A vehicle lateral and longitudinal control method and device based on road boundary constraints

Abstract

The present application relates to a method and apparatus for controlling the lateral and longitudinal directions of a vehicle based on road boundary constraints. The method comprises the following steps: constructing a nonlinear mathematical model of the corresponding vehicle and performing Taylor expansion linearization to obtain a state transition model; utilizing a constrained iterative linear quadratic regulator in combination with the constraints and the state transition model to solve the vehicle's control variable sequence to minimize a cost function; obtaining optimized trajectory information based on the solved control variable sequence; and generating control instructions for the corresponding vehicle based on the optimized trajectory information. This method establishes a vehicle kinematic model using a constrained iterative linear quadratic regulator solution, constructs a cost function for state variables and control variables, and simultaneously combines the constraints of acceleration, front wheel angle, and road boundary information to comprehensively optimize the trajectory information for control tracking. This method improves the controller's autonomous flexibility, eliminates control errors caused by motion coupling, increases control robustness, and significantly enhances vehicle safety.

Description

Technical Field

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

[0002] Currently, autonomous vehicle control technologies are generally divided into two forms: lateral and longitudinal decoupling control and lateral and longitudinal coupling control, which control the vehicle's throttle and steering. Compared to traditional lateral and longitudinal coupling control, decoupling control reduces the control difficulty. However, due to the complex driving conditions of vehicles, there is a complex and strong coupling relationship between lateral and longitudinal motion. Therefore, it can be regarded as a highly nonlinear motion constraint system, which is subject to vehicle parameter uncertainty. This makes lateral and longitudinal coordinated control difficult under actual conditions. At the same time, existing vehicle controllers are highly dependent on input variables, causing the vehicle trajectory under the controller to tend to overlap with the input vehicle trajectory. This makes it impossible for the controller to autonomously adjust the control trajectory to avoid collisions when the input vehicle trajectory conflicts with the road boundary constraints, which reduces vehicle safety. Summary of the Invention

[0003] To overcome the above-mentioned deficiencies of the prior art, the present application provides a method and apparatus for controlling the lateral and longitudinal directions of a vehicle based on road boundary constraints, which specifically adopts the following technical solutions:

[0004] A vehicle lateral and longitudinal control method based on road boundary constraints, the method comprising the following steps:

[0005] Construct a nonlinear mathematical model of the corresponding vehicle and perform Taylor expansion linearization to obtain the state transition model;

[0006] A constrained iterative linear quadratic regulator is used in combination with constraints and a state transition model to solve a control variable sequence of the vehicle to minimize a cost function; the constraints include at least an angular velocity constraint, a curvature constraint, and a corner point constraint;

[0007] Obtain optimized trajectory information based on the control quantity sequence obtained by solving;

[0008] Generate control instructions for the corresponding vehicle based on the optimized trajectory information.

[0009] Optional: When constructing a nonlinear mathematical model of a corresponding vehicle, the vehicle's motion model needs to be simplified to a bicycle model:

[0010] Obtaining the acceleration, front wheel angle, and wheelbase of the vehicle;

[0011] Constructing a first objective function for calculating the vehicle speed based on the acceleration of the vehicle;

[0012] Constructing a second objective function for calculating the vehicle heading angle according to the vehicle speed, the front wheel turning angle and the wheelbase;

[0013] Constructing a third objective function for calculating the lateral coordinate position of the vehicle according to the vehicle speed, acceleration and heading angle;

[0014] Constructing a fourth objective function for calculating the longitudinal coordinate position of the vehicle based on the vehicle speed, acceleration and heading angle;

[0015] A nonlinear mathematical model of the vehicle is obtained by combining the first objective function, the second objective function, the third objective function and the fourth objective function of the vehicle.

[0016] Optionally, the step of performing Taylor expansion linearization on the nonlinear mathematical model of the vehicle to obtain a state transition model includes:

[0017] Setting a state variable sequence and a control variable sequence of the vehicle, and obtaining a nonlinear mathematical model of the corresponding vehicle;

[0018] Performing Taylor expansion on a nonlinear mathematical model of the vehicle according to a state variable sequence and a control variable sequence of the vehicle;

[0019] The state transfer model of the corresponding vehicle is obtained according to the nonlinear mathematical model after Taylor expansion.

