Vehicle adjusting method and device based on driving habits

Abstract

The invention discloses a driving habit-based vehicle adjustment method and device, and the method comprises the steps: controlling an image collection device disposed in a vehicle to collect a face image of a driver at a driving position, and carrying out the recognition of the face image, and determining the identity information of the driver, adjusting the initial parameters of the vehicle according to the identity information of the driver; in the vehicle driving process, driving data of the vehicle are collected in real time, and the driving data are input into a pre-trained driving behavior analysis model to analyze the current driving behavior of a driver; and according to the current driving behavior, determining an adjustment item which needs to adjust the vehicle, and adjusting the vehicle through the adjustment item. The problem of low vehicle adjustment accuracy caused by lack of accuracy in driving behavior evaluation in the prior art is solved.

Description

Technical Field

[0001] The present invention relates to the field of vehicle technology, and in particular to a vehicle adjustment method and device based on driving habits. Background Art

[0002] Driving behavior analysis technology plays an important role in modern traffic management and vehicle safety. With the development of intelligent transportation systems, understanding and predicting driving behavior has become the key to improving road safety, optimizing traffic flow and enhancing driving experience.

[0003] Traditional rule-based or threshold-based driving behavior assessment methods may lack accuracy. For example, simply evaluating driving habits based on hard braking or acceleration may ignore the environmental factors and individual differences behind driving, resulting in low vehicle adjustment accuracy. Summary of the invention

[0004] In view of this, an object of the present invention is to provide a vehicle adjustment method and device based on driving habits, aiming to solve the problem of low accuracy of vehicle adjustment caused by lack of accuracy in driving behavior assessment in the prior art.

[0005] The embodiment of the present invention is implemented as follows: A vehicle adjustment method based on driving habits, the method comprising: Controlling an image acquisition device disposed in the vehicle to acquire a facial image of a driver in a driving position, identifying the facial image to determine the identity information of the driver, and adjusting initial parameters of the vehicle according to the identity information of the driver; During the driving process of the vehicle, the driving data of the vehicle is collected in real time, and the driving data is input into a pre-trained driving behavior analysis model to analyze the current driving behavior of the driver; An adjustment item that needs to be adjusted for the vehicle is determined according to the current driving behavior, and the vehicle is adjusted using the adjustment item.

[0006] Furthermore, in the above-mentioned vehicle adjustment method based on driving habits, the step of controlling the image acquisition device disposed in the vehicle to acquire the facial image of the driver in the driving seat comprises: Recognize the image captured by the image acquisition device to obtain a face area and a background area; The background area is removed, and the face area is fully scanned to obtain pixel values ​​corresponding to a plurality of pixel points in the human body area, and the face image is obtained according to the pixel points and the corresponding pixel values.

[0007] Furthermore, in the above-mentioned vehicle adjustment method based on driving habits, the step of identifying the facial image to determine the identity information of the driver includes: Detecting a number of target facial features contained in the face image, and searching for a corresponding target image in a preset image database according to the target facial features. When the target image is consistent with the facial image, the target identity information corresponding to the target image is used as the identity information of the driver.

[0008] Furthermore, in the above-mentioned vehicle adjustment method based on driving habits, the training process of the driving behavior analysis model includes: Collecting the vehicle historical travel data and the corresponding historical driving behavior to form a training data set, performing data cleaning and preprocessing on the training data set, and standardizing and normalizing the training data set; Establishing a neural network, and inputting the training data set into the neural network for deep learning; The parameter weights in the neural network are updated by back propagation until the error of the neural network is within a preset threshold to obtain the driving behavior analysis model.

[0009] Furthermore, in the above-mentioned vehicle adjustment method based on driving habits, after the step of updating the parameter weights in the neural network by back propagation until the error of the neural network is within a preset threshold to obtain the driving behavior analysis model, the step further includes: Extract the driving behavior analysis model from the training data set according to preset rules A validation set is used, and the validation set is used to perform a validation test on the trained driving behavior analysis model.

[0010] Furthermore, in the above-mentioned vehicle adjustment method based on driving habits, the step of extracting a verification set of the driving behavior analysis model from the training data set according to a preset rule comprises: Extract the driving behavior analysis model from the training data set according to a preset ratio Validation set; The training data set is divided into multiple equal parts, one of which is used as a validation set of the training data set, and the rest is used as a training set of the training data set.

