CN115416588A - Intelligent automobile control method and system based on metadata - Google Patents

Intelligent automobile control method and system based on metadata Download PDF

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Publication number
CN115416588A
CN115416588A CN202210861951.2A CN202210861951A CN115416588A CN 115416588 A CN115416588 A CN 115416588A CN 202210861951 A CN202210861951 A CN 202210861951A CN 115416588 A CN115416588 A CN 115416588A
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control
automobile
processed
condition
data
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王羽
季玮恺
王辉
赵汀
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Shanghai Qiwang Network Technology Co ltd
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Shanghai Qiwang Network Technology Co ltd
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    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B60—VEHICLES IN GENERAL
    • B60R—VEHICLES, VEHICLE FITTINGS, OR VEHICLE PARTS, NOT OTHERWISE PROVIDED FOR
    • B60R16/00—Electric or fluid circuits specially adapted for vehicles and not otherwise provided for; Arrangement of elements of electric or fluid circuits specially adapted for vehicles and not otherwise provided for
    • B60R16/02—Electric or fluid circuits specially adapted for vehicles and not otherwise provided for; Arrangement of elements of electric or fluid circuits specially adapted for vehicles and not otherwise provided for electric constitutive elements
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B60—VEHICLES IN GENERAL
    • B60R—VEHICLES, VEHICLE FITTINGS, OR VEHICLE PARTS, NOT OTHERWISE PROVIDED FOR
    • B60R16/00—Electric or fluid circuits specially adapted for vehicles and not otherwise provided for; Arrangement of elements of electric or fluid circuits specially adapted for vehicles and not otherwise provided for
    • B60R16/02—Electric or fluid circuits specially adapted for vehicles and not otherwise provided for; Arrangement of elements of electric or fluid circuits specially adapted for vehicles and not otherwise provided for electric constitutive elements
    • B60R16/023—Electric or fluid circuits specially adapted for vehicles and not otherwise provided for; Arrangement of elements of electric or fluid circuits specially adapted for vehicles and not otherwise provided for electric constitutive elements for transmission of signals between vehicle parts or subsystems

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  • Mechanical Engineering (AREA)
  • Feedback Control In General (AREA)

Abstract

The method and the system for controlling the intelligent automobile based on the metadata determine automobile control data to be processed covering the prior constraint condition setting of the auxiliary driving data of the intelligent automobile; on the premise that the previously set constraint condition of the automobile control data to be processed is screened to comprise the control condition of the reference scene, determining a control index of the control condition of the reference scene in the previously set constraint condition on the basis of a plurality of uninterrupted automobile control events of the control condition of the reference scene screened to the reference scene in the previously set constraint condition, wherein the reference scene comprises a previously set control condition scene matched with the automobile state items; and generating an automobile state item analysis result of the control condition of the screened reference scene on the premise that the control index does not meet the preset driving instruction. Through the steps, the automobile can be accurately controlled, and thus, the intelligent control precision and accuracy of the automobile can be effectively improved.

Description

Intelligent automobile control method and system based on metadata
Technical Field
The application relates to the technical field of data processing, in particular to an intelligent automobile control method and system based on metadata.
Background
Metadata (Metadata), also called intermediary data and relay data, is data (data about data) describing data, and is mainly information describing data attribute (property) for supporting functions such as indicating storage location, history data, resource search, file record, and the like. Metadata is an electronic catalog, and in order to achieve the goal of creating a catalog, the contents or features of data must be described and collected, so as to achieve the goal of assisting data retrieval.
At present, the technical field related to metadata is more and more extensive, and after the metadata is specifically combined with the intelligent automobile technology, various different route conditions under various scenes may exist to interfere the control of an automobile, so that the reliability and the accuracy of the control of the automobile are difficult to ensure.
Disclosure of Invention
In order to solve the technical problems in the related art, the application provides an intelligent automobile control method and system based on metadata.
