CN112925657A - Vehicle road cloud cooperative processing system and method - Google Patents
Vehicle road cloud cooperative processing system and method Download PDFInfo
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- CN112925657A CN112925657A CN202110065050.8A CN202110065050A CN112925657A CN 112925657 A CN112925657 A CN 112925657A CN 202110065050 A CN202110065050 A CN 202110065050A CN 112925657 A CN112925657 A CN 112925657A
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Abstract
The invention discloses a vehicle road cloud cooperative processing system and a method, wherein the vehicle road cloud cooperative processing system comprises: the vehicle-end equipment is used for acquiring the running parameter information of the target vehicle; the side end equipment is used for acquiring road condition information; the system processing unit is used for carrying out calculation power distribution according to the driving parameter information, the road condition information and calculation power resources of the vehicle-side equipment, the side-end equipment and the cloud server, and calling at least one of the vehicle-side equipment, the side-end equipment and the cloud server to carry out information analysis according to a distribution result. By implementing the method, the system processing unit combines the received running parameter information and road condition information of the target vehicle and allocates corresponding equipment for processing, the flow of data in three-terminal equipment can be realized, the static information and dynamic information of the vehicle can be uploaded to side-end equipment and a cloud server in real time, the computing capacity of the automatic driving system is expanded, and the utilization rate of computing resources in the automatic driving system is improved.
Description
Technical Field
The invention relates to the technical field of computers, in particular to a vehicle road cloud cooperative processing system and method.
Background
In the existing vehicle-road cooperative automatic driving technology, the system architectures of a vehicle, an edge end and a cloud control are independent from each other, upper-layer application, basic software and hardware of each end are strongly coupled, the application can only singly run at one of the vehicle end, the edge end or a cloud end, a developer is required to completely develop a whole set of system from the bottom to the upper layer, and meanwhile, a perception algorithm, a planning algorithm and a control algorithm in automatic driving are all completed at the vehicle end, so that the calculation efficiency of the automatic driving system is low.
Disclosure of Invention
In view of this, the embodiment of the invention provides a vehicle road cloud cooperative processing system and a vehicle road cloud cooperative processing method, so as to solve the problem that an automatic driving system is low in computing efficiency.
According to a first aspect, an embodiment of the present invention provides a vehicle road cloud cooperative processing system, including: the system comprises vehicle-end equipment, side-end equipment, a cloud server and a system processing unit, wherein the vehicle-end equipment is used for acquiring running parameter information of a target vehicle; the side end equipment is used for acquiring road condition information; the system processing unit is used for performing calculation power distribution according to the driving parameter information, the road condition information and calculation power resources of the vehicle-end equipment, the side-end equipment and the cloud server, and calling at least one of the vehicle-end equipment, the side-end equipment and the cloud server to perform information analysis according to a distribution result.
Optionally, the system processing unit is specifically configured to generate a calculation task according to the driving parameter information and the road condition information; calculating power distribution is carried out according to the calculating power resources of the vehicle-side equipment, the side-end equipment and the cloud server and the calculating task; when the computational power resource of the vehicle-end equipment meets a target condition, calling the vehicle-end equipment and the side-end equipment to perform information analysis; the car end equipment comprises: the first perception module is used for acquiring running parameter information of a target vehicle, wherein the running parameter information is used for representing speed information, acceleration information, yaw rate information and image data of the target vehicle; the first calculation server is used for calculating running state information of the target vehicle according to the running parameter information, and generating a first running instruction according to the running state information and a preset algorithm model, wherein the running state information is used for representing the running state information of the target vehicle; the frontend device includes: the second sensing module is used for acquiring road condition information, and the road condition information is used for representing environment information of a driving road surface of the target vehicle; the second calculation server is used for calculating and generating a second driving instruction according to the running state information, the road condition information and a preset algorithm model, wherein the preset algorithm model is generated by the second calculation server according to historical data of a preset time period and an initial training model; the car end equipment still includes: and the control module is used for controlling the target vehicle to run according to the first running instruction and the second running instruction.
Optionally, the system processing unit is specifically configured to generate a calculation task according to the driving parameter information and the road condition information; calculating power distribution is carried out according to the calculating power resources of the vehicle-side equipment, the side-end equipment and the cloud server and the calculating task; when the computational power resource of the vehicle-end equipment does not meet the target condition, calling the side-end equipment and the cloud server to perform information analysis; the frontend device includes: the second sensing module is used for acquiring road condition information, and the road condition information is used for representing environment information of a driving road surface of the target vehicle; the second calculation server is used for receiving the running parameter information and calculating the running state information of the target vehicle according to the running parameter information; generating a third driving command according to the running state information, the road condition information and a preset algorithm model, wherein the preset algorithm model is generated by the second computing server according to historical data of a preset time period and an initial training model; the cloud server comprises: the third calculation server is used for receiving the running parameter information and calculating the running state information of the target vehicle according to the running parameter information; generating a fourth driving command according to the running state information, the road condition information and a preset algorithm model, wherein the preset algorithm model is generated by the third computing server according to historical data of a preset time period and an initial training model; the car end equipment still includes: and the control module is used for controlling the target vehicle to run according to the third running command and the fourth running command.
Optionally, the second computing server is further configured to issue the preset algorithm model to the vehicle-end device; and the third computing server is also used for issuing the preset algorithm model to the vehicle-end equipment.
Optionally, the driving parameter information, the running state information, the road condition information and the preset algorithm model are mutually transmitted among the vehicle-end equipment, the side-end equipment and the cloud server.
Optionally, the system further comprises: and the API is used for transmitting the data on the vehicle-end equipment, the side-end equipment and the cloud server to external equipment.
