CN115009278A

CN115009278A - Cruise control method, device, equipment and storage medium

Info

Publication number: CN115009278A
Application number: CN202210941074.XA
Authority: CN
Inventors: 王秀雷; 赵康荏
Original assignee: Weichai Power Co Ltd
Current assignee: Weichai Power Co Ltd
Priority date: 2022-08-08
Filing date: 2022-08-08
Publication date: 2022-09-06
Anticipated expiration: 2042-08-08
Also published as: CN115009278B

Abstract

The invention discloses a cruise control method, a cruise control device, cruise control equipment and a storage medium, wherein the method comprises the following steps: acquiring the current actual speed of a target vehicle, and inputting the current actual speed and a set target speed into a current PID network model to obtain an output predicted cruise torque; performing a cruise control operation on the target vehicle based on the predicted cruise torque; under the condition that the current iteration times do not meet a preset time threshold value, based on the predicted cruising torque, adjusting model parameters in the current PID network model to obtain an adjusted current PID network model; and taking the adjusted current PID network model as a current PID network model, and repeatedly executing the step of obtaining the current actual speed of the target vehicle. The embodiment of the invention solves the problem that the traditional cruise control method needs to execute a large amount of calibration work, reduces the calibration workload, and improves the adaptability of the cruise control method and the accuracy of cruise control vehicle speed.

Description

Cruise control method, device, equipment and storage medium

Technical Field

The present invention relates to the field of vehicle control technologies, and in particular, to a cruise control method, apparatus, device, and storage medium.

Background

An Adaptive Cruise Control (ACC) system can allow a driver to stably run at a set speed without controlling an accelerator pedal within a certain vehicle speed range, so as to reduce the fatigue degree of the driver and improve the stability, safety, comfort and fuel economy during running.

The ACC mainly includes a command switch, a vehicle speed sensor, an ECU (Electronic Control Unit) and a throttle actuator. The ECU has two signal inputs, one is a command signal for setting a target vehicle speed input by a driver through a command switch, and the other is a feedback signal of an actual vehicle speed acquired by a sensor when the vehicle runs. Most conventional cruise control methods adopt a PID (Proportional, Integral and Differential) algorithm, and based on the two detected input signals, the actual vehicle speed is adjusted through an accelerator actuator so as to keep the actual vehicle speed constant.

The application scenarios of the vehicles involve multiple loads, multiple gears, various vehicle types and the like, and the performance parameters of the vehicles or engines in the same batch also have the problem of production consistency. Therefore, in order to adapt to different application scenarios of the vehicle, the conventional cruise control method needs to perform a large amount of calibration work on the proportional coefficient, the integral coefficient and the differential coefficient in the PID algorithm, which is time-consuming and labor-consuming, and the accuracy of the cruise vehicle speed is not high.

Disclosure of Invention

The embodiment of the invention provides a cruise control method, a cruise control device, cruise control equipment and a storage medium, which are used for solving the problem that a large amount of calibration work needs to be executed in the traditional cruise control method and improving the adaptability of the cruise control method and the accuracy of cruise control vehicle speed.

According to an embodiment of the present invention, there is provided a cruise control method including:

acquiring the current actual speed of a target vehicle, and inputting the current actual speed and a set target speed into a current PID network model to obtain an output predicted cruise torque;

performing a cruise control operation on the target vehicle based on the predicted cruise torque;

under the condition that the current iteration times do not meet a preset time threshold value, based on the predicted cruising torque, adjusting model parameters in the current PID network model to obtain an adjusted current PID network model;

and taking the adjusted current PID network model as a current PID network model, and repeatedly executing the step of obtaining the current actual speed of the target vehicle.

According to another embodiment of the present invention, there is provided a cruise control apparatus including:

the predicted cruise torque determining module is used for acquiring the current actual speed of a target vehicle, and inputting the current actual speed and the set target speed into a current PID network model to obtain the output predicted cruise torque;

a cruise control module that performs a cruise control operation on the target vehicle based on the predicted cruise torque;

the PID neural network adjusting module is used for adjusting model parameters in the current PID network model based on the predicted cruising torque under the condition that the current iteration number does not meet a preset number threshold value to obtain an adjusted current PID network model;

and the repeated execution module is used for taking the adjusted current PID network model as a current PID network model and repeatedly executing the step of obtaining the current actual speed of the target vehicle.

