Vehicle trajectory tracking control method, device, electronic device and storage medium

Abstract

This application relates to the field of vehicle technology and discloses a vehicle trajectory tracking control method, device, electronic device, and storage medium. The method includes: obtaining a vehicle state compensation value at the current moment based on the vehicle's current state prediction value and actual state value; wherein the state prediction value is data obtained by predicting the vehicle's state at the current moment at a target historical moment; predicting the vehicle's target state prediction value within a control time domain after the current moment based on the state compensation value; and performing vehicle trajectory tracking control based on the target state prediction value and information about the road the vehicle is traveling. This application can improve the safety and accuracy of vehicle trajectory tracking control.

Description

Technical Field

[0001] The present application relates to the field of vehicle technology, and in particular to a vehicle trajectory tracking control method, device, electronic device and storage medium. Background Art

[0002] Autonomous driving technology has greatly promoted the development of automotive intelligence, and trajectory tracking technology is a technical integration in the motion control of autonomous driving vehicles. Research on trajectory tracking technology mainly focuses on improving the safety, accuracy and real-time performance of vehicle lateral motion control.

[0003] Some proposals use a linear quadratic regulator (LQR) for lateral control and PID (feedback regulation) for longitudinal control to achieve vehicle trajectory tracking. Others propose building a closed-loop driver-vehicle driving model to improve the driver's ability to track the desired trajectory. These solutions fail to consider the impact of the driving environment on the vehicle and are unable to accurately track the vehicle's trajectory. Summary of the Invention

[0004] In view of the above problems, the present application provides a vehicle trajectory tracking control method, device and storage medium for efficient vehicle trajectory tracking control with concise query statements.

[0005] According to one aspect of the present application, a vehicle trajectory tracking control method is provided, which includes: obtaining a state compensation value of the vehicle at the current moment based on the state prediction value and the actual state value of the vehicle at the current moment; wherein the state prediction value is data obtained by predicting the state of the current moment at a target historical moment; predicting a target state prediction value of the vehicle in a control time domain after the current moment based on the state compensation value; and performing trajectory tracking control on the vehicle based on the target state prediction value and information about the road on which the vehicle is traveling.

[0006] In an optional manner, before obtaining the state compensation value of the vehicle at the current moment based on the predicted state value and the actual state value of the vehicle at the current moment, the method also includes: obtaining the first lateral motion deviation and the first heading angle deviation of the vehicle at the current moment; constructing a fuzzy reasoning relationship between the numerical value of the time distance, the lateral motion deviation and the heading angle deviation; wherein the numerical value of the time distance is the time distance between the current moment and the historical moment; and determining the target historical moment based on the first lateral motion deviation, the first heading angle deviation and the fuzzy reasoning relationship.

[0007] In an optional manner, it is characterized in that the construction of the fuzzy reasoning relationship between the numerical value of the time distance, the lateral motion deviation and the heading angle deviation further includes: setting a proportional relationship between the numerical value of the time distance and the numerical value of the lateral motion deviation; and setting a proportional relationship between the numerical value of the time distance and the numerical value of the heading angle deviation, thereby obtaining the fuzzy reasoning relationship.

[0008] In an optional manner, it is characterized in that the prediction of the target state prediction value of the vehicle in the control time domain after the current moment based on the state compensation value further includes: obtaining the control time domain and the prediction time domain of the vehicle; constructing a prediction equation based on the control, the prediction time domain, the actual state value of the vehicle and the state compensation value; and obtaining the target state prediction value at each moment between the current moment and the control time domain based on the prediction equation.

[0009] In an optional manner, the trajectory tracking control of the vehicle based on the target state prediction value and the road information on which the vehicle is traveling further includes: constructing an elastic space based on the road information on which the vehicle is traveling; wherein the elastic space represents the error space of the vehicle at the intersection of the driving road and the road; and trajectory tracking control of the vehicle based on the elastic space and the target state prediction value.

[0010] In an optional manner, the trajectory tracking control of the vehicle based on the elastic space and the target state prediction value further includes: obtaining the target state of the vehicle at a target moment; wherein the target moment is the moment between the current moment and the control time domain; and performing trajectory tracking control based on the elastic space, the target state and the target state prediction value.

[0011] In an optional manner, the trajectory tracking control based on the elastic space, the target state and the target state prediction value further includes: constructing an optimization model based on the elastic space, the target state and the target state prediction value; obtaining a control sequence based on the optimization model, and performing trajectory tracking control on the vehicle based on the control items of the control sequence.

