Control method and device of vehicle, storage medium and vehicle

Abstract

The embodiment of the application provides a vehicle control method, device, storage medium and vehicle, the method comprises the following steps: generating deviation information according to acquired road information through a pre-look point algorithm; generating a control matrix value according to the deviation information and acquired vehicle information; adjusting a lateral offset according to the control matrix value; generating an incremental output value according to driving information; and adjusting a driving speed according to the incremental output value, so that the control matrix value for controlling the lateral movement of the vehicle is calculated through the pre-look point algorithm and the road information, and the incremental output value for controlling the longitudinal movement of the vehicle is calculated through the driving information, the lateral and longitudinal control of the vehicle adopts different control modes, and the lateral and longitudinal control is cooperatively controlled, so that the vehicle can automatically adjust the lateral offset and the driving speed in a complex environment, and the control precision of the vehicle is improved.

Description

[Technical Field]

[0001] This invention relates to the field of vehicle technology, specifically to a vehicle control method, device, storage medium, and vehicle. [Background Technology]

[0002] During operation, the stability and control precision of autonomous vehicles are crucial. Autonomous vehicles need to drive automatically according to the required speed and path, minimizing both speed and position errors to ensure precise movement along the predetermined trajectory.

[0003] However, in the process of autonomous driving, a single control method is usually used to achieve the lateral and longitudinal movement of the vehicle. This method of achieving autonomous driving with a single control method does not have high control precision. It works fine in simple and closed scenarios, but it will cause deviations in complex scenarios, thus reducing the control effect of autonomous driving. [Summary of the Invention]

[0004] In view of this, embodiments of the present invention provide a vehicle control method, device, storage medium, and vehicle to solve the problem of reduced control effect in the prior art for autonomous driving of vehicles.

[0005] In a first aspect, embodiments of the present invention provide a vehicle control method, comprising:

[0006] Based on the acquired road information, deviation information is generated using a pre-aiming point algorithm.

[0007] Based on the deviation information and the acquired vehicle information, a control matrix value is generated;

[0008] Adjust the lateral offset according to the control matrix value;

[0009] Based on the driving information, generate incremental output values;

[0010] Adjust the driving speed based on the incremental output value.

[0011] In one possible implementation, generating the control matrix value based on the deviation information and the acquired vehicle information includes:

[0012] The control matrix value is generated based on the deviation information and the vehicle information using the linear quadratic regulator LQR algorithm.

[0013] In one possible implementation, generating the control matrix value using a linear quadratic regulator (LQR) algorithm based on the deviation information and the vehicle information includes:

[0014] Based on the deviation information and the vehicle information, a state equation model is generated;

[0015] The first function solution is generated based on the state equation model, objective function, first defined function, and second defined function.

[0016] The control matrix value is generated by the second setting function based on the solution of the first function and the parameters of the state equation.

[0017] In one possible implementation, generating the incremental output value based on the driving information includes:

[0018] The fuzzy PID control algorithm generates incremental output values ​​based on driving information.

[0019] In one possible implementation, the driving information includes first driving information and second driving information, and the fuzzy PID control algorithm includes a fuzzy algorithm and a PID control algorithm; the step of generating incremental output values ​​based on the driving information using the fuzzy PID control algorithm includes:

[0020] Based on the first driving information, the current PID parameters are generated using a fuzzy algorithm.

[0021] The incremental output value is generated by using a PID control algorithm based on the current PID parameters and the second driving information.

[0022] In one possible implementation, the first driving information includes at least one of speed deviation, speed deviation change rate, driving speed, and speed setpoint; the second driving information includes regulator control deviation amount.

[0023] In one possible implementation, the vehicle information includes at least one of the following: vehicle mass, moment of inertia, distance between front wheel centers of gravity and rear wheel centers of gravity, driving speed, front wheel lateral stiffness, rear wheel lateral stiffness, front wheel steering angle measurement, and heading angle.

[0024] Secondly, embodiments of the present invention provide a vehicle control device, comprising:

[0025] The first generation module is used to generate deviation information based on the acquired road information using a pre-aiming point algorithm;

[0026] The second generation module is used to generate control matrix values ​​based on the deviation information and the acquired vehicle information;

[0027] The first adjustment module is used to adjust the lateral offset according to the control matrix value;

[0028] The third generation module is used to generate incremental output values ​​based on driving information;

[0029] The second adjustment module is used to adjust the driving speed according to the incremental output value.

[0030] Thirdly, embodiments of the present invention provide a storage medium including a stored program, wherein, when the program is executed, the device where the storage medium is located is controlled to perform the vehicle control method described in the first aspect or any possible implementation thereof.