[0011] Furthermore, in the above-mentioned vehicle adjustment method based on driving habits, before the step of controlling the image acquisition device disposed in the vehicle to acquire the facial image of the driver in the driving seat, the step further includes: When it is detected that the vehicle is started, a request to start driving habit behavior detection is sent. When an instruction to start driving habit behavior detection is received, the step of controlling the image acquisition device arranged in the vehicle to capture the facial image of the driver in the driving seat is executed.

[0012] Another object of the present invention is to provide a vehicle adjustment device based on driving habits, the device comprising: A collection module, used to control an image collection device arranged in the vehicle to collect a facial image of the driver in the driving seat, identify the facial image to determine the identity information of the driver, and adjust the initial parameters of the vehicle according to the identity information of the driver; An input module, used to collect the driving data of the vehicle in real time during the driving process of the vehicle, and input the driving data into a pre-trained driving behavior analysis model to analyze the current driving behavior of the driver; An adjustment module is used to determine adjustment items that need to be adjusted for the vehicle according to the current driving behavior, and adjust the vehicle through the adjustment items.

[0013] Another object of the present invention is to provide a readable storage medium having a computer program stored thereon, wherein the program, when executed by a processor, implements the steps of any one of the methods described above.

[0014] Another object of the present invention is to provide an electronic device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the steps of the above method when executing the program.

[0015] The present invention controls an image acquisition device arranged in a vehicle to acquire a facial image of the driver in the driving position, identifies the facial image to determine the identity information of the driver, and adjusts the initial parameters of the vehicle according to the identity information of the driver; during the driving process of the vehicle, the driving data of the vehicle is acquired in real time, and the driving data is input into a pre-trained driving behavior analysis model to analyze the current driving behavior of the driver; the adjustment items that need to be adjusted for the vehicle are determined according to the current driving behavior, and the vehicle is adjusted through the adjustment items. By using the driving behavior analysis model to analyze multiple driving data, the accuracy of vehicle adjustment is improved through pattern recognition and analysis of driving behavior, and the vehicle is adjusted, which solves the problem of low accuracy of vehicle adjustment caused by lack of accuracy in driving behavior evaluation in the prior art. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] Figure 1 It is a flowchart of a vehicle adjustment method based on driving habits in a first embodiment of the present invention; Figure 2 Schematic diagram of the structure of a vehicle adjustment device based on driving habits in the third embodiment of the present invention.

[0017] The following specific implementation manner will further illustrate the present invention in conjunction with the above-mentioned drawings. DETAILED DESCRIPTION

[0018] In order to facilitate the understanding of the present invention, the present invention will be described more fully below with reference to the relevant drawings. Several embodiments of the present invention are given in the drawings. However, the present invention can be implemented in many different forms and is not limited to the embodiments described herein. On the contrary, the purpose of providing these embodiments is to make the disclosure of the present invention more thorough and comprehensive.

[0019] It should be noted that when an element is referred to as being "fixed to" another element, it may be directly on the other element or there may be a central element. When an element is considered to be "connected to" another element, it may be directly connected to the other element or there may be a central element at the same time. The terms "vertical", "horizontal", "left", "right" and similar expressions used herein are for illustrative purposes only.

[0020] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as those commonly understood by those skilled in the art to which the present invention belongs. The terms used herein in the specification of the present invention are only for the purpose of describing specific embodiments and are not intended to limit the present invention. The term "and / or" used herein includes any and all combinations of one or more of the related listed items.

[0021] The following will describe in detail how to improve the accuracy of vehicle adjustment by improving the accuracy of driving behavior in conjunction with specific embodiments and drawings.

[0022] Embodiment 1 See also Figure 1 , shown is a vehicle adjustment method based on driving habits in a first embodiment of the present invention, the method includes steps S10~S12.

[0023] Step S10, controlling the image acquisition device disposed in the vehicle to acquire the facial image of the driver in the driving seat, and identifying the facial image to determine the identity information of the driver, and adjusting the initial parameters of the vehicle according to the identity information of the driver.

[0024] Among them, a camera is arranged in the car. After the driver enters the cab, the camera is controlled to capture the image of the driver to determine the driver's identity information, and then adjust the corresponding vehicle parameters according to the previous settings, such as seat height, brake force, steering wheel height, etc.; start sensors, such as acceleration sensors and gyroscopes.

[0025] Specifically, the step of controlling the image acquisition device disposed in the vehicle to acquire the facial image of the driver in the driving seat includes: Recognize the image captured by the image acquisition device to obtain a face area and a background area; The background area is removed, and the face area is fully scanned to obtain pixel values ​​corresponding to a plurality of pixel points in the human body area, and the face image is obtained according to the pixel points and the corresponding pixel values.

