Automatic driving vehicle efficient transverse control method and system and vehicle

Abstract

The invention discloses an efficient transverse control method and system for an automatic driving vehicle and the vehicle, and relates to the technical field of vehicle transverse control, and the method comprises the steps: in the automatic driving process of the vehicle, obtaining the current real-time position information of the vehicle, determining a reference speed, a reference course angle and a reference curvature in combination with a preset reference trajectory, and calculating a current state variable; based on an off-line LQR control gain table, discrete intervals where the reference speed, the reference course angle and the reference curvature are located in respective working ranges are determined, control gains corresponding to the left end point and the right end point of the discrete interval where each reference value is located are determined, and then a control gain interval is determined; according to the positions of the discrete intervals where the reference values are located, control gains are obtained through linear interpolation calculation; according to the control gain corresponding to each reference value and the current state variable, a control law is obtained through calculation, and transverse control is conducted on the vehicle according to the control law. The method can guarantee the calculation precision, improves the calculation speed, and achieves a better transverse control effect.

Description

Technical Field

[0001] The present invention relates to the technical field of vehicle lateral control, and in particular to an efficient lateral control method, system and vehicle for an autonomous driving vehicle. Background Art

[0002] As a key function of autonomous vehicles, the lateral control system can effectively improve driving comfort and safety. The linear quadratic regulator (LQR) control algorithm is a widely used method in the control design and analysis of autonomous vehicles. It ensures accurate trajectory tracking by calculating the steering wheel angle using the LQR controller. However, in actual use, the calculation of the LQR control algorithm is completed through Riccati iteration. The number of iterations is closely related to the stability of the system. A higher number of iterations can enable the vehicle to track quickly, but it also consumes too much computing time, which leads to a low frequency of control commands sent to the chassis for execution, and finally the lateral control effect of the vehicle deteriorates. On the contrary, if the number of iterations is too small, it will take a long time to achieve trajectory tracking within the error tolerance range, resulting in low tracking accuracy. Therefore, for the existing vehicle lateral control method based on LQR control, it is difficult to achieve a good lateral control effect by online iterative calculation of the LQR control law. Summary of the invention

[0003] To address the deficiencies of the above-mentioned prior art, the present invention provides an efficient lateral control method, system and vehicle for an autonomous driving vehicle. By pre-constructing an offline LQR control gain table, the LQR control gain table can be directly called during the actual driving process of the autonomous driving vehicle, and the LQR control law can be quickly obtained by looking up the table, and then the steering wheel angle is calculated for lateral control. In this way, the calculation speed can be effectively improved while ensuring the calculation accuracy, and ultimately a better lateral control effect can be achieved.

[0004] In a first aspect, the present invention provides an efficient lateral control method for an autonomous driving vehicle.

[0005] An efficient lateral control method for an autonomous driving vehicle, comprising:

[0006] During the vehicle's automatic driving process, the vehicle's current real-time position information is obtained, and the reference speed, reference heading angle and reference curvature are determined in combination with the preset reference trajectory, and the current state variables are calculated;

[0007] Based on the offline LQR control gain table, the discrete intervals of the reference speed, reference heading angle and reference curvature within their respective working ranges are determined respectively, and the control gains corresponding to the left and right endpoints of the discrete interval where each reference value is located are determined, and then the control gain interval is determined;

[0008] According to the position of the discrete intervals of the reference speed, reference heading angle, and reference curvature, linear interpolation is performed in the control gain interval corresponding to each reference value to obtain the final control gain;

[0009] The control law is calculated according to the control gains corresponding to the reference speed, reference heading angle, reference curvature and the current state variables, and the vehicle is laterally controlled according to the control law.

[0010] In a second aspect, the present invention provides an efficient lateral control system for an autonomous driving vehicle.

[0011] An efficient lateral control system for an autonomous driving vehicle, comprising:

[0012] The data acquisition module is used to obtain the current real-time position information of the vehicle during the automatic driving process of the vehicle, determine the reference speed, reference heading angle and reference curvature in combination with the preset reference trajectory, and calculate the current state variables;

[0013] The data analysis module is used to determine the discrete intervals of the reference speed, reference heading angle and reference curvature within their respective working ranges based on the offline LQR control gain table, and determine the control gains corresponding to the left and right endpoints of the discrete interval where each reference value is located, and then determine the control gain interval; according to the positions of the discrete intervals where the reference speed, reference heading angle and reference curvature are located, linear interpolation is performed in the control gain interval corresponding to each reference value to obtain the final control gain;

[0014] The lateral control module is used to calculate the control law according to the control gains corresponding to the reference speed, reference heading angle, reference curvature and the current state variables, and to perform lateral control on the vehicle according to the control law.

