CN107229690A - Dynamic High-accuracy map datum processing system and method based on trackside sensor - Google Patents
Dynamic High-accuracy map datum processing system and method based on trackside sensor Download PDFInfo
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Abstract
The invention discloses a kind of Dynamic High-accuracy map datum processing system and method based on trackside sensor, the system includes：Map data collecting end, magnanimity, the map road initial data of diversification are provided for high-precision map generation service end；High-precision map generation service end includes：Map datum processing module, map road initial data is subjected to road feature extraction, hierarchical design is used to all roadway characteristics, same road information is spliced, first roadway characteristic data fusion is completed in roadway characteristic aspect, last to complete image mosaic in image aspect, the result of splicing generates the downward projection figure of road；Map generates visualization model, marks road information on downward projection figure, the road information and downward projection figure that have marked collectively constitute the map datum of high-precision map, and map datum is carried out into visual edit, generates Dynamic High-accuracy map.The present invention can reduce accurately map generalization cost；Realize the quick renewal of Dynamic High-accuracy map.
Description
Technical field
The present invention relates to field of computer technology, and in particular to a kind of Dynamic High-accuracy map number based on trackside sensor
According to processing system and method.
Background technology
Currently, with the fast development of automatic Pilot technology, high-precision map importance is increasingly highlighted, it has also become realize nothing
People drives and the indispensable important ring of intelligent transportation.Existing navigation map precision is not general high, and made with entire road
Road information data are provided for object or navigation instruction issue is carried out, this navigation map is referred to as road grade map, are pair
Actual traffic environment significantly simplifies, and the information content that can be provided is few, and accuracy is low, relatively low to the supporting capacity of driver.
Map required for automatic Pilot will not only possess high accuracy, also possess largely abundant road periphery details,
General map navigation accuracy can only achieve a meter magnitude, and high-precision map can be accurate to 10cm ranks, not only increase track attribute
Related data, also add the multiple types of data such as overhead object, guard rail, barrier, road edge type, roadside terrestrial reference.
The massive map data of polynary isomery needs to take a large amount of memory spaces, and the high-precision map of free hand drawing layer can not meet real-time update
Demand.
The technologies such as deep learning, image recognition can significantly lift map datum in the application in high-precision map field
Collection and treatment effeciency.Automatic Pilot technology and the continuous lifting of user's request, data capacity, accurate journey to high-precision map
Degree, renewal frequency etc. propose higher requirement, and traditional map data collecting draws mode and there is many technical bottlenecks, utilizes
The artificial intelligence technologys such as image recognition, big data processing, deep learning, being capable of automatic identification traffic sign, surface mark, track
Line, signal lamp etc., realize that road and POI are extracted in panoramic picture automation, improve data mart modeling efficiency and update the frequency, protect
Demonstrate,prove the accuracy of data.
The production of high accuracy map resurveys all road informations by the staff of specialty mostly at present, and plans
Periodically most of region is updated again after the completion of collection.The collecting device of this method is often to be mounted with laser radar
Deng the collecting vehicle of special equipment.Joint Japan of the Japanese car manufacturer such as Mitsubishi and Toyota figure business Zenrin is making the dynamic of three-dimensional
State map.It plans to be to use the special purpose vehicle equipped with high end sensor to carry out side to road to paint, and the first step is covering Japan 300
The major highways of kilometer.Here, TomTom and Google also make three-dimensional map using similar fashion.Domestic tradition figure business
High moral meets required 10cm dimension accuracies by way of assembling 2 laser radars and 4 cameras.Tengxun, Baidu,
The companies such as NavInfo are also making high-precision map with similar mode.
