvature respectively are linearly interpolated to obtain interpolated control gain intervals corresponding to the reference speed, the reference heading angle and the reference curvature respectively;

[0013] According to the reference speed, the reference heading angle and the reference curvature, and the interpolated control gain intervals corresponding to the reference speed, the reference heading angle and the reference curvature respectively, final control gains corresponding to the reference speed, the reference heading angle and the reference curvature respectively are obtained;

[0014] According to the final control gains corresponding to the reference speed, the reference heading angle and the reference curvature respectively and the current state variable, a control law is obtained, which is used for lateral control of the vehicle.

[0015] In a second aspect, the application provides an efficient lateral control system for an automatic driving vehicle.

[0016] An efficient lateral control system for an automatic driving vehicle comprises:

[0017] A data acquisition module is configured to acquire real-time position information of the vehicle in the automatic driving process of the vehicle, and calculate a current state variable according to the real-time position information and a preset reference trajectory; and determine a reference speed, a reference heading angle and a reference curvature according to the preset reference trajectory;

[0018] a data analysis module configured to determine, based on an offline linear quadratic regulator (LQR) control gain table, control gains of left and right endpoints of discrete intervals corresponding to the reference speed, the reference heading angle, and the reference curvature in respective working ranges, respectively; obtain control gain intervals corresponding to the reference speed, the reference heading angle, and the reference curvature, respectively, according to the control gains of the left and right endpoints of the discrete intervals corresponding to the reference speed, the reference heading angle, and the reference curvature, respectively; perform linear interpolation on the control gain intervals corresponding to the reference speed, the reference heading angle, and the reference curvature, respectively, to obtain interpolated control gain intervals corresponding to the reference speed, the reference heading angle, and the reference curvature, respectively; and obtain final control gains corresponding to the reference speed, the reference heading angle, and the reference curvature, respectively, according to the reference speed, the reference heading angle, and the reference curvature, and the interpolated control gain intervals corresponding to the reference speed, the reference heading angle, and the reference curvature, respectively.

[0019] a lateral control module configured to obtain a control law for lateral control of the vehicle according to the final control gains corresponding to the reference speed, the reference heading angle, and the reference curvature, and the current state variables.

[0020] In a third aspect, the present application also provides a computer device, which comprises a processor and a memory, and the memory stores a computer program, which is loaded and executed by the processor to implement the efficient lateral control method for an autonomous vehicle.

[0021] In a fourth aspect, the present application also provides a computer readable storage medium, which stores a computer program, which is loaded and executed by a processor to implement the efficient lateral control method for an autonomous vehicle.

[0022] In a fifth aspect, the present application also provides a vehicle, which is controlled laterally by the efficient lateral control method for an autonomous vehicle or comprises the efficient lateral control system for an autonomous vehicle.

[0023] The above one or more technical solutions have the following beneficial effects:

[0024] 1. The present application provides an efficient lateral control method, system, and vehicle for an autonomous vehicle. By pre-constructing an offline LQR control gain table, the LQR control gain table can be directly called in the actual driving process of the autonomous vehicle, and the LQR control law can be quickly obtained by table lookup, and then the steering wheel angle can be calculated for lateral control. This method can effectively improve the calculation speed while ensuring the calculation accuracy, and ultimately achieve better lateral control effect.

[0025] 2. The automatic driving vehicle efficient lateral control method, system and vehicle provided by the application, the core of which is the pre-construction and production of an offline LQR control gain table. In the application, a vehicle kinematics model is constructed, an LQR control model for vehicle lateral tracking control is established, and based on the LQR control model, the control gains corresponding to the endpoints of each discretization interval of the reference speed, reference heading angle and reference curvature in the working range are calculated and stored to obtain an offline LQR control gain table, thereby providing a data basis for subsequent efficient lateral control of the automatic driving vehicle and realizing efficient and rapid vehicle lateral control. BRIEF DESCRIPTION OF DRAWINGS

[0026] The accompanying drawings, which form a part of the specification, are included to provide a further understanding of the application and are incorporated herein in conjunction with the description of the application. The drawings should not be used to construe the specification in a limiting sense.

