CN114348008A - Vehicle control method and device, electronic equipment and storage medium - Google Patents
Vehicle control method and device, electronic equipment and storage medium Download PDFInfo
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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
Technical Field
The present application relates to the field of vehicle control technologies, and in particular, to a vehicle control method and apparatus, an electronic device, and a storage medium.
Background
In the process of driving the vehicle on roads with different working conditions, the proper gear directly influences the dynamic property, the economical efficiency and the riding comfort of the vehicle.
Therefore, how to select a reasonable gear in a semi-automatic or automatic driving vehicle is an urgent technical problem to be solved.
Disclosure of Invention
The application aims to provide a vehicle control method and device, an electronic device and a storage medium.
A first aspect of the present application provides a vehicle control method including:
acquiring a plurality of running parameter values of a target vehicle under a current running condition;
inputting the plurality of running parameter values into a pre-constructed gear determining model to obtain a target gear corresponding to the target vehicle under the current running condition;
and controlling the target vehicle to run according to the target gear.
In one possible implementation manner, in the vehicle control method provided by the present application, 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.
In one possible implementation, in the above vehicle control method provided by the present application, 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.
In one possible implementation manner, in the vehicle control method provided by the present application, 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.
In one possible implementation manner, in the vehicle control method provided by the present application, the vehicle control method further includes:
acquiring a plurality of verified running parameter values when a verified vehicle runs under different working conditions and corresponding optimal gears when the verified vehicle runs 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.
In one possible implementation manner, in the vehicle control method provided by the present application, the driving parameter value includes: the control system comprises measured values of a whole vehicle sensor, input values of whole vehicle analog signals, transmission control information, engine control information and gear lever control information.
In one possible implementation manner, in the vehicle control method provided by the present application, the vehicle completion sensor includes: 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 apparatus 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.
In one possible implementation manner, the vehicle control device provided in the present application further includes: the model training module is used for training to obtain the gear determination model according to the following modes:
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.
In one possible implementation, in the vehicle control device provided by the present application, 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.
In a possible implementation manner, in the vehicle control device provided by the present application, the model training module is specifically configured to:
and reversely correcting the parameter values of the output layer and the hidden layer of the neural network based on the prediction error quantity.
In a possible implementation manner, in the vehicle control device provided in the present application, the model training module is further specifically configured to:
acquiring a plurality of verified running parameter values when a verified vehicle runs under different working conditions and corresponding optimal gears when the verified vehicle runs 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.
In one possible implementation manner, in the vehicle control device provided by the present application, the driving parameter value includes: the control system comprises measured values of a whole vehicle sensor, input values of whole vehicle analog signals, transmission control information, engine control information and gear lever control information.
In one possible implementation manner, in the vehicle control device provided in the present application, the vehicle completion sensor includes: 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: memory, a processor and a computer program stored on the memory and executable on the processor, the processor executing the computer program when executing the computer program to perform the method of the first aspect of the application.
A fourth aspect of the present application provides a computer readable medium having computer readable instructions stored thereon which are executable by a processor to implement the method of the first aspect of the present application.
Compared with the prior art, the vehicle control method, the vehicle control device, the electronic equipment and the storage medium provided by the application acquire a plurality of running parameter values of the target vehicle under the current running working condition; 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 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.
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 only for purposes of illustrating the preferred embodiments and are not to be construed as limiting the application. Also, like reference numerals are used to refer to like parts throughout the drawings. In the drawings:
FIG. 1 illustrates a flow chart of a vehicle control method provided herein;
FIG. 2 illustrates a flow chart of a gear determination model training process provided herein;
FIG. 3 shows a schematic diagram of a neural network provided herein;
FIG. 4 is a flow chart illustrating a training process for a particular gear determination model provided herein;
FIG. 5 illustrates a schematic diagram of a vehicle control arrangement provided herein;
fig. 6 shows a schematic diagram of the flow of the information signal between the various components provided in the present application.
Detailed Description
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 to the embodiments set forth herein. Rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the scope of the disclosure to those skilled in the art.
It is to be noted that, unless otherwise specified, technical or scientific terms used herein shall have the ordinary meaning as understood by those skilled in the art to which this application belongs.
In addition, the terms "first" and "second", etc. are used to distinguish different objects, rather than to describe a particular order. Furthermore, the terms "include" and "have," as well as any variations thereof, are intended to cover non-exclusive inclusions. For example, a process, method, system, article, or apparatus that comprises a list of steps or elements is not limited to only those steps or elements listed, but may alternatively include other steps or elements not listed, or inherent to such process, method, article, or apparatus.
