Snow disaster weather road identification auxiliary driving map processing method and system

Abstract

The invention relates to the technical field of driving, and discloses a snow disaster weather road identification auxiliary driving map processing method and system, through an SOA service architecture, the function level of an enterprise service bus is increased, and the overall service cannot work normally due to a certain problem due to an excessively huge function set. In addition, the normal data scale is still expressed by a map thematic map and symbolization in many multi-terminal accesses in a traditional mode, but a more intuitive and more friendly big data display mode for large-data-volume, multi-category and real-time data does not exist.

Description

Technical Field

[0001] The present invention relates to the field of driving technology, and in particular to a map processing method and system for road recognition-assisted driving in snowstorm weather. Background Art

[0002] With the comprehensive development of information technology, the public has put forward higher requirements for highway services. The traffic information service and highway management system based on the traditional "three major systems" of highway electromechanical and electrical can no longer meet the users' growing information needs. The purpose of building smart highways is to greatly improve the service and management capabilities of highway management departments through the application of new technologies, solve the pain points of the transportation industry, and accelerate the transformation to service-oriented highways.

[0003] In the era of spatial big data, traditional centralized relational databases are no longer able to efficiently store and manage terabytes or even petabytes of data. In particular, traditional databases are no longer able to efficiently process billions of data in a single table. Furthermore, traditional computing capabilities are generally based on a single-machine single process or a single-machine multi-process processing method, and are unable to apply multi-cluster parallel computing capabilities.

[0004] Therefore, we propose a map processing method and system for road recognition assisted driving in snowstorm weather to solve the above problems. Summary of the Invention

[0005] The purpose of the present invention is to provide a method and system for processing road recognition assisted driving maps in snowstorm weather to solve the problem raised in the above background technology that traditional centralized relational databases can no longer achieve efficient storage and management of TB or even PB-level data.

[0006] To achieve the above objectives, the present invention provides the following technical solution: a method for processing a map of road recognition-assisted driving in snowstorm weather, comprising the following steps: S1: Event judgment and confirmation assistance: Data center staff collect various information to judge and confirm emergency events. After confirming the event, they determine the event level and emergency scope. S2: Generate emergency response plan: After the incident is confirmed, the corresponding response plan is activated. The system should be able to identify the various levels of collaborative departments and their responsibilities, as well as the resources required for incident handling, based on the incident type and level, current road network traffic conditions, and weather conditions, and formulate an incident handling plan. The collaborative departments involved in incident handling will be notified to handle the incident. S3: Emergency Response Command: Based on the incident handling plan, the system can disseminate incident information to on-site information boards, websites, and media outlets through information releases, notifying incident handling collaboration departments to handle the incident and informing resource management departments to mobilize resources to assist in the handling of the incident. During the incident handling process, the system can maintain communication with all parties involved in the incident handling process, monitoring the progress and status of the incident handling through aggregated video, data, and audio communications. The incident handling plan can be adjusted in real time based on the incident handling situation. Emergency incident handling information can also be released in real time based on the progress and changes in the incident handling situation. After the incident is handled, emergency response and information release will be carried out to restore normal traffic conditions.

[0007] The present invention also discloses a snowstorm weather road recognition assisted driving map system, including a platform overall architecture, which includes a storage layer, a data layer, a data exchange layer and a presentation layer. The storage layer includes structured data, spatial data, and unstructured data. Structured data is reflected as spatial data, and unstructured data is reflected as file data and configuration file data. The data layer includes file data and map data. Map data is divided into two-dimensional data and three-dimensional data. File data includes map tile data and configuration file data. The data exchange layer includes HTTP(S) and Lserver network services, which are the link between the data layer and the presentation layer. The data in the data layer needs to be published through http or https services or Iserver network services for use by the presentation layer. The presentation layer includes H5 and Android, and the map data is presented through the H5 and Android platforms for users to view.