According to another embodiment of the present invention, there is provided an electronic apparatus including:

at least one processor; and

a memory communicatively coupled to the at least one processor; wherein the content of the first and second substances,

the memory stores a computer program executable by the at least one processor, the computer program being executable by the at least one processor to enable the at least one processor to perform the cruise control method according to any of the embodiments of the present invention.

According to another embodiment of the present invention, there is provided a computer-readable storage medium storing computer instructions for causing a processor to implement a cruise control method according to any one of the embodiments of the present invention when executed.

The technical scheme of the embodiment of the invention obtains the output predicted cruising torque by obtaining the current actual speed of the target vehicle and inputting the current actual speed and the set target speed into the current PID network model, and based on the predicted cruising torque, performing a cruise control operation on the target vehicle, based on the predicted cruise torque in the case where the current iteration number does not satisfy a preset number threshold, adjusting model parameters in the current PID network model to obtain an adjusted current PID network model, taking the adjusted current PID network model as the current PID network model, and the step of obtaining the current actual speed of the target vehicle is repeatedly executed, so that the problem that a large amount of calibration work needs to be executed in the traditional cruise control method is solved, the calibration workload is reduced, and the adaptability of the cruise control method and the accuracy of the cruise control speed are improved.

It should be understood that the statements in this section do not necessarily identify key or critical features of the embodiments of the present invention, nor do they necessarily limit the scope of the invention. Other features of the present invention will become apparent from the following description.

Drawings

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

Fig. 1 is a flowchart of a cruise control method according to an embodiment of the present invention;

fig. 2 is a schematic diagram of a sub-network model according to an embodiment of the present invention;

fig. 3 is a flowchart of a cruise control method according to a second embodiment of the present invention;

FIG. 4 is a schematic diagram illustrating a variation curve of cruise control vehicle speeds at different set target vehicle speeds according to a second embodiment of the present invention;

fig. 5 is a schematic structural diagram of a cruise control apparatus according to a third embodiment of the present invention;

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

Detailed Description

In order to make those skilled in the art better understand the technical solutions of the present invention, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.

It should be noted that the terms "first," "second," and the like in the description and claims of the present invention and in the drawings described above are used for distinguishing between similar elements and not necessarily for describing a particular sequential or chronological order. It is to be understood that the data so used is interchangeable under appropriate circumstances such that the embodiments of the invention described herein are capable of operation in other sequences than those illustrated or described herein. Furthermore, the terms "comprises," "comprising," and "having," and any variations thereof, are intended to cover a non-exclusive inclusion, such that a process, method, system, article, or apparatus that comprises a list of steps or elements is not necessarily limited to those steps or elements expressly listed, but may include other steps or elements not expressly listed or inherent to such process, method, article, or apparatus.

Example one

Fig. 1 is a flowchart of a cruise control method according to an embodiment of the present invention, where the embodiment is applicable to a situation where a driving speed of a vehicle is automatically controlled in a specific scenario, and the method may be executed by a cruise control device, which may be implemented in the form of hardware and/or software, and may be configured in a terminal device. As shown in fig. 1, the method includes:

and S110, acquiring the current actual speed of the target vehicle, and inputting the current actual speed and the set target speed into the current PID network model to obtain the output predicted cruise torque.

For example, the set target vehicle speed may be a target vehicle speed set by a user through a command switch. Specifically, the PID network model includes an input layer, a hidden layer, and an output layer.

In an alternative embodiment, inputting the current actual vehicle speed and the set target vehicle speed into the current PID network model, resulting in an output predicted cruise torque, comprises: outputting the input current actual vehicle speed and the set target vehicle speed to a hidden layer in the current PID network model through an input layer in the current PID network model; outputting a proportional vehicle speed characteristic, an integral vehicle speed characteristic and a differential vehicle speed characteristic based on the input current actual vehicle speed and the set target vehicle speed through a hidden layer in the current PID network model; and outputting the predicted cruising torque based on the input proportional vehicle speed characteristic, the integral vehicle speed characteristic and the differential vehicle speed characteristic through an output layer in the current PID network model.

In an alternative embodiment, the number structure between the number of input neurons contained in the input layer, the number of hidden neurons contained in the hidden layer, and the number of output neurons contained in the output layer of the current PID network model is 2N × 3N, where N is an integer greater than or equal to 1.

