Automatic driving data management method and device, electronic equipment and storage medium

Abstract

The invention discloses an automatic driving data management method and device, electronic equipment and a storage medium, and the method comprises the steps: obtaining automatic driving data, and storing the automatic driving data in an automatic driving data management warehouse; determining a data format corresponding to the automatic driving data, and determining a data processing module corresponding to the data format based on a data type adapter; and converting the automatic driving data into visual data based on the data processing module, and storing the visual data to the automatic driving data management warehouse. Based on the technical scheme, the automatic driving data in different data formats are subjected to data conversion to obtain the corresponding visual data, and the visual data set is stored in the automatic driving data management warehouse, so that the corresponding visual data can be directly called during data playback, and the data playback efficiency is improved. And the management efficiency of the automatic driving data is improved.

Description

Technical Field

[0001] The present invention relates to vehicle data management technology, and in particular to a method, device, electronic device and storage medium for managing autonomous driving data. Background Art

[0002] With the continuous development of autonomous driving technology, in the process of autonomous driving software development, software programs and test vehicles will generate a large amount of sensor and software debugging data during the test process. Developers need to record this data and play it back visually during the debugging process to analyze and debug the data. Due to different hardware brands and software protocols integrated into the vehicle, or different models, this data will be saved in various data formats, and the encoding method, compression format and visualization method of different data formats are also different.

[0003] However, the existing data management method is to store the collected data directly in the database. When data playback is needed, the developer downloads the data for playback by himself, and the data cannot be managed in a unified manner, thereby reducing the data management efficiency. Summary of the invention

[0004] The present invention provides a method, device, electronic device and storage medium for managing autonomous driving data. The autonomous driving data in different data formats are converted and stored in an autonomous driving data management warehouse. The corresponding visual data can be directly called during data playback to solve the problem of low data playback efficiency in existing data management solutions.

[0005] According to one aspect of the present invention, a method for managing autonomous driving data is provided, comprising:

[0006] Acquire autonomous driving data, and store the autonomous driving data in an autonomous driving data management warehouse;

[0007] Determine a data format corresponding to the autonomous driving data, and determine a data processing module corresponding to the data format based on a data type adapter;

[0008] The autonomous driving data is converted into visual data based on the data processing module, and the visual data is stored in the autonomous driving data management warehouse.

[0009] According to another aspect of the present invention, there is provided a device for managing autonomous driving data, comprising:

[0010] A data storage module, used to obtain autonomous driving data and store the autonomous driving data in an autonomous driving data management warehouse;

[0011] a processing mode determination module, configured to determine a data format corresponding to the autonomous driving data, and determine a data processing module corresponding to the data format based on a data type adapter;

[0012] A data conversion module is used to convert the autonomous driving data into visual data based on the data processing module, and store the visual data in the autonomous driving data management warehouse.

[0013] According to another aspect of the present invention, there is provided an electronic device, the electronic device comprising:

[0014] at least one processor; and

[0015] a memory communicatively connected to the at least one processor; wherein,

[0016] The memory stores a computer program that can be executed by the at least one processor, and the computer program is executed by the at least one processor so that the at least one processor can execute the method for managing autonomous driving data described in any embodiment of the present invention.

[0017] According to another aspect of the present invention, a computer-readable storage medium is provided, wherein the computer-readable storage medium stores computer instructions, and the computer instructions are used to enable a processor to implement the method for managing autonomous driving data described in any embodiment of the present invention when executed.

[0018] The technical solution of the embodiment of the present invention obtains the autonomous driving data and stores the autonomous driving data in the autonomous driving data management warehouse, thereby determining the data format corresponding to the autonomous driving data, and determining the data processing module corresponding to the data format based on the data type adapter, and finally converting the autonomous driving data into visual data based on the data processing module, and storing the visual data in the autonomous driving data management warehouse. Based on the above technical solution, the corresponding visual data is obtained by converting the autonomous driving data in different data formats, and the visual data set is stored in the autonomous driving data management warehouse, so that the corresponding visual data can be directly called when the data is played back, thereby improving the management efficiency of the autonomous driving data.