[0027] Fig. 1 is a flowchart of an automatic driving vehicle efficient lateral control method provided by an embodiment of the application;

[0028] Fig. 2 is a schematic diagram of the construction process of an LQR control gain table provided by an embodiment of the application;

[0029] Fig. 3 is a schematic diagram of the construction code of an LQR control gain table provided by an embodiment of the application;

[0030] Fig. 4 is a schematic diagram of the automatic driving vehicle efficient lateral control flow provided by an embodiment of the application;

[0031] Fig. 5 is a schematic diagram of the code for calling an offline LQR control gain table provided by an embodiment of the application. DETAILED DESCRIPTION

[0032] It should be noted that the following detailed description is exemplary only and is intended to provide further description of the specific embodiments in order to provide a further understanding of the application, and is not intended to limit the exemplary embodiments according to the application. Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which the application belongs. In addition, it should be understood that when the terms "comprise" and / or "include" are used in the specification, there is a presence of a feature, step, operation, device, component and / or combination thereof.

[0033] Embodiment One

[0034] To solve the problems in the prior art, the embodiment provides an automatic driving vehicle efficient lateral control method, which pre-constructs an offline LQR control gain table, directly calls the LQR control gain table during actual driving of the automatic driving vehicle, and realizes rapid and accurate vehicle lateral control through table lookup.

[0035] The core of the method proposed in this embodiment is the making of the offline LQR control gain table, specifically: first, determine the parameters such as reference speed ref_v, reference heading angle ref_heading, and reference curvature ref_kappa in the LQR control model, and determine the working range of the above parameters. Discretize the reference speed, reference heading angle, and reference curvature into several small intervals within the working range of the above parameters, and take the reference values at the endpoints of each interval into the model to obtain the control gain through Riccati iteration; second, for each reference speed interval endpoint, each reference heading angle interval endpoint, and each reference curvature interval endpoint, the corresponding control gain is calculated, and after storage, the offline LQR control gain table is constructed.

[0036] When using the offline LQR gain table, the left and right endpoints of the discrete interval are determined according to the current reference speed, reference heading angle, and reference curvature of the vehicle, and the offline LQR control gain table is searched to determine the gain values corresponding to the left and right endpoints of the interval, and the final corresponding gain value is obtained through linear interpolation. The control law is determined according to the gain value, and the vehicle is controlled in the lateral direction according to the control law, thereby achieving efficient and accurate vehicle lateral control.

[0037] The automatic driving vehicle efficient lateral control method provided in this embodiment includes the following steps 110-170:

[0038] Step 110, during the automatic driving of the vehicle, the real-time position information of the vehicle is obtained, and the current state variable is calculated according to the real-time position information and the preset reference trajectory.

[0039] Step 120, according to the preset reference trajectory, determine the reference speed, reference heading angle, and reference curvature.

[0040] Step 130, based on the offline linear quadratic regulator (LQR) control gain table, determine the control gain of the left and right endpoints of the discrete interval corresponding to the reference speed, reference heading angle, and reference curvature in their respective working ranges.

[0041] Step 140, according to the control gain of the left and right endpoints of the discrete interval corresponding to the reference speed, reference heading angle, and reference curvature, obtain the control gain interval corresponding to the reference speed, reference heading angle, and reference curvature.

[0042] Step 150, linearly interpolate the control gain interval corresponding to the reference speed, reference heading angle, and reference curvature to obtain the interpolated control gain interval corresponding to the reference speed, reference heading angle, and reference curvature.

[0043] At step 160, the final control gain corresponding to the reference speed, the reference heading angle and the reference curvature is obtained according to the reference speed, the reference heading angle and the reference curvature, and the interpolated control gain interval corresponding to the reference speed, the reference heading angle and the reference curvature respectively.