[0031] Fourthly, embodiments of the present invention provide a vehicle, including a memory and a processor. The memory is used to store information including program instructions, and the processor is used to control the execution of the program instructions. When the program instructions are loaded and executed by the processor, they implement the steps of the vehicle control method in the first aspect or any possible implementation of the first aspect.

[0032] The present invention provides a vehicle control method, device, storage medium, and vehicle technical solution. Through a pre-aiming point algorithm, deviation information is generated based on acquired road information; a control matrix value is generated based on the deviation information and acquired vehicle information; the lateral offset is adjusted based on the control matrix value; an incremental output value is generated based on driving information; and the driving speed is adjusted based on the incremental output value. Thus, the control matrix value is calculated using the pre-aiming point algorithm and road information, and the incremental output value is calculated using driving information. This allows the vehicle to employ different control methods in its lateral and longitudinal directions, and through coordinated lateral and longitudinal control, the vehicle can automatically adjust its lateral offset and driving speed even in complex environments, improving the vehicle's control accuracy. [Attached Image Description]

[0033] To more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0034] Figure 1 A flowchart of a vehicle control method provided in an embodiment of the present invention;

[0035] Figure 2 A path diagram provided for an embodiment of the present invention;

[0036] Figure 3 A flowchart for generating control matrix values ​​is provided in an embodiment of the present invention;

[0037] Figure 4 A flowchart for generating incremental output values ​​is provided as an embodiment of the present invention;

[0038] Figure 5This is a schematic diagram of the structure of a vehicle control device provided in an embodiment of the present invention;

[0039] Figure 6 This is a schematic diagram of a vehicle provided in an embodiment of the present invention.

Detailed Implementation Methods

[0040] To better understand the technical solution of the present invention, the embodiments of the present invention will be described in detail below with reference to the accompanying drawings.

[0041] It should be understood that the described embodiments are merely some, not all, of the embodiments of the present invention. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without inventive effort are within the scope of protection of the present invention.

[0042] The terminology used in the embodiments of this invention is for the purpose of describing particular embodiments only and is not intended to limit the invention. The singular forms “a,” “the,” and “the” as used in the embodiments of this invention and the appended claims are also intended to include the plural forms unless the context clearly indicates otherwise.

[0043] It should be understood that the term "and / or" used in this article is merely a description of the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A existing alone, A and B existing simultaneously, and B existing alone. Additionally, the character " / " in this article generally indicates that the preceding and following related objects have an "or" relationship.

[0044] It should be understood that although terms such as first, second, third, etc., may be used to describe numbers in embodiments of the present invention, these numbers should not be limited to these terms. These terms are only used to distinguish numbers from each other. For example, without departing from the scope of embodiments of the present invention, a first number may also be referred to as a second number, and similarly, a second number may also be referred to as a first number.

[0045] Depending on the context, the word "if" as used here can be interpreted as "when," "when," "in response to determination," or "in response to detection." Similarly, depending on the context, the phrase "if determination" or "if detection (of the stated condition or event)" can be interpreted as "when determination," "in response to determination," "when detection (of the stated condition or event)," or "in response to detection (of the stated condition or event)."

[0046] Figure 1 A flowchart of a vehicle control method provided in an embodiment of the present invention is shown below. Figure 1 As shown, the method includes:

[0047] Step 101: The vehicle uses a pre-aiming point algorithm to generate deviation information based on the acquired road information.

[0048] In this embodiment of the invention, the aiming point algorithm is a control algorithm proposed based on human driving behavior. Since humans can manipulate the vehicle based on road information and the vehicle's motion state, the vehicle can use the aiming point algorithm to detect the path ahead in advance and calculate the desired curvature of the path. Based on the desired curvature and road information, aiming point position information and closest path information are generated. Deviation information is generated based on the aiming point position information and the current position information. The deviation information includes lateral deviation and heading angle deviation; the aiming point position parameters include the position coordinates of multiple aiming points; and the closest path information includes the position coordinates of the vehicle and the closest point on the path at the current moment.

[0049] The vehicle is equipped with navigation software that provides users with a road navigation line from their current location to their destination. Figure 2 A path diagram provided for an embodiment of the present invention, such as Figure 2 As shown, the vehicle travels along the path in the positive X-axis and positive Y-axis directions, that is, in the direction from P0 to P3. P0 represents the point on the path closest to the vehicle at the current moment; there are multiple preview points P1, P2, and P3. P1 represents the predicted point on the path closest to the vehicle after a travel time T1; P2 represents the predicted point on the path closest to the vehicle after a travel time T2; and P3 represents the predicted point on the path closest to the vehicle after a travel time T3.