The above-mentioned original map information accuracy with special onboard sensor collection is very high, but there is problems with：
1), mobile unit cost is remained high, and precision of information height is gathered using laser radar, of overall importance good, but cost is high
High, data volume is big, and generation image is albedo image, is had differences with real-world scene；
2), data-handling efficiency is relatively low, and map data collecting is long to the cycle for realizing map rejuvenation, it may appear that in map more
The phenomenon that actual road conditions characteristic attribute state changes already when new, it is impossible to the behavioral characteristics letter of timely and effective reaction real road
Breath, hinders the fast development of location-based service, reduces unpiloted safety and reliability；
3) data, collected are dense point clouds, and packing density is very big, the substantial amounts of computing resource of consumption, and the later stage
The figure traffic is high；
4), the link characteristic information content of collection is limited, and specific sensor is needed for some specific roadway characteristics
(the related behavioral characteristics data of such as humiture, road ponding weather) data acquisition, therefore vehicle-mounted acquisition mode can not
Automatic Pilot is met to the demand in terms of high-precision map content.
Therefore, how the high-precision map of low cost, efficiently and accurately production or renewal is urgent problem to be solved.
The content of the invention
In view of this, in order to solve, map acquisition mode data volume in the prior art is big, processing is difficult, cost is high, map
The problem of update cycle is long, the present invention proposes a kind of based on the Dynamic High-accuracy map datum processing system of trackside sensor and side
Method, realizes that high-precision map is generated by trackside sensor and AI technologies, and cost is low, generate result energy effective Feedback road in real time
The current state of feature, accurate driving assistance information is provided for automatic Pilot.
The present invention is solved the above problems by following technological means：
A kind of Dynamic High-accuracy map datum processing system based on trackside sensor, including：
Map data collecting end, magnanimity, the map road original of diversification are provided for generating service end for high-precision map
Beginning data；
High-precision map generates service end, the map road of magnanimity, diversification for the offer of data acquisition end according to the map
Road Raw Data Generation Dynamic High-accuracy map；
The high-precision map generation service end includes：
Map datum processing module, for map road initial data to be carried out into road feature extraction, to all roads
Feature uses hierarchical design, and same road information is spliced, and first completes roadway characteristic data fusion in roadway characteristic aspect,
Last to complete image mosaic in image aspect, the result of splicing generates the downward projection figure of road；
Map generate visualization model, for marking road information on downward projection figure, road information mark with
Downward projection figure collectively constitutes the map datum of high-precision map, and map datum is carried out into visual edit, and generation high accuracy is dynamic
State map.
Further, the map data collecting end includes：
Image data acquiring module, the original road image data of map for gathering magnanimity；
Road condition data acquisition module, the original road conditions data of map for gathering magnanimity.
Further, the map datum processing module includes：
Pre-processing image data unit, becomes for the original road image data of map to be carried out into image rectification, image coordinate
Change, the pretreatment of image projection transformation, after pretreatment adjacent position image data acquiring module collection image overlay region energy
Enough alignment；
Road feature extraction unit, for recognizing road behavioral characteristics from the original road image data of map；According to existing
There is navigation map to carry out the feature modeling that becomes more meticulous, obtain road static nature；Number is done by primitive beginning road conditions data over the ground
According to screening verification, corresponding road semi-static nature and the behavioral characteristics of road half are extracted；
Roadway characteristic design cell, for carrying out hierarchical design to all roadway characteristics from content, first layer is road
Static nature, the second layer is road semi-static nature, and third layer is the behavioral characteristics of road half, and the 4th layer is road behavioral characteristics；
Multidimensional road information anastomosing and splicing unit, for same road information to be spliced, first in roadway characteristic aspect
Roadway characteristic data fusion is completed, pretreated image is finally completed into image mosaic, the result life of splicing in image aspect
Into the downward projection figure of road.
Further, the road feature extraction unit includes：
Behavioral characteristics extract subelement, for setting up a depth using deep learning, the related AI technologies of image recognition
Learn road behavioral characteristics identification model, using deep learning road behavioral characteristics identification model from the original road image number of map
According to middle identification road behavioral characteristics；
Static nature extracts subelement, for carrying out the feature modeling that becomes more meticulous according to existing navigation map, obtains road quiet
State feature；
Semi-static nature extracts subelement, for doing data screening checking by primitive beginning road conditions data over the ground, takes out
Take corresponding road semi-static nature；
Half behavioral characteristics extract subelement, for doing data screening checking by primitive beginning road conditions data over the ground, take out
Take the corresponding behavioral characteristics of road half.