Optionally, the system further comprises: and the encryption module is used for encrypting the driving parameter information, the running state information, the road condition information and the control instruction algorithm model through a preset encryption algorithm and then transmitting the encrypted driving parameter information, the running state information, the road condition information and the control instruction algorithm model.
According to a second aspect, an embodiment of the present invention provides a vehicle route cloud cooperative processing method, which is applied to a vehicle route cloud cooperative processing system including a vehicle end device, an edge end device, a cloud server, and a system processing unit, and includes: acquiring driving parameter information and road condition information of a target vehicle; and performing calculation power distribution according to the driving parameter information, the road condition information and calculation power resources of the vehicle-side equipment, the side-end equipment and the cloud server, and calling at least one of the vehicle-side equipment, the side-end equipment and the cloud server according to a distribution result to perform information analysis.
According to a third aspect, an embodiment of the present invention provides a computer device, including: at least one processor; and a memory communicatively coupled to the at least one processor; wherein the memory stores instructions executable by the one processor to cause the at least one processor to perform the steps of the method for dynamic allocation of network slice resources of the second aspect.
According to a fourth aspect, an embodiment of the present invention provides a computer-readable storage medium, on which a computer program is stored, which, when being executed by a processor, implements the steps of the method for dynamically allocating network slice resources according to the second aspect.
The technical scheme of the invention has the following advantages:
the invention provides a vehicle road cloud cooperative processing system and a method, wherein the system comprises: the system comprises vehicle-end equipment, side-end equipment, a cloud server and a system processing unit, wherein the vehicle-end equipment is used for acquiring running parameter information of a target vehicle; the side end equipment is used for acquiring road condition information; the system processing unit is used for performing calculation power distribution according to the driving parameter information, the road condition information and calculation power resources of the vehicle-side equipment, the side-end equipment and the cloud server, and calling at least one of the vehicle-side equipment, the side-end equipment and the cloud server to perform information analysis according to a distribution result.
By implementing the invention, the problem of low computing efficiency of the automatic driving system caused by mutual independence of system architectures of the vehicle, the side terminal and the cloud control in the related technology is solved, the system processing unit combines the received running parameter information and road condition information of the target vehicle and allocates corresponding equipment for processing, the flow of data in three-terminal equipment pieces can be realized, the static information and dynamic information of the vehicle can be uploaded to the side terminal equipment and the cloud server in real time, the real vehicle can be mapped in the side and cloud images in real time, the computing capability of the automatic driving system is expanded, the utilization rate of computing power resources in the automatic driving system is improved, and the communication delay between data transmission is reduced.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, and it is obvious that the drawings in the following description are some embodiments of the present invention, and other drawings can be obtained by those skilled in the art without creative efforts.
Fig. 1 is a schematic diagram of a specific example of a vehicle road cloud cooperative processing system in the embodiment of the present invention;
fig. 2 is a schematic diagram of another specific example of the vehicle road cloud cooperative processing system in the embodiment of the present invention;
fig. 3 is a schematic diagram illustrating a cloud server included in the vehicle road cloud cooperative processing system according to the embodiment of the present invention;
fig. 4 is a schematic diagram of a cloud control network management framework in the vehicle road cloud cooperative processing system in the embodiment of the present invention;
FIG. 5 is a schematic diagram of computational power monitoring and scheduling in the cloud coprocessing system for a vehicle road according to an embodiment of the invention;
FIG. 6 is a schematic flow chart of a vehicle road cloud cooperative processing method according to an embodiment of the present invention;
FIG. 7 is a diagram showing an exemplary embodiment of a computer device.
Detailed Description
In order to make the objects, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are some, but not all, embodiments of the present invention. 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 invention.
With the advance of the research of the automatic driving vehicle, a plurality of problems need to be solved urgently, on one hand, the coupling relation among upper-layer application, basic software and hardware in the existing automatic driving system architecture is strong, different applications need to rebuild the architecture system according to the user requirements and are not adaptive to each other, so that the repeated development work is increased, and the development efficiency is low; on the other hand, the existing application is bound with physical equipment in the automatic driving system, and the existing automatic driving system does not support an application development system based on cloud computing, so that the application (such as an algorithm model) can only be singly operated at one of a vehicle end, an edge end and a cloud end, the computing system in the automatic driving system is solidified, the computing resource utilization rate is low, and the degradation or the breakdown of the whole system is easily caused by a single-point fault.
In addition, the existing automatic driving system has large communication and data synchronization delay, network optimization and real-time architecture optimization management are lacked, and the architecture of the existing system is developed around an automatic driving function and does not have a development environment aiming at non-automatic driving. Meanwhile, the vehicle, the edge and the cloud have no unified information safety and data safety protection system, so that the industrialization promotion of the intelligent networked vehicle cannot be really realized.
Based on the above background, embodiments of the present invention provide a vehicle road cloud cooperative processing system and method, aiming to implement cooperative work of a vehicle, a vehicle edge, and a cloud in an automatic driving system, and improve the computing efficiency of the automatic driving system.
An embodiment of the present invention provides a vehicle road cloud cooperative processing system, as shown in fig. 1, including: the vehicle-side device 100, the edge-side device 200, the cloud server 300 and the system processing unit 400, wherein,
the vehicle-end device 100 is used for acquiring the running parameter information of the target vehicle; in the present embodiment, the target vehicle may be an autonomous vehicle; the travel parameter information may be speed information of the autonomous vehicle, the speed information may be acquired by an on-board IMU sensor on the autonomous vehicle, and the speed information may include a travel speed, an acceleration, and a yaw rate of the autonomous vehicle; the running parameter information may also be image data information of the surroundings of the autonomous vehicle, which may be acquired by an in-vehicle image pickup apparatus.
