CN111259545A

CN111259545A - Intelligent driving virtual simulation cloud platform

Info

Publication number: CN111259545A
Application number: CN202010043098.4A
Authority: CN
Inventors: 白勍; 王成俊
Original assignee: Zhejiang Geely Holding Group Co Ltd; Geely Automobile Research Institute Ningbo Co Ltd
Current assignee: Zhejiang Geely Holding Group Co Ltd; Geely Automobile Research Institute Ningbo Co Ltd
Priority date: 2020-01-15
Filing date: 2020-01-15
Publication date: 2020-06-09
Anticipated expiration: 2040-01-15
Also published as: CN111259545B

Abstract

The invention provides an intelligent driving virtual simulation cloud platform, which comprises: a map operations management system, the map operations management system comprising: the public cloud module is used for realizing the coding and decoding of the map data file and the operation and calculation of the corresponding map data and informing the result to the client application module; and the client application module is used for displaying and monitoring the data of the public cloud module in real time and providing a visual interface. The map operation management system in the intelligent driving virtual simulation cloud platform provides a high-precision map data management and application solution EHP-EHR based on an AWS public cloud platform, realizes the uniform storage, uniform buffering, uniform decoding and uniform calculation of high-precision map data, provides a uniform service level data access interface for the EHR, and provides bottom-layer high-precision map data and service technology support for the subsequent construction of the virtual simulation platform.

Description

Intelligent driving virtual simulation cloud platform

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 high development of internet technology, the application of intelligent technology is more and more common. In the field of automobiles, concepts of intelligent automobiles and analog simulation systems are well known, and the development, test and verification of automatic driving systems in simulation environments are often twice with half the effort, but the construction of simulation scenes of the driving environments is time-consuming and labor-consuming, and the consistency with the real world is difficult to ensure. However, if a simulation development scene can be constructed based on a high-precision map, the simulation development scene is consistent with the real world, and all lane lines, traffic lights, obstacles, even pictures on a traffic sign board, are directly extracted from data acquired by the high-precision map. Namely, a virtual simulation system based on cloud environment and high-precision map integration is rarely available in the market. In addition, the desktop single-machine virtual simulation system has limited capability of integrating high-precision map data and low universality. The accuracy of the high-accuracy map data of each map quotient is different in each region, namely, the map quotients respectively have the high-accuracy advantage of the regional map data. The virtual simulation system on the market can not flexibly select and load the high-precision map data of different map merchants according to the high-precision degree of the regional map data, and has no high elasticity and high expandability.

In addition, most of high-precision map services on the market provide public data service interfaces, 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 the requirements of customization and personalization. The intelligent driving virtual simulation platform needs to flexibly perform scene management, parameter configuration, map display and the like according to various test scenes, needs good user operation experience to realize omnibearing test verification of vehicles, and cannot meet the requirement if an existing high-precision map service system on the market is directly used. There are some well-known software products of intelligent driving simulation system, but each has its shortcomings. Some systems are single-machine desktop application environments, although analog simulation map scenes can be customized and edited in a VR interface, the system lacks the support of a massive scene library, cannot store and use massive scene data, and cannot support high-concurrency analog simulation tests. Some systems are provided with a server, but lack an analog simulation system supported by high-precision map data, and are not supported by a cloud environment. In a word, based on a cloud computing environment, a high-precision map system and an analog simulation platform, a solution for realizing intelligent driving virtual simulation verification capability is almost not available.

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 an AWS public cloud platform, and based on a high-precision map data management and application solution EHP (electronic Horizon provider) -EHR (electronic Horizon Reconstructor), 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 technical support for the subsequent construction of a virtual simulation platform.

A further object of the present invention is to provide a high-precision map synchronization system of an intelligent driving virtual simulation cloud platform, and provide a solution for secure communication handshake and interconnection access between a client and a high-precision map management system based on an AWS public cloud, so as to implement uniform storage of high-precision map data and provide synchronization management.

The invention further aims to provide a test case-based high-precision map application system of the intelligent driving virtual simulation cloud platform, so that the test case can be used for editing and customizing a high-precision map on line; in the running process of the test flow example, the VR client can visually display the running state of the virtual vehicle in the whole process, and can realize real-time environment perception high-precision positioning and simulation behavior decision support.

Particularly, the invention provides an intelligent driving virtual simulation cloud platform, which comprises: a map operations management system, the map operations management system comprising:

the public cloud module is used for realizing the coding and decoding of the map data file and the operation and calculation of the corresponding map data and informing the result to the client application module;

and the client application module is used for displaying and monitoring the data of the public cloud module in real time and providing a visual interface.

Further, the public cloud module comprises:

the data service module is used for acquiring corresponding map data from a 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 for a system;

the map hot cache module is used for persistence of a map data cloud;

the cloud-client application data interaction module is used for performing data interaction with the client application module, calling the functions of the simulation test flow module of the intelligent driving virtual simulation cloud platform and sending data to the client application module through an authorization mechanism;

the scene use case management module, the data service module, the map decoding service module, the map hot cache module and the cloud-client application data interaction module are respectively in interactive connection with the map data operation module.

