CN111259545B - Intelligent driving virtual simulation cloud platform - Google Patents
Intelligent driving virtual simulation cloud platform Download PDFInfo
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- CN111259545B CN111259545B CN202010043098.4A CN202010043098A CN111259545B CN 111259545 B CN111259545 B CN 111259545B CN 202010043098 A CN202010043098 A CN 202010043098A CN 111259545 B CN111259545 B CN 111259545B
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- G06F16/20—Information retrieval; Database structures therefor; File system structures therefor of structured data, e.g. relational data
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Abstract
The invention provides an intelligent driving virtual simulation cloud platform, which comprises the following components: a map operation management system, the map operation management system comprising: the public cloud module is used for realizing the encoding and decoding of the map data file and the operation calculation of the corresponding map data, and notifying a result to the client application module; and the client application module is used for displaying, monitoring and providing a visual interface for the data of the public cloud module in real time. The invention provides a map operation management system in an intelligent driving virtual simulation cloud platform, and provides an EHP-EHR (advanced high-precision map-based) high-precision map data management and application solution based on an AWS public cloud platform, which realizes unified storage, unified buffering, unified decoding and unified calculation of high-precision map data, provides a unified business level data access interface for the EHR, and provides a bottom-layer high-precision map data and service technical support for the construction of a subsequent virtual simulation platform.
Description
Technical Field
The invention relates to the technical field of electronic information processing, in particular to an intelligent driving virtual simulation cloud platform.
Background
With the advanced development of internet technology, the application of intelligent technology is becoming more and more common. In the field of automobiles, the concepts of intelligent automobiles and analog simulation systems are well known, and automatic driving systems are often developed, tested and verified in a simulation environment with half effort, but the construction of the simulation scene of the driving environment is time-consuming and labor-consuming, and the simulation scene is difficult to ensure to be consistent with the real world. But if the simulation development scene can be built based on the high-precision map, the simulation development scene is very consistent with the real world, and all lane lines, traffic lights, barriers and even pictures on traffic signs are directly extracted from the data acquired by the high-precision map. Namely, virtual simulation systems based on cloud environment and high-precision map integration are rarely available on the market. In addition, the desktop single-machine-version virtual simulation system has limited capability of integrating high-precision map data and low universality. The precision of the map data of each map merchant is different in each region, that is, the map merchants have the advantage of high precision of the regional map data. The virtual simulation system on the market cannot flexibly select and load the high-precision map data of different diagrammers according to the high-precision degree of regional map data, and has no high elasticity and high expandability.
And most of high-precision map services on the market provide a public data service interface, and a client acquires corresponding map data and renders and displays the map data, but cannot flexibly modify the high-precision map data so as to meet customization and individuation requirements. The intelligent driving virtual simulation platform needs to flexibly perform scene management, parameter configuration, map display and the like according to various test scenes, and needs good user operation experience so as to realize omnibearing test verification on vehicles, and if the existing high-precision map service system on the market is directly used, the condition can not be satisfied. There are some relatively well known intelligent driving simulation system software products, but each has shortcomings. Some systems are single-version desktop application environments, and although simulated map scenes can be customized and edited in a VR interface, the support of a massive scene library is lacking, massive scene data cannot be stored and used, and high-concurrency simulated testing cannot be supported. Still other systems, while having a server, lack high-precision map data supported analog simulation systems, nor cloud environment supported. In a word, based on cloud computing environment, high-precision map system and analog simulation platform, the intelligent driving virtual simulation verification capability is hardly realized.
Disclosure of Invention
The invention aims to provide a high-precision map operation management system of an intelligent driving virtual simulation cloud platform, which is based on the high-precision map data management and application solutions EHP (Electronic Horizon Provider) -EHR (Electronic Horizon Reconstructor) of an AWS public cloud platform, realizes unified storage, unified buffering, unified decoding and unified calculation of high-precision map data, provides a unified business level data access interface for EHR, and provides a bottom high-precision map data and service technology support for the construction of a subsequent virtual simulation platform.
The invention further aims to provide a high-precision map synchronization system of an intelligent driving virtual simulation cloud platform, and provides a safe communication handshake and interconnection access solution of a client and a high-precision map management system based on an AWS public cloud, so that unified storage of high-precision map data is realized and synchronization management is provided.
The invention further aims to provide a high-precision map application system based on the test cases of the intelligent driving virtual simulation cloud platform, which realizes online editing and customization of the high-precision map by the test cases; in the running process of the test flow instance, the VR client is visible in the whole process of the running state of the virtual vehicle, and real-time environment perception high-precision positioning and simulation behavior decision support are realized.
In particular, the invention provides an intelligent driving virtual simulation cloud platform, comprising: a map operation management system, the map operation management system comprising:
the public cloud module is used for realizing the encoding and decoding of the map data file and the operation calculation of the corresponding map data, and notifying a result to the client application module;
and the client application module is used for displaying, monitoring and providing a visual interface for the data of the public cloud module in real time.
Further, the public cloud module includes:
the data service module is used for acquiring corresponding map data from the map library according to the requested parameters;
the map data operation module is used for respectively calling the map data in the public cloud module according to the operation request;
the map decoding service module is used for decoding the map data and analyzing the map data into a data format available to the system;
the map thermal caching module is used for persistence of the map data cloud;
the cloud-client application data interaction module is used for carrying out data interaction with the client application module, calling the function of the simulation test flow module of the intelligent driving virtual simulation cloud platform and sending the data to the client application module through an authorization mechanism;
And the scene use case management module, the data service module, the map decoding service module, the map hot buffer module and the cloud-client application data interaction module are respectively and interactively connected with the map data operation module.
Further, the map decoding service module includes:
the 2D map decoding module is used for analyzing the map original file into a plane data point mode;
and the 3D map decoding module is used for analyzing the map original file into a stereoscopic data point mode.
Further, the data service module includes:
the original data storage Amazon S3 is used for storing the map original file;
the map index database MySql is used for storing the relation between the map index and the map original file;
and updating an index processing function Lambda for updating the index of the map.
