A driving habit self-learning method, an automatic driving method, a device, and a vehicle

Abstract

Embodiments of the present application provide a driving habit self-learning method, an automatic driving method, a device and a vehicle. The driving habit self-learning method comprises: acquiring one or more driving data in a process in which a driver drives a vehicle; determining a mode and / or a magnitude corresponding to the one or more driving data; taking the mode and / or the magnitude as identity identification data for identifying an identity of the driver, and collecting driving habit data corresponding to the identity identification; and storing the identity identification, the identity identification data and the driving habit data correspondingly. Through the embodiment, a face recognition system does not need to be installed on the vehicle, and the cost is greatly saved. The driving habit self-learning based on big data is realized.

Description

Technical Field

[0001] This application relates to vehicle autonomous driving technology, and more particularly to a driving habit self-learning method, autonomous driving method, device, and vehicle. Background Technology

[0002] Vehicle driving habit data is usually pre-stored in the vehicle controller. When the driver activates the autonomous driving mode, the vehicle will drive automatically according to this pre-stored driving habit data.

[0003] If you want to update the driving habit data of the in-vehicle software through self-learning, you need to re-identify the driver based on facial recognition, collect and learn the driving habit data, and then store the learned driving habit data in the database corresponding to the driver. Summary of the Invention

[0004] This application provides a driving habit self-learning method, autonomous driving method, device, and vehicle that eliminates the need to install a facial recognition system on the vehicle, significantly reducing costs and enabling driving habit self-learning based on big data.

[0005] This application provides a self-learning method for driving habits, the method may include:

[0006] Acquire one or more types of driving data during the driver's operation of the vehicle;

[0007] Determine the mode and / or magnitude corresponding to the execution of the one or more driving data;

[0008] The pattern and / or magnitude are used as identification data to identify the driver, and driving habit data corresponding to the identification is collected;

[0009] The identity identifier, the identity identifier data, and the driving habit data are stored accordingly.

[0010] In an exemplary embodiment of this application, the driving data may include any one or more of the following: driving mode switch data, driver's seat memory switch data, and accelerator pedal change rate data;

[0011] Determining the mode and / or magnitude corresponding to the execution of the one or more driving data may include:

[0012] Determine the driving mode corresponding to the driving mode switch data;

[0013] Determine the driver's seat memory mode corresponding to the driver's seat memory switch data;

[0014] Determine the order of magnitude of the accelerator pedal change corresponding to the accelerator pedal change rate data.

[0015] In an exemplary embodiment of this application, the step of using the pattern and / or magnitude as identification data to identify the driver may include:

[0016] The different driving modes, different driver seat memory modes, and / or different orders of magnitude of accelerator pedal changes used by the driver during the driving process are used as the identity identification data.

[0017] In an exemplary embodiment of this application, the identity identification data may include: primary identity identification data;

[0018] The step of using the different driving modes, different driver seat memory modes, and / or different orders of magnitude of accelerator pedal changes adopted by the driver during driving as the identification data may include:

[0019] The primary identity data combines different driving modes and different driver seat memory modes as the main identity type to distinguish drivers.

[0020] In an exemplary embodiment of this application, the identity data may further include: segmented identity data;

[0021] The method of using different driving modes, different driver seat memory modes, and / or different orders of magnitude of accelerator pedal changes adopted by the driver during driving as the identification data may further include:

[0022] Different orders of magnitude of accelerator pedal changes are used as the subdivided identity identification data to further subdivide the driver's primary identity type.

[0023] In an exemplary embodiment of this application, the method may further include:

[0024] The identity data and driving habit data corresponding to different drivers are stored in a preset cloud database;

[0025] When the vehicle controller needs a software update, it communicates with the cloud database to integrate the identity data and driving habit data corresponding to different drivers stored in the cloud database into the update software, and then downloads the update software to the vehicle controller.

[0026] In an exemplary embodiment of this application, the method may further include:

[0027] When the identification data obtained based on the types of driving data collected cannot uniquely identify the driver's identity, the types of driving data are increased.

[0028] This application embodiment also provides a driving habit self-learning device, which may include a first processor and a first memory. The first memory stores a first instruction, and when the first instruction is executed by the first processor, the driving habit self-learning method is implemented.