Specifically, the current PID network model includes N groups of sub-network models. Fig. 2 is a schematic diagram of a sub-network model according to an embodiment of the present invention. Specifically, the sub-network model includes two input neurons, and the input data corresponding to each input neuron is the current actual vehicle speed and the set target vehicle speed. The subnetwork model contains three implicit neurons, Proportional (proportionality), Integral (Integral) and Differential (Differential).

The number of the current actual vehicle speed, the set target vehicle speed, the actual vehicle speed feature, the target vehicle speed feature, the proportional vehicle speed feature, the integral vehicle speed feature and the differential vehicle speed feature in the embodiment is N respectively.

Specifically, the input data of the input layer is the same as the output data. Outputting the input current actual vehicle speed and the set target vehicle speed to a hidden layer in a current PID network model, wherein the steps of: and for each sub-network model, outputting the input current actual vehicle speed to a hidden layer in the current PID network model through a first input neuron in the sub-network model, and outputting the input set target vehicle speed to the hidden layer in the current PID network model through a second input neuron in the sub-network model.

For example, the formula corresponding to the input layer of the current PID network model may be represented as:

wherein the content of the first and second substances,

representing the second in the current PID network model

In the sub-network model

The output data corresponding to each of the input neurons,

representing the second in the current PID network model

In the sub-network model

Input data corresponding to the individual input neurons,

representing the current number of iterations. Wherein the content of the first and second substances,

values of (a) include 1,2,3 … N,

is 1, 2.

Specifically, outputting a proportional vehicle speed characteristic, an integral vehicle speed characteristic and a differential vehicle speed characteristic based on the input current actual vehicle speed and the set target vehicle speed includes: determining a hidden vehicle speed characteristic based on the input current actual vehicle speed and the set target vehicle speed through a proportional neuron in the sub-network model for each sub-network model, and taking the hidden vehicle speed characteristic as a proportional vehicle speed characteristic; determining an implicit vehicle speed characteristic based on the input current actual vehicle speed and the set target vehicle speed through an integral neuron in the sub-network model, and determining an integral vehicle speed characteristic based on the implicit vehicle speed characteristic; and determining an implicit vehicle speed characteristic based on the input current actual vehicle speed and the set target vehicle speed through a differential neuron in the sub-network model, and determining a differential vehicle speed characteristic based on the implicit vehicle speed characteristic.

Wherein, for example, the implicit vehicle speed characteristic satisfies the formula:

wherein the content of the first and second substances,

representing the second in the current PID network model

In the sub-network model

The implicit vehicle speed characteristics corresponding to the implicit neurons,

denotes the first

An input neuron and

the weights between the individual hidden neurons. Wherein the content of the first and second substances,

is 1,2, 3.

Therein, exemplary, proportional vehicle speed characteristics

Satisfies the formula:

in this embodiment, the first in the current PID network model

The 1 st hidden neuron in the sub-network model is a proportional neuron.

Therein, exemplary, integral vehicle speed characteristics

Satisfies the formula:

in this embodiment, the first in the current PID network model

The 2 nd hidden neuron in the sub-network model is an integrating neuron.

Therein, exemplary, differential vehicle speed characteristics

Satisfies the formula:

in this embodiment, the first in the current PID network model

The 3 rd implicit neuron in the sub-network model is a differential neuron.

Specifically, the predicted cruise torque output by the output layer of the current PID network model is a weighted sum of a proportional vehicle speed feature, an integral vehicle speed feature and a differential vehicle speed feature. For example, the predicted cruise torque satisfies the formula:

wherein the content of the first and second substances,

is shown as

The predicted cruise torque output by the output neurons in the sub-network model,

indicating the second in the hidden layer

Weights between the hidden neurons and the output layer.

And S120, performing cruise control operation on the target vehicle based on the predicted cruise torque.

Specifically, when N =1, the accelerator opening of the target vehicle is adjusted based on the predicted cruise torque, so as to achieve the purpose of cruise control. Specifically, under the condition that N is not equal to 1, the average cruise torque is determined based on at least two predicted cruise torques, and the accelerator opening degree of the target vehicle is adjusted based on the average cruise torque so as to achieve the purpose of cruise control.

