Intelligent driving data management method and electronic equipment

Abstract

The invention provides an intelligent driving data management method and electronic equipment, and relates to the technical field of intelligent driving data management. According to the method, intelligent driving data generated in the driving process of a vehicle can be received, then the intelligent driving data are stored, and the intelligent driving data are labeled with multi-dimensional labels; next, in response to the at least one user operation instruction, executing a data processing operation associated with the at least one user operation instruction based on the intelligent driving data; acquiring data processing operation on the intelligent driving data each time; and under the condition that the data processing operation meets a preset label updating condition, updating a data use portrait label of the intelligent driving data according to the data processing operation. Therefore, the data use portrait label of the intelligent driving data can be updated along with continuous use of the intelligent driving data due to business requirements. Therefore, the accuracy of marking the intelligent driving data by the multi-dimensional label is high, and the reliability of the multi-dimensional label is high.

Description

Technical Field

[0001] This application relates to the field of intelligent driving data management technology, and in particular to an intelligent driving data management method and electronic device. Background Technology

[0002] With the rapid development of intelligent driving technology, the scale and complexity of intelligent driving data are growing exponentially, thus posing increasing challenges to the storage, management, and application of intelligent driving data. Typically, different intelligent driving data can be tagged with different data labels so that staff can retrieve the data based on these labels and use it to perform business processing operations (such as training intelligent driving models or conducting intelligent driving function tests).

[0003] Currently, after receiving raw autonomous driving data, it can be tagged, and these tags remain fixed. However, autonomous driving data is continuously applied to business processing operations (such as training autonomous driving models or conducting autonomous driving function tests) as needed. If the data tags remain fixed, the accuracy of tagging the autonomous driving data will be low, leading to low reliability of the data tags. Consequently, the reliability of the autonomous driving data retrieved by staff based on these tags will also be low. Summary of the Invention

[0004] This application provides an intelligent driving data management method and electronic device to solve the problem that in the prior art, the data tags for intelligent driving data are fixed and unchanging, which results in low accuracy in tagging intelligent driving data and low reliability of data tags.

[0005] Firstly, this application provides an intelligent driving data management method, applied to a first server. The method provided by this application includes: Receive intelligent driving data generated by the vehicle during driving, including driving scenario data and driving parameters of the vehicle. Store intelligent driving data and tag the intelligent driving data with multi-dimensional labels, including at least the data usage profile label of the intelligent driving data; In response to at least one user operation command, perform data processing operations associated with at least one user operation command based on intelligent driving data; Acquire each data processing operation performed on intelligent driving data; When the data processing operation meets the preset label update conditions, the data usage profile label of the intelligent driving data is updated according to the data processing operation.

[0006] In some implementations, the data usage profile label includes data annotation labels for intelligent driving data. When the data processing operation meets preset label update conditions, the data usage profile label for the intelligent driving data is updated according to the data processing operation, including: When the data processing operation is a data labeling update operation, the data labeling labels of the intelligent driving data are updated based on the data labeling update operation.

[0007] In some implementations, the data usage profile tags include the historical usage frequency of intelligent driving data. When the data processing operation meets preset tag update conditions, the data usage profile tags of the intelligent driving data are updated according to the data processing operation, including: Get the number of times data processing operations are performed on intelligent driving data within a preset period; The current usage frequency of intelligent driving data is determined based on the number of times data processing operations are performed. In cases where the current usage popularity of intelligent driving data differs from its historical usage popularity, the historical usage popularity should be updated based on the current usage popularity.

[0008] In some implementations, the multi-dimensional tags also include data storage tags. After updating historical usage popularity based on current usage popularity, the method provided in this application embodiment further includes: Determine the data storage area that matches the current usage frequency; If the current storage area of ​​intelligent driving data is different from the data storage area that matches the current usage frequency, the intelligent driving data will be migrated from the current storage area to the data storage area that matches the current usage frequency. Update the data storage labels based on the data storage regions that match the current usage popularity.

[0009] In some implementations, the data usage profile label also includes the task type of the data processing operation and the number of times each task type is executed. The task types of the data processing operation include inputting intelligent driving data as training samples into the network to be trained to train the intelligent driving model, and / or feeding intelligent driving data back into the vehicle test bench as test data to test the intelligent driving function of the vehicle.