[0020] Optionally, when solving the control variable sequence of the vehicle, the constraints in the form of inequalities are converted into barrier functions in the form of exponentials;

[0021] Constructing a barrier function of the acceleration constraint according to the maximum allowable value and the minimum allowable value of the vehicle acceleration;

[0022] Constructing an obstacle function of the curvature constraint according to the minimum allowable value and the maximum allowable value of the front wheel turning angle of the vehicle;

[0023] The obstacle function of the corner point constraint is constructed according to the coordinate positions of the vehicle envelope circle and the road boundary discrete points.

[0024] Optionally, the step of solving the control variable sequence of the vehicle by using a constrained iterative linear quadratic regulator in combination with constraint conditions and a state transition model includes:

[0025] Set the cost function and value function of the control variable sequence;

[0026] Perform variational processing on the value function at time k and calculate the optimal control increment at the current time;

[0027] Obtain the key control parameters at the corresponding moment according to the optimal control increment, and update the control variables and state variables;

[0028] Substitute the updated control variables into the state transition model to calculate the state variables at the next moment;

[0029] Calculate the cost change of the control variable before and after the update, and output the updated control variable when the cost change converges.

[0030] Optionally, the cost of the state variable takes into account trajectory following and speed conformity, wherein the cost function of the state variable is constructed based on the lateral coordinate position, longitudinal coordinate position, and speed of the vehicle;

[0031] The cost of the control variable takes into account vehicle stability and vehicle oscillation amplitude, wherein the cost function of the control variable is constructed according to the acceleration and front wheel turning angle of the vehicle.

[0032] Optional: When generating control instructions for the corresponding vehicle based on the optimized trajectory information, it is necessary to monitor whether the acceleration change at time k exceeds the preset threshold and whether the trajectory position collides with the road boundary:

[0033] When it is determined that the acceleration change exceeds a 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;

[0034] When it is determined that the acceleration change 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.

[0035] Optional: Before generating the control instructions 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, the actual trajectory information of the vehicle at the current moment is added to the head end of the tracking trajectory.

[0036] 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 instruction for the vehicle.

[0037] In addition, the present application also discloses a vehicle lateral and longitudinal control device based on road boundary constraints, the device comprising:

[0038] The model building module is used to build a nonlinear mathematical model of the corresponding vehicle and perform Taylor expansion linearization to obtain a state transition model;

[0039] a variable solving module for solving a control variable sequence of the vehicle to minimize a cost function by using a constrained iterative linear quadratic regulator in combination with constraints and a state transition model; the constraints include at least an angular velocity constraint, a curvature constraint, and a corner point constraint;

[0040] Trajectory optimization module, used to obtain optimized trajectory information based on the control quantity sequence obtained by solving;

[0041] The instruction generation module is used to generate control instructions for the corresponding vehicle based on the optimized trajectory information.

[0042] Beneficial effects

[0043] The technical solution of this application has the following beneficial effects:

[0044] The vehicle lateral and longitudinal control method of the present application is based on the lateral and longitudinal coupling control mode. The vehicle kinematic model is established through the constrained iterative linear quadratic regulator solution form, and the cost function of the state quantity and the control quantity is constructed. At the same time, the constraints of acceleration, front wheel angle and road boundary information are combined to optimize the overall trajectory information for control tracking. It improves the autonomous flexibility of the controller, eliminates the control error caused by motion coupling, increases the control robustness, and greatly improves the vehicle safety. BRIEF DESCRIPTION OF THE DRAWINGS

[0045] Figure 1 This is a flow chart of the vehicle lateral and longitudinal control method in an embodiment of the present application.

[0046] Figure 2 This is a schematic diagram of the form in which the enveloping circle completely envelops the vehicle body in the embodiment of the present application.

[0047] Figure 3 Schematic diagram of obtaining local optimal discrete points in corner point constraints in an embodiment of the present application.

[0048] Figure 4 This is a structural diagram of the vehicle lateral and longitudinal control device in an embodiment of the present application.

[0049] Figure 5 This is a structural diagram of an electronic device in an embodiment of the present application. DETAILED DESCRIPTION

[0050] The present application will be further described below in conjunction with the accompanying drawings. The following examples are only used to more clearly illustrate the technical solutions of the present application and are not intended to limit the scope of protection of the present application. It should be noted that the following detailed descriptions are exemplary and are intended to provide further explanation of the present application.