[0008] Preferably, it also includes route design, aerial photography, field image control measurement, provision of topographic map data, aerial triangulation, DEM production, DOM tilt model production and data compilation and submission; According to the scope of the shooting area, use the internationally advanced route planning software FPES; The track coordinates of aerial photography are obtained using GPS navigation.

[0009] Preferably, the requirements for vertical image overlap and oblique image overlap are as follows: The vertical image heading overlap should generally be designed to be 70% to 80%, and the heading overlap in other areas should generally not be less than 60%; The lateral overlap of vertical images should generally be designed to be 50% to 80%, with a minimum of no less than 30%; The heading overlap of oblique images shall not be less than 53%; The laser radar heading overlap should generally be designed to be 70% to 80%; The lateral overlap of the laser radar should generally be designed to be 40% to 45%.

[0010] Preferably, the tilt angle of the film is no greater than 5°, and the maximum does not exceed 12°, and the number of films with an inclination angle exceeding 8° does not exceed 10% of the total.

[0011] Preferably, the image rotation angle is not greater than 15°, and the individual maximum is not more than 30°. The number of photos with a rotation angle exceeding 20° on the same route shall not exceed 3, and the number of photos with a rotation angle exceeding 15° shall not exceed 10% of the total number of photos in the partition. The image inclination angle and the image rotation angle do not reach the maximum value at the same time.

[0012] Preferably, the image meets the requirements of 3cm resolution for producing the main urban area of ​​the city.

[0013] Preferably, the flight quality requirements are as follows: Image overlap: Image overlap meets the design requirements; Image tilt angle: vertical image tilt angle is not greater than 3°, and the maximum is not greater than 6°; Image rotation angle: The vertical image rotation angle shall not exceed 25°, and shall not exceed 35° at most, provided that the image heading and lateral overlap meet the requirements; Route curvature: The route curvature is generally not greater than 1%. When the route length is less than 5000m, the maximum route curvature is not greater than 3%; Altitude maintenance: The altitude difference between adjacent images on the same route shall not exceed 30m; the difference between the maximum altitude and the minimum altitude shall not exceed 50m. The difference between the actual altitude and the designed altitude shall not exceed 50m; Guarantee of coverage of shooting areas and zones: The coverage of shooting areas and zones should generally meet the design requirements; Filling in flight record information: At the end of each flight, the photographer shall fill in the aerial flight record form.

[0014] Preferably, image control points are selected on the acquired aerial images. The following principles should be followed when selecting image control points: Choose the intersection of linear features or corners of features. The intersection angle must be good. Arc-shaped and non-fixed features cannot be used as puncture point targets. Choose a location with little elevation change. Avoid narrow gullies, sharp hilltops, or slopes with sharp elevation changes. When choosing a building as a gizmo, try to choose a bungalow. The image of the image control point target must be clear and distinct. Point targets with images larger than 0.2mm or shadows cannot be used as point targets; Meet the requirements of GPS observation: the receiving equipment and operation are placed at the point, with a wide view to the sky, no large water surface around, and no major obstruction; Image control points should be placed in areas where there is a 6-degree overlap in the aerial photographs. Select image control points every other flight strip, and select one image control point every 8-12 baselines on the flight strip. There must be connecting points between flight strips.

[0015] Preferably, the three-dimensional real scene modeling mainly utilizes oblique photography photos and related parameters and field measurement photo control point data to perform aerial triangulation calculations, three-dimensional reconstruction, model modification, model output and other processes in the three-dimensional real scene modeling software to obtain three-dimensional real scene block model data and true orthogonal image block data.

[0016] Compared with the prior art, the present invention has the following beneficial effects: 1. In the era of spatial big data, traditional centralized relational databases are no longer able to efficiently store and manage terabytes or even petabytes of data. In particular, traditional databases are no longer able to efficiently process billions of data in a single table. Furthermore, traditional computing capabilities are generally based on a single-machine, single-process or single-machine, multi-process approach, and are unable to utilize multi-cluster parallel computing capabilities. Furthermore, the SOA service architecture increases the functionality of the enterprise service bus, but an overly large set of functions makes it more likely that the entire service will malfunction due to a specific issue. Furthermore, traditional multi-terminal access models often use thematic maps and symbolization to express normal data scales, but lack a more intuitive and user-friendly way to display large volumes of data, multiple categories, and real-time data.

