CN114212083A

CN114212083A - Online optimal scheduling networked vehicle multi-target adaptive cruise control method

Info

Publication number: CN114212083A
Application number: CN202210054097.9A
Authority: CN
Inventors: 宋秀兰; 柴伟豪; 王轲; 董汉聪; 何德峰; 卢为党
Original assignee: Zhejiang University of Technology ZJUT
Current assignee: Zhejiang University of Technology ZJUT
Priority date: 2022-01-18
Filing date: 2022-01-18
Publication date: 2022-03-22
Anticipated expiration: 2042-01-18
Also published as: CN114212083B

Abstract

The invention discloses a multi-target self-adaptive cruise control method for online optimization scheduling of networked vehicles, which comprises the steps of obtaining vehicle running information between a target vehicle and a front vehicle, and representing each control target based on the vehicle running information; generating a comprehensive tracking signal of the target vehicle based on each control target, and representing a state vector according to a signal error of the comprehensive tracking signal; generating a state feedback control law according to the state vector, generating an optimization problem equation of the state feedback control law based on the cost function, and solving the state feedback control law based on the optimization problem equation so as to minimize the signal error; and calculating the control input corresponding to the solved state feedback control law, and driving the tracking front vehicle motion state of the target vehicle based on the control input in the current control period. The invention solves the problem of contradiction of a plurality of control targets in the self-adaptive cruise control of the networked vehicles, and ensures the safety of the vehicles and the comfort of passengers while reducing the vehicle-following distance.

Description

Online optimal scheduling networked vehicle multi-target adaptive cruise control method

Technical Field

The application relates to the technical field of adaptive cruise control, in particular to a multi-target adaptive cruise control method for online optimal scheduling of networked vehicles.

Background

The networked vehicle adaptive cruise control is a control method for realizing automatic driving of the vehicle by acquiring vehicle running information (position, speed, acceleration and the like) of a front vehicle and the vehicle through a wireless communication network (special short-range communication, a cellular internet of vehicles and the like) and vehicle-mounted sensors (radars, laser radars, ultrasonic sensors and the like), then establishing a vehicle dynamic state space model, inputting the acquired vehicle running information of the front vehicle and the vehicle into the model, calculating proper throttle opening or brake control quantity, adjusting the distance between the vehicle and the front vehicle, and controlling the speed and the acceleration of the vehicle. When the internet vehicle runs along with the front vehicle on the road, in order to ensure the following target of the vehicle, the speed of the vehicle needs to be consistent with that of the front vehicle, and the inter-vehicle distance is kept at a small distance so as to cruise along with the front vehicle, but the comfort of passengers and the safety of the vehicle cannot be ensured. For example, when the current vehicle state changes frequently, the vehicle may also accelerate or brake frequently to ensure the following target of the vehicle, thereby affecting the comfort of the passengers. Furthermore, the networked vehicle is kept at a small distance from the preceding vehicle in order to ensure a following target, which may lead to rear-end collisions of the vehicle in the event of emergency braking.

For the reasons, the current multi-target adaptive cruise control process of the networked vehicles needs to consider three control targets, namely vehicle following performance, vehicle safety and passenger comfort. However, the existing multi-target adaptive cruise control mode of the networked vehicle has the problem that mutual conflict among a plurality of targets cannot be guaranteed at the same time, model prediction modes are mostly used, and the model prediction method is difficult to realize the all-road-condition stable operation of the networked vehicle adaptive cruise.

Disclosure of Invention

In order to solve the above problems, the embodiment of the application provides a multi-objective adaptive cruise control method for online optimal scheduling of networked vehicles.

