An intelligent driving self-learning method and device, an electronic device, and a storage medium

Abstract

The application provides a kind of intelligent driving self-learning method, device, electronic equipment and storage medium, method includes: obtaining vehicle real-time position information;If vehicle enters the target learning section of pre-set, then create the automatic driving model of target learning section and carry out automatic driving self-learning;If the driving effect of automatic driving self-learning is consistent with the target driving effect of user pre-set, and the driving habit of automatic driving self-learning is consistent with the driving habit of user pre-set, then determine to complete automatic driving self-learning;Based on the automatic driving model after completing automatic driving self-learning, automatic driving is carried out in target learning section, and the automatic driving model is shared;The application further subdivides the function scene of intelligent driving into specific actual road section, uses actual driving condition as model learning target, is easy to complete learning and has higher reliability, and the model after learning success can be shared or traded on the network.

Description

Technical Field

[0001] This application relates to the field of computer applications, specifically to an intelligent driving self-learning method, device, electronic device, and storage medium. Background Technology

[0002] With the rapid development of automotive technology and the improvement of people's living standards, users have increasingly higher demands for intelligent vehicle configurations. The product competition among major OEMs is gradually shifting from electrification and electricization to intelligentization. Intelligent vehicles will become the mainstream of future automotive development. As the core of intelligent vehicles, intelligent driving not only affects the product strength of the vehicle itself, but also the user experience.

[0003] The current research focus of intelligent driving is mainly on key functional technologies such as image recognition and path planning. Research on autonomous driving largely concentrates on achieving road condition learning and planning control, with some research on road segment learning. Patent CN109597317A proposes a self-learning-based vehicle autonomous driving method, system, and electronic device. Based on road-related data collected for the learning route, it performs road environment learning to construct a virtual road scene; plans the vehicle's target trajectory and target speed in the virtual road scene; generates an autonomous driving control model to be trained based on the virtual road scene, target trajectory, and target speed; and trains and verifies the autonomous driving control model to determine whether the learning route is suitable for autonomous driving. Patent CN103383265A proposes an autonomous driving system for automobiles, including a vehicle control unit, a positioning system, an information transceiver device, and a steer-by-wire system. The vehicle control unit records the driving route through the positioning system and exchanges information with the urban road monitoring center through the information transceiver device to correct the road information provided by the urban road monitoring center. The above method mainly achieves road segment self-learning by acquiring road information and municipal traffic signal information. This results in the vehicle's intelligent driving unit needing to store a large amount of data information from multiple road segments. Moreover, users cannot define which road segments need to undergo autonomous driving self-learning, and the autonomous driving style after self-learning has not been integrated into the user's driving habits. Summary of the Invention

[0004] In view of the shortcomings of the prior art described above, the present invention provides an intelligent driving self-learning method, device, electronic device and storage medium to solve the above technical problems.

[0005] The intelligent driving self-learning method provided in this application includes: acquiring real-time vehicle location information; if the vehicle enters a preset target learning road segment, creating an autonomous driving model for the target learning road segment and performing autonomous driving self-learning; if the driving effect of autonomous driving self-learning is consistent with the user's preset target driving effect, and the driving habits of autonomous driving self-learning tend to be consistent with the user's preset driving habits, then determining that autonomous driving self-learning is complete; based on the autonomous driving model after completing autonomous driving self-learning, performing autonomous driving on the target learning road segment, and sharing the autonomous driving model so that other users can perform autonomous driving on the target learning road segment based on the autonomous driving model.

[0006] In one embodiment of this application, if a vehicle enters a portion of a preset target learning road segment, segmented self-learning is performed, and the start position, end position, and route segments of the self-learned road segment are recorded. Multiple portions of the self-learned road segment are combined, and if all segments have undergone segmented self-learning, it is determined that the autonomous driving self-learning of the complete target learning road segment is completed.

[0007] In one embodiment of this application, if the autonomous driving self-learning of the entire target learning road segment is completed, then autonomous driving is permitted in the target learning road segment; if the autonomous driving self-learning of the entire target learning road segment is not completed, then autonomous driving is not permitted in the target learning road segment.