Further, the map decoding service module includes:

the 2D map decoding module is used for analyzing the original map file into a plane data point mode;

and the 3D map decoding module is used for analyzing the map original file into a stereo data point mode.

Further, the data service module includes:

the original data storage Amazon S3 is used for storing the original map file;

the map index database MySql is used for storing the relationship between the map index and the original map 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 data synchronization system is formed by interaction among the client application module, the data service module and the map service module, the map service module is used for connecting a map supplier and acquiring map data in an API mode and providing a synchronous updating interface, the data service module is used for carrying out data interaction with the client application module so as to manage the data after acquiring the data from the map service module, sending the data to the client application module through an authorization mechanism and synchronizing the real-time updated data to the map service module.

Further, the data service module further comprises:

the Http App load balancer is used for carrying out load balancing processing on the HTTP request;

the AirFlow on Amazon EC2 cluster is used for synchronizing the data meeting the updating condition to the map library;

in the data service module, the update index processing function Lambda is further configured to add an index to the latest updated map and place the latest updated map into an index library for unified management; the original data storage Amazon S3 is also used for storing original files of the map data and performing classification management on the original files; and 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 routing service module is used for planning a path 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, performing 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 request of user connection and writing the audited data into the connection object hot cache module;

and the connection object hot cache module is used for storing the relevant data of the state, the address and the characteristics of the user.

Furthermore, a GPB is adopted as a communication protocol between the public cloud module and the client application module.

Furthermore, 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, uniform storage, uniform buffering, uniform decoding and uniform calculation of the high-precision map data are realized, a uniform service level data access interface is provided for the EHR, and bottom-layer high-precision map data and service technology support are provided for the construction of a subsequent virtual simulation platform. And for the management of high-precision map data, an AWS public cloud-based elastic storage resource and a computing resource are adopted, the storage and the use of massive scene data are supported, a series of flexible operation actions such as query, loading, editing and storage of 2D/3D high-precision map data are realized, various test scenes and Corner Case can be customized, and the intelligent driving virtual simulation platform is used for developing, protecting and navigating.

Furthermore, the intelligent driving virtual simulation cloud platform further comprises a map data synchronization system, and the map data synchronization system provides a solution for safe communication handshake and interconnection access between the client and a high-precision map management system based on an AWS public cloud, so that uniform storage of high-precision map data is realized, and synchronous management is provided; and a system administrator can easily monitor and manage the data of the high-precision map through a management interface. And a technical realization basis is provided for subsequently supporting high-concurrency virtual simulation tests. The map data synchronization system realizes a data synchronization scheme that a map supplier data center transmits a high-precision map to an intelligent driving high-precision map management platform, and supports the transmission synchronization of a plurality of map suppliers, different map data sources, different high-precision map data formats and standards and the storage support of massive map original big data. And abstract design is carried out on the synchronization of the high-precision map data of each public cloud service provider. Through the distributed workflow, a data synchronization task is called and monitored, a series of functions such as resource access authorization, key encryption and decryption, compression/decompression algorithm, distributed object storage bucket directory structure, index database construction and the like are performed on high-precision map data of different file formats and contents of various map providers, and the distributed object storage bucket directory structure has excellent safety, flexibility, expansibility, compatibility and openness.

Furthermore, 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, so that the online editing and customization of the test case on the high-precision map are realized; in the running process of the test flow example, the VR client can visually display the running state of the virtual vehicle in the whole process, and can realize real-time environment perception high-precision positioning and simulation behavior decision support.

The above and other objects, advantages and features of the present invention will become more apparent to those skilled in the art from the following detailed description of specific embodiments thereof, taken in conjunction with the accompanying drawings.

Drawings

Some specific embodiments of the invention will be described in detail hereinafter, by way of illustration and not limitation, with reference to the accompanying drawings. The same reference numbers in the drawings identify the same or similar elements or components. Those skilled in the art will appreciate that the drawings are not necessarily drawn to scale. In the drawings:

fig. 1 is a working principle diagram of a map operation management system of a smart driving virtual simulation cloud platform according to an embodiment of the present invention;

fig. 2 is a working schematic diagram of a map data synchronization system of a smart driving virtual simulation cloud platform according to an embodiment of the present invention;

fig. 3 is a working principle diagram of a map application system of a smart driving virtual simulation cloud platform according to an embodiment of the invention.

Reference numerals:

a map operation management system 100;

a map data synchronization system 200;

a map application system 300;

a data service module 10; raw data storage Amazon S311; a 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 heat cache 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; connected object thermal cache module 832;

scenario use case management service 91; a simulation test flow block 92.

Detailed Description

Referring to fig. 1, the intelligent driving virtual simulation cloud platform of the present invention includes a map operation management system 100, and the map operation management system 100 is mainly composed of 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 and calculation of the corresponding map data, and notify the result to the client application module 60. The client application module 60 may be used to display, monitor and provide a visual interface for the 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 uniform storage, uniform buffering, uniform decoding and uniform calculation of high-precision map data, provides a uniform service level data access interface for the EHR, and provides bottom-layer high-precision map data and service technical support for the construction of a subsequent virtual simulation platform. And for the management of high-precision map data, an AWS public cloud-based elastic storage resource and a computing resource are adopted, the storage and the use of massive scene data are supported, a series of flexible operation actions such as query, loading, editing and storage of 2D/3D high-precision map data are realized, various test scenes and Corner Case can be customized, and the intelligent driving virtual simulation platform is used for developing, protecting and navigating.