[0051] Combine Figure 1As shown, the embodiment of the present application discloses a vehicle lateral and longitudinal control method based on road boundary constraints, the method comprising the following steps:

[0052] Obtain the state transition model of the vehicle: Construct a nonlinear mathematical model of the corresponding vehicle and perform Taylor expansion linearization to obtain the state transition model.

[0053] When constructing a nonlinear mathematical model for a vehicle, the vehicle's motion model is first simplified to a bicycle model. This assumes that the vehicle has two wheels, one of which is a front wheel responsible for steering and the other is a rear wheel responsible for driving. Specifically, the simplification process for the vehicle's motion model in this embodiment is as follows:

[0054] First, the acceleration, front wheel turning angle and wheel spacing of the vehicle are obtained; wherein the acceleration and front wheel turning angle of the vehicle can be obtained by the vehicle's own sensors, and the wheel spacing of the vehicle is known data.

[0055] Then, a first objective function for calculating the vehicle speed is constructed according to the vehicle acceleration:

[0056] ;

[0057] A second objective function for calculating the vehicle heading angle is constructed based on the vehicle speed, front wheel turning angle, and wheelbase:

[0058] ;

[0059] A third objective function for calculating the vehicle's lateral coordinate position is constructed based on the vehicle's speed, acceleration, and heading angle:

[0060] ;

[0061] A fourth objective function for calculating the longitudinal coordinate position of the vehicle is constructed based on the vehicle speed, acceleration and heading angle:

[0062] ;

[0063] Finally, the nonlinear mathematical model of the vehicle is obtained by integrating the first objective function, the second objective function, the third objective function and the fourth objective function of the vehicle:

[0064] ;

[0065] in x is the lateral coordinate position of the vehicle in the Cartesian coordinate system; is the vehicle's lateral coordinate position x About time tThe derivative of represents the rate of change of the vehicle's lateral position; y is the longitudinal coordinate position of the vehicle in the Cartesian coordinate system; is the longitudinal coordinate position of the vehicle y About time t The derivative of represents the rate of change of the vehicle's longitudinal position; v is the speed of the vehicle; Represents vehicle speed v About time t The derivative of a is the acceleration of the vehicle; is the heading angle of the vehicle; Represents the vehicle heading angle About time t The derivative of , that is, the rate of change of the vehicle heading angle; is the front wheel turning angle of the vehicle; L is the wheelbase of the vehicle.

[0066] Furthermore, in this embodiment, the step of performing Taylor expansion linearization on the nonlinear mathematical model of the vehicle to obtain a state transition model includes:

[0067] Set the state variable sequence and control variable sequence , and set the nonlinear mathematical model of the corresponding vehicle as ;

[0068] At this time, the corresponding nonlinear mathematical model of the vehicle At the point The Taylor expansion at is:

[0069] ;

[0070] Which sets

[0071] ;

[0072] ;

[0073] Then we can further obtain:

[0074] ;

[0075] ;

[0076] , ;

[0077] Then the state transition model is obtained as:

[0078] ;

[0079] in For the k+1 The state variables of the step; For the k The state variables of the step; For the k The control variable of the step; is the sampling time interval; is the identity matrix.

[0080] Solving the vehicle variable sequence: using a constrained iterative linear quadratic regulator in combination with constraints and a state transition model to solve the vehicle control variable sequence to minimize a cost function; the constraints include at least angular velocity constraints, curvature constraints, and corner constraints.

[0081] This embodiment preferably uses a constrained iterative linear quadratic regulator (CILQR) for the solution. This regulator uses the iterative linear quadratic regulator (iLQR) algorithm and combines constraints to achieve optimal control planning for nonlinear systems. It mainly includes two processes: the reverse solution process and the forward solution process. The specific process is as follows:

[0082] Reverse solution process:

[0083] In this embodiment, the cost of optimizing the entire sequence is set as a function related to the control variable U. Therefore, this embodiment first sets the cost function and value function of the control variable sequence; the cost function includes the terminal cost and the sum of the costs at time k; the value function includes the value function at the terminal time and the value function that is minimized at time k.

[0084] For the optimization of the control variable sequence, the cost function in the initial state is the sum of the costs during the vehicle operation and the terminal cost, that is:

[0085] ;

[0086] Then the cost function from any time t to the terminal is:

[0087] ;

[0088] in , Represents the global cost function in the initial state; Represents the global cost function from any time t to the terminal; Represents the stage cost function at time t. Similarly, Representative Moment The stage cost function when ; Represents the stage cost function at the terminal time N.