[0015] In a third aspect, the present invention further provides an electronic device comprising a memory and a processor, and computer instructions stored in the memory and executed on the processor, wherein when the computer instructions are executed by the processor, the steps of the method described in the first aspect are completed.

[0016] In a fourth aspect, the present invention further provides a computer-readable storage medium for storing computer instructions, which, when executed by a processor, complete the steps of the method described in the first aspect.

[0017] In a fifth aspect, the present invention further provides a vehicle that performs lateral control using an efficient lateral control method for an autonomous driving vehicle as described in the first aspect, or includes an efficient lateral control system for an autonomous driving vehicle as described in the first aspect.

[0018] One or more of the above technical solutions have the following beneficial effects:

[0019] 1. The present invention provides an efficient lateral control method, system and vehicle for an autonomous driving vehicle. By pre-building an offline LQR control gain table, the LQR control gain table can be directly called during the actual driving process of the autonomous driving vehicle, and the LQR control law can be quickly obtained by looking up the table, and then the steering wheel angle is calculated for lateral control. In this way, the calculation speed can be effectively improved while ensuring the calculation accuracy, and ultimately a better lateral control effect can be achieved.

[0020] 2. The core of the efficient lateral control method, system and vehicle for autonomous driving vehicles provided by the present invention is the pre-construction and production of an offline LQR control gain table. In the present invention, by constructing a vehicle kinematic model, an LQR control model for vehicle lateral tracking control is established. Based on the LQR control model, the control gains corresponding to the endpoints of each discretized interval of the reference speed, reference heading angle and reference curvature within the working range are calculated, and an offline LQR control gain table is stored to provide a data basis for the subsequent efficient lateral control of the autonomous driving vehicle, thereby achieving efficient and rapid lateral control of the vehicle. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] The accompanying drawings in the specification, which constitute a part of the present invention, are used to provide a further understanding of the present invention. The exemplary embodiments of the present invention and their descriptions are used to explain the present invention and do not constitute improper limitations on the present invention.

[0022] Figure 1 This is a flow chart of an efficient lateral control method for an autonomous driving vehicle according to an embodiment of the present invention;

[0023] Figure 2 This is a flow chart of constructing an offline LQR control gain table in an embodiment of the present invention. DETAILED DESCRIPTION

[0024] It should be noted that the following detailed descriptions are exemplary only, are intended to describe specific embodiments, are intended to provide further explanation of the present invention, and are not intended to limit exemplary embodiments according to the present invention. Unless otherwise specified, all technical and scientific terms used herein have the same meanings as those of ordinary skill in the art to which the present invention belongs. In addition, it should also be understood that when the terms "comprising" and / or "including" are used in this specification, it indicates the presence of features, steps, operations, devices, components and / or combinations thereof.

[0025] Embodiment 1

[0026] In order to solve the problems existing in the prior art, the present embodiment provides an efficient lateral control method for an autonomous driving vehicle. By pre-building an offline LQR control gain table, the LQR control gain table is directly called during the actual driving process of the autonomous driving vehicle, and fast and accurate vehicle lateral control is achieved by table lookup.

[0027] The core of the method proposed in this embodiment lies in the preparation of an offline LQR control gain table, specifically: first, determine the reference speed ref_v, reference heading angle ref_heading, reference curvature ref_kappa and other parameters in the LQR control model, clarify 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, take the reference value of each interval endpoint and substitute it into the model, perform Riccati iteration to obtain the control gain; secondly, for each reference speed interval endpoint, each reference heading angle interval endpoint and each reference curvature interval endpoint, calculate the corresponding control gain, and construct the offline LQR control gain table after storage. When using the offline LQR gain table, the left and right endpoints of the discrete interval are determined according to the vehicle's current reference speed, reference heading angle, and reference curvature, and the offline LQR control gain table is searched to determine the gain values ​​corresponding to the left and right endpoints of the interval, respectively. The final corresponding gain value is obtained through linear interpolation, and the control law is determined according to the gain value. The vehicle is laterally controlled according to the control law, thereby achieving efficient and accurate vehicle lateral control.