[0026] Among them, when performing image acquisition, it is not necessarily possible to completely capture only the driver's facial image. In order to further improve the accuracy of recognition, after the image is acquired, the image is detected to detect the facial area and the background area, and the background area is removed to extract the facial area. Then, the facial area is fully scanned to obtain the pixel values ​​corresponding to several pixel points in the human body area, and finally the facial image is obtained based on the pixel points and the corresponding pixel values.

[0027] Furthermore, the step of identifying the facial image to determine the identity information of the driver includes: Detecting a number of target facial features contained in the face image, and searching for a corresponding target image in a preset image database according to the target facial features. When the target image is consistent with the facial image, the target identity information corresponding to the target image is used as the identity information of the driver.

[0028] The driver's facial image is stored in advance, and then the recognized facial image is searched in a pre-stored image database to find the corresponding target image, and the driver corresponding to the facial image is determined based on the image.

[0029] Step S11, during the driving process of the vehicle, the driving data of the vehicle is collected in real time, and the driving data is input into a pre-trained driving behavior analysis model to analyze the current driving behavior of the driver.

[0030] Among them, driving data includes motion status, acceleration, braking force, steering angle, etc. The driving behavior analysis model is a model trained based on driving data that masters the internal logic between driving data and driving behavior. The driver's behavior can be analyzed through the driving behavior analysis model, and multiple factors of driving data are comprehensively considered during the analysis. A large amount of driving data is analyzed and modeled through machine learning algorithms to improve the accuracy of driving behavior recognition and reduce the possibility of misjudgment and missed judgment, so that driving behavior can be identified more accurately.

[0031] Step S12, determining adjustment items that need to be adjusted for the vehicle according to the current driving behavior, and adjusting the vehicle according to the adjustment items. Among them, after determining the current driving behavior of the driver, the items that need to be adjusted can be determined according to the current driving behavior of the driver, so that the vehicle can be adjusted according to the adjustment items. For example, if emergency braking is detected and there may be danger, the vehicle will be dynamically adjusted to reduce the risk of the owner: identify whether the vehicle has a large-angle turn, and dynamically adjust the seat belt tightness and seat angle to reduce the risk of the owner. When it is recognized that the vehicle changes lanes or turns, and the turn signal is not turned on, the turn signal will be automatically turned on for the owner. If it is determined that the driving behavior is abnormal, a language broadcast will be made to remind the owner of the potential risks and safe driving. Traditional driving habit assessment methods are often based on general rules or experience, while the driving behavior analysis model using a machine learning algorithm in the embodiment of the present invention can give personalized feedback based on the specific driving behavior of each driver.

[0032] In summary, the vehicle adjustment method based on driving habits in the above-mentioned embodiments of the present invention controls the image acquisition device arranged in the vehicle to acquire the facial image of the driver in the driving seat, and identifies the facial image to determine the identity information of the driver, and adjusts the initial parameters of the vehicle according to the identity information of the driver; during the driving process of the vehicle, the driving data of the vehicle is collected in real time, and the driving data is input into the pre-trained driving behavior analysis model to analyze the current driving behavior of the driver; the adjustment items that need to be adjusted for the vehicle are determined according to the current driving behavior, and the vehicle is adjusted through the adjustment items. By using the driving behavior analysis model to analyze multiple driving data, the accuracy of vehicle adjustment is improved through pattern recognition and analysis of driving behavior, and the vehicle is adjusted, and the problem of low accuracy of vehicle adjustment caused by lack of accuracy in driving behavior evaluation in the prior art is solved.

[0033] Embodiment 2 The embodiment of the present invention also proposes a vehicle adjustment method based on driving habits. The vehicle adjustment method based on driving habits proposed in this embodiment is different from the vehicle adjustment method based on driving habits proposed in the first embodiment in that: Before step S10, the method further includes: When it is detected that the vehicle is started, a request to start driving habit behavior detection is sent. When an instruction to start driving habit behavior detection is received, the step of controlling the image acquisition device arranged in the vehicle to capture the facial image of the driver in the driving seat is executed.

[0034] Among them, after the vehicle is started, the driver enters the car, the driving behavior detection system starts, and sends a request to start driving habit behavior detection, asking the car owner whether to authorize the driving behavior detection behavior. When the instruction to start driving habit behavior detection is received, the car owner agrees to start driving habit behavior detection and turns on the camera to collect the driver's facial image.

