CN114348008A - A vehicle control method, device, electronic device and storage medium - Google Patents

Abstract

The application provides a vehicle control method, a vehicle control device, an electronic device and a storage medium, wherein the method comprises the following steps: acquiring a plurality of running parameter values of a target vehicle under a current running condition; inputting the plurality of driving parameter values into a pre-constructed gear determining model to obtain a target gear corresponding to the target vehicle under the current driving condition; and controlling the target vehicle to run according to the target gear. Compared with the prior art, the optimal gear under the current working condition can be decided according to the specific running working condition through the scheme, and the vehicle can better give consideration to the economy, the dynamic property and the comfort of the whole vehicle when running at the optimal gear.

Description

A vehicle control method, device, electronic device and storage medium

technical field

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

Background technique

When the vehicle is running on roads with different working conditions, the appropriate gear will directly affect the power, economy and ride comfort of the vehicle.

Therefore, in a semi-autonomous or autonomous vehicle, how to select a reasonable gear is a technical problem that needs to be solved urgently.

SUMMARY OF THE INVENTION

The purpose of this application is to provide a vehicle control method, device, electronic device and storage medium.

A first aspect of the present application provides a vehicle control method, including:

Obtain multiple driving parameter values of the target vehicle under the current driving condition;

Inputting the plurality of driving parameter values into a pre-built gear determination model to obtain a target gear corresponding to the target vehicle under current driving conditions;

The target vehicle is controlled to travel according to the target gear.

In a possible implementation, in the above-mentioned vehicle control method provided by the present application, the gear determination model is obtained by training in the following manner:

Obtain multiple sample driving parameter values when the sample vehicle is running under different working conditions, and the corresponding optimal gear when driving under each working condition;

Inputting a plurality of sample driving parameter values corresponding to the sample vehicle when driving under each working condition into the neural network to obtain the corresponding predicted gear when the sample vehicle is driving under the working condition;

determining the prediction error amount of the neural network based on the optimal gear and the predicted gear when the sample vehicle is running under each operating condition;

After adjusting the parameter values of the neural network based on the prediction error amount, return to the step of inputting a plurality of sample driving parameter values corresponding to the sample vehicle driving under each working condition into the neural network, until the neural network The predicted error amount is less than the preset error amount, or after the number of training times reaches the preset number of times, the gear determination model is obtained.

In a possible implementation manner, in the above-mentioned vehicle control method provided in the present application, the neural network includes an input layer, a hidden layer and an output layer;

Described inputting a plurality of sample driving parameter values corresponding to the sample vehicle when driving under each working condition into the neural network, to obtain the corresponding predicted gear when the sample vehicle is driving under the working condition, including:

The multiple sample driving parameter values are input into the input layer of the neural network, and the intermediate value of the output of the hidden layer is obtained through the transfer function of the neurons between the input layer and the hidden layer in the neural network;

Based on the intermediate value of the output of the hidden layer and the transfer function of the neurons between the hidden layer and the output layer in the neural network, the predicted level of the output of the output layer is obtained.

In a possible implementation manner, in the above-mentioned vehicle control method provided by the present application, the adjustment of the parameter value of the neural network based on the prediction error amount includes:

Based on the prediction error amount, the parameter values of the output layer and the hidden layer of the neural network are reversely corrected.

In a possible implementation, in the above-mentioned vehicle control method provided by the present application, the vehicle control method further includes:

Obtain multiple verification driving parameter values when the verification vehicle is running under different working conditions, and the corresponding optimal gear when driving under each working condition;

Inputting a plurality of verification driving parameter values corresponding to the verification vehicle when driving under each working condition into the neural network to obtain the corresponding predicted gear position of the verification vehicle when driving under the operating condition;

Based on the optimal gear and the predicted gear when the verification vehicle is running under each working condition, it is determined that the predicted error amount of the neural network is less than the preset error amount, and the gear determination model is obtained.