[0050] like Figure 2 As shown, the vehicle can detect the path ahead of the vehicle through the pre-aiming point algorithm and navigation software, calculate the expected curvature of the path, determine multiple pre-aiming points P1, P2 and P3 on the path based on the expected curvature and road information, and determine the shortest path point P0; then determine the position coordinates of the shortest path point and the position coordinates of multiple pre-aiming points; finally, based on the position coordinates of the shortest path point and the position coordinates of multiple pre-aiming points, predict the lateral deviation and heading angle deviation between the vehicle's current actual position and the road navigation line.

[0051] Step 102: The vehicle generates control matrix values ​​based on the deviation information and the acquired vehicle information.

[0052] In this embodiment of the invention, the vehicle information includes at least one of the following: vehicle mass, moment of inertia, distance between the front and rear wheel centers of gravity, driving speed, front wheel lateral stiffness, rear wheel lateral stiffness, measured front wheel steering angle, and heading angle. The control matrix value can also be referred to as the calculated front wheel steering angle value.

[0053] Step 103: The vehicle adjusts its lateral offset according to the control matrix value.

[0054] In embodiments of the present invention, such as Figure 2 As shown, the vehicle needs to travel along the arc of the path. The vehicle includes a chassis module, a steering wheel, and wheels. The chassis module controls the steering wheel to turn through a control matrix value, which in turn controls the wheels to turn, causing the vehicle to travel to the left. This achieves the adjustment of the vehicle's lateral offset and thus the adjustment of the vehicle's direction of travel.

[0055] Step 104: The vehicle generates incremental output values ​​based on the driving information.

[0056] In this embodiment of the invention, the driving information includes first driving information and second driving information. The first driving information includes at least one of speed deviation, speed deviation change rate, driving speed, and speed setpoint; the second driving information includes regulator control deviation. The incremental output value includes throttle input or brake input.

[0057] Step 105: The vehicle adjusts its speed according to the incremental output value.

[0058] In this embodiment of the invention, if the incremental output value includes throttle input, the vehicle increases its speed according to the throttle input; if the incremental output value includes brake input, the vehicle decreases its speed according to the brake input. For example, the vehicle includes a vehicle chassis module, an throttle, and a brake. The vehicle chassis module controls the throttle according to the throttle input, thereby increasing the vehicle's speed; the vehicle chassis module controls the brake according to the brake input, thereby decreasing the vehicle's speed.

[0059] In this embodiment of the invention, steps 104 to 105 may be executed before steps 101 to 103, or they may be executed simultaneously with steps 101 to 103. This embodiment of the invention does not limit the execution order of steps 101 to 103 and steps 104 to 105.

[0060] This invention provides a vehicle control method that uses a pre-aiming point algorithm to generate deviation information based on acquired road information; generates a control matrix value based on the deviation information and acquired vehicle information; adjusts the lateral offset based on the control matrix value; generates an incremental output value based on driving information; and adjusts the driving speed based on the incremental output value. This method enables the vehicle to automatically adjust its lateral offset and driving speed even in complex environments through coordinated lateral and longitudinal control, using the pre-aiming point algorithm and road information to calculate the control matrix value and the driving information to calculate the incremental output value. This improves the vehicle's control accuracy.

[0061] In one possible implementation, the user can pre-set a lateral control cycle in the vehicle, for example, 0.03 seconds. The vehicle will execute steps 101 to 103 every 0.03 seconds according to the lateral control cycle, automatically adjusting the lateral offset. A time step is also pre-set in the vehicle, allowing it to automatically execute steps 104 to 105, enabling real-time adjustment of the vehicle's speed.

[0062] In one possible implementation, step 102 may specifically include: the vehicle generating control matrix values ​​based on deviation information and vehicle information using a linear quadratic regulator (LQR) algorithm.

[0063] Figure 3 A flowchart for generating control matrix values ​​is provided as an embodiment of the present invention, such as... Figure 3 As shown, step 102 may specifically include:

[0064] Step 1021: The vehicle generates a state equation model based on the deviation information and vehicle information.