[0089] The value function of this embodiment adopts the cost function of minimizing the control variable sequence:

[0090] The value function at the terminal moment is equal to its terminal cost, that is: ;

[0091] The value function corresponding to the moment k before the terminal moment is:

[0092] ;

[0093] Among them ,but .

[0094] Then, this embodiment performs variational operations on the value function at time k to obtain the variational function of the corresponding value function; Specifically, a slight perturbation of the value function of this embodiment can yield:

[0095] ;

[0096] ;

[0097] Further, yes Performing Taylor expansion yields:

[0098] ;

[0099] in,

[0100]

[0101] Then, the optimal control increment at the current moment is calculated based on the variation function of the value function at time k; the small disturbance required is Make Minimum, that is:

[0102] ;

[0103] Further seek , which is the optimal control increment of k at the current moment.

[0104] Then, the key control parameters at the corresponding moment are obtained based on the optimal control increment calculation; Can be obtained: , Then we get the key control parameters at time k and .

[0105] It should be explained that, in this embodiment, , , , , , , , , , , , , , , , , are all derived from the derivative of the relevant function with respect to the subscript variable. In addition, the current step obtains and Can be used in the next cycle step.

[0106] Forward solution process:

[0107] The updated control variables and state variables are obtained according to the key control parameters obtained; specifically, the key control parameters at each moment calculated in the above reverse solution process are and It can be used to update the corresponding control variables to obtain the optimized optimal control variables. and The process of updating the corresponding control variables is:

[0108] ;

[0109] In each cycle, the cost change at the current moment is calculated for the updated control variables and the control variables before and after the update. Specifically, the costs corresponding to the control variables before and after the update can be calculated according to the value function calculation formula corresponding to the time k in the reverse solution process, and the difference in costs corresponding to the control variables before and after the update can be determined.

[0110] At the same time, the updated control variables are substituted into the state transfer model to calculate the optimized state variables at the next moment;

[0111] When the cost change is converging, it is generally judged whether the difference in costs corresponding to the control variables before and after the update tends to converge, that is, the cost difference is within a certain preset threshold range. If it is satisfied, the loop is stopped and the optimized control variables and state variables are output.

[0112] It should be explained that the cost of the state variables in this embodiment needs to take into account trajectory following and speed conformity, wherein the cost function of the state variables is constructed based on the lateral coordinate position, longitudinal coordinate position, and speed of the vehicle, that is, further optimized as follows:

[0113] ;

[0114] The cost of the control variable needs to take into account vehicle stability and vehicle oscillation amplitude, wherein the cost function of the control variable is constructed based on the acceleration and front wheel angle of the vehicle, that is, further optimized as:

[0115] ;

[0116] in are weight adjustment parameters respectively.

[0117] Since the constraints, 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, the constraints in the form of inequalities need to be converted into corresponding obstacle functions in the form of exponentials when solving the control variable sequence of the vehicle. This embodiment constructs an exponential function , where the constraints and Parameters need to be adjusted later. error When the function is greater than 0, barrier function The value of will approach infinity to achieve the equivalent constraint. At the same time, due to barrier function Since it is a nonlinear function, this embodiment will perform Taylor expansion on it to linearize it. The specific method is as follows:

[0118] The acceleration of the vehicle needs to be within a certain range, that is, , so the barrier function of the acceleration constraint is It can be obtained according to the minimum and maximum allowable values ​​of the front wheel turning angle of the vehicle, that is:

[0119] ;

[0120] in and Parameters for later adjustment; a is the acceleration of the vehicle; a min is the minimum permissible acceleration of the vehicle; a max is the maximum permissible acceleration of the vehicle;

[0121] According to the curvature calculation formula of the vehicle:

[0122] ;

[0123] Due to the vehicle's front wheel angle Range of change Keep in Within this range, the change of vehicle curvature is monotonic, so the curvature constraint can be simplified to an inequality constraint on the vehicle front wheel angle, that is, the obstacle function form of the curvature constraint It can be constructed based on the minimum and maximum allowed values ​​of the vehicle's front wheel angle:

[0124] ;

[0125] in and Parameters for later adjustment; is the front wheel turning angle of the vehicle; is the minimum allowed value of the vehicle's front wheel turning angle; is the maximum allowable value of the vehicle's front wheel steering angle; each time the updated result of the control variable is obtained, the front wheel steering angle in the control variable can be limited to a reasonable range to achieve a hard constraint form.