In a possible implementation manner, in the above-mentioned vehicle control method provided by the present application, the driving parameter values include: the measured value of the vehicle sensor, the input value of the vehicle simulation signal, the transmission control information, the engine control information and shift lever control information.

In a possible implementation manner, in the above-mentioned vehicle control method provided by the present application, the vehicle sensors include: a curve angle sensor, a vehicle speed sensor, a gradient sensor, a temperature sensor, a pressure sensor, and a gravity sensor.

A second aspect of the present application provides a vehicle control device, comprising:

an acquisition module for acquiring multiple driving parameter values of the target vehicle under the current driving condition;

a determination module, configured to input the plurality of driving parameter values into a pre-built gear determination model to obtain a target gear corresponding to the target vehicle under current driving conditions;

The control module is configured to control the target vehicle to drive according to the target gear.

In a possible implementation manner, the above-mentioned vehicle control device provided by the present application further includes: a model training module, configured to obtain the gear determination model by training in the following manner:

Obtain multiple sample driving parameter values when the sample vehicle is running under different working conditions, and the corresponding optimal gear when driving under each working condition;

Inputting a plurality of sample driving parameter values corresponding to the sample vehicle when driving under each working condition into the neural network to obtain the corresponding predicted gear when the sample vehicle is driving under the working condition;

determining the prediction error amount of the neural network based on the optimal gear and the predicted gear when the sample vehicle is running under each operating condition;

After adjusting the parameter values of the neural network based on the prediction error amount, return to the step of inputting a plurality of sample driving parameter values corresponding to the sample vehicle driving under each working condition into the neural network, until the neural network The predicted error amount is less than the preset error amount, or after the number of training times reaches the preset number of times, the gear determination model is obtained.

In a possible implementation manner, in the above-mentioned vehicle control device provided in the present application, the neural network includes an input layer, a hidden layer and an output layer;

Described inputting a plurality of sample driving parameter values corresponding to the sample vehicle when driving under each working condition into the neural network, to obtain the corresponding predicted gear when the sample vehicle is driving under the working condition, including:

The multiple sample driving parameter values are input into the input layer of the neural network, and the intermediate value of the output of the hidden layer is obtained through the transfer function of the neurons between the input layer and the hidden layer in the neural network;

Based on the intermediate value of the output of the hidden layer and the transfer function of the neurons between the hidden layer and the output layer in the neural network, the predicted level of the output of the output layer is obtained.

In a possible implementation, in the above-mentioned vehicle control device provided by the present application, the model training module is specifically used for:

Based on the prediction error amount, the parameter values of the output layer and the hidden layer of the neural network are reversely corrected.

In a possible implementation, in the above-mentioned vehicle control device provided by the present application, the model training module is also specifically used for:

Obtain multiple verification driving parameter values when the verification vehicle is running under different working conditions, and the corresponding optimal gear when driving under each working condition;

Inputting a plurality of verification driving parameter values corresponding to the verification vehicle when driving under each working condition into the neural network to obtain the corresponding predicted gear position of the verification vehicle when driving under the operating condition;

Based on the optimal gear and the predicted gear when the verification vehicle is running under each working condition, it is determined that the predicted error amount of the neural network is less than the preset error amount, and the gear determination model is obtained.

In a possible implementation manner, in the above-mentioned vehicle control device provided in the present application, the driving parameter values include: measurement values of sensors of the entire vehicle, input values of analog signals of the entire vehicle, transmission control information, and engine control information and shift lever control information.

In a possible implementation manner, in the above-mentioned vehicle control device provided in the present application, the vehicle sensors include: a curve angle sensor, a vehicle speed sensor, a gradient sensor, a temperature sensor, a pressure sensor, and a gravity sensor.

A third aspect of the present application provides an electronic device, comprising: a memory, a processor, and a computer program stored on the memory and executable on the processor, the processor executing the computer program to implement The method described in the first aspect of the present application.