S130, judging whether the current iteration number meets a preset number threshold, if so, executing S110, and if not, executing S140.

For example, the preset number threshold may be 200 times.

Specifically, under the condition that the current iteration number does not meet a preset number threshold, the model parameters of the current PID network model need to be adjusted during each iteration, and the step of obtaining the current actual speed of the target vehicle is repeatedly executed based on the adjusted current PID network model. And under the condition that the current times meet a preset time threshold, the model parameters of the current PID network model are optimal, and the step of obtaining the current actual speed of the target vehicle is repeatedly executed directly on the basis of the current PID network model.

And S140, adjusting model parameters in the current PID network model based on the predicted cruising torque to obtain the adjusted current PID network model.

And S150, taking the adjusted current PID network model as a current PID network model, and executing S110.

Specifically, a set cruise torque corresponding to a set target vehicle speed is determined, and model parameters in the current PID network model are adjusted based on the predicted cruise torque and the set cruise torque to obtain an adjusted current PID network model. In an alternative embodiment, the model parameters include a current first weight between the input layer and the hidden layer and a current second weight between the hidden layer and the output layer.

On the basis of the above embodiment, optionally, after the current iteration number meets the preset number threshold, determining a cruising speed difference value based on a set target speed and at least one current actual speed; and under the condition that the cruising speed difference value exceeds a preset vehicle speed difference threshold value, repeatedly executing the step of adjusting the model parameters in the current PID network model based on the predicted cruising torque to obtain the adjusted current PID network model until the cruising speed difference value does not exceed the preset vehicle speed difference threshold value.

Specifically, the cruise vehicle speed difference may represent an absolute value of a difference between the set target vehicle speed and one current actual vehicle speed, or the cruise vehicle speed difference may be an average of absolute values of differences between the set target vehicle speed and at least two current actual vehicle speeds. For example, the cruise vehicle speed differential may be

km/h。

The cruise control method has the advantages that the problem that when the preset time threshold value is set too small, the deviation between the current cruise control speed controlled by the PID network model and the set target speed is large is solved, and the stability and the accuracy of the cruise control speed are further improved.

In the technical scheme of the embodiment, the output predicted cruise torque is obtained by acquiring the current actual speed of the target vehicle and inputting the current actual speed and the set target speed into the current PID network model, and based on the predicted cruise torque, performing a cruise control operation on the target vehicle, based on the predicted cruise torque in the case where the current iteration number does not satisfy a preset number threshold, adjusting model parameters in the current PID network model to obtain an adjusted current PID network model, taking the adjusted current PID network model as the current PID network model, and the step of obtaining the current actual speed of the target vehicle is repeatedly executed, so that the problem that a large amount of calibration work needs to be executed in the traditional cruise control method is solved, the calibration workload is reduced, and the adaptability of the cruise control method and the accuracy of the cruise control speed are improved.

Example two

Fig. 3 is a flowchart of a cruise control method according to a second embodiment of the present invention, where the present embodiment further details technical features of "adjusting model parameters in a current PID network model based on a predicted cruise torque to obtain an adjusted current PID network model" in the above embodiments. As shown in fig. 3, the method includes:

s210, obtaining the current actual speed of the target vehicle, and inputting the current actual speed and the set target speed into the current PID network model to obtain the output predicted cruise torque.

In the above embodiment, the proportional coefficient kp =1, the integral coefficient ki =1, and the differential coefficient kd =1 respectively correspond to the proportional neuron, the integral neuron, and the differential neuron in the hidden layer.

In an alternative embodiment, the proportional, integral and differential neurons in the hidden layer correspond to a proportional coefficient kp =1.5, an integral coefficient ki =0.5 and a differential coefficient kd =5, respectively. Accordingly, proportional vehicle speed characteristics of proportional neuron output

Satisfies the formula:

integral vehicle speed feature of integral neuron output

Satisfies the formula:

differential vehicle speed characterization of differential neuron output

Satisfies the formula:

the advantage of this arrangement is that the accuracy of the predicted cruise torque output by the current PID network model can be further improved.

And S220, performing cruise control operation on the target vehicle based on the predicted cruise torque.

And S230, judging whether the current iteration number meets a preset number threshold, if so, executing S210, and if not, executing S240.

