CN117962929A

CN117962929A - Vehicle track tracking control method and device, electronic equipment and storage medium

Info

Publication number: CN117962929A
Application number: CN202410309290.1A
Authority: CN
Inventors: 谢波; 张操; 苏星溢
Original assignee: Chongqing Selis Phoenix Intelligent Innovation Technology Co ltd
Current assignee: Chongqing Selis Phoenix Intelligent Innovation Technology Co ltd
Priority date: 2024-03-19
Filing date: 2024-03-19
Publication date: 2024-05-03

Abstract

The application relates to the technical field of vehicles, and discloses a vehicle track tracking control method, a device, electronic equipment and a storage medium, wherein the method comprises the following steps: acquiring a state compensation value of the vehicle at the current moment based on a state predicted value and a state actual value of the vehicle at the current moment; the state prediction value is data obtained by predicting the state of the current moment at the target historical moment; predicting a target state predicted value of the vehicle in a control time domain after the current moment based on the state compensation value; and performing track tracking control on the vehicle based on the target state predicted value and the road information on which the vehicle is running. The application can improve the safety and accuracy of vehicle track tracking control.

Description

Vehicle track tracking control method and device, electronic equipment and storage medium

Technical Field

The present application relates to the field of vehicle technologies, and in particular, to a vehicle track tracking control method, a device, an electronic apparatus, and a storage medium.

Background

While the automatic driving technology greatly promotes the intelligent development of automobiles, the track tracking technology is a technology integration in the motion control of the automatic driving vehicles, and the research on the track tracking technology is mainly developed around the improvement of the safety, accuracy and real-time performance of the transverse motion control of the vehicles.

Some schemes propose to use linear quadratic regulators (Linear Quadratic Regulator, LQR) for transverse control and PID (feedback regulation) for longitudinal control to perform vehicle trajectory tracking control; in another writing scheme, a closed-loop driving model of the human-vehicle can be constructed, so that the tracking effect of a driver on an ideal track is improved. These schemes can only take no consideration of the influence of the driving environment on the vehicle, and cannot accurately perform the vehicle trajectory tracking control.

Disclosure of Invention

In view of the above problems, the present application provides a vehicle track tracking control method, apparatus, and storage medium, which are used for efficiently performing vehicle track tracking control, and the query statement is concise.

According to an aspect of the present application, there is provided a vehicle track-following control method including: acquiring a state compensation value of the vehicle at the current moment based on a state predicted value and a state actual value of the vehicle at the current moment; the state prediction value is data obtained by predicting the state of the current moment at the 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 track tracking control on the vehicle based on the target state predicted value and the road information on which the vehicle runs.

In an alternative manner, before the obtaining the state compensation value of the vehicle at the current time based on the state prediction value and the state actual value of the vehicle at the current time, the method further includes: acquiring a first transverse movement deviation and a first heading angle deviation of the vehicle at the current moment; constructing a fuzzy reasoning relation among the numerical value of the time distance, the transverse motion deviation and the course angle deviation; the numerical value of the time distance is the time distance between the current moment and the historical moment; and determining a target historical moment based on the first lateral movement deviation, the first heading angle deviation and the fuzzy inference relation.

In an alternative manner, the method is characterized in that the fuzzy inference relation among the numerical value of the time distance, the lateral motion deviation and the heading angle deviation is constructed, and the method further comprises: setting a proportional relation between the numerical value of the time distance and the numerical value of the transverse movement deviation; and setting a proportional relation between the numerical value of the time distance and the numerical value of the heading angle deviation, so as to obtain the fuzzy reasoning relation.

In an alternative manner, the predicting the target state prediction value of the vehicle in the control time domain after the current time based on the state compensation value further includes: acquiring a control time domain and a prediction time domain of the vehicle; constructing a predictive equation based on the control, the prediction horizon, the actual state value of the vehicle, and the state compensation value; and acquiring a target state predicted value of each moment between the current moment and the control time domain based on the prediction equation.

In an optional manner, the track-following 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 road information on which the vehicle is traveling; wherein the elastic space characterizes an error space at the junction of the vehicle and a road on a running road; and performing track tracking control on the vehicle based on the elastic space and the target state predicted value.

In an optional manner, the track following control is performed on the vehicle based on the elastic space and the target state prediction value, and further includes: acquiring a target state of the vehicle at a target moment; the target time is the time between the current time and a control time domain; and performing track tracking control based on the elastic space, the target state and the target state predicted value.