A fourth aspect of the present application provides a computer-readable medium having computer-readable instructions stored thereon, where the computer-readable instructions can be executed by a processor to implement the method described in the first aspect of the present application.

Compared with the prior art, the vehicle control method, device, electronic device and storage medium provided by the present application can obtain multiple driving parameter values of the target vehicle under the current driving condition; The target gear corresponding to the current driving condition of the target vehicle is obtained; the target vehicle is controlled to drive according to the target gear. Compared with the prior art, through this solution, the optimal gear under the current operating conditions can be determined according to the specific driving conditions, and the vehicle can be driven in the optimal gear to better take into account the economy, power and performance of the vehicle. comfort.

Description of drawings

Various other advantages and benefits will become apparent to those of ordinary skill in the art upon reading the following detailed description of the preferred embodiments. The drawings are for purposes of illustrating preferred embodiments only and are not to be considered limiting of the application. Also, the same reference numerals are used for the same parts throughout the drawings. In the attached image:

FIG. 1 shows a flowchart of a vehicle control method provided by the present application;

Fig. 2 shows the flow chart of the gear determination model training process provided by the present application;

Fig. 3 shows the schematic diagram of the neural network provided by this application;

Fig. 4 shows the flow chart of the training process of a specific gear determination model provided by the present application;

FIG. 5 shows a schematic diagram of a vehicle control device provided by the present application;

FIG. 6 shows a schematic diagram of the signal flow of interactive information between various components provided in this application.

Detailed ways

Exemplary embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. While exemplary embodiments of the present disclosure are shown in the drawings, it should be understood that the present disclosure may be embodied in various forms and should not be limited by the embodiments set forth herein. Rather, these embodiments are provided so that the present disclosure will be more thoroughly understood, and will fully convey the scope of the present disclosure to those skilled in the art.

It should be noted that, unless otherwise specified, the technical or scientific terms used in the present application should have the usual meanings understood by those skilled in the art to which the present application belongs.

In addition, the terms "first" and "second" etc. are used to distinguish different objects, and not to describe a particular order. Furthermore, the terms "comprising" and "having", and any variations thereof, are intended to cover a non-exclusive inclusion. For example, a process, method, system, product or device comprising a series of steps or units is not limited to the listed steps or units, but optionally also includes unlisted steps or units, or optionally also includes For other steps or units inherent to these processes, methods, products or devices.

Embodiments of the present application provide a vehicle control method and device, an electronic device, and a computer-readable storage medium, which are described below with reference to the accompanying drawings.

Please refer to FIG. 1 , which shows a flowchart of a vehicle control method provided by some embodiments of the present application. As shown in FIG. 1 , the vehicle control method may include the following steps:

S101, acquiring multiple driving parameter values of the target vehicle under the current driving condition;

S102, inputting the multiple driving parameter values into a pre-built gear determination model to obtain a target gear corresponding to the target vehicle under current driving conditions;

S103. Control the target vehicle to travel according to the target gear.

The execution subject of the above method may be a vehicle VCU (Vehicle control unit, vehicle controller).

Specifically, the driving parameter values include: the measurement value of the vehicle sensor, the input value of the vehicle simulation signal, the transmission control information, the engine control information and the shift lever control information.

Specifically, the vehicle sensors include: a curve angle sensor, a vehicle speed sensor, a gradient sensor, a temperature sensor, a pressure sensor, and a gravity sensor.

The vehicle simulation signal can be pedal opening (pedal voltage value), such as brake pedal opening, accelerator pedal opening and so on.

Transmission control information can be obtained from TCU (Transmission Control Unit, transmission controller), engine control information can be obtained from engine ECU (Electronic Control Unit, electronic controller), shift lever control information can be executed from the shift lever The gear lever position is obtained at the mechanism, such as forward gear, reverse gear. Figure 6 is a schematic diagram of the signal flow of the interactive information between the various components.