It should be noted that map data management is an important function of the virtual simulation platform. The virtual simulation platform needs to use a high-precision map for 3D scene reconstruction (including static scenes and dynamic scenes) so as to verify various algorithms of the automatic driving vehicle in a simulation environment.

According to an 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 cache module 40, and a cloud-client application data interaction module 50. The data service module 10 may be configured to obtain corresponding map data from the map library according to the requested parameters. The map data operation module 20 may be configured to respectively call specific sub-modules in the public cloud module according to the operation request for the map data. The map decoding service module 30 may be used to decode map data and parse into a data format usable by the system. The map hot cache module 40 may be used for persistence of the map data cloud. The significance of the map hot cache module 40 is that the client application module 60 can quickly obtain map data when using the map data, and does not need to obtain the map data in the cloud database and the distributed object storage every time and then decode the map data, so that the query speed is greatly increased, and the user experience is improved. The cloud-client application data interaction module 50 may perform data interaction with the client application module 60, call a function of the simulation test flow module 92 of the smart driving virtual simulation cloud platform, and send 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 cache module 40 and the cloud-client application data interaction module 50 are respectively in interactive connection 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 searching) scene use cases. The scene use case comprises 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 import and the like. In static map scene management in scene use case management, maps in a high-precision map data management platform are required to be used for calculating Lambda, global path navigation Lambda, map data operation Lambda, high-precision map data and the like.

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 flat data point pattern. The 3D map decode module 32 may be used to parse the map raw file into a stereo data point pattern. The data service module 10 includes a raw data store Amazon S311, a map index database MySql 12, and an update index handling function Lambda 13. Among other things, raw data store Amazon S311 may 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 original file of the map. The update index handling function Lambda13 may be used to update the index of the map. When 2D map loading, displaying, editing and storing and 3D map effect checking are carried out, the client side and the cloud platform interact in an Http Restful short connection mode. When high-precision map data communication is performed between the client and the cloud platform, a communication Protocol adopts GPB (Google Protocol Buffer). The GPB protocol has the following advantages: the method has the advantages of performance (small data volume, high serialization speed and high transmission speed), use (simple use, low maintenance cost, good backward compatibility and good encryption) and use range (cross-platform, cross-language and good expandability). It should be noted that, when performing high-precision map data communication between the client and the cloud platform, a gpb (*** Protocol buffer) communication Protocol is adopted. However, if only the user identity authentication is performed, the short connection can communicate in an http restful/json mode, and the long connection needs to use http restful/json + websocket.

The client application module 60 is one or more of a television large-screen end module, a smartphone end module, a PC end module, and a browser Web end module, that is, the client application module 60 is a television large-screen VR application, a smartphone VR application, a PCVR application, and a browser Web application.

The raw data storage Amazon S3 buckets store map raw data files and thumbnail image files of a map, and different mappers have their own map data file formats, and here, the map data files of different mappers are stored, respectively, considering a plurality of bucket and directory structure divisions. The step of post-trigger execution of the update index processing function Lambda13 as the map raw data storage Amazon S311 triggers the update index processing function Lambda13 each time the contents of the S3 bucket data file are updated. The update index processing function Lambda13 firstly performs file attribute information acquisition and content analysis according to the map file data storage formats of different map vendors, and then generates data records to the map index database MySQL 12 for storage, so that the virtual simulation platform can call, query and use the data records. The map index database MySQL 12 stores the index and attribute information of each high-precision map raw data file.

And the map data operation Lambda is responsible for inquiring corresponding index records in the map index database according to the map data inquiry conditions, and then inquiring map original data files in the original data storage Amazon S311 according to the index database inquiry result. And then calling corresponding 2D and 3D map decoder services respectively according to the map display form requested by the user. Finally, the latest operation sequence data of the map is written into the map hot cache module 40 according to the requirement.

The decoder only interacts data with Lambda of map data operation domain, firstly analyzes the content format of original map data file, then performs reverse deflection calculation decoding on the analyzed result, and finally extracts corresponding 2D map data or 3D map data to return according to the requirement of user request.

Before starting the test flow instance, the scene use case or map data needs to be edited and stored 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, the method supports the generation of a self-defined map, the generation of a map based on high-precision map original data and the generation of a virtual simulation map based on a GAN generation countermeasure network.

The Web user edits the loaded map, and needs to cache the original data of the loaded map first, then cache the latest operation sequence of the edited map, and finally write the edited and integrated map data into the data service domain when the map data are submitted and stored in a unified way, and the original data of the map needs to be written into the map hot cache module 40 in the whole process. In addition, the Web user performs global path navigation planning on the loaded scene map, and data obtained by map decoding is needed, so that the data is also written into a map operation sequence cache. Therefore, here, the distributed Redis is used as a cache of a map operation sequence, and includes original data for map editing and also includes data obtained by decoding a map for global path navigation.

