CN114194201A

CN114194201A - Vehicle control method and device, electronic equipment and storage medium

Info

Publication number: CN114194201A
Application number: CN202111625858.3A
Authority: CN
Inventors: 田鹤; 高洪伟; 吕贵林; 陈涛; 韩爽; 孙玉洋; 杨阳; 刘赫
Original assignee: FAW Group Corp
Current assignee: FAW Group Corp
Priority date: 2021-12-28
Filing date: 2021-12-28
Publication date: 2022-03-18

Abstract

Embodiments of the present invention disclose a vehicle control method, device, electronic device and storage medium. The method includes: determining the traffic participants existing in the detection range corresponding to the target vehicle and the state information of each traffic participant; based on the state information of each traffic participant, the starting point information of the target vehicle, the information of the destination vehicle and the pre-established polynomial The model determines each target trajectory point of the target vehicle and the driving information of the target vehicle corresponding to each target trajectory point; The control parameters of the trajectory point control the target vehicle to travel. In the technical solution of the embodiment of the present invention, the determined trajectory is continuous and smooth, which is beneficial to control the vehicle; the polynomial model has a small amount of calculation and good timeliness; and combined with the model predictive control algorithm to predict the control parameters, the control precision is high.

Description

Vehicle control method and device, electronic equipment and storage medium

Technical Field

The embodiment of the invention relates to the technical field of vehicle control, in particular to a vehicle control method and device, electronic equipment and a storage medium.

Background

In the current society, automobile intellectualization has become an important issue for the development of the automobile industry. How to make intelligent automobile accurately avoid other traffic participants and obstacles becomes an important link for realizing that intelligent automobile guarantees the safe driving of intelligent automobile.

In the prior art, the driving track of the intelligent automobile is determined by adopting a Dijkstra algorithm, an A-x algorithm and other graph searching algorithms derived from the A-x algorithm, so that the intelligent automobile can avoid obstacles to drive according to the driving track. However, the Dijkstra algorithm can cause the increase of time overhead due to the fact that searching is not instructive when the number of nodes increases, the timeliness of the determined driving track is poor, and even the intelligent vehicle is in danger due to wrong driving; the A-star algorithm has complicated calculation process and large calculation amount, and can not determine smooth and continuous vehicle running tracks.

Disclosure of Invention

The embodiment of the invention provides a vehicle control method, a vehicle control device, electronic equipment and a storage medium, wherein a target track point is determined through a pre-established polynomial model, and the determined track is continuous and smooth and is beneficial to controlling a vehicle; the polynomial model has small calculated amount and good timeliness; and the control parameters are predicted by combining a model prediction control algorithm, so that the control precision is high.

In a first aspect, an embodiment of the present invention provides a vehicle control method, including:

determining traffic participants existing in a detection range corresponding to a target vehicle and state information of each traffic participant;

determining each target track point of the target vehicle and the running information of the target vehicle corresponding to each target track point based on the state information of each traffic participant, the running starting point information and the running end point information of the target vehicle and a pre-established polynomial model;

and predicting control parameters of the target vehicle at each target track point based on the running information and a model prediction control algorithm so as to control the target vehicle to run according to the control parameters of each target track point.

In a second aspect, an embodiment of the present invention further provides a vehicle control apparatus, including:

the system comprises a state information determining module, a state information determining module and a state information acquiring module, wherein the state information determining module is used for determining traffic participants existing in a detection range corresponding to a target vehicle and state information of each traffic participant;

the driving information determining module is used for determining each target track point of the target vehicle and driving information of the target vehicle corresponding to each target track point based on the state information of each traffic participant, the driving starting point information and the driving end point information of the target vehicle and a pre-established polynomial model;

and the control vehicle running module is used for predicting the control parameters of the target vehicle at each target track point based on the running information and a model prediction control algorithm so as to control the target vehicle to run according to the control parameters of each target track point.

In a third aspect, an embodiment of the present invention further provides an electronic device, where the electronic device includes:

one or more processors;

a storage device for storing one or more programs,

when the one or more programs are executed by the one or more processors, the one or more processors are caused to implement the vehicle control method provided by any of the embodiments of the present invention.

