Data management system for autonomous vehicle driving

Abstract

The embodiment of the present application discloses a data management system for vehicle autonomous driving. A specific implementation of the method includes: a data management module configured to access and manage vehicle autonomous driving data; an algorithm management module configured to access the associated parameters, version, address, and description information of the autonomous driving algorithm into the system for unified management; and a process management module configured to combine and splice the required data and the required algorithm to construct a business flow according to business needs. This implementation supports the combination of data and algorithms to construct a business flow, thereby improving the efficiency of business processing.

Description

Technical Field

[0001] The embodiments of the present application relate to the field of computer technology, and in particular to a data management system for autonomous vehicle driving. Background Art

[0002] Data is an important asset for enterprises, especially in the information age. Whether it is on public roads or in closed underground mines, the amount of autonomous driving data will continue to grow in a massive manner. How to effectively manage and utilize a large amount of data is particularly important and directly related to the value of data and the benefits of enterprises. Due to the large volume and diversity of data structure of autonomous driving data, traditional data management methods can no longer meet the growing storage and data processing needs. Summary of the invention

[0003] An embodiment of the present application proposes a data management system for vehicle autonomous driving, which system includes: a data management module, configured to access and manage vehicle autonomous driving data; an algorithm management module, configured to access the associated parameters, version, address, and description information of the autonomous driving algorithm into the system for unified management; and a process management module, configured to combine and splice the required data and the required algorithms to construct a business flow according to business needs.

[0004] In some embodiments, vehicle autonomous driving data includes original collected data, pre-processed data, annotated data, perception results data, positioning data, perception algorithm data, and label data; and the data management system adopts a distributed storage form, supports the coexistence and sharing of different data types, and the upper layer of the data management system is accessed through a unified encapsulated interface, and supports real-time query and analysis; the data management system reads data from different storage systems in a mounted manner by setting different connectors, and the data storage system includes a file storage system, a database storage system, and an object storage system.

[0005] In some embodiments, the data management module includes an original data area and a data asset area. The data in the original data area is preprocessed and stored in the data asset area with the data set as the smallest storage unit. The data asset area also includes the result data obtained based on the data set and algorithm custom processing flow.

[0006] In some embodiments, data preprocessing includes data parsing, label extraction, building a data catalog, data cleaning, quality checking, error correction, parsed reorganization, and data migration.

[0007] In some embodiments, label extraction includes: when data is accessed, metadata information and label information are synchronously extracted, put into the label library, and the label library is synchronously improved; when extracting labels, if the labels in the existing label library cannot meet business needs, user-defined labels are obtained, and the classification of labels in the label library is defined according to different business needs.

[0008] In some embodiments, building a data directory includes: building a data directory based on tags on the data, the data directory classification includes business class, management class and quality class, the data directory includes project identification, collection time, collection device, data set ID, data set name, data set size, version description, according to different business needs, the data directory supports custom extension.

[0009] In some embodiments, the data management module is further configured to retain or delete data based on time factors and scene values, wherein negative scene data in the autonomous driving system is retained and the results data obtained from the business flow is fully managed according to versions.

[0010] In some embodiments, the algorithm management module is further configured to store the algorithms as data sets, and to perform data tagging on each algorithm data set to construct an algorithm data directory.

[0011] In some embodiments, business needs include algorithm training, scenario mining, and simulation testing. Business flows generate new data sets that are placed in a data storage system for unified management, and data lineage relationships are established synchronously.

[0012] In some embodiments, the data management system also includes a permission management module, in which a user is associated with at least one role, different roles have different permissions, and user permission management is performed based on roles to control different users' access to different resources under different projects and different operation methods of corresponding resources. Resources include data, algorithms, models, services, functions and application programming interfaces. The operation methods of resources include adding, deleting, modifying and querying. Each operation of each resource is controlled to be opened and closed through the permission management module.