Further, the intelligent driving virtual simulation cloud platform further comprises: the map service module is used for interfacing with a map provider and acquiring map data in an API mode and simultaneously providing a synchronous updating interface, and the data service module is used for carrying out data interaction with the client application module so as to manage the data acquired from the map service module, and sending the data to the client application module through an authorization mechanism and synchronously synchronizing the data updated in real time to the map service module.
Further, the data service module further includes:
the Http App load balancer is used for carrying out load balancing processing on the HTTP request;
AirFlow on Amazon EC2 cluster for synchronizing data satisfying the update condition to the gallery;
in the data service module, the update index processing function Lambda is also used for adding an index to the latest updated map and putting the latest updated map into an index library for unified management; the original data storage Amazon S3 is also used for storing the original file of the map data and carrying out classification management on the original file; the map index database MySql is also used for storing map index data.
Further, the intelligent driving virtual simulation cloud platform further comprises: the map application system based on the test case is formed by configuring the public cloud module and the client application module, and the public cloud module further comprises:
the route service module is used for planning a route of the map data;
the algorithm service module is used for calculating map elements and data according to the user request;
and the communication connection management module is used for managing the data in the system cache, carrying out corresponding operation on the data through the input of the simulation test flow module, and feeding back the result to the cloud-client application data interaction module.
Further, the communication connection management module includes:
the long connection object management module is used for auditing the user connection request and writing the audited data into the connection object hot buffer module;
the connection object hot buffer module is used for storing relevant data of states, addresses and characteristics of users.
Further, GPB is adopted as a communication protocol between the public cloud module and the client application module.
Further, the client application module adopts one or more of a television large screen end module, a smart phone end module, a PC end module and a browser web end module.
The intelligent driving virtual simulation cloud platform comprises a map operation management system, the map operation management system provides a high-precision map data management and application solution EHP-EHR based on an AWS public cloud platform, unified storage, unified buffering, unified decoding and unified calculation of high-precision map data are realized, a unified service level data access interface is provided for the EHR, and bottom high-precision map data and service technology support are provided for the construction of a subsequent virtual simulation platform. And the elastic storage resource and the computing resource based on the AWS public cloud are adopted for the management of the high-precision map data, so that the storage and the use of massive scene data are supported, a series of flexible operation actions such as the inquiry, the loading, the editing, the storage and the like of the 2D/3D high-precision map data are realized, various test scenes and Corner cases can be customized, and the development and the protection navigation of the intelligent driving virtual simulation platform are realized.
Further, the intelligent driving virtual simulation cloud platform also comprises a map data synchronization system, and the map data synchronization system provides a secure communication handshake and interconnection access solution of the client and the high-precision map management system based on the AWS public cloud, so that unified storage of high-precision map data is realized and synchronization management is provided; the system administrator can easily monitor and manage the data of the high-precision map through the management interface. And a technology realization foundation is provided for supporting high concurrency virtual simulation test subsequently. The map data synchronization system realizes a data synchronization scheme that a map provider data center transmits a high-precision map to an intelligent driving high-precision map management platform, and supports transmission synchronization of multiple map providers, different map data sources, different high-precision map data formats and standards and mass map original big data storage. And synchronizing high-precision map data of all public cloud service providers, and carrying out abstract design. The high-precision map data of different file formats and contents of each graphic merchant are subjected to a series of function implementation based on resource access authorization, key encryption and decryption, compression/decompression algorithm, distributed object storage bucket directory structure, index library construction and the like through a distributed workflow, and the method has excellent safety, flexibility, expansibility, compatibility and openness.
Further, the intelligent driving virtual simulation cloud platform also comprises a map application system based on the test case, wherein the map application system is an application solution EHP-EHR of the high-precision map based on the AWS public cloud platform in the intelligent driving virtual simulation test case, and realizes online editing and customization of the high-precision map by the test case; in the running process of the test flow instance, the VR client is visible in the whole process of the running state of the virtual vehicle, and real-time environment perception high-precision positioning and simulation behavior decision support are realized.
The above, as well as additional objectives, advantages, and features of the present invention will become apparent to those skilled in the art from the following detailed description of a specific embodiment of the present invention when read in conjunction with the accompanying drawings.
Drawings
Some specific embodiments of the invention will be described in detail hereinafter by way of example and not by way of limitation with reference to the accompanying drawings. The same reference numbers will be used throughout the drawings to refer to the same or like parts or portions. It will be appreciated by those skilled in the art that the drawings are not necessarily drawn to scale. In the accompanying drawings:
FIG. 1 is a schematic diagram of the operation and management system of a map of an intelligent driving virtual simulation cloud platform according to an embodiment of the present invention;
FIG. 2 is a schematic diagram of the operation of a map data synchronization system for an intelligent driving virtual simulation cloud platform according to an embodiment of the present invention;
fig. 3 is a schematic diagram of the operation of the map application system of the intelligent driving virtual simulation cloud platform according to an embodiment of the present invention.
Reference numerals:
a map operation management system 100;
a map data synchronizing system 200;
a map application system 300;
a data service module 10; storing Amazon S3 11 in the original data; map index database MySql 12; updating an index processing function Lambda 13; http App load balancer 14; airFlow on Amazon EC2 Cluster 15;
a map data operation module 20;
a map decoding service module 30; a 2D map decoding module 31; a 3D map decoding module 32;
a map thermal caching module 40;
a cloud-client application data interaction module 50;
a client application module 60;
a map service module 70;
a routing service module 81; an algorithm service module 82; a communication connection management module 83; a long connection object management module 831; a connection object hot buffer module 832;
a scene use case management service 91; the test flow module 92 is simulated.