[0008] In one embodiment of this application, vehicle operation scenarios are classified into different road condition scenarios and driving scenarios; based on the driving scenarios under different road condition scenarios, different target driving effects and driving habits are set.

[0009] In one embodiment of this application, vehicle operating status information and preference information are collected. The vehicle operating status information includes starting, acceleration, deceleration, and steering operation information under different road conditions. The preference information includes lane line position preferences and vehicle speed preferences. Based on the vehicle operating status information and preference information, different target driving effects and driving habits are set.

[0010] In one embodiment of this application, when other users perform autonomous driving based on the shared autonomous driving model, if some road sections are not within the autonomous driving road sections, a prompt will be made indicating a deviation from the autonomous driving road sections, and the driver will be prompted to take over driving. If the driver does not take over driving, the vehicle will be controlled to pull over to the side of the road and the hazard lights will be turned on as a warning.

[0011] This application also provides an intelligent driving self-learning device, comprising: a navigation module for acquiring real-time vehicle location information; a self-learning module for creating an autonomous driving model for the target learning road segment and performing autonomous driving self-learning if the vehicle enters a preset target learning road segment; determining that autonomous driving self-learning is complete if the driving effect of autonomous driving self-learning is consistent with the user's preset target driving effect and the driving habits of autonomous driving self-learning tend to be consistent with the user's preset driving habits; and an execution module for performing autonomous driving on the target learning road segment based on the autonomous driving model after autonomous driving self-learning is completed, and sharing the autonomous driving model so that other users can perform autonomous driving on the target learning road segment based on the autonomous driving model.

[0012] In one embodiment of this application, a segmented learning module is further included, which is used to perform segmented self-learning if the vehicle enters a part of the preset target learning road segment, and record the start position, end position, and route of the self-learned road segment; combine multiple parts of the segmented self-learned road segment, and if all segments have been self-learned, determine that the autonomous driving self-learning of the complete road segment of the target learning road segment is completed.

[0013] According to one aspect of the embodiments of this application, an electronic device is provided, including: one or more processors; and a storage device for storing one or more programs, which, when executed by the one or more processors, cause the electronic device to implement the intelligent driving self-learning method as described above.

[0014] According to one aspect of the embodiments of this application, a computer-readable storage medium is provided, on which computer-readable instructions are stored, which, when executed by a computer's processor, cause the computer to perform the intelligent driving self-learning method as described above.

[0015] In some embodiments of the technical solutions provided in this application, the intelligent driving self-learning method, device, electronic device and storage medium of this application further subdivide the functional scenarios of intelligent driving into specific actual road segments, record and analyze the user's driving habits, and use actual driving conditions as the model learning target. It is easy to complete the learning and has high reliability. The model after successful learning can be shared or traded online. Users have purchased or used other users' successfully self-learned road segment autonomous driving models, which supplement and improve users' autonomous driving needs in various regions and road segments. This can greatly stimulate the development of autonomous driving and effectively promote the implementation of intelligent driving in the consumer market.

[0016] It should be understood that the above general description and the following detailed description are exemplary and explanatory only, and do not limit this application. Attached Figure Description

[0017] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with this application and, together with the description, serve to explain the principles of this application. It is obvious that the drawings described below are merely some embodiments of this application, and those skilled in the art can obtain other drawings based on these drawings without any inventive effort. In the drawings:

[0018] Figure 1 This is a schematic diagram of the system architecture of an intelligent driving self-learning method, as illustrated in an exemplary embodiment of this application.

[0019] Figure 2 This is a schematic flowchart illustrating an exemplary embodiment of the intelligent driving self-learning method of this application;

[0020] Figure 3 This is a schematic diagram illustrating the self-learning method of intelligent driving in an exemplary embodiment of this application;

[0021] Figure 4 This is a block diagram illustrating an intelligent driving self-learning device in an exemplary embodiment of this application;

[0022] Figure 5 A schematic diagram of the structure of a computer system suitable for implementing the electronic device of the present application is shown. Detailed Implementation

[0023] The embodiments of the present invention will be described below with reference to the accompanying drawings and preferred embodiments. Those skilled in the art can easily understand other advantages and effects of the present invention from the content disclosed in this specification. The present invention can also be implemented or applied through other different specific embodiments, and various details in this specification can also be modified or changed based on different viewpoints and applications without departing from the spirit of the present invention. It should be understood that the preferred embodiments are only for illustrating the present invention and not for limiting the scope of protection of the present invention.

