Vehicle transverse and longitudinal control method and device based on road boundary constraint

Abstract

The invention relates to a vehicle transverse and longitudinal control method and device based on road boundary constraint, and the method comprises the steps: constructing a nonlinear mathematical model of a corresponding vehicle, and carrying out the Taylor expansion linearization, and obtaining a state transition model; solving a control variable sequence of the vehicle by using a constraint iteration linear quadratic regulator in combination with a constraint condition and a state transition model, so as to minimize a cost function; obtaining optimized trajectory information based on the controlled quantity sequence obtained by solving; and generating a control instruction corresponding to the vehicle based on the optimized track information. According to the method, a vehicle kinematic model is established through a constraint iteration linear quadratic regulator solving form, a cost function of a state quantity and a control quantity is constructed, and control tracking is performed by combining constraint conditions of acceleration, a front wheel steering angle and road boundary information and integrally optimizing trajectory information, so that the autonomous flexibility of a controller is improved, and the control precision is improved. The control error caused by motion coupling is eliminated, the control robustness is improved, and the vehicle safety is greatly improved.

Description

Technical Field

[0001] The present application relates to the technical field of vehicle autonomous driving, and specifically relates to a vehicle lateral and longitudinal control method and device based on road boundary constraints. Background Art

[0002] Currently, vehicle autonomous driving control technology generally divides into two forms: lateral and longitudinal decoupled control and lateral and longitudinal coupled control to control the throttle and steering wheel of the vehicle. Compared with traditional lateral and longitudinal coupled control, the decoupled control method reduces the control difficulty. However, due to the complex actual driving conditions of the vehicle, there is a complex strong coupling relationship between the lateral movement and the longitudinal movement. Therefore, it can be regarded as a highly nonlinear motion constraint system, and there will be problems of vehicle parameter uncertainty, making it difficult for lateral and longitudinal coordinated control under actual conditions. At the same time, the controllers of existing vehicles highly depend on the input quantity, making the vehicle trajectory under the controller tend to coincide with the input vehicle trajectory. When the input vehicle trajectory conflicts with the road boundary constraints, the controller cannot autonomously adjust the control trajectory to avoid collisions, which reduces the vehicle safety. Summary of the Invention

[0003] To overcome the above deficiencies of the prior art, the present application provides a vehicle lateral and longitudinal control method and device based on road boundary constraints, and specifically adopts the following technical solutions: A vehicle lateral and longitudinal control method based on road boundary constraints, the method includes the following steps: Construct a nonlinear mathematical model corresponding to the vehicle, and perform Taylor expansion linearization to obtain a state transition model; Use a constrained iterative linear quadratic regulator and combine the constraint conditions and the state transition model to solve the control variable sequence of the vehicle to minimize the cost function; the constraint conditions at least include angular velocity constraint, curvature constraint, and corner constraint; Obtain optimized trajectory information based on the solved control quantity sequence; Generate a control instruction corresponding to the vehicle based on the optimized trajectory information.

[0004] Optionally: when constructing the nonlinear mathematical model corresponding to the vehicle, the motion model of the vehicle needs to be simplified to a bicycle model: Obtain the acceleration, front wheel steering angle, and wheelbase of the vehicle; Construct a first objective function for calculating the vehicle speed according to the acceleration of the vehicle; Construct a second objective function for calculating the vehicle heading angle according to the vehicle speed, front wheel steering angle, and wheelbase; Construct a third objective function for calculating the vehicle lateral coordinate position according to the vehicle speed, acceleration, and heading angle; Construct a fourth objective function for calculating the longitudinal coordinate position of the vehicle based on the vehicle speed, acceleration, and heading angle; Obtain the nonlinear mathematical model of the vehicle by combining the first objective function, second objective function, third objective function, and fourth objective function of the vehicle.

[0005] Optionally: The steps of linearizing the nonlinear mathematical model of the vehicle by Taylor expansion to obtain a state transition model include: Set the state variable sequence and control variable sequence of the vehicle, and obtain the nonlinear mathematical model of the corresponding vehicle; Perform Taylor expansion on the nonlinear mathematical model of the vehicle according to the state variable sequence and control variable sequence of the vehicle; Obtain the state transition model of the corresponding vehicle according to the nonlinear mathematical model after Taylor expansion.