[0019] It should be understood that the contents described in this section are not intended to identify the key or important features of the embodiments of the present invention, nor are they intended to limit the scope of the present invention. Other features of the present invention will become easily understood through the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0021] Figure 1 is a flowchart of a method for managing autonomous driving data provided by an embodiment of the present invention;

[0022] Figure 2 is a schematic diagram of a visualization interface provided by an embodiment of the present invention;

[0023] Figure 3 is a structural block diagram of an automatic driving data management device provided by an embodiment of the present invention;

[0024] Figure 4 It is a schematic diagram of the structure of an electronic device provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0025] In order to enable those skilled in the art to better understand the scheme of the present invention, the technical scheme in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work should fall within the scope of protection of the present invention.

[0026] It should be noted that the terms "first", "second", etc. in the specification and claims of the present invention and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchanged where appropriate, so that the embodiments of the present invention described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device that includes a series of steps or units is not necessarily limited to those steps or units that are clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0027] Embodiment 1

[0028] Figure 1It is a flow chart of a method for managing autonomous driving data provided by an embodiment of the present invention. This embodiment is applicable to the case where the collected autonomous driving data is converted into visual data and the converted visual data is stored in an autonomous driving data management warehouse. The method can be executed by a management device for autonomous driving data. The management device for autonomous driving data can be implemented in the form of hardware and / or software. The management device for autonomous driving data can be configured in an electronic device, which can be a server, a terminal device, etc.

[0029] like Figure 1 As shown, the method includes:

[0030] S110. Acquire autonomous driving data, and store the autonomous driving data in an autonomous driving data management warehouse.

[0031] The autonomous driving data may be data collected by a vehicle with an autonomous driving function during the autonomous driving process, and may include radar point cloud data, image data, video data, and perception data, etc. It should be noted that the autonomous driving data may be data generated in actual application scenarios, or test data generated during the development of the autonomous driving function. The autonomous driving data management warehouse can be understood as a pre-established database for storing autonomous driving data, which may be a database created based on minio, an open source distributed file management system that supports file and directory management.

[0032] Specifically, it can be to use self-driving cars and related equipment (such as lidar, cameras, radars, etc.) to collect data to obtain vehicle driving routes, camera images, lidar data, etc., and then determine the self-driving data based on the collected data, and remotely upload the collected data to the self-driving data management warehouse. It can also be that developers select the self-driving data to be uploaded according to needs and store it in the self-driving data management warehouse. Exemplarily, first create a directory in minio according to business classification, and configure the file upload event of the data directory to trigger the data adapter to run. Developers can upload data files collected by the vehicle through minio's management tool or the front-end page of the data management platform.

[0033] Based on the above technical solution, storing the autonomous driving data in an autonomous driving data management warehouse includes: determining a data format corresponding to the autonomous driving data, and determining a target storage location based on the data format; storing the autonomous driving data in the autonomous driving data management warehouse based on the target storage location.

[0034] The data format may be information used to define the storage format of autonomous driving data. Different types of autonomous driving data correspond to different data formats. For example, radar point clouds are in pcap format, and perception data may be in structured data formats such as JSON, XML, or CSV. The target storage location may be understood as a pre-set data storage path corresponding to the data format.

[0035] Specifically, it is necessary to analyze the data format of the autonomous driving data and determine the corresponding target storage location according to the data format of the autonomous driving data. For example, a mapping relationship table between the data format and the storage path may be pre-established. After acquiring the autonomous driving data, the data format of the autonomous driving data may be obtained by parsing, and the data format may be matched according to the mapping relationship table between the data format and the storage path to obtain the data storage path corresponding to the autonomous driving data, and the autonomous driving data may be stored in the autonomous driving data management warehouse.

[0036] Based on the above technical solution, the acquisition of autonomous driving data includes: in response to the target user's triggering operation on the data upload function, displaying a data upload page corresponding to the data upload function; and obtaining the autonomous driving data uploaded by the target user through the data upload page.

[0037] The target user may be a developer of the autonomous driving function. The data upload function may be understood as a function for starting the autonomous driving data upload. The trigger operation may be the target user triggering the instruction information issued by the control corresponding to the data upload function. The data upload page is a webpage for uploading autonomous driving data.