Therefore, the above-mentioned multiple driving parameters may include vehicle driving parameters such as vehicle speed, pedal opening, load, slope, curve angle, engine speed, gear lever position, temperature, pressure, etc. The impact factor of the excellent grade.

In the present application, a gear determination model is obtained by pre-training based on the influence factor of the above-mentioned optimal gear. As shown in FIG. 2 , the gear determination model is obtained by training in the following manner:

S201, acquiring multiple sample driving parameter values when the sample vehicle is driving under different working conditions, and the corresponding optimal gear when driving under each working condition;

S202, input a plurality of sample driving parameter values corresponding to the sample vehicle when driving under each working condition into the neural network, and obtain the corresponding predicted gear when the sample vehicle is driving under this working condition;

S203, determine the prediction error amount of the neural network based on the optimal gear and the predicted gear when the sample vehicle is running under each operating condition;

S204. After adjusting the parameter values of the neural network based on the prediction error amount, return to the step of inputting a plurality of sample driving parameter values corresponding to the sample vehicle driving under each working condition into the neural network, until the The gear determination model is obtained after the prediction error amount of the neural network is less than the preset error amount, or after the number of training times reaches the preset number of times.

Specifically, the sample driving parameter values may include vehicle driving parameter values such as vehicle speed, pedal opening, load, slope, curve angle, engine speed, gear lever position, temperature, and pressure.

Specifically, the above-mentioned neural network may adopt a BP (Back Propagation, back propagation) neural network. As shown in Figure 3, the neural network includes an input layer, a hidden layer and an output layer.

The above step S202 specifically includes the following steps:

The multiple sample driving parameter values are input into the input layer of the neural network, and the intermediate value of the output of the hidden layer is obtained through the transfer function of the neurons between the input layer and the hidden layer in the neural network;

Based on the intermediate value of the output of the hidden layer and the transfer function of the neurons between the hidden layer and the output layer in the neural network, the predicted level of the output of the output layer is obtained.

Adjusting the parameter values of the neural network based on the prediction error in the above-mentioned step S204 specifically includes: performing reverse correction on the parameter values of the output layer and the hidden layer of the neural network based on the prediction error.

The training method for the above gear determination model further includes the following model verification steps:

Obtain multiple verification driving parameter values when the verification vehicle is running under different working conditions, and the corresponding optimal gear when driving under each working condition;

Inputting a plurality of verification driving parameter values corresponding to the verification vehicle when driving under each working condition into the neural network to obtain the corresponding predicted gear position of the verification vehicle when driving under the operating condition;

Based on the optimal gear and the predicted gear when the verification vehicle is running under each working condition, it is determined that the predicted error amount of the neural network is less than the preset error amount, and the gear determination model is obtained.

As shown in Figure 4, taking the BP neural network as an example, the training process of the above gear determination model is introduced in detail.

Determine the influencing factors, that is, determine the driving parameters that affect the optimal gear under different working conditions;

Obtain sample data, that is, obtain the driving parameter values that affect the optimal gear under different working conditions, and form sample data;

To create a BP neural network model, first initialize the BP neural network model, set the upper limit of the error, and set the number of neurons in different layers.

A large amount of sample data is used to train the model, and the sample driving parameter values corresponding to the optimal gears under different working conditions are input to the input layer of the neural network to train the neural network model.

In the process of deciding the optimal gear by the forward operation, if the difference between the actually calculated optimal gear value and the ideal gear value in the sample data is larger than the set error range, the neural network model will be reversed for error. Correction, the weights and thresholds are adjusted backwards in a way that minimizes the error.

k∈{1,2,…,m}。其 中，T_k表示输出层节点理想输出值，

表示输出层节点输出值。Output layer node error correction:

k∈{1,2,…,m}. Among them, T _k represents the ideal output value of the output layer node,

Represents the output value of the output layer node.