[0017] 2. In the event of an incident or congestion on the road, the system provides users with active control information such as speed coordination, ramp control, queue warning, route guidance, lane speed limit, and available lane control, reminding users to control their speed and use road resources reasonably, reduce the speed difference between vehicles on the road, and remind users to avoid entering congested areas or causing secondary accidents. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] Figure 1 This is a block diagram of the image control measurement system of the present invention; Figure 2 This is a flow chart of the three-dimensional real scene modeling production process of the present invention. DETAILED DESCRIPTION

[0019] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0020] Example 1: Please refer to Figure 1-Figure 2A map processing method for road recognition assisted driving in snowstorm weather is proposed. When an emergency occurs, the emergency level is divided according to the type of emergency, the authority and responsibilities of each unit for handling various emergency events are clarified, and a corresponding emergency handling process is formulated, including the following three steps: S1: Event judgment and confirmation assistance: Data center staff collect various information to judge and confirm emergency events. After confirming the event, they determine the event level and emergency scope. S2: Generate emergency response plan: After the incident is confirmed, the corresponding response plan is activated. The system should be able to identify the various levels of collaborative departments and their responsibilities, as well as the resources required for incident handling, based on the incident type and level, current road network traffic conditions, and weather conditions, and formulate an incident handling plan. The collaborative departments involved in incident handling will be notified to handle the incident. S3: Emergency response command: According to the incident handling plan, the system can release incident information to information boards, websites, media, etc. on the road through information release, notify the incident handling cooperation department to handle the incident, and notify the resource management department to call resources to assist in incident handling. During the incident handling process, the system can maintain communication with all parties involved in the incident handling, understand the progress and status of the incident handling through collected video, data, audio communications, etc., adjust the incident handling plan in real time according to the incident handling situation, and release emergency incident handling information in real time according to the progress and changes of the incident handling. After the incident is handled, emergency handling and information release will be carried out to restore normal traffic conditions.

[0021] The snowstorm weather road recognition assisted driving map system monitors the road traffic status, weather status and emergency resources in real time, realizes accurate monitoring and detection of road traffic situation, emergency resource status, environmental conditions, weather conditions and various events, realizes detection, prediction and early warning of road traffic situation / environmental operating conditions, realizes monitoring and management of road emergency resources, and early warning of various traffic and meteorological events.

[0022] The snowstorm weather road recognition assisted driving map system provides the following services: (1) Visual monitoring and early warning of asset status: Dynamically collect slope structure detection data, tunnel structure detection data, vehicle resources, electromechanical equipment status, electromechanical equipment energy consumption data, etc., and realize real-time visual display of the above data; dynamically analyze the above data and issue early warnings for abnormal situations; judge whether the above data reaches the early warning level based on the threshold value, and if it reaches the early warning level, alarm will be issued, and relevant management personnel will be notified through information technology and realize real-time visual display; monitor the safety status of slope and tunnel structures; collect and analyze the historical status data of the above monitoring facilities, and display them to users in various expression means such as charts, bar charts, and line charts; Full life cycle management of electromechanical facilities: Record management of the entire process of electromechanical equipment from procurement, warehousing, delivery, installation, maintenance, repair, and scrapping, providing query, retrieval, and statistics of the equipment's full life cycle; Record, classify, and count mechanical and electrical equipment failures, verify the causes of failures, clarify and record them, analyze historical equipment failure data, query historical equipment failure patterns, and provide equipment maintenance recommendations. Use economic methods to conduct quantitative economic analysis of equipment assets. Asset security analysis automatically identifies network equipment assets, conducts statistics and full-flow scanning on them, analyzes security vulnerabilities and network threats in the network, and provides security recommendations to users. Provides daily minor maintenance management tools, records various data during the minor repair process, and provides data support for the planning of "maintenance major and medium repairs", including on-site maintenance inspections, maintenance tasks, statistical analysis and other functions.