[0028] The present embodiment provides an efficient lateral control method for an autonomous driving vehicle, comprising the following steps:

[0029] During the vehicle's automatic driving process, the vehicle's current real-time position information is obtained, and the reference speed, reference heading angle and reference curvature are determined in combination with the preset reference trajectory, and the current state variables are calculated;

[0030] Based on the offline LQR control gain table, the discrete intervals of the reference speed, reference heading angle and reference curvature within their respective working ranges are determined respectively, and the control gains corresponding to the left and right endpoints of the discrete interval where each reference value is located are determined, and then the control gain interval is determined;

[0031] According to the position of the discrete intervals of the reference speed, reference heading angle, and reference curvature, linear interpolation is performed in the control gain interval corresponding to each reference value to obtain the final control gain;

[0032] The control law is calculated according to the control gains corresponding to the reference speed, reference heading angle, reference curvature and the current state variables, and the vehicle is laterally controlled according to the control law.

[0033] The following content provides a more detailed introduction to the efficient lateral control method for an autonomous driving vehicle proposed in this embodiment.

[0034] The core of the efficient lateral control method for the autonomous driving vehicle proposed in this embodiment is to pre-build an offline LQR control gain table, such as Figure 2 As shown, the construction of the LQR control gain table includes the following steps:

[0035] Step S1.1: Based on the vehicle kinematics model, an LQR control model for vehicle lateral tracking control is established. The model is:

[0036] e(k+1)=Ae(k)+Bu(k);

[0037] In the above formula, e(k) represents the state variable of the vehicle at the current moment, i.e., at moment k. The state variable of the vehicle includes the error value of the horizontal and vertical coordinates of the vehicle position and the error value of the vehicle heading angle. 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 the next moment, i.e., moment k+1, u(k) represents the control quantity at the current moment, i.e., moment k, and A and B are both system matrices, which are expressed as:

[0038]

[0039] The horizontal and vertical coordinates of the actual position coordinates are expressed as (x, y), the actual heading angle, actual curvature, and actual speed are expressed as heading, v, and a respectively; the horizontal and vertical coordinates of the reference position coordinates are expressed as (x r ,y r ), the reference heading angle, reference curvature, reference velocity, and reference acceleration are respectively 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 axis length, δ r represents the front wheel turning angle of the vehicle, T s Represents the sampling time when the continuous model is discretized.

[0040] Step S1.2, obtaining the actual position information of the vehicle and obtaining the reference value of the vehicle in the preset reference trajectory. The actual position signal includes the actual position coordinates, the actual heading angle, the actual curvature, the actual speed, and the actual acceleration, and the reference value includes the reference position coordinates, the reference heading angle, the reference curvature, the reference speed, and the reference acceleration.

[0041] In fact, the vehicle's automatic driving lateral control system receives the trajectory from the upper planning layer, which is a preset reference trajectory and can be represented by the above reference value, that is, the trajectory usually 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 ) represent the reference position horizontal and vertical coordinates, heading angle, curvature, speed, and acceleration of the trajectory in the global coordinate system. Similarly, the current position information of the vehicle that can be obtained through the vehicle positioning system is (x, y, heading, v, a).

[0042] Step S1.3, based on the actual position coordinates, actual heading angle, reference position coordinates, and reference heading angle of the vehicle obtained above, calculate the state variables of the vehicle, that is, according to the following formula:

[0043] e(k)=(x r (k)-x,y r (k)-y,heading r (k)-heading);

[0044] The vehicle's state variable e(k) is calculated.

[0045] Step S1.4: Based on the preset reference trajectory, determine the working range of the reference speed, reference heading angle, and reference curvature, and discretize the reference speed, reference heading angle, and reference curvature within the working range.

[0046] In this embodiment, the reference speed v r The working range is [0,14], the unit is m / s; the reference heading angle is r The working range is [-pi, +pi], the unit is rad; the reference curvature κ r The working range is [-0.125, 0.125], and the unit is 1 / m.

[0047] Step S1.5, after determining the above model, substitute each discrete point of the reference speed, reference heading angle, reference curvature and the state variable of the vehicle into the LQR control model, set the parameter matrices Q and R in the model, and obtain the control gains of the vehicle position coordinates and heading angle under different reference values ​​through iterative calculation, so as to construct an offline LQR control gain table. Among them, the parameter matrices Q and R in the model are artificially pre-set, and the set of Q and R with the best control effect is determined as the final value through repeated tests.