[0029] This application embodiment also provides an autonomous driving method, based on the identity identifier and corresponding driving habit data obtained by the aforementioned driving habit self-learning method; the method may include:

[0030] Obtain one or more driving data points from the current driver;

[0031] Determine the mode and / or magnitude corresponding to the execution of the one or more driving data;

[0032] The pattern and / or magnitude are compared with the stored identity data, and the driver's identity is determined based on the comparison result.

[0033] Autonomous driving is performed based on the driving habit data corresponding to the identity identifier.

[0034] In an exemplary embodiment of this application, the driving data may include any one or more of the following: driving mode switch data, driver's seat memory switch data, and accelerator pedal change rate data;

[0035] The mode and / or magnitude corresponding to the execution of the driving data may include any one or more of the following: driving mode, driver's seat memory mode, and accelerator pedal change magnitude;

[0036] The identity identifier may include: a primary identity type and a sub-identity type;

[0037] The identity data may include: primary identity data and detailed identity data.

[0038] In an exemplary embodiment of this application, when the driving data includes the driving mode switch data and the driver's seat memory switch data, and the mode and / or magnitude corresponding to the execution of the driving data includes the driving mode and the driver's seat memory mode, the step of comparing the mode and / or magnitude with the stored identity data to determine the driver's identity may include:

[0039] The driving mode and driver's seat memory mode implemented by the driver are compared with the driving mode and driver's seat memory modes contained in the various primary identity data stored.

[0040] When the driving mode and driver seat memory mode implemented by the driver match the driving mode and driver seat memory mode contained in any one or more of the multiple primary identity data, the identity type corresponding to the matched primary identity data is determined as the driver's primary identity type.

[0041] In an exemplary embodiment of this application, the identity identifier may further include: a subdivided identity type; the identity identifier data may further include: subdivided identity identifier data; the driving data may further include: the accelerator pedal change rate data; the mode and / or magnitude corresponding to the driving data being executed may further include: the order of magnitude of the accelerator pedal change.

[0042] The step of comparing the pattern and / or magnitude with the stored identity data to determine the driver's identity may further include:

[0043] The magnitude of the accelerator pedal changes performed by the driver is compared with the magnitude of the accelerator pedal changes contained in the various segmented identity data stored in the database.

[0044] When the magnitude of the accelerator pedal change implemented by the driver matches the magnitude of the accelerator pedal change contained in any one or more of the subdivided identity data, the identity type corresponding to the matched subdivided identity data is determined as the driver's subdivided identity type.

[0045] This application also provides an autonomous driving device, which may include a second processor and a second memory. The second memory stores a second instruction, and when the second instruction is executed by the second processor, the autonomous driving method is implemented.

[0046] This application also provides a vehicle that may include: the driving habit self-learning device and the automatic driving device.

[0047] The driving habit self-learning method of this application embodiment may include: acquiring one or more driving data during the driver's driving process; determining the pattern and / or magnitude corresponding to the execution of the one or more driving data; using the pattern and / or magnitude as identity identifier data to identify the driver, and collecting driving habit data corresponding to the identity identifier; and storing the identity identifier, the identity identifier data, and the driving habit data accordingly. This embodiment eliminates the need to install a facial recognition system in the vehicle, significantly saving costs, and achieves driving habit self-learning based on big data.

[0048] Other features and advantages of this application will be set forth in the following description, and will be apparent in part from the description, or may be learned by practicing the application. Other advantages of this application can be realized and obtained by means of the solutions described in the description and the accompanying drawings. Attached Figure Description

[0049] The accompanying drawings are used to provide an understanding of the technical solutions of this application and constitute a part of the specification. They are used together with the embodiments of this application to explain the technical solutions of this application and do not constitute a limitation on the technical solutions of this application.

[0050] Figure 1 This is a flowchart of the self-learning method for driving habits according to an embodiment of this application;

[0051] Figure 2 This is a schematic diagram of an embodiment of the driving habit self-learning method of this application;

[0052] Figure 3 This is a block diagram of the driving habit self-learning device according to an embodiment of this application;

[0053] Figure 4 This is a flowchart of an autonomous driving method according to an embodiment of this application;

[0054] Figure 5 This is a block diagram of the autonomous driving device according to an embodiment of this application;

[0055] Figure 6 This is a block diagram of the vehicle components according to an embodiment of this application. Detailed Implementation

[0056] This application describes several embodiments, but these descriptions are exemplary and not restrictive, and it will be apparent to those skilled in the art that many more embodiments and implementations are possible within the scope of the embodiments described herein. Although many possible combinations of features are shown in the drawings and discussed in the detailed description, many other combinations of the disclosed features are also possible. Unless specifically limited, any feature or element of any embodiment may be used in combination with, or may replace, any feature or element of any other embodiment.