[0038] Specifically, the target user triggers the data upload function through buttons, links or other interactive elements in the interface. In response to the target user's triggering operation on the control, the target user is presented with a page for data upload. The user selects the local autonomous driving data file through the file selection box and clicks the upload button. When the file is successfully uploaded, the system should store the file in a predetermined location (such as a file server, database, etc.) and record relevant metadata (such as file name, size, upload time, upload user, etc.). After the upload is completed, the system should feedback the upload result to the user. If the upload is successful, a confirmation message can be displayed, and a download link and the option to view the uploaded data can be provided. If the upload fails, an error message should be displayed, and the reason for the failure (such as file type error, file size, network problem, etc.) should be explained so that the user can handle it accordingly. Exemplarily, the data upload page may include: a file selection box for the target user to select autonomous driving data; an upload button for the target user to trigger the file upload operation by clicking; an upload progress bar / prompt information for displaying the progress or status of the file upload, as well as data description information, such as data size, label, classification, etc.

[0039] S120. Determine a data format corresponding to the autonomous driving data, and determine a data processing module corresponding to the data format based on a data type adapter.

[0040] The data type adapter may be a pre-compiled mirror system for converting autonomous driving data in different data formats into visual data. The data processing module may be understood as a software module for converting data in a specific data format.

[0041] Specifically, a mapping relationship between different data formats and data processing modules is established. When the system receives new autonomous driving data, it can automatically identify the data format, find the data processing module corresponding to the data format through the data type adapter, and trigger the corresponding processing flow. In order to ensure that the data format conversion process has better flexibility and scalability, dynamic loading of data type adapters and data processing modules can be implemented, and new adapters or modules can be loaded at runtime without restarting the entire system. This helps to adapt to the ever-changing autonomous driving data formats and processing requirements. Exemplarily, the data adapter runs in a docker image and calls different data adapters according to different data formats. The data format of the visualization data is in ros format, and all data visualization content will be converted to ros format and saved as a visualization package.

[0042] S130. Convert the autonomous driving data into visual data based on the data processing module, and store the visual data in the autonomous driving data management warehouse.

[0043] The visualization data may be a data packet in a specific data format for data visualization, for example, a visualization data packet in ROS format.

[0044] Specifically, visualization data is generated through the output or intermediate results of the data processing module. For example, autonomous driving data in different data formats can be parsed to obtain corresponding parsing results, and the parsing results are converted into ROS format, and the converted visualization data is packaged to obtain a visualization data packet in ROS format.

[0045] On the basis of the above technical solution, the data processing module is used to convert the autonomous driving data into visualization data, including: when the data format is a radar point cloud data format, calling a radar point cloud adaptation module through the data type adapter; parsing the radar point cloud data format based on the radar point cloud adaptation module to obtain a radar point cloud data parsing result, and generating radar point cloud visualization data according to the radar point cloud data parsing result.

[0046] The radar point cloud data format may be a data format of point cloud data acquired by radar, and may be a pcap format. The radar point cloud adaptation module may be a pre-compiled software module for performing data conversion on radar point cloud data.

[0047] Specifically, when the data format of the current autonomous driving data is determined to be the radar point cloud data format by checking the file extension or file header information, the radar point cloud adaptation module is called through the data type adapter. The radar point cloud adaptation module reads the radar point cloud data file and parses it according to the definition of the data format. When the data parsing is completed, the radar point cloud adaptation module generates a parsing result containing all point cloud data, and generates radar point cloud visualization data based on the radar point cloud data parsing result. Exemplarily, the radar point cloud supports radar point cloud packet capture data in pcap format, and the data adapter calls the corresponding pcap radar point cloud adaptation module. The radar point cloud adaptation module is developed based on C++, and its specific processing flow is to use the libpcap library to parse the pcap data, read the parsed data frame according to the PCL radar point cloud format, and call the rosbag library for the parsed radar point cloud data, and write it into the rosbag using the sensor / PointCloud2 format in ros.

[0048] On the basis of the above technical solution, the data processing module is used to convert the autonomous driving data into visualization data, including: when the data format is a video image data format, calling a video image adaptation module through the data type adapter; parsing each image frame in the video image data through the video image adaptation module to obtain an image frame parsing result, and converting the image frame parsing result into a preset picture format, and determining the video image visualization data according to the image frame parsing result of the preset picture format.

[0049] The video image adaptation module may be a pre-compiled software module for performing data conversion on video image data. The preset picture format may be understood as a pre-set picture format.

