A driving mode switching method and device, electronic equipment and storage medium

Abstract

Embodiments of the present application provide a driving mode switching method and device, electronic equipment and storage medium, the method comprising: generating a standard database, the standard database comprising a plurality of working conditions, each working condition corresponding to different historical road condition parameters and driving modes; obtaining the current car road condition parameters; obtaining the similarity between the current car road condition parameters and the historical road condition parameters corresponding to the plurality of working conditions in the standard database; determining the next driving mode from the plurality of working conditions according to the similarity; and switching to the next driving mode. The above embodiments can realize automatic switching of the driving mode.

Description

Technical Field

[0001] This application relates to the field of new energy vehicle technology, and more specifically, to a driving mode switching method, device, electronic device, and storage medium. Background Technology

[0002] Currently, vehicle driving mode switching is primarily based on the driver selecting the mode via the AVNT (Autonomous Driving Mode Unit). Modes without AVNT / AV configuration are switched via a long press on the I-Pedal. Therefore, automatic mode switching based on specific driving conditions is not possible. Considering that different driving modes result in different torque outputs from the accelerator pedal, a mismatch between the driver's selected mode and the corresponding driving conditions would significantly impact the vehicle's power and fuel economy. Summary of the Invention

[0003] To address the aforementioned problems, the purpose of this application is to provide a driving mode switching method, device, electronic device, and storage medium that can achieve automatic switching of driving modes.

[0004] This application provides a driving mode switching method, including:

[0005] Generate a standard database, which includes multiple operating conditions, each corresponding to different historical road condition parameters and driving modes;

[0006] Obtain current vehicle road condition parameters;

[0007] Obtain the similarity between the current vehicle road condition parameters and the historical road condition parameters corresponding to various operating conditions in the standard database;

[0008] The current next driving mode is determined based on the similarity among the various operating conditions;

[0009] Switch to the next driving mode.

[0010] In the above implementation process, a standard database is generated using different historical parameters. The data in the standard database belongs to multiple operating conditions, each corresponding to different historical parameters and driving modes. During vehicle operation, the current road condition parameters are determined. Since drivers typically determine the appropriate driving mode based on their judgment when encountering similar or identical road conditions, the similarity between the current road condition parameters and the historical road condition parameters corresponding to multiple operating conditions in the standard database is obtained. Based on the similarity, the current driving mode is determined among the multiple operating conditions, and then switched to that driving mode. Based on the above implementation method, automatic switching of driving modes can be achieved, overcoming the inaccuracy problem caused by manually determining the driving mode based on road conditions, and improving the overall vehicle's power and economy.

[0011] Furthermore, the standard database includes: a first feature vector corresponding to each working condition;

[0012] The steps for generating the standard database include:

[0013] Obtain multiple historical traffic condition parameters;

[0014] Generate multiple first feature vectors corresponding to the multiple historical road condition parameters;

[0015] The multiple first feature vectors are clustered using a clustering analysis algorithm to obtain the first feature vector corresponding to each working condition.

[0016] In the above implementation process, multiple historical parameters are generated into a first feature vector, which can abstract the historical parameters. Using clustering analysis algorithms, the similarity between different feature vectors can be obtained quickly and accurately, thereby classifying and clustering the first feature vectors. Finally, each working condition corresponds to a different first feature vector.

[0017] Furthermore, the step of obtaining the similarity between the current vehicle road condition parameters and the historical road condition parameters corresponding to various operating conditions in the standard database includes:

[0018] Generate a second feature vector of the current vehicle road condition parameters;

[0019] Calculate the Euclidean distance between the second feature vector and the first feature vector corresponding to each working condition, where the similarity is the Euclidean distance.

[0020] In the above implementation process, the second feature vector of the current vehicle road condition parameters can abstract the current vehicle road condition parameters. Euclidean distance can measure the difference between two feature vectors. Using Euclidean distance as a similarity, the current driving mode can be accurately and quickly determined.