[0006] Optionally: When solving the control variable sequence of the vehicle, convert the constraint conditions in inequality form into barrier functions in exponential form respectively; Construct a barrier function for the acceleration constraint according to the maximum allowable value and minimum allowable value of the vehicle acceleration; Construct a barrier function for the curvature constraint according to the minimum allowable value and maximum allowable value of the vehicle front wheel steering angle; Construct a barrier function for the corner point constraint according to the coordinate positions of the vehicle envelope circle and the discrete points of the road boundary.

[0007] Optionally: The steps of using the constrained iterative linear quadratic regulator and combining the constraint conditions and the state transition model to solve the control quantity sequence of the vehicle include: Set the cost function and value function of the control variable sequence; Perform variational processing on the value function at time k, and calculate and obtain the optimal control increment at the current moment; Obtain the key control parameters at the corresponding moment according to the optimal control increment, and update the control variables and state variables; Substitute the updated control variables into the state transition model to calculate and obtain the state variables at the next moment; Calculate the cost change amount of the control variables before and after the update. When the cost change amount converges, output the updated control variables.

[0008] Optionally: The cost of the state variable considers trajectory following and speed fitting, and the cost function of the state variable is constructed based on the lateral coordinate position, longitudinal coordinate position, and speed of the vehicle; The cost of the control variable considers vehicle smoothness and vehicle oscillation amplitude, and the cost function of the control variable is constructed based on the acceleration and front wheel steering angle of the vehicle.

[0009] Optionally: when generating a control command for the corresponding vehicle based on the optimized trajectory information, it is necessary to monitor whether the acceleration change amount at time k exceeds a preset threshold and whether the trajectory position collides with the road boundary respectively: When it is determined that the acceleration change amount exceeds the preset threshold or the trajectory position collides with the road boundary, an abnormal result is generated and the corresponding vehicle is controlled to brake slowly; When it is determined that the acceleration change amount does not exceed the preset threshold and the trajectory position does not collide with the road boundary, the vehicle travels according to the issued control command.

[0010] Optionally: before generating a control command for the corresponding vehicle based on the optimized trajectory information, it is necessary to match the time information in the optimized trajectory information according to the time information at the current moment, retain the trajectory information after the current moment to form a tracking trajectory, and the tracking trajectory includes multiple tracking trajectory points; at the same time, add the actual trajectory information of the vehicle at the current moment to the head of the tracking trajectory.

[0011] Optionally: The vehicle selects any control variable corresponding to a trajectory tracking point greater than the current moment as a tracking target and generates a control command for the vehicle.

[0012] In addition, the present application also discloses a vehicle longitudinal and lateral control device based on road boundary constraints, and the device includes: A model construction module for constructing a non-linear mathematical model of the corresponding vehicle and performing Taylor expansion linearization to obtain a state transition model; A variable solving module for using a constrained iterative linear quadratic regulator and combining constraint conditions and a state transition model to solve a sequence of control variables of the vehicle to minimize a cost function; the constraint conditions at least include angular velocity constraints, curvature constraints, and corner constraints; A trajectory optimization module for obtaining optimized trajectory information based on the solved control quantity sequence; An instruction generation module for generating a control command for the corresponding vehicle based on the optimized trajectory information.

[0013] Beneficial effects The technical solution of the present application has obtained the following beneficial effects: The vehicle longitudinal and lateral control method of the present application is based on a longitudinal and lateral coupling control method. By establishing a vehicle kinematic model through a constrained iterative linear quadratic regulator solution form, constructing a cost function of state variables and control variables, and combining constraint conditions of acceleration, front wheel angle, and road boundary information, the trajectory information is optimized as a whole for control tracking. It improves the autonomous flexibility of the controller, eliminates control errors caused by motion coupling, increases control robustness, and greatly improves vehicle safety. Description of the drawings

[0014] Figure 1 This is a flowchart of the vehicle longitudinal and lateral control method in the embodiment of the present application.