[0010] In some implementations, the data usage profile tags include historical data lineage. When the data processing operation meets preset tag update conditions, the data usage profile tags of the intelligent driving data are updated according to the data processing operation, including: If the data processing operation is determined to be a data editing operation, the current parent-child data lineage relationship is established between the intelligent driving data before the data editing operation and the intelligent driving data after the data editing operation. Update historical bloodline relationships based on current father-son data.

[0011] In some implementations, when the data processing operation meets preset label update conditions, the data usage profile label of the intelligent driving data is updated according to the data processing operation, including: If the data processing operation meets the preset label update conditions, immediately update the data usage profile label of the intelligent driving data according to the data processing operation; Alternatively, at preset intervals, retrieve data processing operations that meet preset tag update conditions from pre-stored operation logs; Based on the data processing operations that meet the preset label update conditions, update the data use profile labels of intelligent driving data.

[0012] In some implementations, multi-dimensional tags are stored on a second server, and the data use profile tags are updated based on data processing operations for intelligent driving data, including: Generate tag update notifications based on data processing operations; The first server sends a tag update notification to the second server via its API interface, so that the second server updates the data usage profile tags of the pre-stored intelligent driving data according to the tag update notification.

[0013] In some implementations, the driving scene data includes at least environmental image data collected by the vehicle-mounted camera, environmental point cloud data collected by the vehicle-mounted lidar, and distance data to surrounding obstacles collected by the vehicle-mounted millimeter-wave radar. The vehicle's driving parameters include at least the vehicle's GPS positioning data / GNSS positioning data, vehicle attitude data, vehicle speed, vehicle acceleration, and throttle data.

[0014] In a second aspect, this application provides an electronic device including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the computer program, the electronic device performs the method provided in the first aspect of this application.

[0015] Thirdly, this application also provides a storage medium storing a computer program, which, when executed by a processor, causes the computer to perform the method provided in the first aspect of this application.

[0016] Fourthly, this application also provides a computer program product, including a computer program that, when run, causes an electronic device to perform the method provided in the first aspect of this application.

[0017] This application provides an intelligent driving data management method and electronic device, which can receive intelligent driving data generated during vehicle operation, store the intelligent driving data, and assign multi-dimensional tags to the intelligent driving data. Next, in response to at least one user operation command, it performs data processing operations associated with at least one user operation command based on the intelligent driving data; it acquires each data processing operation performed on the intelligent driving data; and when the data processing operation meets preset tag update conditions, it updates the data usage profile tags of the intelligent driving data according to the data processing operation. In this way, the data usage profile tags of the intelligent driving data can be updated as the intelligent driving data is used continuously due to business needs. This ensures high accuracy and reliability of multi-dimensional tagging of intelligent driving data, thereby ensuring high reliability of the intelligent driving data retrieved by staff based on the multi-dimensional tags. Attached Figure Description

[0018] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0019] Figure 1 A flowchart of the intelligent driving data management method provided in the embodiments of this application; Figure 2 A schematic diagram illustrating the interaction between the first server and the second server provided in an embodiment of this application; Figure 3 A functional block diagram of the intelligent driving data management device provided in the embodiments of this application. Detailed Implementation

[0020] Embodiments of the present disclosure will now be described with reference to the accompanying drawings. However, it should be understood that these descriptions are exemplary only and are not intended to limit the scope of the disclosure. Furthermore, descriptions of well-known structures and technologies are omitted in the following description to avoid unnecessarily obscuring the concepts of the present disclosure.

[0021] The accompanying drawings illustrate various structural schematics according to embodiments of the present disclosure. These drawings are not to scale, and some details have been enlarged for clarity, and some details may have been omitted. The shapes of the various regions and layers shown in the drawings, as well as their relative sizes and positional relationships, are merely exemplary and may deviate from reality due to manufacturing tolerances or technical limitations. Furthermore, those skilled in the art can design regions / layers with different shapes, sizes, and relative positions as needed.

[0022] In the context of this disclosure, when a layer / element is referred to as being "above" another layer / element, the layer / element may be directly above the other layer / element, or there may be an intermediate layer / element between them. Additionally, if a layer / element is "above" another layer / element in one orientation, then when the orientation is reversed, the layer / element may be "below" the other layer / element.

