CN113753060B

CN113753060B - Control method, device, computing equipment and medium for vehicle

Info

Publication number: CN113753060B
Application number: CN202011199616.8A
Authority: CN
Inventors: 边学鹏; 窦凤谦; 刘懿; 高萌; 张亮亮
Original assignee: Beijing Jingdong Qianshi Technology Co Ltd
Current assignee: Beijing Jingdong Qianshi Technology Co Ltd
Priority date: 2020-10-30
Filing date: 2020-10-30
Publication date: 2023-05-30
Anticipated expiration: 2040-10-30
Also published as: CN113753060A

Abstract

The present disclosure provides a control method of a vehicle. The method includes obtaining a current rate of the vehicle; determining a target rate interval to which the current rate belongs from a plurality of rate intervals; determining a period prediction rule corresponding to the target rate interval; determining a prediction period according to the current rate and a period prediction rule; performing path prediction according to the prediction period to obtain a prediction result; and controlling the running of the vehicle according to the prediction result. The disclosure also provides a control device, a computing device and a medium of the vehicle.

Description

Control method, device, computing equipment and medium for vehicle

Technical Field

The present disclosure relates to the field of computer technology, and more particularly, to a method, an apparatus, a computing device, and a medium for controlling a vehicle.

Background

Model predictive control algorithms (hereinafter mpc) belong to multi-step predictive algorithms, and mpc has several important parameters: how to design these parameters is an important issue, prediction time domain, control time domain, prediction period, etc.

The related art sets all of these parameters to a fixed value, so that although consistency of the prediction period is ensured, tracking effect is poor for complex trajectories (e.g., complex paths and complex speeds).

Disclosure of Invention

In view of this, the present disclosure provides a control method, apparatus, computing device, and medium for a vehicle.

One aspect of the present disclosure provides a control method of a vehicle, including: acquiring the current speed of the vehicle; determining a target rate interval to which the current rate belongs from a plurality of rate intervals; determining a period prediction rule corresponding to the target rate interval; determining a prediction period according to the current rate and the period prediction rule; performing path prediction according to the prediction period to obtain a prediction result; and controlling the running of the vehicle according to the prediction result.

According to an embodiment of the present disclosure, the method further comprises: obtaining predicted time domain parameter N _p Wherein the N is _p Is a positive integer; determining a functional relationship between a rate within each of the plurality of rate intervals and a predicted distance; and according to the functional relation between the speed and the predicted distance and the predicted time domain parameter N _p And configuring a period prediction rule corresponding to each rate interval.

According to an embodiment of the present disclosure, the determining a functional relationship between the rate and the predicted distance within each of the plurality of rate intervals includes: for each rate interval in the plurality of rate intervals, acquiring a plurality of standard rates in the rate interval and standard prediction distances corresponding to the plurality of standard rates; and fitting the plurality of standard rates and the corresponding standard predicted distances to obtain a functional relationship between the rates and the predicted distances in the rate interval.

According to an embodiment of the present disclosure, the prediction time domain parameter N and the functional relation between the rate and the prediction distance _p Configuring a period prediction rule corresponding to each rate interval, including: the period prediction rule corresponding to each rate interval is configured according to the following formula:

wherein T is the period prediction rule, v is _min For a preset speed minimum, the α, A, B, k1 and k2 are constants, and the A, B, k and k2 are determined according to a functional relationship between the speed and the predicted distance, and c=k1×3-v 2) +b.

According to an embodiment of the present disclosure, the performing path prediction according to the prediction period includes: according to the predicted time domain parameter N _p The prediction period is used for determining a planning model; and determining N using the planning model _p Position coordinates, heading angle and speed of each reference point are used as the prediction results.

According to an embodiment of the present disclosure, the controlling the driving of the vehicle according to the prediction result includes: acquiring control time domain parameter N _c Wherein the N is _c Is a positive integer; generating N according to the prediction result _c A control instruction; according to the N _c Control instructions, in the future N _c And adjusting the running parameters of the vehicle at each moment.

