A design method of intelligent networked vehicle-road cloud integrated information system

Abstract

The application relates to the technical field of intelligent transportation, and discloses a design method of an intelligent networked automobile road cloud integrated information system, which can realize the integration and matching among vehicles, roads and clouds in an intelligent networked automobile road cloud integrated system and comprises the following steps: designing a functional scene of the intelligent networked automobile road cloud integrated system according to the space-time relationship of a road network level driving task; designing a logic scene of the intelligent networked automobile road cloud integrated system according to the functional scene design result and the implementation logic of global optimization of the road network level driving task; and designing a physical scene of the intelligent networked automobile road cloud integrated system according to the logic scene design result and the implementation logic of global optimization of the road network level driving task. The design method is phased, multi-scale, cross-domain and layered, and can provide method support and architecture guidance for the design, modeling, testing and verification of the automobile road cloud integrated system.

Description

Technical Field

[0001] This invention relates to the field of intelligent transportation technology, and in particular to a design method for a traffic information system. Background Technology

[0002] Intelligent Connected Vehicles (ICVs) are a new generation of vehicles equipped with advanced onboard sensors, controllers, actuators, and other devices, and integrated with modern communication and network technologies to achieve intelligent information exchange and sharing. They possess complex environmental perception, intelligent decision-making, collaborative control, and execution functions, enabling safe, comfortable, energy-saving, and efficient driving.

[0003] The vehicle-road-cloud integrated system is a cyber-physical system that integrates the physical and information spaces of people, vehicles, roads, and the cloud through next-generation information and communication technologies. Based on system-wide collaborative perception, decision-making, and control, it enables the safe, energy-saving, comfortable, and efficient operation of intelligent connected vehicle transportation systems.

[0004] The integration of vehicle, road, and cloud in intelligent connected vehicles is an inevitable trend in technological evolution and a consensus in the industry. However, the construction of vehicle, road, and cloud integration in intelligent connected vehicles is currently in its initial stage and has not yet formed a complete system architecture. For example, the time span and spatial scale of existing scenario designs are small, and there is no attention paid to the integration and matching, functional interaction and collaborative fusion between vehicles, roads, and the cloud. Summary of the Invention

[0005] This invention provides a design method for an integrated vehicle-road-cloud system for intelligent connected vehicles, realizing the integration and matching between vehicles, roads, and the cloud in the integrated vehicle-road-cloud system for intelligent connected vehicles.

[0006] This invention provides the following technical solutions:

[0007] A design method for an integrated vehicle-road-cloud information system for intelligent connected vehicles includes the following steps:

[0008] S1. Functional Scenario Design: Based on the spatiotemporal relationship of road network-level driving tasks, design the functional scenarios of the intelligent connected vehicle vehicle-road-cloud integrated system, and describe the vehicle-road-cloud road network-level functions, road segment-level vehicle-road functions, and vehicle-to-vehicle functions in the vehicle body area, as well as the interrelationships between functions at each scale.

[0009] S2. Logical Scenario Design: Based on the functional scenario design results and the implementation logic of global optimization of road network-level driving tasks, design the logical scenario of the intelligent connected vehicle vehicle-road-cloud integrated system, describe the functions of the cloud, road, and vehicle, as well as the logical relationships and interfaces between the above functions.

[0010] S3. Physical Scene Design: Based on the logical scene design results and the implementation logic of global optimization of road network-level driving tasks, design the physical scene of the intelligent connected vehicle vehicle-road-cloud integrated system, and design the specific implementation strategies and input and output parameters of cloud, road, and vehicle functions.

[0011] Step S1 specifically includes the following steps:

[0012] S11. Based on the spatiotemporal relationships of road network-level driving tasks, construct a vehicle-road-cloud integrated scale model, wherein the vehicle-road-cloud integrated scale includes the following scales:

[0013] Vehicle-road-cloud scale refers to the spatiotemporal scope composed of vehicle-road scale and cloud, enabling vehicle-road-cloud interaction and collaboration within the road network.