[0065] In this embodiment of the invention, the deviation information includes the lateral deviation e1 and the first derivative of the lateral deviation. The first derivative of the heading angle deviation e2 and the heading angle deviation Vehicle information includes vehicle mass m and moment of inertia I. z Front wheel center of gravity distance l f Rear wheel center of gravity distance lr, driving speed V x Front wheel lateral stiffness C af Rear wheel lateral stiffness C ar With heading angle Among them, the distance from the center of gravity of the front wheel is l f This indicates the distance from the front wheels to the center of gravity of the vehicle, and the distance from the rear wheels to the center of gravity. r The distance from the rear wheel to the center of gravity of the vehicle; vehicle mass m, moment of inertia I z Front wheel center of gravity distance l f Rear wheel center of gravity distance l r Front wheel lateral stiffness C af Rear wheel lateral stiffness C ar All are constants; travel speed V x This can be obtained through the vehicle's sensors; heading angle. It can be measured via the vehicle's CAN bus; the calculated value σ of the vehicle's front wheel steering angle is an unknown quantity.

[0066] The state equation model is The state equation model can be simplified to The simplified state equation model is defined as Equation (1).

[0067] Among them, the definition For formula (2); Define For formula (3); Define For formula (4); Define Formula (5) is used. It represents the first derivative of x, which is the differential with respect to x.

[0068] Step 1022: The vehicle generates the first function solution based on the state equation model, objective function, first set function and second set function.

[0069] In this embodiment of the invention, the objective function is: The objective function is defined as Equation (6), which can also be called the energy minimization formula or optimization function. In Equations (1) and (6), x is the state matrix; Q is the state weight matrix; R is the control weight matrix; u is the control matrix; T is the matrix transpose, where T represents a matrix calculation method; x, Q, and R are all known quantities. The vehicle includes a state feedback controller; the vehicle designs the state feedback controller using the LQR algorithm to minimize the value of the objective function, resulting in optimal control.

[0070] The first defined function is u = -Kx. Substituting the first defined function into formula (6), we can obtain formula (7). Formula (7) is: Assuming there exists a constant matrix P, formula (8) can be obtained from constant matrix P and formula (7). Formula (8) is: Expanding the differential in formula (8), we obtain formula (9), which is: Based on formula (1) and the first set function, formula (10) can be obtained. Formula (10) represents the first derivative of the state matrix x. The equation, formula (10) is: Substituting formula (10) into formula (9) yields formula (11), which is: Simplifying formula (11) yields formula (12), which is: Analyzing formula (12) reveals that when When the value is 0, formula (12) has a solution. Definition Formula (13) is given by equation (10) and equation (13). Formula (14) can be obtained from equation (14), which is (A-BK). T P+P(A-BK)+Q+K TRK = 0. Rearranging formula (14) yields formula (15), where formula (15) is A. T +PA+Q+K T RK-K T B T P-PBK = 0.

[0071] The second defined function is K = R -1 B T P. Substituting the second defined function into formula (15) yields formula (16), where formula (16) is A. T +PA+Q+K T R(R -1 B T P)-K T B T P-PB(R -1 B T P) = 0. Rearranging formula (16) yields formula (17), where formula (17) is A. T +PA+Q-PB(R -1 B T P) = 0.

[0072] Since A, B, Q and R are all known quantities, the value of the constant matrix P can be calculated according to formula (17), and the value of P is the solution of the first function.

[0073] Step 1023: The vehicle generates control matrix values ​​based on the solution of the first function and the parameters of the state equation through the second setting function.

[0074] In this embodiment of the invention, the value of K can be calculated by substituting the solution of the first function into the second set function. Substituting the calculated value of K and formula (5) into the first set function yields the control matrix value u.

[0075] In one possible implementation, the vehicle information also includes a front wheel steering angle measurement value α. The front wheel steering angle measurement value α can be acquired by the vehicle's sensors. After step 1023, the following steps are also included: Step 1024: The vehicle generates a steering angle deviation based on the control matrix value and the front wheel steering angle measurement value; if it is determined that the steering angle deviation is less than or equal to a steering angle threshold, then step 103 is executed; if it is determined that the steering angle deviation is greater than the steering angle threshold, then the control matrix value is determined to be invalid, and step 101 is executed.

[0076] In this embodiment of the invention, the vehicle compares the control matrix value u predicted by the LQR algorithm with the steering angle deviation and steering angle threshold of the front wheel steering angle measurement value α collected by the sensor; if the steering angle deviation is greater than the steering angle threshold, an error may occur and the vehicle needs to regenerate the control matrix value; if the steering angle deviation is less than or equal to the steering angle threshold, the vehicle executes step 103 to adjust the lateral offset.

[0077] In one possible implementation, step 104 may specifically include: generating incremental output values ​​based on driving information using a fuzzy PID control algorithm.