Another aspect of the present disclosure provides a control apparatus of a vehicle, including: the speed acquisition module is used for acquiring the current speed of the vehicle; the interval determining module is used for determining a target rate interval to which the current rate belongs from a plurality of rate intervals; the rule determining module is used for determining a period prediction rule corresponding to the target rate interval; the period determining module is used for determining a prediction period according to the current speed and the period prediction rule; the prediction module is used for performing path prediction according to the prediction period to obtain a prediction result; and the control module is used for controlling the running of the vehicle according to the prediction result.

According to an embodiment of the present disclosure, the apparatus further comprises: a prediction time domain acquisition module for acquiring a prediction time domain parameter N _p Wherein the N is _p Is a positive integer; a functional relation determining module, configured to determine a functional relation between a rate in each of the plurality of rate intervals and a predicted distance; and a rule configuration module for configuring the time domain parameter N according to the function relation between the speed and the predicted distance and the predicted time domain parameter N _p And configuring a period prediction rule corresponding to each rate interval.

According to an embodiment of the disclosure, the prediction module includes: a model building sub-module for building a model according to the predicted time domain parameter N _p The prediction period is used for determining a planning model; and a model solving sub-module for determining N by using the planning model _p Position coordinates, heading angle and speed of each reference point are used as the prediction results.

Another aspect of the present disclosure provides a computing device comprising: one or more processors; and a storage means for storing one or more programs, which when executed by the one or more processors cause the one or more processors to implement the methods as described above.

Another aspect of the present disclosure provides a computer-readable storage medium storing computer-executable instructions that, when executed, are configured to implement a method as described above.

Another aspect of the present disclosure provides a computer program comprising computer executable instructions which when executed are for implementing a method as described above.

According to an embodiment of the present disclosure, by associating the prediction period with the vehicle speed, the prediction period is made to continuously vary with the vehicle speed. When the vehicle is controlled, a period prediction rule corresponding to the current speed is determined according to the target speed interval to which the current speed belongs, and a prediction period is determined according to the period prediction rule, so that the prediction period is adapted to the vehicle speed. When the path prediction is performed according to the prediction period and the running of the vehicle is controlled according to the prediction result, the smoothness of the vehicle control can be ensured, and the effect is better.

Drawings

The above and other objects, features and advantages of the present disclosure will become more apparent from the following description of embodiments thereof with reference to the accompanying drawings in which:

fig. 1 schematically illustrates an exemplary application scenario in which a control method of a vehicle may be applied according to an embodiment of the present disclosure;

FIG. 2 schematically illustrates a flow chart of a method of controlling a vehicle according to an embodiment of the disclosure;

FIG. 3 schematically illustrates a flow chart of a method of configuring a periodicity prediction rule corresponding to each rate interval according to the present disclosure;

FIG. 4 schematically illustrates a graph of the rate in each rate interval as a function of predicted distance;

fig. 5 schematically illustrates a block diagram of a control device of a vehicle according to an embodiment of the present disclosure; and

fig. 6 schematically illustrates a block diagram of a computer system suitable for implementing methods of embodiments of the present invention, in accordance with embodiments of the present disclosure.

Detailed Description

Hereinafter, embodiments of the present disclosure will be described with reference to the accompanying drawings. It should be understood that the description is only exemplary and is not intended to limit the scope of the present disclosure. In the following detailed description, for purposes of explanation, numerous specific details are set forth in order to provide a thorough understanding of the embodiments of the present disclosure. It may be evident, however, that one or more embodiments may be practiced without these specific details. In addition, in the following description, descriptions of well-known structures and techniques are omitted so as not to unnecessarily obscure the concepts of the present disclosure.

The terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the disclosure. The terms "comprises," "comprising," and/or the like, as used herein, specify the presence of stated features, steps, operations, and/or components, but do not preclude the presence or addition of one or more other features, steps, operations, or components.