[0050] Specifically, the video image adaptation module reads the video file, decodes the video according to the video coding standard (such as H.264, MPEG-4, etc.), reads the image data in the video frame by frame, and parses each image frame. For each image frame, the video image adaptation module extracts the image data and parses the key information such as the pixel value, size, color space, etc. of the image, and then converts the parsed image frame into a preset picture format. Based on the image frame parsing result of the preset picture format, the visual data of the video image is generated, for example, each image frame can be saved as a separate picture file and organized according to the time sequence of the video, and visual data such as video thumbnails and key frame sets can also be generated according to demand. Exemplarily, the video image data format can be a yuv sampled image, or other types of encoded video format data, and then the data adapter calls the image processing module of the corresponding format to use ffmpeg as the image data software parsing library to parse the frame data of the original data, encode the parsed frame data into a jpeg format picture, and write it into the rosbag using the sensor_msgs / CompressedImage format in ros.

[0051] It should be noted that the technical solution of the present invention also supports the conversion of various perception result data; the conversion requires the corresponding professional to provide the data structure of the corresponding data, and the data adapter parses different data formats into the format supported by the front-end rendering. For example, the obstacle data will be converted into the position, size, and speed information of the obstacle, and written into the rosbag using the custom rosmsg format.

[0052] Based on the above technical solution, storing the visualization data in the autonomous driving data management warehouse includes: obtaining a target storage location of autonomous driving data corresponding to the visualization data; and associating and storing the visualization data and the autonomous driving data based on the target storage location.

[0053] Specifically, the two data may be associated in some way (such as file name, timestamp, etc.) and stored in the same directory or adjacent directories. Then, when data visualization is required, the corresponding file can be quickly found through the association relationship, and subsequent data analysis and processing can be performed. Exemplarily, key information associated with the autonomous driving data is extracted from the visualization data, such as timestamp, vehicle ID, trip ID and other information. Using the extracted association information, the target storage location of the autonomous driving data is queried in the autonomous driving data management warehouse, and then the visualization data is associated with the autonomous driving data based on the target storage location. The association field can be added to the database table, and then when the data is visualized, the corresponding visualization data can be quickly found according to the association field.

[0054] On the basis of the above technical solution, after storing the visualization data in the autonomous driving data management warehouse, it also includes: when receiving a visualization instruction, determining the data storage location corresponding to the target autonomous driving data based on the visualization instruction; obtaining a visualization data packet corresponding to the target autonomous driving data based on the data storage location; parsing the visualization data packet based on a visualization player analysis, and generating a visualization interface corresponding to the target autonomous driving data according to the analysis result.

[0055] The visualization instruction may be instruction information sent by the user by triggering the visualization control. The visualization interface may be understood as a web page interface for displaying visualization information.

[0056] Specifically, the received visualization instructions are parsed to determine the target autonomous driving data that the user wants to view. The visualization instructions may contain an identifier of the target data (such as a timestamp, vehicle ID, trip ID, etc.). Then, based on the target data identifier in the visualization instructions, the autonomous driving data management warehouse or database is queried to determine the data storage location corresponding to the target autonomous driving data. Once the data storage location is determined, a visualization data packet corresponding to the target autonomous driving data is obtained according to the data storage location. The visualization data packet may contain multiple files or data streams that together describe the visualization information of the autonomous driving scene. Then, using a visualization player or a corresponding parsing tool, the system parses the visualization data packet and generates a visualization interface corresponding to the target autonomous driving data based on the parsing results. It should be noted that, Figure 2As shown, the visualization interface may include multiple views, such as vehicle driving trajectory diagrams, sensor data charts, video playback, etc. The layout and display mode of the interface can also be adjusted according to user preferences or instructions. Finally, the generated visualization interface is displayed to the user, and the user can adjust the view, zoom the image, view detailed information, etc. through the controls on the interface. The system provides feedback based on the user's interactive behavior, such as updating the view, displaying more data, etc. The technical solution provided by the embodiment of the present invention can pre-compile a data visualization player, which uses js / React to develop pages and uses the Three.js library to develop 3D rendering modules, and supports various mainstream browsers to run. Supports radar point cloud three-dimensional rendering, picture, video rendering, apollo visualization rendering, and perception result rendering. The page supports internal multi-window display of data, each window displays different data types, and the window size and position can be freely configured.