The query, loading, editing and saving of the Web client to the 2D map and the rendering and displaying of the VR client to the 3D map are all performed through the Restful API Gateway.

Through the scheme, the 2D high-precision map is edited through the web browser, customized map data generation is achieved to support future simulation scene definition, and a 2D map data query list needs to be obtained firstly. And then selecting a target map data in the query list, loading and displaying, and when the client is a Web application, sending a 2D map query request to the Restful API Gateway by the client Web application. And calling a map data operation function by the Restful API Gateway to retrieve the map index database MySQL 12 according to the query condition, and returning a map index result set to the map data operation function. And the map data operation function queries the map thumbnail in the map original data storage AmazonS 311 according to the path of the index thumbnail in the index result set to obtain a target map thumbnail and sends the target map thumbnail to the map data operation function, and then the target map thumbnail and the previously obtained target map index data set are packaged in a unified format.

When a user of the Web browser wants to customize high-precision map data, the user needs to load target high-precision map 2D data and render and display the data on an interface. The client Web application sends a target map query request to the Restful API Gateway to call a map data operation function. And inquiring the map data from the map operation sequence cache Redis according to the parameters by the map data operation function and obtaining inquiry result response feedback of the map operation sequence cache Redis. At this time, it will check whether the target map operation sequence exists in the cache Redis, and if so, clear the corresponding map operation sequence cache data record. Meanwhile, according to the parameters, the map data operation function queries the map index database MySQL 12 and returns a query result set to the map data operation function, and the map original data is queried in the map original data storage Amazon S311 to obtain returned query result data. The map data operation function forwards the target map raw data to the 2D map decoder function. And the 2D map decoder analyzes and performs geographical position reverse deflection calculation, and finally feeds back the analyzed map data response to the map data operation function. And simultaneously writing the data into a map operation sequence cache Redis and feeding back the analyzed map data response to a Restful application feedback response query result set to the client Web application. The client Web application obtains triangular faces through calculation, and then the whole interface is rendered according to the triangular faces.

After a Web browser user loads target high-precision map data, interface elements can be edited freely, and then the latest operation sequence data of the map is cached; the map raw data is additionally parsed into an interface rendering data format for display using a 2D map decoder. The client Web application needs to access Restful API Gateway when modifying 2D map information, and after a Web browser edits a map, the client Web application submits map increment editing content to the Restful API Gateway by taking a map increment operation sequence as a parameter to call a map data operation function. The method calculates map incremental data through parameters, and then sends the map operation sequence data to a 2D map decoder function. And the 2D map decoder decodes the current incremental map operation sequence data and performs geographical position reverse deflection calculation. And the 2D map decoder function feeds back the analyzed map data response to the map data operation function. And the map data operation function updates the current map operation sequence data and the data obtained after decoding the current incremental map operation sequence to the cache Redis. And feeding back the map incremental edit result to the Restful API Gateway. The Restful API Gateway feeds back the query result set to the client Web application. And calculating by the client Web application to obtain a triangular surface and render an interface.

After editing the 2D high-precision map data, the Web browser user can store the data cached by the latest operation sequence in an object storage and database in a persistent manner. When the client Web application stores the 2D map information, firstly, a request for storing the edited 2D map data is sent to the Restful API Gateway.

Restful API Gateway calls the map data operation function. And the map data operation function is cached in the latest operation sequence Redis of the map according to the meeting parameters, and the edited original data of the map is obtained. And feeding back the query result response to the map data operation function and generating a thumbnail according to the query result. Selecting the thumbnail and the original map data to be directly replaced and stored to an Amazon S3 bucket file by the map data operation function according to the connection number; or save the thumbnail and map raw data as a new file for Amazon S3 bucket. The update of Amazon S3 will trigger the update map index function of the post-processing. And the update map index function acquires map file information in the event object. And updating and writing the map index information into an index database MySQL. And finally, responding the operation execution result to the client.

After the Web browser client side edits and stores the 2D map data, the target map data can be loaded through the VR desktop client side and displayed in a 3D mode, so that a user can check the self-customized editing effect conveniently. When the VR client side obtains the 3D map information, a query request is sent to a Restful API Gateway, the Restful API Gateway calls a map data operation function according to the 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 S311 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, calculating reverse deflection of the geographic position, and feeding back a map data response to a map data operation function. The map data operation function feeds back a map data response to the Restful API Gateway. And finally, feeding back the query result response to the client application. And calculating by the VR client application to obtain a triangular surface and render a VR interface.

According to an embodiment of the present invention, referring to fig. 2, the intelligent driving virtual simulation cloud platform further includes a map data synchronization system 200, and the client application module 60, the data service module 10 and the map service module 70 interact with each other 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 map cloud platform (i.e., the map service module 70) of a third party. There are two triggers for synchronizing data from the map service module 70 to the map data synchronization system 200. The first is to let the user trigger data synchronization by himself, i.e. the user triggers data synchronization operation through the client application module 60 (which may be a web browser client in particular). The operation instructions are then forwarded to the AirFlow distributed workflow orchestration engine deployed on the Amazon EC2 cluster through the Http App load balancer 14. And finally, the AirFlow executes a Python program to finish downloading the high-precision map data from the map service module 70, and writes the original map data file into the Amazon S3. The second method is to set a timing task Crontab in the AirFlow cluster, and the timing task can trigger a Python program on the AirFlow at regular time, so as to complete data synchronization.