All terms (including technical and scientific terms) used herein have the same meaning as commonly understood by one of ordinary skill in the art unless otherwise defined. It should be noted that the terms used herein should be construed to have meanings consistent with the context of the present specification and should not be construed in an idealized or overly formal manner.

Where expressions like at least one of "A, B and C, etc. are used, the expressions should generally be interpreted in accordance with the meaning as commonly understood by those skilled in the art (e.g.," a system having at least one of A, B and C "shall include, but not be limited to, a system having a alone, B alone, C alone, a and B together, a and C together, B and C together, and/or A, B, C together, etc.). Where a formulation similar to at least one of "A, B or C, etc." is used, in general such a formulation should be interpreted in accordance with the ordinary understanding of one skilled in the art (e.g. "a system with at least one of A, B or C" would include but not be limited to systems with a alone, B alone, C alone, a and B together, a and C together, B and C together, and/or A, B, C together, etc.).

Embodiments of the present disclosure provide a control method of a vehicle and a control device of a vehicle to which the method can be applied. The method includes obtaining a current rate of the vehicle; determining a target rate interval to which the current rate belongs from a plurality of rate intervals; determining a period prediction rule corresponding to the target rate interval; determining a prediction period according to the current rate and a period prediction rule; performing path prediction according to the prediction period to obtain a prediction result; and controlling the running of the vehicle according to the prediction result.

Fig. 1 schematically illustrates an exemplary application scenario 100 in which a control method of a vehicle may be applied according to an embodiment of the present disclosure. It should be noted that fig. 1 illustrates only an example of an application scenario in which the embodiments of the present disclosure may be applied to help those skilled in the art understand the technical content of the present disclosure, but it does not mean that the embodiments of the present disclosure may not be applied to other devices, systems, environments, or scenarios.

As shown in fig. 1, an application scenario 100 according to this embodiment may include a vehicle 10 and a vehicle 20, the vehicle 10 and the vehicle 20 traveling along a lane in directions indicated by arrows, respectively, and during traveling of the two vehicles, traveling paths of the two vehicles are predicted according to a model predictive control algorithm (mpc) and traveling of the two vehicles is controlled according to a prediction result. The current vehicle speed of the vehicle 10 is v1, and the predicted distance of the vehicle 10 is s1. The current vehicle speed of the vehicle 20 is v2, and the predicted distance of the vehicle 20 is s2.

According to the embodiments of the present disclosure, when the vehicles travel at different speeds, the corresponding predicted distances are also different, and the faster the speed of the vehicle, the larger the predicted distance is configured accordingly. In the application scenario 100, the vehicle speed v2 of the vehicle 20 is greater than the vehicle speed v1 of the vehicle 10, and thus the predicted distance s2 of the vehicle 20 is greater than the predicted distance s1 of the vehicle 10.

During the running of the vehicle 10 and the vehicle 20, the vehicle 10 and the vehicle 20 may be controlled by control instructions. In this embodiment, in order to ensure the smoothness of the control command, the predicted distance is effectively related to the vehicle speed, and the predicted period is also related to the vehicle speed, so as to obtain the following formula:

s＝N _p *T*v

wherein s is the predicted distance, N _p To predict the time domain parameter, T is the prediction period and v is the current speed of the vehicle.

The calculation formula of the prediction period can be further deduced from the formula:

according to the embodiment of the present disclosure, the predicted distance s at different speeds may be estimated according to driving experience and real vehicle debugging experience, and the value of s may be set according to the estimation result.

According to embodiments of the present disclosure, the difference in N can be obtained by comparison _p And N _c Under the value, the factors such as amplitude characteristic, phase characteristic, controller frequency, 99 minutes solving time of the controller at the vehicle end and the like of the controller of the vehicle are set to N _p And N _c Size of the product. In addition, in setting the parameter N _p And N _c And the influence of the planned track, the wheelbase of the vehicle, the motor performance of the chassis of the vehicle, the control period, the calculation performance of the industrial personal computer and other factors can be considered.