The side equipment 200 is used for acquiring road condition information; in this embodiment, the road condition information may be environment information on a traveling road surface of the autonomous vehicle, may include information of an incoming vehicle at the front and the rear of the vehicle, signal light information in front of the vehicle, and may further include congestion information of the traveling road surface of the vehicle, and the like, and specifically, the road condition information may be acquired by a camera device and a sensor device provided on a road side.
The system processing unit 400 is configured to perform calculation power allocation according to the driving parameter information, the road condition information, and the calculation power resources of the vehicle-side device 100, the edge-side device 200, and the cloud server 300, and call at least one of the vehicle-side device 100, the edge-side device 200, and the cloud server 300 to perform information analysis according to an allocation result.
In this embodiment, edge computing servers may be disposed on the vehicle-end device 100, the edge-end device 200, and the cloud server 300, that is, computing resources are disposed on the vehicle-end device 100, the edge-end device 200, and the cloud server 300, and the system processing unit 400 may perform distribution of computing tasks according to the current computing resources and current data to be processed of the vehicle-end device 100, the edge-end device 200, and the cloud server 300; specifically, the system processing unit 400 may call one or more of the vehicle-end device 100, the edge-end device 200, and the cloud server 300, and allocate data to be processed, where the data to be processed may include driving parameter information and road condition information.
The invention provides a vehicle road cloud cooperative processing system which comprises vehicle-end equipment, side-end equipment, a cloud server and a system processing unit, wherein the vehicle-end equipment is used for acquiring running parameter information of a target vehicle; the side end equipment is used for acquiring road condition information; the system processing unit is used for performing calculation power distribution according to the driving parameter information, the road condition information and calculation power resources of the vehicle-side equipment, the side-end equipment and the cloud server, and calling at least one of the vehicle-side equipment, the side-end equipment and the cloud server to perform information analysis according to a distribution result.
By implementing the invention, the problem of low computing efficiency of the automatic driving system caused by mutual independence of system architectures of the vehicle, the side terminal and the cloud control in the related technology is solved, the system processing unit combines the received running parameter information and road condition information of the target vehicle and allocates corresponding equipment for processing, the flow of data in three-terminal equipment pieces can be realized, the static information and dynamic information of the vehicle can be uploaded to the side terminal equipment and the cloud server in real time, the real vehicle can be mapped in the side and cloud images in real time, the computing capability of the automatic driving system is expanded, the utilization rate of computing power resources in the automatic driving system is improved, and the communication delay between data transmission is reduced.
As an optional embodiment of the present invention, as shown in fig. 2, the system processing unit 400 is specifically configured to generate a calculation task according to the driving parameter information and the road condition information; calculating power distribution is carried out according to calculating power resources and calculating tasks of the vehicle-end equipment 100, the side-end equipment 200 and the cloud server 300; in this embodiment, the system processing unit 400 may be configured to allocate and schedule computing resources among the vehicle, the edge, and the cloud, specifically, generate a computing task according to the driving parameter information acquired by the vehicle-end device 100 and the road condition information acquired by the edge-end device 200, and then allocate the computing task and allocate the computing resource according to the computing resources and the computing tasks currently existing in the edge computing servers on the vehicle, the edge, and the cloud.
When the computational resources of the vehicle-end equipment 100 meet the target conditions, calling the vehicle-end equipment 100 and the side-end equipment 200 to perform information analysis; in this embodiment, the target condition may be that the calculation power resource of the current calculation server matches the calculation task, or that the signal strength of the current driving area is weak, for example, the signal strength may be lower than-90 dbm, specifically, when the vehicle drives in an area where the communication signal strength is lower than-90 dbm, at this time, the vehicle-end device 100 and the cloud-end device cannot perform real-time data transmission, and the calculation power resource of the vehicle-end device 100 is sufficient, at this time, the calculation task may be analyzed and processed on the vehicle-end device 100 and the edge-end device 200, or only the vehicle-end device 100 may perform analysis and processing of the calculation task.
The vehicle-end device 100 includes:
the first sensing module 101 is configured to acquire driving parameter information of a target vehicle, where the driving parameter information is used to represent speed information, acceleration information, yaw rate information, and image data of the target vehicle;
in this embodiment, the first sensing module 101 may include an on-board IMU sensor and an on-board camera device, where the on-board IMU sensor is configured to acquire speed information of the autonomous vehicle, including running speed information, acceleration information, and yaw rate information; the vehicle-mounted camera equipment is used for acquiring image information of the periphery of the vehicle, and whether the vehicle comes from the front and the rear of the vehicle can be judged according to the pattern data.
The first calculation server 102 is configured to calculate running state information of the target vehicle according to the running parameter information, and generate a first running instruction according to the running state information and a preset algorithm model, where the running state information is used to represent the running state information of the target vehicle; in this embodiment, the operating state information may be used to characterize the position information and the size information of the autonomous vehicle, and specifically, the determination of the vehicle position may be made based on the data information sensed by the sensing device.
In addition, the first computing server is further configured to process a computing task generated according to the driving parameter information, and specifically, the first computing server may compute a first driving instruction of the vehicle according to the computed motion state information and a pre-declared intelligent AI algorithm model, and may include: vehicle turning instruction, vehicle lane change instruction and vehicle overtaking instruction.
Specifically, the pre-declared intelligent AI algorithm model may be issued to the vehicle-end device 100 after the training is completed by the edge-side device 200 and the cloud server 300, that is, the algorithm model for vehicle decision control calculation may be transmitted to the vehicle-end device 100 after the training is completed by the cloud server 300 or the edge calculation performed by the roadside device in advance, and then loaded on the vehicle-end device 100 for the calculation tool of sensing calculation and decision planning.
The edge device 200 includes:
the second sensing module 201 is configured to acquire road condition information, where the road condition information is used to represent environmental information of a driving road surface of a target vehicle; in the present embodiment, the environmental information of the target vehicle traveling road surface may include the vehicle-coming information of the front and rear sides of the vehicle traveling, the road surface information, and the obstacle information on the traveling road surface, and the like.