[0012] According to another aspect of the present application, a vehicle trajectory tracking control device is provided, which includes: a compensation value acquisition module for acquiring a state compensation value of the vehicle at the current moment based on a state prediction value and an actual state value of the vehicle at the current moment; wherein the state prediction value is data obtained by predicting the state of the current moment at a target historical moment; a prediction value acquisition module for predicting a target state prediction value of the vehicle in a control time domain after the current moment based on the state compensation value; and a trajectory tracking control module for performing trajectory tracking control on the vehicle based on the target state prediction value and information about the road on which the vehicle is traveling.

[0013] According to one aspect of the present application, an electronic device is provided, comprising: a controller; and a memory for storing one or more programs, which, when executed by the controller, executes the above-mentioned vehicle trajectory tracking control method.

[0014] According to one aspect of the present application, a computer-readable storage medium is also provided, on which computer-readable instructions are stored. When the computer-readable instructions are executed by a processor of a computer, the computer executes the above-mentioned vehicle trajectory tracking control method.

[0015] According to one aspect of the present application, a computer program product or computer program is also provided, the computer program product or computer program including computer instructions stored in a computer-readable storage medium. A processor of a computer device reads the computer instructions from the computer-readable storage medium and executes the computer instructions, causing the computer device to perform the above-described vehicle trajectory tracking control method.

[0016] This application compensates the vehicle's state through state prediction values ​​and actual state values ​​to ensure the accuracy and anti-interference performance of vehicle trajectory tracking control. At the same time, it combines road information and considers the actual situation of the vehicle during driving to perform vehicle trajectory tracking control and improve the safety and accuracy of vehicle driving.

[0017] The above description is only an overview of the technical solution of the present application. In order to more clearly understand the technical means of the present application, it can be implemented in accordance with the contents of the specification. In order to make the above and other purposes, features and advantages of the present application more obvious and easy to understand, the specific implementation methods of the present application are listed below. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] The accompanying drawings are incorporated into and constitute a part of the specification, illustrate embodiments consistent with the present application, and together with the specification, are used to explain the principles of the present application. Obviously, the drawings described below are only some embodiments of the present application, and it is clear that a person of ordinary skill in the art can derive other drawings based on these drawings without inventive effort.

[0019] Figure 1 It is a flowchart of a vehicle trajectory tracking control method shown in an exemplary embodiment of the present application.

[0020] Figure 2 It is a schematic diagram of vehicle driving state compensation shown in an exemplary embodiment of the present application.

[0021] Figure 3 It is a flow chart of another vehicle trajectory tracking control method shown in an exemplary embodiment of the present application.

[0022] Figure 4 Schematic diagram of vehicle trajectory tracking in elastic space shown in an exemplary embodiment of the present application.

[0023] Figure 5 It is a flow chart of another vehicle trajectory tracking control method shown in an exemplary embodiment of the present application.

[0024] Figure 6 It is a schematic diagram of a target historical moment acquisition process shown in an exemplary embodiment of the present application.

[0025] Figure 7 3 is a comparison diagram of vehicle trajectory tracking effects shown in an exemplary embodiment of the present application.

[0026] Figure 8 It is a structural diagram of a vehicle trajectory tracking control device shown in an exemplary embodiment of the present application.

[0027] Figure 9 It is a structural diagram of a computer system of an electronic device shown in an exemplary embodiment of the present application. DETAILED DESCRIPTION

[0028] Exemplary embodiments will be described in detail herein, with examples illustrated in the accompanying drawings. When the following description refers to the drawings, identical numerals in different figures represent identical or similar elements unless otherwise indicated. The embodiments described in the following exemplary embodiments are not intended to represent all embodiments consistent with the present application. Rather, they are merely examples of apparatus and methods consistent with certain aspects of the present application, as detailed in the appended claims.

[0029] The block diagrams shown in the accompanying drawings are merely functional entities and do not necessarily correspond to physically separate entities. That is, these functional entities may be implemented in software, in one or more hardware modules or integrated circuits, or in different networks and / or processor devices and / or microcontroller devices.

[0030] The flowcharts shown in the accompanying drawings are for illustrative purposes only and do not necessarily include all contents and operations / steps, nor must they be executed in the order described. For example, some operations / steps may be decomposed, while others may be combined or partially combined. Therefore, the actual execution order may vary depending on the actual situation.

[0031] In this application, "plurality" refers to two or more. "And / or" describes the relationship between related objects, indicating that three possible relationships exist. For example, "A and / or B" can mean: A exists alone, A and B exist simultaneously, and B exists alone. The character " / " generally indicates that the related objects are in an "or" relationship.

[0032] This application provides a vehicle trajectory tracking control method, the flow chart of which can be referred to Figure 1 The vehicle trajectory tracking control method includes at least S110 to S130, which are described in detail as follows:

[0033] S110: Obtaining a state compensation value of the vehicle at the current moment based on the state prediction value and the actual state value of the vehicle at the current moment.

