CN112597048A - Automatic driving cloud simulation implementation method, system and medium based on K8S - Google Patents
Automatic driving cloud simulation implementation method, system and medium based on K8S Download PDFInfo
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Abstract
The application discloses a method, a system and a medium for realizing automatic driving cloud simulation based on K8S, which are used for solving the technical problems that the existing automatic driving learning platform is high in construction cost and the cost of the learning and testing automatic driving technology is high. The method comprises the following steps: respective emulated resource request information is received from a number of first terminals. Based on the corresponding emulated resource request information, a resource creation instruction of the K8S application is invoked to create a corresponding number of POD resources. Wherein, the POD resource is used for storing the Docker container. And associating the Docker image in the container image library in the automatic driving cloud simulation platform to the POD resource, and generating access login information of the POD resource. And sending the access login information to the corresponding first terminal so that the first terminal can perform automatic driving simulation corresponding to the user.
Description
Technical Field
The application relates to the technical field of cloud simulation, in particular to a method, a system and a medium for realizing automatic driving cloud simulation based on K8S.
Background
With the continuous development of robots and automatic driving technologies, robots and automatic driving technologies thereof are applied to more and more fields, and the automatic driving application is ubiquitous in scenes such as industrial inspection, market service, restaurant distribution, children toys and the like, so that more and more students and robot enthusiasts are attracted to be added into automatic driving technology learning.
However, in order to learn the automatic driving technology, firstly, hardware for bearing automatic driving is used as a carrier, but the construction cost of the automatic driving learning platform is low, namely thousands of yuan, and more ten thousands of yuan, and a certain space is needed for testing, so that the investment is not small for beginners. Therefore, this raises the beginner's threshold to some extent. In addition, for developers, the automatic driving algorithm needs various scenes for testing, and if the testing is carried out in a real scene, the testing has great risk and high cost.
Therefore, the study and test cost is reduced, which is an urgent problem to be solved in the field of automatic driving.
Disclosure of Invention
The embodiment of the application provides a method, a system and a medium for realizing automatic driving cloud simulation based on K8S, which are used for solving the following technical problems in the prior art: the existing automatic driving learning platform is high in construction cost, and the cost of learning and testing automatic driving technology is high.
The embodiment of the application adopts the following technical scheme:
on one hand, the embodiment of the application provides an automatic driving cloud simulation implementation method based on K8S, and the method comprises the following steps:
respective emulated resource request information is received from a number of first terminals. Based on the corresponding emulated resource request information, a resource creation instruction of the K8S application is invoked to create a corresponding number of POD resources. Wherein, the POD resource is used for storing the Docker container. And associating the Docker image in the container image library in the automatic driving cloud simulation platform to the POD resource, and generating access login information of the POD resource. And sending the access login information to the corresponding first terminal so that the first terminal can perform automatic driving simulation corresponding to the user.
According to the embodiment of the application, the Docker container is managed through the K8S platform, and the automatic telescopic characteristic of the K8S is utilized, so that corresponding resources are provided according to the number of users, corresponding resources can be automatically released when the users leave the platform, and the maximum utilization of the resources is achieved. And moreover, automatic driving simulation of the cloud platform is provided for the user, the user is helped to learn and/or test automatic driving, and the cost for the user to build the platform is saved. And a cheap and efficient autopilot cloud simulation learning platform is provided for users who cannot build the autopilot learning platform.
In an implementation manner of the application, a preset container mirror library is established on an automatic driving cloud simulation platform to store a Docker mirror. And receiving the Docker mirror image established by the second terminal, and putting the Docker mirror image into a preset container mirror image library. Wherein the second terminal has a Docker application container engine.
In an implementation manner of the present application, an access path from front-end access page information to a Docker container in a POD resource is established. The number of valid simulation resource request messages is determined based on the corresponding simulation resource request messages. The quantity of the effective simulation resource request information is obtained by counting of the automatic driving cloud simulation platform. According to the number of valid simulation resource request messages, a resource creation instruction in K8S is invoked to create a corresponding number of POD resources.