[0023] (2) Provide users with more accurate information services, including pre-trip and in-drive information services. Pre-trip information services: The public can obtain information related to highway network infrastructure, traffic conditions and weather conditions, construction information, traffic control information, traffic guidance information, traffic incident information, progress of traffic incident handling, public safety protection measures, etc. through the Internet, radio, telephone, text messages, television, public accounts, navigation software, etc., and can realize travel planning services by visiting the website; In-drive information services: The public can obtain information related to highway network infrastructure, traffic conditions and weather conditions, construction information, traffic control information, traffic guidance information, traffic incident information, progress of traffic incident handling, public safety protection measures, etc. through the Internet, radio, telephone, text messages, public accounts, highway electronic information screen navigation software, etc., and can obtain FM radio services in tunnels, high-precision positioning services for ordinary sections of vehicle-road cooperative test sections, and positioning services in tunnels; provide driving guidance services to users within foggy areas; (3) Parking guidance, smart public toilets, online food ordering, traffic information, weather forecasts and other services are available in the service area; and managers can be provided with analysis and evaluation of pedestrian and vehicle flows, passenger stay time, restaurant retail optimization, and facility optimization.

[0024] (4) In the event of an incident or congestion on the road, the system provides users with active control information such as speed coordination, ramp control, queue warning, route guidance, lane speed limit, and available lane control, reminding users to control speed and use road resources reasonably, reducing the speed difference between vehicles on the road, reminding users to avoid entering congested areas or causing secondary accidents, and providing users with driving safety advice under adverse weather conditions to ensure their driving safety.

[0025] (5) Provide an open testing environment for vehicle-road collaboration pilot projects, conduct pilot tests in various scenarios, conduct vehicle network safety tests, and lay the data and testing foundation for the Ministry to establish standards.

[0026] The present invention also discloses a snowstorm weather road recognition assisted driving map system, including a platform overall architecture, which includes a storage layer, a data layer, a data exchange layer and a presentation layer; The storage layer supports the data layer, is the foundation of the entire system, and is the source of data. The data layer includes structured data and unstructured data. Structured data is reflected as spatial data, and unstructured data is reflected as file data and configuration file data. The data layer is the data generated by further processing the data in the storage layer, including file data and map data. Map data is divided into two-dimensional data and three-dimensional data. File data includes map image data and configuration file data. The data layer is the basis for map display; The data exchange layer is the link between the data layer and the presentation layer. Data from the data layer needs to be published via http or https services or Iserver network services for use by the presentation layer. The presentation layer cannot use data that is not published through the data exchange layer. The presentation layer presents map data through H5 and Android platforms for users to view. This system is embodied in two-dimensional maps, three-dimensional maps, and map functions.

[0027] The overall route of the snowstorm weather road recognition assisted driving map system is: (1) Based on data standards, we will carry out the aggregation, integration, fusion and application of spatiotemporal big data. Based on geographic entity coding, we will load other natural and social information to achieve the aggregation and integration of spatiotemporal big data. The transportation engineering and facilities along the project will be constructed in accordance with the Class A standard of the Highway Engineering Technical Standard. (2) Adopting the integrated means of space, air and land to enrich and update geospatial data. Based on geospatial data, national geographic surveys and spatial planning results, we will use drones to obtain high-resolution images, oblique photogrammetry to quickly obtain three-dimensional models, satellite data to obtain real-time locations and other technical means to establish a data collection and update system, further expand the data content of geospatial data, and carry out dynamic data updates to ensure the freshness and accuracy of geographic information. At the same time, we will gather basic database resources such as natural persons and legal persons, as well as government information resources of various government departments, and build a three-in-one spatiotemporal data model based on "time, space and attributes" to achieve integration with geospatial data, and build a spatiotemporal big data resource system for the entire county. (3) Research on technologies such as ETL of multi-source heterogeneous data and intelligent address matching to realize intelligent and automated fusion processing of multi-source heterogeneous spatiotemporal big data. By studying multi-source heterogeneous data ETL technology based on business models and job management, and intelligent address matching technology based on spatial semantic constraints, a spatiotemporal data fusion technology system integrating data processing, management, and spatialization will be constructed to realize intelligent and automated fusion of spatiotemporal big data.