In a fourth aspect, the embodiments of the present invention further provide a computer-readable storage medium, on which a computer program is stored, where the computer program, when executed by a processor, implements the vehicle control method provided in any embodiment of the present invention.

The vehicle control method provided by the embodiment of the invention determines the traffic participants existing in the detection range corresponding to the target vehicle and the state information of each traffic participant, and determines each target track point of the target vehicle and the running information of the target vehicle corresponding to each target track point through the state information of each traffic participant, the running starting point information and the running end point information of the target vehicle and a pre-established polynomial model; and predicting control parameters of the target vehicle at each target track point based on the running information and a model prediction control algorithm, and controlling the target vehicle to run according to the control parameters of each target track point. According to the embodiment of the invention, the target track point is determined through the pre-established polynomial model, and the determined track is continuous and smooth, so that the control of the vehicle is facilitated; the polynomial model has small calculated amount and good timeliness; and the control parameters are predicted by combining a model prediction control algorithm, so that the control precision is high.

In addition, the vehicle control device, the electronic equipment and the storage medium provided by the invention correspond to the method, and have the same beneficial effects.

Drawings

In order to illustrate the embodiments of the present invention more clearly, the drawings that are needed 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 invention, and that other drawings can be obtained by those skilled in the art without inventive effort.

FIG. 1 is a flow chart of a vehicle control method provided by an embodiment of the present invention;

FIG. 2 is a flow chart of another vehicle control method provided by an embodiment of the present invention;

fig. 3 is a structural diagram of a vehicle control apparatus according to an embodiment of the present invention;

fig. 4 is a structural diagram of an electronic device according to an embodiment of the present invention.

Detailed Description

The present invention will be described in further detail with reference to the accompanying drawings and examples. It is to be understood that the specific embodiments described herein are merely illustrative of the invention and are not limiting of the invention. It should be further noted that, for the convenience of description, only some of the structures related to the present invention are shown in the drawings, not all of the structures.

It should be further noted that, for the convenience of description, only some but not all of the relevant aspects of the present invention are shown in the drawings. Before discussing exemplary embodiments in more detail, it should be noted that some exemplary embodiments are described as processes or methods depicted as flowcharts. Although a flowchart may describe the operations (or steps) as a sequential process, many of the operations can be performed in parallel, concurrently or simultaneously. In addition, the order of the operations may be re-arranged. The process may be terminated when its operations are completed, but may have additional steps not included in the figure. The processes may correspond to methods, functions, procedures, subroutines, and the like.

In order that those skilled in the art will better understand the disclosure, the invention will be described in further detail with reference to the accompanying drawings and specific embodiments.

Example one

Fig. 1 is a flowchart of a vehicle control method according to an embodiment of the present invention. The method may be performed by a vehicle control apparatus, which may be implemented by software and/or hardware, and may be configured in a terminal and/or a server to implement the vehicle control method in the embodiment of the present invention.

As shown in fig. 1, the method of the embodiment may specifically include:

s101, determining the traffic participants existing in the detection range corresponding to the target vehicle and the state information of each traffic participant.

The embodiment of the invention can be applied to application scenes of calling a target vehicle, parking out the target vehicle, parking in the target vehicle and the like by a user. The detection range of the target vehicle in the application scene can be determined, and the traffic participants existing in the detection range and the state information of each traffic participant can be determined.

Alternatively, the detection range may be a circular area having the start position and the end position of the target vehicle as diameters; the present invention is not limited to this embodiment, and the present invention may also be a rectangular area with the start position and the end position of the target vehicle as diagonal lines.

Further, the traffic participants existing in the detection range are determined. It is noted that the traffic participant includes at least one of a motor vehicle, a non-motor vehicle, a pedestrian, an animal, and a transportation device. The traffic participants in the detection range and the state information of each traffic participant can be obtained through detection equipment such as image acquisition equipment, laser radar and millimeter radar.