[0034] In this embodiment, while the vehicle is driving, the vehicle can receive information about the road it is traveling on. At the same time, the vehicle's planning module will plan the vehicle's status at different times, so as to track and control the vehicle's trajectory based on the planned status and the predicted status at the corresponding time.

[0035] In this embodiment, the vehicle obtains a state prediction value and a state actual value at each moment, wherein the prediction value is data obtained by predicting the state of the vehicle at the current moment at the target historical moment.

[0036] In some embodiments, the state parameters of the vehicle may include the lateral velocity, yaw rate and longitudinal velocity of the vehicle, ie, the state prediction value and the state actual value, ie, the specific numerical values ​​corresponding to the corresponding state parameters.

[0037] In this embodiment, the target historical moment can be obtained by performing fuzzy logic reasoning and defuzzification on the lateral motion deviation and heading angle deviation of the vehicle at the current moment, thereby obtaining the target historical moment corresponding to the current moment.

[0038] For reference Figure 2The vehicle driving state compensation schematic diagram shown is Figure 2 In the figure, the dotted line is the predicted vehicle trajectory, the solid line is the actual vehicle trajectory, u is the control increment / control item, k is set to the current moment, and kw is the target historical moment. The vehicle can obtain the corresponding predicted state at any time. Therefore, the state prediction value of the vehicle at the target historical moment is first recorded, and then the state prediction value is applied to the actual state value at the current moment to obtain the state compensation value, thereby compensating for the state deviation problem caused by the time-varying vehicle system parameters during vehicle movement.

[0039] In this embodiment, the state compensation value is calculated as:

[0040] f(k)=x(k)-x(kw)

[0041] Among them, x(k) is the actual state value at time k, x(kw) is the state prediction value obtained by predicting the state at time k from time kw, and f(k) is the state compensation value at time k.

[0042] S120: Predicting a target state prediction value of the vehicle in a control time domain after the current moment based on the state compensation value.

[0043] In this embodiment, S120 may include:

[0044] S10: Obtain the control time domain and prediction time domain of the vehicle.

[0045] S11: Construct a prediction equation based on the control, prediction time domain, the actual state value of the vehicle, and the state compensation value.

[0046] S12: Obtain the target state prediction value at each moment between the current moment and the control time domain based on the prediction equation.

[0047] In this embodiment, the control time domain and the prediction time domain of the vehicle are obtained; and a state output relational expression is constructed based on the prediction time domain, the state supplementary value at each moment of the prediction time domain, and the control time domain.

[0048] The control time domain is the time it takes for the vehicle to reach the target control amount after accepting the control parameters, and the prediction time domain is the time it takes to perform state prediction at the current moment.

[0049] In this embodiment, the prediction equation Y(k) of the current state can be:

[0050] Y(k)=(γ+EW f )X(k)+θΔU(k)-EW f X(kw)

[0051]

[0052]

[0053]

[0054]

[0055]

[0056]

[0057]

[0058] Where x(k) is the actual state value of the vehicle at time k), Δu(k) is the system control increment of the vehicle at time k, that is, the control quantity / control item of the vehicle at time k, ΔU(k) ​​is the control sequence, A, B, C are the dynamic parameters of the vehicle, N p is the prediction time domain, N c For the control time domain, y(k+1) is the state prediction value at time k+1 predicted at time k. It can be seen that the prediction equation can predict the state from time k to k+N at time k. p The target state prediction value at the moment, that is, the target state prediction value at each moment between the current moment and the control time domain.

[0059] Further, integrate the above formula and add k+N p The target state prediction value at the moment can be:

[0060]

[0061] Among them, y(k+N p |k) is the predicted vehicle at k+N at time k p The target state prediction value at time k+i, f(k+i) is the state compensation value of the vehicle at time k+i, Δu(k-1+i) is the system control increment of the vehicle, W f It is the adjustable coefficient of the state compensation value.

[0062] It can be understood that in this embodiment, the y(k+N p |k) can predict the state of the vehicle in the control domain at time k. This equation not only includes compensation at the current moment, but also performs real-time compensation during the prediction process based on the predictive control principle.

[0063] In this embodiment, the state prediction value of the current moment in S110 is not performed, and can also be obtained through the prediction equation established at the target historical moment, that is, at the target historical moment, the same as the current moment, a prediction equation is also established. The prediction equation of the target historical moment can calculate the target state prediction value of each moment between the target historical moment and the prediction time domain, and the state prediction value of the current moment is the target state prediction value of the current moment calculated by the prediction equation at the target historical moment.

[0064] In this embodiment, the target state prediction value is optimized by using the compensation values ​​at different times, so that the predicted target state prediction value is more accurate, which facilitates subsequent vehicle trajectory tracking control.

[0065] S130: Performing trajectory tracking control on the vehicle based on the target state prediction value and the road information on which the vehicle is traveling.