The embodiment of the application provides 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.
Referring to fig. 1, which shows a flowchart of a vehicle control method provided in some embodiments of the present application, as shown in fig. 1, the vehicle control method may include the following steps:
s101, obtaining a plurality of running parameter values of a target vehicle under a current running condition;
s102, 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 S103, controlling the target vehicle to run according to the target gear.
The subject of execution of the above method may be a Vehicle VCU (Vehicle control unit).
Specifically, the driving parameter values include: the control system comprises a measured value of a whole vehicle sensor, an input value of a whole vehicle analog signal, gearbox control information, engine control information and gear lever control information.
Specifically, whole car sensor includes: a curve angle sensor, a vehicle speed sensor, a gradient sensor, a temperature sensor, a pressure sensor and a gravity sensor.
The vehicle analog signal may be a pedal opening (pedal voltage value), such as a brake pedal opening, an accelerator pedal opening, or the like.
The Transmission Control information may be obtained from a TCU (Transmission Control Unit), the engine Control information may be obtained from an engine ECU (Electronic Control Unit), and the shift lever Control information may be obtained from a shift lever actuator as a shift lever position, such as a forward shift position and a reverse shift position. Fig. 6 is a schematic diagram showing the flow of information signals exchanged among the various components.
Therefore, the plurality of driving parameters may include vehicle driving parameters such as vehicle speed, pedal opening, load, gradient, curve angle, engine speed, gear position, temperature, pressure, and the like, which are factors influencing optimal gears of the vehicle under different conditions.
In the present application, a gear determination model is obtained by pre-training based on the influence factor of the optimal gear, as shown in fig. 2, the gear determination model is obtained by training in the following manner:
s201, obtaining a plurality of sample running parameter values of a sample vehicle running under different working conditions and corresponding optimal gears running under each working condition;
s202, inputting a plurality of corresponding sample running parameter values of the sample vehicle running under each working condition into a neural network to obtain a corresponding predicted gear of the sample vehicle running under the working condition;
s203, determining a prediction error amount of the neural network based on the optimal gear and the predicted gear of the sample vehicle in running under each working condition;
and S204, 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 running under each working condition into the neural network until the predicted error amount of the neural network is smaller than the preset error amount or the training times reach the preset times, and obtaining the gear determination model.
Specifically, the sample travel parameter values may include vehicle travel parameter values such as vehicle speed, pedal opening, load, grade, curve angle, engine speed, shift lever position, temperature, pressure, and the like.
Specifically, the neural network may adopt a BP (Back Propagation) neural network. As shown in fig. 3, the neural network includes an input layer, a hidden layer, and an output layer.
The step S202 specifically includes 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.
In the step S204, adjusting the parameter value of the neural network based on the prediction error amount specifically 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.
The training method of the gear determination model further comprises the following model verification steps:
acquiring a plurality of verified running parameter values when a verified vehicle runs under different working conditions and corresponding optimal gears when the verified vehicle runs 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.
As shown in fig. 4, the training process of the gear determination model is described in detail by taking a BP neural network as an example.
Determining an influence factor, namely determining driving parameters influencing optimal gears under different working conditions;
obtaining sample data, namely obtaining driving parameter values influencing optimal gears under different working conditions to form the sample data;
and (3) creating a BP neural network model, initializing the BP neural network model, setting an error upper limit value, and setting the number of neurons in different layers.
And training the model by using a large amount of sample data, inputting the sample driving parameter values corresponding to the optimal gears under different working conditions into a neural network input layer, and training the neural network model.
In the process of deciding the optimal gear by 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, reverse error correction is carried out on the neural network model, and the weight and the threshold are adjusted backwards in a mode of minimizing errors.
Output layer node error correction:
k is equal to {1,2, …, m }. Wherein, TkRepresenting the ideal output value of the output layer node,
representing the output layer node output value.
Hidden layer node error correction:
wherein,
representing hidden layer node output value, WjkRepresenting the hidden layer weights.
And correcting the weights and the threshold values of the neurons in different layers through the calculated node error correction quantity.
Weight and threshold modification between the output layer and the hidden layer:
θk(t+1)＝θk(t)+βδk。
weight and threshold modification between the hidden layer and the input samples:
θj(t+1)＝θj(t)+βδj。
and finally, training and verifying the model by using a large amount of optimal gear real vehicle data under different working conditions to obtain an optimal gear selection control strategy suitable for complex driving conditions.