In one implementation manner of the present application, first terminal account information corresponding to corresponding simulation resource request information is determined. The first terminal account information at least comprises one or more of the following items: account name, account balance, account age. And judging whether the corresponding simulation resource request information meets the preset condition or not based on the first terminal account information. And under the condition that the corresponding simulation resource request information meets the preset condition, determining the corresponding simulation resource request information as effective simulation resource request information.
In an implementation manner of the present application, in a case that creation of a POD resource is completed, a corresponding container is created to the POD resource through a container establishment instruction. And pulling the Docker mirror image in the preset container mirror image library to a corresponding container, so as to associate the Docker mirror image in the preset container mirror image library to the POD resource.
In an implementation manner of the present application, corresponding access login information is generated according to the effective simulation resource request information, and the access login information is sent to the corresponding first terminal. And receiving access login information from the first terminal, and verifying the validity of the access login information according to the access login information record table. And the access login information recording table is stored in the automatic driving cloud simulation platform. And under the condition that the validity of the access login information is effective, establishing connection with the corresponding first terminal. And sending the display desktop to the corresponding first terminal through the VNC based on the connection with the connection terminal.
In an implementation manner of the present application, the Docker image includes an autopilot simulation development environment, and the autopilot simulation development environment includes a robot operating system and robot simulation software. And sending the display desktop information of the robot operating system and the robot simulation software to the corresponding first terminal through the VNC.
On the other hand, the embodiment of the application provides an automatic driving cloud simulation implementation system based on K8S, and the system includes:
at least one processor; and a memory communicatively coupled to the at least one processor. Wherein the memory stores instructions executable by the at least one processor to enable the at least one processor to: respective emulated resource request information is received from a number of first terminals. Based on the corresponding emulated resource request information, a resource creation instruction of the K8S application is invoked to create a corresponding number of POD resources. Wherein the POD resource is for storing the container. And associating the Docker image in the container image library in the automatic driving cloud simulation platform to the POD resource, and generating access login information of the POD resource. And sending the access login information to the corresponding first terminal so that the first terminal can perform automatic driving simulation corresponding to the user.
In an implementation manner of the application, a preset container mirror library is established on an automatic driving cloud simulation platform to store a Docker mirror. And receiving the Docker mirror image established by the second terminal, and putting the Docker mirror image into a preset container mirror image library. Wherein the second terminal has a Docker application container engine.
In another aspect, an embodiment of the present application provides a K8S-based automated driving cloud simulation implementation medium, where the medium stores computer instructions for executing the automated driving cloud simulation implementation method.
Drawings
The accompanying drawings, which are included to provide a further understanding of the application and are incorporated in and constitute a part of this application, illustrate embodiment(s) of the application and together with the description serve to explain the application and not to limit the application. In the drawings:
fig. 1 is a flowchart of an implementation method of an automated driving cloud simulation based on K8S according to an embodiment of the present application;
fig. 2 is a schematic diagram of an automated driving cloud simulation platform based on K8S according to an embodiment of the present application;
fig. 3 is a schematic structural diagram of an automated driving cloud simulation implementation system based on K8S according to an embodiment of the present application.
Detailed Description
In order to make the objects, technical solutions and advantages of the present application more apparent, the technical solutions of the present application will be described in detail and completely with reference to the following specific embodiments of the present application and the accompanying drawings. It should be apparent that the described embodiments are only some of the embodiments of the present application, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present application.
With the rapid development of the automatic driving technology in recent years, more and more users participate. The learning and/or testing of automatic driving through a real environment consumes too many unnecessary resources and, in some scenarios, there are some risks that are difficult to avoid. Therefore, people have begun to learn and test the automatic driving by simulation techniques. However, building an autopilot learning platform requires a learner to invest in high cost, and in future development, the built autopilot learning platform may be eliminated, and the investment and the output are not in direct proportion to the learner.