[0014] Vehicle-to-road scale refers to the spatiotemporal scope composed of vehicle-to-vehicle scale and road end scale, realizing the interaction and coordination between vehicles and roads within the road segment;

[0015] Vehicle-to-vehicle scale refers to the spatiotemporal range consisting of the vehicle body and the adjacent vehicles around the vehicle body, enabling interaction and collaboration between vehicles;

[0016] S12. Based on the integrated vehicle-road-cloud scale model, describe the following functions:

[0017] Cloud-road-network level functions describe the integrated vehicle-road-cloud functions of intelligent connected vehicles at the vehicle-road-cloud scale, and describe the top-level functions of cloud, road-cloud collaboration, and vehicle-cloud collaboration at the vehicle-road-cloud scale.

[0018] Road segment-level vehicle-road functions describe road end and vehicle-road cooperative functions at the vehicle-road scale.

[0019] Vehicle-to-vehicle functions are described in terms of vehicle-to-vehicle scale.

[0020] The interrelationships between functions at various scales are described, including the interaction and coordination between vehicle-road-cloud scale functions and vehicle-road scale functions, as well as vehicle-to-vehicle scale functions; the interaction and coordination between vehicle-road scale functions and vehicle-to-vehicle scale functions under cloud-based coordination and decision-making; and the interaction and coordination between vehicle-to-vehicle scale functions under roadside coordination and decision-making.

[0021] Step S2 specifically includes the following steps:

[0022] S21. Based on the implementation logic of global optimization of road network-level driving tasks, construct a vehicle-road-cloud integrated cross-domain model, including the following logical domains:

[0023] In the cloud, it is responsible for realizing road network-level cloud functions at the vehicle-road-cloud scale, and for information interaction between the integrated vehicle-road-cloud system and external systems;

[0024] At the roadside, it undertakes the implementation of road-cloud collaborative functions at the vehicle-road-cloud scale and the implementation of roadside functions at the vehicle-road scale;

[0025] On the vehicle side, it undertakes the implementation of road network-level vehicle-cloud collaborative functions at the vehicle-road-cloud scale, the implementation of vehicle-road collaborative functions at the vehicle-road scale, and the implementation of vehicle functions at the vehicle-to-vehicle scale.

[0026] S22. Based on the integrated vehicle-road-cloud cross-domain model, design the logical functions of the cloud, road, and vehicle, as well as the logical relationships and interfaces between these functions, including:

[0027] The cloud communicates with roadside and vehicle terminals, receiving data uploaded by them. This data includes location information, real-time traffic perception data, real-time vehicle operating status, real-time vehicle perception data, and / or alarm events. Based on the road network's relationships, the cloud performs vehicle-road-cloud fusion perception to form vehicle-road-cloud fusion information. This information includes traffic situation information, traffic decision information, traffic control information, early warning events, and / or vehicle driving decision information. The cloud also interacts with external systems, including meteorological systems, traffic management systems, emergency medical systems, map systems, and / or positioning systems. Based on the road network's tasks and the acquired information, the cloud makes collaborative decisions to obtain road network traffic situation, traffic decision information, and / or vehicle driving decision information, which is then distributed to the roadside and vehicle terminals.

[0028] The roadside unit communicates with the cloud, other roadside units, and vehicle terminals. It sends real-time dynamic traffic perception data of the road segment to the cloud, traffic situation information and / or traffic decision information of the road segment to surrounding roadside units, and road segment-level dynamic traffic perception data to vehicle terminals. It receives road network traffic situation and / or traffic decision information from the cloud, road segment traffic situation information and / or road segment traffic decision information from surrounding roadside units, and real-time operating status and perception data of vehicle terminals within the road segment. It achieves vehicle-road-cloud fusion perception according to the correlation of road segments, forming traffic situation information, traffic decision information, traffic control information, early warning events, and / or vehicle driving decision information of the road segment and then distributes them to the vehicle terminals.

[0029] The vehicle communicates with the cloud, roadside, and other vehicles, sending real-time operating status and / or perception data to the cloud, roadside, and other vehicles; it receives road network traffic conditions and / or vehicle driving decision information from the cloud, as well as vehicle driving decision information, road segment traffic perception information, and road segment traffic condition information from the roadside; it also receives real-time operating status and perception data from vehicles around the vehicle, achieving vehicle-road-cloud fusion perception based on the vehicle's location to form vehicle driving decision information.

[0030] In step S3, the physical scenario of the intelligent connected vehicle vehicle-road-cloud integrated system method is a layered model, wherein:

[0031] The cloud includes an integrated foundation layer, a domain-specific standard component layer, and a standardized hierarchical shared interface layer;

[0032] The roadside includes a roadside data processing layer, a roadside fusion perception and collaborative decision-making layer, and a roadside control layer;

[0033] The vehicle-side system includes a vehicle-side data processing layer, a vehicle-side fusion perception and collaborative decision-making layer, and a vehicle-side control layer.