In a first aspect, an embodiment of the present application provides a method for online optimal scheduling of networked vehicles for multi-objective adaptive cruise control, where the method includes:

acquiring vehicle running information between a target vehicle and a front vehicle, and representing each control target of the networked vehicle adaptive cruise control system based on the vehicle running information;

generating a comprehensive tracking signal of the target vehicle based on each control target, and characterizing a state vector according to a signal error of the comprehensive tracking signal;

generating a state feedback control law according to the state vector, generating an optimization problem equation of the state feedback control law based on a cost function, and solving the state feedback control law based on the optimization problem equation so as to minimize the signal error;

and calculating the control input corresponding to the solved state feedback control law, and driving the tracking front vehicle motion state of the target vehicle based on the control input in the current control period.

Preferably, the acquiring vehicle running information between the target vehicle and the preceding vehicle, and characterizing each control target of the online vehicle adaptive cruise control system based on the vehicle running information includes:

acquiring vehicle running information between a target vehicle and a front vehicle, and establishing an internet vehicle kinematic equation based on the vehicle running information to represent a relative distance between the target vehicle and the front vehicle, a relative speed between the target vehicle and the front vehicle, and an acceleration of the target vehicle;

and characterizing control targets of the online vehicle adaptive cruise control system based on the relative distance, the relative speed and the acceleration, wherein the control targets comprise passenger comfort, vehicle safety and vehicle following performance.

Preferably, the generating a comprehensive tracking signal of the target vehicle based on each of the control targets and characterizing a state vector according to a signal error of the comprehensive tracking signal includes:

setting adjustable real number weight parameters, distributing the adjustable real number weight parameters to the control targets respectively, and generating comprehensive tracking signals of the target vehicle based on the control targets and the adjustable real number parameters;

and characterizing a state vector according to the signal error of the comprehensive tracking signal.

Preferably, the characterizing the state vector according to the signal error of the integrated tracking signal includes:

constructing a first transfer matrix according to the relative distance, the relative speed and the acceleration, and generating a discrete state space model of the networked vehicle adaptive cruise control system based on the first transfer matrix;

and constructing a third transpose matrix based on the signal error of the integrated tracking signal and a second transpose matrix, and converting the discrete state space model into a discrete augmented state space model according to the third transpose matrix, wherein the third transpose matrix is a state vector of the discrete augmented state space model, and the second transpose matrix is a transpose matrix of the first transpose matrix.

Preferably, the generating a state feedback control law according to the state vector includes:

defining a first solving matrix and a second solving matrix, constructing a state feedback control matrix according to the first solving matrix and the second solving matrix, and generating a state feedback control law based on the state feedback control matrix and the state vector.

Preferably, the solving the state feedback control law based on the optimization problem equation to minimize the signal error includes:

initializing each adjustable real number weight parameter and calculating an initial state vector;

solving the first solving matrix and the second solving matrix based on the optimization problem equation and preset linear matrix inequality constraint formulas, and calculating and solving the state feedback control law;

determining a control input based on the state feedback control law to minimize the signal error.

Preferably, the method further comprises:

recalculating the control input when a next control cycle is entered, and repeating the step of driving the tracked preceding vehicle motion state of the target vehicle based on the control input at the current control cycle.

In a second aspect, an embodiment of the present application provides an online optimal scheduling networked vehicle multi-objective adaptive cruise control device, where the device includes:

the acquisition module is used for acquiring vehicle running information between a target vehicle and a front vehicle and representing each control target of the networked vehicle adaptive cruise control system based on the vehicle running information;

the generating module is used for generating a comprehensive tracking signal of the target vehicle based on each control target and representing a state vector according to a signal error of the comprehensive tracking signal;

the solving module is used for generating a state feedback control law according to the state vector, generating an optimization problem equation of the state feedback control law based on a cost function, and solving the state feedback control law based on the optimization problem equation so as to minimize the signal error;

and the control module is used for calculating control input corresponding to the solved state feedback control law and driving the tracking front vehicle motion state of the target vehicle based on the control input in the current control period.

In a third aspect, an embodiment of the present application provides an electronic device, which includes a memory, a processor, and a computer program stored on the memory and executable on the processor, where the processor executes the computer program to implement the steps of the method as provided in the first aspect or any one of the possible implementation manners of the first aspect.