In order to solve the problems, the embodiment of the application provides the automatic driving cloud simulation platform for the learners to rent, and the automatic driving learning and testing can be carried out without investing a large amount of cost for the learners and constructing hardware facilities of the cloud simulation platform. According to the embodiment of the application, a friendly cloud platform is built by means of a K8S technology and a Docker technology. K8S is known collectively as kubernets and is an open source for managing containerized applications on multiple hosts of a cloud platform. Docker is an open source application container engine, so that developers can pack their applications and dependency packages into a portable image, and then distribute the image to any popular Linux or Windows machine, and also realize virtualization.
In the embodiment of the present application, the execution subject is an automatic driving cloud simulation server.
The technical solutions provided by the embodiments of the present application are described in detail below with reference to the accompanying drawings.
The embodiment of the application provides an automatic driving cloud simulation implementation method based on K8S, and as shown in FIG. 1, the method may include steps S101-S104:
s101, receiving corresponding simulation resource request information from a plurality of first terminals.
In some embodiments of the Application, a user sends simulation resource request information to the automatic driving cloud simulation platform through a web interface or an Application (APP) of a first terminal. The automatic driving cloud simulation platform performs information interaction with a user through a World Wide Web (Web) front-end page. It should be noted that the first terminal may be a mobile phone, a notebook computer, or other devices, which is not limited in this application.
In addition, the device for the user to perform the autopilot cloud simulation is not necessarily the first terminal, and the first terminal may be a tool for the user to submit simulation resource request information, and after the user applies for the POD resource, the autopilot cloud simulation may be performed on other terminal devices. The POD resource is composed of one or more containers, and in K8S, a POD is the smallest schedulable atomic unit and is created by K8S.
In other embodiments of the present application, in order to prevent a user from maliciously interfering with normal use of the cloud simulation platform by applying for a plurality of simulation resource requests, the user needs to submit an Internet Protocol (IP) Address of the user for automatically driving the cloud simulation device in the simulation resource request information. In actual use, the IP address can be tracked and verified by the automatic driving cloud simulation platform, and when a user wants to perform automatic driving cloud simulation, if the automatic driving cloud simulation platform fails to verify the IP address of the automatic driving cloud simulation terminal, the automatic driving cloud simulation POD resource cannot be sent to a terminal interface of the user.
Before the automatic driving cloud simulation platform receives simulation resource request information from a plurality of first terminals, a container mirror image library is established on the automatic driving cloud simulation platform through Docker software to store Docker mirror images. The second terminal can pack the ROS development environment of the robot operating system, a Gazebo (one of the robot simulation software) of the robot simulation software and the basic operating environment of the Linux operating system into a Docker mirror image. The second terminal has a Docker application container engine therein, and it should be further noted that the second terminal may be a desktop computer, a notebook computer, and the like, which is not limited in this application.
In other embodiments of the application, the autopilot cloud simulation platform may further write a Dockerfile, where the work content of the file includes an ROS installation command, an Gazebo environment installation, a Virtual Network Controller (VNC) Server installation, multiple Virtual desktops, and a mapping port. And generating a Docker container through a Docker result line of Docker software, wherein the Docker container is used for placing a Docker mirror image, supports the running of the Docker mirror image and provides automatic driving simulation service for users.
And S102, calling a resource creating instruction of a Kubernetes (K8S for short) application based on the corresponding simulation resource request information to create a corresponding number of POD resources.
In some embodiments of the present application, a POD resource is a collection of containers that store and manage Docker containers, a POD typically having one or more Docker containers therein, and a single POD in the present application having only one Docker container therein.
The automatic driving cloud simulation platform interacts with a user through a WEB front end to obtain simulation resource request information of a user applying POD resources, the WEB front end obtains a first terminal account name corresponding to the simulation resource request information, and the automatic driving cloud simulation platform inquires first terminal account information in an account information book. And judging whether the requirements in the simulation resource request information can be met or not according to the first terminal account information in the account information book. For example, the simulation resource request information of the user a requests to use the autopilot cloud simulation resource for 20 days, the autopilot cloud simulation platform sets the unit price of the POD resource to be 1.8 yuan per day, in the account information book of the user a, the account balance of the user a is 37 yuan, the account balance of the user a can meet the simulation resource request information of the user a, and the autopilot cloud simulation platform determines that the simulation resource request information of the user a is effective simulation resource request information.