[0034] The beneficial effects of this invention are as follows: The design method of the intelligent connected vehicle-road-cloud integrated system of this invention adopts a phased design approach. In the functional scenario design, functions are gradually decomposed according to the road network level, road segment level, and the scale around the vehicle body, highlighting the concept of network-enabled empowerment. It describes the functions of each scale from large scale to small scale, as well as the interaction and collaborative relationships between functions at each scale, ensuring accurate decomposition between large-scale and small-scale functions. The physical scenario design embodies two major technical features: layered decoupling and cross-domain sharing. It breaks through data barriers between different fields and regions, realizing the integration of basic data and cross-domain sharing of infrastructure, services, and platforms. It can provide methodological support and architectural guidance for the design, modeling, testing, and verification of vehicle-road-cloud integrated systems. Attached Figure Description

[0035] The accompanying drawings, which are included to provide a further understanding of the invention and form part of this invention, illustrate exemplary embodiments of the invention and are used to explain the invention, but do not constitute an undue limitation of the invention. In the drawings:

[0036] Figure 1 This is a flowchart illustrating the design method of an integrated vehicle-road-cloud information system for intelligent connected vehicles according to the present invention.

[0037] Figure 2 This is a schematic diagram illustrating the functional scenarios in this invention;

[0038] Figure 3 This is a schematic diagram illustrating the logical scenario in this invention;

[0039] Figure 4 This is a schematic diagram of the cloud-based physical model in this invention;

[0040] Figure 5 This is a schematic diagram of the physical model of the path end in this invention;

[0041] Figure 6 This is a schematic diagram of the vehicle-side physical model in this invention. Detailed Implementation

[0042] The specific embodiments of the present invention will now be described with reference to the accompanying drawings.

[0043] This embodiment uses a model-based systems engineering (MBSE) approach to model the integrated vehicle-road-cloud information system for intelligent connected vehicles, including the following steps:

[0044] S1. Functional Scenario Design. See also Figure 2 The vehicle-road-cloud integrated system needs to be able to describe the collaborative perception and decision-making of the road network-level vehicle-road-cloud system, enabling global optimization of the road network-level operation. The road network includes many road segments, and each road segment contains many vehicles with different performance characteristics and driving tasks. In other words, both the road network and road segments serve the vehicles. By optimizing vehicle operation, the road network traffic conditions and driving tasks are optimized. The characteristic of vehicle-road-cloud integration is highlighting the value of connectivity and intelligence at a large scale, achieving fusion perception and collaborative decision-making among multiple entities on a large scale. Therefore, this embodiment designs the functional scenarios of the intelligent connected vehicle-road-cloud integrated system based on the spatiotemporal relationships of road network-level driving tasks, describing the road network-level functions of the vehicle-road-cloud system, the road segment-level vehicle-road functions, and the vehicle-to-vehicle functions within the vehicle's body area, as well as the interrelationships between these functions at various scales. Specifically, it includes the following steps:

[0045] S11. Based on the spatiotemporal relationships of road network-level driving tasks, construct a vehicle-road-cloud integrated scale model, wherein the vehicle-road-cloud integrated scale includes the following scales:

[0046] Vehicle-road-cloud scale refers to the spatiotemporal scope composed of vehicle-road scale and cloud, enabling vehicle-to-vehicle interaction and collaboration, vehicle-road interaction and collaboration, and vehicle-road-cloud interaction and collaboration within the road network.

[0047] The vehicle-to-road scale is a spatiotemporal range consisting of vehicle-to-vehicle scale and road end, realizing the interaction and coordination between vehicles and roads within the road segment. Based on the actual situation, the length covered by each road end facility is about 500 meters.

[0048] The vehicle-to-vehicle scale is the spatiotemporal range consisting of the vehicle body and the adjacent vehicles around the vehicle body, realizing the interaction and cooperation between vehicles. Depending on the actual situation, this spatiotemporal range can be limited to within 20 meters of the vehicle body, within 1 second, and the number of adjacent vehicles can be limited to 4-8 vehicles.