[0021] Furthermore, the step of switching to the next driving mode includes:

[0022] Get the duration of the current driving mode;

[0023] Switch to the next driving mode when the duration exceeds the minimum time.

[0024] In the above implementation process, in order to avoid the frequency of road condition recognition switching too quickly, a minimum time is set, and the driving mode is switched only when the minimum time is exceeded. Based on the above implementation method, the computing resources of the controller can be saved.

[0025] Secondly, embodiments of this application provide a driving mode switching device, including:

[0026] The database generation module is used to generate a standard database, which includes multiple operating conditions, each of which corresponds to different historical road condition parameters and driving modes.

[0027] The parameter acquisition module is used to acquire current vehicle road condition parameters;

[0028] The similarity acquisition module is used to obtain the similarity between the current vehicle road condition parameters and the historical road condition parameters corresponding to various working conditions in the standard database;

[0029] The determination module determines the current driving mode based on the similarity among the various operating conditions;

[0030] The switching module switches to the driving mode.

[0031] In the above implementation process, a standard database is generated using different historical parameters. The data in the standard database belongs to multiple operating conditions, each corresponding to different historical parameters and driving modes. During vehicle operation, the current road condition parameters are determined. Since drivers typically determine the appropriate driving mode based on their judgment when encountering similar or identical road conditions, the similarity between the current road condition parameters and the historical road condition parameters corresponding to multiple operating conditions in the standard database is obtained. Based on the similarity, the current driving mode is determined among the multiple operating conditions, and then switched to that driving mode. Based on the above implementation method, automatic switching of driving modes can be achieved, overcoming the inaccuracy problem caused by manually determining the driving mode based on road conditions, and improving the overall vehicle's power and economy.

[0032] Furthermore, the standard database includes: a first feature vector corresponding to each working condition; the database generation module is also used to obtain multiple historical road condition parameters;

[0033] Generate multiple first feature vectors corresponding to the multiple historical road condition parameters;

[0034] The multiple first feature vectors are clustered using a clustering analysis algorithm to obtain the first feature vector corresponding to each working condition.

[0035] In the above implementation process, multiple historical parameters are generated into a first feature vector, which can abstract the historical parameters. By using clustering analysis algorithms, the similarity between different feature vectors can be obtained quickly and accurately, thereby classifying and clustering the first feature vectors, and finally obtaining the first feature vector corresponding to each working condition.

[0036] Furthermore, the determining module is also used to generate a second feature vector of the current vehicle road condition parameters;

[0037] Calculate the Euclidean distance between the second feature vector and the first feature vector corresponding to each working condition, where the similarity is the Euclidean distance.

[0038] In the above implementation process, the second feature vector of the current vehicle road condition parameters can abstract the current vehicle road condition parameters. Euclidean distance can measure the difference between two feature vectors. Using Euclidean distance as a similarity, the current driving mode can be accurately and quickly determined.

[0039] Furthermore, the switching module is also used to obtain the duration of the current driving mode;

[0040] Whether to switch to the current driving mode is determined based on the duration of the current driving mode.

[0041] In the above implementation process, in order to avoid the frequency of road condition recognition switching too quickly, a minimum time is set, and the driving mode is switched only when the minimum time is exceeded. Based on the above implementation method, the computing resources of the controller can be saved.

[0042] Thirdly, an electronic device provided in this application includes: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the steps of the method as described in any of the first aspects.

[0043] Fourthly, embodiments of this application provide a computer-readable storage medium storing instructions that, when executed on a computer, cause the computer to perform the method described in any of the first aspects.

[0044] Other features and advantages disclosed in this application will be set forth in the following description, or some features and advantages may be inferred from the description or determined without doubt, or may be learned by practicing the above-described technology disclosed in this application.

[0045] To make the above-mentioned objectives, features and advantages of this application more apparent and understandable, preferred embodiments are described below in detail with reference to the accompanying drawings. Attached Figure Description

[0046] To more clearly illustrate the technical solutions of the embodiments of this application, the accompanying drawings used in the embodiments of this application will be briefly introduced below. It should be understood that the following drawings only show some embodiments of this application and should not be regarded as a limitation of the scope. For those skilled in the art, other related drawings can be obtained based on these drawings without creative effort.