Optionally, determining traffic participants existing in a detection range corresponding to the target vehicle and state information of each traffic participant includes: receiving first state information sent by a target vehicle, second state information sent by each adjacent vehicle in a detection range and third state information sent by road side equipment; performing information fusion processing on the first state information, the second state information and the third state information to generate fusion information; and determining the traffic participants existing in the detection range and the state information of the traffic participants based on the fusion information.

Wherein the status information includes at least one of position, speed, acceleration, and heading angle of the traffic participant. The state information can be acquired from the target vehicle side, the adjacent vehicle side and the road side equipment respectively, so that the condition of detection omission caused by the fact that single side equipment is in a sensing blind area which is difficult to cover or the sight line is blocked by a tall obstacle is avoided, and the comprehensiveness and accuracy of detecting traffic participants and state information are improved.

Specifically, the first state information sent by the target vehicle, the second state information sent by the adjacent vehicle and the third state information sent by the road side equipment can be respectively obtained, and the first state information, the second state information and the third state information are subjected to information fusion processing to eliminate redundant state information in the state information, so that the detection accuracy is improved. And determining the state information of each traffic participant in the detection range based on the fusion information.

S102, determining each target track point of the target vehicle and the running information of the target vehicle corresponding to each target track point based on the state information of each traffic participant, the running starting point information and the running end point information of the target vehicle and a pre-established polynomial model.

In particular implementations, travel start point information and travel end point information for a target vehicle are determined. The travel start point information includes at least one of position, speed, acceleration, and heading angle of the target vehicle at the travel start point. The driving end information includes at least one of position, speed, acceleration, and heading angle of the target vehicle at the driving end.

Furthermore, a map of a detection range pre-stored by the cloud is acquired, and a target track of the target vehicle from the driving starting point to the driving end point can be determined by inputting the map, the state information, the driving starting point information and the driving end point information into a pre-established polynomial model, wherein the target track can be determined by two or more target track points. Illustratively, the polynomial model may be a polynomial model corresponding to a polynomial of five or more times.

Optionally, determining each target track point of the target vehicle based on the state information of each traffic participant, the driving start point information and the driving end point information of the target vehicle, and a polynomial model established in advance, includes: and inputting at least one item of information of the driving starting point information, the driving end point information and the position, the speed, the acceleration and the course angle of the traffic participant into a pre-established polynomial model, and determining each target track point of the target vehicle based on the output result of the polynomial model.

Optionally, determining the driving information of the target vehicle corresponding to each target track point includes: and determining at least one item of information of the coordinate position, the course angle, the running speed, the running acceleration and the front wheel rotation angle of the target vehicle corresponding to each target track point.

It should be noted that the output result of the polynomial model needs to satisfy the vehicle kinematics model. And forming a track point set based on the determined coordinate position, the course angle, the running speed, the running acceleration and the front wheel rotation angle of the target vehicle at each target track point for track tracking. For example, the coordinate position of the target vehicle may be an abscissa and an ordinate in a specified coordinate system; the coordinate may also be a longitude and latitude coordinate in a world coordinate system, and the embodiment of the present invention is not limited. Information such as the coordinate position, the heading angle, the traveling speed, the traveling acceleration, and the front wheel turning angle of the target vehicle at the target locus point can be output in the track data format.

S103, predicting control parameters of the target vehicle at each target track point based on the running information and the model prediction control algorithm, and controlling the target vehicle to run according to the control parameters of each target track point.

Alternatively, the Control parameters of the target vehicle at each target locus point may be predicted based on an MPC (Model Predictive Control) algorithm. Specifically, predicting the control parameters of the target vehicle at each target track point based on the running information and the model predictive control algorithm includes: and predicting the control speed, the steering angle speed and the control acceleration of the target vehicle at each target track point based on the running information and the model prediction control algorithm.

Specifically, the determined running information may be understood as parameters such as speed, acceleration, course angle, front wheel rotation angle and the like which are expected to be reached by the vehicle at each target track point, and the expected output parameters have deviation from the actually generated effect due to external interference, model system errors and the like in the actual running process of the target vehicle. In order to ensure that the target vehicle can run according to the expected value, the control parameters of the target vehicle at the target track point can be predicted through an MPC algorithm based on the state information at the current moment. As known to those skilled in the art, the MPC algorithm solves the optimization problem by constructing a cost function and constraints that are consistent with vehicle kinematics and dynamics to obtain an optimal control sequence for a future time, such as a sequence of speed, acceleration, front wheel steering angle, and steering wheel steering angle.