It should be noted that once the map raw data is successfully written into Amazon S3 of the data service module 10, the update index lambda function is automatically triggered, and the function parses the map data file in Amazon S3, obtains the necessary map attribute metadata information, and creates a record index into the MySql database according to the metadata information. After the index record is successfully established in MySql, the URL field in the index record indicates the actual storage position of the map original file 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 may first enter the MySql database of the data service module 10 according to the upper layer service request to query the target map index, and then query 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 be able to interface and synchronize data with the map data services of various mappers. Common high-precision map service providers generally publish map data to public clouds after map building. High-precision map data are synchronized on public clouds of all the households, and generally, the method is simple and easy to implement, namely, a map core service cluster of a map provider exports the shared high-precision map data to an object storage service, such as an OSS of Ali cloud, a COS of Tencent cloud, an OBS of Huashi cloud and the like. The map data file is compressed or uncompressed according to external requirements, then a plaintext is encrypted by using a key, and then the map data file is stored in an object storage service according to a certain directory structure rule. On the high-precision map data object storage service, various access authentication mechanisms are 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 receiving a request for a resource, the object storage service will check the corresponding ACL to verify that the requestor possesses the required access rights. In addition, the map data of each map provider has its own content format, and can provide customized content according to the needs of the user, including but not limited to a graphic format, a graphic index format, an attribute data format, geographic coordinate system and projection information, a geometric space index, an attribute index of an active field in a list, a geocoding index, an attribute index of a file, metadata, character coding, and the like.

In some embodiments of the invention, referring to fig. 2, the data service module 10 further comprises an Http App load balancer 14 and an AirFlow on Amazon EC2 cluster 15. The Http App load balancer 14 may be configured to perform load balancing processing on the Http request. The AirFlow on Amazon EC2 cluster 15 may be used to synchronize data meeting the update conditions to the map library. In the data service module 10, the update index processing function Lambda13 is further configured to add an index to the newly updated map and place the newly updated map into the index repository for unified management. The original data storage Amazon S311 is also used for storing original files of map data and performing classification management on the original files, 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 an Http App seven-layer load balancing service of a cloud platform data service domain through an Http Restful protocol, and then the function of synchronizing high-precision map data between cloud platforms is started.

The data service module 10 is composed of an Http App load balancer 14, an AirFlow on Amazon EC2 cluster 15, a map raw data storage Amazon S311, an update index processing function Lambda13 and a map index database MySql 12. 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 be live elastic extended and highly available. Each EC2 service node has the AirFlow service system precision installed. When the map data synchronization task is started, there are 2 ways: 1. the user manually starts: and manually starting a data synchronization task by a user administrator through a Web management interface of the AirFlow, and monitoring the execution state of the task through a real-time interface. AirFlow timed start: and setting a timer in the AirFlow, triggering a Python program at regular time, and automatically starting a data synchronization task. The high-precision map data synchronization program is written in a Python language, is also deployed on the EC2 cluster, and is used for task scheduling, business arrangement and visual monitoring by AirFlow.

The cloud platform and other external cloud platforms can be connected in an internet mode, encrypted data are transmitted through an HTTPS protocol, and a VPN (virtual private network) security tunnel can be built on the internet to perform a data synchronization transmission mechanism with higher security. After the intelligent driving simulation cloud platform receives the data file, decryption is carried out through the secret key. And decompressing the decrypted high-precision map data according to a certain data compression mode if necessary. After decompression, there may be a series of file generation, which needs to store the decompressed map file set into amazon s3 of the map original data according to different map merchants and a certain directory structure. And updating the index processing function Lambda13, namely triggering the post-trigger execution step of the map original data storage Amazon S311, wherein the updating of the index processing function Lambda13 is triggered every time the contents of the S3 bucket data file are updated. The Lambda function firstly acquires file attribute information and analyzes the content according to the map file data storage formats of different map merchants, and then generates data for recording to a map index database MySQL 12 for storage so that a virtual simulation platform can call, inquire and use the data.