In a fourth aspect, the present application provides a computer-readable storage medium, on which a computer program is stored, where the computer program, when executed by a processor, implements the method as provided in the first aspect or any one of the possible implementations of the first aspect.

The invention has the beneficial effects that: 1. the comprehensive tracking signal is designed, the feedback control law is designed to ensure that the error between the vehicle state and the tracking signal is minimum, a plurality of control targets of the vehicle are ensured, the problem that contradiction exists among a plurality of control targets in the self-adaptive cruise control of the networked vehicle is solved, and the safety of the vehicle and the comfort of passengers are ensured while the vehicle following distance is reduced.

2. The multi-target self-adaptive cruise control system for the vehicle has the advantages of less adjustable parameters, convenience in operation, simplicity in calculation, high calculation speed and good real-time property.

3. And the feedback control law is calculated based on the linear matrix inequality, so that the calculation speed is high and the real-time performance is good.

4. Through on-line calculation, the optimal control law can be calculated according to the real-time state change of the vehicle to adjust the vehicle state, so that the multi-target self-adaptive cruise of the vehicle can be better realized.

Drawings

In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings needed to be used in the embodiments will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments of the present application, and it is obvious for those skilled in the art to obtain other drawings without creative efforts.

Fig. 1 is a schematic flow chart of a multi-objective adaptive cruise control method for online optimal scheduling of networked vehicles according to an embodiment of the present application;

FIG. 2 is a schematic diagram of a vehicle distance variation curve between a target vehicle and a preceding vehicle in the vehicle multi-target adaptive cruise control provided by the embodiment of the application;

FIG. 3 is a target vehicle speed variation curve in the vehicle multi-target adaptive cruise control provided by the embodiment of the present application;

FIG. 4 is a target vehicle control input variation curve in the vehicle multi-target adaptive cruise control provided by the embodiment of the present application;

fig. 5 is a schematic structural diagram of an online optimal scheduling networked vehicle multi-objective adaptive cruise control device according to an embodiment of the present application;

fig. 6 is a schematic structural diagram of an electronic device according to an embodiment of the present application.

Detailed Description

The technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application.

In the following description, the terms "first" and "second" are used for descriptive purposes only and are not intended to indicate or imply relative importance. The following description provides embodiments of the present application, where different embodiments may be substituted or combined, and thus the present application is intended to include all possible combinations of the same and/or different embodiments described. Thus, if one embodiment includes feature A, B, C and another embodiment includes feature B, D, then this application should also be considered to include an embodiment that includes one or more of all other possible combinations of A, B, C, D, even though this embodiment may not be explicitly recited in text below.

The following description provides examples, and does not limit the scope, applicability, or examples set forth in the claims. Changes may be made in the function and arrangement of elements described without departing from the scope of the disclosure. Various examples may omit, substitute, or add various procedures or components as appropriate. For example, the described methods may be performed in an order different than the order described, and various steps may be added, omitted, or combined. Furthermore, features described with respect to some examples may be combined into other examples.

Referring to fig. 1, fig. 1 is a schematic flowchart of a multi-objective adaptive cruise control method for online optimal scheduling networked vehicles according to an embodiment of the present application. In an embodiment of the present application, the method includes:

s101, vehicle running information between a target vehicle and a front vehicle is obtained, and each control target of the networked vehicle adaptive cruise control system is represented based on the vehicle running information.

The execution main body can be a cloud server of the internet vehicle self-adaptive cruise control system.

In the embodiment of the application, the target vehicle can acquire the vehicle running information between the target vehicle and the front vehicle through the vehicle-mounted sensor in the running process, and the vehicle running information is uploaded to the cloud server. After the cloud server acquires the vehicle running information, the cloud server respectively represents a plurality of control targets of the networked vehicle adaptive cruise control system on the basis, so that the control targets can practically represent the requirements of the vehicle multi-target adaptive cruise control field on the accuracy and the real-time performance of the vehicle running information such as vehicle safety distance, relative speed and acceleration control.