[0044] At step 170, the control law is obtained according to the final control gain corresponding to the reference speed, the reference heading angle and the reference curvature and the current state variable, and the control law is used for lateral control of the vehicle.

[0045] The automatic driving vehicle efficient lateral control method proposed in the embodiment is described in more detail as follows.

[0046] The core of the automatic driving vehicle efficient lateral control method proposed in the embodiment is to construct an offline LQR control gain table in advance, as shown in FIG. 2, and the construction of the LQR control gain table includes the following steps.

[0047] At step S1.1, an LQR control model for vehicle lateral control is established based on a vehicle kinematics model, and the model is as follows:

[0048] e(k+1)=Ae(k)+Bu(k);

[0049] In the above formula, e(k) represents the state variable of the vehicle at time k, the state variable of the vehicle includes the error value of the vehicle position horizontal and vertical coordinates and the error value of the vehicle heading angle, and the error value is the difference between the actual value and the reference value, i.e. e(k)=(x r (k)-x,y r (k)-y,heading r (k)-heading), e(k+1) represents the state variable of the vehicle at time k+1, u(k) represents the control amount at time k, and A and B are system matrices, which are represented as follows:

[0050] wherein the horizontal and vertical coordinates of the actual position coordinates are represented as (x, y), the actual heading angle, the actual curvature and the actual speed are represented as heading, v and a respectively, the horizontal and vertical coordinates of the reference position coordinates are represented as (x r ,y r ), the reference heading angle, the reference curvature, the reference speed and the reference acceleration are represented as heading r , κ r , v r , a r , δ r =arctan(L*κ r ), represents the reference heading angle, L represents the vehicle axle length, δ r represents the vehicle front wheel steering angle, and Ts This represents the sampling time when the continuous model is discretized.

[0051] Step S1.2: Obtain the vehicle's actual position information and the vehicle's reference values ​​within the preset reference trajectory. The actual position information includes actual position coordinates, actual heading angle, actual curvature, actual velocity, and actual acceleration. The reference values ​​include reference position coordinates, reference heading angle, reference curvature, reference velocity, and reference acceleration.

[0052] In practice, the vehicle's autonomous driving lateral control system receives a trajectory from the upper planning layer. This trajectory is a preset reference trajectory, which can be represented by the aforementioned reference values. That is, the trajectory typically includes (x... r ,y r ,heading r ,κ r ,v r ,a r ), (x r ,y r ,heading r ,κ r ,v r ,a r The x, y, heading, v, and a values ​​represent the reference position of the trajectory in the global coordinate system, respectively. Similarly, the current position information of the vehicle, which can be obtained through the vehicle positioning system, is (x, y, heading, v, a).

[0053] Step S1.3: Based on the vehicle's actual position coordinates, actual heading angle, reference position coordinates, and reference heading angle obtained above, calculate the vehicle's state variables, i.e., according to the following formula: e(k)=(x r (k)-x,y r (k)-y,heading r (k)-heading);

[0054] The state variable e(k) of the vehicle is calculated.

[0055] Step S1.4: Based on the preset reference trajectory, determine the working range corresponding to the reference speed, reference heading angle, and reference curvature, respectively.

[0056] In this embodiment, the reference velocity v r The working range is [0,14], and the unit is m / s; the reference heading angle is... r The operating range is [-pi, +pi], in rad; the reference curvature κ r The working range is [-0.125, 0.125], and the unit is 1 / m.

[0057] Step S1.5, discretize the working range corresponding to the reference speed, the reference heading angle and the reference curvature respectively to obtain discrete points in the working range corresponding to the reference speed, the reference heading angle and the reference curvature respectively.

[0058] Step S1.6, input the discrete points corresponding to the reference speed, the reference heading angle and the reference curvature respectively and the state variables of the vehicle into the LQR control model, obtain the control gain corresponding to different reference values respectively by iterative calculation based on the parameter matrix Q and R in the LQR control model, and construct an offline LQR control gain table.