[0126] Furthermore, in this embodiment, the corner constraint is essentially a road boundary constraint, which constrains the vehicle from exceeding the road boundary. This 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 discretized road boundaries on both sides. Figure 2 As shown, in this embodiment, n enveloping circles 2 are used to completely envelop the vehicle body. Preferably, n is an odd number so that the center of the vehicle body coincides with the center of an intermediate circle. Then, the radius and center distance of each circle are calculated:

[0127] ;

[0128] ;

[0129] The center position of any enveloping circle 2 can be calculated by the vehicle center position, the distance between the center points, and the vehicle heading angle. 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 must be located within the road boundary, the distance between the discrete point and the center position must be greater than the circle radius, 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 circle radius. At this time, the obstacle function form of the corner point constraint is It can be constructed based on the coordinate positions of the vehicle envelope circle and the discrete points on the road boundary:

[0130] ;

[0131] in and Parameters for later adjustment; dis is the minimum distance between the discrete points on the road boundary and the center of the vehicle envelope circle; ( x,y ) are the coordinates of discrete points on the road boundary; ( x circle ,y circle ) is the coordinate of the center of the vehicle envelope circle.

[0132] In addition, assuming that the number of discrete points on the road boundary is m and the number of circle centers is n, and that each state variable on the trajectory needs to be calculated for the corner point constraint, and there are k state variables, the traversal loop is time-consuming. Therefore, this embodiment simplifies the above solution process:

[0133] First, it is known that the trajectory points are arranged along the direction of forward movement 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 first trajectory point position, the minimum boundary point that can be found at the next trajectory point position should be located near it and extend backward. 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 of the traversal of 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 the traversal.

[0134] In addition, in this embodiment, since the road boundaries are mostly straight lines, or part of them are curves with curvature within a reasonable range, the local optimum is sought instead of the global optimum, that is, the minimum value calculated by the discrete points of the newly appeared road boundaries during the traversal process is obtained. distance When it is greater than the currently recorded minimum distance value, it can be considered that the traversal has passed the optimal value point and there is no need to continue traversing. The recorded minimum distance value is used as the optimal value point. Figure 3 As 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 optimal value point. At this time, the distance value a is used as the optimal value point.

[0135] Obtaining optimized vehicle trajectory information: Obtaining optimized trajectory information based on the control quantity sequence obtained by solving.

[0136] Furthermore, before generating the control instructions for the corresponding vehicle, this embodiment also needs to match the time information in the optimized trajectory information with the time information at the current moment. Due to control errors, if the planning results containing position information and speed and acceleration information are directly input into the vehicle controller, it does not take into account the vehicle's current position, posture, and movement conditions, and the control effect will be greatly reduced. Therefore, this embodiment retains the trajectory information after the current moment to form a tracking trajectory, wherein 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 end of the tracking trajectory. In addition, under normal circumstances, the first point in the trajectory information output is tracked, but this cannot guarantee real-time performance. Therefore, the vehicle in this embodiment can select any control variable corresponding to a trajectory tracking point greater than the current moment as the tracking target to generate the control instructions for the vehicle.

[0137] Generate vehicle control instructions: Generate control instructions for the corresponding vehicle based on the optimized trajectory information.

[0138] Furthermore, in order to avoid abnormal situations such as acceleration jitter and the optimized trajectory crossing the road boundary during vehicle control, this embodiment generates control instructions for the corresponding vehicle based on the optimized trajectory information. Therefore, it is necessary to monitor whether the acceleration change at time k exceeds the preset threshold and whether the trajectory position collides with the road boundary:

[0139] When it is determined that the acceleration change exceeds a 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;

[0140] When it is determined that the acceleration change 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.

[0141] In addition, Figure 4 As shown, the present application also discloses a vehicle lateral and longitudinal control device based on road boundary constraints, the device comprising:

[0142] The model building module is used to build a nonlinear mathematical model of the corresponding vehicle and perform Taylor expansion linearization to obtain a state transition model;

[0143] a variable solving module for solving a control variable sequence of the vehicle to minimize a cost function by using a constrained iterative linear quadratic regulator in combination with constraints and a state transition model; the constraints include at least an angular velocity constraint, a curvature constraint, and a corner point constraint;

[0144] Trajectory optimization module, used to obtain optimized trajectory information based on the control quantity sequence obtained by solving;

[0145] The instruction generation module is used to generate control instructions for the corresponding vehicle based on the optimized trajectory information.