After T is calculated, T is taken as a prediction period, N is taken as _p And N _c And respectively carrying out mpc rolling optimization solution based on a vehicle kinematics or dynamics model for a prediction time domain and a control time domain. Final output N _p Each predicted point and N _c A control instruction. The N then occurs to the vehicle _c And a control instruction to control the running of the vehicle.

Fig. 2 schematically illustrates a flowchart of a control method of a vehicle according to an embodiment of the present disclosure.

As shown in fig. 2, the method includes the following operations S210 to S260.

In operation S210, a current speed of the vehicle is acquired.

Then, in operation S220, a target rate interval to which the current rate belongs is determined from among a plurality of rate intervals.

In operation S230, a period prediction rule corresponding to the target rate interval is determined.

In operation S240, a prediction period is determined according to the current rate and the period prediction rule.

In operation S250, path prediction is performed according to a prediction period to obtain a prediction result.

In operation S260, the traveling of the vehicle is controlled according to the prediction result.

According to embodiments of the present disclosure, the current speed of the vehicle may be obtained from data detected by a speedometer sensor inside the vehicle, or may also be measured by a speed measuring device outside the vehicle. It should be noted that, in practical application, any other means may be used to obtain the current speed of the vehicle, and the method for obtaining the current speed of the vehicle is not specifically limited in this disclosure.

According to an embodiment of the present disclosure, when the vehicles travel at different speeds, the corresponding predicted distances are different, and the faster the speed of the vehicle, the greater the predicted distance. Therefore, in this embodiment, the rate may be divided into a plurality of rate intervals in advance, each of which corresponds to one of the period prediction rules, so that the prediction period determined according to the period prediction rules may vary with the change in the vehicle speed, and continuity is maintained at all times.

According to embodiments of the present disclosure, the time domain parameter N may be predicted from _p And predicting the period, determining a planning model, and then determining N using the planning model _p Position coordinates, heading angle and speed of each reference point are used as prediction results.

In the present embodiment, after T is acquired, N may be obtained by backward interpolation with T as a period and the nearest track point to the vehicle as a starting point _p A plurality of track points as reference points, wherein the N is _p Each trace point is on a trace.

In this embodiment, the reference point elementThe elements may include, for example, { x, y, θ, v }, where x, y are position coordinates, θ is heading angle, and v is velocity. Thus N _p The reference points may form a single frame of size N _p *4, i.e. as a prediction result.

According to embodiments of the present disclosure, the planning model may include, for example, a vehicle kinematics or dynamics model. Illustratively, the planning model is as shown in the following equation,

x(k+1)＝f1(x(k)，T)；

y(k+1)＝f2(y(k)，T)；

θ(k+1)＝f3(θ(k)，T)；

v(k+1)＝f4(v(k)，T)；

wherein k is E [0, N _p ]F1, f2, f3 and f4 represent the x, y, θ, v at the current time, calculated from the x, y, θ, v and T at the previous time, respectively.

According to embodiments of the present disclosure, a control time domain parameter N may be obtained _c Wherein N is _c Is a positive integer, and generates N according to the prediction result _c Control instructions, then according to N _c Control instructions, in the future N _c And adjusting the running parameters of the vehicle at each moment so as to realize the control of the vehicle.

The method of configuring the period prediction rule corresponding to each rate interval is further described below.

Fig. 3 schematically illustrates a flow chart of a method of configuring a periodicity prediction rule corresponding to each rate interval according to the present disclosure.

As shown in fig. 3, the method includes the following operations S310 to S330. Operations S310 to S330 may be performed, for example, before operation S210.

In operation S310, a predicted time domain parameter N is obtained _p Wherein N is _p Is a positive integer.