[0057] The technical solution of the embodiment of the present invention obtains the autonomous driving data and stores the autonomous driving data in the autonomous driving data management warehouse, thereby determining the data format corresponding to the autonomous driving data, and determining the data processing module corresponding to the data format based on the data type adapter, and finally converting the autonomous driving data into visual data based on the data processing module, and storing the visual data in the autonomous driving data management warehouse. Based on the above technical solution, the corresponding visual data is obtained by converting the autonomous driving data in different data formats, and the visual data set is stored in the autonomous driving data management warehouse, so that the corresponding visual data can be directly called when the data is played back, thereby improving the management efficiency of the autonomous driving data.

[0058] Embodiment 2

[0059] Figure 3 This is a structural block diagram of a device for managing autonomous driving data provided by an embodiment of the present invention. Figure 3 As shown, the device includes: a data storage module 310 , a processing mode determination module 320 and a data conversion module 330 .

[0060] A data storage module 310, configured to obtain autonomous driving data and store the autonomous driving data in an autonomous driving data management warehouse;

[0061] a processing mode determination module 320, configured to determine a data format corresponding to the autonomous driving data, and determine a data processing module corresponding to the data format based on a data type adapter;

[0062] The data conversion module 330 is used to convert the autonomous driving data into visual data based on the data processing module, and store the visual data in the autonomous driving data management warehouse.

[0063] Based on the above technical solution, the data storage module is used to determine the data format corresponding to the autonomous driving data, and determine the target storage location based on the data format; and store the autonomous driving data in the autonomous driving data management warehouse based on the target storage location.

[0064] Based on the above technical solution, the data conversion module is used to obtain a target storage location of the autonomous driving data corresponding to the visualization data; and store the visualization data and the autonomous driving data in association with each other based on the target storage location.

[0065] On the basis of the above technical solution, the data conversion module is used to call the radar point cloud adaptation module through the data type adapter when the data format is the radar point cloud data format; parse the radar point cloud data format based on the radar point cloud adaptation module to obtain the radar point cloud data parsing result, and generate radar point cloud visualization data according to the radar point cloud data parsing result.

[0066] On the basis of the above technical solution, the data conversion module is used to call the video image adaptation module through the data type adapter when the data format is a video image data format; parse each image frame in the video image data through the video image adaptation module to obtain an image frame parsing result, and convert the image frame parsing result into a preset picture format, and determine the video image visualization data according to the image frame parsing result of the preset picture format.

[0067] Based on the above technical solution, the data storage module is used to display a data upload page corresponding to the data upload function in response to the target user's triggering operation on the data upload function; and obtain the autonomous driving data uploaded by the target user through the data upload page.

[0068] Based on the above technical solution, the device includes: a data visualization module, which is used to determine the data storage location corresponding to the target autonomous driving data based on the visualization instruction when a visualization instruction is received after the visualization data is stored in the autonomous driving data management warehouse; obtain a visualization data packet corresponding to the target autonomous driving data based on the data storage location; parse the visualization data packet based on a visualization player analysis, and generate a visualization interface corresponding to the target autonomous driving data according to the analysis result.

[0069] The technical solution of the embodiment of the present invention obtains the autonomous driving data and stores the autonomous driving data in the autonomous driving data management warehouse, thereby determining the data format corresponding to the autonomous driving data, and determining the data processing module corresponding to the data format based on the data type adapter, and finally converting the autonomous driving data into visual data based on the data processing module, and storing the visual data in the autonomous driving data management warehouse. Based on the above technical solution, the corresponding visual data is obtained by converting the autonomous driving data in different data formats, and the visual data set is stored in the autonomous driving data management warehouse, so that the corresponding visual data can be directly called when the data is played back, thereby improving the management efficiency of the autonomous driving data.

[0070] The management device for autonomous driving data provided in an embodiment of the present invention can execute the management method for autonomous driving data provided in any embodiment of the present invention, and has functional modules and beneficial effects corresponding to the execution method.

[0071] Embodiment 3

[0072] Figure 4 A schematic diagram of the structure of an electronic device 10 that can be used to implement an embodiment of the present invention is shown. The electronic device is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device can also represent various forms of mobile devices, such as personal digital processing, cellular phones, smart phones, wearable devices (such as helmets, glasses, watches, etc.) and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely examples and are not intended to limit the implementation of the present invention described and / or required herein.