[0066] like Figure 3 Flowchart of another proposed vehicle trajectory tracking control method. During the driving process, the vehicle receives road information, which enters the perception module for analysis. On the other hand, the planning module plans the target state y of the vehicle at each moment. d ,Thus, the vehicle trajectory tracking control is performed through the target state and the results analyzed by the perception module.

[0067] This embodiment introduces road information into the constraints of the vehicle trajectory tracking optimization problem, considers the impact of road information on vehicle trajectory tracking control, and further improves the accuracy of vehicle trajectory tracking control.

[0068] In some embodiments, S130 includes:

[0069] S20: Constructing an elastic space based on information about the road on which the vehicle is traveling; wherein the elastic space represents an error space at a location where the vehicle intersects with the road on which it is traveling.

[0070] S21: Perform trajectory tracking control on the vehicle based on the elastic space and the target state prediction value.

[0071] In this embodiment, based on the road information, an elastic space is constructed, such as Figure 4 This is a vehicle trajectory tracking diagram in an elastic space shown in an embodiment. Figure 4 In the figure, the solid line represents the edge of the road, the dotted line represents the intersection of the road, the dotted line represents the center line of the road, and the dotted line represents the elastic space.

[0072] In this way, trajectory tracking control can be performed based on elastic space and state prediction equations.

[0073] In some embodiments, S21 may further include:

[0074] S30: Obtaining a target state of the vehicle at a target time; wherein the target time is a time between the current time and the control time domain.

[0075] S31: Perform trajectory tracking control based on the elastic space, the target state, and the target state prediction value.

[0076] In this embodiment, a vehicle trajectory tracking control optimization problem can be constructed based on the elastic space, target state, and target predicted state, and the following optimization model can be obtained:

[0077]

[0078] S1=s1(y(k+i)-y d (k+i)) 2

[0079]

[0080] S4=s4Δu(k+i-1) 2

[0081] Among them, S1 is used to ensure the trajectory tracking accuracy of the vehicle, S2 and S3 represent the optimization goals of the vehicle in the road elastic space, S4 represents the execution control dissipation, and e y (k+i) is the predicted lateral position deviation of the vehicle at time k+i. y (k+i) is a state equation. Based on the predictive control principle, the predicted value can be obtained. The predictive control principle can refer to y(k+N p |k), y(k+i) is the target state prediction value of the vehicle at time k+i, y d (k+i) is the target state of the vehicle at the predicted time k+i, d l (k+i) represents the position information on the left side of the road, that is, the distance between the vehicle and the left side of the road, d r (k+i) represents the position information of the right side of the road, that is, the distance between the vehicle and the right side of the road, d b is the length of the defined elastic space, d l (k+i), d r (k+i) and d b The length unit is the same as , which can be m (meter), km (kilometer), etc., and can be set by yourself. s1, s2, s3, and s4 represent the adjustment coefficients of the corresponding trajectory tracking accuracy, left side of the road safety, right side of the road safety, and controller dissipation, respectively. is the heading angle deviation at the current moment.

[0082] In this way, the optimal control sequence ΔU can be obtained by combining the system state prediction equation Y(k) containing the adaptive historical state compensation and the optimization problem S in the elastic space.* , ΔU * The difference between ΔU(k) ​​and ΔU(k) ​​is that ΔU(k) ​​is a generalized form of the control sequence, while ΔU * is the optimal solution obtained after solving the optimization problem, ΔU * is the final control sequence acting on vehicle control, the ΔU * There are multiple control items in the control sequence, each of which can be regarded as a control quantity and can be controlled based on the control items in the control sequence.

[0083] Specifically, the system state prediction equation Y(k) and the optimization problem are about ΔU * A quadratic equation, by solving the quadratic equation, we can get the result of the optimization problem ΔU * , which corresponds to Figure 3 , and then select the first control item Δu of the optimal control sequence * , Δu * That is Figure 3 The control quantity in , which ultimately acts on the vehicle system to achieve trajectory tracking control.

[0084] This embodiment proposes a vehicle trajectory tracking control method that compensates the vehicle state through state prediction values ​​and actual state values ​​to ensure the accuracy and anti-interference performance of vehicle trajectory tracking control. At the same time, it combines road information and considers the actual situation of the vehicle during driving to perform vehicle trajectory tracking control, thereby improving the safety and accuracy of vehicle driving.

[0085] In another exemplary embodiment of the present application, a method for obtaining a target historical moment is described in detail, such as Figure 5 As shown, in Figure 1 The vehicle trajectory tracking control method shown in FIG. 1 may further include steps S510 to S530 before step S110, which are described in detail as follows:

[0086] S510: Acquire a first lateral motion deviation and a first heading angle deviation of the vehicle at the current moment.