[0015] Figure 2 This is a schematic diagram of the form of using an envelope circle to completely envelope the vehicle body in the embodiment of the present application.

[0016] Figure 3 This is a schematic diagram of obtaining discrete points of local optimum in corner point constraints in the embodiment of the present application.

[0017] Figure 4 This is a structural diagram of the vehicle longitudinal and lateral control device in the embodiment of the present application.

[0018] Figure 5 This is a structural diagram of an electronic device in the embodiment of the present application. Detailed implementation manners

[0019] The present application will be further described below with reference to the accompanying drawings. The following embodiments are only used to more clearly illustrate the technical solutions of the present application and should not be used to limit the protection scope of the present application. It should be noted that the following detailed descriptions are all exemplary and are intended to provide further descriptions of the present application.

[0020] Combined with Figure 1 As shown, the embodiment of the present application discloses a vehicle longitudinal and lateral control method based on road boundary constraints, and the method includes the following steps: Obtain the state transition model of the vehicle: construct a nonlinear mathematical model corresponding to the vehicle, and perform Taylor expansion linearization to obtain the state transition model.

[0021] Among them, when constructing the nonlinear mathematical model corresponding to the vehicle, first simplify the motion model of the vehicle into a bicycle model, that is, assume that the vehicle includes two wheels, one of which is the front wheel responsible for vehicle steering; the other is the rear wheel responsible for vehicle driving. Specifically, the simplification process of the motion model of the vehicle in this embodiment is as follows: First, obtain the acceleration, front wheel angle, and wheelbase of the vehicle; among them, the acceleration and front wheel angle of the vehicle can be obtained through the vehicle's own sensors, and the wheelbase of the vehicle belongs to known data.

[0022] Subsequently, construct a first objective function for calculating the vehicle speed according to the acceleration of the vehicle: ; Construct a second objective function for calculating the vehicle heading angle according to the vehicle speed, front wheel angle, and wheelbase: ; Construct a third objective function for calculating the lateral coordinate position of the vehicle based on the vehicle speed, acceleration, and heading angle: ; Construct a fourth objective function for calculating the longitudinal coordinate position of the vehicle based on the vehicle speed, acceleration, and heading angle: ; Finally, obtain the nonlinear mathematical model of the vehicle by comprehensively considering the first, second, third, and fourth objective functions of the vehicle: ; where x is the lateral coordinate position of the vehicle in the Cartesian coordinate system; is the lateral coordinate position of the vehicle x derived with respect to time t , which represents the lateral position change rate of the vehicle; y is the longitudinal coordinate position of the vehicle in the Cartesian coordinate system; is the longitudinal coordinate position of the vehicle y derived with respect to time t , which represents the longitudinal position change rate of the vehicle; v is the vehicle speed; represents the vehicle speed v derived with respect to time t ; a is the vehicle acceleration; is the vehicle heading angle; represents the vehicle heading angle derived with respect to time t , which is the change rate of the vehicle heading angle; is the front wheel steering angle of the vehicle; L is the wheelbase of the vehicle.

[0023] Furthermore, the steps for linearizing the nonlinear mathematical model of the vehicle by Taylor expansion to obtain the state transition model in this embodiment include: Set the state variable sequence and the control variable sequence , and set the corresponding nonlinear mathematical model of the vehicle as ; At this time, the Taylor expansion of the corresponding nonlinear mathematical model of the vehicle at the point is: ; where it is set that ; ; Then, it can be further obtained that: ; ; , ; Then, the state transition model obtained is: ; where is the state variable at the k+1 th step; is the state variable at the k th step; is the control variable at the k th step; is the sampling time interval; is the identity matrix.

[0024] Solve for the vehicle variable sequence: Using the constrained iterative linear quadratic regulator and combining the constraint conditions and the state transition model, solve the control variable sequence of the vehicle to minimize the cost function; the constraint conditions at least include angular velocity constraints, curvature constraints, and corner constraints.