[0059] The parameter matrix Q and R in the LQR control model are artificially pre-set, and a group of Q and R with the optimal control effect determined through repeated tests is taken as the final value.

[0060] The construction of the above LQR control gain table can be realized through the pseudo code shown in FIG. 3.

[0061] Finally, the gain files k1.dat, k2.dat and k3.dat calculated in FIG. 3 are saved to construct the offline LQR control gain table for subsequent calling.

[0062] In the embodiment, during the automatic driving of the vehicle, the offline LQR control gain table is used for efficient and accurate lateral control of the vehicle, as shown in FIG. 4, which specifically includes:

[0063] Step S2.1, the positioning system of the automatic driving vehicle is used to obtain the real-time position information of the vehicle, which includes real-time position coordinates (including the horizontal coordinate and the vertical coordinate of the real-time position), real-time heading angle, real-time speed and real-time acceleration (x, y, heading, v, a), on the basis of which, the preset reference trajectory (x r ,y r ,heading r ,κ r ,v r ,a r ) given by the planning layer is combined to determine the reference speed, the reference heading angle and the reference curvature, so as to calculate the current state variable e(k).

[0064] Step S2.2, based on the offline LQR control gain table, the reference speed v r , the reference heading angle heading r and the reference curvature κ rThe corresponding discrete intervals of the reference speed, the reference heading angle and the reference curvature are determined in the respective working ranges, i.e. the corresponding discrete intervals of the lists ref_v(p), ref_heading(q) and ref_kappa(r) are determined. On this basis, the control gains of the left and right endpoints of the discrete intervals corresponding to the reference speed, the reference heading angle and the reference curvature are determined, and the control gain values of the left and right endpoints of the discrete intervals corresponding to the reference speed, the reference heading angle and the reference curvature are denoted as [k1_l, k1_r], [k2_l, k2_r] and [k3_l, k3_r], so as to determine the control gain intervals corresponding to the reference speed, the reference heading angle and the reference curvature respectively.

[0065] Step S2.3. Linear interpolation is performed on the control gain intervals corresponding to the reference speed, the reference heading angle and the reference curvature respectively to obtain the interpolated control gain intervals corresponding to the reference speed, the reference heading angle and the reference curvature respectively. According to the reference speed v r , the reference heading angle heading r and the reference curvature κ r , and the interpolated control gain intervals corresponding to the reference speed, the reference heading angle and the reference curvature respectively, the final control gains corresponding to the reference speed, the reference heading angle and the reference curvature respectively are obtained, i.e. k1, k2 and k3 are obtained.

[0066] The final control gains obtained by linear interpolation are as follows: firstly, the total distances of the three reference parameters, i.e. the reference speed v r , the reference heading angle heading r and the reference curvature κ r , to the left and right endpoints of the respective corresponding discrete intervals are calculated, and are denoted as d l and d r ; secondly, the difference between the gain values of the left and right endpoints of the discrete interval corresponding to each reference parameter is multiplied by the slope of the total distance of the left and right endpoints, and the product is added to the sum of the gain values of the left endpoint of the discrete interval corresponding to the reference parameter, so as to obtain the final control gain of the reference parameter. For example, the current reference speed v r , the reference heading angle heading r and the reference curvature κ rLet v_x, heading_x, and kappa_x be the reference values. The intervals for v_x, heading_x, and kappa_x are determined to be [v1, v2], heading_x to be [heading1, heading2], and kappa_x to be [kappa1, kappa2]. Given that the control gain matrix K = [k1_l, k2_l, k3_l] corresponds to reference speed, reference heading angle, and reference curvature v1, heading1, and kappa1 respectively, and the control gain matrix K = [k2_r, k2_r, k3_r] corresponds to reference speed, reference heading angle, and reference curvature v2, heading2, and kappa2 respectively, the following formula is used to calculate: d l =sqrt((v_x-v1)^2+(heading_x-heading1)^2+(kappa_x- kappa1)^2); d r =sqrt((v2-v1)^2+(heading2-heading1)^2+(kappa2- kappa1)^2);