Further, the automatic driving cloud simulation platform can count the number of effective simulation resource request information, and accordingly create POD resources according to the number of the effective simulation resource request information, so that the user requirements are met. For example, the number of current simulation resource request messages is 100, the number of valid simulation resource request messages is 80, and the autopilot cloud simulation platform creates 80 POD resources.
In addition, after the corresponding POD resource is created for the user, the server of the cloud simulation platform deducts the account balance of the user correspondingly.
S103, associating the Docker image in the container image library in the automatic driving cloud simulation platform to POD resources, and generating access login information of the POD resources.
In some embodiments of the application, after the autopilot cloud simulation platform creates POD resources for the first terminal, the K8S may automatically pull a Docker image in the container image library into a Docker container, where the Docker container is located in the POD.
Through the method, the association between the Docker image and the POD resource can be realized. After the POD resource is prepared, the user is given login credentials to confirm the identity of the user applying for the POD resource. The user login credentials are access login information, an access login information record table is established on a cloud simulation platform, and when a user successfully applies for POD resources, the account name, the terminal IP, the account use duration and the account check code of the user are recorded in the access login information record table. The account check code may be randomly generated by a hash algorithm or may be obtained in other ways. The cloud simulation platform sends the account name, the terminal IP and the account check code to the corresponding first terminal as access login information.
And S104, sending the access login information to the corresponding first terminal so that the first terminal can perform automatic driving simulation corresponding to the user.
In some embodiments of the application, when the first terminal wants to use POD resources of the autopilot cloud simulation, firstly, login is performed through a cloud simulation platform webpage or an APP, that is, the access login information is sent through a WEB front-end interaction page, the autopilot cloud simulation platform compares the access login information sent by the first terminal with the access login information sent by the access login information record table, and whether the current time exceeds the account use duration is judged. And under the condition that the comparison result is correct and the current time does not exceed the account use duration, judging that the validity of the access login information is valid, and establishing the connection between the automatic driving cloud simulation platform and the first terminal.
The automatic driving cloud simulation platform is connected with the first terminal through the VNC and sends a plurality of interfaces of mirror images in the Docker container, wherein the interfaces at least comprise an ROS interface and a Gazebo interface, and the automatic driving simulation requirements of users are met.
In addition, this application embodiment provides the cloud platform of autopilot simulation for a plurality of users through K8S, and POD resource adds corresponding to user's quantity, and in actual use, the user probably drops POD resource voluntarily or user's account duration of use does not have the surplus, and autopilot cloud simulation platform detects whether two kinds of condition take place, and when detecting that corresponding condition takes place, autopilot cloud simulation platform deletes corresponding POD resource of user through K8S, releases its occupation space to the utilization ratio of resource has been guaranteed.
According to the scheme, the automatic driving cloud simulation implementation method based on K8S is provided, a Docker mirror image containing a robot simulation environment is placed in a container, and arranged by using K8S to form a POD cluster, so that the container is better managed, multiple interfaces of robot simulation are achieved and sent to terminal equipment of a user, and the user can carry out automatic driving simulation. The robot simulation environment at least comprises an ROS development environment, Gazebo software and a linux basic operation environment, and the plurality of interfaces at least comprise an ROS interface and a Gazebo interface, so that a user can be helped to better perform automatic driving simulation. On the cloud platform, automatic driving cloud simulation is realized, a complex and high-cost hardware environment does not need to be built locally, and simulation cost is greatly reduced for a user.
For better understanding of the cloud simulation platform of the present application, a schematic diagram of an autopilot cloud simulation platform is provided, as shown in fig. 2:
The K8S platform interacts with the user's terminal device through the WEB front end to verify whether the user can apply for POD (the smallest schedulable atomic unit in K8S, with one or more containers in it), etc. And the WEB front end feeds back the number of the users according to the verified n users, and creates n PODs with corresponding number through a K8S platform.