[0078] In this embodiment of the invention, PID is an abbreviation for Proportional, Integral, and Differential. Driving information includes first driving information and second driving information. The first driving information includes at least one of speed deviation e, speed deviation change rate ec, speed setpoint, and driving speed; the second driving information includes the regulator control deviation e(t). The vehicle calculates the output increment value at fixed time intervals and executes step 105, thereby enabling real-time adjustment of the vehicle's driving speed. The fuzzy PID control algorithm includes both fuzzy algorithms and PID control algorithms.

[0079] Figure 4 A flowchart for generating incremental output values ​​is provided as an embodiment of the present invention, such as... Figure 4 As shown, step 2024 may specifically include:

[0080] Step 1041: The vehicle generates the current PID parameters based on the first driving information using a fuzzy algorithm.

[0081] In this embodiment of the invention, the current PID parameters include the regulator amplification coefficient, the partial regulation coefficient, and the derivative regulation coefficient. Step 1041 may specifically include:

[0082] Step 1041a: The vehicle obtains the membership function corresponding to the PID correction value of the current PID parameter based on the first driving information.

[0083] In this embodiment of the invention, the current PID parameters include the regulator amplification factor, the partial regulation factor, and the derivative regulation factor. The regulator amplification factor is determined by k. p This indicates that the adjustment coefficient is determined by k. i This indicates that the differential adjustment coefficient is determined by k. d Indicates the PID correction value corresponding to the current PID parameter; k p The corresponding correction value is determined by Δk. p It means that k i The corresponding correction value is determined by Δk. i It means that k d The corresponding correction value is determined by Δk. dThe first driving information includes speed deviation *e* and the rate of change of speed deviation *ec*. The vehicle includes a vehicle controller. In the vehicle controller, the input quantities are speed deviation *e* and the rate of change of speed deviation *ec*. The universe of discourse for speed deviation *e* is [-14, 14], and the fuzzy language set is [rapid deceleration, slight deceleration, slight deceleration, hold, slight acceleration, slight acceleration, rapid acceleration]; the universe of discourse for the rate of change of speed deviation *ec* is [-2, 2], and the fuzzy language set is [rapid deceleration, slight deceleration, slight deceleration, hold, slight acceleration, slight acceleration, rapid acceleration]. The membership functions for speed deviation *e* and the rate of change of speed deviation *ec* are the *guassmf* membership function, Δk p Δk i With Δk d The membership function is an asymmetric triangular membership function.

[0084] Step 1041b: The vehicle generates a fuzzy inference result corresponding to the PID correction value based on the first driving information according to the fuzzy rules of the fuzzy algorithm.

[0085] In this embodiment of the invention, the first driving information further includes driving speed and a speed setting value. The driving speed is the current speed of the vehicle, which can be collected by sensors; the speed setting value can be the maximum speed limit required on the road the vehicle is traveling on, which can be obtained by navigation software.

[0086] Fuzzy rules include: fuzzy rule 1), fuzzy rule 2), and fuzzy rule 3.

[0087] Fuzzy rule 1) When the speed setpoint and the actual driving speed differ too much, select the larger k value. p and smaller k d Meanwhile, to avoid large overshoot, the integral adjustment coefficient is limited, and k is taken as k. i =0. That is, Δk p The value should reach the level that currently needs to be generated, k. p Compared to the previously calculated k p The effect should be significant; the value of Δkd should reach the level of k that needs to be generated. d Compared to the previously calculated k d Small effect; Δk i The value of should be such that the k to be generated is currently required. i The effect is to approach or equal to 0. For example, when the difference between the speed setpoint and the driving speed is greater than the first threshold, fuzzy rule 1 is used.

[0088] Fuzzy rule 2) When both the velocity deviation e and the rate of change of velocity deviation ec are in their corresponding intermediate regions, in order to ensure the response speed and reduce the system response overshoot, k p It should be slightly smaller; because ki The value of k has a significant impact on the system response, therefore... i A smaller value should be chosen; k d Choose an appropriate value. That is, Δk p The value should reach the level that currently needs to be generated, k. p Compared to the previously calculated k p Small effect; Δk d The value should reach the level that currently needs to be generated, k. d Compared with the previously calculated k d The effect of approaching or equalizing; Δk i The value of should be such that the k to be generated is currently required. i Compared to the previously calculated k i Small effect. The intermediate region corresponding to the velocity deviation e is a subset of the universe of discourse where the velocity deviation e is located. For example, if the universe of discourse for the velocity deviation e is [-14, 14], the intermediate region corresponding to the velocity deviation e is [-7, 7]. The intermediate region corresponding to the velocity deviation change rate ec is a subset of the universe of discourse where the velocity deviation change rate ec is located. For example, if the universe of discourse for the velocity deviation change rate ec is [-2, 2], the intermediate region corresponding to the velocity deviation change rate ec is [0, 1]. When the velocity deviation e is greater than or equal to -7 and less than or equal to 7, and the velocity deviation change rate ec is greater than or equal to 0 and less than or equal to 1, fuzzy rule 2 is applied.