Detailed Description
Referring to fig. 1, the intelligent driving virtual simulation cloud platform of the present invention includes a map operation management system 100, where the map operation management system 100 mainly includes two major parts, namely a public cloud module and a client application module 60. The public cloud module may adopt an ABS public cloud platform, and the public cloud module may be configured to implement encoding and decoding of the map data file and operation calculation of the corresponding map data, and notify the client application module 60 of the result. The client application module 60 may be configured to display, monitor, and provide a visual interface for data of the public cloud module in real time. The intelligent driving virtual simulation cloud platform comprises a map operation management system 100, wherein the map operation management system 100 provides a high-precision map data management and application solution EHP-EHR based on an AWS public cloud platform, realizes unified storage, unified buffering, unified decoding and unified calculation of high-precision map data, provides a unified service level data access interface for the EHR, and provides bottom-layer high-precision map data and service technology support for the establishment of a subsequent virtual simulation platform. And the elastic storage resource and the computing resource based on the AWS public cloud are adopted for the management of the high-precision map data, so that the storage and the use of massive scene data are supported, a series of flexible operation actions such as the inquiry, the loading, the editing, the storage and the like of the 2D/3D high-precision map data are realized, various test scenes and Corner cases can be customized, and the development and the protection navigation of the intelligent driving virtual simulation platform are realized.
It should be noted that map data management is an important function of the virtual simulation platform. The virtual simulation platform uses high-precision map to reconstruct a 3D scene (including a static scene and a dynamic scene) so as to verify various algorithms of the automatic driving vehicle in a simulation environment.
According to one embodiment of the present invention, as shown in fig. 1, the public cloud module mainly comprises a data service module 10, a map data operation module 20, a map decoding service module 30, a map hot buffer module 40, and a cloud-client application data interaction module 50. Wherein the data service module 10 may be configured to obtain corresponding map data from a map library according to the requested parameters. The map data operation module 20 may be used to call specific sub-modules in the public cloud module according to the operation request. The map decoding service module 30 may be used to decode map data and analyze it into a data format usable by the system. The map hot buffer module 40 may be used for persistence of the cloud of map data. The map thermal buffering module 40 has the significance that when the client application module 60 uses map data, the map thermal buffering module can be obtained quickly, and the map thermal buffering module does not need to be obtained in the cloud database and the distributed object storage every time and then decoded, so that the query speed is greatly improved, and the use experience of a user is improved. The cloud-client application data interaction module 50 may interact with the client application module 60 and call the functions of the simulation test flow module 92 of the intelligent driving virtual simulation cloud platform, and send the data to the client application module 60 through an authorization mechanism. The scene use case management module, the data service module 10, the map decoding service module 30, the map hot buffer module 40 and the cloud-client application data interaction module 50 are respectively and interactively connected with the map data operation module 20. The scene use case management module is a scene use case management service 91, and is mainly used for managing (adding, deleting, changing and checking) scene use cases. The scene use cases comprise static map scene management, dynamic traffic flow management, weather setting, path planning, vehicle sensor simulation configuration, vehicle dynamics simulation configuration, intelligent driving decision algorithm interface importing and the like. The static map scene management in the scene use case management needs to use map calculation Lambda, global path navigation Lambda, map data operation Lambda, high-precision map data and the like in the high-precision map data management platform.
In some embodiments of the present invention, referring to fig. 1, the map decoding service module 30 includes a 2D map decoding module 31 and a 3D map decoding module 32. The 2D map decoding module 31 may be used to parse the map raw file into a pattern of flat data points. The 3D map decoding module 32 may be used to parse the map raw file into stereoscopic data point patterns. The data service module 10 includes an original data store Amazon S3 11, a map index database MySql 12, and an update index processing function Lambda 13. Wherein the raw data store Amazon S3 11 can be used to store map raw files. The map index database MySql 12 may be used to store the relationship between the map index and the map raw file. The update index processing function Lambda 13 may be used to update the index of the map. And when 2D map loading, displaying, editing and storing and 3D map effect viewing are performed, the client side interacts with the cloud platform in an Http Restful short connection mode. When the client side and the cloud platform are in high-precision map data communication, a communication protocol adopts GPB ((Google Protocol Buffer)). The GPB protocol has the following advantages: performance (small data volume, high serialization speed, high transmission speed), usage (simple use, low maintenance cost, good backward compatibility, good encryption), and usage scope (cross-platform, cross-language, good scalability). Of course, it should be noted that, when the client side and the cloud platform perform high-precision map data communication, a GPB (Google Protocol Buffer) communication protocol is adopted. However, if only the user identity authentication is performed, the short connection can be communicated in an http restful/json mode, and the long connection needs to be communicated in an http restful/json+websocket mode.
The client application module 60 adopts one or more of a television large screen end module, a smart phone end module, a PC end module and a browser Web end module, that is, the client application module 60 adopts a television large screen VR application, a smart phone VR application, a PCVR application and a browser Web application.
The original data storage Amazon S3 stores map original data files and map thumbnail files in barrels, and different map merchants have own map data file formats, wherein the map data files of different map merchants are respectively stored by considering a plurality of barrel and directory structure divisions. The update index processing function Lambda 13 will be used as a post-trigger execution step of storing Amazon S3 11 as map raw data, and each time the content of the S3 barrel data file is updated, the update index processing function Lambda 13 will be triggered. The update index processing function Lambda 13 firstly acquires file attribute information and analyzes content according to map file data storage formats of different image merchants, and then generates data records to a map index database MySQL 12 for storage according to acquired and analyzed results, so that a virtual simulation platform can call and inquire the data for use. The map index database MySQL 12 stores index and attribute information of each high-precision map raw data file.
Map data operation Lambda is responsible for inquiring corresponding index records in a map index database according to map data inquiring conditions, and then inquiring map original data files in an original data storage Amazon S3 11 according to an index database inquiring result. And respectively calling corresponding 2D and 3D map decoder services according to the map display form requested by the user. Finally, the map latest operation sequence data is written into the map thermal buffer module 40 as required.
The decoder only carries out Lambda interactive data with the map data operation field, firstly carries out content format analysis on the map original data file, then carries out reverse deflection calculation decoding on the analysis result, and finally extracts corresponding 2D map data or 3D map data according to the requirement of a user request and returns the data.
Before the test flow instance is started, the scene use case or the map data needs to be edited and saved in advance. Here, a 2D map or a 2D map template corresponding to a scene use case is loaded through a web browser. When a user loads and edits a map, custom map generation, map generation based on high-precision map raw data, and virtual simulation map generation based on a GAN generation countermeasure network are supported.