In operation S320, a functional relationship between the rate and the predicted distance within each of the plurality of rate intervals is determined.

In operation S330, a time domain parameter N is predicted according to a functional relationship between the rate and the predicted distance _p A period prediction rule corresponding to each rate interval is configured.

According to an embodiment of the present disclosure, a plurality of standard rates within a rate interval and standard prediction distances corresponding to the plurality of standard rates may be acquired for each of the plurality of rate intervals. The standard speed and the standard prediction distance can be preset manually according to driving experience and real vehicle debugging experience and serve as calculated standard values. And then fitting by using a plurality of standard rates and corresponding standard prediction distances to obtain a functional relation between the rates and the prediction distances in the rate interval.

Illustratively, in this embodiment, the rate interval may include a first interval, a second interval, a third interval, and a fourth interval. The first interval is [0, v 1), the second interval is [ v1, v2 ], the third interval is [ v2, v 3), and the fourth interval is [ v3, v 4).

According to an embodiment of the present disclosure, if the speed is in a first interval, the period prediction rule includes calculating a prediction period according to a first formula, wherein the first formula is

/>

Wherein T is a prediction period, N _p To predict the time domain parameters, v is the velocity, v _min For a preset speed minimum value, α is a constant set to prevent calculation errors when v=0, and a is a constant. The value of a may be obtained by fitting a plurality of standard rates and corresponding standard prediction distances within the first interval.

If the speed is in the second interval, the period prediction rule includes calculating a prediction period according to a second formula, wherein the second formula is

T＝B。

Wherein, B is a constant, and the value of B can be obtained by fitting a plurality of standard rates and corresponding standard prediction distances in the second interval.

If the speed is in the third interval, the period prediction rule includes calculating a prediction period according to a third formula, where the third formula is

k1*(v-v2)+B。

Wherein k1 is a constant, and the value of k1 can be obtained by fitting a plurality of standard rates and corresponding standard prediction distances in the third interval.

If the speed is in the fourth interval, the period prediction rule includes calculating a prediction period according to a fourth formula, wherein the fourth formula is

k2*(v-v3)+C。

Where k2 is a constant, and the value of k2 may be obtained by fitting a plurality of standard rates and corresponding standard prediction distances in the third interval, where c=k1×3-v 2) +b.

If the speed does not belong to any of the first, second, third and fourth intervals, the error is reported, and the subsequent operation is not executed.

For example, the first interval is 0-1m/s, the second interval is 1.5-5m/s, the third interval is 5-11m/s, and the fourth interval is 11-22m/s. The standard rate and corresponding standard predicted distance for each interval are shown in table 1. Fig. 4 schematically shows a schematic diagram of the functional relationship between the velocity and the predicted distance in each velocity interval, and the fitting process is performed on the data in table 1, so that a fitted image, for example, as shown in fig. 4, can be obtained.

TABLE 1

Speed value or speed interval (m/s)	Prediction distance (m)
		0.0-1.0	1.5
1.5	2.25
		5.0	7.5
11.0(40km/h)	30
		22.0(80km/h)	80

Illustratively, in N _p For example, =15, the data in table 1 is interpolated to solve the functional relationship between the vehicle speed and the predicted distance, and the relationship between T and v can be obtained by combining the above formula as follows:

when the vehicle is in a low-speed stage, for example, the vehicle is under 1.0m/s, the locking prediction distance is 1.5m, and the maximum locking prediction period is 0.5s. The prediction period is kept constant at 0.1s when the vehicle speed is in the range of 1.0m/s to 5.0 m/s. The predicted period t= 0.013333 (v-5.0) +0.1 when the vehicle speed is in the range of 5.om/s to 11.0 m/s. The predicted period t= 0.010909 (v-11.0) +0.18 for vehicle speeds in the range of 11.0m/s to 22.0 m/s.