[0047] Figure 1A flowchart illustrating the driving mode switching method provided in an embodiment of this application;

[0048] Figure 2 This is a schematic diagram of the driving mode switching device provided in the embodiments of this application;

[0049] Figure 3 This is a schematic diagram of the structure of an electronic device provided in an embodiment of this application. Detailed Implementation

[0050] The technical solutions in the embodiments of this application will now be described with reference to the accompanying drawings.

[0051] It should be noted that similar reference numerals and letters in the following figures indicate similar items; therefore, once an item is defined in one figure, it does not need to be further defined and explained in subsequent figures. Furthermore, in the description of this application, terms such as "first," "second," etc., are used only to distinguish descriptions and should not be construed as indicating or implying relative importance.

[0052] Example 1

[0053] See Figure 1 This application provides a driving mode switching method, including:

[0054] S1: Generate a standard database, which includes various operating conditions, each of which corresponds to different historical road condition parameters and driving modes;

[0055] S2: Obtain current vehicle road condition parameters;

[0056] S3: Obtain the similarity between the current vehicle road condition parameters and the historical road condition parameters corresponding to various operating conditions in the standard database;

[0057] S4: Determine the next driving mode based on similarity across multiple operating conditions;

[0058] S5: Switch to the next driving mode.

[0059] In the above implementation process, a standard database is generated using different historical parameters. The data in the standard database belongs to multiple operating conditions, each corresponding to different historical parameters and driving modes. During vehicle operation, the current road condition parameters are determined. Since drivers typically determine the appropriate driving mode based on their judgment when encountering similar or identical road conditions, the similarity between the current road condition parameters and the historical road condition parameters corresponding to multiple operating conditions in the standard database is obtained. Based on the similarity, the current driving mode is determined among the multiple operating conditions, and then switched to that driving mode. Based on the above implementation method, automatic switching of driving modes can be achieved, overcoming the inaccuracy problem caused by manually determining the driving mode based on road conditions, and improving the overall vehicle's power and economy.

[0060] Furthermore, embodiments of this application provide various vehicle driving parameters, including:

[0061] Top speed: v max :v max =max(v); average vehicle speed v ave : In the formula, t represents the vehicle's operating time step (s); Δt represents the sampling time (s) during the vehicle's operation, which is taken as 120s in this invention; the average vehicle speed v dri : In the formula, t' represents the time (s) during which the vehicle's speed is greater than 0 during the entire driving process. The percentage of total speed time t idle :

[0062] Average acceleration a ave : In the formula a acc This represents the value when the vehicle's acceleration is greater than 0 (m / s²). 2 );t acc This represents the vehicle's acceleration time (s). Average deceleration d ave : In the formula a dcc This represents the value when the vehicle's acceleration is less than 0 (m / s²). 2 ), t dcc The deceleration time (s) and standard deviation of acceleration a of the entire vehicle are given. dev :

[0063] In the formula E(a acc 2 (E(a) represents the expected value of the square of the acceleration, (E(a)) acc )) 2 The square of the expected acceleration; the standard deviation of the deceleration d dev :

[0064] In the formula E(a dcc 2 (E(a) represents the expected value of the square of the deceleration.) dcc )) 2 The expression represents the square of the expected deceleration; the maximum acceleration a max :a max =max(a), maximum deceleration d max =|min(a)|.

[0065] Further, the standard database includes: a first feature vector corresponding to each working condition; S1 includes:

[0066] Obtain multiple historical traffic condition parameters;

[0067] Generate multiple first feature vectors corresponding to the multiple historical road condition parameters;

[0068] The multiple first feature vectors are clustered using a clustering analysis algorithm to obtain the first feature vector corresponding to each working condition.

[0069] In the above implementation process, multiple historical parameters are generated into a first feature vector, which can abstract the historical parameters. Using clustering analysis algorithms, the similarity between different feature vectors can be obtained quickly and accurately, thereby clustering the first feature vectors and finally obtaining the first feature vector corresponding to each working condition.