The second calculation server 202 is used for calculating and generating a second driving instruction according to the running state information, the road condition information and a preset algorithm model, wherein the preset algorithm model is generated by the second calculation server according to historical data of a preset time period and training of an initial training model; in this embodiment, the second computing server may generate the preset algorithm model after training for a preset time period according to the historical data and the initial training model within the preset time period.
Specifically, the second calculation server may further process a calculation task generated according to the traffic information, specifically, analyze and process the received traffic information and the preset algorithm model to generate a second driving instruction, where the driving instruction may include a vehicle turning instruction, a vehicle lane change instruction, and a vehicle overtaking instruction. For example, when the received road condition information indicates that an obstacle exists in front of the driving road surface of the vehicle, the edge calculation server in the edge device 200 may generate a vehicle lane change instruction at this time, so as to prevent the autonomous driving vehicle from contacting the obstacle, and prevent the vehicle from causing an accident, thereby improving the safety of the autonomous driving vehicle of the autonomous driving system.
The vehicle-end device 100 further includes:
and the control module 103 is used for controlling the target vehicle to run according to the first running instruction and the second running instruction. In the present embodiment, the first travel instruction may be a "vehicle lane change instruction", and the second travel instruction may be a "vehicle lane change instruction", at which time, when the control module in the end-of-vehicle device 100 receives the "vehicle lane change instruction", the control module may control the vehicle to perform lane change of the travel road under the condition that the traffic regulation is complied with, for example, lane change from the first lane to the second lane.
According to the vehicle road cloud cooperative processing system provided by the embodiment of the invention, the preset algorithm model is combined with the flow among the vehicle-end equipment, the side-end equipment and the cloud server, so that when the computing resources of the vehicle-end equipment are sufficient, the sensing data can be directly processed at the vehicle-end equipment and the side-end equipment to generate the driving instruction, the vehicle can be controlled in time, and the safety of the automatic driving system is improved.
As an optional embodiment of the present invention, as shown in fig. 3, the system processing unit 400 is specifically configured to generate a calculation task according to the driving parameter information and the road condition information; calculating power distribution is carried out according to calculating power resources and calculating tasks of the vehicle-end equipment 100, the side-end equipment 200 and the cloud server 300; in this embodiment, the system processing unit 400 may be configured to allocate and schedule computing resources among the vehicle, the edge, and the cloud, specifically, generate a computing task according to the driving parameter information acquired by the vehicle-end device 100 and the road condition information acquired by the edge-end device 200, and then allocate the computing task and allocate the computing resource according to the computing resources and the computing tasks currently existing in the edge computing servers on the vehicle, the edge, and the cloud.
When the computing resources of the vehicle-end device 100 do not meet the target conditions, the side-end device 200 and the cloud server 300 are called to perform information analysis; in this embodiment, the target condition may be that the calculation power resource of the current calculation server matches the calculation task, or that the signal intensity of the current driving area is weak, specifically, when the vehicle drives in an area with strong communication signal intensity, the vehicle-end device 100 and the cloud-end device may perform real-time data transmission, and the calculation power resource of the vehicle-end device 100 is insufficient and cannot meet the requirement of data processing, at this time, one or more of the side-end device 200 and the cloud-end server 300 may be called to perform analysis processing on the calculation task.
The edge device 200 includes:
the second sensing module 201 is configured to acquire road condition information, where the road condition information is used to represent environmental information of a driving road surface of a target vehicle; in the present embodiment, the environmental information of the target vehicle traveling road surface may include the vehicle-coming information of the front and rear sides of the vehicle traveling, the road surface information, and the obstacle information on the traveling road surface, and the like.
The second calculation server 202 is used for receiving the running parameter information and calculating the running state information of the target vehicle according to the running parameter information; generating a third driving command according to the running state information, the road condition information and a preset algorithm model, wherein the preset algorithm model is generated by the second computing server according to historical data of a preset time period and the initial training model; in this embodiment, the second computing server may generate the preset algorithm model after training for a preset time period according to the historical data and the initial training model within the preset time period.
Specifically, the second computing server may further process a computing task generated according to the traffic information, and specifically, analyze and process the received traffic information and the preset algorithm model to generate a third driving instruction, where the driving instruction may include a vehicle turning instruction, a vehicle lane change instruction, and a vehicle overtaking instruction. For example, when the received road condition information indicates that an obstacle exists in front of the driving road surface of the vehicle, the edge calculation server in the edge device 200 may generate a vehicle lane change instruction at this time, so as to prevent the autonomous driving vehicle from contacting the obstacle, and prevent the vehicle from causing an accident, thereby improving the safety of the autonomous driving vehicle of the autonomous driving system.
a third calculation server 301, configured to receive the driving parameter information, and calculate the operating state information of the target vehicle according to the driving parameter information; in this embodiment, the third calculation server in the cloud server 300 may receive the driving parameter information acquired by the sensing device in the vehicle-end device 100, and then calculate the running state information of the target vehicle according to the driving parameter information, that is, calculate the current position information of the autonomous vehicle, and the like.
Generating a fourth driving command according to the running state information, the road condition information and a preset algorithm model, wherein the preset algorithm model is generated by the third computing server according to historical data of a preset time period and the initial training model;
in this embodiment, the third computing server may further process a computing task generated according to the traffic information, and specifically, according to the received traffic information and the preset algorithm model, perform analysis processing to generate a fourth driving instruction, where the driving instruction may include a vehicle turning instruction, a vehicle lane changing instruction, and a vehicle overtaking instruction. For example, when the received road condition information indicates that an obstacle exists in front of the driving road surface of the vehicle, the edge calculation server in the edge device 200 may generate a vehicle lane change instruction at this time, so as to prevent the autonomous driving vehicle from contacting the obstacle, and prevent the vehicle from causing an accident, thereby improving the safety of the autonomous driving vehicle of the autonomous driving system.