[0089] Fuzzy rule 3) When the error between the speed setpoint and the driving speed is small, the speed deviation e and the rate of change of speed deviation ec are small. In order to make the system have better steady-state performance, k should be increased. p and k i The value of k, and at the same time, when the rate of change of velocity deviation ec is small. d A larger value should be chosen; when the rate of change of velocity deviation (ec) is large, k d Take the smaller value; when the speed deviation e equals the rate of change of speed deviation ec, k d Choose an appropriate value. That is, Δk p The value should reach the level that currently needs to be generated, k. p Compared to the previously calculated k p Large effect; Δk i The value should reach the level that currently needs to be generated, k. i Compared to the previously calculated k i The large effect; when the speed deviation e is greater than the rate of change of speed deviation ec, Δk d The value should reach the level that currently needs to be generated, k. d Compared to the previously calculated k d The large effect; when the speed deviation e is less than the rate of change of speed deviation ec, Δk dThe value should reach the level that currently needs to be generated, k. d Compared to the previously calculated k d Small effect; when the velocity deviation e equals the rate of change of velocity deviation ec, Δk d The value should reach the level that currently needs to be generated, k. d Compared with the previously calculated k d The effect is to approximate or equalize. For example, when the difference between the speed setpoint and the driving speed is less than the second threshold, fuzzy rule 3 is used.

[0090] Step 1041c: The vehicle generates a PID correction value based on the fuzzy inference result and the membership function.

[0091] In this embodiment of the invention, the vehicle uses an asymmetric triangular membership function and the principle of maximum membership to defuzzify the fuzzy inference result, thereby obtaining a suitable Δk. p Δk i With Δk d The value of .

[0092] Step 1041d: The vehicle generates the current PID parameters based on the PID correction value using the correction function.

[0093] In this embodiment of the invention, the correction function includes a regulator amplification coefficient correction function, a partial regulation coefficient correction function, and a derivative regulation coefficient correction function. The regulator amplification coefficient correction function is: k p =k′ p +Δk p The adjustment coefficient correction function is: k i =k′ i +Δk i The differential adjustment coefficient correction function is: k d =k′ d +Δk d Where, k′ p 、k′ i 、k′ d These are the current PID parameters calculated in the previous step. Therefore, the vehicle can adjust the PID parameters in real time using a fuzzy algorithm.

[0094] Step 1042: The vehicle generates incremental output values ​​based on the current PID parameters and the second driving information using the PID control algorithm.

[0095] In this embodiment of the invention, the PID control algorithm is as follows: Among them, u k Let e(t) represent the incremental output value of the k-th sampling, and let k represent the control deviation of the regulator. p T represents the amplification factor of the regulator. i T represents the integral adjustment coefficient.d This represents the differential adjustment coefficient. Thus, the vehicle, through dual-input, multi-rule fuzzy inference logic, detects the values ​​of speed deviation *e* and the rate of change of speed deviation *ec*, as well as the values ​​of driving speed and speed setpoint during the calculation process. Based on fuzzy rules, it finds the fuzzy relationship between the PID correction value and the speed deviation *e* and the rate of change of speed deviation *ec*, or the fuzzy relationship between driving speed and speed setpoint, obtaining the fuzzy inference result of the fuzzy rules. Then, it defuzzifies the inference result using the maximum membership principle to obtain a suitable PID correction value. Furthermore, it obtains the current PID parameters of the PID control algorithm through the correction function and the PID correction value, and finally calculates the incremental output value, enabling the vehicle to adjust the current PID parameters of the PID control algorithm in real time.

[0096] This invention provides a vehicle control method. Using a pre-aiming algorithm, deviation information is generated based on acquired road information; a control matrix value is generated based on the deviation information and acquired vehicle information; the lateral offset is adjusted based on the control matrix value; an incremental output value is generated based on driving information; and the driving speed is adjusted based on the incremental output value. This allows the vehicle to achieve lateral offset control through the pre-aiming algorithm and the LQR algorithm, and to control the vehicle's driving speed through a fuzzy PID control algorithm. By employing a combined lateral and longitudinal control approach, different algorithms are used to calculate the control matrix value and incremental output value in real time, enabling the vehicle to automatically adjust its lateral offset and driving speed even in complex environments, thus improving the vehicle's control accuracy.