[0028] The key technical routes of the snowstorm weather road recognition assisted driving map system are: (1) Based on distributed spatiotemporal big data storage technology, in the era of spatial big data, traditional centralized relational databases can no longer achieve efficient storage and management of TB or even PB-level data, especially traditional databases can no longer achieve efficient processing of single-table data at the billion level. At the same time, traditional computing capabilities are generally single-machine single-process or single-machine multi-process processing methods, and cannot apply the ability of multi-cluster parallel computing. Moreover, through the SOA service architecture, the functional level of the enterprise service bus has been increased. An overly large set of functions is also more likely to cause the entire service to malfunction due to a certain problem. In addition, many multi-terminal accesses in the traditional model still use map thematic maps and symbolization to express normal data scale, but there is no more intuitive and friendly big data display method for large amounts of data, multiple categories, and real-time data; In the era of spatial big data, the technical architecture has also evolved from the traditional model to the distributed technical architecture. Distributed technical architecture can be simply understood as solving the two most core technical needs of storage and computing through a cluster parallel working mode. Specifically, it is to improve the storage capacity of massive spatiotemporal data through the support of distributed file systems and distributed databases; to expand the distributed computing capabilities of traditional spatial analysis operators by integrating the industry's mainstream distributed computing frameworks; at the same time, the SOA service architecture will continue to achieve functional decoupling, and services will be split through a more streamlined microservice architecture, and accurate resource matching of microservices and external calls to RESTAPI will be achieved; in addition, the ability to visualize big data will be strengthened and expanded at each terminal; The technical solution for improving the capacity of massive spatiotemporal data based on big data technology mainly provides traditional GIS users with large-scale data storage and computing capabilities. Especially in traditional strong GIS industries such as land, surveying and mapping, the amount of user data continues to increase dramatically with the accumulation of time, but the relevant data types and spatial analysis operators remain basically unchanged.

[0029] (2) Address intelligent matching engine based on spatial semantic constraints. The place name knowledge graph factory is the basis of the place name address coding engine. It pre-processes and analyzes the basic spatial data of place names, effectively explores the spatial distribution pattern of place name relevance, and forms a fuzzy place name database with place name semantic entities as the basic organizational unit. The place name records in the fuzzy place name database correspond to the spatial distribution probability field of place name relevance. On the basis of place name semantic entities, the address database is organized and standardized. During the pre-processing of the spatial data set, the spatial data will be classified according to the data semantics, including administrative division data, place name data, traffic data and point of interest data. When the spatial data is stored in the database, various types of data will be weighted according to the sample data semantic weight model. The weight value will directly affect the place name relevance probability field result; (3) Lightweight engine rendering technology based on vector maps uses high-definition vector map rendering technology to quickly provide map mapping and ensure high map clarity. By using dynamic interactive technology, the color, transparency, texture, etc. of map elements can be changed at will, and buildings can be automatically and quickly constructed in three dimensions, achieving all-round, multi-angle, and multi-level panoramic browsing.

[0030] (4) Support high-performance rendering technology for oblique photography and three-dimensional models. New technologies such as laser point cloud and oblique photogrammetry have been applied in many fields and provide fast and reliable basic data for three-dimensional modeling, making large-scale three-dimensional construction and application cheaper and more convenient. At the same time, it also puts forward higher requirements for the performance of three-dimensional construction and application. This project will use technologies such as three-dimensional data storage, three-dimensional rendering, three-dimensional analysis, and oblique photography data query and analysis to improve the performance of three-dimensional construction and application.