In a first aspect, a method for intelligent vehicle control based on metadata is provided, the method at least comprising: determining automobile control data to be processed covering the prior constraint condition setting of the intelligent automobile auxiliary driving data; on the premise that the previously set constraint condition of the automobile control data to be processed is screened to include a control condition of a reference scene, determining a control index of the control condition of the reference scene in the previously set constraint condition based on a plurality of uninterrupted automobile control events of the control condition of the reference scene screened in the previously set constraint condition, wherein the plurality of uninterrupted automobile control events covers the automobile control data to be processed; the reference scene comprises a previously set control condition scene matched with the automobile state items; and generating an automobile state item analysis result of the control condition of the reference scene which is screened on the premise that the control index does not meet the preset driving instruction.
In an independently implemented embodiment, the determining vehicle control data to be processed that covers previously set constraints for performing intelligent vehicle-assisted driving data includes: and screening out the automobile control data to be processed at intervals of the auxiliary driving evaluation result from the driving data set recorded by the artificial intelligence thread according to the previously set auxiliary driving evaluation result, wherein the automobile control data to be processed covers the previously set constraint condition for carrying out the intelligent automobile auxiliary driving data.
In an independently implemented embodiment, the determining a control indicator for the control condition of the reference scene in the previously set constraints based on the filtering of the plurality of uninterrupted vehicle control events to the control condition of the reference scene in the previously set constraints comprises: determining an automobile control event collection quantification result X by combining the prior set driving instruction; determining a front X group of automobile control events of the automobile control data to be processed, which are close to the automobile control data to be processed, in a driving data set recorded by an artificial intelligence thread, and a rear X group of automobile control events of the automobile control data to be processed, which are close to the automobile control data to be processed, in the driving data set, and taking the front X group of automobile control events, the automobile control data to be processed and the rear X group of automobile control events as operation data items to be processed; determining, from the operational data items to be processed, a reference operational data item that covers a control condition of the reference scene in the previously set constraint condition; determining a control index of the control condition of the reference scene in the previously set constraint condition in combination with the number of vehicle control events included in the reference operational data item.
In an independently implemented embodiment, the determining a control indicator for the control condition of the reference scene in the previously set constraints based on the filtering of the plurality of uninterrupted vehicle control events to the control condition of the reference scene in the previously set constraints comprises: on the premise that the automobile control data to be processed screened in the previously set constraint condition covers the control condition of the reference scene, determining a control index of the control condition of the reference scene in the previously set constraint condition based on the number of the automobile control data to be processed of the control condition of the reference scene which has been screened recently uninterruptedly, wherein the automobile control data to be processed of the control condition of the reference scene which has been screened recently uninterruptedly comprises the automobile control data to be processed of the control condition of the reference scene which has been screened in real time and a plurality of other automobile control data to be processed which are uninterruptedly before the automobile control data to be processed of the control condition of the reference scene which has been screened in real time; and covering the control conditions of the reference scene in the previously set constraint conditions of the other automobile control data to be processed.
In a separately implemented embodiment, the control conditions of the reference scene are filtered according to the following steps: inputting the automobile control data to be processed into an artificial intelligence thread, selecting the automobile control event description of the automobile control data to be processed with the constraint condition set in advance through the artificial intelligence thread, and outputting a reference identification result of the automobile control event by combining the automobile control event description, wherein the reference identification result is used for indicating whether the control condition of the reference scene is covered in the constraint condition set in advance.
In an embodiment, after determining the vehicle control data to be processed that covers the previously set constraints for the intelligent vehicle-assisted driving data, the method further comprises: determining the collected positioning data of at least one previously set constraint condition; and screening the control conditions of the reference scene in the previously set constraint conditions in combination with the positioning data.
In a second aspect, an intelligent vehicle control system based on metadata is provided, comprising a processor and a memory, which are communicated with each other, wherein the processor is used for reading a computer program from the memory and executing the computer program to realize the method.
The intelligent automobile control method and system based on the metadata determine automobile control data to be processed covering the prior constraint condition setting of the intelligent automobile auxiliary driving data; on the premise that the previously set constraint condition of the automobile control data to be processed is screened to comprise the control condition of the reference scene, determining a control index of the control condition of the reference scene in the previously set constraint condition on the basis of a plurality of uninterrupted automobile control events of the control condition of the reference scene screened to the reference scene in the previously set constraint condition, wherein the reference scene comprises a previously set control condition scene matched with the automobile state items; and on the premise that the control index does not meet the preset driving instruction, generating an automobile state item analysis result of the control condition of the screened reference scene. Through the steps, the automobile can be accurately controlled, and thus, the intelligent control precision and accuracy of the automobile can be effectively improved.