Taking POD-1 (the first POD, corresponding to user 1) as an example, there is a Docker (application container engine) container, in which a Virtual Network Console (VNC), an ROS (robot operating system), a Gazebo, and an Ubuntu (one of linux systems) are stored, and the Docker container adopts an Ubuntu base image and installs the ROS, so that the Docker supports a robot communication interaction function.
It should be noted that the ROS and Gazebo interfaces are simultaneously displayed on the terminal device of the user. The number of PODs of the K8S platform corresponds to the number of user terminals, i.e. a one-to-one relationship.
The embodiment of the application provides an automatic driving cloud simulation implementation system based on K8S, as shown in FIG. 3, the system includes:
at least one processor; and a memory communicatively coupled to the at least one processor; wherein the memory stores instructions executable by the at least one processor to enable the at least one processor to: receiving corresponding simulation resource request information from a plurality of first terminals; based on the corresponding simulation resource request information, calling a resource creation instruction of the K8S application to create a corresponding number of POD resources; wherein the POD resource is used for storing the container; associating a Docker image in a container image library in the automatic driving cloud simulation platform to POD resources, and generating access login information of the POD resources; and sending the access login information to the corresponding first terminal so that the first terminal can perform automatic driving simulation corresponding to the user.
The processor is also used for establishing a preset container mirror image library on the automatic driving cloud simulation platform so as to store a Docker mirror image; receiving a Docker mirror image established by a second terminal, and putting the Docker mirror image into a preset container mirror image library; wherein the second terminal has a Docker application container engine.
The embodiment of the application provides an automatic driving cloud simulation implementation medium based on K8S, and the medium stores computer instructions for executing the automatic driving cloud simulation implementation method.
The embodiments in the present application are described in a progressive manner, and the same and similar parts among the embodiments can be referred to each other, and each embodiment focuses on the differences from the other embodiments. In particular, the system and media embodiments are substantially similar to the method embodiments and therefore are described in a relatively simple manner, where relevant, reference may be made to some descriptions of the method embodiments.
The system and the medium provided by the embodiment of the application correspond to the method one to one, so the system and the medium also have the beneficial technical effects similar to the corresponding method.
As will be appreciated by one skilled in the art, embodiments of the present application may be provided as a method, system, or computer program product. Accordingly, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present application may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The present application is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the application. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
In a typical configuration, a computing device includes one or more processors (CPUs), input/output interfaces, network interfaces, and memory.
The memory may include forms of volatile memory in a computer readable medium, Random Access Memory (RAM) and/or non-volatile memory, such as Read Only Memory (ROM) or flash memory (flash RAM). Memory is an example of a computer-readable medium.
Computer-readable media, including both non-transitory and non-transitory, removable and non-removable media, may implement information storage by any method or technology. The information may be computer readable instructions, data structures, modules of a program, or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), Static Random Access Memory (SRAM), Dynamic Random Access Memory (DRAM), other types of Random Access Memory (RAM), Read Only Memory (ROM), Electrically Erasable Programmable Read Only Memory (EEPROM), flash memory or other memory technology, compact disc read only memory (CD-ROM), Digital Versatile Discs (DVD) or other optical storage, magnetic cassettes, magnetic tape magnetic disk storage or other magnetic storage devices, or any other non-transmission medium that can be used to store information that can be accessed by a computing device. As defined herein, a computer readable medium does not include a transitory computer readable medium such as a modulated data signal and a carrier wave.
It should also be noted that the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising an … …" does not exclude the presence of other like elements in a process, method, article, or apparatus that comprises the element.
The above description is only an example of the present application and is not intended to limit the present application. Various modifications and changes may occur to those skilled in the art. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application should be included in the scope of the claims of the present application.
Claims (10)
1. A K8S-based automatic driving cloud simulation implementation method is characterized by comprising the following steps:
receiving corresponding simulation resource request information from a plurality of first terminals;
based on the corresponding simulation resource request information, invoking a resource creation instruction of the K8S application to create a corresponding number of POD resources; wherein the POD resource is used for storing a Docker container;
associating a Docker image in a container image library in an automatic driving cloud simulation platform to the POD resource, and generating access login information of the POD resource;
and sending the access login information to a corresponding first terminal so as to enable a user corresponding to the first terminal to perform automatic driving simulation.