[0031] A snowstorm weather road recognition assisted driving map system also includes route design, aerial photography, field image control measurement, provision of topographic map data, aerial triangulation, DEM production, DOM tilt model production and data compilation and submission; According to the scope of the shooting area, the internationally advanced route design software FPES is used. This software can fully consider factors such as flight strip length, sun angle, light intensity and ground elevation changes to automatically design routes. At the same time, in order to avoid the lack of lateral overlap caused by high mountains, this shooting area also combines SRTM 90-meter DTM data for route design.

[0032] The following are the flight requirements: (1) The track coordinates of aerial photography are based on GPS navigation; (2) Requirements for vertical image overlap and oblique image overlap are as follows: The vertical image heading overlap should generally be designed to be 70% to 80%, and the heading overlap in other areas should generally not be less than 60%; The lateral overlap of vertical images should generally be designed to be 50% to 80%, with a minimum of no less than 30%; The heading overlap of oblique images shall not be less than 53%; The laser radar heading overlap should generally be designed to be 70% to 80%; The lateral overlap of the laser radar should generally be designed to be 40% to 45%; (3) The image inclination angle is not greater than 5°, the maximum is not more than 12°, and the number of images with an inclination angle exceeding 8° is not more than 10% of the total; (4) The image rotation angle shall not exceed 15°, and the individual maximum shall not exceed 30°. The number of images with a rotation angle exceeding 20° on the same route shall not exceed 3. The number of images with a rotation angle exceeding 15° shall not exceed 10% of the total number of images in the subarea. The image inclination angle and the image rotation angle shall not reach their maximum values ​​at the same time. (5) The image should meet the requirements of 3cm resolution for the main urban area of ​​the city; (6) Flight quality requirements are as follows: Image overlap: Image overlap meets the design requirements; Image tilt angle: vertical image tilt angle is not greater than 3°, and the maximum is not greater than 6°; Image rotation angle: The vertical image rotation angle shall not exceed 25°, and shall not exceed 35° at most, provided that the image heading and lateral overlap meet the requirements; Route curvature: The route curvature is generally not greater than 1%. When the route length is less than 5000m, the maximum route curvature is not greater than 3%; Altitude maintenance: The altitude difference between adjacent images on the same route shall not exceed 30m; the difference between the maximum altitude and the minimum altitude shall not exceed 50m. The difference between the actual altitude and the designed altitude shall not exceed 50m; Guarantee of coverage of shooting areas and zones: The coverage of shooting areas and zones should generally meet the design requirements; Filling in flight record information: At the end of each flight, the photographer shall fill in the aerial flight record form.

[0033] Select image control points on the acquired aerial image. Follow the following principles when selecting image control points: (1) Choose the intersection of linear features or corners of features. The intersection angle must be good. Arc-shaped and non-fixed features cannot be used as puncture point targets. (2) Choose a place with little elevation change. Avoid choosing a narrow gully, a sharp hilltop, or a slope with a sharp elevation change. When choosing a house as a control point, try to choose a bungalow. (3) The image of the image control point target must be clear and distinct. Point targets with an image larger than 0.2 mm or shadows cannot be used as point targets; (4) Meet the requirements of GPS observation: the receiving equipment and operation are placed at the point, the view to the sky is wide, there is no large water surface around, and there is no major obstruction; (5) GCPs should be placed in areas where the aerial photos overlap by 6 degrees. (6) Select image control points every other flight strip. Select an image control point every 8-12 baselines on the flight strip. There must be connection points between flight strips.