It is not difficult to find that the above strategy cuts the speed range, but the smoothness of the control instruction is fundamentally ensured by ensuring the continuity of the vehicle prediction distance, the occurrence of phenomena such as abrupt change and the like of the control instruction is effectively prevented, and the tracking precision of the control system under different vehicle speeds is ensured.

Fig. 5 schematically shows a block diagram of a control device of a vehicle according to an embodiment of the present disclosure.

As shown in fig. 5, the apparatus 500 includes a rate acquisition module 510, an interval determination module 520, a rule determination module 530, a period determination module 540, a prediction module 550, and a control module 560.

A speed acquisition module 510, configured to acquire a current speed of the vehicle.

The interval determining module 520 is configured to determine a target rate interval to which the current rate belongs from a plurality of rate intervals.

A rule determination module 530 is configured to determine a cycle prediction rule corresponding to the target rate interval.

The period determining module 540 is configured to determine a prediction period according to the current rate and the period prediction rule.

The prediction module 550 is configured to perform path prediction according to the prediction period, so as to obtain a prediction result.

And a control module 560 for controlling the running of the vehicle according to the prediction result.

According to another embodiment of the disclosure, the apparatus may further include a prediction time domain acquisition module, a functional relationship determination module, and a rule configuration module. The prediction time domain acquisition module is used for acquiring a prediction time domain parameter N _p Wherein N is _p Is a positive integer. And the functional relation determining module is used for determining a functional relation between the speed and the predicted distance in each speed interval in the plurality of speed intervals. Rule configurationA module for predicting a time domain parameter N according to a functional relation between the velocity and the predicted distance _p A period prediction rule corresponding to each rate interval is configured.

According to another embodiment of the present disclosure, the prediction module may include a model building sub-module and a model solving sub-module. Wherein, the model building sub-module is used for predicting the time domain parameter N according to the prediction _p And predicting the period to determine a planning model. A model solving sub-module for determining N by using the planning model _p Position coordinates, heading angle and speed of each reference point are used as prediction results.

Any number of modules, sub-modules, units, sub-units, or at least some of the functionality of any number of the sub-units according to embodiments of the present disclosure may be implemented in one module. Any one or more of the modules, sub-modules, units, sub-units according to embodiments of the present disclosure may be implemented as split into multiple modules. Any one or more of the modules, sub-modules, units, sub-units according to embodiments of the present disclosure may be implemented at least in part as a hardware circuit, such as a Field Programmable Gate Array (FPGA), a Programmable Logic Array (PLA), a system-on-chip, a system-on-substrate, a system-on-package, an Application Specific Integrated Circuit (ASIC), or in any other reasonable manner of hardware or firmware that integrates or encapsulates the circuit, or in any one of or a suitable combination of three of software, hardware, and firmware. Alternatively, one or more of the modules, sub-modules, units, sub-units according to embodiments of the present disclosure may be at least partially implemented as computer program modules, which when executed, may perform the corresponding functions.

For example, any of the rate acquisition module 510, interval determination module 520, rule determination module 530, period determination module 540, prediction module 550, and control module 560 may be combined in one module to be implemented, or any of the modules may be split into multiple modules. Alternatively, at least some of the functionality of one or more of the modules may be combined with at least some of the functionality of other modules and implemented in one module. According to embodiments of the present disclosure, at least one of the rate acquisition module 510, interval determination module 520, rule determination module 530, period determination module 540, prediction module 550, and control module 560 may be implemented at least in part as hardware circuitry, such as a Field Programmable Gate Array (FPGA), programmable Logic Array (PLA), system-on-chip, system-on-substrate, system-on-package, application Specific Integrated Circuit (ASIC), or hardware or firmware in any other reasonable manner of integrating or packaging the circuitry, or any one of or a suitable combination of any of the three. Alternatively, at least one of the rate acquisition module 510, the interval determination module 520, the rule determination module 530, the period determination module 540, the prediction module 550, and the control module 560 may be at least partially implemented as a computer program module, which when executed, may perform the corresponding functions.