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In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings that are required to be used in the embodiments will be briefly described below, it should be understood that the following drawings only illustrate some embodiments of the present application and therefore should not be considered as limiting the scope, and for those skilled in the art, other related drawings can be obtained from the drawings without inventive effort.
Fig. 1 is a flowchart of an intelligent vehicle control method based on metadata according to an embodiment of the present application.
Fig. 2 is a block diagram of an intelligent vehicle control device based on metadata according to an embodiment of the present application.
Fig. 3 is an architecture diagram of an intelligent vehicle control system based on metadata according to an embodiment of the present application.
Detailed Description
In order to better understand the technical solutions, the technical solutions of the present application are described in detail below with reference to the drawings and specific embodiments, and it should be understood that the specific features in the embodiments and examples of the present application are detailed descriptions of the technical solutions of the present application, and are not limitations of the technical solutions of the present application, and the technical features in the embodiments and examples of the present application may be combined with each other without conflict.
Referring to fig. 1, a method for controlling an intelligent vehicle based on metadata is shown, which may include the following steps 100-300.
Step 100, determining automobile control data to be processed covering the prior constraint condition setting of the intelligent automobile auxiliary driving data;
200, on the premise that the previously set constraint condition of the automobile control data to be processed is screened to include a control condition of a reference scene, determining a control index of the control condition of the reference scene in the previously set constraint condition based on a plurality of uninterrupted automobile control events of the control condition of the reference scene screened to the previously set constraint condition, wherein the plurality of uninterrupted automobile control events cover the automobile control data to be processed; the reference scene comprises a previously set control condition scene matched with the automobile state items;
and 300, generating an automobile state item analysis result of the control condition of the reference scene which is screened on the premise that the control index does not meet the preset driving instruction.
It can be understood that, when the technical solutions described in the above steps 100 to 300 are executed, the vehicle control data to be processed covering the prior constraint condition setting of the intelligent vehicle auxiliary driving data is determined; on the premise that the previously set constraint condition of the automobile control data to be processed is screened to comprise the control condition of the reference scene, determining a control index of the control condition of the reference scene in the previously set constraint condition on the basis of a plurality of uninterrupted automobile control events of the control condition of the reference scene screened to the reference scene in the previously set constraint condition, wherein the reference scene comprises a previously set control condition scene matched with the automobile state items; and on the premise that the control index does not meet the preset driving instruction, generating an automobile state item analysis result of the control condition of the screened reference scene. Through the steps, the automobile can be accurately controlled, and thus the intelligent control precision and accuracy of the automobile can be effectively improved.
In one possible embodiment, when determining the vehicle control data to be processed covering the previously set constraint conditions for performing intelligent vehicle driving assistance data, there may be a problem that the result of driving assistance evaluation is inaccurate, so that it is difficult to accurately determine the vehicle control data to be processed, and in order to improve the above technical problem, the step of determining the vehicle control data to be processed covering the previously set constraint conditions for performing intelligent vehicle driving assistance data described in step 100 may specifically include the content described in step 110 below.
And 110, screening out the automobile control data to be processed from the driving data set recorded by the artificial intelligence thread at intervals of the auxiliary driving evaluation result according to the previously set auxiliary driving evaluation result, wherein the automobile control data to be processed covers the previously set constraint condition for carrying out the intelligent automobile auxiliary driving data.
It can be understood that when the contents described in the above step 110 are executed, and the vehicle control data to be processed covering the previously set constraint condition for performing intelligent vehicle auxiliary driving data is determined, the problem that the auxiliary driving evaluation result is inaccurate is improved, so that the vehicle control data to be processed can be accurately determined.
In a possible embodiment, based on the fact that when the previously-set constraint condition is applied to the plurality of uninterrupted vehicle control events of the control condition of the reference scene, the previously-set driving indication may be inaccurate, so that it is difficult to accurately determine the control index of the control condition of the reference scene in the previously-set constraint condition, in order to improve the above technical problem, the step of determining the control index of the control condition of the reference scene in the previously-set constraint condition in the plurality of uninterrupted vehicle control events of the control condition of the reference scene applied to the previously-set constraint condition in the step 200 may specifically include the following step 210.