The Web user edits the loaded map, and needs to buffer the loaded map original data first, then buffer the latest operation sequence of the edited map, and finally write the edited and complete map data into the data service domain when the edited and complete map data is submitted to be saved uniformly, and the whole process needs to write the map original data into the map thermal buffer module 40. In addition, the Web user performs global path navigation planning on the loaded scene map, and data after map decoding is needed, so that the data is also written into a map operation sequence cache. Therefore, the distributed Redis is used as a cache of the map operation sequence, and contains the original data for map editing and also contains the data after map decoding for global path navigation.
The query, loading, editing and storing of the 2D map by the Web client and the rendering and displaying of the 3D map by the VR client all go through Restful API Gateway.
Through the scheme, the 2D high-precision map is edited through the web browser, customized map data generation is achieved, future simulation scene definition is supported, and a 2D map data query list is needed to be obtained first. And then selecting one target map data in the query list for loading and displaying, and when the client is a Web application, sending a 2D map query request to Restful API Gateway by the client Web application. Restful API Gateway the map data operation function is invoked to retrieve the map index database MySQL 12 according to the query condition, and return the map index result set to the map data operation function. And the map data operation function queries the map thumbnail in the map raw data storage Amazon S3 according to the path of the index thumbnail in the index result set to obtain a target map thumbnail for the map data operation function, and then the target map thumbnail and the target map index data set acquired before are packaged in a unified format.
The user of the Web browser wants to customize the high-precision map data, and needs to load the 2D data of the target precision map and render and display the data on the interface. The client Web application sends a target map query request to Restful API Gateway invoking a map data manipulation function. And the map data operation function inquires map data in the map operation sequence buffer Redis according to the parameters and obtains inquiry result response feedback of the map operation sequence buffer Redis. At this time, it checks whether the target map operation sequence exists in the cache Redis, and if so, clears the corresponding map operation sequence cache data record. Meanwhile, the map data operation function queries the map index database MySQL 12 to return a query result set to the map data operation function according to the parameters, queries target map original data in the map original data storage Amazon S3 11, and acquires returned query result data. The map data manipulation function forwards the target map raw data to the 2D map decoder function. And the 2D map decoder analyzes and carries out geographic position reverse deflection calculation, and finally feeds the analyzed map data response back to the map data operation function. And simultaneously writing the data into a map operation sequence cache Redis, and feeding back the resolved map data response to Restful API Gateway to feed back a response query result set to the client Web application. The client Web application is computed to obtain triangle facets, and then renders the entire interface according to these triangle facets.
After loading target high-precision map data, a user of the Web browser can freely edit interface elements and then cache the latest operation sequence data of the map; the map raw data is further parsed into an interface rendering data format for display with a 2D map decoder. The client-side Web application needs to access Restful API Gateway to modify the 2D map information, and after the Web browser edits the map operation, the map incremental operation sequence is used as a parameter to submit map incremental editing content to Restful API Gateway to call the map data operation function. The map incremental data is calculated through parameters, and then the map operation sequence data is sent to a 2D map decoder function. The 2D map decoder decodes the map operation sequence data of the current increment and performs geographic position reverse deflection calculation. The 2D map decoder function feeds back the parsed map data response to the map data operation function. And the map data operation function updates the data of the map operation sequence and the data decoded by the map operation sequence of the increment to the cache Redis. And feeds back the map incremental editing result to Restful API Gateway. Restful API Gateway feeds back the answer query result set to the client Web application. The client Web application obtains the triangle surface and renders the interface through calculation.
After editing the 2D high-precision map data, the Web browser user can uniformly and durably store the data cached by the latest operation sequence into an object storage and database. When the client Web application saves the 2D map information, a request for saving the edited 2D map data is sent to Restful API Gateway.
Restful API Gateway invokes a map data manipulation function. And the map data operation function obtains the edited map original data from the map latest operation sequence buffer Redis according to the meeting parameters. And feeding back the query result response to the map data operation function and generating a thumbnail according to the query result. The map data operation function selects to directly replace and store the thumbnail and the map original data into Amazon S3 barrel files according to the connection quantity; or save the thumbnail and map raw data as new files of Amazon S3 bucket. The update of Amazon S3 will trigger a post-processed update map index function. Updating the map index function obtains map file information in the event object. And updating and writing map index information into an index database MySQL. And finally, responding the operation execution result to the client.
After the Web browser client edits and saves the 2D map data, the target map data can be loaded through the VR desktop client and displayed in a 3D mode, so that a user can check the self-customized editing effect. When the VR client acquires the 3D map information, a query request is sent to Restful API Gateway and Restful API Gateway, a map data operation function is called according to parameters to query the map index database MySQL 12, and a query result set is returned to the map data operation function. And the map data operation function obtains target map original data from the map original data storage Amazon S3 according to the result. The data is forwarded to a 3D map decoder function. The method comprises the steps of decoding original data of a target map, performing geographic position reverse deflection calculation, and feeding back map data response to a map data operation function. The map data manipulation function feeds back a map data response to Restful API Gateway. And finally, feeding back the query result response to the client application. And the VR client application obtains the triangle surface through calculation and renders the VR interface.
Referring to fig. 2, the intelligent driving virtual simulation cloud platform further includes a map data synchronization system 200 according to one embodiment of the present invention, and the client application module 60, the data service module 10, and the map service module 70 interact to form the map data synchronization system 200.
The analog simulation platform needs to be modeled with high-precision map data. The high-precision map data originates from a third party's map cloud platform (i.e., map service module 70). There are two triggering ways to synchronize data from the map service module 70 to the map data synchronization system 200. The first is to let the user trigger the data synchronization by himself, i.e. the user triggers the data synchronization operation by means of the client application module 60, which may in particular be a web browser client. The operation instructions are then forwarded to the air flow distributed workflow orchestration engine deployed on the Amazon EC2 cluster by Http App load balancer 14. Finally, the air flow executes the Python program to complete the downloading of the high-precision map data from the map service module 70, and writes the map raw data file to Amazon S3. And secondly, setting a timing task Crontab in the AirFlow cluster, wherein the timing task can trigger a Python program on the AirFlow at fixed time, so that data synchronization is completed.