In one possible embodiment, step S101 includes:

In the embodiment of the application, an internet vehicle kinematic equation can be established according to vehicle running information, the relative distance between a target vehicle and a front vehicle, the relative speed between the target vehicle and the front vehicle and the acceleration of the target vehicle can be represented respectively through the internet vehicle kinematic equation, and then the cloud server can represent each control target based on the data.

Specifically, the first calculation formula of the networked vehicle kinematic equation is as follows:

wherein the variable k represents the time of day,

respectively representing the position and speed of the target vehicle;

respectively representing the position and the speed of the front vehicle;

representing acceleration and control input of the target vehicle, respectively;

a vehicle dynamics constant representing a target vehicle;

represents a sampling period;

the relative distance and relative speed of the target vehicle and the leading vehicle, respectively.

The second calculation formula to characterize passenger comfort is:

the third calculation formula to characterize vehicle safety is:

the fourth calculation formula for characterizing vehicle followability is:

wherein the content of the first and second substances,

represents a maximum acceleration of the target vehicle;

to representAn acceleration change amount of the target vehicle;

represents a minimum safe distance of the vehicle;

representing a headway of the target vehicle;

respectively representing an ideal inter-vehicle distance and an ideal speed difference between the target vehicle and the front vehicle;

and the error between the ideal vehicle-to-vehicle distance and the actual vehicle-to-vehicle distance between the target vehicle and the front vehicle is represented.

S102, generating a comprehensive tracking signal of the target vehicle based on each control target, and representing a state vector according to a signal error of the comprehensive tracking signal.

In the embodiment of the application, after each control target is determined by the characterization, in order to solve the problem that the plurality of control targets are contradictory, the cloud server integrates each control target, so that the comprehensive tracking signal is generated, and in the subsequent process of specifically calculating the comprehensive tracking signal, the safety of a vehicle and the comfort of passengers and other control targets can be guaranteed while the vehicle following distance is reduced. In actual conditions, an error exists between an ideal comprehensive tracking signal and an actual comprehensive tracking signal, so that the signal error of the comprehensive tracking signal is determined according to a characterization calculation formula corresponding to the generated comprehensive tracking signal, and the state vector of the vehicle is characterized by the signal error.

In one possible embodiment, step S102 includes:

In the embodiment of the present application, an adjustable real number weight parameter will be set

The weighting coefficients of the relative distance, the relative speed and the acceleration are respectively expressed, and a fifth calculation formula corresponding to the designed vehicle comprehensive tracking signal is as follows:

wherein the content of the first and second substances,

represents the designed integrated tracking signal of the host vehicle,

indicating the front vehicle acceleration.

In one possible embodiment, the characterizing the state vector from the signal error of the integrated tracking signal comprises:

In the embodiment of the application, a first rotation matrix is constructed according to the relative distance, the relative speed and the acceleration

Obtaining the discrete value of the networked vehicle adaptive cruise control system according to the first rotation matrix and the first calculation formulaThe state space model corresponds to a sixth calculation formula as follows:

wherein the content of the first and second substances,

。

signal error of the integrated tracking signal is

Will define a third transpose matrix

And further converting the discrete state space model into a discrete augmented state space model, wherein a corresponding seventh calculation formula is as follows:

wherein the content of the first and second substances,

；

。

the state vector can be aligned through the finally obtained discrete augmentation state space model

And performing iterative computation.

S103, generating a state feedback control law according to the state vector, generating an optimization problem equation of the state feedback control law based on a cost function, and solving the state feedback control law based on the optimization problem equation so as to minimize the signal error.