[0070] In one implementation, all parameters are normalized using the following formula before clustering:

[0071]

[0072] In the formula x * σ represents the standardized parameter; x represents the parameter to be standardized; σ represents the mean of the parameter to be standardized; σ represents the standard deviation of the parameter to be standardized.

[0073]

[0074] Furthermore, S3 includes:

[0075] Generate a second feature vector of the current vehicle road condition parameters;

[0076] Calculate the Euclidean distance between the second feature vector and the first feature vector corresponding to each working condition, where the similarity is the Euclidean distance.

[0077] For example, a 120-second driving condition segment can be selected for calculation, and then the calculated data can be fused into a database matrix for comparison.

[0078]

[0079] In the formula x 11 ,x 12 ,…,x 1n This represents the feature parameter values ​​obtained within a 120-second information segment under the current driving conditions; x mn Let represent the value of the nth characteristic parameter under the (m-1)th working condition. Then the Euclidean distance calculation formula can be expressed as:

[0080]

[0081] Each time new road condition parameters are added to the matrix, the same type of feature parameters must be standardized using the database standardization function.

[0082] In the above implementation process, the second feature vector of the current vehicle road condition parameters can abstract the current vehicle road condition parameters. Euclidean distance can measure the difference between two feature vectors. Using Euclidean distance as a similarity, the current driving mode can be accurately and quickly determined.

[0083] For example, the first type of driving condition is defined as the city center driving condition in this invention, and the corresponding driving condition is defined as ECO+ in this invention; the second type of driving condition is defined as the urban driving condition in this invention, and the corresponding driving condition is defined as ECO mode in this invention; the third type of driving condition is defined as the suburban driving condition in this invention, and the corresponding driving condition is defined as Normal mode in this invention; the fourth type of driving condition is defined as the far suburban driving condition in this invention, and the corresponding driving mode is defined as sport- mode in this invention; the fifth type of driving condition is defined as the highway driving condition in this invention, and the corresponding driving mode is defined as sport mode in this invention.

[0084] Furthermore, the step of switching to the next driving mode includes:

[0085] Get the duration of the current driving mode;

[0086] Switch to the next driving mode when the duration exceeds the minimum time.

[0087] The minimum duration of maintenance can be determined based on specific experimental calibration.

[0088] In the above implementation process, in order to avoid the frequency of road condition recognition switching too quickly, a minimum time is set, and the driving mode is switched only when the minimum time is exceeded. Based on the above implementation method, the computing resources of the controller can be saved.

[0089] Example 2

[0090] See Figure 2 This application provides a driving mode switching device, including:

[0091] Database generation module 1 is used to generate a standard database, which includes multiple operating conditions, each of which corresponds to different historical road condition parameters and driving modes.

[0092] Parameter acquisition module 2 is used to acquire current vehicle road condition parameters;

[0093] Similarity acquisition module 3 is used to acquire the similarity between the current vehicle road condition parameters and the historical road condition parameters corresponding to various working conditions in the standard database;

[0094] Module 4 is used to determine the current driving mode in the multiple operating conditions based on the similarity.

[0095] Switching module 5 is used to switch to the driving mode.

[0096] In the above implementation process, a standard database is generated using different historical parameters. The data in the standard database belongs to multiple operating conditions, each corresponding to different historical parameters and driving modes. During vehicle operation, the current road condition parameters are determined. Since drivers typically determine the appropriate driving mode based on their judgment when encountering similar or identical road conditions, the similarity between the current road condition parameters and the historical road condition parameters corresponding to multiple operating conditions in the standard database is obtained. Based on the similarity, the current driving mode is determined among the multiple operating conditions, and then switched to that driving mode. Based on the above implementation method, automatic switching of driving modes can be achieved, overcoming the inaccuracy problem caused by manually determining the driving mode based on road conditions, and improving the overall vehicle's power and economy.