In an optional manner, the track 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 predicted value; and obtaining a control sequence based on the optimization model so as to carry out track tracking control on the vehicle based on control items of the control sequence.

According to another aspect of the present application, there is provided a vehicle track following control device including: the compensation value acquisition module is used for acquiring a state compensation value of the vehicle at the current moment based on a state predicted value and a state actual value of the vehicle at the current moment; the state prediction value is data obtained by predicting the state of the current moment at the target historical moment; a predicted value obtaining module, configured to predict a target state predicted value of the vehicle in a control time domain after the current time based on the state compensation value; and the track tracking control module is used for carrying out track tracking control on the vehicle based on the target state predicted value and the road information on which the vehicle is driven.

According to an aspect of the present application, there is provided an electronic apparatus including: a controller; and a memory for storing one or more programs which, when executed by the controller, perform the vehicle trajectory tracking control method described above.

According to an aspect of the present application, there is also provided a computer-readable storage medium having stored thereon computer-readable instructions which, when executed by a processor of a computer, cause the computer to perform the above-described vehicle track-following control method.

According to one aspect of the present application, there is also provided a computer program product or computer program comprising computer instructions stored in a computer readable storage medium. The processor of the computer device reads the computer instructions from the computer-readable storage medium, and the processor executes the computer instructions so that the computer device performs the vehicle track following control method described above.

The application compensates the state of the vehicle through the state predicted value and the state actual value, ensures the accuracy and anti-interference performance of the vehicle track tracking control, and simultaneously considers the actual condition of the vehicle in the running process by combining the road information, thereby carrying out the vehicle track tracking control and improving the safety and accuracy of the vehicle running.

The foregoing description is only an overview of the present application, and is intended to be implemented in accordance with the teachings of the present application in order that the same may be more clearly understood and to make the same and other objects, features and advantages of the present application more readily apparent.

Drawings

The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the application and together with the description, serve to explain the principles of the application. It is evident that the drawings in the following description are only some embodiments of the present application and that other drawings may be obtained from these drawings without inventive effort for a person of ordinary skill in the art.

Fig. 1 is a flowchart illustrating a vehicle track following control method according to an exemplary embodiment of the present application.

Fig. 2 is a schematic diagram showing a vehicle running state compensation according to an exemplary embodiment of the present application.

Fig. 3 is a flow chart illustrating another vehicle track-following control method according to an exemplary embodiment of the present application.

Fig. 4 is a schematic diagram illustrating vehicle trajectory tracking in elastic space according to an exemplary embodiment of the present application.

Fig. 5 is a flow chart illustrating another vehicle track-following control method according to an exemplary embodiment of the present application.

Fig. 6 is a schematic diagram of a target historical moment acquisition flow according to an exemplary embodiment of the application.

Fig. 7 is a graph showing a comparison of a vehicle track following effect according to an exemplary embodiment of the present application.

Fig. 8 is a schematic structural view of a vehicle track-following control device according to an exemplary embodiment of the present application.

Fig. 9 is a schematic diagram of a computer system of an electronic device according to an exemplary embodiment of the present application.

Detailed Description

Reference will now be made in detail to exemplary embodiments, examples of which are illustrated in the accompanying drawings. When the following description refers to the accompanying drawings, the same numbers in different drawings refer to the same or similar elements, unless otherwise indicated. The implementations described in the following exemplary examples do not represent all implementations consistent with the application. Rather, they are merely examples of apparatus and methods consistent with aspects of the application as detailed in the accompanying claims.

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

The flow diagrams depicted in the figures are exemplary only, and do not necessarily include all of the elements and operations/steps, nor must they be performed in the order described. For example, some operations/steps may be decomposed, and some operations/steps may be combined or partially combined, so that the order of actual execution may be changed according to actual situations.

In the present application, the term "plurality" means two or more. "and/or" describes an association relationship of an association object, meaning that there may be three relationships, e.g., a and/or B may represent: a exists alone, A and B exist together, and B exists alone. The character "/" generally indicates that the context-dependent object is an "or" relationship.

The application provides a vehicle track tracking control method, and a flow diagram can refer to fig. 1. The vehicle track following control method at least comprises S110 to S130, and is described in detail as follows:

S110: and acquiring a state compensation value of the vehicle at the current moment based on the state predicted value and the state actual value of the vehicle at the current moment.