[0023] The technical solutions of this application and how they solve the aforementioned technical problems will be described in detail below with specific embodiments. These specific embodiments can be combined with each other, and the same or similar concepts or processes may not be repeated in some embodiments. The embodiments of this application will now be described with reference to the accompanying drawings.

[0024] Please see Figure 1 This application provides an intelligent driving data management method, applied to a first server. The method provided in this application includes: S101: Receives intelligent driving data generated during vehicle operation.

[0025] Intelligent driving data includes driving scenario data and vehicle driving parameters. For example, driving scenario data includes at least environmental image data collected by the vehicle's onboard camera, environmental point cloud data collected by the vehicle's onboard LiDAR, and distance data to surrounding obstacles collected by the vehicle's millimeter-wave radar. Vehicle driving parameters include at least the vehicle's GPS / GNSS positioning data, vehicle attitude data, vehicle speed, vehicle acceleration, and throttle data. It should be noted that the term "vehicle" refers to all vehicles within the specified area.

[0026] S102: Stores intelligent driving data and adds multi-dimensional labels to the intelligent driving data.

[0027] The multi-dimensional tags include at least a data usage profile tag for intelligent driving data. For example, the data usage profile tag may include, but is not limited to, the total number of historical uses of intelligent driving data, historical usage popularity, task type of data processing operation, execution count of each task type, usage permissions (e.g., which users have access and which are prohibited from using the data), security and compliance information (e.g., whether sensitive data in the intelligent driving data has been masked), and data annotation tags (e.g., the data type and core data content of the annotations). Specifically, the multi-dimensional tags can be represented by a set of fields consisting of multiple fields with different dimensions. For example, field 1 represents the total number of historical uses, field 2 represents the historical usage popularity, field 3 represents usage permissions, and so on.

[0028] In addition, multi-dimensional tags also include storage tags for intelligent driving data. For example, storage tags for intelligent driving data may include, but are not limited to, data size, data format, and data storage location. Multi-dimensional tags may also include attribute tags for intelligent driving data. For example, attribute tags for intelligent driving data may include, but are not limited to, the source of intelligent driving data (such as from cameras or LiDAR) and data quality (such as image clarity or the strength of communication signals when transmitting intelligent driving data).

[0029] S103: In response to at least one user operation command, perform a data processing operation associated with at least one user operation command based on intelligent driving data.

[0030] For example, at least one data processing operation associated with a user operation command includes, but is not limited to, data annotation and update operations, data editing operations, inputting intelligent driving data as training samples into the network to be trained to train the intelligent driving model, and / or feeding intelligent driving data back into the vehicle test bench as test data to test the intelligent driving function of the vehicle.

[0031] S104: Obtain the data processing operations performed on intelligent driving data each time.

[0032] S105: When the data processing operation meets the preset label update conditions, update the data usage profile label of the intelligent driving data according to the data processing operation.

[0033] Specifically, if the data processing operation meets the preset label update conditions, the data usage profile label of the intelligent driving data can be updated immediately according to the data processing operation; or, every preset period (such as 8:00 a.m. every day), the data processing operation that meets the preset label update conditions can be obtained from the pre-stored operation log; and the data usage profile label of the intelligent driving data can be updated according to the obtained data processing operation that meets the preset label update conditions.

[0034] For example, when the data uses profile tags that include data annotation tags for intelligent driving data, S105 can be specifically implemented such that if the data processing operation is a data annotation update operation, the data annotation tags for intelligent driving data are updated based on the data annotation update operation. For example, when intelligent driving data includes environmental image data, if the environmental image data was previously annotated with the core data content "pedestrian," and if the staff finds that the environmental image data also includes "truck," then the environmental image data can also be annotated with "truck" (i.e., in response to the user's operation command, based on the operation of annotating the environmental image data with "truck"), thereby identifying the data annotation update operation and updating the data annotation tags for intelligent driving data.