In the embodiment of the application, a state feedback control law can be designed and generated according to the state vector represented by calculation, and an optimization problem equation with the state feedback control law can be generated according to a cost function. In the foregoing process, the calculation data such as the state feedback control law and the state vector are all calculation expressions represented by the vehicle running information. After the optimization problem equation is generated, the actual solution calculation is performed on the state feedback control law, so that the finally obtained signal error of the state feedback control law under the current vehicle running information is minimized.

In one embodiment, the generating a state feedback control law according to the state vector includes:

In the embodiment of the present application, a first solving matrix will be defined

And a second solution matrix

According to the first solving matrix and the second solving matrix, a state feedback control matrix can be constructed

The state feedback control law can be designed and generated according to the state feedback control matrix and the state vector

。

The cost function is that, the eighth calculation formula corresponding to the optimization problem equation is as follows:

wherein the content of the first and second substances,

is a system transfer function;

is given

A performance limit;

；

；

；

；

the adjustable real number weight parameters are respectively a tracking error weight and a control input weight; j represents the time step predicted backwards at the current time k; l denotes the time step from time zero to time k-1.

In one embodiment, said solving said state feedback control law based on said optimization problem equation to minimize said signal error comprises:

In the embodiment of the application, the matrix can be initialized by initializing each adjustable real number weight parameter

And further can calculate the initial state vector

. According to the first solving matrix and the second solving matrix, the optimization problem equation can be adjusted to be

By giving

、

And

the first solution matrix can be based on the optimization problem equation and the constraint equation of each linear matrix inequality

And the second solution matrix

Solving is carried out to calculate a state feedback control matrix

Then calculates the state feedback control law

And further find the control input

。

In this application, three linear matrix inequality constraint equations are set, and the corresponding ninth calculation formula, tenth calculation formula, and eleventh calculation formula are as follows:

wherein the content of the first and second substances,

is a relaxation variable and takes a value of [0, 1%](ii) a And I is an identity matrix.

And S104, calculating the control input corresponding to the solved state feedback control law, and driving the tracking front vehicle motion state of the target vehicle based on the control input in the current control period.

In the embodiment of the application, after the state feedback control law is solved, the cloud server can determine the control input corresponding to the state feedback control law, and the control input obtained at the moment is the optimal control input of the safety of the vehicle and the comfort of passengers while the vehicle following distance is reduced under the current vehicle running information. Therefore, the cloud server drives the tracking front vehicle motion state of the target vehicle according to the finally calculated control input in the current control period.

In one embodiment, the method further comprises:

In the embodiment of the application, in the next control period, the real-time vehicle running information of the target vehicle and the front vehicle is acquired again and new control input is calculated

And repeating the steps in this way, and realizing the multi-target self-adaptive cruise of the networked vehicles.

Illustratively, an actual operation process corresponding to the online optimal scheduling networked vehicle multi-target adaptive cruise control method is as follows:

1. input device

Safe distance

Dynamic constant of the vehicle

Sampling period

=0.1s, headway

=1s，

Constraint conditions

=1, relaxation variables

=0.6, control input constraints

Acceleration of front vehicle 0 to 64 seconds

After that

。

2. Calculating a state feedback control matrix of the vehicle multi-target self-adaptive cruise system at the moment k =1 through a written calculation program, and substituting input parameters into a linear matrix inequality

To find out the ones meeting the conditions

Then calculating a state feedback control matrix

。

3. After obtaining the state feedback control matrix, further calculating the control input

Driving the vehicle to track the motion state of the front vehicle; in the next control period, the real-time vehicle running information of the vehicle and the front vehicle is obtained again and new control input is calculated

The actual control effect is shown in fig. 2, 3 and 4, and fig. 2 is a vehicle-to-vehicle distance variation curve of a vehicle and a preceding vehicle in the vehicle multi-target adaptive cruise control; FIG. 3 is a curve of speed variation of the vehicle in the multi-target adaptive cruise control; FIG. 4 is a diagram illustrating a variation curve of the vehicle control input in the multi-target adaptive cruise control.