S240, determining a cruise torque difference value based on the predicted cruise torque and the set cruise torque corresponding to the set target vehicle speed.

Wherein, illustratively, the cruise torque differential

Satisfies the formula:

wherein the content of the first and second substances,

indicating the set cruise torque.

And S250, determining a next first weight and a next second weight based on the cruise torque difference, the current first weight and the current second weight.

In an alternative embodiment, determining the next first weight and the next second weight based on the cruise torque difference, the current first weight, and the current second weight includes: determining a current first gradient based on the cruise torque difference, the learning rate and the current first weight, and determining a current second gradient based on the cruise torque difference, the learning rate and the current second weight; based on the current first gradient and the current second gradient, a next first weight and a next second weight are determined.

Wherein, for example, the next first weight satisfies the formula:

wherein the content of the first and second substances,

is shown as

At the time of the next iteration, the

First in the sub-network model

An input neuron and

the current first weight between the implicit neurons,

is shown as

At the time of the next iteration, the

First in the sub-network model

An input neuron and

the next first weight between the implicit neurons,

representing the scale factor, reflecting the learning rate in the training process of the current PID network model,

denotes the first

First in the sub-network model

An input neuron and

the current first gradient for each implied neuron.

Wherein, for example, the next second weight satisfies the formula:

wherein the content of the first and second substances,

is shown as

At the time of the next iteration, the

First in the sub-network model

A current second weight between the hidden neuron and the output neuron,

is shown as

At the time of the next iteration, the

First in the sub-network model

A next second weight between the hidden neuron and the output neuron,

is shown as

First in the sub-network model

A current second gradient of the number of hidden neurons corresponding to the output neuron.

In an alternative embodiment, determining the next first weight and the next second weight based on the current first gradient and the current second gradient comprises: constructing a current first momentum item based on the current first weight and the previous first weight, and determining a next first weight based on the current first gradient and the current first momentum item; and constructing a current second momentum item based on the current second weight and the previous second weight, and determining a next second weight based on the current second gradient and the current second momentum item.

Wherein, for example, the next first weight satisfies the formula:

wherein the content of the first and second substances,

a first momentum term is represented in the first image,

representing the momentum factor.

Wherein, for example, the next second weight satisfies the formula:

wherein the content of the first and second substances,

a second momentum term is represented as a function of,

to representA momentum factor.

And S260, constructing the adjusted current PID network model based on the next first weight and the next second weight.

And S270, taking the adjusted current PID network model as the current PID network model, and executing S210.

Fig. 4 is a schematic diagram of a variation curve of cruise control vehicle speeds at different set target vehicle speeds according to a second embodiment of the present invention. Specifically, the abscissa of the three coordinate systems is normalized time, and the ordinate is normalized vehicle speed. The normalized set target vehicle speeds corresponding to the three coordinate systems in fig. 4 are 0.7, 0.4, and 0.6, respectively, and the fluctuation curves in the three coordinate systems respectively represent the variation curves of the cruise control vehicle speeds at different set target vehicle speeds. Specifically, the iteration number is in a positive correlation with time. As can be seen from fig. 4, when the current iteration number does not exceed the preset number threshold, the cruise control vehicle speed is in a state of large fluctuation, and when the current iteration number exceeds the preset number threshold, the cruise control vehicle speed tends to be stable.

The technical scheme of the embodiment comprises the steps of determining a cruise torque difference value based on a predicted cruise torque and a set cruise torque corresponding to a set target vehicle speed; determining a next first weight and a next second weight based on the cruise torque difference, the current first weight and the current second weight; and constructing the adjusted current PID network model based on the next first weight and the next second weight, solving the problem of poor optimization effect of the current PID network model, improving the time for the cruise control vehicle speed to reach a stable state, reducing the training period of the current PID network model, and further improving the accuracy of the cruise control vehicle speed.

EXAMPLE III

Fig. 5 is a schematic structural diagram of a cruise control apparatus according to a third embodiment of the present invention. As shown in fig. 5, the apparatus includes: a predicted cruise torque determination module 310, a cruise control module 320, a PID neural network adjustment module 330, and a repeat execution module 340.