It should be noted that once the map raw data is successfully written into Amazon S3 of the data service module 10, an update index lambda function is automatically triggered, which parses the map data file in Amazon S3 to obtain necessary map attribute metadata information, and creates a record index into MySql database according to the metadata information. After the MySql is successfully built, the URL field in the index record indicates the location where the map original file is actually stored on Amamzon S3, and other map metadata information may be used as a query condition for lambda query in the map data operation module 20.
The lambda of the map data operation module 20 can firstly search the MySql database of the data service module 10 for the target map index according to the upper layer service request, and then search the Amazon S3 for the original data file of the target map according to the index information.
The map service module 70 is intended to interface and synchronize data with the map data services of the various vendors. Common high-precision map service providers typically post-map creation, release map data onto public clouds. The high-precision map data are synchronized on public clouds, and it is generally simple to lead the sharable high-precision map data out of a map core service cluster of a map merchant into an object storage service, such as an OSS of an ali cloud, a COS of a messenger cloud, an OBS of a cloud, and the like. The map data file is first considered for compression or non-compression according to external needs, then the plaintext is encrypted using the key, and then stored in the object storage service according to a certain directory structure rule. On the high-precision map data object storage service, various access authentication mechanisms are to be set. Taking the AWS public cloud platform as an example, an Access Control List (ACL) of the object storage service may manage access rights to buckets and objects. Each bucket and object has an ACL attached as a child resource. It defines which cloud accounts or groups are to be granted access rights and the type of access. Upon receipt of a request for a resource, the object store service will check the corresponding ACL to verify whether the requestor possesses the required access rights. In addition, the map data of each map merchant has own content format, and customized content can be provided according to the requirements of users, including but not limited to graphic format, graphic index format, attribute data format, geographic coordinate system and projection information, geometric body space index, attribute index of active fields in a list, geocode index, attribute index of files, metadata, character codes and the like.
In some embodiments of the present invention, referring to fig. 2, the data service module 10 further includes Http App load balancer 14 and AirFlow on Amazon EC cluster 15. Wherein the Http App load balancer 14 may be used to load balance Http requests. AirFlow on Amazon EC2 cluster 15 may be used to synchronize data meeting the update condition to a gallery. In the data service module 10, the update index processing function Lambda 13 is also used for adding an index to the latest updated map, and placing the latest updated map into an index library for unified management. The original data storage Amazon S3 11 is also used for storing and sorting and managing the original files of map data, and the map index database MySql 12 is also used for storing map index data.
Specifically, in the map data synchronization system 200 of the intelligent driving virtual simulation cloud platform of the present invention, the client application module 60 mainly refers to a browser Web application, which allows a system administrator to manually trigger a map data synchronization service function after authentication. The browser client is connected with the Http App seven-layer load balancing service of the cloud platform data service domain through the Http Restful protocol, and then the function of synchronizing high-precision map data among the cloud platforms is started.
The data service module 10 is composed of a Http App load equalizer 14, airFlow on Amazon EC cluster 15, a map raw data storage Amazon S3 11, an update index processing function Lambda 13, and a map index database MySql 12. The key processing components for map data synchronization are deployed on Amazon EC2 cluster 15, which consists of a series of Amazon EC2 elastic computing clouds to live elastic expansion and high availability. Each EC2 service node has an AirFlow service system accuracy installed. When the map data synchronization task is started, there are 2 modes: 1. the user manually activates: and a user manager manually starts a data synchronization task through a Web management interface of the AirFlow, and monitors the execution state of the task in real time. AirFlow timing start: setting a timer in the AirFlow, triggering a Python program at fixed time, and automatically starting a data synchronization task. The high-precision map data synchronization program is written in the Python language, is also deployed on the EC2 cluster, and is subjected to task scheduling, service scheduling and visual monitoring by the AirFlow.
The cloud platform and other external cloud platforms can be connected in an internet mode, encrypted data is transmitted by using an HTTPS protocol, and a VPN security tunnel can be built on the internet to perform a data synchronous transmission mechanism with higher security. After receiving the data file, the intelligent driving simulation cloud platform decrypts the data file through the secret key. For the decrypted high-precision map data, decompression is performed in accordance with a certain data compression scheme, if necessary. After decompression, a series of files may be generated, and the decompressed map file set needs to be stored in Amazon S3 of the map raw data according to different vendors and a certain directory structure. Updating the index processing function Lambda 13, wherein the step is performed as the post-trigger of storing Amazon S3 as the map original data, and each time the content of the S3 barrel data file is updated, the index processing function Lambda 13 is triggered. According to the Lambda function, firstly, file attribute information is acquired and content is analyzed according to map file data storage formats of different image merchants, then, the acquired and analyzed results are generated and data are recorded in a map index database MySQL 12 to be stored, so that a virtual simulation platform can call and inquire the results for use.
The map data synchronization system 200 can achieve high-precision map data synchronization by the following scheme. And the map merchant stores the openable high-precision map data on the cloud platform. And according to the secret key, locally encrypting and storing the map data, exposing the storage address of the high-precision map to the cloud platform for access, and setting access control authority. The system synchronizes the map in two ways, either manually or timer triggered. The former is that the client Web application sends a data synchronization request to the Http App load balance after meeting the synchronization condition. The latter is the triggering of the data synchronization task by the AirFlow system timer running on Amazon EC 2. The Http App load balancing service distributes task flows to corresponding air flow cluster environments running on Amazon EC2 according to load policies. The air flow service starts a data synchronization processing program written by a Python language, sends a request for acquiring each file attribute information of the graph quotient object storage catalog to the Http App load balancing service, and returns an access graph quotient data object storage End Point to a query result set. Htpp App load balancing returns the query result set to the Python handler. After obtaining the merchant file, the Python processing program will send a request for acquiring attribute information of the history map file to the map raw data Amazon S3. Map raw data Amazon S3 will return a query result set. And meanwhile, the Python processing program compares and calculates the attributes of the new file and the old file and generates a file list to be synchronized. When the list of files to be synchronized exists, namely, under the condition that updating is needed, the Python processing program sends a map file request to the Http App load balancing service. The Http App load balancing service accesses the graph business data object to store the End Point, and returns to the target map file set. After the Python processing program obtains the map increment complete file, the key is used for decrypting the latest increment file. And finally updating the file content to the local Amazon S3. At this time, amazon S3 triggers the post-processing logic function Lambda to acquire map file information in the event object, writes map index information into the database, and updates the index library.