[0034] 3D real scene modeling mainly uses oblique photography and related parameters and field measurement photo control point data to perform aerial triangulation calculations, 3D reconstruction, model modification, model output and other processes in 3D real scene modeling software to obtain 3D real scene block model data and true orthogonal image block data. The 3D real scene modeling production process is as follows: (1) Data preparation: Collect oblique camera photo data and related information, camera data, and image control point results; (2) Aerial triangulation: The purpose of aerial triangulation calculation for oblique camera measurement is to automatically estimate the attribute information of each camera and the position and angular element information of each photo, accurately grasp the attributes of each input image group (the photos obtained by each camera constitute a separate image group) and the pose information of each photo, and use it to perform three-dimensional reconstruction; (3) 3D reconstruction: 3D reconstruction of the data that has completed aerial triangulation calculation to generate a 3D model; (4) Model modification: The 3D real scene model output by 3D reconstruction may include structural defects, such as invisible areas, reflective surfaces, water surfaces, planes without feature information, etc., and the reference 3D model needs to be modified as a modified model for 3D reconstruction to replace the model automatically generated by the software; (5) Point cloud modification; (6) Model output: For 3D models that have been reconstructed and do not need to be modified, they can be output as data in different formats according to the needs of subsequent production steps.

[0035] Among them, the data interaction between the OBU end and the APP end is uniformly filled in JSON format and then compressed into unformatted data transmission; The OBU periodically reports vehicle parameter information to the APP for display, with a period of 200ms; The OBU periodically reports the location information of the vehicle and surrounding vehicles detected by the radar to the APP for display. The period is 500ms. By default, the first object of the JSON data is the vehicle data, followed by other remote vehicle data. If no remote vehicle is detected, only the vehicle data is reported. After receiving the vehicle location information, the APP returns the lane number of the vehicle and the up and down directions of the vehicle to the OBU; When the OBU triggers a V2V scene, it reports the scene message to the APP for display. There may be zero or more scenes triggered at the same time. When "scene": {} is sent, it means the V2V scene ends. When a service area event occurs, stop broadcasting the service area information; when the service area event ends, continue broadcasting the service area information; When there is a policy broadcast, the lane information in the policy message is displayed; when the policy broadcast stops, the lane status is displayed.

[0036] The contents not described in detail in this specification belong to the prior art known to those skilled in the art.

[0037] Although the present invention has been described in detail with reference to the aforementioned embodiments, it is still possible for those skilled in the art to modify the technical solutions described in the aforementioned embodiments, or to make equivalent substitutions for some of the technical features therein. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the scope of protection of the present invention.

Claims

1. A method for processing maps for road recognition assisted driving in snowstorm weather, characterized in that: The following steps are involved: S1: Event judgment and confirmation assistance: Data center staff collect various information to judge and confirm emergency events. After confirming the event, they determine the event level and emergency scope. S2: Generate emergency response plan: After the incident is confirmed, the corresponding response plan will be activated. The system should define the collaborative departments at all levels and their responsibilities, as well as the resources required for incident handling, based on the incident type and level, current road network traffic conditions, and weather conditions, and formulate an incident handling plan. S3: Emergency response command: According to the incident handling plan, the system will release the incident information to road information boards, websites, media, etc. through information release, notify the incident handling cooperation department to handle the incident, and notify the resource management department to call resources to assist in incident handling. During the incident handling process, the system will maintain communication with all parties involved in the incident handling, and understand the progress and status of the incident handling through collected video, data, audio communications, etc., adjust the incident handling plan in real time according to the incident handling situation, and release emergency incident handling information in real time according to the progress and changes of the incident handling. After the incident is handled, the incident emergency handling information will be released to restore normal traffic conditions.

2. The snowstorm weather road recognition assisted driving map system according to claim 1 comprises a platform overall architecture, which includes a storage layer, a data layer, a data exchange layer and a presentation layer. It is characterized by: The storage layer includes structured data, spatial data, and unstructured data. Structured data is reflected as spatial data, and unstructured data is reflected as file data and configuration file data. The data layer includes file data and map data. Map data is divided into two-dimensional data and three-dimensional data. File data includes map tile data and configuration file data. The data exchange layer includes HTTP(S) and Lserver network services, which are the link between the data layer and the presentation layer. The data in the data layer needs to be published through http or https services or Iserver network services for use by the presentation layer. The presentation layer includes H5 and Android, and the map data is presented through the H5 and Android platforms for users to view.