其中，

表示隐 含层节点输出值，W_jk表示隐含层权值。Error correction of hidden layer nodes:

in,

represents the output value of the hidden layer node, and W _jk represents the hidden layer weight.

The weights and thresholds of neurons in different layers are corrected through the calculated node error correction.

θ_k(t+1)＝θ_k(t)+βδ_k。Weight and threshold correction between output layer and hidden layer:

θ _k (t+1)=θ _k (t)+βδ _k .

θ_j(t+1)＝θ_j(t)+βδ_j。Weight and threshold correction between hidden layer and input samples:

θ _j (t+1)=θ _j (t)+βδ _j .

Finally, a large number of optimal gear real vehicle data under different working conditions are used to train and verify the model, and the optimal gear selection control strategy for complex driving conditions is obtained.

Through this intelligent gear selection control method, the shift of the vehicle is no longer limited by a specific shift line (the curve corresponding to the calibrated operating condition and the gear position), and the optimal driving condition under the current operating condition can be determined according to the specific driving condition. The optimal gear can better take into account the economy, power and comfort of the vehicle.

First, the target gear selection is no longer purely dependent on the shift line, but is determined by a nonlinear relationship determined by many influencing factors, so this intelligent gear selection control method is more suitable for complex and changeable actual driving conditions; second, The method can obtain the corresponding relationship between the working condition and the optimal gear only through the training of sample data for different hardware selection, so it has strong versatility and scalability.

The vehicle control method provided by the embodiment of the present application obtains multiple driving parameter values of the target vehicle under the current driving condition; inputs the multiple driving parameter values into a pre-built gear determination model, and obtains The target gear corresponding to the current driving condition; the target vehicle is controlled to drive according to the target gear. Compared with the prior art, through this solution, the optimal gear under the current operating conditions can be determined according to the specific driving conditions, and the vehicle can travel in the optimal gear to better take into account the economy, power and performance of the vehicle. comfort.

In the above-mentioned embodiments, a vehicle control method is provided, and correspondingly, the present application also provides a vehicle control device, which can be implemented by software, hardware or a combination of software and hardware. For example, the vehicle control device may include integrated or separate functional modules or units to perform corresponding steps in the above-described methods. Please refer to FIG. 5, which shows a schematic diagram of a vehicle control device provided by some embodiments of the present application. Since the apparatus embodiments are basically similar to the method embodiments, the description is relatively simple, and reference may be made to some descriptions of the method embodiments for related parts. The apparatus embodiments described below are merely illustrative.

As shown in FIG. 5 , the vehicle control device 10 may include:

an obtaining module 101, configured to obtain a plurality of driving parameter values of the target vehicle under the current driving condition;

A determination module 102, configured to input the plurality of driving parameter values into a pre-built gear determination model to obtain a target gear corresponding to the target vehicle under the current driving condition;

The control module 103 is configured to control the target vehicle to drive according to the target gear.

In a possible implementation manner, the above-mentioned vehicle control device provided by the present application further includes: a model training module, configured to obtain the gear determination model by training in the following manner:

Obtain multiple sample driving parameter values when the sample vehicle is running under different working conditions, and the corresponding optimal gear when driving under each working condition;

Inputting a plurality of sample driving parameter values corresponding to the sample vehicle when driving under each working condition into the neural network to obtain the corresponding predicted gear when the sample vehicle is driving under the working condition;

determining the prediction error amount of the neural network based on the optimal gear and the predicted gear when the sample vehicle is running under each operating condition;

After adjusting the parameter values of the neural network based on the prediction error amount, return to the step of inputting a plurality of sample driving parameter values corresponding to the sample vehicle driving under each working condition into the neural network, until the neural network The predicted error amount is less than the preset error amount, or after the number of training times reaches the preset number of times, the gear determination model is obtained.