[0067] k1_x=k1_l+(k1_r-k1_l)*(d l / d r );

[0068] k2_x=k2_l+(k2_r-k2_l)*(d l / d r );

[0069] k3_x=k3_l+(k3_r-k3_l)*(d l / d r );

[0070] The final control gains k1_x, k2_x, and k3_x corresponding to the reference velocity v_x, reference heading angle heading_x, and reference curvature kappa_x are obtained through the above calculations, which gives k1, k2, and k3.

[0071] Step S2.4, based on the reference speed v r Reference heading angle r Reference curvature κ r Using the corresponding final control gain and current state variable e(k), the control law u = (k1, k2, k3) is calculated. T *e(k) performs lateral control of the vehicle according to the control law.

[0072] The above method for calling the offline LQR control gain table can be implemented using the pseudocode shown in Figure 5:

[0073] The control law is quickly calculated in the above manner, and the vehicle is controlled in the lateral direction according to the control law, so that efficient and accurate vehicle lateral control is achieved.

[0074] Embodiment Two

[0075] The embodiment provides an efficient lateral control system for an autonomous vehicle, which comprises:

[0076] The data acquisition module is configured to acquire real-time position information of the vehicle during autonomous driving of the vehicle, calculate a current state variable according to the real-time position information and a preset reference trajectory, and determine a reference speed, a reference heading angle and a reference curvature according to the preset reference trajectory.

[0077] The data analysis module is configured to determine control gains of left and right endpoints of discrete intervals corresponding to the reference speed, the reference heading angle and the reference curvature respectively in respective working ranges based on an offline linear quadratic regulator (LQR) control gain table, obtain control gain intervals corresponding to the reference speed, the reference heading angle and the reference curvature respectively according to the control gains of the left and right endpoints of the discrete intervals corresponding to the reference speed, the reference heading angle and the reference curvature respectively, perform linear interpolation on the control gain intervals corresponding to the reference speed, the reference heading angle and the reference curvature respectively to obtain interpolated control gain intervals corresponding to the reference speed, the reference heading angle and the reference curvature respectively, and obtain final control gains corresponding to the reference speed, the reference heading angle and the reference curvature respectively according to the reference speed, the reference heading angle and the reference curvature and the interpolated control gain intervals corresponding to the reference speed, the reference heading angle and the reference curvature respectively.

[0078] The lateral control module is configured to obtain a control law according to the final control gains corresponding to the reference speed, the reference heading angle and the reference curvature respectively and the current state variable, and the control law is used for lateral control of the vehicle.

[0079] Embodiment Three

[0080] The embodiment provides a computer device, which comprises a processor and a memory, the memory stores a computer program, the computer program is loaded and executed by the processor to implement an efficient lateral control method for an autonomous vehicle.

[0081] Embodiment Four

[0082] The embodiment also provides a computer readable storage medium, wherein a computer program is stored in the computer readable storage medium, the computer program is loaded and executed by a processor to implement the automatic driving vehicle efficient lateral control method.

[0083] Embodiment five

[0084] The embodiment also provides a vehicle, which is controlled laterally by using the automatic driving vehicle efficient lateral control method or comprises the automatic driving vehicle efficient lateral control system.

[0085] The steps and method embodiments two to five correspond to the method embodiment one, and the specific embodiments can be referred to the related description part of the embodiment one. The term "computer readable storage medium" should be understood as including a single medium or multiple media of one or more instruction sets; and should also be understood as including any medium capable of storing, encoding or carrying the instruction set for execution by the processor and causing the processor to perform any method in the present application.

[0086] Those skilled in the art should understand that each module or step of the present application described above can be realized by a general computer device, alternatively, they can be realized by program codes executable by a computing device, so that they can be stored in a storage device and executed by a computing device, or they can be respectively manufactured into each integrated circuit module, or a plurality of modules or steps among them can be manufactured into a single integrated circuit module to realize. The present application is not limited to any specific combination of hardware and software.