[0025] In this embodiment, it is preferably solved using a constrained iterative linear quadratic regulator (CILQR). This regulator adopts the iterative linear quadratic regulator (iLQR) algorithm and combines the constraint conditions to achieve the optimal control planning of the nonlinear system, mainly including two processes: the backward solution process and the forward solution process. The specific process is as follows: Backward solution process: In this embodiment, the cost optimized for the entire sequence is set as a function related to the control variable U. Therefore, in this embodiment, the cost function and the value function of the control variable sequence are first set; where the cost function includes the terminal cost and the total cost at time k; the value function includes the value function at the terminal time and the value function that is minimized at time k.

[0026] For the optimization of the control variable sequence, the cost function in its initial state is the total cost during the vehicle operation and the terminal cost, that is: ; Then, the cost function from any time t to the terminal is: ; where , represents the global cost function in the initial state; represents the global cost function from any moment t to the terminal; represents the stage cost function at moment t. Similarly, represents the moment when the stage cost function; represents the stage cost function at the terminal moment N.

[0027] The value function in this embodiment adopts the cost function that minimizes the control variable sequence: where the value function at the terminal moment is equal to its terminal cost, that is: ; and the value function corresponding to the moment k before the terminal moment is: ; where let , then .

[0028] Subsequently, this embodiment performs variational operations on the value function at moment k to obtain the variational function of the corresponding value function; among them . Specifically, a small perturbation to the value function of this embodiment can obtain: ; ; Furthermore, performing Taylor expansion on can obtain: ; where, After that, the optimal control increment at the current moment is calculated according to the variational function of the value function at moment k; it is required that the set small perturbation makes the smallest, that is: ; Furthermore, obtaining , which is the optimal control increment at the current moment k.

[0029] Then, the key control parameters at the corresponding moment are calculated based on the optimal control increment; among them, according to can be obtained: , . Then the key control parameters at moment k and are obtained.

[0030] It should be explained that in this embodiment, the above , , , , , , , , , , , , , , , , All are obtained by taking the derivative of the relevant function with respect to the subscript variable. In addition, the and obtained in the current step can be used in the next loop step.

[0031] Forward solution process: Update the control variable and state variable according to the obtained key control parameters; specifically, the key control parameters at each moment calculated in the above reverse solution process and can be used to update the corresponding control variable to obtain the optimized optimal control variable. Among them, based on the key control parameters and the process of updating the corresponding control variable is: ; Among them, the cost change amount at the current moment is calculated for the updated control variable and the control variable before update in each loop process; specifically, the costs corresponding to the updated and unupdated control variables can be calculated respectively according to the value function calculation formula corresponding to time k in the reverse solution process, and the difference in costs corresponding to the updated and unupdated control variables can be judged.

[0032] At the same time, substitute the updated control variable into the state transition model to calculate the optimized state variable at the next moment; When the cost change amount converges, generally judge whether the difference in costs corresponding to the updated and unupdated control variables converges, that is, the difference in costs is within a certain preset threshold range. If it is satisfied, stop the loop and output the optimized control variable and state variable.

[0033] It should be explained that in this embodiment, the cost of the state variable needs to consider trajectory following and speed fitting. Among them, the cost function of the state variable is constructed according to the lateral coordinate position, longitudinal coordinate position and speed of the vehicle, that is, it is further optimized as: ; The cost of the control variable needs to consider vehicle smoothness and vehicle oscillation amplitude. Among them, the cost function of the control variable is constructed according to the acceleration and front wheel angle of the vehicle, that is, it is further optimized as: ; wherein are respectively weight adjustment parameters.