The online optimal scheduling networked vehicle multi-target adaptive cruise control device provided by the embodiment of the application is described in detail below with reference to fig. 5. It should be noted that, the networked vehicle multi-objective adaptive cruise control device for online optimal scheduling shown in fig. 5 is used for executing the method of the embodiment shown in fig. 1 of the present application, and for convenience of description, only the portion related to the embodiment of the present application is shown, and details of the specific technology are not disclosed, please refer to the embodiment shown in fig. 1 of the present application.

Referring to fig. 5, fig. 5 is a schematic structural diagram of a multi-objective adaptive cruise control device for networked vehicles with online optimized scheduling provided by an embodiment of the present application. As shown in fig. 5, the apparatus includes:

the acquiring module 501 is used for acquiring vehicle running information between a target vehicle and a preceding vehicle and representing each control target of the networked vehicle adaptive cruise control system based on the vehicle running information;

a generating module 502, configured to generate a comprehensive tracking signal of the target vehicle based on each control target, and characterize a state vector according to a signal error of the comprehensive tracking signal;

a solving module 503, configured to generate a state feedback control law according to the state vector, generate an optimization problem equation of the state feedback control law based on a cost function, and solve the state feedback control law based on the optimization problem equation, so as to minimize the signal error;

and the control module 504 is configured to calculate a control input corresponding to the solved state feedback control law, and drive the tracked front vehicle motion state of the target vehicle based on the control input in the current control period.

In one possible implementation, the obtaining module 501 includes:

the system comprises an acquisition unit, a calculation unit and a control unit, wherein the acquisition unit is used for acquiring vehicle running information between a target vehicle and a front vehicle, and establishing an internet vehicle kinematic equation based on the vehicle running information so as to represent the relative distance between the target vehicle and the front vehicle, the relative speed between the target vehicle and the front vehicle and the acceleration of the target vehicle;

and the first characterization unit is used for characterizing control targets of the networked vehicle adaptive cruise control system based on the relative distance, the relative speed and the acceleration, wherein the control targets comprise passenger comfort, vehicle safety and vehicle following performance.

In one possible implementation, the generation module 502 includes:

the setting unit is used for setting adjustable real number weight parameters, distributing the adjustable real number weight parameters to the control targets respectively, and then generating comprehensive tracking signals of the target vehicle based on the control targets and the adjustable real number parameters;

and the second characterization unit is used for characterizing the state vector according to the signal error of the comprehensive tracking signal.

In one possible implementation, the generating module 502 further includes:

the first construction unit is used for constructing a first transfer matrix according to the relative distance, the relative speed and the acceleration, and generating a discrete state space model of the networked vehicle adaptive cruise control system based on the first transfer matrix;

a second constructing unit, configured to construct a third transpose matrix based on a signal error of the integrated tracking signal and a second transpose matrix, and convert the discrete state space model into a discrete augmented state space model according to the third transpose matrix, where the third transpose matrix is a state vector of the discrete augmented state space model, and the second transpose matrix is a transpose matrix of the first transpose matrix.

In one possible implementation, the solving module 503 includes:

and the defining unit is used for defining a first solving matrix and a second solving matrix, constructing a state feedback control matrix according to the first solving matrix and the second solving matrix, and generating a state feedback control law based on the state feedback control matrix and the state vector.

In one possible implementation, the solving module 503 further includes:

the first calculation unit is used for initializing each adjustable real number weight parameter and calculating an initial state vector;

the second calculation unit is used for solving the first solving matrix and the second solving matrix based on the optimization problem equation and preset constraint equations of various linear matrixes, and calculating and solving the state feedback control law;

a third calculation unit for determining a control input based on the state feedback control law for minimizing the signal error.

In one embodiment, the apparatus further comprises:

and the repeating module is used for recalculating the control input when entering the next control period and repeating the step of driving the tracked front vehicle motion state of the target vehicle based on the control input in the current control period.