The map data synchronization system 200 can achieve high-precision map data synchronization by the following scheme. The map businessmen store openable high-precision map data on the cloud platform of the businessmen. And according to the secret key, map data are encrypted and stored locally, a storage address of the high-precision map is exposed to the cloud platform for access, and access control authority is set. The system synchronizes the map using two means, manual trigger or timer trigger. The former is that after the client Web application meets the synchronization condition, the client Web application sends a data synchronization request to the Http App for load balancing. The latter is the data synchronization task triggered by the AirFlow system timer running on Amazon EC 2. The Http App load balancing service distributes the task flows to corresponding AirFlow cluster environments running on the Amazon EC2 according to the load policies. The AirFlow service starts a data synchronization processing program written by a Python language, sends a request for obtaining the attribute information of each file of the graph businessman object storage directory to the Http App load balancing service, and returns an access graph businessman data object storage End Point to a query result set. And returning the query result set to the Python processing program by the Http App load balancing. After obtaining the map quotient file, the Python processing program sends a request for obtaining the attribute information of the history map file to the map original data Amazon S3. Map raw data Amazon S3 will return the query result set. And simultaneously, 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, that is, under the condition that the files need to be updated, the Python processing program sends a map file request to the HttpApp load balancing service. The Http App load balancing service accesses the graph data object storage End Point and returns a target map file set. And after the Python processing program obtains the map increment complete file, decrypting the latest increment file by using the key. Finally, the file content is updated to local Amazon S3. At this time, Amazon S3 triggers the post-processing logic function Lambda to acquire map file information from the event object, and writes the map index information into the database to update the index database.

According to an 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 routing service module 81 may be configured to perform path planning on the map data. The algorithm service module 82 may be used to compute 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 the 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 thermal cache module 832. The long connection object management module 831 may be configured to audit a request for a user connection and write the audited data into the connection object hot cache module 832. The connection object hot cache module 832 may be used to store relevant data for the status, address, and features of the 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 the map rendering data cache Redis and then plan the global path navigation. When two main function points are used for the Lambda, and one function point is a scene use case at a web end, a starting point and an ending point of a virtual simulation vehicle traveling task need to be designed in advance, and then the Lambda is called to generate a global navigation path and is visualized on a web browser. This allows the user to continually make adjustments to the global path design to meet the needs of the simulated test path. And the other is that after the test flow example is started, the virtual vehicle redesigns and plans the global path as necessary according to the environment identified by the surroundings 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 a threshold value, the global path navigation is considered to be required again, and the like).

The most central content of the algorithm service module 82 is the map computation Lambda function. There are the following main functional points for the Lambda. After the test process 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 using the map.

After the long Connection handshake is established between the VR client and the WebSocket gateway, the Lambda long Connection authorized party writes the test flow instance ID, the API ID and the Connection ID into the Redis of the domain. Then, the Web management client can query the connection object hot cache Redis data record through the long connection object management Lambda, and display all online VR clients (including which VR client is connected with which test flow instance). And finally, when the VR client is disconnected from the WebSocket gateway, the data records in the hot cache Redis of the connection object are cleaned through the management of the long connection object Lambda.

Through the scheme, the scene use cases are designed, a query list needs to be obtained firstly, then one scene use case is selected for editing, viewing and storing, the client Web application sends a 2D scene use case query request to the Restful API Gateway to forward the query request to the scene use case management service 91, and the result is returned to the map data operation function. And after the map data operation function retrieves the map index database MySQL 12 result according to the query condition and returns the result, the map data operation function queries the map thumbnail in the map original data storage Amazon S311 according to the result, and returns the target map query result set list to the scene use case management service 91 after obtaining the result. The scenario use case management service 91 feeds back the query result set list to the restful api Gateway. And finally, feeding a response to the client Web application by the query result set list, and finally rendering a query result set list interface.

After obtaining the 2D scene case list, a user can select one to open, and the selected scene 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 the Restful API Gateway, which forwards the query request to the scenario case management service 91. The scenario use case management service 91 will call a map data operation function. And inquiring the latest operation sequence cache data of the map from the latest operation sequence cache Redis of the map by the map data operation function according to the parameters. And feeding back the query result response to the map data operation function. And when the latest operation sequence of the target map exists in the cache, clearing the cache data of the latest operation sequence of the map corresponding to the session ID. And meanwhile, the map index database MySQL 12 is retrieved according to the parameters. And after obtaining the result, obtaining the target map original data in the map original data storage Amazon S311 and returning the query result data to the map data operation function. The map data operation function forwards the target map raw data to the 2D map decoder function. And the 2D map decoder analyzes the original data format of the target map, performs reverse deflection calculation of the geographic position and feeds back the result response to the map data operation function. And the map data operation function writes the original map data and the decoded data into the latest operation sequence cache Redis of the map. And finally feeds back the result to the scenario case management service 91. The scenario use case management service 91 responds the static map scenario information feedback to Restful API Gateway. The Restful API Gateway feeds back the query result set response to the client Web application. And calculating by the client Web application to obtain a triangular surface and render an interface.

After the client Web application loads the 2D scene map, interface elements can be freely edited, and then the latest operation sequence data of the map is cached. The map raw data is additionally parsed into an interface rendering data format for display using a 2D map decoder. The client Web application sends an edit request to the Restful API Gateway. Restful API Gateway calls the scenario use case management service 91. The scenario use case management service 91 calls a map data operation function. It sends the sequence data of the scene map operation to the 2D map decoder function. And the 2D map decoder decodes the scene map operation sequence data of the increment and performs reverse deflection calculation of the geographic position. And finally, feeding back the analyzed scene map data response to the map data operation function. The map data operation function updates the current map operation sequence data and the current incremental map operation sequence decoded data to the cache Redis, and returns the processing result to the scene use case management service 91. The scenario use case management service 91 feeds back the map delta editing result to the Restful API Gateway. The Restful API Gateway returns a response to the client Web application. And calculating by the client Web application to obtain a triangular surface and render an interface.