[0035] In addition, in some optional embodiments of the present invention, the training process of the driving behavior analysis model includes: Collecting the vehicle historical travel data and the corresponding historical driving behavior to form a training data set, performing data cleaning and preprocessing on the training data set, and standardizing and normalizing the training data set; Establishing a neural network, and inputting the training data set into the neural network for deep learning; The parameter weights in the neural network are updated by back propagation until the error of the neural network is within a preset threshold to obtain the driving behavior analysis model.

[0036] Among them, you can choose to fill missing values ​​or delete missing data. Identify and process outliers to prevent them from affecting model training. And standardize and normalize the data. The learning algorithm of the neural network is adopted. The basic principle of the neural network is to simulate the function of the human brain nervous system. Through the connection and calculation of multiple nodes (also called neurons), the combination and output of nonlinear models are realized. The basic principle of the neural network has three characteristics: nonlinear function: the neural network adopts nonlinear functions, so that it can simulate the complex system of the real world; parameter weight: the neural network adopts parameter weight to achieve automatic learning and pattern recognition. These weights can be used to measure the interaction between each neuron, and they are constantly adjusted as the training progresses; back propagation algorithm: the neural network adopts the back propagation algorithm to better adjust the weights. The back propagation algorithm adjusts the weights by backpropagating the error from the output layer to the input layer, so that the network can better fit the data. Specifically, after the training data set is input, the neural network receives data from the input layer, calculates through the hidden layer, and finally outputs the prediction result. Each neuron has an activation function to increase the nonlinear ability of the network.

[0037] The neural network calculates the error based on the predicted result and the true label, and then back-propagates the error layer by layer from the output layer to the input layer, updating the weights and biases in sequence, so that the network's prediction ability gradually improves. The back-propagation algorithm usually uses the gradient descent method or its variants to optimize the network's parameters.

[0038] In addition, in some optional embodiments of the present invention, after the model training is completed, a verification set of the driving behavior analysis model can be extracted from the training data set according to preset rules, and the trained driving behavior analysis model can be verified and tested using the verification set. Specifically, the verification set of the driving behavior analysis model is extracted from the training data set according to a preset ratio; wherein the training data set is divided into multiple equal parts, one of which is used as the verification set of the training data set, and the rest is used as the training set of the training data set.

[0039] The vehicle adjustment method based on driving habits in the above-mentioned embodiment of the present invention controls the image acquisition device arranged in the vehicle to acquire the facial image of the driver in the driving seat, and identifies the facial image to determine the identity information of the driver, and adjusts the initial parameters of the vehicle according to the identity information of the driver; during the driving process of the vehicle, the driving data of the vehicle is collected in real time, and the driving data is input into the pre-trained driving behavior analysis model to analyze the current driving behavior of the driver; the adjustment items that need to be adjusted for the vehicle are determined according to the current driving behavior, and the vehicle is adjusted according to the adjustment items. By analyzing multiple driving data using the driving behavior analysis model, the accuracy of vehicle adjustment is improved through pattern recognition and analysis of driving behavior, and the vehicle is adjusted, and the problem of low accuracy of vehicle adjustment caused by lack of accuracy in driving behavior evaluation in the prior art is solved.

[0040] Embodiment 3 See also Figure 2 , shown is a vehicle adjustment device based on driving habits proposed in the third embodiment of the present invention, the device comprising: The acquisition module 100 is used to control the image acquisition device arranged in the vehicle to acquire the facial image of the driver in the driving seat, identify the facial image to determine the identity information of the driver, and adjust the initial parameters of the vehicle according to the identity information of the driver; An input module 200 is used to collect the driving data of the vehicle in real time during the driving process of the vehicle, and input the driving data into a pre-trained driving behavior analysis model to analyze the current driving behavior of the driver; The adjustment module 300 is used to determine the adjustment items that need to be adjusted for the vehicle according to the current driving behavior, and adjust the vehicle according to the adjustment items.

[0041] Furthermore, in the above-mentioned vehicle adjustment device based on driving habits, the step of controlling the image acquisition device disposed in the vehicle to acquire the facial image of the driver in the driving seat comprises: Recognize the image captured by the image acquisition device to obtain a face area and a background area; The background area is removed, and the face area is fully scanned to obtain pixel values ​​corresponding to a plurality of pixel points in the human body area, and the face image is obtained according to the pixel points and the corresponding pixel values.