[0048] The construction of the above LQR control gain table can be realized by the following pseudo code:

[0049]

[0050] Finally, the gain files k1.dat, k2.dat, and k3.dat calculated above are saved to construct an offline LQR control gain table for subsequent use.

[0051] In this embodiment, during the automatic driving process of the vehicle, an offline LQR control gain table is used to perform efficient and accurate lateral control of the vehicle, such as Figure 1 As shown, specifically including:

[0052] Step S2.1, using the positioning system of the autonomous driving vehicle, obtain the current real-time position information of the vehicle, which includes the real-time position coordinates (including the horizontal and vertical coordinates of the position), the real-time heading angle, the real-time speed, and the real-time acceleration (x, y, heading, v, a). On this basis, combined with the preset reference trajectory (x r ,y r ,heading r ,κ r ,v r ,a r ), determine the reference speed, reference heading angle and reference curvature, and use them to calculate the current state variable e(k).

[0053] Step S2.2: Based on the offline LQR control gain table, the reference speed v is determined by looking up the table according to the determined reference speed, reference heading angle and reference curvature. r , reference heading angle r and the reference curvature κ rThe discrete intervals in their respective working ranges are determined in the lists ref_v(p), ref_heading(q), and ref_kappa(r). On this basis, the control gains corresponding to the left and right endpoints of the discrete intervals where each reference value is located are determined, and the corresponding control gain values ​​are recorded as [k1_l, k1_r], [k2_l, k2_r], and [k3_l, k3_r] to determine the control gain interval.

[0054] Step S2.3: According to the reference speed v r , reference heading angle r , reference curvature κ r The position of the discrete interval is linearly interpolated in the control gain interval corresponding to each reference value to obtain the final control gain, that is, k1, k2, and k3.

[0055] The final control gain is obtained by linear interpolation calculation as follows: First, calculate the reference speed v r , reference heading angle r and the reference curvature κ r The total distances of these three reference parameters to the left and right endpoints of their respective discrete intervals are denoted as d l ,d r ; Secondly, the ratio of the total distance between the left and right endpoints is taken as the slope. For each reference parameter, the difference between the gain values ​​of the left and right endpoints of the discrete interval where the reference parameter is located is multiplied by the slope, and the sum of the product and the gain value corresponding to the left endpoint of the discrete interval where the reference parameter is located is taken as the control gain corresponding to the reference parameter. For example, the current reference speed v r , reference heading angle r and the reference curvature κ r Recorded as v_x, heading_x, kappa_x, find and determine that the interval of v_x is [v1, v2], the interval of heading_x is [heading1, heading2], and the interval of kappa_x is [kappa1, kappa2]. It is known that when the reference speed, reference heading angle, and reference curvature are v1, heading1, and kappa1 respectively, the corresponding control gain matrix K = [k1_l, k2_l, k3_l], and when the reference speed, reference heading angle, and reference curvature are v2, heading2, and kappa2 respectively, the corresponding control gain matrix K = [k2_r, k2_r, k3_r]. Based on the above parameters, the calculation is performed by the following formula:

[0056] d l=sqrt((v_x-v1)^2+(heading_x-heading1)^2+(kappa_x-kappa1)^2);

[0057] d r =sqrt((v2-v1)^2+(heading2-heading1)^2+(kappa2-kappa1)^2);

[0058] k1_x=k1_l+(k1_r-k1_l)*(d l / d r );

[0059] k2_x=k2_l+(k2_r-k2_l)*(d l / d r );

[0060] k3_x=k3_l+(k3_r-k3_l)*(d l / d r );

[0061] Through the above calculation, we can obtain the control gains k1_x, k2_x, k3_x corresponding to the reference speed v_x, the reference heading angle heading_x and the reference curvature kappa_x, that is, we can obtain k1, k2, k3.

[0062] Step S2.4: According to the reference speed v r , reference heading angle r , reference curvature κ r The corresponding control gain and current state variable e(k) are used to calculate the control law u=(k1, k2, k3) T *e(k), the vehicle is laterally controlled according to the control law.

[0063] The above method of calling the offline LQR control gain table can be implemented by the following pseudo code:

[0064]

[0065] The control law is quickly calculated in the above manner, and the vehicle is laterally controlled according to the control law, thereby achieving efficient and accurate lateral control of the vehicle.