[0087] The above only describes the preferred embodiments of the present application, and the specific embodiments of the present application are described in combination with the drawings, but the present application is not limited thereto, and those skilled in the art should understand that various modifications or changes made on the basis of the technical solutions of the present application without creative labor are still within the protection scope of the present application.

Claims

1. A method for efficient lateral control of an autonomous vehicle, the method being performed by a computer device, comprising: acquiring real-time position information of the vehicle during autonomous driving of the vehicle, and calculating a current state variable based on the real-time position information and a preset reference trajectory; determining a reference speed, a reference heading angle and a reference curvature based on the preset reference trajectory; determining control gains of left and right endpoints of discrete intervals corresponding to the reference speed, the reference heading angle and the reference curvature respectively in respective working ranges based on an offline linear quadratic regulator (LQR) control gain table; obtaining control gain intervals corresponding to the reference speed, the reference heading angle and the reference curvature respectively based on the control gains of the left and right endpoints of the discrete intervals corresponding to the reference speed, the reference heading angle and the reference curvature respectively; performing linear interpolation on the control gain intervals corresponding to the reference speed, the reference heading angle and the reference curvature respectively to obtain interpolated control gain intervals corresponding to the reference speed, the reference heading angle and the reference curvature respectively; obtaining final control gains corresponding to the reference speed, the reference heading angle and the reference curvature respectively based on the reference speed, the reference heading angle and the reference curvature and the interpolated control gain intervals corresponding to the reference speed, the reference heading angle and the reference curvature respectively; obtaining a control law based on the final control gains corresponding to the reference speed, the reference heading angle and the reference curvature respectively and the current state variable, the control law being used for lateral control of the vehicle.

2. The efficient lateral control method for an autonomous vehicle of claim 1, wherein, The real-time position information includes real-time position coordinates, a real-time heading angle, a real-time speed and a real-time acceleration, and the real-time position coordinates include a horizontal coordinate and a vertical coordinate of a real-time position.

3. The efficient lateral control method for an autonomous vehicle of claim 1, wherein, The method further comprises: establishing an LQR control model for vehicle lateral control based on a vehicle kinematic model; acquiring actual position information of the vehicle and a reference value of the vehicle in the preset reference trajectory, wherein the actual position information includes actual position coordinates, an actual heading angle, an actual curvature, an actual speed and an actual acceleration, and the reference value includes a reference position coordinate, a reference heading angle, a reference curvature, a reference speed and a reference acceleration; calculating a state variable of the vehicle based on the actual position coordinates, the actual heading angle, the reference position coordinate and the reference heading angle; determining working ranges corresponding to the reference speed, the reference heading angle and the reference curvature respectively based on the preset reference trajectory; discretizing the working ranges corresponding to the reference speed, the reference heading angle and the reference curvature respectively to obtain discrete points in the working ranges corresponding to the reference speed, the reference heading angle and the reference curvature respectively; inputting the discrete points corresponding to the reference speed, the reference heading angle and the reference curvature respectively and the state variable of the vehicle into the LQR control model, and obtaining control gains corresponding to different reference values respectively by iterative calculation based on parameter matrices Q and R in the LQR control model to construct the offline LQR control gain table.

4. The efficient lateral control method for an autonomous vehicle of claim 3, wherein, The LQR control model is: e(k+1)=Ae(k)+Bu(k); In the above formula, e(k) represents a state variable of the vehicle at time k, e(k+1) represents a state variable of the vehicle at time k+1, u(k) represents a control variable at time k, A and B are system matrices, and are respectively represented as: wherein the horizontal and vertical coordinates of the actual position coordinates are denoted as (x, y), the actual heading, the actual curvature and the actual speed are denoted as heading, v, a respectively; the horizontal and vertical coordinates of the reference position coordinates are denoted as (x r ,y r ), the reference heading, the reference curvature, the reference speed and the reference acceleration are denoted as heading r , κ r , v r , a r respectively, and δ r = arctan(L * κ r ), denotes the reference heading angle, L denotes the vehicle wheelbase, δ r denotes the vehicle front wheel steering angle, T s denotes the sampling time when the continuous model is discretized.