[0146] The device provided in the embodiment of the present application can achieve Figure 1 To avoid repetition, the various processes implemented in the method embodiment will not be described here.

[0147] like Figure 5 As shown, the embodiment of the present application also provides an electronic device, including a processor and a memory, a program or instruction stored in the memory and capable of running on the processor, and when the program or instruction is executed by the processor, the following is achieved: Figure 1 The various processes of the method embodiment shown in the figure can achieve the same technical effect. To avoid repetition, they will not be described here.

[0148] 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, the above Figure 1 The various processes of the method embodiments described above can achieve the same technical effects, and will not be described again here to avoid repetition.

[0149] The present application also provides a computer program product, including computer instructions, which, when executed by a processor, implement the above Figure 1 The various processes of the method embodiments described above can achieve the same technical effects, and will not be described again here to avoid repetition.

[0150] It should be understood that "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, "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 the various embodiments of the present application, the size of the serial numbers of the above-mentioned processes does not mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present application. The above-mentioned serial numbers of the embodiments of the present application are for description only and do not represent the advantages and disadvantages of the embodiments.

[0151] It should be noted that, in this document, the terms "comprises," "includes," or any other variations thereof are intended to encompass non-exclusive inclusion, such that a process, method, article, or apparatus comprising a series of elements includes not only those elements but also other elements not explicitly listed, or elements inherent to such process, method, article, or apparatus. In the absence of further limitations, an element defined by the phrase "comprising a ..." does not exclude the presence of other identical elements in the process, method, article, or apparatus comprising the element.

[0152] In the several embodiments provided in this application, it should be understood that the disclosed devices and methods can be implemented in other ways. The device embodiments described above are merely schematic. For example, the division of the units is merely 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 can be through some interfaces, and the indirect coupling or communication connection of the devices or units can be electrical, mechanical or other forms.

[0153] The units described above as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units; they may be located in one place or distributed across multiple network units; some or all of the units may be selected according to actual needs to achieve the purpose of the scheme of this embodiment.

[0154] In addition, all functional units in the embodiments of the present application can be integrated into one processing unit, or each unit can be a separate unit, or two or more units can be integrated into one unit; the above-mentioned integrated units can be implemented in the form of hardware or in the form of hardware plus software functional units.

[0155] Those skilled in the art will understand that all or part of the steps of implementing the above-mentioned method embodiment can be completed by hardware related to program instructions, and the aforementioned program can be stored in a computer-readable storage medium. When the program is executed, it executes the steps of the above-mentioned method embodiment; and the aforementioned storage medium includes: mobile storage devices, read-only memories (ROM), magnetic disks or optical disks, and other media that can store program codes.

[0156] Alternatively, if the above-mentioned integrated unit of the present application is implemented in the form of a software function module and sold or used as an independent product, it can also be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the embodiment of the present application is essentially or the part that contributes to the prior art can be embodied in the form of a software product, and the computer software product is stored in a storage medium, including a number of instructions for enabling a device (which can be a terminal or platform, etc.) to execute all or part of the methods described in each embodiment of the present application. The aforementioned storage medium includes: various media that can store program codes, such as mobile storage devices, ROMs, magnetic disks or optical disks.

[0157] The above is only a preferred embodiment of the present application. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the technical principles of the present application. These improvements and modifications should also be regarded as the scope of protection of the present application.

Claims

1. A vehicle lateral and longitudinal control method based on road boundary constraints, characterized in that: The method comprises the following steps: Construct a nonlinear mathematical model of the corresponding vehicle and perform Taylor expansion linearization to obtain the state transition model; A constrained iterative linear quadratic regulator is used in combination with constraints and a state transition model to solve a control variable sequence of the vehicle to minimize a cost function; the constraints include at least an angular velocity constraint, a curvature constraint, and a corner point constraint; The step of solving the control variable sequence of the vehicle includes: Setting a cost function and a value function for a control variable sequence; the cost of the state variable considers trajectory following and speed conformance, wherein 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 stability and vehicle oscillation amplitude, wherein the cost function of the control variable is constructed based on the acceleration and front wheel angle of the vehicle; Perform variational processing on the value function at time k and calculate the optimal control increment at the current time; 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 of the control variable before and after the update, and output the updated control variable when the cost change converges; Obtain optimized trajectory information based on the control variable sequence obtained by solving; Generate control instructions for the corresponding vehicle based on the optimized trajectory information; at the same time, monitor whether the acceleration change at time k exceeds the preset threshold and whether the trajectory position collides with the road boundary: When it is determined that the acceleration change exceeds a 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 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.