Fig. 6 schematically illustrates a block diagram of a computer system suitable for implementing the above-described method according to an embodiment of the present disclosure. The computer system illustrated in fig. 6 is merely an example and should not be construed as limiting the functionality and scope of use of the embodiments of the present disclosure.

As shown in fig. 6, a computer system 600 according to an embodiment of the present disclosure includes a processor 601 that can perform various appropriate actions and processes according to a program stored in a Read Only Memory (ROM) 602 or a program loaded from a storage section 608 into a Random Access Memory (RAM) 603. The processor 601 may include, for example, a general purpose microprocessor (e.g., a CPU), an instruction set processor and/or an associated chipset and/or a special purpose microprocessor (e.g., an Application Specific Integrated Circuit (ASIC)), or the like. Processor 601 may also include on-board memory for caching purposes. The processor 601 may comprise a single processing unit or a plurality of processing units for performing different actions of the method flows according to embodiments of the disclosure.

In the RAM 603, various programs and data required for the operation of the system 600 are stored. The processor 601, the ROM 602, and the RAM 603 are connected to each other through a bus 604. The processor 601 performs various operations of the method flow according to the embodiments of the present disclosure by executing programs in the ROM 602 and/or the RAM 603. Note that the program may be stored in one or more memories other than the ROM 602 and the RAM 603. The processor 601 may also perform various operations of the method flow according to embodiments of the present disclosure by executing programs stored in the one or more memories.

According to an embodiment of the present disclosure, the system 600 may further include an input/output (I/O) interface 605, the input/output (I/O) interface 605 also being connected to the bus 604. The system 600 may also include one or more of the following components connected to the I/O interface 605: an input portion 606 including a keyboard, mouse, etc.; an output portion 607 including a Cathode Ray Tube (CRT), a Liquid Crystal Display (LCD), and the like, a speaker, and the like; a storage section 608 including a hard disk and the like; and a communication section 609 including a network interface card such as a LAN card, a modem, or the like. The communication section 609 performs communication processing via a network such as the internet. The drive 610 is also connected to the I/O interface 605 as needed. Removable media 611 such as a magnetic disk, an optical disk, a magneto-optical disk, a semiconductor memory, or the like is installed as needed on drive 610 so that a computer program read therefrom is installed as needed into storage section 608.

According to embodiments of the present disclosure, the method flow according to embodiments of the present disclosure may be implemented as a computer software program. For example, embodiments of the present disclosure include a computer program product comprising a computer program embodied on a computer readable storage medium, the computer program comprising program code for performing the method shown in the flowcharts. In such an embodiment, the computer program may be downloaded and installed from a network through the communication portion 609, and/or installed from the removable medium 611. The above-described functions defined in the system of the embodiments of the present disclosure are performed when the computer program is executed by the processor 601. The systems, devices, apparatus, modules, units, etc. described above may be implemented by computer program modules according to embodiments of the disclosure.

The present disclosure also provides a computer-readable storage medium that may be embodied in the apparatus/device/system described in the above embodiments; or may exist alone without being assembled into the apparatus/device/system. The computer-readable storage medium carries one or more programs which, when executed, implement methods in accordance with embodiments of the present disclosure.

According to embodiments of the present disclosure, the computer-readable storage medium may be a non-volatile computer-readable storage medium, which may include, for example, but is not limited to: 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), 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 disclosure, 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. For example, according to embodiments of the present disclosure, the computer-readable storage medium may include ROM 602 and/or RAM 603 and/or one or more memories other than ROM 602 and RAM 603 described above.

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 disclosure. In this regard, 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.

Those skilled in the art will appreciate that the features recited in the various embodiments of the disclosure and/or in the claims may be provided in a variety of combinations and/or combinations, even if such combinations or combinations are not explicitly recited in the disclosure. In particular, the features recited in the various embodiments of the present disclosure and/or the claims may be variously combined and/or combined without departing from the spirit and teachings of the present disclosure. All such combinations and/or combinations fall within the scope of the present disclosure.