[0035] For example, data usage profile tags include the historical usage popularity of intelligent driving data. S105 can be specifically implemented as follows: Obtain the number of times data processing operations are performed on intelligent driving data within a preset period; determine the current usage popularity of intelligent driving data based on the number of data processing operations performed (e.g., determine the frequency range to which the number of data processing operations belongs, and find the current usage popularity of intelligent driving data from a preset mapping table based on the frequency range; for example, the current usage popularity is divided into three levels: cold, medium, and hot, with different frequency ranges corresponding to different levels of popularity); if the current usage popularity of intelligent driving data is inconsistent with the historical usage popularity, update the historical usage popularity based on the current usage popularity. For example, if the historical usage popularity is "cold" and the current usage popularity is "hot," then the historical usage popularity of "cold" can be updated to the current usage popularity of "hot."

[0036] Furthermore, when the multi-dimensional tags also include data storage tags, the method provided in this application embodiment further includes: determining a data storage area matching the current usage popularity; if the current storage area of ​​the intelligent driving data differs from the data storage area matching the current usage popularity, migrating the intelligent driving data from the current storage area to the data storage area matching the current usage popularity; and updating the data storage tags based on the data storage area matching the current usage popularity. In this way, intelligent driving data with different usage popularity can be stored in different storage areas for easy retrieval by staff.

[0037] In addition, the data usage profile label also includes the task type of the data processing operation and the number of times each task type is executed. The task types of data processing operations include using intelligent driving data as training samples to input into the network to train the intelligent driving model, and / or using intelligent driving data as test data to feed back into the vehicle test bench to test the vehicle's intelligent driving functions. Understandably, the number of times each task type is executed is updated as the task is performed.

[0038] In addition, when the data uses profile tags that include historical data lineage, if the data processing operation is determined to be a data editing operation, the current parent-child data lineage relationship will be established between the intelligent driving data before the data editing operation and the intelligent driving data after the data editing operation (e.g., if a processing operation is performed on a certain environmental image, such as rotation and sharpness processing, the current parent-child data lineage relationship between the environmental image before the processing operation and the environmental image after the processing operation will be established); the historical data lineage relationship will be updated based on the current parent-child data lineage relationship.

[0039] In addition, such as Figure 2As shown, the first server can communicate with the second server via an API interface. Multi-dimensional tags are stored on the second server, which can generate tag update notifications based on data processing operations. The first server sends these notifications to the second server via its API interface, enabling the second server to update the pre-stored intelligent driving data's data usage profile tags accordingly. This decouples intelligent driving data storage from intelligent driving data tag management. The second server can focus on tag management and computation, while the first server focuses on intelligent driving data management. Asynchronous data exchange between the two servers is achieved through a message queue, ensuring the scalability and high performance of the system comprised of the first and second servers.

[0040] In summary, this application provides an intelligent driving data management method that can receive intelligent driving data generated during vehicle operation, store the intelligent driving data, and assign multi-dimensional tags to the intelligent driving data. Next, in response to at least one user operation command, it performs data processing operations associated with the intelligent driving data based on the intelligent driving data; it acquires each data processing operation performed on the intelligent driving data; and when the data processing operation meets preset tag update conditions, it updates the data usage profile tags of the intelligent driving data according to the data processing operation. This allows the data usage profile tags of the intelligent driving data to be updated as the intelligent driving data is used continuously due to business needs. This ensures high accuracy and reliability of multi-dimensional tagging of intelligent driving data, thereby ensuring high reliability of the intelligent driving data retrieved by staff based on the multi-dimensional tags.

[0041] Please see Figure 3 This application also provides an intelligent driving data management device applied to a first server. It should be noted that the intelligent driving data management device provided in this application has the same basic principle and technical effects as the above embodiments. For the sake of brevity, any parts not mentioned in this application can be referred to the corresponding content in the above embodiments. For example... Figure 3 As shown, the apparatus provided in this application embodiment includes a data receiving unit, a data storage unit, a data operation unit, an operation acquisition unit, and a tag updating unit, wherein... The data receiving unit is used to receive intelligent driving data generated by the vehicle during driving. The intelligent driving data includes driving scenario data and driving parameters of the vehicle.

[0042] For example, the driving scene data includes at least environmental image data collected by the vehicle camera, environmental point cloud data collected by the vehicle lidar, and distance data to surrounding obstacles collected by the vehicle millimeter-wave radar. The vehicle's driving parameters include at least the vehicle's GPS positioning data / GNSS positioning data, vehicle attitude data, vehicle speed, vehicle acceleration, and throttle data.