2. The method of claim 1, wherein before receiving emulated resource request information from a number of first terminals, the method further comprises;
establishing the preset container mirror image library on the automatic driving cloud simulation platform to store the Docker mirror image;
receiving a Docker mirror image established by a second terminal, and putting the Docker mirror image into the preset container mirror image library; wherein the second terminal has a Docker application container engine.
3. The method of claim 1, wherein before invoking the resource creation instruction of the K8S application to create the corresponding number of POD resources based on the corresponding emulated resource request information, the method further comprises:
establishing an access path from front-end access page information to a Docker container in the POD resource;
determining the number of effective simulation resource request information based on the corresponding simulation resource request information; the quantity of the effective simulation resource request information is obtained by counting of an automatic driving cloud simulation platform;
and according to the quantity of the effective simulation resource request information, calling a resource creating instruction in the K8S to create a corresponding quantity of POD resources.
4. The method according to claim 3, wherein the determining the number of valid simulation resource request messages based on the corresponding simulation resource request messages specifically comprises:
determining first terminal account information corresponding to the corresponding simulation resource request information; wherein the first terminal account information at least comprises one or more of the following items: account name, account balance, account age;
judging whether the corresponding simulation resource request information meets a preset condition or not based on the first terminal account information;
and determining the corresponding simulation resource request information as the effective simulation resource request information under the condition that the corresponding simulation resource request information meets the preset condition.
5. The method according to claim 3, wherein associating the Docker image in a container image library in the autopilot cloud simulation platform with the POD resource and generating the access login information of the POD resource comprises:
under the condition that the POD resource is established, establishing a corresponding container to the POD resource through a container establishing instruction;
and pulling the Docker mirror image in the preset container mirror image library to the corresponding container, so as to associate the Docker mirror image in the preset container mirror image library to the POD resource.
6. The method according to claim 5, wherein the sending the access login information to a corresponding first terminal for a user corresponding to the first terminal to perform automatic driving simulation specifically comprises:
generating corresponding access login information according to the effective simulation resource request information, and sending the access login information to a corresponding first terminal;
receiving access login information from the first terminal, and verifying the validity of the access login information according to an access login information record table; the access login information record table is stored in the automatic driving cloud simulation platform;
establishing a connection with the corresponding first terminal under the condition that the validity of the access login information is valid;
and sending a display desktop to the corresponding first terminal through a Virtual Network Console (VNC) based on the connection with the connection terminal.
7. The method of claim 6, wherein sending a display desktop to the corresponding first terminal via a Virtual Network Console (VNC) based on the connection to the corresponding first terminal, the method further comprising:
the Docker mirror image comprises an automatic driving simulation development environment, and the automatic driving simulation development environment comprises a robot operating system and robot simulation software;
and sending the display desktop information of the robot operating system and the robot simulation software to the corresponding first terminal through the VNC.
8. A K8S-based automatic driving cloud simulation implementation system is characterized by comprising:
at least one processor; and a memory communicatively coupled to the at least one processor; wherein the memory stores instructions executable by the at least one processor to enable the at least one processor to:
receiving corresponding simulation resource request information from a plurality of first terminals;
based on the corresponding simulation resource request information, invoking a resource creation instruction of the K8S application to create a corresponding number of POD resources; wherein the POD resource is for storing a container;
associating a Docker image in a container image library in an automatic driving cloud simulation platform to the POD resource, and generating access login information of the POD resource;
and sending the access login information to a corresponding first terminal so as to enable a user corresponding to the first terminal to perform automatic driving simulation.
9. The system of claim 8, wherein the processor is further configured to:
establishing the preset container mirror image library on the automatic driving cloud simulation platform to store the Docker mirror image;
receiving a Docker mirror image established by a second terminal, and putting the Docker mirror image into the preset container mirror image library; wherein the second terminal has a Docker application container engine.
10. A K8S-based autonomous driving cloud simulation implementation medium, wherein the medium stores the computer instructions of claims 1-7 for executing an autonomous driving cloud simulation implementation method.
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