[0057] This application includes and contemplates combinations of features and elements known to those skilled in the art. The embodiments, features, and elements disclosed in this application may also be combined with any conventional features or elements to form a unique inventive scheme as defined by the claims. Any feature or element of any embodiment may also be combined with features or elements from other inventive schemes to form another unique inventive scheme as defined by the claims. Therefore, it should be understood that any feature shown and / or discussed in this application may be implemented individually or in any suitable combination. Therefore, the embodiments are not limited except by the limitations imposed by the appended claims and their equivalents. Furthermore, various modifications and changes may be made within the scope of the appended claims.

[0058] Furthermore, in describing representative embodiments, the specification may have presented methods and / or processes as a specific sequence of steps. However, the method or process should not be limited to the specific order of steps described herein, to the extent that it does not depend on such a specific order. As will be understood by those skilled in the art, other sequences of steps are also possible. Therefore, the specific order of steps set forth in the specification should not be construed as a limitation of the claims. Moreover, the claims concerning the method and / or process should not be limited to the steps performed in the written order, and those skilled in the art will readily understand that these orders can be varied and still remain within the spirit and scope of the embodiments of this application.

[0059] This application provides a self-learning method for driving habits, such as... Figure 1 As shown, the method may include steps S101-S104:

[0060] S101. Acquire one or more types of driving data during the driver's operation of the vehicle;

[0061] S102. Determine the mode and / or magnitude corresponding to the execution of one or more driving data;

[0062] S103. Use the pattern and / or magnitude as identification data to identify the driver, and collect driving habit data corresponding to the identification.

[0063] S104. Store the identity identifier, the identity identifier data, and the driving habit data accordingly.

[0064] In an exemplary embodiment of this application, the driving data may include, but is not limited to, any one or more of the following: driving mode switch data, driver's seat memory switch data, and accelerator pedal change rate data.

[0065] In the exemplary embodiments of this application, unlike traditional solutions that rely on facial recognition systems to identify drivers and collect related driving habit data, the solutions in this application can identify the vehicle's driver and their driving habit data based on driving mode switch data, driver's seat memory switch data, and accelerator pedal change rate data. For example, the identity of the corresponding driver can be determined based on the patterns and / or magnitudes of driving data used by different drivers, and the driving habits of that driver can be determined accordingly.

[0066] In an exemplary embodiment of this application, determining the mode and / or magnitude corresponding to the execution of the one or more driving data may include:

[0067] Determine the driving mode corresponding to the driving mode switch data;

[0068] Determine the driver's seat memory mode corresponding to the driver's seat memory switch data;

[0069] Determine the order of magnitude of the accelerator pedal change corresponding to the accelerator pedal change rate data.

[0070] In an exemplary embodiment of this application, the step of using the pattern and / or magnitude as identification data to identify the driver may include:

[0071] The different driving modes, different driver seat memory modes, and / or different orders of magnitude of accelerator pedal changes used by the driver during the driving process are used as the identity identification data.

[0072] In an exemplary embodiment of this application, the identity identification data may include: primary identity identification data;

[0073] The step of using the different driving modes, different driver seat memory modes, and / or different orders of magnitude of accelerator pedal changes adopted by the driver during driving as the identification data may include:

[0074] The primary identity data combines different driving modes and different driver seat memory modes as the main identity type to distinguish drivers.

[0075] In an exemplary embodiment of this application, the driving mode may include, but is not limited to, a first driving mode and a second driving mode; the driver's seat memory mode may include, but is not limited to, a first driver's seat memory mode and a second driver's seat memory mode.

[0076] In an exemplary embodiment of this application, the driver's primary identity type can be determined based on a combination of different driving modes and different driver seat memory modes, for example:

[0077] When a driver's driving data includes data corresponding to the first driving mode and the first driver's seat memory mode, the driver can be identified as a Class A driver.