Example two

Fig. 2 is a flowchart of another vehicle control method according to an embodiment of the present invention. The present embodiment is optimized based on the above technical solutions. Optionally, the method further includes: acquiring actual driving parameters of a target vehicle at a target track point; and correcting parameters in the model prediction control algorithm based on the actual running parameters and the control parameters corresponding to the target track points.

The same or corresponding terms as those in the above embodiments are not explained in detail herein.

S201, determining the traffic participants existing in the detection range corresponding to the target vehicle and the state information of each traffic participant.

Optionally, before determining the traffic participants and the state information of each traffic participant existing in the detection range corresponding to the target vehicle, the method further includes: and establishing a rectangular area which takes the current position of the target vehicle as the center and takes the preset length as the side length, and determining the rectangular area as a detection range.

Specifically, a person skilled in the art may determine a specific value of the preset length according to the distance between the current position and the end position. Illustratively, the preset length is 15 meters, and the detection range is a square area with a side length of 15 meters and a current position as a center.

S202, determining each target track point of the target vehicle and the running information of the target vehicle corresponding to each target track point based on the state information of each traffic participant, the running starting point information and the running end point information of the target vehicle and a pre-established polynomial model.

And S203, predicting the control parameters of the target vehicle at each target track point based on the running information and the model prediction control algorithm so as to control the target vehicle to run according to the control parameters of each target track point.

And S204, acquiring actual running parameters of the target vehicle at the target track points, and correcting parameters in the model predictive control algorithm based on the actual running parameters and the control parameters corresponding to the target track points.

In the embodiment of the invention, in order to improve the accuracy of the predicted control parameters of each target track point, the parameters in the model prediction control algorithm can be corrected through the actual running parameters of the target vehicle at the target track point.

Specifically, the actual running parameters of the target vehicle at each target track point are recorded in the actual running process of the target vehicle, and the actual running parameters comprise the actual speed, the actual acceleration and the actual front wheel rotation angle of the target vehicle. Comparing the actual driving parameters with the predicted control parameters, and calculating the difference between the actual driving parameters and the predicted control parameters; if the difference is larger than the preset threshold, adjusting parameters in a model predictive control algorithm based on the difference between the actual driving parameter and the predicted control parameter so that the difference between the actual driving parameter and the predicted control parameter is within an allowable error range; if the difference is less than or equal to the preset threshold, no operation is required. Furthermore, the model predictive control algorithm can be updated based on the corrected parameters so as to improve the accuracy of the prediction result.

According to the embodiment of the invention, the target track point is determined through the pre-established polynomial model, and the determined track is continuous and smooth, so that the control of the vehicle is facilitated; parameters in the model predictive control algorithm are corrected based on actual running parameters of the target vehicle, and the model predictive control algorithm is updated, so that the accuracy of a prediction result is improved.

EXAMPLE III

Fig. 3 is a block diagram of a vehicle control device according to an embodiment of the present invention, which is configured to execute a vehicle control method according to any of the embodiments. The device and the vehicle control method of each embodiment belong to the same inventive concept, and details which are not described in detail in the embodiment of the vehicle control device can refer to the embodiment of the vehicle control method. The device may specifically comprise:

a state information determining module 10, configured to determine traffic participants existing in a detection range corresponding to the target vehicle and state information of each traffic participant;

the driving information determining module 11 is configured to determine each target track point of the target vehicle and driving information of the target vehicle corresponding to each target track point based on the state information of each traffic participant, the driving start point information and the driving end point information of the target vehicle, and a polynomial model established in advance;

and the control vehicle running module 12 is used for predicting the control parameters of the target vehicle at each target track point based on the running information and the model prediction control algorithm so as to control the target vehicle to run according to the control parameters of each target track point.