[0013] The vehicle autonomous driving data management system provided in the embodiment of the present application includes a data management module, which is configured to access and manage the vehicle autonomous driving data; an algorithm management module, which is configured to access the associated parameters, version, address, and description information of the autonomous driving algorithm into the system for unified management; and a process management module, which is configured to combine and splice the required data and the required algorithms to construct a business flow according to business needs. The embodiment of the present application supports the combination of data and algorithms to construct a business flow, thereby improving the efficiency of business processing. BRIEF DESCRIPTION OF THE DRAWINGS

[0014] Other features, objects and advantages of the present application will become more apparent by reading the detailed description of non-limiting embodiments made with reference to the following drawings:

[0015] Figure 1 It is a structural diagram of a data management system for vehicle autonomous driving of the present application;

[0016] Figure 2 is a schematic diagram of the original data area and the data asset area in an embodiment of the present application;

[0017] Figure 3 It is a schematic diagram of the project application in the embodiment of the present application;

[0018] Figure 4 It is a schematic diagram of a user rights management framework in an embodiment of the present application;

[0019] Figure 5 is a schematic diagram of a project-based data management system in an embodiment of the present application;

[0020] Figure 6 It is a schematic diagram of the structure of a computer system suitable for implementing some embodiments of the present application. DETAILED DESCRIPTION

[0021] The present application will be further described in detail below in conjunction with the accompanying drawings and embodiments. It is to be understood that the specific embodiments described herein are only used to explain the relevant invention, rather than to limit the invention. It should also be noted that, for ease of description, only the parts related to the relevant invention are shown in the accompanying drawings.

[0022] It should be noted that, in the absence of conflict, the embodiments and features in the embodiments of the present application can be combined with each other. The present application will be described in detail below with reference to the accompanying drawings and in combination with the embodiments.

[0023] Continue to refer Figure 1 , shows a structure 100 of a data management system for vehicle autonomous driving of the present application. The data management system for vehicle autonomous driving includes the following modules:

[0024] The data management module 101 is configured to access and manage vehicle autonomous driving data.

[0025] In this embodiment, the data management system can adopt distributed systems and cloud computing technology, can dynamically increase or decrease computing and storage resources, has strong scalability, and can adopt a multi-node redundant architecture to ensure the high availability of the system and improve the stability of the system.

[0026] The data management module can access data from various sensors of the vehicle, including but not limited to radar, LiDAR, camera, inertial measurement unit (IMU), etc., as well as data from the vehicle's internal and external networks. It manages the data generated by autonomous vehicles, including data classification, labeling, indexing, and querying, to facilitate rapid retrieval and use of data. In addition, it can also conduct in-depth analysis of the data to extract valuable information, support decision-making, and realize intelligent application of data through machine learning and artificial intelligence technologies.

[0027] In some optional implementations of this embodiment, the vehicle automatic driving data includes original collected data, pre-processed data, annotated data, perception results data, positioning data, perception algorithm data, and label data; and the data management system adopts a distributed storage form to support the coexistence and sharing of different data types. The upper layer of the data management system is accessed through a unified encapsulated interface, and supports real-time query and analysis; the data management system reads data from different storage systems in a mounted manner by setting different connectors, and the data storage system includes a file storage system, a database storage system, and an object storage system. The data management system of the big data architecture adopts a distributed storage system, is compatible with all types of data, is accessed through a unified encapsulated interface, and can support real-time query and analysis at the same time, bringing more convenience to enterprises in data governance.

[0028] In some optional implementations of this embodiment, see Figure 2 The data management module can include the original data area and the data asset area. The data in the original data area is pre-processed and stored in the data asset area with the data set as the smallest storage unit. The data asset area also includes the result data obtained based on the data set and the algorithm custom processing flow. The data set is the smallest unit of data storage. In order to facilitate the flexible use of the business party, the data set has a small granularity as much as possible and is easy to split and combine.

[0029] In some optional implementations of this embodiment, data preprocessing includes data parsing, label extraction, building a data catalog, data cleaning, quality checking, error correction, parsing reorganization and data migration.