[0073] like Figure 4 As shown, the electronic device 10 includes at least one processor 11, and a memory connected to the at least one processor 11, such as a read-only memory (ROM) 12, a random access memory (RAM) 13, etc., wherein the memory stores a computer program that can be executed by at least one processor, and the processor 11 can perform various appropriate actions and processes according to the computer program stored in the read-only memory (ROM) 12 or the computer program loaded from the storage unit 18 to the random access memory (RAM) 13. In the RAM 13, various programs and data required for the operation of the electronic device 10 can also be stored. The processor 11, the ROM 12, and the RAM 13 are connected to each other through a bus 14. An input / output (I / O) interface 15 is also connected to the bus 14.

[0074] A number of components in the electronic device 10 are connected to the I / O interface 15, including: an input unit 16, such as a keyboard, a mouse, etc.; an output unit 17, such as various types of displays, speakers, etc.; a storage unit 18, such as a disk, an optical disk, etc.; and a communication unit 19, such as a network card, a modem, a wireless communication transceiver, etc. The communication unit 19 allows the electronic device 10 to exchange information / data with other devices through a computer network such as the Internet and / or various telecommunication networks.

[0075] The processor 11 may be a variety of general and / or special processing components with processing and computing capabilities. Some examples of the processor 11 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various special artificial intelligence (AI) computing chips, various processors running machine learning model algorithms, a digital signal processor (DSP), and any appropriate processor, controller, microcontroller, etc. The processor 11 executes the various methods and processes described above, such as a method for managing autonomous driving data.

[0076] In some embodiments, the method for managing autonomous driving data may be implemented as a computer program, which is tangibly contained in a computer-readable storage medium, such as a storage unit 18. In some embodiments, part or all of the computer program may be loaded and / or installed on the electronic device 10 via the ROM 12 and / or the communication unit 19. When the computer program is loaded into the RAM 13 and executed by the processor 11, one or more steps of the method for managing autonomous driving data described above may be performed. Alternatively, in other embodiments, the processor 11 may be configured to execute the method for managing autonomous driving data in any other appropriate manner (e.g., by means of firmware).

[0077] Various implementations of the systems and techniques described above herein can be implemented in digital electronic circuit systems, integrated circuit systems, field programmable gate arrays (FPGAs), application specific integrated circuits (ASICs), application specific standard products (ASSPs), systems on chips (SOCs), load programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various implementations can include: being implemented in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which can be a special purpose or general purpose programmable processor that can receive data and instructions from a storage system, at least one input device, and at least one output device, and transmit data and instructions to the storage system, the at least one input device, and the at least one output device.

[0078] Computer programs for implementing the methods of the present invention may be written in any combination of one or more programming languages. These computer programs may be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing device, so that when the computer program is executed by the processor, the functions / operations specified in the flow chart and / or block diagram are implemented. The computer program may be executed entirely on the machine, partially on the machine, partially on the machine and partially on a remote machine as a stand-alone software package, or entirely on a remote machine or server.

[0079] In the context of the present invention, a computer-readable storage medium may be a tangible medium that may contain or store a computer program for use by or in combination with an instruction execution system, device or equipment. A computer-readable storage medium may include, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, devices or equipment, or any suitable combination of the foregoing. Alternatively, a computer-readable storage medium may be a machine-readable signal medium. A more specific example of a machine-readable storage medium may include an electrical connection based on one or more lines, 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 foregoing.

[0080] To provide interaction with a user, the systems and techniques described herein may be implemented on an electronic device having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user; and a keyboard and a pointing device (e.g., a mouse or trackball) through which the user can provide input to the electronic device. Other types of devices may also be used to provide interaction with the user; for example, the feedback provided to the user may be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user may be received in any form (including acoustic input, voice input, or tactile input).

[0081] The systems and techniques described herein may be implemented in a computing system that includes backend components (e.g., as a data server), or a computing system that includes middleware components (e.g., an application server), or a computing system that includes frontend components (e.g., a user computer with a graphical user interface or a web browser through which a user can interact with implementations of the systems and techniques described herein), or a computing system that includes any combination of such backend components, middleware components, or frontend components. The components of the system may be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: a local area network (LAN), a wide area network (WAN), a blockchain network, and the Internet.