[0097] Figure 5 This is a schematic diagram of the structure of a vehicle control device provided in an embodiment of the present invention, as shown below. Figure 5 As shown, the device includes

[0098] The first generation module 11 is used to generate deviation information based on the acquired road information using a pre-aiming point algorithm; the second generation module 12 is used to generate control matrix values ​​based on the deviation information and the acquired vehicle information; the first adjustment module 13 is used to adjust the lateral offset based on the control matrix values; the third generation module 14 is used to generate incremental output values ​​based on driving information; and the second adjustment module 15 is used to adjust the driving speed based on the incremental output values.

[0099] In this embodiment of the invention, the second generation module 12 is specifically used to generate control matrix values ​​based on deviation information and vehicle information using the linear quadratic regulator LQR algorithm.

[0100] In this embodiment of the invention, the second generation module 12 is specifically used to generate a state equation model based on deviation information and vehicle information; generate a first function solution based on the state equation model, objective function, first set function and second set function; and generate a control matrix value based on the first function solution and state equation parameters through the second set function.

[0101] In this embodiment of the invention, the third generation module 14 is specifically used to generate incremental output values ​​based on driving information using a fuzzy PID control algorithm.

[0102] In this embodiment of the invention, the driving information includes first driving information and second driving information, and the fuzzy PID control algorithm includes a fuzzy algorithm and a PID control algorithm; the third generation module 14 is specifically used to generate the current PID parameters based on the first driving information using the fuzzy algorithm; and to generate an incremental output value based on the current PID parameters and the second driving information using the PID control algorithm.

[0103] In this embodiment of the invention, the first driving information includes at least one of speed deviation, speed deviation change rate, driving speed, and speed set value; the second driving information includes regulator control deviation amount.

[0104] In this embodiment of the invention, the vehicle information includes at least one of the following: vehicle mass, moment of inertia, distance between the front wheel center of gravity and the rear wheel center of gravity, driving speed, front wheel lateral stiffness, rear wheel lateral stiffness, front wheel steering angle measurement, and heading angle.

[0105] This invention provides a vehicle control device that generates deviation information based on acquired road information using a pre-aiming algorithm; generates a control matrix value based on the deviation information and acquired vehicle information; adjusts the lateral offset based on the control matrix value; generates an incremental output value based on driving information; and adjusts the driving speed based on the incremental output value. This allows the control matrix value to be calculated using the pre-aiming algorithm and road information, and the incremental output value to be calculated using driving information, enabling the vehicle to employ different control methods in the lateral and longitudinal directions. This coordinated lateral and longitudinal control method allows the vehicle to automatically adjust its lateral offset and driving speed even in complex environments, improving the vehicle's control accuracy.

[0106] This invention provides a storage medium that includes a stored program. When the program runs, it controls the device where the storage medium is located to execute the steps of the vehicle control method described above. For a detailed description, please refer to the embodiments of the vehicle control method described above.

[0107] This invention provides a vehicle, including a memory and a processor. The memory stores information including program instructions, and the processor controls the execution of the program instructions. When the program instructions are loaded and executed by the processor, they implement the steps of the above-described vehicle control method. For a detailed description, please refer to the embodiments of the above-described vehicle control method.

[0108] Figure 6 This is a schematic diagram of a vehicle provided as an embodiment of the present invention. (See diagram below.) Figure 6As shown, the vehicle 30 in this embodiment includes a processor 31, a memory 32, and a computer program 33 stored in the memory 32 and executable on the processor 31. When the computer program 33 is executed by the processor 31, it implements the image processing method described in the embodiment; to avoid repetition, it will not be described in detail here. Alternatively, when the computer program is executed by the processor 31, it implements the functions of each model / unit in the vehicle control device described in the embodiment; to avoid repetition, it will not be described in detail here.

[0109] Vehicle 30 includes, but is not limited to, a processor 31 and a memory 32. Those skilled in the art will understand that... Figure 6 This is merely an example of vehicle 30 and does not constitute a limitation on vehicle 30. It may include more or fewer components than shown, or combine certain components, or different components. For example, vehicle 30 may also include input / output devices, network access devices, buses, etc.

[0110] The processor 31 may be a Central Processing Unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor may be a microprocessor or any conventional processor.