[0034] Since the constraint conditions, namely acceleration constraint, curvature constraint and corner constraint, are considered in the process of solving the control variable sequence of the vehicle in this embodiment, when solving the control variable sequence of the vehicle, the constraint conditions in the form of inequalities need to be converted into the corresponding barrier functions in the exponential form respectively; an exponential function is constructed in this embodiment, where the and parameters of the constraint conditions need to be adjusted later. When error the function is greater than 0, the value of barrier function will approach infinity to achieve equivalent constraints. At the same time, since barrier function is a non-linear function, it will be Taylor-expanded in this embodiment for linearization. The specific method is as follows: Since the acceleration of the vehicle needs to satisfy within a certain specific range, that is, satisfy , the barrier function form of the acceleration constraint can be obtained according to the minimum allowable value and the maximum allowable value of the front wheel steering angle of the vehicle, that is: ; wherein and are parameters for later adjustment; a is the acceleration of the vehicle; a min is the minimum allowable value of the acceleration of the vehicle; a max is the maximum allowable value of the acceleration of the vehicle; According to the curvature calculation formula of the vehicle: ; Since the change range of the front wheel steering angle of the vehicle remains within , and the change of the vehicle curvature is monotonic within this range, the curvature constraint can be simplified to an inequality constraint on the front wheel steering angle of the vehicle, that is, the barrier function form of the curvature constraint can be constructed according to the minimum allowable value and the maximum allowable value of the front wheel steering angle of the vehicle: ; wherein and are parameters for later adjustment; is the front wheel steering angle of the vehicle; is the minimum allowable value of the front wheel steering angle of the vehicle; is the maximum allowable value of the front wheel angle of the vehicle; each time the updated result of the control variable is obtained, the front wheel angle in the control variable can be limited within a reasonable range to achieve a form of hard constraint.

[0035] Furthermore, in this embodiment, the corner point constraint is essentially a road boundary constraint, which restricts the vehicle from exceeding the road boundary. It is achieved by drawing a circle on the vehicle body and calculating the closest distance between the center of the circle and the boundary points of the discrete road boundaries on both sides. For example Figure 2 As shown, in this embodiment, n enclosing circles 2 are used to completely enclose the vehicle body. Preferably, n is an odd number, so that the center position of the vehicle body coincides with the center of an intermediate circle. Subsequently, the radius of each circle and the center distance are calculated: ; ; Based on the vehicle center position, the center distance, and the heading angle of the vehicle, the center position of any enclosing circle 2 can be calculated. By traversing the discrete points 1 on both sides of the road boundary, the distance between each discrete point 1 and the center position can be calculated. Since the entire vehicle needs to be located inside the road boundary, the distance between the discrete point and the center position must be greater than the radius of the circle, that is, the ratio of the two must be greater than 1. Therefore, the corner point constraint can be converted into a constraint on the ratio of the distance between the discrete point and the center of the circle to the radius of the circle. At this time, the obstacle function form of the corner point constraint can be constructed according to the coordinate positions of the vehicle enclosing circle and the discrete points of the road boundary: ; where and are parameters for later parameter adjustment; dis is the minimum distance between the discrete point of the road boundary and the center of the vehicle enclosing circle; ( x,y ) are the coordinates of the discrete point of the road boundary; ( x circle ,y circle ) are the coordinates of the center of the vehicle enclosing circle.

[0036] In addition, it is assumed that the number of discrete points of the road boundary is m, the number of center points is n, and the corner point constraint needs to be calculated for each state quantity on the trajectory, and there are k state quantities. At this time, the traversal loop is time-consuming. Therefore, this embodiment simplifies the above solution process: First, it is known that the trajectory points are arranged along the forward direction of the road, and the discrete points of the road boundary also extend forward in a certain order. For the index value of the minimum boundary point found at the position of the first trajectory point, the minimum boundary point that can be found at the position of the next trajectory point should be extended backward near it. Therefore, in this embodiment, there is no need to traverse the discrete points of the road boundary from the beginning. It is only necessary to start from the position of the discrete point matched by the current trajectory point and traverse backward. For example, in this embodiment, the starting position for traversing the next trajectory point is set to min(the position of the discrete point of the road boundary matched by the current trajectory point - 5, 0), forming a continuously shrinking sliding window to reduce the complexity of traversal.

[0037] In addition, in this embodiment, since the road boundaries are mostly straight lines or some are curves with a curvature within a reasonable range, the local optimum is used to replace the global optimum. That is, when it is found during the traversal that the minimum distance calculated from the newly emerging discrete points of the road boundary is greater than the currently recorded minimum distance value, it can be considered that the traversal has passed the optimum value point at this time, and there is no need to continue traversing backward. The recorded minimum distance value is used as the optimum value point. As Figure 3 shown, the recorded minimum distance value is a. At this time, continue to traverse backward and find that the calculated distance value b is greater than the recorded minimum distance value a. It can be judged that the traversal has passed the optimum value point, and at this time, the distance value a is used as the optimum value point.