[0082] A computing system may include a client and a server. The client and the server are generally remote from each other and usually interact through a communication network. The client and server relationship is generated by computer programs running on the corresponding computers and having a client-server relationship with each other. The server may be a cloud server, also known as a cloud computing server or cloud host, which is a host product in the cloud computing service system to solve the defects of difficult management and weak business scalability in traditional physical hosts and VPS services.

[0083] It should be understood that the various forms of processes shown above can be used to reorder, add or delete steps. For example, the steps described in the present invention can be executed in parallel, sequentially or in different orders, as long as the desired results of the technical solution of the present invention can be achieved, and this document does not limit this.

[0084] The above specific implementations do not constitute a limitation on the protection scope of the present invention. It should be understood by those skilled in the art that various modifications, combinations, sub-combinations and substitutions can be made according to design requirements and other factors. Any modification, equivalent substitution and improvement made within the spirit and principle of the present invention should be included in the protection scope of the present invention.

Claims

1. A method for managing autonomous driving data, characterized in that: include: Acquire autonomous driving data, and store the autonomous driving data in an autonomous driving data management warehouse; Determine a data format corresponding to the autonomous driving data, and determine a data processing module corresponding to the data format based on a data type adapter; The autonomous driving data is converted into visual data based on the data processing module, and the visual data is stored in the autonomous driving data management warehouse.

2. The method according to claim 1, characterized in that The storing the autonomous driving data in an autonomous driving data management warehouse includes: determining a data format corresponding to the autonomous driving data, and determining a target storage location based on the data format; The autonomous driving data is stored in the autonomous driving data management warehouse based on the target storage location.

3. The method according to claim 1, characterized in that The storing the visualization data into the autonomous driving data management warehouse includes: Obtaining a target storage location of autonomous driving data corresponding to the visualization data; The visualization data and the autonomous driving data are stored in association with each other based on the target storage location.

4. The method according to claim 1, characterized in that: The converting the autonomous driving data into visual data based on the data processing module includes: In the case where the data format is a radar point cloud data format, calling a radar point cloud adaptation module through the data type adapter; The radar point cloud data format is parsed based on the radar point cloud adaptation module to obtain a radar point cloud data parsing result, and radar point cloud visualization data is generated according to the radar point cloud data parsing result.

5. The method according to claim 1, characterized in that: The converting the autonomous driving data into visual data based on the data processing module includes: In the case where the data format is a video image data format, calling a video image adaptation module through the data type adapter; The video image adaptation module parses each image frame in the video image data to obtain an image frame parsing result, converts the image frame parsing result into a preset picture format, and determines the video image visualization data according to the image frame parsing result in the preset picture format.

6. The method according to claim 1, characterized in that The obtaining of the autonomous driving data includes: In response to a triggering operation of a data upload function by a target user, displaying a data upload page corresponding to the data upload function; Obtain the autonomous driving data uploaded by the target user through the data upload page.

7. The method according to claim 1, characterized in that After storing the visualization data in the autonomous driving data management warehouse, the method further includes: When receiving the visualization instruction, determining a data storage location corresponding to the target autonomous driving data based on the visualization instruction; Acquire a visualization data packet corresponding to the target autonomous driving data based on the data storage location; The visualization data packet is parsed based on the visualization player analysis, and a visualization interface corresponding to the target autonomous driving data is generated according to the analysis result.

8. An automatic driving data management device, characterized in that: include: A data storage module, used to obtain autonomous driving data and store the autonomous driving data in an autonomous driving data management warehouse; a processing mode determination module, configured to determine a data format corresponding to the autonomous driving data, and determine a data processing module corresponding to the data format based on a data type adapter; A data conversion module is used to convert the autonomous driving data into visual data based on the data processing module, and store the visual data in the autonomous driving data management warehouse.

9. An electronic device, characterized in that: The electronic device comprises: at least one processor; and a memory communicatively connected to the at least one processor; wherein, The memory stores a computer program that can be executed by the at least one processor, and the computer program is executed by the at least one processor so that the at least one processor can execute the method for managing autonomous driving data as described in any one of claims 1-7.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores computer instructions, and the computer instructions are used to enable a processor to implement the method for managing autonomous driving data according to any one of claims 1 to 7 when executed.

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