In this embodiment, the vehicle may receive road information on which the vehicle travels during the traveling process of the vehicle, and the vehicle planning module plans the states of the vehicle at different times, so as to perform vehicle track tracking control on the vehicle based on the planned states and the states of the predicted corresponding times.

In this embodiment, the vehicle obtains a state prediction value and a state actual value at each time, where the prediction value is data obtained by predicting the state of the vehicle at the current time at the target historical time.

In some embodiments, the state parameters of the vehicle may include a lateral speed of the vehicle, a yaw rate of the vehicle, and a longitudinal speed, i.e., a state prediction value, and a state actual value, i.e., a specific value corresponding to the state parameter.

In this embodiment, the target historical moment may be obtained by performing fuzzy logic reasoning and defuzzification according to the lateral movement deviation and the heading angle deviation of the vehicle at the current moment, so as to obtain the target historical moment corresponding to the current moment.

Referring to the vehicle running state compensation schematic diagram shown in fig. 2, a dotted line is a predicted vehicle running track diagram, a solid line is an actual running track diagram of a vehicle, u is a control increment/control item, k is a current time, k-w is a target history time, and the vehicle can obtain a corresponding predicted state at any time.

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

f(k)＝x(k)-x(k-w)

Wherein x (k) is the actual state value at time k, x (k-w) is the state prediction value obtained by predicting time k at time k-w, and f (k) is the state compensation value at time k.

S120: and predicting a target state predicted value of the vehicle in a control time domain after the current moment based on the state compensation value.

In this embodiment, S120 may include:

S10: a control horizon and a prediction horizon of the vehicle are obtained.

S11: a predictive equation is constructed based on the control, the prediction horizon, the actual state value of the vehicle, and the state compensation value.

S12: and acquiring a target state predicted value of each moment between the current moment and the control time domain based on a prediction equation.

In this embodiment, a control time domain and a prediction time domain of a vehicle are acquired; and constructing a state output relation based on the predicted time domain, the state supplement value of each time of the predicted time domain and the control time domain.

The control time domain is the time length of the vehicle reaching the target control quantity after receiving the control parameters, and the prediction time domain is the time length capable of carrying out state prediction at the current moment.

In this embodiment, the prediction equation Y (k) of the current state may be:

Y(k)＝(γ+EW_f)X(k)+θΔU(k)-EW_fX(k-w)

Where x (k) is an actual state value of the vehicle at time k), Δu (k) is a system control increment of the vehicle at time k, that is, a control quantity/control item of the vehicle at time k, Δu (k) is a control sequence, A, B, C is a kinetic parameter of the vehicle, N _p is a prediction horizon, N _c is a control horizon, and y (k+1) is a state prediction value at time k+1 predicted at time k, which can be seen that the prediction equation predicts a target state prediction value from time k to time k+n _p at time k, that is, a target state prediction value at each time between the current time and the control horizon.

Further, by integrating the above formulas, the target state prediction value at time k+n _p may be:

Where y (k+N _p |k) is a target state predicted value of the vehicle at k+N _p, f (k+i) is a state compensation value of the vehicle at k+i, Δu (k-1+i) is a system control increment of the vehicle, and W _f is an adjustable coefficient of the state compensation value.

It will be appreciated that in the present embodiment, the equation of y (k+n _p |k) is established, and the state of the vehicle in the control time domain can be predicted at the time k, and the equation includes not only the compensation at the current time, but also the real-time compensation based on the prediction control principle during the prediction process.

In this embodiment, instead of the state predicted value at the current time in S110, the state predicted value at the current time may be obtained by a prediction equation established by the target historical time, that is, at the target historical time, the same as the current time, and the prediction equation of the target historical time may calculate the target state predicted value at each time between the target historical time and the prediction time domain, and the state predicted value at the current time is the target state predicted value at the current time calculated by the prediction equation.

In the embodiment, the target state predicted value is optimized through the compensation values at different moments, so that the predicted target state predicted value is accurate and higher, and subsequent vehicle track tracking control is facilitated.

S130: and performing track tracking control on the vehicle based on the target state predicted value and the road information on which the vehicle is running.

As shown in fig. 3, another flow chart of a vehicle track tracking control method is provided, in which the vehicle receives road information during driving, the road information enters a sensing module for analysis, and on the other hand, a planning module plans a target state y _d of the vehicle at each moment, so as to perform vehicle track tracking control according to the target state and the result analyzed by the sensing module.