According to one embodiment of the present invention, referring to fig. 3, the intelligent driving virtual simulation cloud platform further includes a map application system 300 based on a test case, the map application system 300 is configured by a public cloud module and a client application module 60, and the public cloud module further includes a routing service module 81, an algorithm service module 82, and a communication connection management module 83. The route service module 81 may be used for planning a path of map data. The algorithm service module 82 may be used to calculate map elements and data for user requests. The communication connection management module 83 may be configured to manage data in the system cache, perform corresponding operations on the data through input of the simulation test flow module 92, and feed back the result to the cloud-client application data interaction module 50.
The communication connection management module 83 includes a long connection object management module 831 and a connection object hot buffer module 832. The long connection object management module 831 may be configured to audit a request for user connection, and write the audited data into the connection object hot cache module 832. The connection object hot buffer module 832 may be used to store data related to the status, address, and characteristics of a user.
Specifically, as shown in fig. 3, the most core content of the routing service module 81 is a global path navigation Lambda function. The global path navigation Lambda function needs to acquire specified map data from a map rendering data cache Redis, and then planning the global path navigation. When one of the two main function points is used for the Lambda and the web end is used as a scene case, a starting point and an ending point of a virtual simulation vehicle travel task need to be designed in advance, and then the Lambda is called to generate a global navigation path and visualized on a web browser. By doing so, the user can continuously adjust the global path design so as to meet the requirements of the simulation test path. The other is that after the test flow example is started, the virtual vehicle needs to redesign and plan the global path when necessary according to the environment recognized by the surrounding and the actual driving route in the driving process (for example, the deviation between the actual driving route and the preset global navigation route reaches the threshold value, and the global path navigation is considered to be needed to be reworked, etc.).
The most central content of the algorithm service module 82 is a map calculation Lambda function. There are the following main functional points to use for this Lambda. After the test flow example is started, real-time high-precision positioning (environment sensing stage) of the virtual vehicle, local path planning, intelligent decision and the like are all used for calculating Lambda by the map.
After the VR client establishes a long Connection handshake with the WebSocket gateway, the Lambda long Connection authorizer writes the test flow instance ID, the API ID and the Connection ID into the Redis of the domain. The Web management client may then query the connection object hot-cache dis data record via the long connection object management Lambda and display all online VR clients (including which VR client connected which test flow instance). Finally, when the VR client is disconnected with the WebSocket gateway, the data record in the connection object hot cache Redis is cleaned through the long connection object management Lambda.
Through the above scheme, the scenario cases are designed, firstly, a query list is needed to be obtained, then, one scenario case is selected for editing, viewing and saving, the client side Web application sends a 2D scenario case query request to Restful API Gateway, forwards the query request to the scenario case management service 91, and returns the result to the map data operation function. After the map data operation function retrieves the map index database MySQL 12 result according to the query condition and returns, the map data operation function queries the map thumbnail in Amazon S3 11 according to the result to the map raw data storage, and returns the target map query result set list to the scene use case management service 91 after obtaining the result. The scenario case management service 91 feeds back a query result set list to Restful API Gateway. And feeding back a response to the final query result set list to the client Web application, and finally rendering a query result set list interface.
After the user obtains the 2D scene use case list, one of the two sets of the 2D scene use cases can be selected to be opened, and the selected scene use case binds the corresponding high-precision map, so that the 2D scene map can be rendered on the interface. The client Web application sends a 2D map query request to Restful API Gateway, restful API Gateway forwarding the query request to the scenario case management service 91. The scenario case management service 91 will call the map data operation function. And the map data operation function queries the map latest operation sequence cache data in the map latest operation sequence cache Redis according to the parameters. And feeding back the query result response to the map data operation function. And when the target map latest operation sequence exists in the cache, clearing the map latest operation sequence cache data corresponding to the session ID. And at the same time retrieves the map index database MySQL 12 according to the parameters. And obtaining target map original data from the map original data storage Amazon S3 11 after obtaining the result, and returning the query result data to the map data operation function. The map data manipulation function forwards the target map raw data to the 2D map decoder function. The 2D map decoder analyzes the original data format of the target map, performs geographic position feedback deflection calculation, and then feeds back a result response to the map data operation function. The map data operation function writes both the map raw data and the decoded data into the map latest operation sequence buffer dis. The result is finally fed back to the scene use case management service 91. The scene use case management service 91 feeds back static map scene information to Restful API Gateway. Restful API Gateway feeds back the query result set answer to the client Web application. The client Web application obtains the triangle surface and renders the interface through calculation.
After loading the 2D scene map, the client Web application can freely edit the interface elements and then buffer the latest operation sequence data of the map. The map raw data is further parsed into an interface rendering data format for display with a 2D map decoder. The client Web application sends an edit request to Restful API Gateway. Restful API Gateway invokes the scenario case management service 91. The scene use case management service 91 calls a map data operation function. It sends the current scene map operation sequence data to the 2D map decoder function. And the 2D map decoder decodes the scene map operation sequence data of the current increment and performs geographic position reverse deflection calculation. And finally, feeding back the resolved scene map data response to the map data operation function. The map data operation function updates both the current map operation sequence data and the data decoded by the current incremental map operation sequence to the cache Redis and returns the processing result to the scene use case management service 91. The scene use case management service 91 feeds back the map increment editing result to Restful API Gateway. Restful API Gateway returns a response to the client Web application. The client Web application obtains the triangle surface and renders the interface through calculation.
After 2D scene map data is edited, the client Web application can uniformly and durably store the data cached by the latest operation sequence into an object storage and database. The client Web application sends a warranty request to Restful API Gateway. Restful API Gateway forwards the request to the scenario case management service 91. The scene use case management service 91 calls a map data operation function according to the parameters. And the map data operation function acquires the edited map original data from the map latest operation sequence buffer Redis and feeds back the query result response to the map data operation function. And when the map data operation function checks that the incremental data of the map operation exists, generating a thumbnail corresponding to the map, and saving the thumbnail and the map original data as a new file of the Amazon S3 barrel. And feeds back the save operation response to the scene use case management service 91. The scenario case management service 91 establishes a mapping relationship. And returns the processing result to Restful API Gateway. Restful API Gateway feeds back the operation execution result response to the client Web application. While an update of the map raw data store Amazon S3 11 will trigger an update of the map index function. The update map index function obtains map file information in the event object and updates and writes the map index information into the map index database MySQL 12.