[0043] The data storage unit is used to store intelligent driving data and to tag the intelligent driving data with multi-dimensional labels, wherein the multi-dimensional labels include at least the data usage profile label of the intelligent driving data.

[0044] A data operation unit is used to perform data processing operations associated with at least one user operation command based on intelligent driving data in response to at least one user operation command.

[0045] The operation acquisition unit is used to acquire each data processing operation performed on the intelligent driving data.

[0046] The tag update unit is used to update the data usage profile tags of intelligent driving data according to the data processing operation when the data processing operation meets the preset tag update conditions.

[0047] In some implementations, the data uses profile tags including data annotation tags for intelligent driving data. The tag update unit is specifically used to update the data annotation tags of intelligent driving data based on the data annotation update operation when the data processing operation is a data annotation update operation.

[0048] In some implementations, the data usage profile label includes the historical usage popularity of intelligent driving data. The label update unit is specifically used to obtain the number of times data processing operations are performed on intelligent driving data within a preset period; determine the current usage popularity of intelligent driving data based on the number of data processing operations performed; and update the historical usage popularity based on the current usage popularity if the current usage popularity of intelligent driving data is inconsistent with the historical usage popularity.

[0049] In some implementations, the multi-dimensional tag also includes a data storage tag. The apparatus provided in this application embodiment further includes a storage area determination unit, used to determine a data storage area that matches the current usage frequency. A data migration unit is used to migrate the intelligent driving data from its current storage area to a data storage area that matches the current usage frequency when the current storage area of ​​the intelligent driving data differs from the data storage area that matches the current usage frequency. Thus, the aforementioned tag update unit is also used to update the data storage tag based on the data storage area that matches the current usage frequency.

[0050] In some implementations, the data usage profile label also includes the task type of the data processing operation and the number of times each task type is executed. The task types of the data processing operation include inputting intelligent driving data as training samples into the network to be trained to train the intelligent driving model, and / or feeding intelligent driving data back into the vehicle test bench as test data to test the intelligent driving function of the vehicle.

[0051] In some implementations, the data uses profile tags including historical data lineage. The tag update unit is specifically used to establish a current parent-child data lineage relationship between the intelligent driving data before the data editing operation and the intelligent driving data after the data editing operation when the data processing operation is determined to be a data editing operation; and to update the historical data lineage relationship based on the current parent-child data lineage relationship.

[0052] In some implementations, the tag updating unit is specifically used to immediately update the data usage profile tag of the intelligent driving data according to the data processing operation when the data processing operation meets the preset tag updating conditions; or, at preset intervals, to obtain the data processing operation that meets the preset tag updating conditions from the pre-stored operation log; and to update the data usage profile tag of the intelligent driving data according to the obtained data processing operation that meets the preset tag updating conditions.

[0053] In some implementations, multi-dimensional tags are stored on a second server. The tag update unit is specifically used to generate tag update notifications based on data processing operations. The tag update notifications are sent to the second server via the API interface of the first server, so that the second server updates the data usage profile tags of the pre-stored intelligent driving data according to the tag update notifications.

[0054] In addition, embodiments of this application provide an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, the electronic device performs the method provided in the above embodiments of this application.

[0055] In addition, this application embodiment also provides a storage medium storing a computer program, which, when executed by a processor, causes the computer to perform the method provided in the above embodiments of this application.

[0056] In addition, this application also provides a computer program product, including a computer program that, when run, causes an electronic device to perform the method provided in the above embodiments of this application.

[0057] The above description does not provide detailed technical specifications regarding the structure of each layer. However, those skilled in the art should understand that layers and regions of desired shapes can be formed using various technical means. Furthermore, to form the same structure, those skilled in the art can also design methods that are not entirely identical to those described above. Additionally, although various embodiments have been described above, this does not mean that the measures in the various embodiments cannot be advantageously combined.

[0058] Although preferred embodiments of this application have been described, those skilled in the art, upon learning the basic inventive concept, can make other changes and modifications to these embodiments. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments as well as all changes and modifications falling within the scope of this application.

[0059] Obviously, those skilled in the art can make various modifications and variations to this application without departing from the spirit and scope of this application. Therefore, if such modifications and variations fall within the scope of the claims of this application and their equivalents, this application also intends to include such modifications and variations.