2. The vehicle lateral and longitudinal control method according to claim 1, characterized in that: When constructing a nonlinear mathematical model of a vehicle, the vehicle's motion model needs to be simplified to a bicycle model: Obtaining the acceleration, front wheel angle, and wheelbase of the vehicle; Constructing a first objective function for calculating the speed of the vehicle according to the acceleration of the vehicle; Constructing a second objective function for calculating the heading angle of the vehicle according to the speed, front wheel turning angle and wheel spacing of the vehicle; Constructing a third objective function for calculating the lateral coordinate position of the vehicle according to the speed, acceleration and heading angle of the vehicle; Constructing a fourth objective function for calculating the longitudinal coordinate position of the vehicle according to the speed, acceleration and heading angle of the vehicle; A nonlinear mathematical model of the vehicle is obtained by combining the first objective function, the second objective function, the third objective function and the fourth objective function of the vehicle.

3. The vehicle lateral and longitudinal control method according to claim 2, characterized in that: The step of performing Taylor expansion linearization on the nonlinear mathematical model of the vehicle to obtain a state transition model includes: Setting a state variable sequence and a control variable sequence of the vehicle, and obtaining a nonlinear mathematical model of the corresponding vehicle; Performing Taylor expansion on a nonlinear mathematical model of the vehicle according to a state variable sequence and a control variable sequence of the vehicle; The state transfer model of the corresponding vehicle is obtained according to the nonlinear mathematical model after Taylor expansion.

4. The vehicle lateral and longitudinal control method according to claim 2, characterized in that: When solving the control variable sequence of the vehicle, the constraints in the form of inequalities are converted into barrier functions in the form of exponentials: Constructing a barrier function of the acceleration constraint according to the maximum allowable value and the minimum allowable value of the vehicle acceleration; Constructing an obstacle function of the curvature constraint according to the minimum allowable value and the maximum allowable value of the front wheel turning angle of the vehicle; The obstacle function of the corner point constraint is constructed according to the coordinate positions of the vehicle envelope circle and the road boundary discrete points.

5. The vehicle lateral and longitudinal control method according to claim 1, characterized in that: Before generating the control instructions for the corresponding vehicle based on the optimized trajectory information, the time information in the optimized trajectory information needs to be matched with the time information at the current moment, and the trajectory information after the current moment is retained to form a tracking trajectory, which 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 end of the tracking trajectory.

6. The vehicle lateral and longitudinal control method according to claim 5, 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.

7. A vehicle lateral and longitudinal control device based on road boundary constraints, characterized in that: The device comprises: The model building module is used to build a nonlinear mathematical model of the corresponding vehicle and perform Taylor expansion linearization to obtain a state transition model; A variable solving module is configured to solve the control variable sequence of the vehicle by using a constrained iterative linear quadratic regulator in combination with constraint conditions and a state transition model to minimize a cost function; the constraint conditions include at least an angular velocity constraint, a curvature constraint, and a corner constraint; wherein the steps of solving the control variable sequence of the vehicle include: Setting a cost function and a value function for a control variable sequence; the cost of the state variable considers trajectory following and speed conformance, wherein 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 stability and vehicle oscillation amplitude, wherein the cost function of the control variable is constructed based on the acceleration and front wheel angle of the vehicle; Perform variational processing on the value function at time k and calculate the optimal control increment at the current time; 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 of the control variable before and after the update, and output the updated control variable when the cost change converges; Trajectory optimization module, used to obtain optimized trajectory information based on the control quantity sequence obtained by solving; The command generation module is used to generate control commands for the corresponding vehicle based on the optimized trajectory information. It also monitors whether the acceleration change at time k exceeds the preset threshold and whether the trajectory position collides with the road boundary: When it is determined that the acceleration change exceeds a 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 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.

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