On the basis of any optional technical solution in the embodiment of the present invention, optionally, the module 10 for determining status information includes:

the fusion processing unit is used for receiving first state information sent by the target vehicle, second state information sent by each adjacent vehicle in the detection range and third state information sent by the road side equipment; performing information fusion processing on the first state information, the second state information and the third state information to generate fusion information; and determining the traffic participants existing in the detection range and the state information of the traffic participants based on the fusion information.

On the basis of any optional technical scheme in the embodiment of the invention, optionally, the state information comprises at least one item of information of the position, the speed, the acceleration and the course angle of the traffic participant;

the travel information determining module 11 includes:

and the track point determining unit is used for inputting the driving starting point information, the driving end point information and at least one item of information of the position, the speed, the acceleration and the course angle of the traffic participant into a pre-established polynomial model, and determining each target track point of the target vehicle based on the output result of the polynomial model.

On the basis of any optional technical solution in the embodiment of the present invention, optionally, the driving information determining module 11 includes:

and the travel information determining unit is used for determining at least one item of information of the coordinate position, the course angle, the travel speed, the travel acceleration and the front wheel rotation angle of the target vehicle corresponding to each target track point.

On the basis of any optional technical solution in the embodiment of the present invention, optionally, the module 12 for controlling vehicle running includes:

and a control vehicle running unit for predicting a control speed, a steering angle speed and a control acceleration of the target vehicle at each target locus point based on the running information and the model predictive control algorithm.

On the basis of any optional technical solution in the embodiment of the present invention, optionally, the apparatus further includes:

the correction module is used for acquiring the actual driving parameters of the target vehicle at the target track points; and correcting parameters in the model prediction control algorithm based on the actual running parameters and the control parameters corresponding to the target track points.

and the detection range establishing unit is used for establishing a rectangular area which takes the current position of the target vehicle as the center and takes the preset length as the side length before determining the traffic participants existing in the detection range corresponding to the target vehicle and the state information of each traffic participant, and determining the rectangular area as the detection range.

The vehicle control device provided by the embodiment of the invention can realize the following method: determining traffic participants existing in a detection range corresponding to the target vehicle and state information of each traffic participant; determining each target track point of the target vehicle and the running information of the target vehicle corresponding to each target track point based on the state information of each traffic participant, the running starting point information and the running end point information of the target vehicle and a pre-established polynomial model; and predicting control parameters of the target vehicle at each target track point based on the running information and the model prediction control algorithm so as to control the target vehicle to run according to the control parameters of each target track point. According to the embodiment of the invention, the target track point is determined through the pre-established polynomial model, and the determined track is continuous and smooth, so that the control of the vehicle is facilitated; the polynomial model has small calculated amount and good timeliness; and the control parameters are predicted by combining a model prediction control algorithm, so that the control precision is high.

It should be noted that, in the embodiment of the vehicle control device, the included units and modules are merely divided according to the functional logic, but are not limited to the above division as long as the corresponding functions can be realized; in addition, specific names of the functional units are only for convenience of distinguishing from each other, and are not used for limiting the protection scope of the present invention.

Example four

Fig. 4 is a structural diagram of an electronic device according to an embodiment of the present invention. FIG. 4 illustrates a block diagram of an exemplary electronic device 20 suitable for use in implementing embodiments of the present invention. The illustrated electronic device 20 is merely an example and should not be used to limit the functionality or scope of embodiments of the present invention.

As shown in fig. 4, the electronic device 20 is embodied in the form of a general purpose computing device. The components of the electronic device 20 may include, but are not limited to: one or more processors or processing units 201, a system memory 202, and a bus 203 that couples the various system components (including the system memory 202 and the processing unit 201).

Bus 203 represents one or more of any of several types of bus structures, including a memory bus or memory controller, a peripheral bus, an accelerated graphics port, and a processor or local bus using any of a variety of bus architectures. By way of example, such architectures include, but are not limited to, Industry Standard Architecture (ISA) bus, micro-channel architecture (MAC) bus, enhanced ISA bus, Video Electronics Standards Association (VESA) local bus, and Peripheral Component Interconnect (PCI) bus.