The predicted cruise torque determining module 310 is used for acquiring the current actual vehicle speed of the target vehicle, and inputting the current actual vehicle speed and the set target vehicle speed into the current PID network model to obtain the output predicted cruise torque;

a cruise control module 320 for performing a cruise control operation on a target vehicle based on the predicted cruise torque;

the PID neural network adjusting module 330 is configured to adjust a model parameter in the current PID network model based on the predicted cruise torque under the condition that the current iteration number does not meet a preset number threshold, so as to obtain an adjusted current PID network model;

and a repeated execution module 340, configured to use the adjusted current PID network model as a current PID network model, and repeatedly execute the step of obtaining the current actual vehicle speed of the target vehicle.

According to the technical scheme of the embodiment, the problem that a large amount of calibration work needs to be performed in a traditional cruise control method is solved, calibration workload is reduced, and adaptability of the cruise control method and accuracy of the cruise control speed are improved.

Based on the above embodiment, the predicted cruise torque determination module 310 is optionally specifically configured to:

outputting the input current actual vehicle speed and the set target vehicle speed to a hidden layer in the current PID network model through an input layer in the current PID network model;

outputting a proportional vehicle speed characteristic, an integral vehicle speed characteristic and a differential vehicle speed characteristic based on the input current actual vehicle speed and the set target vehicle speed through a hidden layer in the current PID network model;

and outputting the predicted cruising torque based on the input proportional vehicle speed characteristic, the integral vehicle speed characteristic and the differential vehicle speed characteristic through an output layer in the current PID network model.

Based on the above embodiment, optionally, the number structure between the number of input neurons included in the input layer, the number of hidden neurons included in the hidden layer, and the number of output neurons included in the output layer of the current PID network model is 2N × 3N × N, where N is an integer greater than or equal to 1.

On the basis of the foregoing embodiment, optionally, the model parameters include a current first weight between the input layer and the hidden layer and a current second weight between the hidden layer and the output layer, and accordingly, the PID neural network adjusting module 330 includes:

a cruise torque difference value determination unit for determining a cruise torque difference value based on the predicted cruise torque and a set cruise torque corresponding to a set target vehicle speed;

a next weight determination unit, configured to determine a next first weight and a next second weight based on the cruise torque difference, the current first weight, and the current second weight;

and the current PID network model adjusting unit is used for constructing an adjusted current PID network model based on the next first weight and the next second weight.

On the basis of the foregoing embodiment, optionally, the next weight determining unit includes:

a current gradient determination subunit, configured to determine a current first gradient based on the cruise torque difference, the learning rate, and the current first weight, and determine a current second gradient based on the cruise torque difference, the learning rate, and the current second weight;

and the next weight determining subunit is used for determining a next first weight and a next second weight based on the current first gradient and the current second gradient.

On the basis of the foregoing embodiment, optionally, the next weight determination subunit is specifically configured to:

constructing a current first momentum item based on the current first weight and the previous first weight, and determining a next first weight based on the current first gradient and the current first momentum item;

and constructing a current second momentum item based on the current second weight and the previous second weight, and determining a next second weight based on the current second gradient and the current second momentum item.

On the basis of the above embodiment, optionally, the apparatus further includes:

the cruise speed difference determining module is used for determining a cruise speed difference based on a set target speed and at least one current actual speed after the current iteration number meets a preset number threshold;

and under the condition that the cruising speed difference value exceeds the preset vehicle speed difference threshold value, repeatedly executing the step of adjusting the model parameters in the current PID network model based on the predicted cruising torque to obtain the adjusted current PID network model until the cruising speed difference value does not exceed the preset vehicle speed difference threshold value.

The cruise control device provided by the embodiment of the invention can execute the cruise control method provided by any embodiment of the invention, and has corresponding functional modules and beneficial effects of the execution method.

Example four

Fig. 6 is a schematic structural diagram of an electronic device according to a fourth embodiment of the present invention. The electronic device 10 is intended to represent various forms of digital computers, such as laptops, desktops, workstations, personal digital assistants, servers, blade servers, mainframes, and other appropriate computers. The electronic device may also represent various forms of mobile devices, such as personal digital assistants, cellular phones, smart phones, wearable devices (e.g., helmets, glasses, watches, etc.), and other similar computing devices. The components shown herein, their connections and relationships, and their functions, are meant to be exemplary only, and are not meant to limit implementations of the inventions described and/or claimed herein.