[0049] S12. Based on the integrated vehicle-road-cloud scale model, describe the following functions:

[0050] Cloud-road-network level functions describe the integrated vehicle-road-cloud functions of intelligent connected vehicles at the vehicle-road-cloud scale, and describe the top-level functions of cloud, road-cloud collaboration, and vehicle-cloud collaboration at the vehicle-road-cloud scale.

[0051] Road segment-level vehicle-road functions describe road end and vehicle-road cooperative functions at the vehicle-road scale.

[0052] Vehicle-to-vehicle functions are described in terms of vehicle-to-vehicle scale.

[0053] The interrelationships between functions at various scales are described, including the interaction and coordination between vehicle-road-cloud scale functions and vehicle-road scale functions, as well as vehicle-to-vehicle scale functions; the interaction and coordination between vehicle-road scale functions and vehicle-to-vehicle scale functions are also described under cloud-based coordinated decision-making.

[0054] Based on the above functions, the implementation logic of the global optimization function for road network-level driving tasks at the vehicle-road-cloud scale can be described.

[0055] S2. Logical scenario design, see [link / reference] Figure 3 Based on the functional scenario design results and the implementation logic of global optimization of road network-level driving tasks, the cloud receives real-time operating status data and perception data from the roadside and vehicle-side, integrates perceived road network traffic conditions and traffic decision information, and vehicle driving decision information, and collaboratively makes operational suggestions for the roadside and vehicle-side within the road network. The roadside and vehicle-side, based on their own perception data and combined with real-time dynamic traffic perception data and operational suggestions issued by the cloud, collaboratively make specific operational setpoints for the roadside and vehicle-side and implement control. The logical scenario of the intelligent connected vehicle vehicle-road-cloud integrated system is designed, describing the functions of the cloud, roadside, and vehicle-side, as well as the logical relationships and interfaces between these functions; specifically, it includes the following steps:

[0056] S21. Based on the implementation logic of global optimization of road network-level driving tasks, construct a vehicle-road-cloud integrated cross-domain model, including the following logical domains:

[0057] In the cloud, it is responsible for realizing road network-level cloud functions at the vehicle-road-cloud scale, and for information interaction between the integrated vehicle-road-cloud system and external systems;

[0058] The roadside is responsible for realizing roadside functions at the road network level at the vehicle-road-cloud scale, and also for realizing roadside functions at the vehicle-road scale.

[0059] On the vehicle side, it undertakes the implementation of road network-level vehicle-side functions at the vehicle-road-cloud scale, the implementation of vehicle-side functions at the vehicle-road scale, and the implementation of vehicle functions at the vehicle-to-vehicle scale.

[0060] S22. Based on the integrated vehicle-road-cloud cross-domain model, design the logical functions of the cloud, road, and vehicle, as well as the logical relationships and interfaces between these functions, including:

[0061] The cloud communicates with roadside and vehicle terminals, receiving data uploaded by them. This data includes location information, real-time traffic perception data, real-time vehicle operating status, real-time vehicle perception data, and / or alarm events. Based on the road network's relationships, the cloud performs vehicle-road-cloud fusion perception to form vehicle-road-cloud fusion information. This information includes traffic situation information, traffic decision information, traffic control information, early warning events, and / or vehicle driving decision information. The cloud also interacts with external systems, including meteorological systems, traffic management systems, emergency medical systems, map systems, and / or positioning systems. Based on the road network's tasks and the acquired information, the cloud makes collaborative decisions to obtain road network traffic situation, traffic decision information, and / or vehicle driving decision information, which is then distributed to the roadside and vehicle terminals.

[0062] The roadside unit communicates with the cloud, other roadside units, and vehicle terminals. It sends real-time dynamic traffic perception data of the road segment to the cloud, traffic situation information and / or traffic decision information of the road segment to surrounding roadside units, and road segment-level dynamic traffic perception data to vehicle terminals. It receives road network traffic situation and / or traffic decision information from the cloud, road segment traffic situation information and / or road segment traffic decision information from surrounding roadside units, and real-time operating status and perception data of vehicle terminals within the road segment. It achieves vehicle-road-cloud fusion perception according to the correlation of road segments, forming traffic situation information, traffic decision information, traffic control information, early warning events, and / or vehicle driving decision information of the road segment and then distributes them to the vehicle terminals.