[0097] Furthermore, the standard database includes: a first feature vector corresponding to each working condition; the database generation module is also used to obtain multiple historical road condition parameters;

[0098] Generate multiple first feature vectors corresponding to the multiple historical road condition parameters;

[0099] The multiple first feature vectors are clustered using a clustering analysis algorithm to obtain the first feature vector corresponding to each working condition.

[0100] In the above implementation process, multiple historical parameters are generated into a first feature vector, which can abstract the historical parameters. By using clustering analysis algorithms, the similarity between different feature vectors can be obtained quickly and accurately, thereby classifying the first feature vector and finally obtaining the first feature vector corresponding to each working condition.

[0101] Furthermore, the determining module is also used to generate a second feature vector of the current vehicle road condition parameters;

[0102] Calculate the Euclidean distance between the second feature vector and the first feature vector corresponding to each working condition, where the similarity is the Euclidean distance.

[0103] In the above implementation process, the second feature vector of the current vehicle road condition parameters can abstract the current vehicle road condition parameters. Euclidean distance can measure the difference between two feature vectors. Using Euclidean distance as a similarity, the current driving mode can be accurately and quickly determined.

[0104] Furthermore, the switching module is also used to obtain the duration of the current driving mode;

[0105] Whether to switch to the current driving mode is determined based on the duration of the current driving mode.

[0106] In the above implementation process, in order to avoid the frequency of road condition recognition switching too quickly, a minimum time is set, and the driving mode is switched only when the minimum time is exceeded. Based on the above implementation method, the computing resources of the controller can be saved.

[0107] This application also provides an electronic device, please refer to [link to application]. Figure 3 , Figure 3 This is a structural block diagram of an electronic device provided in an embodiment of this application. The electronic device may include a processor 31, a communication interface 32, a memory 33, and at least one communication bus 34. The communication bus 34 is used to enable direct communication between these components. In this embodiment, the communication interface 32 of the electronic device is used for signaling or data communication with other node devices. The processor 31 may be an integrated circuit chip with signal processing capabilities.

[0108] The processor 31 described above can be a general-purpose processor, including a central processing unit (CPU), a network processor (NP), etc.; it can also be a digital signal processor (DSP), an application-specific integrated circuit (ASIC), an off-the-shelf programmable gate array (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, or discrete hardware components. It can implement or execute the methods, steps, and logic block diagrams disclosed in the embodiments of this application. The general-purpose processor can be a microprocessor, or the processor 31 can be any conventional processor.

[0109] The memory 33 may be, but is not limited to, Random Access Memory (RAM), Read Only Memory (ROM), Programmable Read-Only Memory (PROM), Erasable Programmable Read-Only Memory (EPROM), Electrically Erasable Programmable Read-Only Memory (EEPROM), etc. The memory 33 stores computer-readable instructions. When these computer-readable instructions are executed by the processor 31, the electronic device can perform the aforementioned operations. Figures 1 to 2 The various steps involved in the method implementation examples.

[0110] Alternatively, the electronic device may also include a storage controller and an input / output unit.

[0111] The memory 33, storage controller, processor 31, peripheral interface, and input / output unit are electrically connected directly or indirectly to achieve data transmission or interaction. For example, these components can be electrically connected to each other through one or more communication buses 34. The processor 31 is used to execute executable modules stored in the memory 33, such as software function modules or computer programs included in electronic devices.

[0112] The input / output unit is used to provide users with the ability to create tasks and to set optional start periods or preset execution times for those tasks, thereby enabling user-server interaction. The input / output unit may be, but is not limited to, a mouse and keyboard.

[0113] Understandable. Figure 3 The structure shown is for illustrative purposes only; the electronic device may also include components that are more advanced than those shown. Figure 3 The more or fewer components shown, or having the same Figure 3 The different configurations shown. Figure 3 The components shown can be implemented using hardware, software, or a combination thereof.

[0114] This application also provides a computer-readable storage medium storing instructions. When the instructions are executed on a computer, the computer program is executed by a processor to implement the method described in the method embodiment. To avoid repetition, the details will not be repeated here.