Further, the multidimensional road information anastomosing and splicing unit includes：
Roadway characteristic anastomosing and splicing subelement, for all roadway characteristics of extraction to be spliced according to Dividing Characteristics,
First splice static nature, finally merge behavioral characteristics；
Image mosaic subelement, for pretreated image to be carried out into anastomosing and splicing into a basic downward projection
Figure.
Further, the map generation visualization model includes：
Geography information mark unit, for marking road information on downward projection figure, road information mark with bow
The map datum of high-precision map is collectively constituted depending on perspective view；
Geography information calibration verification unit, for carrying out calibration verification to the road information marked；
Map generation unit, for map datum to be carried out into visual edit, generates Dynamic High-accuracy map.
Further, described image data acquisition module is camera, and the road condition data acquisition module includes GPS, temperature
Humidity sensor, ponding sensor.
Further, the camera is on the security monitoring camera and Internet of Things wisdom light pole used in urban transportation
The camera of carry；GPS is the GPS Base Station in current smart city, and Temperature Humidity Sensor, ponding sensor are Internet of Things wisdom
Integrated Temperature Humidity Sensor, ponding sensor on light pole；The original road image data of map and the original road conditions of map
Data generate service end by the Common Gateway module transfer that is configured on Internet of Things wisdom light pole to high-precision map.
A kind of Dynamic High-accuracy map data processing method based on trackside sensor, including：
S1, the original road image data of map and the original road conditions data of map for gathering magnanimity；
S2, the original road image data of map are carried out to image rectification, image coordinate conversion, the pre- place of image projection transformation
Reason, the image overlay region of the image data acquiring module collection of adjacent position can align after pretreatment；
S3, set up using deep learning, the related AI technologies of image recognition a deep learning road behavioral characteristics identification
Model, recognizes that road dynamic is special using deep learning road behavioral characteristics identification model from the original road image data of map
Levy；
The feature modeling that becomes more meticulous is carried out according to existing navigation map, road static nature is obtained；
Data screening checking is done by primitive beginning road conditions data over the ground, corresponding road semi-static nature and road is extracted
The behavioral characteristics of road half；
S4, from content all roadway characteristics are carried out with hierarchical design, first layer is road static nature, and the second layer is
Road semi-static nature, third layer is the behavioral characteristics of road half, and the 4th layer is road behavioral characteristics；
S5, same road information spliced, first complete roadway characteristic data fusion in roadway characteristic aspect, finally will
Pretreated image completes image mosaic in image aspect, and the result of splicing generates the downward projection figure of road；
S6, on downward projection figure road information is marked, the road information and downward projection figure marked collectively constitutes height
The map datum of precision map；
S7, the road information progress calibration verification to having marked；
S8, map datum carries out to visual edit, generate Dynamic High-accuracy map.
Further, in step S1, the original road image data of map of magnanimity are gathered using camera, using GPS, temperature
Humidity sensor, ponding sensor locality primitive beginning road conditions data；
The camera is that carry is taken the photograph on security monitoring camera and Internet of Things wisdom light pole used in urban transportation
As head；GPS is the GPS Base Station in current smart city, and Temperature Humidity Sensor, ponding sensor are on Internet of Things wisdom light pole
Integrated Temperature Humidity Sensor, ponding sensor；The original road image data of map and the original road conditions data of map pass through
The Common Gateway module transfer configured on Internet of Things wisdom light pole generates service end to high-precision map.