Step 210, determining an automobile control event collection quantification result X by combining the prior set driving instruction; determining a front X group of automobile control events of the automobile control data to be processed, which are close to the automobile control data to be processed, in a driving data set recorded by an artificial intelligence thread, and a rear X group of automobile control events of the automobile control data to be processed, which are close to the automobile control data to be processed, in the driving data set, and taking the front X group of automobile control events, the automobile control data to be processed and the rear X group of automobile control events as operation data items to be processed; determining, from the operational data items to be processed, a reference operational data item that covers a control condition of the reference scene in the previously set constraint condition; determining a control index of the control condition of the reference scene in the previously set constraint condition in combination with the number of vehicle control events included in the reference operational data item.
It can be understood that, when the content described in the above step 210 is executed, the problem that the pre-set driving indication is inaccurate is solved when the plurality of uninterrupted automobile control events of the control condition of the reference scene are screened out based on the pre-set constraint condition, so that the control index of the control condition of the reference scene in the pre-set constraint condition can be accurately determined.
In a possible embodiment, when the previous setting constraint is applied to screen the plurality of uninterrupted vehicle control events of the control condition of the reference scene, there may be a problem that the number of vehicle control data to be processed is inaccurate, so that it is difficult to accurately determine the control index of the control condition of the reference scene in the previous setting constraint, and in order to improve the above technical problem, the step of determining the control index of the control condition of the reference scene in the previous setting constraint based on the plurality of uninterrupted vehicle control events of the control condition of the reference scene screened in the previous setting constraint, which is described in step 200, may specifically include the content described in the following step a 1.
A1, on the premise that the control condition of the reference scene is covered by the automobile control data to be processed screened in the previously set constraint condition, determining a control index of the control condition of the reference scene in the previously set constraint condition based on the number of the automobile control data to be processed which have been screened recently and uninterruptedly to the control condition of the reference scene, wherein the automobile control data to be processed which have been screened recently and uninterruptedly to the control condition of the reference scene comprises the automobile control data to be processed which have been screened in real time to the control condition of the reference scene and a plurality of other uninterrupted automobile control data to be processed which are ahead of the automobile control data to be processed which have been screened in real time to the control condition of the reference scene; and covering the control conditions of the reference scene in the previously set constraint conditions of the other automobile control data to be processed.
It can be understood that, when the content described in the above step a1 is executed, when the previously-set constraint condition is screened for a plurality of uninterrupted vehicle control events of the control condition of the reference scene, the problem that the number of vehicle control data to be processed is inaccurate is solved, so that the control index of the control condition of the reference scene in the previously-set constraint condition can be accurately determined.
In this embodiment, the control conditions of the reference scene are filtered according to the following steps: inputting the automobile control data to be processed into an artificial intelligence thread, selecting the automobile control event description of the automobile control data to be processed with the constraint condition set in advance through the artificial intelligence thread, and outputting a reference identification result of the automobile control event by combining the automobile control event description, wherein the reference identification result is used for indicating whether the control condition of the reference scene is covered in the constraint condition set in advance.
Based on the above basis, after determining the vehicle control data to be processed covering the previously set constraint condition for performing the intelligent vehicle auxiliary driving data, the following may be further included: determining the collected positioning data of at least one previously set constraint condition; and screening the control conditions of the reference scene in the previously set constraint conditions by combining the positioning data.
It can be understood that, through the above steps, the control conditions of the reference scene in the previously set constraints can be accurately screened.