Electronic device 20 typically includes a variety of computer system readable media. Such media may be any available media that is accessible by electronic device 20 and includes both volatile and nonvolatile media, removable and non-removable media.

The system memory 202 may include computer system readable media in the form of volatile memory, such as Random Access Memory (RAM)204 and/or cache memory 205. The electronic device 20 may further include other removable/non-removable, volatile/nonvolatile computer system storage media. By way of example only, the storage system 206 may be used to read from and write to non-removable, nonvolatile magnetic media. A magnetic disk drive for reading from and writing to a removable, nonvolatile magnetic disk (e.g., a "floppy disk") and an optical disk drive for reading from or writing to a removable, nonvolatile optical disk (e.g., a CD-ROM, DVD-ROM, or other optical media) may be provided. In these cases, each drive may be connected to bus 203 by one or more data media interfaces. Memory 202 may include at least one program product having a set (e.g., at least one) of program modules that are configured to carry out the functions of embodiments of the invention.

A program/utility 208 having a set (at least one) of program modules 207 may be stored, for example, in memory 202, such program modules 207 including, but not limited to, an operating system, one or more application programs, other program modules, and program data, each of which examples or some combination thereof may comprise an implementation of a network environment. Program modules 207 generally perform the functions and/or methodologies of embodiments of the present invention as described herein.

The electronic device 20 may also communicate with one or more external devices 209 (e.g., keyboard, pointing device, display 210, etc.), with one or more devices that enable a user to interact with the electronic device 20, and/or with any devices (e.g., network card, modem, etc.) that enable the electronic device 20 to communicate with one or more other computing devices. Such communication may occur via input/output (I/O) interfaces 211. Also, the electronic device 20 may communicate with one or more networks (e.g., a Local Area Network (LAN), a Wide Area Network (WAN), and/or a public network, such as the Internet) via the network adapter 212. As shown, the network adapter 212 communicates with other modules of the electronic device 20 over the bus 203. It should be understood that other hardware and/or software modules may be used in conjunction with electronic device 20, including but not limited to: microcode, device drivers, redundant processing units, external disk drive arrays, RAID systems, tape drives, and data backup storage systems, among others.

The processing unit 201 executes various functional applications and data processing by running a program stored in the system memory 202.

The electronic equipment provided by the invention can realize the following method: determining traffic participants existing in a detection range corresponding to the target vehicle and state information of each traffic participant; determining each target track point of the target vehicle and the running information of the target vehicle corresponding to each target track point based on the state information of each traffic participant, the running starting point information and the running end point information of the target vehicle and a pre-established polynomial model; and predicting control parameters of the target vehicle at each target track point based on the running information and the model prediction control algorithm so as to control the target vehicle to run according to the control parameters of each target track point. According to the embodiment of the invention, the target track point is determined through the pre-established polynomial model, and the determined track is continuous and smooth, so that the control of the vehicle is facilitated; the polynomial model has small calculated amount and good timeliness; and the control parameters are predicted by combining a model prediction control algorithm, so that the control precision is high.

EXAMPLE six

Embodiments of the present invention provide a storage medium containing computer-executable instructions which, when executed by a computer processor, are operable to perform a method of vehicle control, the method comprising:

determining traffic participants existing in a detection range corresponding to the target vehicle and state information of each traffic participant; determining each target track point of the target vehicle and the running information of the target vehicle corresponding to each target track point based on the state information of each traffic participant, the running starting point information and the running end point information of the target vehicle and a pre-established polynomial model; and predicting control parameters of the target vehicle at each target track point based on the running information and the model prediction control algorithm so as to control the target vehicle to run according to the control parameters of each target track point. According to the embodiment of the invention, the target track point is determined through the pre-established polynomial model, and the determined track is continuous and smooth, so that the control of the vehicle is facilitated; the polynomial model has small calculated amount and good timeliness; and the control parameters are predicted by combining a model prediction control algorithm, so that the control precision is high.

Of course, the storage medium containing the computer-executable instructions provided by the embodiments of the present invention is not limited to the method operations described above, and may also perform related operations in the vehicle control method provided by any embodiment of the present invention.