It is clear to a person skilled in the art that the solution according to the embodiments of the present application can be implemented by means of software and/or hardware. The "unit" and "module" in this specification refer to software and/or hardware that can perform a specific function independently or in cooperation with other components, where the hardware may be, for example, a Field-Programmable Gate Array (FPGA), an Integrated Circuit (IC), or the like.

Each processing unit and/or module in the embodiments of the present application may be implemented by an analog circuit that implements the functions described in the embodiments of the present application, or may be implemented by software that executes the functions described in the embodiments of the present application.

Referring to fig. 6, a schematic structural diagram of an electronic device according to an embodiment of the present application is shown, where the electronic device may be used to implement the method in the embodiment shown in fig. 1. As shown in fig. 6, the electronic device 600 may include: at least one central processor 601, at least one network interface 604, a user interface 603, a memory 605, at least one communication bus 602.

Wherein a communication bus 602 is used to enable the connection communication between these components.

The user interface 603 may include a Display screen (Display) and a Camera (Camera), and the optional user interface 603 may also include a standard wired interface and a wireless interface.

The network interface 604 may optionally include a standard wired interface, a wireless interface (e.g., WI-FI interface).

Central processor 601 may include one or more processing cores, among others. The central processor 601 connects the various parts within the overall electronic device 600 using various interfaces and lines, and performs various functions of the terminal 600 and processes data by executing or executing instructions, programs, code sets, or instruction sets stored in the memory 605, as well as calling data stored in the memory 605. Optionally, the central Processing unit 601 may be implemented in at least one hardware form of Digital Signal Processing (DSP), Field-Programmable Gate Array (FPGA), and Programmable Logic Array (PLA). The Central Processing Unit 601 may integrate one or a combination of a Central Processing Unit (CPU), a Graphics Processing Unit (GPU), a modem, and the like. Wherein, the CPU mainly processes an operating system, a user interface, an application program and the like; the GPU is used for rendering and drawing the content required to be displayed by the display screen; the modem is used to handle wireless communications. It is understood that the modem may not be integrated into the central processor 601, but may be implemented by a single chip.

The Memory 605 may include a Random Access Memory (RAM) or a Read-Only Memory (Read-Only Memory). Optionally, the memory 605 includes a non-transitory computer-readable medium. The memory 605 may be used to store instructions, programs, code, sets of codes, or sets of instructions. The memory 605 may include a stored program area and a stored data area, wherein the stored program area may store instructions for implementing an operating system, instructions for at least one function (such as a touch function, a sound playing function, an image playing function, etc.), instructions for implementing the various method embodiments described above, and the like; the storage data area may store data and the like referred to in the above respective method embodiments. The memory 605 may alternatively be at least one storage device located remotely from the central processor 601. As shown in fig. 6, memory 605, which is one type of computer storage medium, may include an operating system, a network communication module, a user interface module, and program instructions.

In the electronic device 600 shown in fig. 6, the user interface 603 is mainly used for providing an input interface for a user to obtain data input by the user; the central processor 601 may be configured to invoke the online optimal scheduling networked vehicle multi-objective adaptive cruise control application stored in the memory 605, and specifically perform the following operations:

The present application also provides a computer-readable storage medium having stored thereon a computer program which, when executed by a processor, performs the steps of the above-described method. The computer-readable storage medium may include, but is not limited to, any type of disk including floppy disks, optical disks, DVD, CD-ROMs, microdrive, and magneto-optical disks, ROMs, RAMs, EPROMs, EEPROMs, DRAMs, VRAMs, flash memory devices, magnetic or optical cards, nanosystems (including molecular memory ICs), or any type of media or device suitable for storing instructions and/or data.