On the basis of the above, please refer to fig. 2 in combination, there is provided a metadata-based intelligent vehicle control apparatus 200, which is applied to a metadata-based intelligent vehicle control system, and the apparatus includes:
a data determination module 210, configured to determine vehicle control data to be processed that covers a previously set constraint condition for performing the intelligent vehicle auxiliary driving data;
an index determining module 220, configured to determine a control index of the control condition of the reference scene in the previously set constraint condition based on a plurality of uninterrupted vehicle control events of the control condition of the reference scene screened from the previously set constraint condition on the premise that the previously set constraint condition of the vehicle control data to be processed includes the control condition of the reference scene, where the uninterrupted vehicle control events cover the vehicle control data to be processed; the reference scene comprises a control condition scene which is set in advance and matched with the automobile state items;
and a result analysis module 230, configured to generate an automobile state item analysis result of the control condition of the reference scene that is screened on the premise that the control index does not satisfy a previously set driving instruction.
On the basis of the above, please refer to fig. 3, which shows a metadata-based intelligent vehicle control system 300, which includes a processor 310 and a memory 320, which are communicated with each other, wherein the processor 310 is configured to read a computer program from the memory 320 and execute the computer program to implement the above method.
On the basis of the above, there is also provided a computer-readable storage medium on which a computer program is stored, which when executed implements the above-described method.
In conclusion, based on the scheme, the vehicle control data to be processed covering the constraint conditions set in advance for the intelligent vehicle auxiliary driving data is determined; on the premise that the previously set constraint conditions of the automobile control data to be processed are screened to include the control conditions of the reference scene, determining control indexes of the control conditions of the reference scene in the previously set constraint conditions on the basis of a plurality of uninterrupted automobile control events of the control conditions of the reference scene screened from the previously set constraint conditions, wherein the reference scene includes the previously set control condition scene matched with the automobile state items; and generating an automobile state item analysis result of the control condition of the screened reference scene on the premise that the control index does not meet the preset driving instruction. Through the steps, the automobile can be accurately controlled, and thus, the intelligent control precision and accuracy of the automobile can be effectively improved.
It should be appreciated that the system and its modules shown above may be implemented in a variety of ways. For example, in some embodiments, the system and its modules may be implemented in hardware, software, or a combination of software and hardware. Wherein the hardware portion may be implemented using dedicated logic; the software portions may be stored in a memory for execution by a suitable instruction execution system, such as a microprocessor or specially designed hardware. Those skilled in the art will appreciate that the methods and systems described above may be implemented using computer executable instructions and/or embodied in processor control code, for example such code provided on a carrier medium such as a diskette, CD-or DVD-ROM, programmable memory such as read-only memory (firmware), or a data carrier such as an optical or electronic signal carrier. The system and its modules of the present application may be implemented not only by hardware circuits such as very large scale integrated circuits or gate arrays, semiconductors such as logic chips, transistors, or programmable hardware devices such as field programmable gate arrays, programmable logic devices, etc., but also by software executed by various types of processors, for example, or by a combination of the above hardware circuits and software (e.g., firmware).
It is to be noted that different embodiments may produce different advantages, and in different embodiments, the advantages that may be produced may be any one or combination of the above, or any other advantages that may be obtained.
Having thus described the basic concept, it will be apparent to those skilled in the art that the foregoing detailed disclosure is to be considered merely illustrative and not restrictive of the broad application. Various modifications, improvements and adaptations to the present application may occur to those skilled in the art, though not expressly described herein. Such modifications, improvements and adaptations are proposed in the present application and thus fall within the spirit and scope of the exemplary embodiments of the present application.
Also, the present application uses specific words to describe embodiments of the application. Reference throughout this specification to "one embodiment," "an embodiment," and/or "some embodiments" means that a particular feature, structure, or characteristic described in connection with at least one embodiment of the present application is included in at least one embodiment of the present application. Therefore, it is emphasized and should be appreciated that two or more references to "an embodiment" or "one embodiment" or "an alternative embodiment" in various places throughout this specification are not necessarily all referring to the same embodiment. Furthermore, some features, structures, or characteristics of one or more embodiments of the present application may be combined as appropriate.
Moreover, those skilled in the art will appreciate that aspects of the present application may be illustrated and described in terms of several patentable species or situations, including any new and useful combination of processes, machines, manufacture, or materials, or any new and useful improvement thereof. Accordingly, various aspects of the present application may be embodied entirely in hardware, entirely in software (including firmware, resident software, micro-code, etc.) or in a combination of hardware and software. The above hardware or software may be referred to as "data block," module, "" engine, "" unit, "" component, "or" system. Furthermore, aspects of the present application may be represented as a computer product, including computer readable program code, embodied in one or more computer readable media.