[0078] When a driver's driving data includes data corresponding to the first driving mode and the second driver's seat memory mode, the driver can be identified as a Class B driver.

[0079] When a driver's driving data includes data corresponding to the second driving mode and the first driver's seat memory mode, the driver can be identified as a Class C driver.

[0080] When a driver's driving data includes data corresponding to the second driving mode and the second driver's seat memory mode, the driver can be identified as a Class D driver.

[0081] In an exemplary embodiment of this application, the identity data may further include: segmented identity data;

[0082] The method of using different driving modes, different driver seat memory modes, and / or different orders of magnitude of accelerator pedal changes adopted by the driver during driving as the identification data may further include:

[0083] Different orders of magnitude of accelerator pedal changes are used as the subdivided identity identification data to further subdivide the driver's primary identity type.

[0084] In an exemplary embodiment of this application, the order of magnitude of the accelerator pedal change may include, but is not limited to, a first accelerator pedal order of magnitude and a second accelerator pedal order of magnitude.

[0085] In an exemplary embodiment of this application, after determining the driver's primary identity type, the driver's corresponding sub-identity type can be determined based on different orders of magnitude of accelerator pedal changes, for example:

[0086] If the driver is identified as a Class A driver, and the magnitude of the change in accelerator pedal position is the same as that of the first accelerator pedal position, the driver will be further classified into Class A1 driving habits.

[0087] If the driver is identified as a Class A driver, and the magnitude of the accelerator pedal change is the same as that of the second accelerator pedal change, the driver will be further classified into Class A2 driving habits.

[0088] If the driver is identified as a Class B driver, and the magnitude of the change in accelerator pedal position is the same as that of the first accelerator pedal position, the driver will be further classified into Class B1 driving habits.

[0089] If the driver is identified as a Class B driver, and the magnitude of the accelerator pedal change is the same as that of the second accelerator pedal, the driver will be further classified into Class B2 driving habits.

[0090] If the driver is identified as a Class C driver, and the magnitude of the accelerator pedal change is the same as the magnitude of the first accelerator pedal change, the driver will be further subdivided into Class C1 driving habits.

[0091] If the driver is identified as a Class C driver, and the magnitude of the accelerator pedal change is the same as that of the second accelerator pedal change, the driver will be further classified into Class C2 driving habits.

[0092] If the driver is identified as a Class D driver, and the magnitude of the accelerator pedal change is the same as the magnitude of the first accelerator pedal change, the driver will be further subdivided into Class D1 driving habits.

[0093] If the driver is identified as a Class D driver, and the magnitude of the accelerator pedal change is the same as that of the second accelerator pedal change, the driver will be further classified into Class D2 driving habits.

[0094] In an exemplary embodiment of this application, the magnitude of the accelerator pedal change can be represented by the number of times the accelerator pedal change rate exceeds a threshold within a preset time period.

[0095] In exemplary embodiments of this application, for example, as Figure 2As shown, if the vehicle has two memory switches 1 and 2 for the driver's seat, when the driver selects memory switch 1 and the driving mode is Sport, they are classified as a Class A driver. If the accelerator pedal change rate exceeds the threshold more than 6 times within 20 minutes, it can be defined as an A1 driving habit; if the accelerator pedal change rate exceeds the threshold less than 6 times within 20 minutes, it can be defined as an A2 driving habit. When the driver selects memory switch 1 and the driving mode is Eco, they are classified as a Class B driver. If the accelerator pedal change rate exceeds the threshold more than 5 times within 20 minutes, it can be defined as a B1 driving habit; if the accelerator pedal change rate exceeds the threshold less than 5 times within 20 minutes, it can be defined as a B1 driving habit. The following driving habits are defined as follows: A driver with memory switch 2 and driving mode selected as Sport can be classified as a Class C driver. If the accelerator pedal change rate exceeds the threshold more than 6 times within 20 minutes, the driver can be classified as a Class C1 driver. If the accelerator pedal change rate exceeds the threshold less than 6 times within 20 minutes, the driver can be classified as a Class C2 driver. A driver with memory switch 2 and driving mode selected as Eco can be classified as a Class D driver. If the accelerator pedal change rate exceeds the threshold more than 5 times within 20 minutes, the driver can be classified as a Class D1 driver. If the accelerator pedal change rate exceeds the threshold less than 5 times within 20 minutes, the driver can be classified as a Class D2 driver.