[0111] The memory 32 can be an internal storage unit of the vehicle 30, such as a hard drive or RAM. The memory 32 can also be an external storage device of the vehicle 30, such as a plug-in hard drive, SmartMedia Card (SMC), Secure Digital (SD) card, or Flash Card. Furthermore, the memory 32 can include both internal and external storage units of the vehicle 30. The memory 32 is used to store computer programs and other programs and data required by the vehicle 30. The memory 32 can also be used to temporarily store data that has been output or will be output.

[0112] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working processes of the systems, devices, and units described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here.

[0113] In the embodiments provided by this invention, it should be understood that the disclosed systems, apparatuses, and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be an indirect coupling or communication connection between devices or units through some interfaces, and may be electrical, mechanical, or other forms.

[0114] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0115] Furthermore, the functional units in the various embodiments of the present invention can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or in the form of hardware plus software functional units.

[0116] The integrated units implemented as software functional units described above can be stored in a computer-readable storage medium. These software functional units, stored in a storage medium, include several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) or processor to execute some steps of the methods described in the various embodiments of the present invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0117] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. A method for controlling a vehicle, characterized in that, include: Based on the acquired road information, deviation information is generated using a pre-aiming point algorithm. Based on the deviation information and the acquired vehicle information, a control matrix value is generated; Adjust the lateral offset according to the control matrix value; Based on the driving information, generate incremental output values; Adjust the driving speed according to the incremental output value; The steps of generating deviation information based on the acquired road information using the pre-aiming point algorithm, generating control matrix values ​​based on the deviation information and the acquired vehicle information, and adjusting the lateral offset based on the control matrix values, and generating incremental output values ​​based on the driving information and adjusting the driving speed based on the incremental output values ​​are two independent steps. The step of generating control matrix values ​​based on the deviation information and the acquired vehicle information includes: The control matrix value is generated based on the deviation information and the vehicle information using the linear quadratic regulator LQR algorithm. The process of generating control matrix values ​​using the linear quadratic regulator (LQR) algorithm, based on the deviation information and the vehicle information, includes: Based on the deviation information and the vehicle information, a state equation model is generated; The first function solution is generated based on the state equation model, objective function, first defined function, and second defined function. The control matrix value is generated by the second setting function based on the solution of the first function and the parameters of the state equation.

2. The method according to claim 1, characterized in that, The step of generating incremental output values ​​based on driving information includes: The fuzzy PID control algorithm generates incremental output values ​​based on driving information.

3. The method according to claim 2, characterized in that, The driving information includes first driving information and second driving information, and the fuzzy PID control algorithm includes fuzzy algorithm and PID control algorithm; The process of generating incremental output values ​​based on driving information using a fuzzy PID control algorithm includes: Based on the first driving information, the current PID parameters are generated using a fuzzy algorithm. The incremental output value is generated based on the current PID parameters and the second driving information using a PID control algorithm.

4. The method according to claim 3, characterized in that, The first driving information includes at least one of speed deviation, speed deviation change rate, driving speed, and speed set value; the second driving information includes regulator control deviation amount.

5. The method according to any one of claims 1 to 4, characterized in that, The vehicle information includes at least one of the following: vehicle mass, moment of inertia, distance between front wheel center of gravity and rear wheel center of gravity, driving speed, front wheel lateral stiffness, rear wheel lateral stiffness, front wheel steering angle measurement, and heading angle.

6. A vehicle control device, characterized in that, include: The first generation module is used to generate deviation information based on the acquired road information using a pre-aiming point algorithm; The second generation module is used to generate control matrix values ​​based on the deviation information and the acquired vehicle information; The first adjustment module is used to adjust the lateral offset according to the control matrix value; The third generation module is used to generate incremental output values ​​based on driving information; The second adjustment module is used to adjust the driving speed according to the incremental output value; The vehicle control device is used to execute the vehicle control method according to claim 1; The second generation module is specifically used for: Based on the deviation information and the vehicle information, a state equation model is generated; The first function solution is generated based on the state equation model, objective function, first defined function, and second defined function. The control matrix value is generated by the second setting function based on the solution of the first function and the parameters of the state equation.

7. A storage medium, characterized in that, The storage medium includes a stored program, wherein, when the program is executed, it controls the device containing the storage medium to perform the vehicle control method according to any one of claims 1 to 5.

8. A vehicle comprising a memory and a processor, the memory for storing information including program instructions, the processor for controlling the execution of the program instructions, characterized in that, When the program instructions are loaded and executed by the processor, they implement the steps of the vehicle control method according to any one of claims 1 to 5.

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