The vehicle-end device 100 further includes:
and the control module is used for controlling the target vehicle to run according to the third running command and the fourth running command. In this embodiment, the third traveling instruction may be a "vehicle lane change instruction", and the fourth traveling instruction may be a "vehicle lane change instruction", at which time, when the control module in the end-of-vehicle device 100 receives the "vehicle lane change instruction", the control module may control the vehicle to perform lane change of the traveling road under the condition that the traffic regulation is complied with, for example, lane change from the first lane to the second lane.
The vehicle road cloud cooperative processing system provided by the embodiment of the invention combines the flow of the preset algorithm model among the vehicle-end equipment, the side-end equipment and the cloud server, can call the computing resources in the side-end equipment and the cloud server to process the sensing data and generate a driving instruction when the computing resources of the vehicle-end equipment are insufficient, can timely control the vehicle, improves the safety of an automatic driving system, can construct full-function operating system basic software on the cloud server based on a digital twinning technology, realizes real-time communication and non-real-time communication between the vehicle-end equipment and the cloud server, and realizes synchronization of algorithm data, application data and the sensing data.
As an optional implementation manner of the present invention, the second computing server is further configured to issue the preset algorithm model to the vehicle-end device; and the third calculation server is also used for issuing the preset algorithm model to the vehicle-end equipment. In this embodiment, the edge calculation server in the edge device and the edge calculation server in the cloud server may generate an intelligent AI algorithm model in advance according to historical data and an initial training model in a preset time period, and then transmit the model to the vehicle-side device to serve as a calculation tool for perception calculation and decision planning of the vehicle-side device.
As an optional implementation manner of the present invention, the driving parameter information, the operating state information, the road condition information, and the preset algorithm model are mutually transmitted among the vehicle-side device, the edge-side device, and the cloud server. In this embodiment, the vehicle-end device in the vehicle-road cloud cooperative processing system further includes a cloud control module, and the cloud control module includes a cloud control data collection module and a cloud control pluggable algorithm module.
Specifically, cloud control module is based on the twin technique of digit, realizes the real-time receiving and dispatching function of data at car end equipment, way end equipment, high in the clouds equipment, and wherein, the data that supports receiving and dispatching include: the sensing data comprise road condition information sensed by the side equipment, vehicle-end equipment identification driving parameter information, and position, size and motion state information of traffic participants (such as automatic driving vehicles) generated by calculation through the road condition information and the driving parameter information; control data, namely control instruction information generated by vehicle-end equipment and control instruction information generated by roadside equipment (such as traffic signal equipment), wherein the control instruction information comprises a vehicle turning instruction, a lane change instruction, an overtaking instruction and the like; the algorithm data, i.e. the AI algorithm model for vehicle decision control calculation, may be a calculation tool generated by the cloud server and the edge calculation server trained.
As an optional embodiment of the present invention, the system further comprises: and the API is used for transmitting the data on the vehicle-end equipment, the side-end equipment and the cloud server to the external equipment. In this embodiment, the vehicle-road cloud cooperative processing system further includes a plurality of standardized API interfaces, which can provide computing resources for the external device through the interface vehicle-side cloud, and provide calling efficiency.
As an optional embodiment of the present invention, the system further comprises: and the encryption module is used for encrypting the driving parameter information, the running state information, the road condition information and the control instruction algorithm model through a preset encryption algorithm and then transmitting the encrypted driving parameter information, the running state information, the road condition information and the control instruction algorithm model. In this embodiment, vehicle end equipment among the vehicle way cloud cooperative processing system, all dispose encryption module on road end equipment and the high in the clouds server, be used for encrypting each data that will transmit between the car, the way, the cloud three-terminal, transmit after the data after generating the encryption, can ensure to run through the car, the limit, the basic information safety of cloud three-terminal, data safety, functional safety, thereby guarantee system fail safe nature, specifically, encryption module specifically includes federal AI technique and attribute ID management tool, in the practical application scene, can provide data and the safety service of different grades according to different demand subjects such as car factory, national supervision agency.
The vehicle road cloud cooperative processing system in the above embodiment is described in detail below with reference to a specific embodiment:
the cloud control platform system comprises a cloud control application platform and a cloud control basic platform, wherein the cloud control application platform comprises a collection of various application software, such as an automatic driving application, roadside intelligent cooperation, various cloud control applications and the like, specifically, the cloud control application platform can comprise a non-real-time application and a real-time coordination application in a plurality of management mechanisms, the management of a third-party mechanism also comprises a non-real-time application and a real-time coordination application, and an automobile enterprise also comprises a non-real-time application and a real-time coordination application; the cloud control basic platform comprises cloud control basic software and a cloud control basic framework, and specifically, the cloud control basic software comprises cloud basic software (cloud), roadside basic software (edge) and an intelligent automobile (vehicle) operating system, wherein the cloud basic software (cloud) comprises functional software and system software and is used for supporting normal operation of a cloud end, the roadside basic software (edge) comprises the functional software and the system software and is used for supporting normal operation of a road end, and the intelligent automobile (vehicle) operating system comprises the functional software and the system software and is used for supporting normal operation of the intelligent automobile.
Specifically, the cloud control basic framework comprises: the central cloud, the regional cloud and the edge cloud can be used for monitoring, collecting, calculating, safely storing and transmitting data.
The cloud control platform system can perform data interaction with an external platform, and for example, a map data platform, a traffic management data platform, a GPS data platform, a meteorological data platform, and the like can be included.