[0038] Obtain the optimized trajectory information of the vehicle: Obtain the optimized trajectory information based on the obtained control quantity sequence.

[0039] Furthermore, before generating the control instruction for the corresponding vehicle in this embodiment, it is also necessary to match the time information in the optimized trajectory information according to the time information at the current moment. Due to control errors, if the planning result containing position information, speed and acceleration information is directly input into the vehicle's controller without considering the current position attitude and motion situation of the vehicle, the control effect will be greatly reduced. Therefore, in this embodiment, the trajectory information after the current moment is retained to form a tracking trajectory, where the tracking trajectory includes multiple tracking trajectory points; at the same time, the actual trajectory information of the vehicle at the current moment is added to the head of the tracking trajectory. In addition, normally, the first point in the tracked output trajectory information is sufficient, but this cannot guarantee real-time performance. Therefore, in this embodiment, the vehicle can select any control variable corresponding to a trajectory tracking point greater than the current moment as the tracking target to generate the control instruction for the vehicle.

[0040] Generate the control instruction for the vehicle: Generate the corresponding control instruction for the vehicle based on the optimized trajectory information.

[0041] Further, in order to avoid abnormal situations such as acceleration jitter and the optimized trajectory crossing the road boundary during the vehicle control process in this embodiment, when generating the control instruction for the corresponding vehicle according to the optimized trajectory information, it is necessary to monitor whether the acceleration change amount at time k exceeds the preset threshold and whether the trajectory position collides with the road boundary respectively: When it is determined that the acceleration change amount exceeds the preset threshold or the trajectory position collides with the road boundary, an abnormal result is generated and the corresponding vehicle is controlled to brake slowly; When it is determined that the acceleration change amount does not exceed the preset threshold and the trajectory position does not collide with the road boundary, the vehicle travels according to the issued control instruction.

[0042] In addition, as Figure 4 shown, the present application also discloses a vehicle longitudinal and lateral control device based on road boundary constraints, and the device includes: A model construction module, configured to construct a non-linear mathematical model of the corresponding vehicle and perform Taylor expansion linearization to obtain a state transition model; A variable solving module, configured to use a constrained iterative linear quadratic regulator and combine constraint conditions and the state transition model to solve the control variable sequence of the vehicle to minimize the cost function; the constraint conditions at least include angular velocity constraint, curvature constraint, and corner constraint; A trajectory optimization module, configured to obtain optimized trajectory information based on the solved control quantity sequence; An instruction generation module, configured to generate a control instruction for the corresponding vehicle based on the optimized trajectory information.

[0043] The device provided in the embodiment of the present application can implement Figure 1 each process implemented by the method embodiment. To avoid repetition, it will not be elaborated here.

[0044] As Figure 5 shown, the embodiment of the present application also provides an electronic device, including a processor and a memory, a program or instruction stored on the memory and executable on the processor, and when the program or instruction is executed by the processor, it implements each process of the method embodiment as Figure 1 shown, and can achieve the same technical effect. To avoid repetition, it will not be elaborated here.

[0045] The embodiment of the present application also provides a readable storage medium, on which a program or instruction is stored, and when the program or instruction is executed by the processor, it implements each process of the above-mentioned Figure 1 method embodiment, and can achieve the same technical effect. To avoid repetition, it will not be elaborated here.

[0046] The embodiment of the present application also provides a computer program product, including computer instructions, and when the computer instructions are executed by the processor, they implement the above-mentionedFigure 1 Each process of the method embodiment described above can achieve the same technical effect. To avoid repetition, it will not be elaborated here.

[0047] It should be understood that the "one embodiment" or "an embodiment" mentioned throughout the specification means that the specific features, structures or characteristics related to the embodiment are included in at least one embodiment of the present application. Therefore, the "in one embodiment" or "in an embodiment" appearing throughout the specification does not necessarily refer to the same embodiment. In addition, these specific features, structures or characteristics can be combined in one or more embodiments in any suitable manner. It should be understood that in various embodiments of the present application, the sequence numbers of the above processes do not mean the order of execution. The order of execution of each process should be determined by its function and internal logic, and should not constitute any limitation to the implementation process of the embodiments of the present application. The sequence numbers of the embodiments of the present application are only for description and do not represent the advantages or disadvantages of the embodiments.