In a possible implementation manner, in the above-mentioned vehicle control device provided in the present application, the neural network includes an input layer, a hidden layer and an output layer;

Described inputting a plurality of sample driving parameter values corresponding to the sample vehicle when driving under each working condition into the neural network, to obtain the corresponding predicted gear when the sample vehicle is driving under the working condition, including:

The multiple sample driving parameter values are input into the input layer of the neural network, and the intermediate value of the output of the hidden layer is obtained through the transfer function of the neurons between the input layer and the hidden layer in the neural network;

Based on the intermediate value of the output of the hidden layer and the transfer function of the neurons between the hidden layer and the output layer in the neural network, the predicted level of the output of the output layer is obtained.

In a possible implementation, in the above-mentioned vehicle control device provided by the present application, the model training module is specifically used for:

Based on the prediction error amount, the parameter values of the output layer and the hidden layer of the neural network are reversely corrected.

In a possible implementation, in the above-mentioned vehicle control device provided by the present application, the model training module is also specifically used for:

Obtain multiple verification driving parameter values when the verification vehicle is running under different working conditions, and the corresponding optimal gear when driving under each working condition;

Inputting a plurality of verification driving parameter values corresponding to the verification vehicle when driving under each working condition into the neural network to obtain the corresponding predicted gear position of the verification vehicle when driving under the operating condition;

Based on the optimal gear and the predicted gear when the verification vehicle is running under each working condition, it is determined that the predicted error amount of the neural network is less than the preset error amount, and the gear determination model is obtained.

In a possible implementation manner, in the above-mentioned vehicle control device provided in the present application, the driving parameter values include: measured values of sensors of the entire vehicle, input values of analog signals of the entire vehicle, transmission control information, and engine control information and shift lever control information.

In a possible implementation manner, in the above-mentioned vehicle control device provided in the present application, the vehicle sensors include: a curve angle sensor, a vehicle speed sensor, a gradient sensor, a temperature sensor, a pressure sensor, and a gravity sensor.

The vehicle control device provided by the embodiment of the present application can decide the optimal gear under the current working condition according to the specific driving condition, and the vehicle can better take into account the economy, power and comfort of the whole vehicle when running in the optimal gear. sex.

The embodiments of the present application further provide an electronic device corresponding to the vehicle control method provided by the foregoing embodiments, the electronic device may be a vehicle VCU, a mobile phone, a notebook computer, a tablet computer, a desktop computer, etc., to perform the above-mentioned vehicle control method.

The electronic device provided by the embodiment of the present application and the vehicle control method provided by the embodiment of the present application are based on the same inventive concept, and have the same beneficial effects as the method adopted, operated or realized.

Embodiments of the present application also provide a computer-readable storage medium corresponding to the vehicle control method provided by the foregoing embodiments, on which a computer program (ie, a program product) is stored, and when the computer program is run by a processor, the The vehicle control method provided by any of the foregoing embodiments is executed.

It should be noted that examples of the computer-readable storage medium may also include, but are not limited to, phase-change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random Access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other optical and magnetic storage media will not be repeated here.

The computer-readable storage medium provided by the above-mentioned embodiments of the present application and the vehicle control method provided by the embodiments of the present application are based on the same inventive concept, and have the same beneficial effects as the methods used, run or implemented by the stored application programs.

It should be noted that the flowchart and block diagrams in the Figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods and computer program products according to various embodiments of the present application. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of code that contains one or more functions for implementing the specified logical function(s). executable instructions. 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 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 is also noted that each block of the block diagrams and/or flowchart illustrations, and combinations of blocks in the block diagrams and/or flowchart illustrations, can be implemented in dedicated hardware-based systems that perform the specified functions or actions , or can be implemented in a combination of dedicated hardware and computer instructions.

Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present application, but not to limit them; although the present application has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that: The technical solutions described in the foregoing embodiments can still be modified, or some or all of the technical features thereof can be equivalently replaced; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the technical solutions of the embodiments of the present application. scope, which shall be included in the scope of the claims and description of the present application.