[0042] Furthermore, in the above-mentioned vehicle adjustment device based on driving habits, the step of identifying the facial image to determine the identity information of the driver includes: Detecting a number of target facial features contained in the face image, and searching for a corresponding target image in a preset image database according to the target facial features. When the target image is consistent with the facial image, the target identity information corresponding to the target image is used as the identity information of the driver. Furthermore, in the above-mentioned vehicle adjustment device based on driving habits, the training process of the driving behavior analysis model includes: Collecting the vehicle historical travel data and the corresponding historical driving behavior to form a training data set, performing data cleaning and preprocessing on the training data set, and standardizing and normalizing the training data set; Establishing a neural network, and inputting the training data set into the neural network for deep learning; The parameter weights in the neural network are updated by back propagation until the error of the neural network is within a preset threshold to obtain the driving behavior analysis model.

[0043] Furthermore, the above-mentioned vehicle adjustment device based on driving habits, wherein the step of updating the parameter weights in the neural network by back propagation until the error of the neural network is within a preset threshold to obtain the driving behavior analysis model further includes: Extract the driving behavior analysis model from the training data set according to preset rules A validation set is used, and the validation set is used to perform a validation test on the trained driving behavior analysis model.

[0044] Furthermore, in the above-mentioned vehicle adjustment device based on driving habits, the step of extracting a verification set of the driving behavior analysis model from the training data set according to a preset rule comprises: Extract the driving behavior analysis model from the training data set according to a preset ratio Validation set; The training data set is divided into multiple equal parts, one of which is used as a validation set of the training data set, and the rest is used as a training set of the training data set.

[0045] Furthermore, in the above-mentioned vehicle adjustment device based on driving habits, before the step of controlling the image acquisition device disposed in the vehicle to acquire the facial image of the driver in the driving seat, the step further includes: When it is detected that the vehicle is started, a request to start driving habit behavior detection is sent. When an instruction to start driving habit behavior detection is received, the step of controlling the image acquisition device arranged in the vehicle to capture the facial image of the driver in the driving seat is executed.

[0046] The functions or operation steps implemented when the above modules are executed are substantially the same as those in the above method embodiments, and will not be repeated here.

[0047] Embodiment 4 Another aspect of the present invention further provides a readable storage medium having a computer program stored thereon, wherein the program, when executed by a processor, implements the steps of the method described in the above-mentioned embodiments 1 to 2.

[0048] The technical features of the above embodiments can be combined arbitrarily. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0049] Those skilled in the art will appreciate that the logic and / or steps represented in the flowchart or otherwise described herein, for example, may be considered as an ordered list of executable instructions for implementing logical functions, and may be specifically implemented in any computer-readable storage medium for use by an instruction execution system, device or apparatus (such as a computer-based system, a system including a processor, or other system that can fetch instructions from an instruction execution system, device or apparatus and execute instructions), or in conjunction with such instruction execution systems, devices or apparatuses. For purposes of this specification, a "computer-readable storage medium" may be any device that can contain, store, communicate, propagate or transmit a program for use by an instruction execution system, device or apparatus, or in conjunction with such instruction execution systems, devices or apparatuses.

[0050] More specific examples (a non-exhaustive list) of computer-readable storage media include the following: an electrical connection with one or more wires (electronic device), a portable computer disk case (magnetic device), a random access memory (RAM), a read-only memory (ROM), an erasable and programmable read-only memory (EPROM or flash memory), an optical fiber device, and a portable compact disk read-only memory (CDROM). In addition, the computer-readable storage medium may even be a paper or other suitable medium on which the program is printed, since the program may be obtained electronically, for example, by optically scanning the paper or other medium, followed by editing, deciphering or, if necessary, processing in another suitable manner, and then stored in a computer memory.

[0051] It should be understood that the various parts of the present invention can be implemented by hardware, software, firmware or a combination thereof. In the above-mentioned embodiments, multiple steps or methods can be implemented by software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented by hardware, as in another embodiment, it can be implemented by any one of the following technologies known in the art or a combination thereof: a discrete logic circuit having a logic gate circuit for implementing a logic function for a data signal, a dedicated integrated circuit having a suitable combination of logic gate circuits, a programmable gate array (PGA), a field programmable gate array (FPGA), etc.

[0052] In the description of this specification, the description with reference to the terms "one embodiment", "some embodiments", "examples", "specific examples", or "some examples" means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present invention. In this specification, the schematic representation of the above terms does not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described may be combined in any one or more embodiments or examples in a suitable manner.