In the embodiment, the road information is introduced into the constraint of the vehicle track tracking optimization problem, and the influence of the road information on the vehicle track tracking control is considered, so that the accuracy of the vehicle track tracking control is further improved.

In some embodiments, S130 comprises:

S20: constructing an elastic space based on road information on which the vehicle is traveling; wherein the elastic space characterizes an error space of the junction between the vehicle and the road on the running road.

S21: and performing track tracking control on the vehicle based on the elastic space and the target state predicted value.

In this embodiment, the elastic space is constructed based on the road information, and as shown in fig. 4, which is a vehicle track following diagram under the elastic space in one embodiment, in fig. 4, the solid line represents the road edge, the dotted line represents the road junction, the dash-dot line represents the road center line, and the dotted line is the elastic space.

Thus, trajectory tracking control can be performed based on the elastic space and the state prediction equation.

In some embodiments, S21 may further include:

s30: acquiring a target state of the vehicle at a target moment; the target time is the time between the current time and the control time domain.

S31: and performing track tracking control based on the elastic space, the target state and the target state predicted value.

In this embodiment, the vehicle track tracking control optimization problem can be constructed based on the elastic space, the target state and the target prediction state, so that the following optimization model can be obtained:

S₁＝s₁(y(k+i)-y_d(k+i))²

S₄＝s₄Δu(k+i-1)²

Wherein S ₁ is used for ensuring track tracking accuracy of the vehicle, S ₂、S₃ is used for respectively representing an optimization target of the vehicle in the road elastic space, S ₄ is used for executing control dissipation, e _y (k+i) is used for predicting the transverse position deviation of the vehicle at the moment of k+i, e _y (k+i) is a state equation, a prediction value can be obtained based on a prediction control principle, the prediction control principle can refer to a derivation process of y (k+N _p |k), y (k+i) is used for predicting the target state prediction value of the vehicle at the moment of k+i, y _d (k+i) is used for predicting the target state of the vehicle at the moment of k+i, d _l (k+i) is used for representing the left side position information of the road, namely the length of the vehicle at the left side of the road, d _r (k+i) is used for representing the right side position information of the road, namely the length of the vehicle at the right side of the road, d _b is used for defining the length of the elastic space, d _l(k+i)、d_r (k+i) and d _b are used for obtaining the same length units, m (meter), km (kilo), and the like can be used for respectively representing the track tracking accuracy of the road safety factor, safety factor on the left side and safety regulator, safety factor on the road, respectively, Is the heading angle deviation at the current moment.

In this way, by combining the system state prediction equation Y (k) including the adaptive historical state compensation amount and the optimization problem S in the elastic space, the optimal control sequences Δu ^*,ΔU^* and Δu (k) can be obtained, which are different in that Δu (k) is a generalized form of the control sequence, Δu ^* is an optimal solution obtained after the optimization problem is solved, Δu ^* is a control sequence that acts on the vehicle control last, and a plurality of control items exist in Δu ^*, each of which can be regarded as a control amount and can be controlled based on the control items in the control sequence.

Specifically, the system state prediction equation Y (k) and the optimization problem are a quadratic equation about Δu ^*, and the result Δu ^* of the optimization problem can be obtained by solving the quadratic equation, where the process corresponds to the optimization solution in fig. 3, and then the first control term Δu ^*,Δu^* of the optimal control sequence is selected to be the control quantity in fig. 3, and the control quantity finally acts on the vehicle system to implement the track tracking control.

The embodiment provides a vehicle track tracking control method, which compensates the state of a vehicle through a state predicted value and a state actual value, ensures the accuracy and anti-interference performance of vehicle track tracking control, and simultaneously considers the actual condition of the vehicle in the running process by combining road information, so as to carry out vehicle track tracking control and improve the running safety and accuracy of the vehicle.

In another exemplary embodiment of the present application, the method for acquiring the target historical time is described in detail, as shown in fig. 5, and S510 to S530 may be further included before S110 in the vehicle track tracking control method shown in fig. 1, which is described in detail as follows:

S510: and acquiring a first transverse movement deviation and a first heading angle deviation of the vehicle at the current moment.

In this embodiment, referring to the target history time acquisition flow schematic shown in fig. 6, the target history time corresponding to the current time is self-adapted based on the lateral motion deviation and the heading angle deviation in the tracking state of the vehicle track, and specifically implemented through fuzzy logic reasoning and defuzzification.