Compared with prior art, beneficial effects of the present invention are as follows：
1), the camera of present invention collection trackside image is deployed in trackside infrastructure (light pole, high hack lever), compares car
The map datum for carrying camera collection has more real-time effectiveness；
2), the present invention can gather high-precision in real time by the multiple sensors terminal device of the integrated lamp stand carry of Internet of Things
The Dynamic and Multi dimensional information (surface gathered water, humidity, weather, street sign indicator etc.) of map is spent, these information only need to backstage and carry out letter
Single ground verification process, it is possible to be published to the application platform of high-precision map, real time service is in intelligent transportation field；
3), the map data collecting in the present invention takes full advantage of infrastructure (wisdom street lamp, the road of current smart city
Side camera, GPS Base Station), by sharing the trackside approach sensor of smart city, and the automatic data collection of map datum is uploaded
Mode, accurately map generalization cost can be reduced from many aspects；
4), the present invention passes through to roadway characteristic hierarchical design, Dividing Characteristics anastomosing and splicing and the number for supporting incremental update
Stored according to form, the quick renewal of Dynamic High-accuracy map can be achieved.
Brief description of the drawings
Technical scheme in order to illustrate the embodiments of the present invention more clearly, makes required in being described below to embodiment
Accompanying drawing is briefly described, it should be apparent that, drawings in the following description are only some embodiments of the present invention, for
For those of ordinary skill in the art, on the premise of not paying creative work, other can also be obtained according to these accompanying drawings
Accompanying drawing.
Fig. 1 is the structural representation of the Dynamic High-accuracy map datum processing system of the invention based on trackside sensor；
Fig. 2 is the topological diagram of the Dynamic High-accuracy map datum processing system of the invention based on trackside sensor；
Fig. 3 is the workflow diagram of the Dynamic High-accuracy map datum processing system of the invention based on trackside sensor；
Fig. 4 is the camera deployment diagram of present invention collection map；
Fig. 5 is the flow chart of the Dynamic High-accuracy map data processing method of the invention based on trackside sensor.
Embodiment
In order to facilitate the understanding of the purposes, features and advantages of the present invention, below in conjunction with accompanying drawing and specifically
Embodiment technical scheme is described in detail.It is pointed out that described embodiment is only this hair
Bright a part of embodiment, rather than whole embodiments, based on the embodiment in the present invention, those of ordinary skill in the art are not having
There is the every other embodiment made and obtained under the premise of creative work, belong to the scope of protection of the invention.
Embodiment 1
As shown in figure 1, the present invention provides a kind of Dynamic High-accuracy map datum processing system based on trackside sensor, bag
Include：
Map data collecting end, magnanimity, the map road original of diversification are provided for generating service end for high-precision map
Beginning data；
High-precision map generates service end, the map road of magnanimity, diversification for the offer of data acquisition end according to the map
Road Raw Data Generation Dynamic High-accuracy map；
The high-precision map generation service end includes：
Map datum processing module, for map road initial data to be carried out into road feature extraction, to all roads
Feature uses hierarchical design, and same road information is spliced, and first completes roadway characteristic data fusion in roadway characteristic aspect,
Last to complete image mosaic in image aspect, the result of splicing generates the downward projection figure of road；
Map generate visualization model, for marking road information on downward projection figure, road information mark with
Downward projection figure collectively constitutes the map datum of high-precision map, and map datum is carried out into visual edit, and generation high accuracy is dynamic
State map.
The map data collecting end includes：
Image data acquiring module, the original road image data of map for gathering magnanimity；
Road condition data acquisition module, the original road conditions data of map for gathering magnanimity.
Described image data acquisition module is camera, and the road condition data acquisition module includes GPS, temperature and humidity sensing
Device, ponding sensor etc..
The camera is that carry is taken the photograph on security monitoring camera and Internet of Things wisdom light pole used in urban transportation
As head；GPS is the GPS Base Station in current smart city, and Temperature Humidity Sensor, ponding sensor are on Internet of Things wisdom light pole
Integrated Temperature Humidity Sensor, ponding sensor.