As shown in fig. 6, the electronic device 10 includes at least one processor 11, and a memory communicatively connected to the at least one processor 11, such as a Read Only Memory (ROM) 12, a Random Access Memory (RAM) 13, and the like, wherein the memory stores a computer program executable by the at least one processor, and the processor 11 can perform various suitable actions and processes according to the computer program stored in the Read Only Memory (ROM) 12 or the computer program loaded from a storage unit 18 into the Random Access Memory (RAM) 13. In the RAM 13, various programs and data necessary for the operation of the electronic apparatus 10 can also be stored. The processor 11, the ROM 12, and the RAM 13 are connected to each other via a bus 14. An input/output (I/O) interface 15 is also connected to bus 14.

A number of components in the electronic device 10 are connected to the I/O interface 15, including: an input unit 16 such as a keyboard, a mouse, or the like; an output unit 17 such as various types of displays, speakers, and the like; a storage unit 18 such as a magnetic disk, an optical disk, or the like; and a communication unit 19 such as a network card, modem, wireless communication transceiver, etc. The communication unit 19 allows the electronic device 10 to exchange information/data with other devices via a computer network such as the internet and/or various telecommunication networks.

The processor 11 may be a variety of general and/or special purpose processing components having processing and computing capabilities. Some examples of processor 11 include, but are not limited to, a Central Processing Unit (CPU), a Graphics Processing Unit (GPU), various specialized Artificial Intelligence (AI) computing chips, various processors running machine learning model algorithms, a Digital Signal Processor (DSP), and any suitable processor, controller, microcontroller, or the like. The processor 11 performs the various methods and processes described above, such as the cruise control method.

In some embodiments, the cruise control method may be implemented as a computer program tangibly embodied on a computer-readable storage medium, such as storage unit 18. In some embodiments, part or all of the computer program may be loaded and/or installed onto the electronic device 10 via the ROM 12 and/or the communication unit 19. When the computer program is loaded into RAM 13 and executed by processor 11, one or more steps of the cruise control method described above may be performed. Alternatively, in other embodiments, the processor 11 may be configured to perform the cruise control method by any other suitable means (e.g., by means of firmware).

Various implementations of the systems and techniques described here above may be implemented in digital electronic circuitry, integrated circuitry, Field Programmable Gate Arrays (FPGAs), Application Specific Integrated Circuits (ASICs), Application Specific Standard Products (ASSPs), system on a chip (SOCs), load programmable logic devices (CPLDs), computer hardware, firmware, software, and/or combinations thereof. These various embodiments may include: implemented in one or more computer programs that are executable and/or interpretable on a programmable system including at least one programmable processor, which may be special or general purpose, receiving data and instructions from, and transmitting data and instructions to, a storage system, at least one input device, and at least one output device.

The computer program for implementing the cruise control method of the present invention may be written in any combination of one or more programming languages. These computer programs may be provided to a processor of a general purpose computer, special purpose computer, or other programmable data processing apparatus, such that the computer programs, when executed by the processor, cause the functions/acts specified in the flowchart and/or block diagram block or blocks to be performed. A computer program can execute entirely on a machine, partly on a machine, as a stand-alone software package partly on a machine and partly on a remote machine or entirely on a remote machine or server.

EXAMPLE five

An embodiment of the present invention further provides a computer-readable storage medium, where computer instructions are stored, and the computer instructions are used to enable a processor to execute a cruise control method, where the method includes:

under the condition that the current iteration times do not meet a preset time threshold value, model parameters in the current PID network model are adjusted based on the predicted cruise torque, and the adjusted current PID network model is obtained;

In the context of the present invention, a computer-readable storage medium may be a tangible medium that can contain, or store a computer program for use by or in connection with an instruction execution system, apparatus, or device. A computer readable storage medium may include, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any suitable combination of the foregoing. Alternatively, the computer readable storage medium may be a machine readable signal medium. More specific examples of a machine-readable storage medium would include an electrical connection based on 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.

To provide for interaction with a user, the systems and techniques described here can be implemented on an electronic device having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to a user; and a keyboard and a pointing device (e.g., a mouse or a trackball) by which a user can provide input to the electronic device. Other kinds of devices may also be used to provide for interaction with a user; for example, feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user may be received in any form, including acoustic, speech, or tactile input.