[0024] It should be noted that the illustrations provided in the following embodiments are only schematic representations of the basic concept of the present invention. Therefore, the drawings only show the components related to the present invention and are not drawn according to the actual number, shape and size of the components in the actual implementation. In the actual implementation, the form, quantity and proportion of each component can be arbitrarily changed, and the layout of the components may also be more complex.

[0025] In the following description, numerous details are explored to provide a more thorough explanation of embodiments of the invention. However, it will be apparent to those skilled in the art that embodiments of the invention may be practiced without these specific details. In other embodiments, well-known structures and devices are shown in block diagram form rather than in detail to avoid obscuring embodiments of the invention.

[0026] In this application, "multiple" refers to two or more. "And / or" describes the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A alone, A and B simultaneously, or B alone. The character " / " generally indicates that the preceding and following related objects have an "or" relationship.

[0027] First, it's important to clarify that automatic driving systems employ advanced communication, computer, network, and control technologies to achieve real-time, continuous train control. Utilizing modern communication methods and directly interacting with the train, they enable two-way data communication between the train and the ground, boasting high transmission speeds and large data volumes. This allows subsequent train tracking and the control center to promptly obtain the precise location of the preceding train, resulting in more flexible operation management, more effective control, and better adaptation to the demands of automatic train operation. Currently, most automatic driving products are hardware-packaged software installations. This approach offers limited customization of the automatic driving model and reduces user participation in promotion, hindering the widespread adoption of automatic driving systems.

[0028] Figure 1 This is a schematic diagram illustrating an exemplary system architecture as shown in an exemplary embodiment of this application.

[0029] Reference Figure 1 As shown, the system architecture may include a vehicle 101, a cloud 102, and a computer device 103. The computer device 103 may be at least one of a desktop graphics processing unit (GPU) computer, a GPU computing cluster, or a neural network computer. Technical personnel can use the vehicle 101 to implement intelligent driving self-learning. The vehicle 101 can share models through the cloud 102 and the computer device 103. By placing the required autonomous driving model into the cloud 102, the cloud 102 distributes the autonomous driving model to other vehicles for other users to share and execute autonomous driving on corresponding road sections.

[0030] It should be noted that the intelligent driving self-learning method provided in this application embodiment is generally executed by vehicle 101.

[0031] The implementation details of the technical solutions in the embodiments of this application are described in detail below:

[0032] Figure 2 This is a flowchart illustrating an exemplary embodiment of the intelligent driving self-learning method, which can be executed by the vehicle. (Refer to...) Figure 2 As shown, this intelligent driving self-learning method includes at least steps S210 to S240, which are described in detail below:

[0033] In step S210, the real-time location information of the vehicle is obtained.

[0034] In one embodiment of this application, the real-time location information of the vehicle is first obtained. This application can further subdivide the functional scenarios of intelligent driving into specific actual road segments through the real-time location information, such as from home to a shopping mall, or from the main entrance of a shopping mall to a parking space in the underground garage of the mall. For this road segment, the information such as lane markings, signs, lanes, and road conditions that affect the performance of intelligent driving is relatively stable. Intelligent driving can automatically build a model of this road segment using the existing scenario-based atomic service models, and record and analyze the user's driving habits. The actual driving situation is used as the model learning target, which is easy to complete the learning and has high reliability.

[0035] In one embodiment of this application, the driver can enable and disable the autonomous driving self-learning of a certain road segment through the human-machine interface of the smart cockpit. The self-learning road segment can be set through the in-vehicle navigation map or determined by the driver's free driving. The intelligent driving unit will record the road segment information, including the start position, end position, and road segments passed through, so that when entering the road segment again, it can automatically determine and prompt that it has entered the historical self-learning road segment or prompt the driver whether to enable the autonomous driving function of the road segment.