In this embodiment, a first lateral movement deviation e _y and a first heading angle deviation at the current time are obtainedThe first lateral movement deviation e _y and the first heading angle deviation/>And outputting a target historical moment after fuzzy logic reasoning and defuzzification.

S520: and constructing a fuzzy reasoning relation among the numerical value of the time distance, the transverse motion deviation and the heading angle deviation.

In this embodiment, the value of the time distance is the time distance between the current time and the historical time.

Specifically, an input amount of a lateral motion deviation and a heading angle deviation of the vehicle is constructed:

E_y＝{E₀,E₁,E₂,E₃,E₄}

W＝{W₀,W₁,W₂,W₃,W₄}

Wherein E _y＝{E₀,E₁,E₂,E₃,E₄ represents the lateral motion deviation of different values, respectively, the larger the subscript is, the larger the value is, The course angle deviation representing different values respectively, the larger the subscript is, the larger the numerical degree is, of course, the input quantity E _y,/>, defined aboveIn other embodiments, the number of the input parameters is different, the fuzzy processing result is different, and the more the number of the input parameters is, the more complex the operation is, so that the corresponding number can be set in consideration of reducing the complexity of the rule and improving the effectiveness, for example, 5 numbers are selected to correspond to the input, and other numbers can be used at other times.

E_y、The specific values of (2) may be set according to human experience and the driving characteristics of the vehicle.

In this embodiment, there is a normalized mapping relationship, that is, a fuzzy inference relationship, between the numerical value of the time distance, the lateral motion deviation and the heading angle deviation, so that the determined input quantity of the lateral motion deviation and the heading angle deviation can obtain a historical moment output, for example, the set w= { W ₀,W₁,W₂,W₃,W₄ } represents different historical moments, the larger the subscript is, the closer the subscript is to the current moment, and according to the characteristics of MPC rolling optimization, the more the historical state is close to the current moment, the more obvious the real-time state compensation of the system is. The time distance in this embodiment is the difference between any value in W and the current time.

In the present embodiment, E _y,For the input of the blurring process, W is the corresponding output.

S530: the target historical moment is determined based on the first lateral movement deviation, the first heading angle deviation and the fuzzy inference relationship.

In this embodiment, according to the characteristics of MPC rolling optimization, the more the historical state is near the current time, the more obvious the real-time state compensation of the system is, and the fuzzy inference relation is set: setting a proportional relation between the numerical value of each time distance and the numerical value of the transverse movement deviation, and setting a proportional relation between the numerical value of each time distance and the numerical value of the heading angle deviation, so as to obtain a fuzzy reasoning relation; and determining a corresponding target historical moment in the fuzzy reasoning relation based on the first transverse movement deviation value and the first heading angle deviation value of the vehicle at the current moment.

The method is characterized by depending on MPC rolling time domain optimization, the more obvious the compensation effect of the historical time is, the more suitable for the situation with larger deviation, but the more easy the situation of overcompensation is, the more the historical time far from the current time is, the only slight compensation is generated, and the method is suitable for the situation with smaller deviation.

The fuzzy inference relationship can be determined by table 1:

TABLE 1

In this embodiment, when the lateral deviation of the vehicle gradually increases, that is, the corresponding fuzzy input variable E _y increases, the corresponding selection history time W gradually increases; when the course angle deviation of the vehicle is gradually increased, namely corresponding to the fuzzy input variableThe increase corresponds to a gradual increase in the selection history time W, but by an increase in magnitude less than the lateral deviation logic.

Based on the vehicle track tracking control method provided by the application, in the embodiment, the effectiveness test is carried out on the vehicle track tracking control method, the vehicle track tracking effect diagram obtained by verifying the vehicle track tracking control by carsim and simulink (two tools) is shown in fig. 7, in the verification method, the comparison is carried out by the conventional vehicle track tracking and the vehicle track tracking method in the embodiment, in fig. 7, the dotted line is the effect curve of the conventional vehicle track tracking mode, and the solid line is the effect curve of the vehicle track tracking method in the embodiment; as can be seen from fig. 7, the vehicle track tracking method in the embodiment can effectively improve the accuracy of vehicle track tracking, and has a certain capability of resisting external interference.