The map datum processing module includes：
Pre-processing image data unit, becomes for the original road image data of map to be carried out into image rectification, image coordinate
Change, the pretreatment of image projection transformation, after pretreatment adjacent position image data acquiring module collection image overlay region energy
Enough alignment；
Road feature extraction unit, for recognizing road behavioral characteristics from the original road image data of map；According to existing
There is navigation map to carry out the feature modeling that becomes more meticulous, obtain road static nature；Number is done by primitive beginning road conditions data over the ground
According to screening verification, corresponding road semi-static nature and the behavioral characteristics of road half are extracted；
Roadway characteristic design cell, for carrying out hierarchical design to all roadway characteristics from content, first layer is road
Static nature, the second layer is road semi-static nature, and third layer is the behavioral characteristics of road half, and the 4th layer is road behavioral characteristics；
Multidimensional road information anastomosing and splicing unit, for same road information to be spliced, first in roadway characteristic aspect
Roadway characteristic data fusion is completed, pretreated image is finally completed into image mosaic, the result life of splicing in image aspect
Into the downward projection figure of road.
The road feature extraction unit includes：
Behavioral characteristics extract subelement, for setting up a depth using deep learning, the related AI technologies of image recognition
Learn road behavioral characteristics identification model, using deep learning road behavioral characteristics identification model from the original road image number of map
According to middle identification road behavioral characteristics；
Static nature extracts subelement, for carrying out the feature modeling that becomes more meticulous according to existing navigation map, obtains road quiet
State feature；
Semi-static nature extracts subelement, for doing data screening checking by primitive beginning road conditions data over the ground, takes out
Take corresponding road semi-static nature；
Half behavioral characteristics extract subelement, for doing data screening checking by primitive beginning road conditions data over the ground, take out
Take the corresponding behavioral characteristics of road half.
The multidimensional road information anastomosing and splicing unit includes：
Roadway characteristic anastomosing and splicing subelement, for all roadway characteristics of extraction to be spliced according to Dividing Characteristics,
First splice static nature, finally merge behavioral characteristics；
Image mosaic subelement, for pretreated image to be carried out into anastomosing and splicing into a basic downward projection
Figure.
The map generation visualization model includes：
Geography information mark unit, for marking road information on downward projection figure, road information mark with bow
The map datum of high-precision map is collectively constituted depending on perspective view；
Geography information calibration verification unit, for carrying out calibration verification to the road information marked；
Map generation unit, for map datum to be carried out into visual edit, generates Dynamic High-accuracy map.
As shown in Fig. 2 the IMAQ in map data collecting end, camera used is the security protection used in urban transportation
The camera of carry on monitoring camera and wisdom light pole.As smart city construction reaches its maturity, the covering of camera is got over
Come more intensive, the road attribute image in each camera collection region correspondence monitoring range uploads to high-precision map
Generate service end.As shown in figure 4, the camera carry of collection road image is on the trackside facilities such as light pole, high hack lever.For
Other road conditions attributes that camera cannot be obtained such as (humiture, surface gathered water data), pass through Internet of Things integrated lamp
Integrated Temperature Humidity Sensor, ponding sensor are obtained on bar, and the sensing equipment terminal on Internet of Things integration lamp stand can
According to circumstances flexibly apolegamy, data arrive high-precision map generation service end by the Common Gateway module transfer that is configured on lamp stand.
Roadway characteristic design cell, according to demand of the automatic Pilot to high-precision map, enters from content to roadway characteristic
Row Modeling and Design, the feature of hierarchical design road network.First layer is the static nature of road, and static nature packet contains：Road
The position of the position of route lane line, traffic signals and traffic sign；The Back ground Informations such as road ID, shape, the gradient, width.
The second layer is the semi-static nature of road：Traffic rule information (such as tide section), extensively road construction information, regional day
Gas information (sleety weather) etc.；Third layer is half multidate information of roadway characteristic：Traffic accident position, traffic congestion position, friendship
Logical ponding position, road hollow position, road barricade object location etc.；4th layer of roadway characteristic is multidate information：Pedestrian, automobile,
The target such as bicycle, motorcycle changing coordinates, movement locus.The result of roadway characteristic design cell is by all roads of design
The abstract data entity and object in high-precision map of characteristic information.The principle that roadway characteristic design is used is that difference is driven automatically
Rank is sailed to high-precision map content and the demand of precision, as automatic Pilot rank is constantly lifted, roadway characteristic needs continuous
Refine and abundant.