The vehicle cloud collaborative computing infrastructure is a group of common basic software sets distributed and deployed on an intelligent vehicle computing platform, a road side unit computing platform and a multilevel cloud platform based on 5G + V2X. Specifically, the method is divided into: a Vehicle-side operating system component (Vehicle OS), an Edge-side operating system component (Edge Light OS), and a Cloud Full-function operating system component (Cloud Full OS). The three-layer component is provided with a universal standard API interface which can be used for transmitting data between the three-layer component and between the component and an external device.
First, Vehicle-end operating system (Vehicle OS): the vehicle-end equipment in the vehicle-end operation system vehicle-road cloud cooperative processing system further comprises a cloud control module, and the cloud control module comprises a cloud control data collection module and a cloud control pluggable algorithm module.
Specifically, cloud control module is based on the twin technique of digit, realizes the real-time receiving and dispatching function of data at car end equipment, way end equipment, high in the clouds equipment, and wherein, the data that supports receiving and dispatching include: the sensing data comprise road condition information sensed by the side equipment, vehicle-end equipment identification driving parameter information, and position, size and motion state information of traffic participants (such as automatic driving vehicles) generated by calculation through the road condition information and the driving parameter information; control data, namely control instruction information generated by vehicle-end equipment and control instruction information generated by roadside equipment (such as traffic signal equipment), wherein the control instruction information comprises a vehicle turning instruction, a lane change instruction, an overtaking instruction and the like; the algorithm data, i.e. the AI algorithm model for vehicle decision control calculation, may be a calculation tool generated by the cloud server and the edge calculation server trained.
Second, Edge lightweight operating system (Edge Light OS): the vehicle cloud collaborative computing basic software can run a mirror image component at the Edge side (Edge), and real-time communication or non-real-time communication between the vehicle end equipment and the Edge end equipment, and between the Edge end equipment and the cloud server is realized based on a digital twin technology.
Therefore, the application component can be directly issued to the vehicle-end equipment, or issued to the vehicle-end equipment after the calculation of the side-end equipment is completed; specifically, the application component may be divided into several cases, for example, a sensing module may be arranged on the edge device to sense road condition information of a driving road of the vehicle for further calculation, or the sensing module may receive sensing data uploaded by the vehicle-end device and then process the data on the edge device, or may plan the edge device to realize smooth movement of the sensing data, the algorithm model, and the control instruction at the vehicle-end and the edge.
In a third aspect, Cloud Full-function operating system (Cloud Full OS): the vehicle-side cloud cooperative computing basic software is a full-function operating system basic software constructed on the basis of a digital twin technology at a cloud end and operated on a cloud end server, so that real-time and non-real-time communication from the inside of a vehicle to the cloud end can be realized, and algorithm and application and original data synchronization can be realized. In addition, the cloud basic software is provided with an information security module and a data security configuration tool, and configuration management of cloud data security is achieved.
As shown in fig. 4: the vehicle-road cloud cooperative system further comprises a unified interoperable digital twin container which is constructed on the vehicle, the edge and the cloud based on a data real-time synchronous transmission tool (such as digital twin, container management, mirror image management and the like), so that the static information and the dynamic information of the vehicle can be uploaded in millisecond-level real-time, namely the real-time mapping of the real vehicle can be realized in the mirror image of the edge and the cloud, namely, the Open Stack network management tool is composed of a cloud control tool Controller (a data transmission instruction issuing device) and executing tools (Switch) which run on the edge end and the vehicle end, and the control tool and the executing tools are decoupled in a transparent mode. And transmitting a transmission instruction from the central cloud to each mobile edge computing server (MEC), wherein the cloud Controller unit is used as a control end for overall computing power distribution, and the Switch built at the road side and the vehicle side is used for switching and executing data transmission according to the instruction transmitted by the Controller. Therefore, effective dynamic arrangement and distribution of data and computing resources are realized, and the optimization goal of the overall cloud control function is realized.
As shown in fig. 5, the system further includes a time synchronization module and a calculation power monitoring and scheduling module, which are configured to automatically flow data to be processed and algorithms at the plurality of vehicle ends 1, the road sections 2 and the cloud ends 1, specifically, the data in the vehicle ends 1 may include calculation power resources and algorithms, the data in the road ends 2 may include calculation power resources and algorithms, and the data in the cloud ends 1 may include calculation power resources and algorithms, so that the calculation power of the automatic driving system is expanded, the utilization rate of the calculation power resources in the automatic driving system is improved, and the communication delay between data transmissions is reduced.
Specifically, the vehicle-end device uploads the static data and the dynamic data to a plurality of RSU base stations (namely, roadside devices), and the vehicle data are mirrored on an edge computing server and a central cloud server in the roadside devices based on a digital twin technology.
Specifically, based on the preset K8S container management tool, the automatic driving can be split into three sub-modules of sensing, planning and controlling, each sub-module runs in one container, the three sub-modules do not need to be bound to a vehicle end to run, and dynamic allocation can be performed according to the efficiency and the requirement of calculating the bearing capacity. For example, the sensing and control sub-module operates at the vehicle end, and the planning sub-module operates at the cloud end and then issues the result to the vehicle end for execution, so that the optimization of the overall calculation is realized.
Specifically, the system further comprises an Open Stack network management tool which is composed of a cloud control tool (Controller) and an execution tool (Switch) running at the side end and the vehicle end, and transparent decoupling between the control tool and the execution tool is achieved. The cloud Controller unit is responsible for serving as a control end of the overall calculation power distribution, and the Switch built at the road side and the vehicle end is responsible for switching and executing data transmission according to the instruction issued by the Controller. Therefore, effective dynamic arrangement and distribution of data and computing resources are realized, and the optimization goal of the overall cloud control function is realized. Through the construction of the Open Stack cloud control network management architecture, effective dynamic arrangement and distribution of data and computing resources can be realized, and thus the optimization target of the overall cloud control function is achieved.