After the client Web application edits and saves the 2D scene map data, the target scene map data can be loaded through the VR desktop client and displayed in a 3D mode, so that a user can check the customized editing effect. The VR client sends a request to query for 3D map scene data to Restful API Gateway. Restful API Gateway forwards the request to the scenario case management service 91. The scene use case management service 91 calls a map data operation function according to the parameters. The map data manipulation function queries the map index database MySQL 12 according to the parameters. The map index database MySQL 12 returns the query result set to the map data manipulation function.
The map data operation function acquires the target map raw data from the raw data storage Amazon S3 11 according to the return result. And forwards the target map raw data to the 3D map decoder function. And the 3D map decoder analyzes the original data format of the target map, performs geographic position anti-deflection calculation, and then feeds back the analyzed map data response to the map data operation function. The map data manipulation function feeds back a map data response to the scene use case management service 91. The scene use case management service 91 feeds back a map data response to Restful API Gateway. Restful API Gateway feeds back the query result answer to the VR client application. And the VR client application obtains the triangle surface through calculation and renders the interface.
After the scene map is edited and stored by the user, the global path navigation planning can be performed on the scene map, and the planned global path can be displayed on the Web browser for the user to view. When a client Web application opens a scenario use case. And sets a start node and an end node on the 2D map of the scene use case. At this point the global navigation path is requested to Restful API Gateway. Restful API Gateway forwards the request to the scenario case management service 91. The scenario case management service 91 invokes a global path navigation function according to the parameters. The map operation sequence buffer Redis acquires corresponding map rendering data according to the map operation sequence buffer Redis, and the query result set is returned to the global path navigation function. The global path navigation function performs the optimal map global path planning calculation and returns the calculation result set to the scene use case management service 91. The scenario management service 91 feeds back the calculation result set to Restful API Gateway. The final Restful API Gateway feeds back the calculation result set to the client Web application for the client Web application to render the interface.
After the scene use case editing, saving and global path navigation planning are completed, the test flow can be started. The test flow instance starts the process, and map original data and decoded rendering data are required to be written into a distributed cache so as to be used for subsequent environment-aware high-precision positioning, simulation behavior decision, real-time global path navigation planning and the like. The client Web application sends a launch test procedure instance request to Restful API Gateway. Restful API Gateway forwards the scenario case ID to the test flow case management service. The test flow instance management service (simulation test flow module 92) generates an identification and mapping relation according to the algorithm and forwards the identification and mapping relation to the scene use instance management service 91. The scene use case management service 91 calls a map data operation function according to the parameters. The map data operation function acquires map real-time cache data from the map real-time data cache Redis. When the map data manipulation function checks that the target map data does not exist in the cache, the map index database MySQL 12 is queried according to the parameters. And returning the query result set to the map data operation function. The map data manipulation function acquires the target map raw data from the map raw data storage Amazon S3 11. And the data is returned to the map data manipulation function. The map data manipulation function forwards the target map raw data to the 3D map decoder function. And the 3D map decoder analyzes the original data format of the target map, performs geographic position reverse deflection calculation, and finally feeds the analyzed map data response back to the map data operation function. The map data manipulation function writes both the map raw data and the decoded rendering data into the map real-time data hot buffer dis while feeding back a map rendering data response to the scene use case management service 91. The scenario case management service 91 feeds back the loading scenario case result response to the test flow case management service. It feeds back a boot test flow instance result answer to Restful API Gateway. Restful API Gateway feeds back the launch test procedure instance result answer to the client Web application.
In the process of executing a certain test flow instance, the virtual simulation platform needs to continuously perform real-time environment-aware high-precision positioning on the virtual vehicle (in the simulation test environment, the test flow instance has a emperor view angle, a simulation sensor can directly provide real-time environment-aware information for the virtual vehicle, and then the virtual vehicle needs to be positioned with high precision), so that a reference basis is provided for subsequent real-time vehicle behavior decisions. After the test flow instance is started, various map real-time calculation requests are triggered to the test flow instance management service according to the parameter period. The test flow instance management service forwards the parameters to the scenario case management service 91. The scene use case management service 91 calls the map calculation function according to the parameters to query the map real-time data hot cache Redis. The map real-time data hot buffer Redis feeds back the query result response to the map calculation function. The map calculation function performs map calculation based on the inputted request condition and the obtained map data, and feeds back a calculation result response to the scene use case management service 91. The scenario case management service 91 feeds back the calculation result response to the test flow case management service. The test flow instance management service feeds back the calculation result response to the context aware high precision positioning service, which pushes the map calculation result to the VR client application through WebSocket API Gateway. And rendering an interface by the VR client application to finish the new real-time high-precision positioning display of the virtual vehicle.
In the process of executing a certain test flow instance, the virtual simulation platform needs to continuously conduct real-time behavior decision on the virtual vehicle according to the environment sensing result so as to complete verification of rationality of a decision algorithm. When the simulated platform detects that the running route of the virtual vehicle is inconsistent with the planned route of the global path navigation planned in advance, the virtual vehicle needs to be subjected to the global path navigation rescheduling by utilizing the high-precision map data. The simulated behavior decision service sends a real-time path planning request to the test flow instance management service. The test flow instance management service forwards the request to the scenario case management service 91. The scenario case management service 91 invokes a global path navigation function. And obtaining corresponding map data from the global path navigation function parameters in the map real-time data cache Redis. And performing optimal map global path navigation calculation according to the current position information of the vehicle and the map data information. Finally, the result response is fed back to the scene use case management service 91. The scenario case management service 91 feeds back the calculation result response to the test flow case management service. The test flow instance management service feeds back the calculation result response to the simulation behavior decision service. After the simulated behavior decision service obtains the calculation result, the map calculation result is pushed to the VR client application through WebSocket API Gateway. And the VR client side application renders an interface to finish the display of the new global path navigation planning of the virtual vehicle.