[0036] In step S220, if the vehicle enters the preset target learning road segment, an autonomous driving model of the target learning road segment is created and autonomous driving self-learning is performed.

[0037] In one embodiment of this application, if a vehicle enters a preset target learning road segment, an autonomous driving model for the target learning road segment is created and autonomous driving self-learning is performed. The system can determine the necessary basic algorithm modules based on the road segment driven by the user and automatically create a suitable road segment algorithm model. This embodiment may involve three main layers: road segment services, scenario services, and basic algorithms. Intelligent driving can automatically retrieve and create a scenario service model as the basic model for autonomous driving based on the user's driving route. Supplementary models to the basic model are created based on the road segment's specific markings, routes, and user driving behavior. Model creation calls functions from the basic algorithm layer as needed. Model creation can be completed when the user sets the start of self-learning and gradually supplemented and improved during the self-learning process, or it can be created and improved after completing one or more drives on that road segment.

[0038] In step S230, if the driving effect of autonomous driving self-learning is consistent with the target driving effect preset by the user, and the driving habits of autonomous driving self-learning tend to be consistent with the driving habits preset by the user, then autonomous driving self-learning is determined to be completed.

[0039] In one embodiment of this application, if a vehicle enters a portion of a preset target learning road segment, segmented self-learning is performed, and the start position, end position, and route segments of the self-learned road segment are recorded. Multiple portions of the self-learned road segment are combined, and if all segments have undergone segmented self-learning, it is determined that the autonomous driving self-learning of the complete target learning road segment is completed.

[0040] like Figure 3 As shown, the basic algorithm layer contains various fundamental algorithms required for intelligent driving, such as speech recognition, navigation and positioning, trajectory prediction, path planning, and vehicle acceleration and deceleration control. These algorithms can be called by upper layers. The scenario service layer categorizes vehicle operation scenarios, primarily determining the necessary basic algorithm modules based on the user's driving route and automatically creating appropriate route algorithm models. The route service layer records specific road conditions, signs, and traffic flow to refine and supplement the basic model, and performs self-learning based on the user's actual driving habits to achieve comprehensive perception and control of the selected route environment. The user operation layer mainly includes the human-machine interaction functions of the intelligent cockpit and the user's driving behavior. Human-machine interaction content primarily includes the driver's operation via instruments, central control, remote control devices, or voice gestures to activate the route autonomous driving self-learning switch, prompts upon completion of route autonomous driving self-learning, and prompts and operations for user confirmation of whether to activate route autonomous driving. It also includes the intelligent cockpit automatically recognizing driver expressions and voice to make differentiated adjustments based on driving habits.

[0041] In this embodiment, the self-learning process mainly involves two layers: road segment services and user operations. The automatically created autonomous driving model learns based on the user's driving habits and performance, with the user's driving performance serving as the final criterion for determining the completion of self-learning. When the autonomous driving model learns to match the user's driving performance and habits, self-learning is complete, and the system can prompt the user that the autonomous driving function can be activated for that road segment. Examples of user driving habits are shown in Table 1, including information such as starting, acceleration / deceleration, and steering operations under different road conditions, as well as the driver's preferences for the vehicle's position in the lane and driving speed. User driving habits can be statistically analyzed after classifying road conditions and driving scenarios. Driving habits include driver operation habits and driving habits under various road conditions, such as driving in the middle of the road, coasting and decelerating in advance at traffic lights, and idling or using low throttle in underground parking garages. Furthermore, when the vehicle enters a small section of the road segment instead of the entire segment, and the segment has not yet completed autonomous driving self-learning, the system will prompt the driver that they have entered a historical self-learning segment and ask whether to activate the autonomous driving self-learning function for that segment. If enabled, the system will learn the autonomous driving capabilities of that short segment of road as part of the learning process for the entire road segment. The autonomous driving self-learning process is considered complete when multiple short segments (including overlapping segments) can be pieced together to form the complete road segment, or when the entire road segment is driven multiple times and the autonomous driving self-learning process is completed. Only after the entire road segment has completed its autonomous driving self-learning process will the driver be prompted whether to activate the autonomous driving function for that segment.