Particularly, safety control modules are arranged at the three ends of the vehicle, the edge and the cloud, and the safety and reliability of the system are guaranteed. The related technologies comprise a federal AI technology for providing encryption privacy, an attribute ID management tool and the like, and can provide data and safety services of different levels according to different requirements of automobile factories, national regulatory agencies and the like.
According to the vehicle road cloud cooperative system provided by the embodiment of the invention, the software and hardware decoupling basic framework of vehicle gauge level real-time safety is constructed, the upper application and the bottom software development are realized to be transparent and decoupled, ICT related application can smoothly walk at the cloud three ends of the vehicle edge, and a user only needs to focus on the development of an application function without paying attention to a physical carrier for application realization, so that the industry division cooperation is promoted, and the problem of repeated development of application developers is solved.
Specifically, an operation system with heterogeneous distribution is established at a vehicle end, the operation system is mapped at an edge end and a cloud end based on a twin technology, and meanwhile, the automatic driving operation system is subjected to sub-module (sensing, planning and controlling) splitting and containerization management, so that dynamic arrangement is realized. The architecture can relieve the minimum configuration requirement of hardware resources of the single-point terminal, and relieve the fixed collocation of an algorithm and hardware. When a certain calculation program is faced, the calculation resources can be dynamically arranged and distributed at three ends effectively according to the principles of efficiency, real-time requirements and the like, so that the current 'incapable of calculating' is changed into the calculable, and the problem of calculation load is solved.
Specifically, by establishing an edge cloud and central cloud multi-level cloud platform architecture, a network management, container management and cloud computing real-time unified management system is perfected, and the delay problem can be solved. The global planning of the edge cloud and the local planning of the vehicle end are effectively combined, the intelligent level, the safety, the riding comfort and the energy economy of a single vehicle can be greatly improved, and the global optimization effect on urban traffic is achieved.
Through the mapping of the intelligent network connection operating system on three ends, more non-automatic driving application development can be supported. Meanwhile, technologies such as an embedded cloud computing management tool, an information safety and data safety protection tool and a multi-level AI basic framework are integrated, and the full-ecological construction of the intelligent networked automobile can be promoted.
The embodiment of the present invention further provides a vehicle route cloud cooperative processing method, which is applied to a vehicle route cloud cooperative processing system including a vehicle end device, a side end device, a cloud server, and a system processing unit, and as shown in fig. 6, the method includes:
step S11: acquiring driving parameter information and road condition information of a target vehicle; for detailed implementation, reference is made to the description of the embodiments of the system described above.
Step S12: and performing calculation power distribution according to the driving parameter information, the road condition information and calculation power resources of the vehicle-side equipment, the side-end equipment and the cloud server, and calling at least one of the vehicle-side equipment, the side-end equipment and the cloud server according to a distribution result to perform information analysis. For detailed implementation, reference is made to the description of the embodiments of the system described above.
The invention provides a vehicle road cloud cooperative processing method, which comprises the following steps: acquiring driving parameter information and road condition information of a target vehicle; and performing calculation power distribution according to the driving parameter information, the road condition information and calculation power resources of the vehicle-side equipment, the side-end equipment and the cloud server, and calling at least one of the vehicle-side equipment, the side-end equipment and the cloud server according to a distribution result to perform information analysis. By implementing the invention, the problem of low computing efficiency of the automatic driving system caused by mutual independence of system architectures of the vehicle, the side terminal and the cloud control in the related technology is solved, the system processing unit combines the received running parameter information and road condition information of the target vehicle and allocates corresponding equipment for processing, the flow of data in three-terminal equipment pieces can be realized, the static information and dynamic information of the vehicle can be uploaded to the side terminal equipment and the cloud server in real time, the real vehicle can be mapped in the side and cloud images in real time, the computing capability of the automatic driving system is expanded, the utilization rate of computing power resources in the automatic driving system is improved, and the communication delay between data transmission is reduced.
An embodiment of the present invention further provides a computer device, as shown in fig. 7, the computer device may include a processor 31 and a memory 32, where the processor 31 and the memory 32 may be connected by a bus 30 or in another manner, and fig. 7 takes the connection by the bus 30 as an example.
The processor 31 may be a Central Processing Unit (CPU). The Processor 31 may also be other general purpose processors, Digital Signal Processors (DSPs), Application Specific Integrated Circuits (ASICs), Field Programmable Gate Arrays (FPGAs) or other Programmable logic devices, discrete Gate or transistor logic devices, discrete hardware components, or combinations thereof.
The memory 32, which is a non-transitory computer readable storage medium, may be used for storing non-transitory software programs, non-transitory computer executable programs, and modules, such as program instructions/modules corresponding to the method for dynamically allocating network slice resources in the embodiment of the present invention. The processor 31 executes various functional applications and data processing of the processor by running non-transitory software programs, instructions and modules stored in the memory 32, that is, the method for dynamically allocating network slice resources in the above method embodiments is implemented.
The memory 32 may include a storage program area and a storage data area, wherein the storage program area may store an operating system, an application program required for at least one function; the storage data area may store data created by the processor 31, and the like. Further, the memory 32 may include high speed random access memory, and may also include non-transitory memory, such as at least one magnetic disk storage device, flash memory device, or other non-transitory solid state storage device. In some embodiments, the memory 32 may optionally include memory located remotely from the processor 31, and these remote memories may be connected to the processor 31 via a network. Examples of such networks include, but are not limited to, the internet, intranets, local area networks, mobile communication networks, and combinations thereof.
The one or more modules are stored in the memory 32 and, when executed by the processor 31, perform a method for dynamic allocation of network slice resources as in the embodiment shown in fig. 6.