5. The efficient lateral control method for an autonomous vehicle of claim 4, wherein, The state variables of the vehicle include error values of vehicle position horizontal and vertical coordinates and an error value of a vehicle heading angle, and the error values are differences between actual values and reference values.

6. An efficient lateral control system for an autonomous vehicle, comprising: a data acquisition module configured to, during autonomous driving of the vehicle, acquire real-time position information of the vehicle, and calculate a current state variable based on the real-time position information and a preset reference trajectory; determine a reference speed, a reference heading angle, and a reference curvature based on the preset reference trajectory; a data analysis module configured to, based on an offline linear quadratic regulator (LQR) control gain table, determine control gains of left and right endpoints of a discrete interval corresponding to each of the reference speed, the reference heading angle, and the reference curvature in a respective working range, and obtain a control gain interval corresponding to each of the reference speed, the reference heading angle, and the reference curvature based on the control gains of the left and right endpoints of the discrete interval corresponding to each of the reference speed, the reference heading angle, and the reference curvature; perform linear interpolation on the control gain intervals corresponding to each of the reference speed, the reference heading angle, and the reference curvature to obtain interpolated control gain intervals corresponding to each of the reference speed, the reference heading angle, and the reference curvature; obtain final control gains corresponding to each of the reference speed, the reference heading angle, and the reference curvature based on the reference speed, the reference heading angle, the reference curvature, and the interpolated control gain intervals corresponding to each of the reference speed, the reference heading angle, and the reference curvature; a lateral control module configured to obtain a control law based on the final control gains corresponding to each of the reference speed, the reference heading angle, and the reference curvature and the current state variable, and use the control law to perform lateral control on the vehicle.

7. The efficient lateral control system for an autonomous vehicle of claim 6, wherein, The data analysis module is further configured to: establish an LQR control model for vehicle lateral control based on a vehicle kinematic model; acquire actual position information of the vehicle and a reference value of the vehicle in the preset reference trajectory; the actual position information includes actual position coordinates, an actual heading angle, an actual curvature, an actual speed, and an actual acceleration, and the reference value includes reference position coordinates, a reference heading angle, a reference curvature, a reference speed, and a reference acceleration; calculate state variables of the vehicle based on the actual position coordinates, the actual heading angle, the reference position coordinates, and the reference heading angle; determine working ranges corresponding to each of the reference speed, the reference heading angle, and the reference curvature based on the preset reference trajectory; discretize the working ranges corresponding to each of the reference speed, the reference heading angle, and the reference curvature to obtain discrete points in the working ranges corresponding to each of the reference speed, the reference heading angle, and the reference curvature. The discrete points corresponding to the reference speed, the reference heading angle and the reference curvature respectively and the state variables of the vehicle are input into the LQR control model, and based on the parameter matrices Q and R in the LQR control model, control gains corresponding to different reference values are obtained through iterative calculation, and the offline LQR control gain table is constructed. 8.A computer device, comprising a processor and a memory, wherein the memory stores a computer program, and the computer program is loaded and executed by the processor to implement the automatic driving vehicle efficient lateral control method according to any one of claims 1 to 5. 9.A computer readable storage medium, wherein the computer readable storage medium stores a computer program, and the computer program is loaded and executed by a processor to implement the automatic driving vehicle efficient lateral control method according to any one of claims 1 to 5. 10.A vehicle, which is controlled in lateral direction by the automatic driving vehicle efficient lateral control method according to any one of claims 1 to 5, or comprises the automatic driving vehicle efficient lateral control system according to any one of claims 6 to 7.

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