[0063] The vehicle communicates with the cloud, roadside, and other vehicles, sending real-time operating status and / or perception data to the cloud, roadside, and other vehicles; it receives road network traffic conditions and / or vehicle driving decision information from the cloud, as well as vehicle driving decision information, road segment traffic perception information, and road segment traffic condition information from the roadside; it also receives real-time operating status and perception data from vehicles around the vehicle, achieving vehicle-road-cloud fusion perception based on the vehicle's location to form vehicle driving decision information.

[0064] S3. Physical Scenario Design: Based on the results of the logical scenario design and the implementation logic of global optimization of road network-level driving tasks, design the physical scenario of the intelligent connected vehicle vehicle-road-cloud integrated system. Design the specific implementation strategies and input / output parameter values ​​for cloud, road, and vehicle-side functions, ultimately enabling global optimization decisions for driving tasks of various entities at the road network level. The physical scenario of the intelligent connected vehicle vehicle-road-cloud integrated system is a layered model, in which:

[0065] See Figure 4The cloud-based physical model comprises a cloud-integrated foundation layer, a cloud-domain standard components layer, and a cloud-standardized hierarchical sharing interface layer. The standardized hierarchical sharing interface layer connects to external systems related to traffic services, including meteorological systems, traffic management systems, emergency medical systems, map systems, and positioning systems. The cloud-domain standard components layer primarily acquires basic road network traffic information and multi-source data spatiotemporal synchronization from the cloud-integrated foundation layer, enabling cloud-based fusion perception and collaborative decision-making functions. It also transmits dynamic changes in road network traffic to the cloud-standardized hierarchical sharing interface layer and collaborative decision-making information to the cloud-integrated foundation layer. The fusion perception and collaborative decision-making functions mainly include road network traffic dynamic situation perception, road network vehicle dynamic situation perception, and road network traffic status perception. This allows for the acquisition of road network vehicle operating status, road network traffic warning events, traffic light timing parameter suggestions for road segments, speed limit suggestions for road segments, and vehicle speed suggestions within the road network. Furthermore, it provides event warning and alert services, including forward congestion alerts, beyond-line-of-sight and blind spot perception, red light violation warnings, and forward collision warnings. Collision warning and collision warning for vulnerable road users; the cloud-integrated base layer is mainly used to store road-end and vehicle-end data, cloud-sensing result data, and collaborative decision-making data. The road-end data includes basic road network information obtained from the road network layer, road network traffic facility layer, road network temporary operation layer, road network target layer, road network environment layer, and road network data communication layer. The basic road network information is part of the basic road network traffic information. The basic road network traffic information, including vehicle perception data, vehicle status data, vehicle positioning data, road-end perception data, and real-time traffic light phase information, undergoes spatiotemporal fusion processing to sense road network-level road hazard information, road network-level safety warning information, road network-level emergency events, road network-level traffic event information, and road network-level vehicle fault information. It also performs spatiotemporal synchronization of multi-source road segment data with the cloud domain standard component layer and obtains road segment collaborative decision-making information from the cloud domain standard component layer to provide road network-level safety event reminder services, collision warning reminder services, traffic dynamics, traffic control information, vehicle-road cooperative information, and beyond-line-of-sight and blind spot perception.

[0066] See Figure 5The roadside physical model includes a roadside data processing layer, a roadside fusion perception and collaborative decision-making layer, and a roadside control layer. The roadside data processing layer primarily stores roadside and vehicle-side data. This roadside data includes basic road information obtained from the road segment layer, road segment traffic facility layer, road segment temporary operation layer, road segment target layer, road segment environment layer, and road segment data communication layer. This basic road information is part of the basic traffic information for the road segment. The basic traffic information is fused spatiotemporally with previous road segment vehicle dynamic change data, road segment vehicle status data and perception data, vehicle positioning data, roadside perception data, real-time traffic light phase information, and cloud-based decision-making information. It also senses road segment hazard information, road segment safety warning information, road segment emergency events, road segment traffic incident information, and road segment vehicle fault information. Furthermore, it performs spatiotemporal synchronization of multi-source road segment data with the roadside fusion perception and collaborative decision-making layer and obtains road segment collaborative decision-making information from it. The system provides services such as road safety incident alerts, road collision warnings, road traffic dynamics, road traffic control information, road vehicle-road cooperative information, and beyond-line-of-sight and blind spot perception. The roadside fusion perception and collaborative decision-making layer primarily implements fusion perception and collaborative decision-making functions, including road traffic dynamics perception, road traffic status perception, and road vehicle dynamics perception. It obtains information such as road vehicle operating status, road traffic safety warnings, traffic light timings and vehicle speed suggestions, speed limit control, and road event perception. It provides event warning alerts and interacts with the road control layer to provide road vehicle-road cooperative decision-making information. The event warning alerts include forward congestion alerts, beyond-line-of-sight and blind spot perception, red light violation warnings, forward collision warnings, and collision warnings for vulnerable road users. The roadside control layer primarily controls road traffic facilities, including C-V2X (cellular...) Vehicle-to-everything (RSU) refers to roadside units (roadside units) such as cellular vehicle-to-everything (V2X) systems, traffic signals, traffic signs, and variable message signs.