[0042] Table 1

[0043]

[0044] In step S240, based on the autonomous driving model after completing autonomous driving self-learning, autonomous driving is performed on the target learning road segment, and the autonomous driving model is shared so that other users can perform autonomous driving on the target learning road segment based on the autonomous driving model.

[0045] In one embodiment of this application, when other users execute autonomous driving based on the shared autonomous driving model, if some road segments are not within the autonomous driving range, a warning will be issued indicating a deviation from the autonomous driving range, and the driver will be prompted to take over driving. If the driver does not take over, the vehicle will pull over and activate the hazard lights as a warning. After the autonomous driving segment has completed self-learning, the user can choose to enable or disable the autonomous driving function for the self-learned segment according to the prompts. When the user activates the autonomous driving function, as long as the vehicle is traveling on that road segment or a small section of that road segment, the autonomous driving function can work normally on all the small road segments included in that road segment. When the driver activates the autonomous driving function for a certain road segment, but some road segments are not within the autonomous driving range, the system will issue a warning indicating a deviation from the autonomous driving range and prompt the driver to take over driving. If the driver does not take over driving, the vehicle will automatically pull over and activate the hazard lights.

[0046] In one embodiment of this application, a user can specify one or more commonly used road segments for autonomous driving self-learning. The successfully learned model can be shared or traded online. Users can purchase or use autonomous driving models of road segments that have been successfully self-learned by other users, supplementing and improving the user's autonomous driving needs in various regions and road segments.

[0047] The following describes an embodiment of the apparatus described in this application, which can be used to execute the self-learning method described in the above embodiments of this application. For details not disclosed in the apparatus embodiments of this application, please refer to the embodiments of the self-learning method described above.

[0048] Figure 4 This is a block diagram illustrating an intelligent driving self-learning device according to an exemplary embodiment of this application. The device can be applied to… Figure 1 The implementation environment shown is specifically configured in vehicle 101. This device can also be applied to other exemplary implementation environments and specifically configured in other devices. This embodiment does not limit the implementation environment to which the device is applicable.

[0049] The intelligent driving self-learning device in this embodiment includes:

[0050] The navigation module is used to obtain the vehicle's real-time location information;

[0051] The self-learning module is used to create an autonomous driving model of the target learning road segment and perform autonomous driving self-learning if the vehicle enters the preset target learning road segment; if the driving effect of autonomous driving self-learning is consistent with the user's preset target driving effect, and the driving habits of autonomous driving self-learning tend to be consistent with the user's preset driving habits, then the autonomous driving self-learning is determined to be completed.

[0052] The execution module is used to perform autonomous driving on the target learning road segment based on the autonomous driving model after it has completed autonomous driving self-learning, and to share the autonomous driving model so that other users can perform autonomous driving on the target learning road segment based on the autonomous driving model.

[0053] Embodiments of this application also provide an electronic device, including: one or more processors; and a storage device for storing one or more programs, which, when executed by the one or more processors, cause the electronic device to implement the image processing methods provided in the above embodiments.

[0054] Figure 5 A schematic diagram of a computer system suitable for implementing the embodiments of this application is shown. It should be noted that... Figure 5 The computer system 1200 of the electronic device shown is merely an example and should not impose any limitation on the functionality and scope of use of the embodiments of this application.

[0055] like Figure 5 As shown, the computer system 1200 includes a Central Processing Unit (CPU) 1201, which can perform various appropriate actions and processes based on programs stored in Read-Only Memory (ROM) 1202 or programs loaded from storage portion 1208 into Random Access Memory (RAM) 1203, such as performing the methods described in the above embodiments. The RAM 1203 also stores various programs and data required for system operation. The CPU 1201, ROM 1202, and RAM 1203 are interconnected via a bus 1204. An Input / Output (I / O) interface 1205 is also connected to the bus 1204.