The computer storage medium may comprise a propagated data signal with the computer program code embodied therewith, for example, on a baseband or as part of a carrier wave. The propagated signal may take any of a variety of forms, including electromagnetic, optical, etc., or any suitable combination. A computer storage medium may be any computer-readable medium that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device. Program code on a computer storage medium may be propagated over any suitable medium, including radio, cable, fiber optic cable, RF, or the like, or any combination of the preceding.
Computer program code required for operation of various portions of the present application may be written in any one or more programming languages, including an object oriented programming language such as Java, scala, smalltalk, eiffel, JADE, emerald, C + +, C #, VB.NET, python, and the like, a conventional programming language such as C, visual Basic, fortran 2003, perl, COBOL 2002, PHP, ABAP, a dynamic programming language such as Python, ruby, and Groovy, or other programming languages, and the like. The program code may execute entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer or server. In the latter scenario, the remote computer may be connected to the user's computer through any form of network, such as a Local Area Network (LAN) or a Wide Area Network (WAN), or the connection may be made to an external computer (for example, through the Internet), or in a cloud computing environment, or as a service using, for example, software as a service (SaaS).
Additionally, unless explicitly recited in the claims, the order of processing elements and sequences, use of numbers and letters, or use of other designations in this application is not intended to limit the order of the processes and methods in this application. While various presently contemplated embodiments of the invention have been discussed in the foregoing disclosure by way of example, it is to be understood that such detail is solely for that purpose and that the appended claims are not limited to the disclosed embodiments, but, on the contrary, are intended to cover all modifications and equivalent arrangements that are within the spirit and scope of the embodiments herein. For example, although the system components described above may be implemented by hardware devices, they may also be implemented by software-only solutions, such as installing the described system on an existing server or mobile device.
Similarly, it should be noted that in the preceding description of embodiments of the application, various features are sometimes grouped together in a single embodiment, figure, or description thereof for the purpose of streamlining the disclosure aiding in the understanding of one or more of the embodiments. This method of disclosure, however, is not intended to require more features than are expressly recited in the claims. Indeed, the embodiments may be characterized as having less than all of the features of a single disclosed embodiment.
Numerals describing the number of components, attributes, etc. are used in some embodiments, it being understood that such numerals used in the description of the embodiments are modified in some instances by the use of the modifier "about", "approximately" or "substantially". Unless otherwise indicated, "about", "approximately" or "substantially" indicates that the numbers allow for adaptive variation. Accordingly, in some embodiments, the numerical parameters set forth in the specification and claims are approximations that may vary depending upon the desired properties sought to be obtained by a particular embodiment. In some embodiments, the numerical parameter should take into account the specified significant digits and employ a general digit-preserving approach. Notwithstanding that the numerical ranges and parameters setting forth the broad scope of the range are approximations, in the specific examples, such numerical values are set forth as precisely as possible within the scope of the application.
Each patent, patent application publication, and other material, such as articles, books, specifications, publications, documents, and the like, cited in this application is hereby incorporated by reference in its entirety. Except where the application is filed in a manner inconsistent or contrary to the present disclosure, and except where the claim is filed in its broadest scope (whether present or later appended to the application) as well. It is to be understood that the descriptions, definitions and/or uses of terms in the attached materials of this application shall control if they are inconsistent or inconsistent with the statements and/or uses of this application.
Finally, it should be understood that the embodiments described herein are merely illustrative of the principles of embodiments of the present application. Other variations are also possible within the scope of the present application. Thus, by way of example, and not limitation, alternative configurations of the embodiments of the present application can be viewed as being consistent with the teachings of the present application. Accordingly, the embodiments of the present application are not limited to only those embodiments explicitly described and depicted herein.
The above are merely examples of the present application and are not intended to limit the present application. Various modifications and changes may occur to those skilled in the art. Any modification, equivalent replacement, improvement or the like made within the spirit and principle of the present application shall be included in the scope of the claims of the present application.