3. The snowstorm weather road recognition assisted driving map system according to claim 2 further includes route planning, aerial photography, field image control measurement, provision of topographic map data, aerial triangulation, DEM production, DOM tilt model production and data compilation and submission; in, According to the scope of the shooting area, use the internationally advanced route planning software FPES; The track coordinates of aerial photography are obtained using GPS navigation.

4. The snowstorm weather road recognition assisted driving map system according to claim 2, characterized in that: The requirements for vertical image overlap and oblique image overlap are as follows: The vertical image heading overlap should generally be designed to be 70% to 80%, and the heading overlap in other areas should generally not be less than 60%; The lateral overlap of vertical images should generally be designed to be 50% to 80%, with a minimum of no less than 30%; The heading overlap of oblique images shall not be less than 53%; The laser radar heading overlap should generally be designed to be 70% to 80%; The lateral overlap of the laser radar should generally be designed to be 40% to 45%.

5. The snowstorm weather road recognition assisted driving map system according to claim 4, characterized in that: The inclination angle of the film shall not exceed 5°, and the maximum shall not exceed 12°. The number of films with an inclination exceeding 8° shall not exceed 10% of the total.

6. The snowstorm weather road recognition assisted driving map system according to claim 5, characterized in that: The image rotation angle shall not exceed 15°, and the individual maximum shall not exceed 30°. The number of images with a rotation angle exceeding 20° on the same route shall not exceed 3. The number of images with a rotation angle exceeding 15° shall not exceed 10% of the total number of images in the partition. The image inclination angle and the image rotation angle shall not reach the maximum value at the same time.

7. The snowstorm weather road recognition assisted driving map system according to claim 6, characterized in that: The images meet the requirements of producing 3cm resolution in the main urban area of ​​the city.

8. The snowstorm weather road recognition assisted driving map system according to claim 7, characterized in that: The flight quality requirements are as follows: Image overlap: Image overlap meets the design requirements; Image tilt angle: vertical image tilt angle is not greater than 3°, and the maximum is not greater than 6°; Image rotation angle: The vertical image rotation angle shall not exceed 25°, and shall not exceed 35° at most, provided that the image heading and lateral overlap meet the requirements; Route curvature: The route curvature is generally not greater than 1%. When the route length is less than 5000m, the maximum route curvature is not greater than 3%; Altitude maintenance: The altitude difference between adjacent images on the same route shall not exceed 30m; the difference between the maximum altitude and the minimum altitude shall not exceed 50m. The difference between the actual altitude and the designed altitude shall not exceed 50m; Guarantee of coverage of shooting areas and zones: Coverage of shooting areas and zones meets the design requirements; Filling in flight record information: At the end of each flight, the photographer shall fill in the aerial flight record form.

9. The snowstorm weather road recognition assisted driving map system according to claim 8, characterized in that: Select image control points on the acquired aerial image. Follow the following principles when selecting image control points: The intersection of linear features or corners should be selected. The intersection angle must be good. Arc-shaped and non-fixed features cannot be used as puncture point targets. Choose a location with little elevation change, not a narrow gully, a sharp hilltop, or a slope with a sharp elevation change. When choosing a house as a gizmo, choose a bungalow. The image of the image control point target must be clear and distinct. Point targets with images larger than 0.2mm or shadows cannot be used as point targets; Meet the requirements of GPS observation: the receiving equipment and operation are placed at the point, with a wide view to the sky, no large water surface around, and no major obstruction; The image control points are placed at the locations where the aerial photos overlap by 6 degrees; Select image control points every other flight strip, and select one image control point every 8-12 baselines on the flight strip. There must be connecting points between flight strips.

10. The snowstorm weather road recognition assisted driving map system according to claim 8, characterized in that: Three-dimensional real-scene modeling mainly uses oblique photography photos and related parameters and field measurement photo control point data to perform aerial triangulation calculations, three-dimensional reconstruction, model modification, model output and other processes in the three-dimensional real-scene modeling software to obtain three-dimensional real-scene block model data and true orthogonal image block data.

Images