Claims

1. A method for intelligent driving data management, characterized in that, Applied to a first server, the method includes: Receive intelligent driving data generated by the vehicle during driving, wherein the intelligent driving data includes driving scenario data of the vehicle and driving parameters of the vehicle. The intelligent driving data is stored and multi-dimensional tags are added to the intelligent driving data, wherein the multi-dimensional tags include at least the data usage profile tags of the intelligent driving data; In response to at least one user operation command, perform a data processing operation associated with the at least one user operation command based on the intelligent driving data; Acquire each data processing operation performed on the intelligent driving data; If the data processing operation meets the preset label update conditions, the data usage profile label of the intelligent driving data is updated according to the data processing operation.

2. The method according to claim 1, characterized in that, The data usage profile tags include the data labeling tags of the intelligent driving data. When the data processing operation meets preset tag update conditions, the data usage profile tags of the intelligent driving data are updated according to the data processing operation, including: When the data processing operation is a data labeling update operation, the data labeling tags of the intelligent driving data are updated based on the data labeling update operation.

3. The method according to claim 1, characterized in that, The data usage profile tags include the historical usage frequency of the intelligent driving data. When the data processing operation meets preset tag update conditions, the data usage profile tags of the intelligent driving data are updated according to the data processing operation, including: The number of times data processing operations are performed on the intelligent driving data within a preset period is obtained; The current usage frequency of the intelligent driving data is determined based on the number of times data processing operations are performed. If the current usage popularity of the intelligent driving data is inconsistent with the historical usage popularity, the historical usage popularity shall be updated based on the current usage popularity.

4. The method according to claim 3, characterized in that, The multi-dimensional tags also include data storage tags. After updating the historical usage popularity based on the current usage popularity, the method further includes: Determine a data storage area that matches the current usage frequency; If the current storage area of ​​the intelligent driving data is different from the data storage area that matches the current usage popularity, the intelligent driving data will be migrated from the current storage area to the data storage area that matches the current usage popularity. Update the data storage label based on the data storage area that matches the current usage popularity.

5. The method according to claim 1, characterized in that, The data use profile tags also include tags for characterizing the task type of the data processing operation and the number of times each task type is executed. The task types of the data processing operation include inputting the intelligent driving data as training samples into the network to be trained to train the intelligent driving model, and / or feeding the intelligent driving data back into the vehicle test bench as test data to test the intelligent driving function of the vehicle.

6. The method according to claim 1, characterized in that, The data usage profile tags include historical data lineage. When the data processing operation meets preset tag update conditions, the data usage profile tags of the intelligent driving data are updated according to the data processing operation, including: If the data processing operation is determined to be a data editing operation, a current parent-child data lineage relationship is established between the intelligent driving data before the data editing operation and the intelligent driving data after the data editing operation. Update the historical bloodline relationship based on the current parent-child bloodline relationship.

7. The method according to claim 1, characterized in that, The step of updating the data usage profile label of the intelligent driving data according to the data processing operation when the data processing operation meets the preset label update conditions includes: If the data processing operation meets the preset tag update conditions, the data usage profile tag of the intelligent driving data is immediately updated according to the data processing operation; Alternatively, at preset intervals, retrieve data processing operations that meet preset tag update conditions from pre-stored operation logs; Based on the data processing operations that meet the preset label update conditions, the data use profile labels of the intelligent driving data are updated.

8. The method according to claim 1, characterized in that, The multi-dimensional tags are stored on a second server, and the data usage profile tags of the intelligent driving data are updated according to the data processing operations, including: Based on the data processing operations, a tag update notification is generated; The first server sends a tag update notification to the second server via its API interface, so that the second server updates the data usage profile tag of the pre-stored intelligent driving data according to the tag update notification.

9. The method according to any one of claims 1-8, characterized in that, The driving scenario data includes at least environmental image data collected by the vehicle-mounted camera, environmental point cloud data collected by the vehicle-mounted lidar, and distance data to surrounding obstacles collected by the vehicle-mounted millimeter-wave radar. The vehicle's driving parameters include at least the vehicle's GPS positioning data / GNSS positioning data, vehicle attitude data, vehicle speed, vehicle acceleration, and throttle data.

10. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the computer program, it causes the electronic device to perform the method as described in any one of claims 1 to 9.

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