In summary, the intelligent driving virtual simulation cloud platform comprises a map operation management system 100, and the map operation management system 100 provides a map data management and application solution EHP-EHR based on an AWS public cloud platform, realizes unified storage, unified buffering, unified decoding and unified calculation of high-precision map data, provides a unified service level data access interface for the EHR, and provides bottom-layer high-precision map data and service technology support for the establishment of a subsequent virtual simulation platform. And the elastic storage resource and the computing resource based on the AWS public cloud are adopted for the management of the high-precision map data, so that the storage and the use of massive scene data are supported, a series of flexible operation actions such as the inquiry, the loading, the editing, the storage and the like of the 2D/3D high-precision map data are realized, various test scenes and Corner cases can be customized, and the development and the protection navigation of the intelligent driving virtual simulation platform are realized. The intelligent driving virtual simulation cloud platform also comprises a map data synchronization system 200, wherein the map data synchronization system 200 provides a secure communication handshake and interconnection access solution of a client and a high-precision map management system based on AWS public cloud, realizes unified storage of high-precision map data and provides synchronous management; the system administrator can easily monitor and manage the data of the high-precision map through the management interface. And a technology realization foundation is provided for supporting high concurrency virtual simulation test subsequently. The map data synchronization system realizes a data synchronization scheme that a map provider data center transmits a high-precision map to an intelligent driving high-precision map management platform, and supports transmission synchronization of multiple map providers, different map data sources, different high-precision map data formats and standards and mass map original big data storage. And synchronizing high-precision map data of all public cloud service providers, and carrying out abstract design. The high-precision map data of different file formats and contents of each graphic merchant are subjected to a series of function implementation based on resource access authorization, key encryption and decryption, compression/decompression algorithm, distributed object storage bucket directory structure, index library construction and the like through a distributed workflow, and the method has excellent safety, flexibility, expansibility, compatibility and openness. In addition, the intelligent driving virtual simulation cloud platform further comprises a high-precision map application system 300 based on the test case, wherein the map application system 300 is an application solution EHP-EHR of the high-precision map based on the AWS public cloud platform in the intelligent driving virtual simulation test case, and online editing and customization of the high-precision map by the test case are realized; in the running process of the test flow instance, the VR client is visible in the whole process of the running state of the virtual vehicle, and real-time environment perception high-precision positioning and simulation behavior decision support are realized.
By now it should be appreciated by those skilled in the art that while a number of exemplary embodiments of the invention have been shown and described herein in detail, many other variations or modifications of the invention consistent with the principles of the invention may be directly ascertained or inferred from the present disclosure without departing from the spirit and scope of the invention. Accordingly, the scope of the present invention should be understood and deemed to cover all such other variations or modifications.
Claims (9)
1. An intelligent driving virtual simulation cloud platform, which is characterized by comprising: a map operation management system, the map operation management system comprising:
the public cloud module is used for realizing the encoding and decoding of the map data file and the operation calculation of the corresponding map data, and notifying a result to the client application module;
the client application module is used for displaying, monitoring and providing a visual interface for the data of the public cloud module in real time;
the public cloud module comprises:
the data service module is used for acquiring corresponding map data from the map library according to the requested parameters;
the map data operation module is used for respectively calling the map data in the public cloud module according to the operation request;
The map decoding service module is used for decoding the map data and analyzing the map data into a data format available to the system;
the map thermal caching module is used for persistence of the map data cloud;
the cloud-client application data interaction module is used for carrying out data interaction with the client application module, calling the function of the simulation test flow module of the intelligent driving virtual simulation cloud platform and sending the data to the client application module through an authorization mechanism;
and the scene use case management module, the data service module, the map decoding service module, the map hot buffer module and the cloud-client application data interaction module are respectively and interactively connected with the map data operation module.
2. The intelligent driving virtual simulation cloud platform of claim 1, wherein the map decoding service module comprises:
the 2D map decoding module is used for analyzing the map original file into a plane data point mode;
and the 3D map decoding module is used for analyzing the map original file into a stereoscopic data point mode.
3. The intelligent driving virtual simulation cloud platform of claim 2, wherein the data service module comprises:
The original data storage Amazon S3 is used for storing the map original file;
the map index database MySql is used for storing the relation between the map index and the map original file;
and updating an index processing function Lambda for updating the index of the map.
4. The intelligent driving virtual simulation cloud platform of claim 3, further comprising: and the client application module, the data service module and the map service module interact to form the map data synchronization system.
5. The intelligent driving virtual simulation cloud platform of claim 4, wherein the data service module further comprises:
the Http App load balancer is used for carrying out load balancing processing on the HTTP request;
AirFlow on Amazon EC2 cluster for synchronizing data satisfying the update condition to the gallery;
in the data service module, the update index processing function Lambda is also used for adding an index to the latest updated map and putting the latest updated map into an index library for unified management; the original data storage Amazon S3 is also used for storing the original file of the map data and carrying out classification management on the original file; the map index database MySql is also used for storing map index data.
6. The intelligent driving virtual simulation cloud platform of claim 2, further comprising: the map application system based on the test case is formed by configuring the public cloud module and the client application module, and the public cloud module further comprises:
the route service module is used for planning a route of the map data;
the algorithm service module is used for calculating map elements and data according to the user request;
and the communication connection management module is used for managing the data in the system cache, carrying out corresponding operation on the data through the input of the simulation test flow module, and feeding back the result to the cloud-client application data interaction module.
7. The intelligent driving virtual simulation cloud platform of claim 6, wherein the communication connection management module comprises:
the long connection object management module is used for auditing the user connection request and writing the audited data into the connection object hot buffer module;
the connection object hot buffer module is used for storing relevant data of states, addresses and characteristics of users.
8. The intelligent driving virtual simulation cloud platform of any of claims 1-7, wherein GPB is employed as a communication protocol between the public cloud module and the client application module.
9. The intelligent driving virtual simulation cloud platform of any of claims 1-7, wherein the client application module employs one or more of a television large screen end module, a smart phone end module, a PC end module, and a browser web end module.
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