[0115] In the several embodiments provided in this application, it should be understood that the disclosed apparatus and methods can also be implemented in other ways. The apparatus embodiments described above are merely illustrative. For example, the flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of apparatus, methods, and computer program products according to various embodiments of this application. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of code containing one or more executable instructions for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions marked in the blocks may occur in a different order than those marked in the drawings. For example, two consecutive blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in a block diagram and / or flowchart, and combinations of blocks in block diagrams and / or flowcharts, can be implemented using a dedicated hardware-based system that performs the specified function or action, or using a combination of dedicated hardware and computer instructions.

[0116] In addition, the functional modules in the various embodiments of this application can be integrated together to form an independent part, or each module can exist independently, or two or more modules can be integrated to form an independent part.

[0117] If the aforementioned functions are implemented as software functional modules and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or a portion of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this application. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0118] The above description is merely an embodiment of this application and is not intended to limit the scope of protection of this application. Various modifications and variations can be made to this application by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the scope of protection of this application. It should be noted that similar reference numerals and letters in the following figures indicate similar items; therefore, once an item is defined in one figure, it does not need to be further defined and explained in subsequent figures.

[0119] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

[0120] It should be noted that, in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitations, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element.

Claims

1. A method for switching driving modes, characterized in that, include: Generate a standard database, which includes multiple operating conditions, each corresponding to different historical road condition parameters and driving modes; Obtain current vehicle road condition parameters; Obtain the similarity between the current vehicle road condition parameters and the historical road condition parameters corresponding to various operating conditions in the standard database; The next driving mode is determined based on the similarity among the various operating conditions; Switch to the next driving mode; The standard database includes: a first feature vector corresponding to each working condition; The steps for generating the standard database include: Obtain multiple historical traffic condition parameters; Generate multiple first feature vectors corresponding to the multiple historical road condition parameters; The multiple first feature vectors are classified using a clustering analysis algorithm to obtain the first feature vector corresponding to each working condition.

2. The driving mode switching method according to claim 1, characterized in that, The step of obtaining the similarity between the current vehicle road condition parameters and the historical road condition parameters corresponding to various operating conditions in the standard database includes: Generate a second feature vector of the current vehicle road condition parameters; Calculate the Euclidean distance between the second feature vector and the first feature vector corresponding to each working condition, where the similarity is the Euclidean distance.

3. The driving mode switching method according to claim 2, characterized in that, The step of switching to the next driving mode includes: Get the duration of the current driving mode; Switch to the next driving mode when the duration exceeds the minimum time.

4. A driving mode switching device, characterized in that, include: The database generation module is used to generate a standard database, which includes multiple operating conditions, each of which corresponds to different historical road condition parameters and driving modes; The parameter acquisition module is used to acquire current vehicle road condition parameters; The similarity acquisition module is used to obtain the similarity between the current vehicle road condition parameters and the historical road condition parameters corresponding to various working conditions in the standard database; A determination module is used to determine the next driving mode in the multiple operating conditions based on the similarity. A switching module is used to switch to the next driving mode; The standard database includes: a first feature vector corresponding to each working condition; the database generation module is also used to obtain multiple historical road condition parameters. Generate multiple first feature vectors corresponding to the multiple historical road condition parameters; The multiple first feature vectors are classified using a clustering analysis algorithm to obtain the first feature vector corresponding to each working condition.

5. The driving mode switching device according to claim 4, characterized in that, The determining module is also used to generate a second feature vector of the current vehicle road condition parameters; Calculate the Euclidean distance between the second feature vector and the first feature vector corresponding to each working condition, where the similarity is the Euclidean distance.

6. The driving mode switching device according to claim 5, characterized in that, The switching module is also used to obtain the duration of the current driving mode; Switch to the next driving mode when the duration exceeds the minimum time.

7. An electronic device, characterized in that, include: A memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor, when executing the computer program, implements the steps of the method as described in any one of claims 1-3.

8. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores instructions that, when executed on a computer, cause the computer to perform the method as described in any one of claims 1-3.

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