It should be noted that, for simplicity of description, the above-mentioned method embodiments are described as a series of acts or combination of acts, but those skilled in the art will recognize that the present application is not limited by the order of acts described, as some steps may occur in other orders or concurrently depending on the application. Further, those skilled in the art should also appreciate that the embodiments described in the specification are preferred embodiments and that the acts and modules referred to are not necessarily required in this application.

In the foregoing embodiments, the descriptions of the respective embodiments have respective emphasis, and for parts that are not described in detail in a certain embodiment, reference may be made to related descriptions of other embodiments.

In the embodiments provided in the present application, it should be understood that the disclosed apparatus may be implemented in other manners. For example, the above-described embodiments of the apparatus are merely illustrative, and for example, the division of the units is only one type of division of logical functions, and there may be other divisions when actually implementing, for example, a plurality of units or components may be combined or may be integrated into another system, or some features may be omitted, or not implemented. In addition, the shown or discussed mutual coupling or direct coupling or communication connection may be an indirect coupling or communication connection of some service interfaces, devices or units, and may be an electrical or other form.

The units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of the embodiment.

In addition, functional units in the embodiments of the present application may be integrated into one processing unit, or each unit may exist alone physically, or two or more units are integrated into one unit. The integrated unit can be realized in a form of hardware, and can also be realized in a form of a software functional unit.

The integrated unit, if implemented in the form of a software functional unit and sold or used as a stand-alone product, may be stored in a computer readable memory. Based on such understanding, the technical solution of the present application may be substantially implemented or a part of or all or part of the technical solution contributing to the prior art may be embodied in the form of a software product stored in a memory, and including several instructions for causing a computer device (which may be a personal computer, a server, or a network device) to execute all or part of the steps of the method described in the embodiments of the present application. And the aforementioned memory comprises: various media capable of storing program codes, such as a usb disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a removable hard disk, a magnetic disk, or an optical disk.

Those skilled in the art will appreciate that all or part of the steps in the methods of the above embodiments may be implemented by a program, which is stored in a computer-readable memory, and the memory may include: flash disks, Read-Only memories (ROMs), Random Access Memories (RAMs), magnetic or optical disks, and the like.

The above description is only an exemplary embodiment of the present disclosure, and the scope of the present disclosure should not be limited thereby. That is, all equivalent changes and modifications made in accordance with the teachings of the present disclosure are intended to be included within the scope of the present disclosure. Embodiments of the present disclosure will be readily apparent to those skilled in the art from consideration of the specification and practice of the disclosure herein. This application is intended to cover any variations, uses, or adaptations of the disclosure following, in general, the principles of the disclosure and including such departures from the present disclosure as come within known or customary practice within the art to which the disclosure pertains. It is intended that the specification and examples be considered as exemplary only, with a true scope and spirit of the disclosure being indicated by the following claims.

Claims

1. A multi-target adaptive cruise control method for online optimal scheduling networked vehicles is characterized by comprising the following steps:

2. The method according to claim 1, wherein the obtaining of vehicle driving information between a target vehicle and a preceding vehicle and the characterization of each control target of the networked vehicle adaptive cruise control system based on the vehicle driving information comprises:

3. The method of claim 2, wherein said generating a composite tracking signal for the target vehicle based on each of the control targets and characterizing a state vector from a signal error of the composite tracking signal comprises:

4. The method of claim 3, wherein characterizing the state vector from the signal error of the integrated tracking signal comprises:

5. The method of claim 4, wherein generating a state feedback control law from the state vector comprises:

6. The method of claim 5, wherein solving the state feedback control law based on the optimization problem equation to minimize the signal error comprises:

7. The method of claim 1, further comprising:

8. An online optimal scheduling networked vehicle multi-target adaptive cruise control device is characterized by comprising:

9. An electronic device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, characterized in that the steps of the method according to any of claims 1-7 are implemented when the computer program is executed by the processor.

10. A computer-readable storage medium, on which a computer program is stored which, when being executed by a processor, carries out the steps of the method according to any one of claims 1 to 7.