Claims (7)

1. An intelligent vehicle control method based on metadata, characterized in that the method at least comprises:
determining automobile control data to be processed covering the prior constraint condition setting of the intelligent automobile auxiliary driving data;
on the premise that the previously set constraint condition of the automobile control data to be processed is screened to include a control condition of a reference scene, determining a control index of the control condition of the reference scene in the previously set constraint condition based on a plurality of uninterrupted automobile control events of the control condition of the reference scene screened in the previously set constraint condition, wherein the plurality of uninterrupted automobile control events covers the automobile control data to be processed; the reference scene comprises a previously set control condition scene matched with the automobile state items;
and on the premise that the control index does not meet the preset driving instruction, generating a screened automobile state item analysis result of the control condition of the reference scene.
2. The intelligent vehicle control method based on metadata according to claim 1, wherein the determining of the vehicle control data to be processed covering the previously set constraints for performing the intelligent vehicle auxiliary driving data comprises: and screening out the automobile control data to be processed at intervals of the auxiliary driving evaluation result from the driving data set recorded by the artificial intelligence thread according to the previously set auxiliary driving evaluation result, wherein the automobile control data to be processed covers the previously set constraint condition for carrying out the intelligent automobile auxiliary driving data.
3. The intelligent metadata-based vehicle control method of claim 2, wherein determining the control indicator for the control condition of the reference scene in the previously set constraints based on the uninterrupted plurality of vehicle control events that were screened for the control condition of the reference scene in the previously set constraints comprises:
determining an automobile control event collection quantification result X by combining the prior set driving instruction; determining a front X group of automobile control events of the automobile control data to be processed, which are close to the automobile control data to be processed, in a driving data set recorded by an artificial intelligence thread, and a rear X group of automobile control events of the automobile control data to be processed, which are close to the automobile control data to be processed, in the driving data set, and taking the front X group of automobile control events, the automobile control data to be processed and the rear X group of automobile control events as operation data items to be processed; determining, from the operational data items to be processed, a reference operational data item that covers a control condition of the reference scene in the previously set constraint condition; determining a control index of the control condition of the reference scene in the previously set constraint condition in combination with the number of vehicle control events included in the reference operational data item.
4. The intelligent metadata-based vehicle control method according to claim 1 or 2, wherein the determining the control index of the control condition of the reference scene in the previously set constraint condition based on the uninterrupted plurality of vehicle control events of the control condition of the reference scene screened in the previously set constraint condition comprises:
on the premise that the automobile control data to be processed screened in the previously set constraint condition covers the control condition of the reference scene, determining a control index of the control condition of the reference scene in the previously set constraint condition based on the number of the automobile control data to be processed of the control condition of the reference scene which has been screened recently uninterruptedly, wherein the automobile control data to be processed of the control condition of the reference scene which has been screened recently uninterruptedly comprises the automobile control data to be processed of the control condition of the reference scene which has been screened in real time and a plurality of other automobile control data to be processed which are uninterruptedly before the automobile control data to be processed of the control condition of the reference scene which has been screened in real time; and covering the control condition of the reference scene in the previously set constraint condition of the other automobile control data to be processed.
5. The intelligent vehicle control method based on metadata according to claim 4, wherein the control conditions of the reference scene are filtered according to the following steps: inputting the automobile control data to be processed into an artificial intelligence thread, selecting the automobile control event description of the automobile control data to be processed with the constraint condition set in advance through the artificial intelligence thread, and outputting a reference identification result of the automobile control event by combining the automobile control event description, wherein the reference identification result is used for indicating whether the control condition of the reference scene is covered in the constraint condition set in advance.
6. The intelligent vehicle control method based on metadata according to claim 5, further comprising, after determining the vehicle control data to be processed that covers the previously set constraints for performing the intelligent vehicle auxiliary driving data: determining the collected positioning data with at least one constraint condition set in advance; and screening the control conditions of the reference scene in the previously set constraint conditions by combining the positioning data.
7. An intelligent metadata-based vehicle control system comprising a processor and a memory in communication with each other, the processor being configured to read a computer program from the memory and execute the computer program to implement the method of any one of claims 1-6.
CN202210861951.2A 2022-07-22 2022-07-22 Intelligent automobile control method and system based on metadata Pending CN115416588A (en)

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