The systems and techniques described here can be implemented in a computing system that includes a back-end component (e.g., as a data server), or that includes a middleware component (e.g., an application server), or that includes a front-end component (e.g., a user computer having a graphical user interface or a web browser through which a user can interact with an implementation of the systems and techniques described here), or any combination of such back-end, middleware, or front-end components. The components of the system can be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: local Area Networks (LANs), Wide Area Networks (WANs), blockchain networks, and the internet.

The computing system may include clients and servers. A client and server are generally remote from each other and typically interact through a communication network. The relationship of client and server arises by virtue of computer programs running on the respective computers and having a client-server relationship to each other. The server can be a cloud server, also called a cloud computing server or a cloud host, and is a host product in a cloud computing service system, so that the defects of high management difficulty and weak service expansibility in the traditional physical host and VPS service are overcome.

It should be understood that various forms of the flows shown above may be used, with steps reordered, added, or deleted. For example, the steps described in the present invention may be executed in parallel, sequentially, or in different orders, and are not limited herein as long as the desired results of the technical solution of the present invention can be achieved.

The above-described embodiments should not be construed as limiting the scope of the invention. It should be understood by those skilled in the art that various modifications, combinations, sub-combinations and substitutions may be made in accordance with design requirements and other factors. Any modification, equivalent replacement, and improvement made within the spirit and principle of the present invention should be included in the protection scope of the present invention.

Claims

1. A cruise control method, characterized by comprising:

2. The method of claim 1, wherein inputting the current actual vehicle speed and the set target vehicle speed into a current PID network model results in an output predicted cruise torque, comprising:

outputting a proportional vehicle speed characteristic, an integral vehicle speed characteristic and a differential vehicle speed characteristic through a hidden layer in the current PID network model based on the input current actual vehicle speed and the set target vehicle speed;

and outputting the predicted cruising torque based on the input proportional vehicle speed characteristic, integral vehicle speed characteristic and differential vehicle speed characteristic through an output layer in the current PID network model.

3. The method according to claim 2, wherein the number structure between the number of input neurons contained in the input layer, the number of hidden neurons contained in the hidden layer, and the number of output neurons contained in the output layer of the current PID network model is 2N x 3N x N, where N is an integer greater than or equal to 1.

4. The method according to claim 2, wherein the model parameters include a current first weight between the input layer and the hidden layer and a current second weight between the hidden layer and the output layer, and accordingly, the adjusting the model parameters in the current PID network model based on the predicted cruise torque to obtain an adjusted current PID network model includes:

determining a cruise torque difference value based on the predicted cruise torque and a set cruise torque corresponding to the set target vehicle speed;

determining a next first weight and a next second weight based on the cruise torque difference, the current first weight and the current second weight;

and constructing the adjusted current PID network model based on the next first weight and the next second weight.

5. The method of claim 4, wherein determining a next first weight and a next second weight based on the cruise torque difference, the current first weight, and the current second weight comprises:

determining a current first gradient based on the cruise torque difference, a learning rate, and the current first weight, and determining a current second gradient based on the cruise torque difference, the learning rate, and the current second weight;

determining a next first weight and a next second weight based on the current first gradient and the current second gradient.

6. The method of claim 5, wherein determining the next first weight and the next second weight based on the current first gradient and the current second gradient comprises:

constructing a current first momentum item based on the current first weight and a previous first weight, and determining a next first weight based on the current first gradient and the current first momentum item;

7. The method according to any one of claims 1-6, characterized in that the method comprises:

determining a cruising speed difference value based on a set target speed and at least one current actual speed after the current iteration times meet a preset time threshold value;

and under the condition that the cruising speed difference value exceeds a preset speed difference threshold value, repeatedly executing the step of adjusting model parameters in the current PID network model based on the predicted cruising torque to obtain the adjusted current PID network model until the cruising speed difference value does not exceed the preset speed difference threshold value.

8. A cruise control apparatus, characterized by comprising:

9. An electronic device, characterized in that the electronic device comprises:

at least one processor; and

a memory communicatively coupled to the at least one processor; wherein, the first and the second end of the pipe are connected with each other,

the memory stores a computer program executable by the at least one processor to enable the at least one processor to perform the cruise control method according to any of claims 1-7.

10. A computer-readable storage medium storing computer instructions for causing a processor to perform the cruise control method according to any one of claims 1-7 when executed.