Computer storage media for embodiments of the invention may employ any combination of one or more computer-readable media. The computer readable medium may be a computer readable signal medium or a computer readable storage medium. A computer readable storage medium may be, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination of the foregoing. More specific examples (a non-exhaustive list) of the computer readable storage medium would include the following: 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 (EPROM or 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.

A computer readable signal medium may include a propagated data signal with computer readable program code embodied therein, for example, in baseband or as part of a carrier wave. Such a propagated data signal may take many forms, including, but not limited to, electro-magnetic, optical, or any suitable combination thereof. 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.

Program code embodied on a computer readable medium may be transmitted using any appropriate medium, including but not limited to wireless, wireline, optical fiber cable, RF, etc., or any suitable combination of the foregoing.

Computer program code for carrying out operations for embodiments of the present invention may be written in any combination of one or more programming languages, including an object oriented programming language such as Java, Smalltalk, C + + or the like and conventional procedural programming languages, such as the "C" programming language or similar programming languages. The program code may execute entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer through any type of network, including a Local Area Network (LAN) or a Wide Area Network (WAN), or the connection may be made to an external computer (for example, through the Internet using an Internet service provider).

It is to be noted that the foregoing is only illustrative of the preferred embodiments of the present invention and the technical principles employed. It will be understood by those skilled in the art that the present invention is not limited to the particular embodiments described herein, but is capable of various obvious changes, rearrangements and substitutions as will now become apparent to those skilled in the art without departing from the scope of the invention. Therefore, although the present invention has been described in greater detail by the above embodiments, the present invention is not limited to the above embodiments, and may include other equivalent embodiments without departing from the spirit of the present invention, and the scope of the present invention is determined by the scope of the appended claims.

Claims

1. A vehicle control method characterized by comprising:

2. The method of claim 1, wherein the determining of the traffic participants and the state information of each of the traffic participants existing within the detection range corresponding to the target vehicle comprises:

receiving first state information sent by the target vehicle, second state information sent by each adjacent vehicle in the detection range and third state information sent by road side equipment;

performing information fusion processing on the first state information, the second state information and the third state information to generate fusion information;

determining the traffic participants existing in the detection range and the state information of the traffic participants based on the fusion information.

3. The method of claim 1, wherein the status information includes at least one of location, speed, acceleration, and heading angle of the traffic participant; wherein,

the determining each target track point of the target vehicle based on the state information of each traffic participant, the driving start point information, the driving end point information of the target vehicle and a polynomial model established in advance comprises:

and inputting the driving starting point information, the driving end point information and at least one item of information of the position, the speed, the acceleration and the course angle of the traffic participant into the pre-established polynomial model, and determining each target track point of the target vehicle based on the output result of the polynomial model.

4. The method of claim 3, wherein determining the travel information of the target vehicle corresponding to each target track point comprises:

and determining at least one item of information of the coordinate position, the course angle, the running speed, the running acceleration and the front wheel rotation angle of the target vehicle corresponding to each target track point.

5. The method of claim 1, wherein predicting control parameters of the target vehicle at each of the target trajectory points based on the travel information and a model predictive control algorithm comprises:

and predicting the control speed, the steering angle speed and the control acceleration of the target vehicle at each target track point based on the running information and the model predictive control algorithm.

6. The method of claim 1, further comprising:

acquiring actual running parameters of the target vehicle at the target track points;

and correcting parameters in the model predictive control algorithm based on the actual running parameters and the control parameters corresponding to the target track points.

7. The method according to claim 1, further comprising, before determining the traffic participants and the status information of each of the traffic participants existing within the detection range corresponding to the target vehicle:

and establishing a rectangular area with the current position of the target vehicle as the center and the preset length as the side length, and determining the rectangular area as the detection range.

8. A vehicle control apparatus characterized by comprising:

9. An electronic device, comprising:

one or more processors;

a memory for storing one or more programs;

when executed by the one or more processors, cause the one or more processors to implement the vehicle control method of any one of claims 1-7.

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