[0087] In this embodiment, reference Figure 6 The target historical moment acquisition process diagram shown is based on the lateral motion deviation and heading angle deviation of the vehicle trajectory tracking state to adapt to the target historical moment corresponding to the current moment, which is specifically achieved through fuzzy logic reasoning and defuzzification.

[0088] In this embodiment, the first lateral motion deviation e at the current moment is obtained. y And the first heading angle deviation The first lateral movement deviation e y And the first heading angle deviation After fuzzy logic reasoning and defuzzification, a target historical moment is output.

[0089] S520: Constructing a fuzzy reasoning relationship between the value of the time distance, the lateral motion deviation, and the heading angle deviation.

[0090] In this embodiment, the value of the time distance is the time distance between the current moment and the historical moment.

[0091] Specifically, construct the input of the vehicle's lateral motion deviation and heading angle deviation:

[0092] E y ={E0, E1, E2, E3, E4}

[0093]

[0094] W = {W0, W1, W2, W3, W4}

[0095] Among them, E y ={E0, E1, E2, E3, E4} represent different values ​​of lateral motion deviation, the larger the subscript, the larger the value. Represents different values ​​of heading angle deviation, the larger the subscript, the greater the value. Of course, the input E defined above y 、 The number is for illustration only. In other embodiments, it can be other numbers. The fuzzy processing results will be different if the number of inputs for each parameter is different. The more inputs there are, the more complicated the operation will be. In this way, the corresponding number can be set to reduce the complexity of the rule and improve its effectiveness. For example, 5 corresponding inputs are selected here. At other times, it can be other numbers.

[0096] E y 、 The specific value can be set according to human experience and the driving characteristics of the vehicle.

[0097] In this embodiment, a normalized mapping relationship, or fuzzy inference, exists between the temporal distance value, the lateral motion deviation, and the heading angle deviation. This relationship allows the determined lateral motion deviation and heading angle deviation inputs to generate a historical moment output. For example, W = {W0, W1, W2, W3, W4} represents different historical moments, with larger subscripts indicating closer proximity to the current moment. Based on the characteristics of MPC rolling optimization, historical states closer to the current moment more significantly compensate for the system's real-time state. In this embodiment, the temporal distance is the difference between any value in W and the current moment.

[0098] In this embodiment, during the fuzzy processing, E y 、 is the input of the fuzzy processing, and W is the corresponding output.

[0099] S530: Determine the target historical moment based on the first lateral motion deviation, the first heading angle deviation, and the fuzzy reasoning relationship.

[0100] In this embodiment, according to the characteristics of MPC rolling optimization, the closer the historical state is to the current moment, the more obvious the compensation for the real-time state of the system is, and a fuzzy reasoning relationship is set: the numerical value of each time distance is set to be proportional to the numerical value of the lateral motion deviation, and the numerical value of each time distance is set to be proportional to the numerical value of the heading angle deviation, so as to obtain a fuzzy reasoning relationship; based on the first lateral motion deviation value and the first heading angle deviation value of the vehicle at the current moment, the corresponding target historical moment is determined in the fuzzy reasoning relationship.

[0101] That is, it relies on the characteristics of MPC rolling time domain optimization. The closer the historical moment is to the current moment, the more obvious the compensation effect is. It is suitable for situations with large deviations, but it is prone to overcompensation. The farther the historical moment is from the current moment, the more slight the compensation is. It is suitable for situations with small deviations.

[0102] The fuzzy reasoning relationship can be determined by Table 1:

[0103] Table 1

[0104]

[0105] In this embodiment, when the lateral deviation of the vehicle gradually increases, the corresponding fuzzy input variable E y Increases, corresponding to the selection of historical moments W gradually increases; when the vehicle's heading angle deviation gradually increases, that is, the corresponding fuzzy input variable Increases, corresponding to the selected historical moment W gradually increases, but the increase is smaller than the horizontal deviation logic.

[0106] Based on the vehicle trajectory tracking control method provided by this application, the effectiveness of the vehicle trajectory tracking control method is tested in this embodiment. The vehicle trajectory tracking control is verified by carsim and simulink (two tools). The obtained vehicle trajectory tracking effect diagram is shown as follows: Figure 7 As shown, in this verification method, conventional vehicle trajectory tracking and the vehicle trajectory tracking method in this embodiment are compared. Figure 7 In the figure, the dotted line is the effect curve of the conventional vehicle trajectory tracking method, and the solid line is the effect curve of the vehicle trajectory tracking method in this embodiment; Figure 7 It can be seen that the vehicle trajectory tracking method in this embodiment can effectively improve the accuracy of vehicle trajectory tracking and has a certain ability to resist external interference.