The embodiments of the present disclosure are described above. However, these examples are for illustrative purposes only and are not intended to limit the scope of the present disclosure. Although the embodiments are described above separately, this does not mean that the measures in the embodiments cannot be used advantageously in combination. The scope of the disclosure is defined by the appended claims and equivalents thereof. Various alternatives and modifications can be made by those skilled in the art without departing from the scope of the disclosure, and such alternatives and modifications are intended to fall within the scope of the disclosure.

Claims

1. A control method of a vehicle, comprising:

acquiring the current speed of the vehicle;

determining a target rate interval to which the current rate belongs from a plurality of rate intervals;

determining a period prediction rule corresponding to the target rate interval;

determining a prediction period according to the current rate and the period prediction rule;

performing path prediction according to the prediction period to obtain a prediction result; and

controlling the running of the vehicle according to the prediction result;

the method further comprises the following steps:

obtaining predicted time domain parameter N _p Wherein the N is _p Is a positive integer;

determining a functional relationship between a rate within each of the plurality of rate intervals and a predicted distance; and

based on the functional relation between the velocity and the predicted distance and the predicted time domain parameter N _p Configuring a period prediction rule corresponding to each rate interval;

wherein said time domain prediction parameter N is based on a functional relationship between said rate and a predicted distance _p Configuring a period prediction rule corresponding to each rate interval, including:

the period prediction rule corresponding to each rate interval is configured according to the following formula:

2. The method of claim 1, wherein the determining a functional relationship between a rate within each of the plurality of rate intervals and a predicted distance comprises:

for each rate interval in the plurality of rate intervals, acquiring a plurality of standard rates in the rate interval and standard prediction distances corresponding to the plurality of standard rates; and

and fitting the plurality of standard rates and the corresponding standard predicted distances to obtain a functional relation between the rates and the predicted distances in the rate interval.

3. The method of claim 1, wherein the performing path prediction according to the prediction period comprises:

according to the predicted time domain parameter N _p The prediction period is used for determining a planning model; and

determining N using the planning model _p Position coordinates, heading angle and speed of each reference point are used as the prediction results.

4. The method of claim 2, wherein the controlling the travel of the vehicle according to the prediction result includes:

acquiring control time domain parameter N _c Wherein the N is _c Is a positive integer;

generating N according to the prediction result _c A control instruction; and

according to the N _c Control instructions, in the future N _c And adjusting the running parameters of the vehicle at each moment.

5. A control device of a vehicle, comprising:

the speed acquisition module is used for acquiring the current speed of the vehicle;

the interval determining module is used for determining a target rate interval to which the current rate belongs from a plurality of rate intervals;

the rule determining module is used for determining a period prediction rule corresponding to the target rate interval;

the period determining module is used for determining a prediction period according to the current speed and the period prediction rule;

the prediction module is used for performing path prediction according to the prediction period to obtain a prediction result; and

the control module is used for controlling the running of the vehicle according to the prediction result;

the device further comprises:

a prediction time domain acquisition module for acquiring a prediction time domain parameter N _p Wherein the N is _p Is a positive integer;

a functional relation determining module, configured to determine a functional relation between a rate in each of the plurality of rate intervals and a predicted distance; and

rule configuration module for predicting distance according to the rateFunctional relation between and said predicted time domain parameter N _p Configuring a period prediction rule corresponding to each rate interval;

6. A computing device, comprising:

one or more processors;

storage means for storing one or more programs,

wherein the one or more programs, when executed by the one or more processors, cause the one or more processors to implement the method of any of claims 1 to 4.

7. A computer readable storage medium having stored thereon executable instructions which when executed by a processor cause the processor to implement the method of any one of claims 1 to 4.