[0067] See Figure 6The vehicle-side physical model includes a vehicle-side data processing layer, a vehicle-side fusion perception and collaborative decision-making layer, and a vehicle-side control layer. The vehicle-side data processing layer primarily stores vehicle-side data, including the vehicle's digital identity and multi-source data. This multi-source data includes dynamic change data of vehicles surrounding the vehicle, status data and perception data of vehicles surrounding the vehicle, basic road traffic information, vehicle positioning data, roadside collaborative decision-making information, and cloud-based collaborative decision-making information. This multi-source data undergoes spatiotemporal fusion processing to obtain road hazard information and safety warning information around the vehicle, and is synchronized with the vehicle-side fusion perception and collaborative decision-making layer. The vehicle-side data processing layer also receives collaborative decision-making information from the multi-source data and the vehicle-side fusion perception and collaborative decision-making layer to provide road safety event alert services. The system includes: a road segment collision warning and reminder service; road segment traffic dynamic situation; road segment traffic control information; road segment vehicle-road cooperative information; and road segment beyond-line-of-sight and blind spot perception. The vehicle-side fusion perception and collaborative decision-making layer is mainly used to realize the vehicle-side fusion perception function and collaborative decision-making function, including perception of the dynamic situation of traffic around the vehicle, perception of the traffic status around the vehicle, perception of traffic safety warning events within the vehicle's vicinity, vehicle-to-vehicle collaborative decision-making, vehicle trajectory planning, and road hazard warning events, including forward congestion warning, red light violation warning, forward collision warning, and collision warning for vulnerable road users. The vehicle-side control layer mainly includes vehicle-side control facilities to realize vehicle control, including lateral speed control, lateral acceleration control, longitudinal speed control, longitudinal acceleration control, and warning event response.

[0068] Those skilled in the art will understand that embodiments of the present invention can be provided as methods, systems, or computer program products. Therefore, the present invention can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention can take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage and optical storage) containing computer-usable program code.

[0069] Obviously, those skilled in the art can make various modifications and variations to this invention without departing from its spirit and scope. Therefore, if these modifications and variations fall within the scope of the claims of this invention and their equivalents, this invention also intends to include these modifications and variations.