[0030] In some optional implementations of this embodiment, label extraction includes: when data is accessed, metadata information and label information are extracted synchronously, put into the label library, and the label library is improved synchronously; when extracting labels, if the labels in the existing label library cannot meet the business needs, user-defined labels are obtained, and the classification of labels in the label library is defined according to different business needs. Label customization can be performed, and users are supported to use any label in the label library to label data, which further improves the flexibility of the data system.

[0031] In some optional implementations of this embodiment, building a data directory includes: building a data directory based on the labels on the data, the data directory classification includes business category, management category and quality category, the data directory includes project identification, acquisition time, acquisition equipment, data set ID, data set name, data set size, version description, and the data directory supports custom extensions according to different business needs. Fast query can be achieved based on the data directory and dimensions, and the required data set can be accurately located. Building a data directory based on the data set is a prerequisite for effective data management. The query service is provided through the data directory, which has strong scalability. The data directory can be expanded as the business expands without the need for additional development workload.

[0032] In some optional implementations of this embodiment, the data management module is further configured to retain or delete data based on time factors and scene value, wherein negative scene data in the autonomous driving system is retained, and the result data obtained from the business flow is fully managed according to the version. For source data and intermediate process data, in order to reduce the cost of data storage, it is necessary to screen out data with preservation value and filter out data that is worthless or has large errors and is unusable. After data quality inspection and cleaning, the correct data is retained, and data with relatively high value needs to be further screened out from the correct data. The value of data is mainly considered from the perspective of time and scene value. On the one hand, the newly collected source data is saved as much as possible, and the relatively old data is deleted; on the other hand, the negative case bad case scene data in the autonomous driving system can be retained, and can continue to be used to use robust system algorithms.

[0033] The algorithm management module 102 is configured to connect the associated parameters, version, address, and description information of the autonomous driving algorithm to the system for unified management.

[0034] The data management system must not only have data management capabilities, but also support data analysis, statistics and processing in order to help enterprises and organizations make more valuable decisions. Data processing is inseparable from algorithms. Algorithms are a special type of data and also need to be managed in a unified manner. The relevant parameters, versions, addresses, descriptions and other information of all mature algorithms can be connected to the system for unified management. Similar to the way of data access, each algorithm is also stored as a data set, and data is labeled based on each algorithm data set to build an algorithm data directory. Rapid retrieval of algorithms is achieved based on the algorithm data directory.

[0035] In some optional implementations of this embodiment, the algorithm management module is further configured to store the algorithms as data sets, and to perform data labeling on the basis of each algorithm data set to construct an algorithm data directory.

[0036] The process management module 103 is configured to combine and splice the required data and required algorithms to construct a business flow according to business needs. Figure 3 , Figure 3 It demonstrated the basic capabilities based on data management, algorithm management, and process management, combined with data authority management, to complete project applications such as perception algorithm training, perception algorithm evaluation, and simulation testing.

[0037] In some optional implementations of this embodiment, business needs may include algorithm training, scenario mining, simulation testing, etc. New data sets generated by business flows can be put into the data storage system for unified management, and data lineage relationships can be established synchronously.

[0038] In some optional implementations of this embodiment, the data management system further includes a rights management module, see Figure 4 In the permission management module, a user can belong to different user groups. Each user is associated with at least one role. Different roles have different permissions. User permission management is performed based on roles to control different users' access to different resources under different projects and the different operation methods of corresponding resources. Resources include data, algorithms, models, services, functions, and application programming interfaces, etc. The operation methods of resources include adding, deleting, modifying, and querying. Each operation of each resource is controlled and opened and closed through the permission management module. Data is an important asset of an enterprise, and ensuring data security is of paramount importance. In order to fully ensure data security, permission management is required to control the entry and exit of data. See Figure 5 , Figure 5 shows a project-based data management system, Figure 5 Projects, functions, and permissions are abstracted in the framework, among which the functional module is the smallest granularity of permissions.