The details of the computer device can be understood by referring to the corresponding related description and effects in the embodiment shown in fig. 6, and are not described herein again.
The embodiment of the present invention further provides a non-transitory computer readable medium, where the non-transitory computer readable storage medium stores a computer instruction, and the computer instruction is used to enable a computer to execute the method for dynamically allocating network slice resources described in any one of the above embodiments, where the storage medium may be a magnetic Disk, an optical Disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a Flash Memory (Flash Memory), a Hard Disk (Hard Disk Drive, abbreviated as HDD), or a Solid-State Drive (SSD); the storage medium may also comprise a combination of memories of the kind described above.
It should be understood that the above examples are only for clarity of illustration and are not intended to limit the embodiments. Other variations and modifications will be apparent to persons skilled in the art in light of the above description. And are neither required nor exhaustive of all embodiments. And obvious variations or modifications therefrom are within the scope of the invention.
Claims (10)
1. A vehicle road cloud coprocessing system, characterized by comprising: vehicle-end equipment, side-end equipment, cloud server and system processing unit, wherein,
the vehicle-end equipment is used for acquiring the running parameter information of the target vehicle;
the side end equipment is used for acquiring road condition information;
the system processing unit is used for performing calculation power distribution according to the driving parameter information, the road condition information and calculation power resources of the vehicle-end equipment, the side-end equipment and the cloud server, and calling at least one of the vehicle-end equipment, the side-end equipment and the cloud server to perform information analysis according to a distribution result.
2. The system according to claim 1, wherein the system processing unit is specifically configured to generate a calculation task according to the driving parameter information and the traffic information; calculating power distribution is carried out according to the calculating power resources of the vehicle-side equipment, the side-end equipment and the cloud server and the calculating task;
when the computational power resource of the vehicle-end equipment meets a target condition, calling the vehicle-end equipment and the side-end equipment to perform information analysis;
the car end equipment comprises:
the first perception module is used for acquiring running parameter information of a target vehicle, wherein the running parameter information is used for representing speed information, acceleration information, yaw rate information and image data of the target vehicle;
the first calculation server is used for calculating running state information of the target vehicle according to the running parameter information, and generating a first running instruction according to the running state information and a preset algorithm model, wherein the running state information is used for representing the running state information of the target vehicle;
the frontend device includes:
the second sensing module is used for acquiring road condition information, and the road condition information is used for representing environment information of a driving road surface of the target vehicle;
the second calculation server is used for calculating and generating a second driving instruction according to the running state information, the road condition information and a preset algorithm model, wherein the preset algorithm model is generated by the second calculation server according to historical data of a preset time period and an initial training model;
the car end equipment still includes:
and the control module is used for controlling the target vehicle to run according to the first running instruction and the second running instruction.
3. The system according to claim 1, wherein the system processing unit is specifically configured to generate a calculation task according to the driving parameter information and the traffic information; calculating power distribution is carried out according to the calculating power resources of the vehicle-side equipment, the side-end equipment and the cloud server and the calculating task;
when the computational power resource of the vehicle-end equipment does not meet the target condition, calling the side-end equipment and the cloud server to perform information analysis;
the frontend device includes:
the second sensing module is used for acquiring road condition information, and the road condition information is used for representing environment information of a driving road surface of the target vehicle;
the second calculation server is used for receiving the running parameter information and calculating the running state information of the target vehicle according to the running parameter information;
generating a third driving command according to the running state information, the road condition information and a preset algorithm model, wherein the preset algorithm model is generated by the second computing server according to historical data of a preset time period and an initial training model;
the cloud server comprises:
the third calculation server is used for receiving the running parameter information and calculating the running state information of the target vehicle according to the running parameter information;
generating a fourth driving command according to the running state information, the road condition information and a preset algorithm model, wherein the preset algorithm model is generated by the third computing server according to historical data of a preset time period and an initial training model;
the car end equipment still includes:
and the control module is used for controlling the target vehicle to run according to the third running command and the fourth running command.
4. The system of claim 3, wherein the second computing server is further configured to issue the preset algorithm model to the vehicle-end device; and the third computing server is also used for issuing the preset algorithm model to the vehicle-end equipment.
5. The system of claim 1, wherein the driving parameter information, the operating status information, the road condition information and the predetermined algorithm model are mutually transmitted among the vehicle-side device, the edge-side device and the cloud server.
6. The system of claim 1, further comprising:
and the API is used for transmitting the data on the vehicle-end equipment, the side-end equipment and the cloud server to external equipment.
7. The system of claim 5 or 6, further comprising:
and the encryption module is used for encrypting the driving parameter information, the running state information, the road condition information and the control instruction algorithm model through a preset encryption algorithm and then transmitting the encrypted driving parameter information, the running state information, the road condition information and the control instruction algorithm model.
8. The vehicle road cloud cooperative processing method is applied to a vehicle road cloud cooperative processing system comprising vehicle end equipment, side end equipment, a cloud server and a system processing unit, and comprises the following steps:
acquiring driving parameter information and road condition information of a target vehicle;
and performing calculation power distribution according to the driving parameter information, the road condition information and calculation power resources of the vehicle-side equipment, the side-end equipment and the cloud server, and calling at least one of the vehicle-side equipment, the side-end equipment and the cloud server according to a distribution result to perform information analysis.
9. A computer device, comprising: at least one processor; and a memory communicatively coupled to the at least one processor; wherein the memory stores instructions executable by the one processor to cause the at least one processor to perform the steps of the vehicle road cloud co-processing method of claim 8.
10. A computer-readable storage medium, on which a computer program is stored, wherein the computer program, when executed by a processor, implements the steps of the vehicle road cloud co-processing method according to claim 8.
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