Claims

1. A method for designing an intelligent vehicle-road cloud integrated information system, characterized in that, Comprising the following steps: S1. Functional scenario design, according to the space-time relationship of the road network level driving task, the functional scenario of the intelligent network connected automobile and the cloud integrated system is designed, the functions of the cloud, the road and the vehicle at the road network level, the road section level and the vehicle body area are described, and the mutual relationship between the functions at different scales is described; Specifically comprising the following steps: S11. According to the space-time relationship of the road network level driving task, the cloud integrated scale model is constructed, and the cloud integrated scale includes the following scales: Cloud scale, the space-time range composed of road scale and cloud, realizing the interaction and cooperation of cloud in the road network range; Road scale, the space-time range composed of vehicle scale and road end, realizing the interaction and cooperation of road in the road section range; Vehicle scale, the space-time range composed of self-vehicle body and adjacent vehicles around the self-vehicle body, realizing the interaction and cooperation between vehicles; S12. According to the cloud integrated scale model, the following functions are described: Cloud network level function, describing the intelligent network connected automobile and cloud integrated function at the cloud scale, describing the top-level function of cloud end, road cloud cooperation and vehicle cloud cooperation at the cloud scale; Road section level road function, describing road end and road cooperation function at road scale; Self-vehicle body area vehicle function, describing vehicle end function at vehicle scale; The mutual relationship between the functions at different scales, the interaction and cooperation of the cloud scale function, the road scale function and the vehicle scale function are described; The interaction and cooperation of the road scale function and the vehicle scale function under the coordination and decision of the cloud end are described; The interaction and cooperation of the vehicle scale function under the coordination and decision of the road end are described; S2. Logic scenario design, according to the functional scenario design result and the implementation logic of the global optimization of the road network level driving task, the logic scenario of the intelligent network connected automobile and cloud integrated system is designed, the functions of the cloud end, the road end and the vehicle end are described, and the logical relationship and interface between the functions are described; S3. Physical scenario design, according to the logic scenario design result and the implementation logic of the global optimization of the road network level driving task, the physical scenario of the intelligent network connected automobile and cloud integrated system is designed, the specific implementation strategy and input and output parameters of the cloud end, road end and vehicle end function are designed; The physical scenario is a hierarchical model, wherein: The cloud end includes cloud end integrated base layer, cloud end field standard part layer and cloud end standardized hierarchical sharing interface layer; The road end includes road end data processing layer, road end fusion perception and cooperative decision making layer, road end control layer; The vehicle end includes vehicle end data processing layer, vehicle end fusion perception and cooperative decision making layer, vehicle end control layer; The cloud end field standard part layer is used to obtain the road network traffic basic information and the space-time synchronization of multi-source data from the cloud end integrated base layer, realize the fusion perception function and cooperative decision making function of the cloud end, and transmit the road network traffic dynamic change information to the cloud end standardized hierarchical sharing interface layer, and transmit the cooperative decision making information to the cloud end integrated base layer.

2. The method of claim 1, wherein, Step S2 specifically comprises the following steps: S21. According to the implementation logic of the global optimization of the road network level driving task, the cloud integrated cross-domain model is constructed, including the following logical domains: The cloud end bears the realization of the road network level cloud end function of the cloud vehicle scale, and bears the information interaction function of the integrated cloud vehicle system and external systems. The road end bears the realization of the road network level road cloud cooperation function of the cloud vehicle scale, and bears the realization of the road end function of the cloud vehicle scale. The vehicle end bears the realization of the road network level vehicle cloud cooperation function of the cloud vehicle scale, bears the realization of the cloud vehicle scale, and bears the realization of the vehicle function of the vehicle scale. S22. According to the integrated cross-domain model of cloud vehicle, the logical functions of cloud end, road end and vehicle end are designed, as well as the logical relationship and interface between the above functions, wherein: The cloud end communicates with the road end and the vehicle end, receives the data uploaded by the road end and the vehicle end, the data uploaded by the road end and the vehicle end includes position information, real-time traffic perception data, real-time running state of vehicle, real-time perception data of vehicle and / or alarm event; according to the correlation of road network, the cloud vehicle fusion perception is formed, the cloud vehicle fusion information includes traffic situation information, traffic decision information, traffic control information, early warning event and / or vehicle driving decision information; the cloud end also interacts with external systems, the external systems include meteorological system, traffic control system, emergency system, map system and / or positioning system, and makes cooperative decision according to the task of road network and the obtained information, obtains road traffic situation, traffic decision information and / or vehicle driving decision information and sends them to the road end and the vehicle end; The road end communicates with the cloud end, other road ends and vehicle ends, sends real-time dynamic traffic perception data of road section to the cloud end, sends road section traffic situation information and / or road section traffic decision information to surrounding road ends, and sends road section level dynamic traffic perception data to vehicle ends; receives road network road traffic situation and / or traffic decision information from the cloud end, receives road section traffic situation information and / or road section traffic decision information from surrounding road ends, receives real-time running state and perception data of vehicle ends in the road section, realizes cloud vehicle fusion perception according to the correlation of road section, forms road section traffic situation information, traffic decision information, traffic control information, early warning event and / or vehicle driving decision information and sends them to vehicle ends; The vehicle end communicates with the cloud end, the road end and other vehicle ends, sends real-time running state and / or perception data of the vehicle end to the cloud end, sends real-time running state and / or perception data of the vehicle end to the road end, and sends real-time running state and / or perception data of the vehicle end to other vehicle ends; receives road network road traffic situation and / or vehicle driving decision information from the cloud end, receives vehicle driving decision information, road section traffic perception information and road section traffic situation information from the road end, receives real-time running state and perception data of vehicle ends around the vehicle, realizes cloud vehicle fusion perception according to the position of the vehicle end, and forms vehicle driving decision information.

Images