The longitudinal speed of the vehicle is set to be 60km/h (kilometer per hour), the track tracking task of the double lane change working condition is completed, a crosswind disturbance is simulated at the time of about 18s (seconds), and the effects of the conventional vehicle track tracking and the vehicle track tracking method in the embodiment are finally compared.

Another aspect of the present application further provides a vehicle track following control device, as shown in fig. 8, and fig. 8 is a schematic structural diagram of the vehicle track following control device according to an exemplary embodiment of the present application. The vehicle track following control device 800 includes: a compensation value obtaining module 810, configured to obtain a state compensation value of the vehicle at the current time based on the state prediction value and the state actual value of the vehicle at the current time; the state prediction value is data obtained by predicting the state of the current moment at the target historical moment; a predicted value obtaining module 830, configured to predict a target state predicted value of the vehicle in a control time domain after the current time based on the state compensation value; the track tracking control module 850 is configured to perform track tracking control on the vehicle based on the target state prediction value and road information on which the vehicle is traveling.

In an alternative manner, the vehicle track following control device 800 further includes: the deviation data acquisition module is used for acquiring a first transverse movement deviation and a first heading angle deviation of the vehicle at the current moment; the relation construction module is used for constructing a fuzzy reasoning relation among the numerical value of the time distance, the transverse motion deviation and the course angle deviation; the time distance value is the time distance between the current moment and the historical moment; and the target historical moment determining module is used for determining the target historical moment based on the first transverse movement deviation, the first heading angle deviation and the fuzzy reasoning relation.

In an alternative manner, the target historical moment determining module further includes: the first setting unit is used for setting a proportional relation between the numerical value of the time distance and the numerical value of the transverse movement deviation; the second setting unit is used for setting a proportional relation between the numerical value of the time distance and the numerical value of the heading angle deviation so as to obtain a fuzzy reasoning relation.

In an alternative manner, the predictor obtaining module 830 further includes: the time domain acquisition unit is used for acquiring a control time domain and a prediction time domain of the vehicle; a prediction equation acquisition unit for constructing a prediction equation based on the control, the prediction horizon, the actual state value of the vehicle, and the state compensation value; and the predicted value acquisition unit is used for acquiring a target state predicted value of each moment between the current moment and the control time domain based on the prediction equation.

In an alternative approach, the trajectory tracking control module 850 further includes: constructing a state output relation of the vehicle in a prediction time domain based on the state compensation value, further comprising:

An elastic space construction unit for constructing an elastic space based on road information on which the vehicle is traveling; the elastic space represents an error space of the junction between the vehicle and the road on the running road; and the track tracking control unit is used for carrying out track tracking control on the vehicle based on the elastic space and the target state predicted value.

In an alternative manner, the trajectory tracking control unit further includes: the target state acquisition plate is used for acquiring the target state of the vehicle at the target moment; the target time is the time between the current time and the control time domain; and the track tracking control plate is used for carrying out track tracking control based on the elastic space, the target state and the target state predicted value.

In an alternative way, the track following control board further comprises: the optimization model building sub-plate is used for building an optimization model based on the elastic space, the target state and the target state predicted value; and the track tracking control sub-block is used for obtaining a control sequence based on the optimization model so as to control the track tracking control of the vehicle based on the control items of the control sequence.

The vehicle track tracking control device in the embodiment compensates the state of the vehicle through the state predicted value and the state actual value, ensures the accuracy and anti-interference performance of vehicle track tracking control, and simultaneously considers the actual condition of the vehicle in the running process by combining road information, so that the vehicle track tracking control is performed, and the running safety and accuracy of the vehicle are improved.

It should be noted that, the vehicle track following control device provided in the foregoing embodiment and the vehicle track following control method provided in the foregoing embodiment belong to the same concept, and a specific manner in which each module and unit perform an operation has been described in detail in the method embodiment, which is not repeated herein.

Another aspect of the present application also provides an electronic device, including: a controller; and a memory for storing one or more programs that, when executed by the controller, perform the vehicle trajectory tracking control method described above.

Referring to fig. 9, fig. 9 is a schematic diagram of a computer system of an electronic device according to an exemplary embodiment of the present application, which is suitable for implementing the electronic device according to the embodiment of the present application.

It should be noted that, the computer system 900 of the electronic device shown in fig. 9 is only an example, and should not impose any limitation on the functions and the application scope of the embodiments of the present application.