[0056] The following components are connected to I / O interface 1205: an input section 1206 including a keyboard, mouse, etc.; an output section 1207 including a cathode ray tube (CRT), liquid crystal display (LCD), etc., and speakers, etc.; a storage section 1208 including a hard disk, etc.; and a communication section 1209 including a network interface card such as a LAN (Local Area Network) card, modem, etc. The communication section 1209 performs communication processing via a network such as the Internet. A drive 1210 is also connected to I / O interface 1205 as needed. Removable media 1211, such as a disk, optical disk, magneto-optical disk, semiconductor memory, etc., are installed on drive 1210 as needed so that computer programs read from them can be installed into storage section 1208 as needed.

[0057] Specifically, according to embodiments of this application, the processes described above with reference to the flowcharts can be implemented as computer software programs. For example, embodiments of this application include a computer program product comprising a computer program carried on a computer-readable medium, the computer program including a computer program for performing the methods shown in the flowcharts. In such embodiments, the computer program can be downloaded and installed from a network via communication section 1209, and / or installed from removable medium 1211. When the computer program is executed by central processing unit (CPU) 1201, it performs various functions defined in the system of this application.

[0058] It should be noted that the computer-readable medium shown in the embodiments of this application can be a computer-readable signal medium or a computer-readable storage medium, or any combination of the two. A computer-readable storage medium can be, for example, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination thereof. More specific examples of a computer-readable storage medium may include, but are not limited to: an electrical connection having one or more wires, a portable computer disk, a hard disk, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM), flash memory, optical fiber, portable compact disc read-only memory (CD-ROM), optical storage device, magnetic storage device, or any suitable combination thereof. In this application, a computer-readable signal medium may include a data signal propagated in baseband or as part of a carrier wave, carrying a computer-readable computer program. Such propagated data signals can take various forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination thereof. Computer-readable signal media can also be any computer-readable medium other than computer-readable storage media, which can send, propagate, or transmit a program for use by or in connection with an instruction execution system, apparatus, or device. The computer program contained on the computer-readable medium can be transmitted using any suitable medium, including but not limited to wireless, wired, etc., or any suitable combination thereof.

[0059] The flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of this application. Each block in a flowchart or block diagram may represent a module, segment, or portion of code, which contains one or more executable instructions for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions indicated in the blocks may occur in a different order than those indicated in the drawings. For example, two consecutively indicated 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 or flowchart, and combinations of blocks in a block diagram or flowchart, can be implemented using a dedicated hardware-based system that performs the specified function or operation, or using a combination of dedicated hardware and computer instructions.

[0060] The units described in the embodiments of this application can be implemented in software or hardware, and the described units can also be located in a processor. The names of these units do not necessarily limit the specific unit itself.

[0061] Another aspect of this application provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the traffic condition refresh method as described above. This computer-readable storage medium may be included in the electronic device described in the above embodiments, or it may exist independently and not incorporated into the electronic device.

[0062] Another aspect of this application provides a computer program product or computer program including computer instructions stored in a computer-readable storage medium. A processor of a computer device reads the computer instructions from the computer-readable storage medium and executes the computer instructions, causing the computer device to perform the traffic update method provided in the various embodiments described above.

[0063] The above embodiments are merely illustrative of the principles and effects of the present invention and are not intended to limit the invention. Any person skilled in the art can modify or alter the above embodiments without departing from the spirit and scope of the present invention. Therefore, all equivalent modifications or alterations made by those skilled in the art without departing from the spirit and technical concept disclosed in the present invention should still be covered by the claims of the present invention.

Claims

1. A self-learning method for intelligent driving, characterized in that, include: Obtain real-time vehicle location information; The functional scenarios of intelligent driving are further subdivided into specific actual road segments by using real-time location information; If the vehicle enters the preset target learning section, an autonomous driving model of the target learning section is created and autonomous driving self-learning is performed. If the driving effect of autonomous driving self-learning is consistent with the user's preset target driving effect, and the driving habits of autonomous driving self-learning tend to be consistent with the user's preset driving habits, then autonomous driving self-learning is considered to be complete. Vehicle operation scenarios are categorized into different road condition scenarios and driving scenarios; based on the driving scenarios under different road condition scenarios, different target driving effects and driving habits are set; Based on the autonomous driving model that has completed autonomous driving self-learning, autonomous driving is performed on the target learning road segment, and the autonomous driving model is shared so that other users can perform autonomous driving on the target learning road segment based on the autonomous driving model.