[0096] In an exemplary embodiment of this application, if there are more than two options for the driving mode and driver's seat memory switch in the vehicle, the ability to define driving habits can be added.

[0097] In an exemplary embodiment of this application, the method may further include:

[0098] When the identification data obtained based on the types of driving data collected cannot uniquely identify the driver's identity, the types of driving data are increased.

[0099] In an exemplary embodiment of this application, data related to driving habits (i.e., driving habit data) in the vehicle controller can be linked to each driver.

[0100] In an exemplary embodiment of this application, the method may further include:

[0101] The identity data and driving habit data corresponding to different drivers are stored in a preset cloud database;

[0102] When the vehicle controller needs a software update, it communicates with the cloud database to integrate the identity data and driving habit data corresponding to different drivers stored in the cloud database into the update software, and then downloads the update software to the vehicle controller.

[0103] In an exemplary embodiment of this application, each driver's driving habit data can be stored in different storage spaces in the cloud database, so that each driver has separate driving habit data in the cloud database.

[0104] In an exemplary embodiment of this application, when the software of a new vehicle controller needs to be updated, the driving habit data of each driver can first be integrated with the new vehicle controller software in a cloud database. The driving habit data can be stored in the cloud database in a calibration data format and integrated into a writable Hex file using calibration data integration tools such as CRETA.

[0105] In an exemplary embodiment of this application, the integrated software can be downloaded to the vehicle controller.

[0106] This application also provides a driving habit self-learning device 1, such as... Figure 3 As shown, it may include a first processor 11 and a first memory 12. The first memory 12 stores a first instruction. When the first instruction is executed by the first processor 11, the driving habit self-learning method is implemented.

[0107] In the exemplary embodiments of this application, any of the above-described driving habit self-learning methods are applicable to the embodiments of the driving habit self-learning device 1, and will not be described in detail here.

[0108] This application also provides an autonomous driving method, such as... Figure 4 As shown, the identity identifier and corresponding driving habit data are obtained based on the aforementioned driving habit self-learning method; the method may include steps S201-S204:

[0109] S201. Obtain one or more driving data of the current driver;

[0110] S202, Determine the mode and / or magnitude corresponding to the execution of one or more driving data;

[0111] S203. Compare the pattern and / or magnitude with the stored identity data, and determine the driver's identity based on the comparison result;

[0112] S204. Perform autonomous driving based on the driving habit data corresponding to the identity identifier.

[0113] In an exemplary embodiment of this application, the driving data may include, but is not limited to, any one or more of the following: driving mode switch data, driver's seat memory switch data, and accelerator pedal change rate data.

[0114] In an exemplary embodiment of this application, after the driver gets into the vehicle, the driver's identity information can be determined based on the driving mode and the driver's seat memory switch, and the corresponding driving habit data can be selected. If the same driving mode switch and driver's seat memory switch are used by different drivers, the corresponding driving habit data can be selected based on the rate of change of the accelerator pedal.

[0115] In an exemplary embodiment of this application, newly learned data is stored in a cloud database as the driver presses the pedal while driving.

[0116] In an exemplary embodiment of this application, the mode and / or magnitude corresponding to the execution of the driving data may include any one or more of the following: driving mode, driver's seat memory mode, and accelerator pedal change magnitude;

[0117] The identity identifier may include: a primary identity type and a sub-identity type;

[0118] The identity data may include: primary identity data and detailed identity data.

[0119] In an exemplary embodiment of this application, when the driving data includes the driving mode switch data and the driver's seat memory switch data, and the mode and / or magnitude corresponding to the execution of the driving data includes the driving mode and the driver's seat memory mode, the step of comparing the mode and / or magnitude with the stored identity data to determine the driver's identity may include:

[0120] The driving mode and driver's seat memory mode implemented by the driver are compared with the driving mode and driver's seat memory modes contained in the various primary identity data stored.

[0121] When the driving mode and driver seat memory mode implemented by the driver match the driving mode and driver seat memory mode contained in any one or more of the multiple primary identity data, the identity type corresponding to the matched primary identity data is determined as the driver's primary identity type.