[0039] The system provided by the above-mentioned embodiments of the present application is configured to access and manage vehicle autonomous driving data through a data management module; the algorithm management module is configured to access the associated parameters, version, address, and description information of the autonomous driving algorithm into the system for unified management; the process management module is configured to combine and splice the required data and the required algorithms to construct a business flow according to business needs, supports the combination of data and algorithms to construct a business flow, and improves the efficiency of business processing.

[0040] Reference below Figure 6 , which shows a schematic diagram of the structure of a computer system 600 suitable for implementing an embodiment of the present application. Figure 6 The computer system shown is merely an example and should not bring any limitation to the functions and scope of use of the embodiments of the present application.

[0041] like Figure 6As shown, the computer system 600 includes a central processing unit (CPU) 601, which can perform various appropriate actions and processes according to a program stored in a read-only memory (ROM) 602 or a program loaded from a storage part 608 into a random access memory (RAM) 603. In the RAM 603, various programs and data required for the operation of the system 600 are also stored. The CPU 601, the ROM 602, and the RAM 603 are connected to each other via a bus 604. An input / output (I / O) interface 605 is also connected to the bus 604.

[0042] The following components can be connected to the I / O interface 605: an input section 606 including such as a keyboard, a mouse, etc.; an output section 607 including such as a cathode ray tube (CRT), a liquid crystal display (LCD), etc., and a speaker, etc.; a storage section 608 including a hard disk, etc.; and a communication section 609 including a network interface card such as a LAN card, a modem, etc. The communication section 609 performs communication processing via a network such as the Internet. A drive 610 is also connected to the I / O interface 605 as needed. A removable medium 611, such as a magnetic disk, an optical disk, a magneto-optical disk, a semiconductor memory, etc., is installed on the drive 610 as needed, so that a computer program read therefrom is installed into the storage section 608 as needed.

[0043] In particular, according to an embodiment of the present disclosure, the process described above with reference to the flowchart can be implemented as a computer software program. For example, an embodiment of the present disclosure includes a computer program product, which includes a computer program carried on a computer-readable medium, and the computer program includes a program code for executing the method shown in the flowchart. In such an embodiment, the computer program can be downloaded and installed from the network through the communication part 609, and / or installed from the removable medium 611. When the computer program is executed by the central processing unit (CPU) 601, the above functions defined in the method of the present application are executed. It should be noted that the computer-readable medium described in the present application can be a computer-readable signal medium or a computer-readable medium or any combination of the above two. The computer-readable medium can be, for example, - but not limited to - an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, device or device, or any combination of the above. More specific examples of computer-readable media may include, but are not limited to, an electrical connection with one or more conductors, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above. In the present application, a computer-readable medium may be any tangible medium containing or storing a program that can be used by or in combination with an instruction execution system, an apparatus or a device. In the present application, a computer-readable signal medium may include a data signal propagated in a baseband or as part of a carrier wave, which carries a computer-readable program code. Such propagated data signals may take a variety of forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination of the above. A computer-readable signal medium may also be any computer-readable medium other than a computer-readable medium, which may send, propagate, or transmit a program for use by or in combination with an instruction execution system, an apparatus or a device. The program code contained on the computer-readable medium may be transmitted using any appropriate medium, including but not limited to: wireless, wire, optical cable, RF, etc., or any suitable combination of the above.

[0044] Computer program code for performing the operations of the present application may be written in one or more programming languages ​​or a combination thereof, including object-oriented programming languages, such as Java, Smalltalk, C++, and conventional procedural programming languages, such as C or similar programming languages. The program code may be executed entirely on the user's computer, partially on the user's computer, as a separate software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer via any type of network, including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computer (e.g., via the Internet using an Internet service provider).

[0045] The flow chart and block diagram in the accompanying drawings illustrate the possible architecture, function and operation of the system, method and computer program product according to various embodiments of the present application. In this regard, each square box in the flow chart or block diagram can represent a module, a program segment or a part of a code, and the module, the program segment or a part of the code contains one or more executable instructions for realizing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the square box can also occur in a sequence different from that marked in the accompanying drawings. For example, two square boxes represented in succession can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each square box in the block diagram and / or flow chart, and the combination of the square boxes in the block diagram and / or flow chart can be implemented with a dedicated hardware-based system that performs a specified function or operation, or can be implemented with a combination of dedicated hardware and computer instructions.