[0053] The above-mentioned embodiments only express several implementation methods of the present invention, and the description thereof is relatively specific and detailed, but it cannot be understood as limiting the scope of the patent of the present invention. It should be pointed out that, for ordinary technicians in this field, several variations and improvements can be made without departing from the concept of the present invention, which all belong to the protection scope of the present invention. Therefore, the protection scope of the patent of the present invention shall be subject to the attached claims.

Claims

1. A vehicle adjustment method based on driving habits, characterized in that: The method comprises: Controlling an image acquisition device disposed in the vehicle to acquire a facial image of a driver in a driving position, identifying the facial image to determine the identity information of the driver, and adjusting initial parameters of the vehicle according to the identity information of the driver; During the driving process of the vehicle, the driving data of the vehicle is collected in real time, and the driving data is input into a pre-trained driving behavior analysis model to analyze the current driving behavior of the driver; An adjustment item that needs to be adjusted for the vehicle is determined according to the current driving behavior, and the vehicle is adjusted using the adjustment item.

2. The vehicle adjustment method based on driving habits according to claim 1, characterized in that: The step of controlling the image acquisition device disposed in the vehicle to acquire the facial image of the driver in the driving seat comprises: Recognize the image captured by the image acquisition device to obtain a face area and a background area; The background area is removed, and the face area is fully scanned to obtain pixel values ​​corresponding to a plurality of pixel points in the human body area, and the face image is obtained according to the pixel points and the corresponding pixel values.

3. The vehicle adjustment method based on driving habits according to claim 1, characterized in that: The step of identifying the facial image and determining the identity information of the driver comprises: Detecting a number of target facial features contained in the face image, and searching for a corresponding target image in a preset image database according to the target facial features. When the target image is consistent with the facial image, the target identity information corresponding to the target image is used as the identity information of the driver.

4. The vehicle adjustment method based on driving habits according to claim 1, characterized in that: The training process of the driving behavior analysis model includes: Collecting the vehicle historical travel data and the corresponding historical driving behavior to form a training data set, performing data cleaning and preprocessing on the training data set, and standardizing and normalizing the training data set; Establishing a neural network, and inputting the training data set into the neural network for deep learning; The parameter weights in the neural network are updated by back propagation until the error of the neural network is within a preset threshold to obtain the driving behavior analysis model.

5. The vehicle adjustment method based on driving habits according to claim 4, characterized in that: The step of updating the parameter weights in the neural network by back propagation until the error of the neural network is within a preset threshold to obtain the driving behavior analysis model further includes: Extract the driving behavior analysis model from the training data set according to preset rules A validation set is used, and the validation set is used to perform a validation test on the trained driving behavior analysis model.

6. The vehicle adjustment method based on driving habits according to claim 5, characterized in that: The step of extracting a verification set of the driving behavior analysis model from the training data set according to a preset rule comprises: Extract the driving behavior analysis model from the training data set according to a preset ratio Validation set; The training data set is divided into multiple equal parts, one of which is used as a validation set of the training data set, and the rest is used as a training set of the training data set.

7. The vehicle adjustment method based on driving habits according to claim 1, characterized in that: Before the step of controlling the image acquisition device arranged in the vehicle to acquire the facial image of the driver in the driving seat, the step further includes: When it is detected that the vehicle is started, a request to start driving habit behavior detection is sent. When an instruction to start driving habit behavior detection is received, the step of controlling the image acquisition device arranged in the vehicle to capture the facial image of the driver in the driving seat is executed.

8. A vehicle adjustment device based on driving habits, characterized in that: The device comprises: an acquisition module, used to control an image acquisition device disposed in the vehicle to acquire a facial image of a driver in a driving position, identify the facial image to determine the identity information of the driver, and adjust the initial parameters of the vehicle according to the identity information of the driver; An input module, used to collect the driving data of the vehicle in real time during the driving process of the vehicle, and input the driving data into a pre-trained driving behavior analysis model to analyze the current driving behavior of the driver; An adjustment module is used to determine adjustment items that need to be adjusted for the vehicle according to the current driving behavior, and adjust the vehicle through the adjustment items.

9. A storage medium having a computer program stored thereon, characterized in that: When the program is executed by a processor, the steps of the method according to any one of claims 1 to 6 are implemented.

10. An electronic device, characterized in that: The method comprises a memory, a processor and a computer program stored in the memory and executable on the processor, wherein when the processor executes the program, the steps of the method according to any one of claims 1 to 6 are implemented.

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