[0107] The longitudinal speed of the vehicle is set to 60 km / h (kilometers per hour), the double lane change working condition trajectory tracking task is completed, and a crosswind interference is simulated in about 18 seconds. Finally, the effects of conventional vehicle trajectory tracking and the vehicle trajectory tracking method in this embodiment are compared. It can be seen that the trajectory tracking method proposed in this application can effectively improve the safety and accuracy of vehicle driving, and has a certain ability to resist external interference.

[0108] Another aspect of the present application also provides a vehicle trajectory tracking control device, such as Figure 8 As shown, Figure 8 : This is a schematic diagram of the structure of a vehicle trajectory tracking control device according to an exemplary embodiment of the present application. The vehicle trajectory tracking control device 800 includes: a compensation value acquisition module 810, which is used to obtain a state compensation value of the vehicle at the current moment based on the vehicle's state prediction value and actual state value at the current moment; wherein the state prediction value is the data obtained by predicting the state at the current moment at the target historical moment; a prediction value acquisition module 830, which is used to predict the target state prediction value of the vehicle within the control time domain after the current moment based on the state compensation value; and a trajectory tracking control module 850, which is used to perform trajectory tracking control on the vehicle based on the target state prediction value and information about the road on which the vehicle is traveling.

[0109] In an optional manner, the vehicle trajectory tracking control device 800 also includes: a deviation data acquisition module, used to obtain the first lateral motion deviation and the first heading angle deviation of the vehicle at the current moment; a relationship construction module, used to construct a fuzzy reasoning relationship between the numerical value of the time distance, the lateral motion deviation and the heading angle deviation; wherein the numerical value of the time distance is the time distance between the current moment and the historical moment; a target historical moment determination module, used to determine the target historical moment based on the first lateral motion deviation, the first heading angle deviation and the fuzzy reasoning relationship.

[0110] In an optional manner, the target historical moment determination module further includes: a first setting unit, used to set the value of the time distance to be proportional to the value of the lateral motion deviation; a second setting unit, used to set the value of the time distance to be proportional to the value of the heading angle deviation, thereby obtaining a fuzzy reasoning relationship.

[0111] In an optional manner, the prediction value acquisition module 830 further includes: a time domain acquisition unit for acquiring the control time domain and prediction time domain of the vehicle; a prediction equation acquisition unit for constructing a prediction equation based on the control, prediction time domain, actual state value of the vehicle and state compensation value; a prediction value acquisition unit for acquiring the target state prediction value at each moment between the current moment and the control time domain based on the prediction equation.

[0112] In an optional manner, the trajectory tracking control module 850 further includes: constructing a state output relationship of the vehicle in the prediction time domain based on the state compensation value, further including:

[0113] An elastic space construction unit is used to construct an elastic space based on the road information on which the vehicle is traveling; wherein the elastic space represents the error space at the intersection of the vehicle and the road on which the vehicle is traveling; and a trajectory tracking control unit is used to perform trajectory tracking control on the vehicle based on the elastic space and the target state prediction value.

[0114] In an optional manner, the trajectory tracking control unit further includes: a target state acquisition module, used to obtain the target state of the vehicle at a target moment; wherein the target moment is a moment between the current moment and the control time domain; and a trajectory tracking control module, used to perform trajectory tracking control based on the elastic space, the target state, and the target state prediction value.

[0115] In an optional manner, the trajectory tracking control module further includes: an optimization model establishment sub-module, which is used to build an optimization model based on the elastic space, the target state and the target state prediction value; and a trajectory tracking control sub-module, which is used to obtain a control sequence based on the optimization model and perform trajectory tracking control on the vehicle with control items based on the control sequence.

[0116] The vehicle trajectory tracking control device in this embodiment compensates for the vehicle state through the state prediction value and the state actual value to ensure the accuracy and anti-interference performance of the vehicle trajectory tracking control. At the same time, it combines road information and considers the actual situation of the vehicle during driving to perform vehicle trajectory tracking control and improve the safety and accuracy of vehicle driving.

[0117] It should be noted that the vehicle trajectory tracking control device provided in the above embodiment and the vehicle trajectory tracking control method provided in the above embodiment belong to the same concept, and the specific manner in which each module and unit performs operations has been described in detail in the method embodiment and will not be repeated here.

[0118] Another aspect of the present application also provides an electronic device, including: a controller; a memory for storing one or more programs, which, when executed by the controller, executes the above-mentioned vehicle trajectory tracking control method.

[0119] See also Figure 9 , Figure 9 1 is a schematic diagram of the structure of a computer system of an electronic device shown in an exemplary embodiment of the present application, which shows a schematic diagram of the structure of a computer system of an electronic device suitable for implementing an embodiment of the present application.

[0120] It should be noted that Figure 9The computer system 900 of the electronic device shown is only an example and should not bring any limitation to the functions and scope of use of the embodiments of the present application.