By the intelligent gear selection control method, the gear shifting of the vehicle is not limited by a specific gear shifting line (a calibrated working condition and gear corresponding curve), the optimal gear under the current working condition can be decided according to the specific running working condition, and the economy, the dynamic property and the comfort of the whole vehicle can be better considered.
Firstly, the target gear selection does not simply depend on a gear shifting line, but is determined by a nonlinear relation determined by a plurality of influence factors, so that the intelligent gear selection control method can be more suitable for complex and variable actual driving conditions; secondly, the method can obtain the corresponding relation between the working condition and the optimal gear only through training of sample data aiming at different hardware types, so that the universality and the expansibility are strong.
According to the vehicle control method provided by the embodiment of the application, a plurality of running parameter values of a target vehicle under the current running working condition are obtained; 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.
In the foregoing embodiments, a vehicle control method is provided, and correspondingly, the present application also provides a vehicle control device, which may be implemented by software, hardware, or a combination of software and hardware. For example, the vehicle control device may comprise integrated or separate functional modules or units to perform the corresponding steps of the above-described methods. Referring to fig. 5, a schematic diagram of a vehicle control device provided in some embodiments of the present application is shown. Since the apparatus embodiments are substantially similar to the method embodiments, they are described in a relatively simple manner, and reference may be made to some of the descriptions of the method embodiments for relevant points. The device embodiments described below are merely illustrative.
As shown in fig. 5, the vehicle control device 10 may include:
the system comprises an obtaining module 101, configured to obtain multiple driving parameter values of a target vehicle under a current driving condition;
the determining module 102 is configured to input the multiple driving parameter values into a gear determining model that is constructed in advance, so as to obtain a target gear corresponding to the target vehicle in a current driving condition;
and the control module 103 is used for controlling the target vehicle to run according to the target gear.
In one possible implementation manner, the vehicle control device provided in the present application further includes: the model training module is used for training to obtain the gear determination model according to the following modes:
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.
In one possible implementation, in the vehicle control device provided by the present application, 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.
In a possible implementation manner, in the vehicle control device provided by the present application, the model training module is specifically configured to:
and reversely correcting the parameter values of the output layer and the hidden layer of the neural network based on the prediction error quantity.
In a possible implementation manner, in the vehicle control device provided in the present application, the model training module is further specifically configured to:
acquiring a plurality of verified running parameter values when a verified vehicle runs under different working conditions and corresponding optimal gears when the verified vehicle runs 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.
In one possible implementation manner, in the vehicle control device provided by the present application, the driving parameter value includes: the control system comprises measured values of a whole vehicle sensor, input values of whole vehicle analog signals, transmission control information, engine control information and gear lever control information.
In one possible implementation manner, in the vehicle control device provided in the present application, the vehicle completion sensor includes: 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 that this application embodiment provided can decide the best gear under the current operating mode according to specific operating mode, and the vehicle goes with best gear can be better compromise whole car economic nature, dynamic property and travelling comfort.
The embodiment of the present application further provides an electronic device corresponding to the vehicle control method provided in the foregoing embodiment, where the electronic device may be a vehicle VCU, a mobile phone, a notebook computer, a tablet computer, a desktop computer, or the like, so as to execute the vehicle control method.
The electronic equipment provided by the embodiment of the application and the vehicle control method provided by the embodiment of the application have the same beneficial effects as the method adopted, operated or realized by the electronic equipment.
The present embodiment also provides a computer-readable storage medium corresponding to the vehicle control method provided by the foregoing embodiment, and a computer program (i.e., a program product) is stored thereon, and when being executed by a processor, the computer program will execute the vehicle control method provided by any of the foregoing embodiments.
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, which are not described in detail herein.
The computer-readable storage medium provided by the above-mentioned embodiment of the present application and the vehicle control method provided by the embodiment of the present application have the same beneficial effects as the method adopted, operated or implemented by the application program stored in the computer-readable storage medium.
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, 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 and/or flowchart illustration, and combinations of blocks in the block diagrams and/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.
Finally, it should be noted that: the above embodiments are only used for illustrating the technical solutions of the present application, and not for limiting the same; although the present application has been described in detail with reference to the foregoing embodiments, it should be understood by those of ordinary skill in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some or all of the technical features may be equivalently replaced; the modifications and the substitutions do not cause the essence of the corresponding technical solutions to depart from the scope of the technical solutions of the embodiments of the present application, and the corresponding technical solutions are all covered in the claims and the specification 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.
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