After the 2D scene map data is edited, the client Web application can uniformly store the data cached by the latest operation sequence into an object storage and database in a persistent manner. The client Web application sends a guarantee request to Restful ap gateway. The Restful API Gateway forwards the request to the scenario use case management service 91. The scenario use case management service 91 calls a map data operation function according to the parameter. And the map data operation function is cached in the latest map operation sequence Redis to obtain edited map original data, and the query result response is fed back to the map data operation function. And if the map data operation function checks that the incremental data of the map operation exists, generating a thumbnail corresponding to the map, and storing the thumbnail and the original data of the map as a new file of the Amazon S3 bucket. And feeds back the save operation response to the scenario use case management service 91. The scenario use case management service 91 establishes a mapping relationship. And returns the processing result to Restful API Gateway. The Restful API Gateway feeds back the operation execution result response to the client Web application. Meanwhile, the updating of the map raw data storage Amazon S311 will trigger the updating of the map index function. And the map updating index function acquires map file information in the event object and updates and writes the map index information into a map index database MySQL 12.

After the client Web application edits and stores 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 self-customized editing effect conveniently. The VR client sends a request to query 3D map scene data to Restful API Gateway. The Restful API Gateway forwards the request to the scenario use case management service 91. The scenario use case management service 91 calls a map data operation function according to the parameter. The map data operation function queries the map index database MySQL 12 according to the parameters. And the map index database MySQL 12 returns the query result set to the map data operation function.

The map data operation function acquires the original data of the target map from the original data storage Amazon S311 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 reverse deflection calculation of the geographic position and then feeds back the analyzed map data response to the map data operation function. The map data operation function feeds back the map data response to the scenario use case management service 91. The scenario use case management service 91 feeds back the map data response to Restful API Gateway. And the Restful API Gateway feeds the query result response back to the VR client application. And calculating by the VR client application to obtain a triangular surface and render an interface.

After the user edits and saves the scene map, the global path navigation planning can be carried out on the scene map, and the planned global path can be displayed on a Web browser so as to be convenient for the user to check. When a client Web application opens a scenario use case. And a start node and an end node are set on the 2D map of the scene use case. At which point the global navigation path request is given to Restful API Gateway. The Restful API Gateway forwards the request to the scenario use case management service 91. The scenario use case management service 91 calls a global path navigation function according to the parameter. And the corresponding map rendering data is acquired from the map operation sequence cache Redis and the query result set is returned to the global path navigation function. The global path navigation function performs optimal map global path planning calculation and then returns a calculation result set to the scene use case management service 91. The scenario use case management service 91 feeds back the calculation result set to the Restful API Gateway. And finally, the Restful API Gateway feeds back the calculation result set to the client Web application for rendering the interface by the client Web application.

After scene case editing, saving and global path navigation planning are completed, the test flow can be started. In the starting process of the test flow example, the original map data and the decoded rendering data are required to be written into the distributed cache, so that the subsequent environment perception high-precision positioning, the simulation behavior decision, the real-time global path navigation planning and the like can be conveniently used. The client Web application sends a request to start a test flow instance to the Restful API Gateway. The Restful API Gateway forwards the scenario case ID to the test flow instance management service. The test flow instance management service (simulation test flow module 92) generates an identifier according to the algorithm and maps the identifier and forwards the identifier to the scenario case management service 91. The scenario use case management service 91 calls a map data operation function according to the parameter. And the map data operation function acquires the map real-time cache data from the map real-time data cache Redis. And when the map data operation function checks that the target map data does not exist in the cache, inquiring the map index database MySQL 12 according to the parameters. And returning the query result set to the map data operation function. The map data operation function acquires target map raw data from the map raw data storage Amazon S311. And the data is returned to the map data operation function. The map data operation 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 reverse deflection calculation of the geographic position and finally feeds back the analyzed map data response to the map data operation function. The map data operation function writes both the original map data and the decoded rendering data into the real-time map data thermal cache Redis and simultaneously feeds back the map rendering data response to the scene use case management service 91. The scenario case management service 91 feeds back the response of the loaded scenario case result to the test flow case management service. It feeds back the result response of the initiated test flow instance to the Restful API Gateway. And the Restful API Gateway feeds back the result response of the test flow instance starting to the client Web application.

In the process of executing a certain test flow example, the virtual simulation platform needs to constantly perform high-precision positioning of real-time environment perception on the virtual vehicle (in the simulation test environment, the test flow example has a view angle of the god, a sensor can be simulated to directly provide real-time environment perception information for the virtual vehicle, and then the virtual vehicle needs to be positioned at high precision), so that a reference basis is provided for subsequent real-time vehicle behavior decision. And after the test process example is started, triggering various map real-time calculation requests to the test process example 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 a map computation function according to the parameters to query the map real-time data thermal cache Redis. And the map real-time data thermal cache Redis feeds the query result response back to the map calculation function. The map calculation function performs map calculation based on the input request condition and the obtained map data, and feeds back a calculation result response to the scenario case management service 91. The scenario case management service 91 feeds back the calculation result response to the test flow case management service. And the test flow instance management service feeds back the calculation result response to the environment-aware high-precision positioning suit, and pushes the map calculation result to the VR client application through the WebSocket API Gateway. And the VR client side is used for rendering an interface to finish the new real-time high-precision positioning display of the virtual vehicle.