[0121] like Figure 9 As shown, the computer system 900 includes a central processing unit (CPU) 901, which can perform various appropriate actions and processes according to the program stored in the read-only memory (ROM) 902 or the program loaded from the storage part 908 into the random access memory (RAM) 903, such as executing the method in the above embodiment. Various programs and data required for system operation are also stored in the RAM 903. The CPU 901, ROM 902 and RAM 903 are connected to each other via a bus 904. An input / output (I / O) interface 905 is also connected to the bus 904.

[0122] The following components are connected to the I / O interface 905: an input section 906 including a keyboard, a mouse, and the like; an output section 907 including devices such as a cathode ray tube (CRT), a liquid crystal display (LCD), and a speaker; a storage section 908 including a hard disk and the like; and a communication section 909 including a network interface card such as a LAN (Local Area Network) card or a modem. The communication section 909 performs communication processing via a network such as the Internet. A drive 910 is also connected to the I / O interface 905 as needed. Removable media 911, such as a magnetic disk, an optical disk, a magneto-optical disk, a semiconductor memory, and the like, are installed in the drive 910 as needed, so that computer programs read therefrom can be installed into the storage section 908 as needed.

[0123] In particular, according to an embodiment of the present application, the process described above with reference to the flowchart can be implemented as a computer software program. For example, an embodiment of the present application includes a computer program product, which includes a computer program carried on a computer-readable medium, and the computer program includes a computer program for executing the method shown in the flowchart. In such an embodiment, the computer program can be downloaded and installed from a network via the communication section 909, and / or installed from a removable medium 911. When the computer program is executed by the central processing unit (CPU) 901, the various functions defined in the system of the present application are executed.

[0124] It should be noted that the computer-readable medium shown in the embodiments of the present application can be a computer-readable signal medium or a computer-readable storage medium or any combination of the above two. The computer-readable storage medium can be, for example, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, device or device, or any combination of the above. More specific examples of computer-readable storage media can include, but are not limited to: an electrical connection with one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM), a flash memory, an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above. In the present application, a computer-readable storage medium can be any tangible medium containing or storing a program that can be used by or in combination with an instruction execution system, device or device. In the present application, a computer-readable signal medium can include a data signal propagated in baseband or as part of a carrier wave, which carries a computer-readable computer program. Such propagated data signals may take various forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination thereof. A computer-readable signal medium may also be any computer-readable medium other than a computer-readable storage medium that can transmit, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device. A computer program embodied on a computer-readable medium may be transmitted using any suitable medium, including but not limited to wireless, wired, or any suitable combination thereof.

[0125] The flowcharts and block diagrams in the accompanying drawings illustrate the possible implementation architecture, functions and operations of the systems, methods and computer program products according to various embodiments of the present application. Among them, each box in the flowchart or block diagram can represent a module, program segment, or part of the code, and the above-mentioned module, program segment, or part of the code contains one or more executable instructions for implementing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the box can also occur in an order different from that marked in the accompanying drawings. For example, two boxes represented in succession can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each box in the block diagram or flowchart, and the combination of boxes in the block diagram or flowchart, can be implemented with a dedicated hardware-based system that performs the specified function or operation, or can be implemented with a combination of dedicated hardware and computer instructions.

[0126] The units involved in the embodiments described in this application may be implemented by software or hardware, and the units described may also be set in a processor. In some cases, the names of these units do not constitute limitations on the units themselves.

[0127] Another aspect of the present application provides a computer-readable storage medium having a computer program stored thereon. When executed by a processor, the computer program implements the vehicle trajectory tracking control method described above. The computer-readable storage medium may be included in the electronic device described in the above embodiments, or may exist independently and not be incorporated into the electronic device.

[0128] Another aspect of the present application provides a computer program product or computer program, which includes computer instructions stored in a computer-readable storage medium. A processor of a computer device reads the computer instructions from the computer-readable storage medium and executes the computer instructions, causing the computer device to perform the vehicle trajectory tracking control method provided in each of the above embodiments.

[0129] According to one aspect of an embodiment of the present application, a computer system is further provided, including a central processing unit (CPU), which can perform various appropriate actions and processes according to a program stored in a read-only memory (ROM) or a program loaded from a storage portion into a random access memory (RAM), such as executing the method in the above embodiment. Various programs and data required for system operation are also stored in the RAM. The CPU, ROM, and RAM are connected to each other via a bus. An input / output (I / O) interface is also connected to the bus.