As shown in fig. 9, the computer system 900 includes a central processing unit (Central Processing Unit, CPU) 901 which can perform various appropriate actions and processes, such as performing the methods in the above-described embodiments, according to a program stored in a Read-Only Memory (ROM) 902 or a program loaded from a storage portion 908 into a random access Memory (Random Access Memory, RAM) 903. In the RAM 903, various programs and data required for system operation are also stored. The CPU 901, ROM 902, and RAM 903 are connected to each other through a bus 904. An Input/Output (I/O) interface 905 is also connected to bus 904.

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 portion 907 including a speaker and the like, such as a Cathode Ray Tube (CRT), a Liquid crystal display (Liquid CRYSTAL DISPLAY, LCD), and the like; a storage portion 908 including a hard disk or the like; and a communication section 909 including a network interface card such as a LAN (Local Area Network ) card, a modem, or the like. The communication section 909 performs communication processing via a network such as the internet. The drive 910 is also connected to the I/O interface 905 as needed. A removable medium 911 such as a magnetic disk, an optical disk, a magneto-optical disk, a semiconductor memory, or the like is installed as needed on the drive 910 so that a computer program read out therefrom is installed into the storage section 908 as needed.

In particular, according to embodiments of the present application, the processes described above with reference to flowcharts may be implemented as computer software programs. For example, embodiments of the present application include a computer program product comprising a computer program embodied on a computer readable medium, the computer program comprising a computer program for performing the method shown in the flowchart. In such an embodiment, the computer program may be downloaded and installed from the network via the communication portion 909 and/or installed from the removable medium 911. When the computer program is executed by a Central Processing Unit (CPU) 901, various functions defined in the system of the present application are performed.

It should be noted that, the computer readable medium shown in the embodiments of the present application may be a computer readable signal medium or a computer readable storage medium, or any combination of the two. The computer readable storage medium may be, for example, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination thereof. More specific examples of the computer-readable storage medium may include, but are not limited to: an electrical connection having one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-Only Memory (ROM), an erasable programmable read-Only Memory (Erasable Programmable Read Only Memory, EPROM), a flash Memory, an optical fiber, a portable compact disc read-Only Memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing. In the context of this document, a computer readable storage medium may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device. In the present application, however, a computer-readable signal medium may include a data signal propagated in baseband or as part of a carrier wave, with a computer-readable computer program embodied therein. Such a propagated data signal may take any of a variety of forms, including, but not limited to, electro-magnetic, optical, or any suitable combination of the foregoing. A computer readable signal medium may also be any computer readable medium that is not a computer readable storage medium and that can communicate, 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 appropriate medium, including but not limited to: wireless, wired, etc., or any suitable combination of the foregoing.

The flowcharts and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods and computer program products according to various embodiments of the present application. Where each block in the flowchart or block diagrams may represent a module, segment, or portion of code, which comprises one or more executable instructions for implementing the specified logical function(s). It should also be noted that, in some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams or flowchart illustration, and combinations of blocks in the block diagrams or flowchart illustration, can be implemented by special purpose hardware-based systems which perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.

The units involved in the embodiments of the present application may be implemented by software, or may be implemented by hardware, and the described units may also be provided in a processor. Wherein the names of the units do not constitute a limitation of the units themselves in some cases.

Another aspect of the present application also provides a computer-readable storage medium having stored thereon a computer program which, when executed by a processor, implements a vehicle trajectory tracking control method as before. The computer-readable storage medium may be included in the electronic device described in the above embodiment or may exist alone without being incorporated in the electronic device.

Another aspect of the application also provides a computer program product or computer program comprising computer instructions stored in a computer readable storage medium. The processor of the computer device reads the computer instructions from the computer-readable storage medium, and the processor executes the computer instructions so that the computer device executes the vehicle track following control method provided in the above-described respective embodiments.

According to an aspect of the embodiment of the present application, there is also provided a computer system including a central processing unit (Central Processing Unit, CPU) that can perform various appropriate actions and processes, such as performing the method in the above-described embodiment, according to a program stored in a Read-Only Memory (ROM) or a program loaded from a storage section into a random access Memory (Random Access Memory, RAM). In the RAM, various programs and data required for the system operation are also stored. The CPU, ROM and RAM are connected to each other by a bus. An Input/Output (I/O) interface is also connected to the bus.