In the process of executing a certain test flow example, the virtual simulation platform needs to continuously perform real-time behavior decision on the virtual vehicle according to the environment sensing result so as to complete verification of the rationality of the decision algorithm. When the simulation platform detects that the driving route of the virtual vehicle is inconsistent with the global route navigation planning route planned in advance, the simulation platform needs to utilize high-precision map data to carry out global route navigation replanning on the virtual vehicle. And the simulation 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 use case management service 91 calls a global path navigation function. And the global path navigation function parameter is sent to the map real-time data cache Redis to obtain corresponding map data. And carrying out optimal map global path navigation calculation according to the current position information of the vehicle and the map data information. And finally feeds back the 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. And the test flow instance management service feeds back the calculation result response to the simulation behavior decision service. And after the simulation behavior decision service obtains the calculation result, pushing the map calculation result to the VR client application through WebSocket APIGateway. And the VR client side renders an interface to finish displaying the new global path navigation plan of the virtual vehicle.

In summary, 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 provides a map data management and application solution EHP-EHR based on an AWS public cloud platform, so as to implement uniform storage, uniform buffering, uniform decoding, and uniform computation of high-precision map data, provide a uniform service level data access interface for the EHR, and provide underlying high-precision map data and service technical support for the subsequent establishment of a virtual simulation platform. And for the management of high-precision map data, an AWS public cloud-based elastic storage resource and a computing resource are adopted, the storage and the use of massive scene data are supported, a series of flexible operation actions such as query, loading, editing and storage of 2D/3D high-precision map data are realized, various test scenes and Corner Case can be customized, and the intelligent driving virtual simulation platform is used for developing, protecting and navigating. The intelligent driving virtual simulation cloud platform further comprises a map data synchronization system 200, wherein the map data synchronization system 200 provides a safe communication handshake and interconnection access solution for a client and a high-precision map management system based on an AWS public cloud, so that uniform storage of high-precision map data is realized, and synchronous management is provided; and a system administrator can easily monitor and manage the data of the high-precision map through a management interface. And a technical realization basis is provided for subsequently supporting high-concurrency virtual simulation tests. The map data synchronization system realizes a data synchronization scheme that a map supplier data center transmits a high-precision map to an intelligent driving high-precision map management platform, and supports the transmission synchronization of a plurality of map suppliers, different map data sources, different high-precision map data formats and standards and the storage support of massive map original big data. And abstract design is carried out on the synchronization of the high-precision map data of each public cloud service provider. Through the distributed workflow, a data synchronization task is called and monitored, a series of functions such as resource access authorization, key encryption and decryption, compression/decompression algorithm, distributed object storage bucket directory structure, index database construction and the like are performed on high-precision map data of different file formats and contents of various map providers, and the distributed object storage bucket directory structure 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 test case on the high-precision map are realized; in the running process of the test flow example, the VR client can visually display the running state of the virtual vehicle in the whole process, and can realize real-time environment perception high-precision positioning and simulation behavior decision support.

Thus, it should be appreciated by those skilled in the art that while a number of exemplary embodiments of the invention have been illustrated and described in detail herein, many other variations or modifications consistent with the principles of the invention may be directly determined or derived from the disclosure of the present invention without departing from the spirit and scope of the invention. Accordingly, the scope of the invention should be understood and interpreted to cover all such other variations or modifications.

Claims

1. The utility model provides a virtual emulation cloud platform is driven to intelligence which characterized in that includes: a map operations management system, the map operations management system comprising:

2. The intelligent driving virtual simulation cloud platform of claim 1, wherein the public cloud module comprises:

the map hot cache module is used for persistence of a map data cloud;

3. The intelligent driving virtual simulation cloud platform of claim 2, wherein the map decoding service module comprises:

4. The intelligent driving virtual simulation cloud platform of claim 3, wherein the data service module comprises:

the original data storage Amazon S3 is used for storing the original map file;

5. The intelligent driving virtual simulation cloud platform of claim 4, further comprising: the map data synchronization system is formed by interaction among the client application module, the data service module and the map service module.

6. The intelligent driving virtual simulation cloud platform of claim 5, wherein the data service module further comprises:

7. The intelligent driving virtual simulation cloud platform of claim 3, 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 routing service module is used for planning a path of the map data;

8. The intelligent driving virtual simulation cloud platform of claim 7, wherein the communication connection management module comprises:

9. The intelligent driving virtual simulation cloud platform according to any one of claims 1-8, wherein a GPB is adopted as a communication protocol between the public cloud module and the client application module.

10. The intelligent driving virtual simulation cloud platform according to any one of claims 1-8, wherein the client application module employs one or more of a television large screen end module, a smartphone end module, a PC end module, and a browser web end module.