[0066] Embodiment 2

[0067] This embodiment provides an efficient lateral control system for an autonomous driving vehicle, including:

[0068] The data acquisition module is used to obtain the current real-time position information of the vehicle during the automatic driving process of the vehicle, determine the reference speed, reference heading angle and reference curvature in combination with the preset reference trajectory, and calculate the current state variables;

[0069] The data analysis module is used to determine the discrete intervals of the reference speed, reference heading angle and reference curvature within their respective working ranges based on the offline LQR control gain table, and determine the control gains corresponding to the left and right endpoints of the discrete interval where each reference value is located, and then determine the control gain interval; according to the positions of the discrete intervals where the reference speed, reference heading angle and reference curvature are located, linear interpolation is performed in the control gain interval corresponding to each reference value to obtain the final control gain;

[0070] The lateral control module is used to calculate the control law according to the control gains corresponding to the reference speed, reference heading angle, reference curvature and the current state variables, and to perform lateral control on the vehicle according to the control law.

[0071] Embodiment 3

[0072] This embodiment provides an electronic device, including a memory and a processor, and computer instructions stored in the memory and executed on the processor. When the computer instructions are executed by the processor, the steps of the above-mentioned method for efficient lateral control of an autonomous driving vehicle are completed.

[0073] Embodiment 4

[0074] This embodiment also provides a computer-readable storage medium for storing computer instructions, which, when executed by a processor, complete the steps of the above-mentioned method for efficient lateral control of an autonomous driving vehicle.

[0075] Embodiment 5

[0076] This embodiment also provides a vehicle that uses an efficient lateral control method for an autonomous driving vehicle as described above for lateral control, or includes an efficient lateral control system for an autonomous driving vehicle as described above.

[0077] The steps involved in the above embodiments 2 to 5 correspond to the method embodiment 1. For the specific implementation, please refer to the relevant description part of embodiment 1. The term "computer-readable storage medium" should be understood as a single medium or multiple media including one or more instruction sets; it should also be understood to include any medium that can store, encode or carry an instruction set for execution by a processor and enable the processor to execute any method in the present invention.

[0078] Those skilled in the art should understand that the modules or steps of the present invention described above can be implemented by a general-purpose computer device, or alternatively, they can be implemented by a program code executable by a computing device, so that they can be stored in a storage device and executed by the computing device, or they can be made into individual integrated circuit modules, or multiple modules or steps therein can be made into a single integrated circuit module for implementation. The present invention is not limited to any specific combination of hardware and software.

[0079] The above description is only a preferred embodiment of the present invention. Although the specific implementation mode of the present invention is described in conjunction with the accompanying drawings, it is not a limitation of the protection scope of the present invention. Those skilled in the art should understand that on the basis of the technical solution of the present invention, various modifications or variations that can be made by those skilled in the art without creative work are still within the protection scope of the present invention.

Claims

1. An efficient lateral control method for an autonomous driving vehicle, characterized in that: include: During the vehicle's automatic driving process, the vehicle's current real-time position information is obtained, and the reference speed, reference heading angle and reference curvature are determined in combination with the preset reference trajectory, and the current state variables are calculated; Based on the offline LQR control gain table, the discrete intervals of the reference speed, reference heading angle and reference curvature within their respective working ranges are determined respectively, and the control gains corresponding to the left and right endpoints of the discrete interval where each reference value is located are determined, and then the control gain interval is determined; According to the position of the discrete intervals of the reference speed, reference heading angle, and reference curvature, linear interpolation is performed in the control gain interval corresponding to each reference value to obtain the final control gain; The control law is calculated according to the control gains corresponding to the reference speed, reference heading angle, reference curvature and the current state variables, and the vehicle is laterally controlled according to the control law.

2. The efficient lateral control method for an autonomous driving vehicle according to claim 1, characterized in that: The real-time position information includes real-time position coordinates, real-time heading angle, real-time speed, and real-time acceleration; the position coordinates include the horizontal and vertical coordinates of the position.

3. The efficient lateral control method for an autonomous driving vehicle according to claim 1, characterized in that: The construction of the offline LQR control gain table includes: Based on the vehicle kinematics model, the LQR control model of vehicle lateral tracking control is established; Acquire the actual position information of the vehicle and obtain the reference value of the vehicle in the preset reference trajectory; the actual position signal includes the actual position coordinates, the actual heading angle, the actual curvature, the actual speed, and the actual acceleration; the reference value includes the reference position coordinates, the reference heading angle, the reference curvature, the reference speed, and the reference acceleration; Calculate the state variables of the vehicle based on the actual position coordinates, actual heading angle, reference position coordinates, and reference heading angle of the vehicle; Based on the preset reference trajectory, determine the working range of the reference speed, reference heading angle, and reference curvature, and discretize the reference speed, reference heading angle, and reference curvature within the working range; Each discrete point of reference speed, reference heading angle, reference curvature and the state variables of the vehicle are substituted into the LQR control model, and the parameter matrices Q and R in the model are set. The control gains of the vehicle position coordinates and heading angle under different reference values ​​are obtained through iterative calculation, so as to construct an offline LQR control gain table.