The following components are connected to the I/O interface: an input section including a keyboard, a mouse, etc.; an output section including a Cathode Ray Tube (CRT), a Liquid crystal display (Liquid CRYSTAL DISPLAY, LCD), and a speaker; a storage section including a hard disk or the like; and a communication section including a network interface card such as a LAN (Local Area Network ) card, a modem, or the like. The communication section performs communication processing via a network such as the internet. The drives are also connected to the I/O interfaces as needed. Removable media such as magnetic disks, optical disks, magneto-optical disks, semiconductor memories, and the like are mounted on the drive as needed so that a computer program read therefrom is mounted into the storage section as needed.

The foregoing is merely illustrative of the preferred embodiments of the present application and is not intended to limit the embodiments of the present application, and those skilled in the art can easily make corresponding variations or modifications according to the main concept and spirit of the present application, so that the protection scope of the present application shall be defined by the claims.

Claims

1. A vehicle trajectory tracking control method, characterized by comprising:

Acquiring a state compensation value of the vehicle at the current moment based on a state predicted value and a state actual value of the vehicle at the current moment; the state prediction value is data obtained by predicting the state of the current moment at the 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 track tracking control on the vehicle based on the target state predicted value and the road information on which the vehicle runs.

2. The method according to claim 1, characterized in that before the obtaining the state compensation value of the vehicle at the present moment based on the state prediction value and the state actual value of the vehicle at the present moment, the method further comprises:

Acquiring a first transverse movement deviation and a first heading angle deviation of the vehicle at the current moment;

Constructing a fuzzy reasoning relation among the numerical value of the time distance, the transverse motion deviation and the course angle deviation; the numerical value of the time distance is the time distance between the current moment and the historical moment;

And determining a target historical moment based on the first lateral movement deviation, the first heading angle deviation and the fuzzy inference relation.

3. The method of claim 2, wherein constructing a fuzzy inference relationship between the numerical value of the temporal distance, the lateral motion bias, and the heading angle bias, further comprises:

Setting a proportional relation between the numerical value of the time distance and the numerical value of the transverse movement deviation;

setting a proportional relation between the numerical value of the time distance and the numerical value of the heading angle deviation, so as to obtain the fuzzy reasoning relation.

4. The method according to claim 1, wherein the predicting a target state prediction value of the vehicle in a control time domain after the current time based on the state compensation value further comprises:

acquiring a control time domain and a prediction time domain of the vehicle;

constructing a predictive equation based on the control, the prediction horizon, the actual state value of the vehicle, and the state compensation value;

And acquiring a target state predicted value of each moment between the current moment and the control time domain based on the prediction equation.

5. The method according to claim 1, wherein the trajectory tracking control of the vehicle based on the target state predicted value and road information on which the vehicle is traveling, further comprises:

Constructing an elastic space based on road information on which the vehicle is traveling; wherein the elastic space characterizes an error space at the junction of the vehicle and a road on a running road;

And performing track tracking control on the vehicle based on the elastic space and the target state predicted value.

6. The method of claim 5, wherein the trajectory tracking control of the vehicle based on the elastic space and the target state prediction value further comprises:

acquiring a target state of the vehicle at a target moment; the target time is the time between the current time and a control time domain;

And performing track tracking control based on the elastic space, the target state and the target state predicted value.

7. The method of claim 6, wherein the trajectory tracking control based on the elastic space, the target state, and the target state prediction value, further comprises:

Constructing an optimization model based on the elastic space, the target state and the target state predicted value;

and obtaining a control sequence based on the optimization model so as to carry out track tracking control on the vehicle based on control items of the control sequence.

8. A vehicle track following control device, characterized by comprising:

the compensation value acquisition module is used for acquiring a state compensation value of the vehicle at the current moment based on a state predicted value and a state actual value of the vehicle at the current moment; the state prediction value is data obtained by predicting the state of the current moment at the target historical moment;

A predicted value obtaining module, configured to predict a target state predicted value of the vehicle in a control time domain after the current time based on the state compensation value;

and the track tracking control module is used for carrying out track tracking control on the vehicle based on the target state predicted value and the road information on which the vehicle is driven.

9. An electronic device, comprising:

A controller;

a memory for storing one or more programs that, when executed by the controller, cause the controller to implement the vehicle trajectory tracking control method of any one of claims 1 to 7.

10. A computer-readable storage medium having stored thereon computer-readable instructions that, when executed by a processor of a computer, cause the computer to perform the vehicle trajectory tracking control method of any one of claims 1 to 7.