[0122] In an exemplary embodiment of this application, the identity identifier may further include: a subdivided identity type; the identity identifier data may further include: subdivided identity identifier data; the driving data may further include: the accelerator pedal change rate data; the mode and / or magnitude corresponding to the driving data being executed may further include: the order of magnitude of the accelerator pedal change.

[0123] The step of comparing the pattern and / or magnitude with the stored identity data to determine the driver's identity may further include:

[0124] The magnitude of the accelerator pedal changes performed by the driver is compared with the magnitude of the accelerator pedal changes contained in the various segmented identity data stored in the database.

[0125] When the magnitude of the accelerator pedal change implemented by the driver matches the magnitude of the accelerator pedal change contained in any one or more of the subdivided identity data, the identity type corresponding to the matched subdivided identity data is determined as the driver's subdivided identity type.

[0126] In the exemplary embodiments of this application, any of the above-described driving habit self-learning methods are applicable to the embodiments of the driving habit self-learning device 1, and will not be described in detail here.

[0127] This application also provides an autonomous driving device 2, such as... Figure 5 As shown, it may include a second processor 21 and a second memory 22. The second memory 22 stores a second instruction. When the second instruction is executed by the second processor 22, the autonomous driving method is implemented.

[0128] In the exemplary embodiments of this application, any of the above-described driving habit self-learning methods are applicable to the second embodiment of the autonomous driving device, and will not be described in detail here.

[0129] This application also provides a vehicle 3, such as... Figure 6 As shown, it may include: the driving habit self-learning device 1 and the automatic driving device 2.

[0130] In the exemplary embodiments of this application, any of the above-described driving habit self-learning methods are applicable to the vehicle in embodiment 3, and will not be described in detail here.

[0131] The driving habit self-learning method of this application embodiment may include: acquiring one or more driving data during the driver's driving process; determining the pattern and / or magnitude corresponding to the execution of the one or more driving data; using the pattern and / or magnitude as identity identifier data to identify the driver, and collecting driving habit data corresponding to the identity identifier; and storing the identity identifier, the identity identifier data, and the driving habit data accordingly. This embodiment eliminates the need to install a facial recognition system in the vehicle, significantly saving costs, and achieves driving habit self-learning based on big data.

[0132] It will be understood by those skilled in the art that all or some of the steps, systems, or apparatuses disclosed above, and their functional modules / units, can be implemented as software, firmware, hardware, or suitable combinations thereof. In hardware implementations, the division between functional modules / units mentioned above does not necessarily correspond to the division of physical components; for example, a physical component may have multiple functions, or a function or step may be performed collaboratively by several physical components. Some or all components may be implemented as software executed by a processor, such as a digital signal processor or microprocessor, or as hardware, or as an integrated circuit, such as an application-specific integrated circuit (ASIC). Such software may be distributed on a computer-readable medium, which may include computer storage media (or non-transitory media) and communication media (or transient media). As is known to those skilled in the art, the term computer storage media includes volatile and non-volatile, removable and non-removable media implemented in any method or technology for storing information (such as computer-readable instructions, data structures, program modules, or other data). Computer storage media include, but are not limited to, RAM, ROM, EEPROM, flash memory or other memory technologies, CD-ROM, digital versatile disc (DVD) or other optical disc storage, magnetic cartridges, magnetic tape, disk storage or other magnetic storage devices, or any other medium that can be used to store desired information and can be accessed by a computer. Furthermore, it is well known to those skilled in the art that communication media typically contain computer-readable instructions, data structures, program modules, or other data in modulated data signals such as carrier waves or other transmission mechanisms, and may include any information delivery medium.

Claims

1. A self-learning method for driving habits, characterized in that, The method includes: Acquire one or more driving data during the driver's operation of the vehicle, wherein the driving data includes any one or more of the following: driving mode switch data, driver's seat memory switch data, and accelerator pedal change rate data; Determine the mode and / or magnitude corresponding to the execution of the one or more driving data; The pattern and / or magnitude are used as identification data to identify the driver, and driving habit data corresponding to the identification is collected. The identification data includes: primary identification data and subdivided identification data. The identity identifier, the identity identifier data, and the driving habit data are stored accordingly; The identification data that uses the pattern and / or magnitude as an identifier for the driver includes: The identification data includes using different driving modes, different driver seat memory modes, and / or different accelerator pedal change orders of magnitude as the driver's driving process. This includes: combining different driving modes and different driver seat memory modes as the main identification data to distinguish the driver's main identity type; and using different accelerator pedal change orders of magnitude as the subdivided identification data to further subdivide the driver's main identity type.