[0046] The above description is only a preferred embodiment of the present application and an explanation of the technical principles used. Those skilled in the art should understand that the scope of the invention involved in the present application is not limited to the technical solution formed by a specific combination of the above technical features, but should also cover other technical solutions formed by any combination of the above technical features or their equivalent features without departing from the above invention concept. For example, the above features are replaced with the technical features with similar functions disclosed in this application (but not limited to) by each other to form a technical solution.

Claims

1. A data management system for vehicle automatic driving, characterized in that: The data management system comprises: A data management module configured to access and manage vehicle autonomous driving data; The algorithm management module is configured to connect the associated parameters, versions, addresses, and description information of the autonomous driving algorithm to the system for unified management; The process management module is configured to combine and splice the required data and required algorithms to build business flows according to business needs; Among them, data preprocessing includes data parsing, label extraction, building a data catalog, data cleaning, quality inspection, error correction, parsing reorganization and data migration; The data management module is further configured to retain or delete data based on time factors and scene values, wherein the negative scene data in the autonomous driving system is retained, and the result data obtained from the business flow is fully managed according to the version; for source data and intermediate process data, in order to reduce the cost of data storage, it is necessary to screen out data with preservation value and filter out data that is worthless or has large errors and is unusable; after data quality inspection and cleaning, the correct data is retained, and the data with relatively large value needs to be further screened out from the correct data; data value includes time and scene value. On the one hand, the newly collected source data is saved and the relatively old data is deleted; on the other hand, the negative case scene data in the autonomous driving system is retained and continued to be used to strengthen the system algorithm; The data management system further includes a rights management module, in which a user is associated with at least one role, different roles have different rights, and user rights management is performed based on roles to control different users to access different resources under different projects and different operation modes of corresponding resources. The resources include data, algorithms, models, services, functions and application programming interfaces. The operation modes of the resources include adding, deleting, modifying and querying. Each operation of each resource is controlled to be opened and closed through the rights management module; Among them, the vehicle automatic driving data includes original collected data, pre-processed data, annotated data, perception results data, positioning data, perception algorithm data, and label data; and the data management system adopts a distributed storage form, supports the coexistence and sharing of different data types, and the upper layer of the data management system is accessed through a unified encapsulated interface, and supports real-time query and analysis; the data management system reads data from different storage systems in a mounted manner by setting different connectors, and the data storage system includes a file storage system, a database storage system, and an object storage system.

2. The system according to claim 1, characterized in that The data management module includes an original data area and a data asset area. The data in the original data area is pre-processed and stored in the data asset area with the data set as the smallest storage unit. The data asset area also includes the result data obtained based on the data set and the algorithm custom processing flow.

3. The system according to claim 1, characterized in that The label extraction includes: When data is accessed, metadata information and tag information are extracted synchronously, put into the tag library, and the tag library is improved synchronously; When extracting tags, if the tags in the existing tag library cannot meet the business needs, obtain user-defined tags. The classification of tags in the tag library is defined according to different business needs.

4. The system according to claim 1, characterized in that The construction data directory includes: A data catalog is constructed based on the labels on the data. The data catalog categories include business category, management category, and quality category. The data catalog includes project identification, collection time, collection device, data set ID, data set name, data set size, and version description. The data catalog supports custom extensions based on different business needs.

5. The system according to claim 1, characterized in that The algorithm management module is further configured to store the algorithms as data sets, and to perform data labeling on each algorithm data set to construct an algorithm data directory.

6. The system according to claim 1, characterized in that The business needs include algorithm training, scenario mining, and simulation testing. The business flow generates a new data set and puts it into the data storage system for unified management, and simultaneously establishes data lineage relationships.

Images