2. The intelligent driving self-learning method according to claim 1, characterized in that, After obtaining the vehicle's real-time location information, it also includes: If a vehicle enters a portion of the preset target learning road segment, it will perform segmented self-learning and record the start position, end position, and road segments traversed in the self-learning segment. The multiple road segments that have undergone segmented self-learning are combined. If all segments have undergone segmented self-learning, then the complete autonomous driving self-learning of the target learning road segment is determined to be complete.

3. The intelligent driving self-learning method according to claim 2, characterized in that, Based on the autonomous driving model that has completed autonomous driving self-learning, before autonomous driving is performed on the target learning road segment, the following is also included: If the autonomous driving self-learning of the entire target learning road segment is completed, then autonomous driving is permitted on the target learning road segment. If the autonomous driving self-learning of the entire target learning road segment is not completed, autonomous driving is not allowed on the target learning road segment.

4. The intelligent driving self-learning method according to claim 1, characterized in that, Based on different driving scenarios under various road conditions, different target driving effects and driving habits are set, including: Collect vehicle operating status information and preference information. The vehicle operating status information includes starting, acceleration, deceleration, and steering operation information under different road conditions. The preference information includes lane line position preference and vehicle speed preference. Based on the vehicle operating status information and preference information, different target driving effects and driving habits are set.

5. The intelligent driving self-learning method according to claim 1, characterized in that, Based on the autonomous driving model that has completed self-learning, autonomous driving is performed on the target learning road segment. After sharing the autonomous driving model, the process further includes: When other users perform autonomous driving based on the shared autonomous driving model, if there are sections of road that are not within the autonomous driving range, a warning will be issued indicating a deviation from the autonomous driving range, and the driver will be prompted to take over driving. If the driver does not take over driving, the vehicle will be pulled over to the side of the road and the hazard lights will be turned on as a warning.

6. An intelligent driving self-learning device, characterized in that, include: The navigation module is used to obtain the vehicle's real-time location information; The self-learning module is used to further subdivide the functional scenarios of intelligent driving into specific actual road segments through real-time location information. If the vehicle enters the preset target learning segment, an autonomous driving model of the target learning segment is created and autonomous driving self-learning is performed. If the driving effect of autonomous driving self-learning is consistent with the target driving effect preset by the user, and the driving habits of autonomous driving self-learning tend to be consistent with the driving habits preset by the user, then autonomous driving self-learning is considered to be completed. The execution module is used to perform autonomous driving on the target learning road segment based on the autonomous driving model after completing autonomous driving self-learning, and to share the autonomous driving model so that other users can perform autonomous driving on the target learning road segment based on the autonomous driving model; Vehicle operation scenarios are categorized into different road condition scenarios and driving scenarios; based on the driving scenarios under different road condition scenarios, different target driving effects and driving habits are set.

7. The intelligent driving self-learning device according to claim 6, characterized in that, Also includes: The segmented learning module is used to perform segmented self-learning if a vehicle enters a part of the preset target learning road segment, and records the start position, end position, and road segments passed through the self-learned road segment. The multiple road segments that have undergone segmented self-learning are combined. If all segments have undergone segmented self-learning, then the complete autonomous driving self-learning of the target learning road segment is determined to be complete.

8. An electronic device, characterized in that, include: One or more processors; A storage device for storing one or more programs, which, when executed by the one or more processors, cause the electronic device to implement the intelligent driving self-learning method as described in any one of claims 1 to 5.

9. A computer-readable storage medium, characterized in that, It stores computer-readable instructions, which, when executed by the computer's processor, cause the computer to perform the intelligent driving self-learning method as described in any one of claims 1 to 5.

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