[0130] The following components are connected to the I / O interface: an input section including a keyboard, mouse, etc.; an output section including a cathode ray tube (CRT), liquid crystal display (LCD), and speakers; a storage section including a hard disk; and a communication section including a network interface card such as a LAN (Local Area Network) card and a modem. The communication section performs communication processing via a network such as the Internet. A drive is also connected to the I / O interface as needed. Removable media such as magnetic disks, optical disks, magneto-optical disks, semiconductor memories, etc. are installed in the drive as needed so that computer programs read from them can be installed into the storage section as needed.

[0131] The above content is only a preferred exemplary embodiment of the present application and is not intended to limit the implementation scheme of the present application. Ordinary technicians in this field can easily make corresponding changes or modifications based on the main ideas and spirit of the present application. Therefore, the scope of protection of the present application shall be based on the scope of protection required by the claims.

Claims

1. A vehicle trajectory tracking control method, characterized in that: The method comprises: Obtaining a first lateral motion deviation and a first heading angle deviation of the vehicle at a current moment; Constructing a fuzzy reasoning relationship between the value of the time distance, the lateral motion deviation, and the heading angle deviation; wherein the value of the time distance is the time distance between the current moment and the historical moment; determining a target historical moment based on the first lateral motion deviation, the first heading angle deviation, and the fuzzy reasoning relationship; Obtaining a state compensation value of the vehicle at the current moment based on a state prediction value and a state actual value of the vehicle at the current moment; wherein the state prediction value is data obtained by predicting the state at the current moment at a target historical moment; predicting a target state prediction value of the vehicle in a control time domain after the current moment based on the state compensation value; The vehicle is tracked and controlled based on the target state prediction value and information about the road on which the vehicle is traveling.

2. The method according to claim 1, characterized in that The constructing of the fuzzy inference relationship between the value of the time distance, the lateral motion deviation, and the heading angle deviation further includes: Setting a proportional relationship between the value of the time distance and the value of the lateral movement deviation; The value of the time distance is set to be proportional to the value of the heading angle deviation, thereby obtaining the fuzzy inference relationship.

3. The method according to claim 1, characterized in that The method of predicting a target state prediction value of the vehicle in a control time domain after the current moment based on the state compensation value further includes: Obtaining a control time domain and a prediction time domain of the vehicle; Constructing a prediction equation based on the control, the prediction time domain, the actual value of the vehicle state, and the state compensation value; A target state prediction value at each moment between the current moment and the control time domain is obtained based on the prediction equation.

4. The method according to claim 1, wherein The performing trajectory tracking control on the vehicle based on the target state prediction value and the road information on which the vehicle is traveling further includes: Based on the road information on which the vehicle is traveling, an elastic space is constructed; wherein the elastic space represents the error space at the intersection of the vehicle and the road; The vehicle is subjected to trajectory tracking control based on the elastic space and the target state prediction value.

5. The method according to claim 4, characterized in that The performing trajectory tracking control on the vehicle based on the elastic space and the target state prediction value further includes: Obtaining a target state of the vehicle at a target time; wherein the target time is a time between the current time and the control time domain; Trajectory tracking control is performed based on the elastic space, the target state, and the target state prediction value.

6. The method according to claim 5, characterized in that The performing trajectory tracking control based on the elastic space, the target state, and the target state prediction value further includes: Building an optimization model based on the elastic space, the target state, and the target state prediction value; A control sequence is obtained based on the optimization model, and trajectory tracking control is performed on the vehicle based on control items of the control sequence.

7. A vehicle trajectory tracking control device, characterized in that: The vehicle trajectory tracking control device comprises: a deviation data acquisition module, configured to acquire a first lateral motion deviation and a first heading angle deviation of the vehicle at a current moment; A relationship building module, configured to build a fuzzy reasoning relationship between a time distance value, a lateral motion deviation, and a heading angle deviation; wherein the time distance value is the time distance between the current moment and the historical moment; a target historical moment determination module, configured to determine a target historical moment based on the first lateral motion deviation, the first heading angle deviation, and the fuzzy reasoning relationship; a compensation value acquisition module, configured to acquire a state compensation value of the vehicle at the current moment based on a state prediction value and an actual state value of the vehicle at the current moment; wherein the state prediction value is data obtained by predicting the state at the current moment at a target historical moment; A prediction value acquisition module, configured to predict a target state prediction value of the vehicle in a control time domain after the current moment based on the state compensation value; A trajectory tracking control module is used to perform trajectory tracking control on the vehicle based on the target state prediction value and the road information on which the vehicle is traveling.

8. An electronic device, characterized in that: include: Controller; A memory for storing one or more programs, which, when executed by a controller, enables the controller to implement the vehicle trajectory tracking control method according to any one of claims 1 to 6.

9. A computer-readable storage medium, characterized in that Computer-readable instructions are stored thereon, and when the computer-readable instructions are executed by a processor of a computer, the computer is caused to execute the vehicle trajectory tracking control method according to any one of claims 1 to 6.

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