[0048] It should be noted that in this article, the term "comprising", "including" or any other variant thereof is intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements not only includes those elements, but also includes other elements not expressly listed, or further includes elements inherent to such process, method, article or device. Without further limitation, an element defined by the statement "including a..." does not exclude the existence of additional identical elements in the process, method, article or device including the element.

[0049] In several embodiments provided in the present application, it should be understood that the disclosed devices and methods can be implemented in other ways. The device embodiments described above are only illustrative. For example, the division of the units is only a logical function division. In actual implementation, there may be other division methods, such as: multiple units or components can be combined, or can be integrated into another device, or some features can be ignored, or not executed. In addition, the coupling, direct coupling or communication connection between the components shown or discussed with each other can be through some interfaces. The indirect coupling or communication connection of the devices or units can be electrical, mechanical or other forms.

[0050] The units described above as separate components may or may not be physically separated, and the components shown as units may or may not be physical units; they can be located in one place or distributed to multiple network units; some or all of the units can be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0051] In addition, in each embodiment of the present application, each functional unit can be fully integrated into one processing unit, or each unit can be separately regarded as one unit, or two or more units can be integrated into one unit; the above-mentioned integrated unit can be implemented in the form of hardware, or in the form of a hardware plus software functional unit.

[0052] Those of ordinary skill in the art can understand that all or part of the steps of implementing the above method embodiments can be completed by hardware related to program instructions. The foregoing program can be stored in a computer-readable storage medium. When the program is executed, it performs the steps including the above method embodiments; and the foregoing storage medium includes: removable storage devices, read-only memory (ROM), magnetic disks, or optical discs and other various media that can store program codes.

[0053] Alternatively, if the above-mentioned integrated unit of the present application is implemented in the form of a software functional module and sold or used as an independent product, it can also be stored in a computer-readable storage medium. Based on such an understanding, the technical solution of the embodiments of the present application, in essence or the part that contributes to the prior art, can be embodied in the form of a software product. The computer software product is stored in a storage medium and includes several instructions for causing a device (which can be a terminal or a platform, etc.) to execute all or part of the methods described in the various embodiments of the present application. And the foregoing storage medium includes: removable storage devices, ROM, magnetic disks, or optical discs and other various media that can store program codes.

[0054] The above is only the preferred embodiment of the present application. It should be noted that for those of ordinary skill in the art, without departing from the technical principle of the present application, several improvements and modifications can be made, and these improvements and modifications should also be regarded as the protection scope of the present application.

Claims

1. A vehicle longitudinal and lateral control method based on road boundary constraints, characterized in that, The method includes the following steps: Construct a non - linear mathematical model corresponding to the vehicle, and perform Taylor expansion linearization to obtain a state transition model; Use a constrained iterative linear quadratic regulator and combine the constraint conditions and the state transition model to solve the control variable sequence of the vehicle to minimize the cost function; the constraint conditions at least include angular velocity constraint, curvature constraint, and corner constraint; Obtain the optimized trajectory information based on the solved control variable sequence; Generate a control instruction corresponding to the vehicle based on the optimized trajectory information.

2. The vehicle lateral and longitudinal control method according to claim 1, wherein, When constructing the non - linear mathematical model corresponding to the vehicle, the motion model of the vehicle needs to be simplified to a bicycle model: Obtain the acceleration, front wheel steering angle, and wheelbase of the vehicle; Construct a first objective function for calculating the vehicle speed according to the acceleration of the vehicle; Construct a second objective function for calculating the vehicle heading angle according to the vehicle speed, front wheel steering angle, and wheelbase; Construct a third objective function for calculating the lateral coordinate position of the vehicle according to the vehicle speed, acceleration, and heading angle; Construct a fourth objective function for calculating the longitudinal coordinate position of the vehicle according to the vehicle speed, acceleration, and heading angle; Combine the first objective function, second objective function, third objective function, and fourth objective function of the vehicle to obtain the non - linear mathematical model of the vehicle.