2. The self-learning method for driving habits according to claim 1, characterized in that, Determining the mode and / or magnitude corresponding to the execution of the one or more driving data includes: Determine the driving mode corresponding to the driving mode switch data; Determine the driver's seat memory mode corresponding to the driver's seat memory switch data; Determine the order of magnitude of the accelerator pedal change corresponding to the accelerator pedal change rate data.

3. The self-learning method for driving habits according to any one of claims 1-2, characterized in that, The method further includes: The identity data and driving habit data corresponding to different drivers are stored in a preset cloud database; When the vehicle controller needs a software update, it communicates with the cloud database to integrate the identity data and driving habit data corresponding to different drivers stored in the cloud database into the update software, and then downloads the update software to the vehicle controller.

4. The self-learning method for driving habits according to any one of claims 1-2, characterized in that, The method further includes: When the identification data obtained based on the types of driving data collected cannot uniquely identify the driver's identity, the types of driving data are increased.

5. A driving habit self-learning device, comprising a first processor and a first memory, wherein the first memory stores a first instruction, characterized in that, When the first instruction is executed by the first processor, the driving habit self-learning method as described in any one of claims 1-4 is implemented.

6. An autonomous driving method, characterized in that, Based on the identity identifier and corresponding driving habit data obtained by the driving habit self-learning method according to any one of claims 1-4; the method includes: Obtain one or more driving data points from the current driver; Determine the mode and / or magnitude corresponding to the execution of the one or more driving data; The pattern and / or magnitude are compared with the stored identity data, and the driver's identity is determined based on the comparison result. Autonomous driving is performed based on the driving habit data corresponding to the identity identifier.

7. The autonomous driving method according to claim 6, characterized in that, The driving data includes any one or more of the following: driving mode switch data, driver's seat memory switch data, and accelerator pedal change rate data; The mode and / or magnitude corresponding to the execution of the driving data includes any one or more of the following: driving mode, driver's seat memory mode, and accelerator pedal change magnitude; The identity identifier includes: primary identity type and sub-identity type; The identity identification data includes: primary identity identification data and detailed identity identification data.

8. The autonomous driving method according to claim 7, characterized in that, When the driving data includes the driving mode switch data and the driver's seat memory switch data, and the mode and / or magnitude corresponding to the execution of the driving data includes: driving mode and driver's seat memory mode, the step of comparing the mode and / or magnitude with the stored identity data to determine the driver's identity includes: The driving mode and driver's seat memory mode implemented by the driver are compared with the driving mode and driver's seat memory modes contained in the stored multiple primary identity data. When the driving mode and driver seat memory mode implemented by the driver match the driving mode and driver seat memory mode contained in any one or more of the multiple primary identity data, the identity type corresponding to the matched primary identity data is determined as the driver's primary identity type.

9. The autonomous driving method according to claim 8, characterized in that, The identity identifier further includes: a detailed identity type; the identity identifier data further includes: detailed identity identifier data; the driving data further includes: accelerator pedal change rate data; the mode and / or magnitude corresponding to the driving data when it is executed further includes: the order of magnitude of accelerator pedal change; The step of comparing the pattern and / or magnitude with the stored identity data to determine the driver's identity further includes: The magnitude of the accelerator pedal changes performed by the driver is compared with the magnitude of the accelerator pedal changes contained in the various segmented identity data stored in the database. When the magnitude of the accelerator pedal change implemented by the driver matches the magnitude of the accelerator pedal change contained in any one or more of the subdivided identity data, the identity type corresponding to the matched subdivided identity data is determined as the driver's subdivided identity type.

10. An autonomous driving device, comprising a second processor and a second memory, wherein the second memory stores second instructions, characterized in that, When the second instruction is executed by the second processor, the autonomous driving method as described in any one of claims 6-9 is implemented.

11. A vehicle, characterized in that, include: The driving habit self-learning device as described in claim 5 and the automatic driving device as described in claim 10.

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