4. The efficient lateral control method for an autonomous driving vehicle according to claim 3, characterized in that: The LQR control model is: e(k+1)=Ae(k)+Bu(k); In the above formula, e(k) represents the state variable of the vehicle at the current moment, i.e., time k; e(k+1) represents the state variable of the vehicle at the next moment, i.e., time k+1; u(k) represents the control quantity at the current moment, i.e., time k; A and B are both system matrices, which are expressed as: The horizontal and vertical coordinates of the actual position coordinates are expressed as (x, y), the actual heading angle, actual curvature, and actual speed are expressed as heading, v, and a respectively; the horizontal and vertical coordinates of the reference position coordinates are expressed as (x r ,y r ), the reference heading angle, reference curvature, reference velocity, and reference acceleration are respectively 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 axis length, δ r represents the front wheel turning angle of the vehicle, T s Represents the sampling time when the continuous model is discretized.

5. The efficient lateral control method for an autonomous driving vehicle according to claim 4, characterized in that: The state variables of the vehicle include error values ​​of the horizontal and vertical coordinates of the vehicle position and an error value of the vehicle heading angle, and the error value is the difference between an actual value and a reference value.

6. An efficient lateral control system for an autonomous driving vehicle, characterized in that: include: The data acquisition module is used to obtain the current real-time position information of the vehicle during the automatic driving process of the vehicle, determine the reference speed, reference heading angle and reference curvature in combination with the preset reference trajectory, and calculate the current state variables; The data analysis module is used to determine the discrete intervals of the reference speed, reference heading angle and reference curvature within their respective working ranges based on the offline LQR control gain table, and determine the control gains corresponding to the left and right endpoints of the discrete interval where each reference value is located, and then determine the control gain interval; according to the positions of the discrete intervals where the reference speed, reference heading angle and reference curvature are located, linear interpolation is performed in the control gain interval corresponding to each reference value to obtain the final control gain; The lateral control module is used to calculate the control law according to the control gains corresponding to the reference speed, reference heading angle, reference curvature and the current state variables, and to perform lateral control on the vehicle according to the control law.

7. An efficient lateral control system for an autonomous driving vehicle as claimed in claim 6, characterized in that: The construction of the offline LQR control gain table includes: Based on the vehicle kinematics model, the LQR control model of vehicle lateral tracking control is established; Acquire the actual position information of the vehicle and obtain the reference value of the vehicle in the preset reference trajectory; the actual position signal includes the actual position coordinates, the actual heading angle, the actual curvature, the actual speed, and the actual acceleration; the reference value includes the reference position coordinates, the reference heading angle, the reference curvature, the reference speed, and the reference acceleration; Calculate the state variables of the vehicle based on the actual position coordinates, actual heading angle, reference position coordinates, and reference heading angle of the vehicle; Based on the preset reference trajectory, determine the working range of the reference speed, reference heading angle, and reference curvature, and discretize the reference speed, reference heading angle, and reference curvature within the working range; Each discrete point of reference speed, reference heading angle, reference curvature and the state variables of the vehicle are substituted into the LQR control model, and the parameter matrices Q and R in the model are set. The control gains of the vehicle position coordinates and heading angle under different reference values ​​are obtained through iterative calculation, so as to construct an offline LQR control gain table.

8. An electronic device, characterized in that: The method comprises a memory and a processor and computer instructions stored in the memory and executed on the processor. When the computer instructions are executed by the processor, the steps of an efficient lateral control method for an autonomous driving vehicle as described in any one of claims 1 to 5 are completed.

9. A computer-readable storage medium, characterized in that: Used to store computer instructions, which, when executed by a processor, complete the steps of an efficient lateral control method for an autonomous driving vehicle as described in any one of claims 1-5.

10. A vehicle, characterized in that: The method for efficient lateral control of an autonomous driving vehicle as described in any one of claims 1-5 is used for lateral control, or the method includes an efficient lateral control system for an autonomous driving vehicle as described in any one of claims 1-5.

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