3. The vehicle lateral and longitudinal control method according to claim 2, characterized in that, The steps of performing Taylor expansion linearization on the non - linear mathematical model of the vehicle to obtain a state transition model include: Set the state variable sequence and control variable sequence of the vehicle, and obtain the non - linear mathematical model of the corresponding vehicle; Perform Taylor expansion on the non - linear mathematical model of the vehicle according to the state variable sequence and control variable sequence of the vehicle; Obtain the state transition model of the corresponding vehicle according to the Taylor - expanded non - linear mathematical model.

4. The vehicle longitudinal and lateral control method according to claim 2, characterized in that, When solving the control variable sequence of the vehicle, convert the inequality - form constraint conditions into exponential - form barrier functions respectively: Construct a barrier function for the acceleration constraint according to the maximum and minimum allowable values of the vehicle acceleration; Construct a barrier function for the curvature constraint according to the minimum and maximum allowable values of the vehicle front wheel steering angle; Construct a barrier function for the corner constraint according to the coordinate positions of the vehicle envelope circle and the discrete points of the road boundary.

5. The vehicle lateral and longitudinal control method according to claim 1, characterized in that, The steps of using a constrained iterative linear quadratic regulator and combining the constraint conditions and the state transition model to solve the control variable sequence of the vehicle include: Set the cost function and value function of the control variable sequence; Perform variational processing on the value function at time k, and calculate and obtain the optimal control increment at the current moment; Obtain the key control parameters at the corresponding moment according to the optimal control increment, and update the control variables and state variables; Substitute the updated control variables into the state transition model to calculate the state variables at the next moment; Calculate the cost change amount of the control variables before and after the update. When the cost change amount converges, output the updated control variables.

6. The vehicle lateral and longitudinal control method according to claim 5, characterized in that, The cost of the state variables considers trajectory following and speed fitting, and the cost function of the state variables is constructed according to the lateral coordinate position, longitudinal coordinate position, and speed of the vehicle; The cost of the control variable takes into account vehicle smoothness and vehicle oscillation amplitude, where the cost function of the control variable is constructed based on the acceleration and front wheel angle of the vehicle.

7. The vehicle longitudinal and lateral control method according to claim 6, wherein When generating a control instruction for a corresponding vehicle according to the optimized trajectory information, it is necessary to monitor whether the acceleration change amount at time k exceeds a preset threshold and whether the trajectory position collides with the road boundary respectively: When it is determined that the acceleration change amount exceeds the preset threshold or the trajectory position collides with the road boundary, an abnormal result is generated and the corresponding vehicle is controlled to brake slowly; When it is determined that the acceleration change amount does not exceed the preset threshold and the trajectory position does not collide with the road boundary, the vehicle travels according to the issued control instruction.

8. The vehicle lateral and longitudinal control method according to claim 1, wherein Before generating a control instruction for a corresponding vehicle based on the optimized trajectory information, it is necessary to match the time information in the optimized trajectory information according to the time information at the current moment, and retain the trajectory information after the current moment to form a tracking trajectory, where the tracking trajectory includes multiple tracking trajectory points; at the same time, the actual trajectory information of the vehicle at the current moment is added to the head of the tracking trajectory.

9. The vehicle lateral and longitudinal control method according to claim 8, characterized in that The vehicle selects any control variable corresponding to a trajectory tracking point greater than the current moment as a tracking target and generates a control instruction for the vehicle.

10. A vehicle longitudinal and lateral control device based on road boundary constraints, characterized in that, The device includes: A model construction module for constructing a nonlinear mathematical model of a corresponding vehicle and performing Taylor expansion linearization to obtain a state transition model; A variable solution module for using a constrained iterative linear quadratic regulator and combining constraint conditions and a state transition model to solve a control variable sequence of the vehicle to minimize a cost function; the constraint conditions at least include angular velocity constraints, curvature constraints, and corner constraints; A trajectory optimization module for obtaining optimized trajectory information based on the solved control quantity sequence; An instruction generation module for generating a control instruction for a corresponding vehicle based on the optimized trajectory information.

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