Claims (10)

1. A vehicle control method characterized by comprising:

acquiring a plurality of running parameter values of a target vehicle under a current running condition;

inputting the plurality of driving parameter values into a pre-constructed gear determining model to obtain a target gear corresponding to the target vehicle under the current driving condition;

and controlling the target vehicle to run according to the target gear.

2. The vehicle control method according to claim 1, characterized in that the gear determination model is trained in the following manner:

obtaining a plurality of sample running parameter values of a sample vehicle running under different working conditions and a corresponding optimal gear when the sample vehicle runs under each working condition;

inputting a plurality of sample running parameter values corresponding to the running of the sample vehicle under each working condition into a neural network to obtain a corresponding predicted gear when the sample vehicle runs under the working condition;

determining a prediction error amount of the neural network based on an optimal gear and a predicted gear when the sample vehicle runs under each working condition;

and after the parameter values of the neural network are adjusted based on the predicted error amount, returning to the step of inputting a plurality of corresponding sample running parameter values of the sample vehicle in running under each working condition into the neural network until the predicted error amount of the neural network is smaller than a preset error amount or the training times reach preset times, and obtaining the gear determination model.

3. The vehicle control method according to claim 2, characterized in that the neural network includes an input layer, a hidden layer, and an output layer;

the step of inputting a plurality of sample running parameter values corresponding to the sample vehicle running under each working condition into a neural network to obtain a corresponding predicted gear when the sample vehicle runs under the working condition comprises the following steps:

inputting the plurality of sample driving parameter values into an input layer of the neural network, and obtaining an intermediate value output by a hidden layer through a transfer function of a neuron between the input layer and the hidden layer in the neural network;

and obtaining a prediction gear output by the output layer based on the intermediate value output by the hidden layer and the transfer function of the neuron between the hidden layer and the output layer in the neural network.

4. The vehicle control method according to claim 3, wherein the adjusting the parameter value of the neural network based on the prediction error amount includes:

and reversely correcting the parameter values of the output layer and the hidden layer of the neural network based on the prediction error quantity.

5. The vehicle control method according to claim 3 or 4, characterized by further comprising:

acquiring a plurality of verified driving parameter values when the verified vehicle drives under different working conditions and corresponding optimal gears when the verified vehicle drives under each working condition;

inputting a plurality of corresponding verified running parameter values of the verified vehicle running under each working condition into a neural network to obtain a corresponding predicted gear of the verified vehicle running under the working condition;

and obtaining the gear determination model under the condition that the prediction error amount of the neural network is determined to be smaller than a preset error amount on the basis of the optimal gear and the predicted gear when the verified vehicle runs under each working condition.

6. The vehicle control method according to claim 1, characterized in that the running parameter value includes:

the control system comprises measured values of a whole vehicle sensor, input values of whole vehicle analog signals, gearbox control information, engine control information and gear lever control information.

7. The vehicle control method according to claim 6, wherein the entire vehicle sensor includes:

a curve angle sensor, a vehicle speed sensor, a gradient sensor, a temperature sensor, a pressure sensor and a gravity sensor.

8. A vehicle control apparatus characterized by comprising:

the system comprises an acquisition module, a control module and a control module, wherein the acquisition module is used for acquiring a plurality of running parameter values of a target vehicle under a current running condition;

the determining module is used for inputting the plurality of driving parameter values into a gear determining model which is constructed in advance to obtain a target gear corresponding to the target vehicle under the current driving condition;

and the control module is used for controlling the target vehicle to run according to the target gear.

9. An electronic device, comprising: memory, processor and computer program stored on the memory and executable on the processor, characterized in that the processor executes when executing the computer program to implement the method according to any of claims 1 to 7.

10. A computer-readable storage medium having computer-readable instructions stored thereon, the computer-readable instructions being executable by a processor to implement the method of any one of claims 1 to 7.

Images