Pre-processing image data unit is to the original road image data of map of upload firstly the need of progress image rectification, figure
As a series of pretreatments such as coordinate transform, image projection transformations；The view data under the same coordinate system after for having handled,
Road behavioral characteristics are extracted using the related AI technologies such as deep learning, image recognition, such as using the good depth of precondition
Dynamic road feature (signal lamp, pedestrian, automobile, bicycle, motorcycle) in the high-precision map of learning model identification.
Multidimensional road information anastomosing and splicing is spliced for same road information in high-precision map, first special in road
Levy aspect and complete the analysis of roadway characteristic data fusion, finally complete image mosaic, the result generation road of splicing in image aspect
Top view, eventually through the mark of road attribute, roadway characteristic database visual edit generate Dynamic High-accuracy map.
Dynamic High-accuracy map datum processing system proposed by the present invention based on trackside sensor, incorporates wisdom road
Lamp, trackside camera, the infrastructure such as communication base station, can effectively reduce accurately map generalization cost.Road in the present invention
Side sensing data processing, the road feature extraction analysis based on AI, the analysis fusion of multidimensional roadway characteristic are passed in service end or high in the clouds
Sense device end only needs to be responsible for the collection of data with uploading data to service background, therefore not excessive to sensing equipment terminal
The requirement of storage computing resource, realize the feasibility that whole map generates scheme.
As shown in figure 3, the Dynamic High-accuracy map datum processing system workflow of the invention based on trackside sensor is such as
Under：
1), the covering of trackside camera.The present invention is used for the camera installation and deployment for gathering trackside view data in road
On the light pole of both sides, develop with the wisdomization in city, the relevant infrastructure construction of the street lamps of town road both sides is all the more
Perfect, distance is general at 30 meters or so between town road street lamp.As shown in Figure 4, it is assumed that the camera for gathering trackside image
Visual angle is θ degree, and light pole is a height of m meters, and camera can gather the scope of imageRice.By calculating, as long as collection
The camera view angle theta of trackside image is more thanCamera pickup area scope l is then more than 30 meters, based on trackside shooting
The road data of head collection would not be omitted.
2), the camera data for the stable section of roadway characteristic can be uploaded at service end by some cycles
Reason, for the section (such as crossroad) that roadway characteristic is complicated and changeable or traffic active safety demand levels are high, need to shorten upload
Cycle of the image to service end, it is ensured that the characteristic accurate and effective in the dynamic road section of acquisition.
3), the integrated lamp stand covering of city Internet of Things.Internet of Things integration lamp stand can integrated Internet of Things charging pile, intelligence photograph
The functions such as bright, monitoring camera, micro weather station, electronic bulletin screen, alarm button, each several part is integrated in modular fashion, can
According to circumstances flexibly apolegamy.The present invention utilizes the sensing equipment collection condition of road surface data on integrated lamp stand, integrated lamp stand
Upper each sensing equipment data pass through the Common Gateway module transfer that is configured on lamp stand to map datum processing service end, map datum
Each several part remote control, remote management, data acquisition, data point can be carried out by unified management platform by handling service end
Analysis, news release, malfunction monitoring etc..
4), image data space conversion process.Because the deployment of city street lamp is arranged with different positions, therefore carry exists
Camera deployment and arrangement mode on light pole are also not quite similar, and cannot ensure the camera of all collection road image data
In approximately the same plane, therefore need to carry out all original images coordinate transform and projective transformation processing.Adjacent bit after processing
Putting the image overlay region of camera collection can align, and the image after alignment is easy to road feature extraction below to splice and map
Image co-registration is spliced.
5), roadway characteristic entity design.It is right according to content and precision two aspect demand of the automatic Pilot to high-precision map
All roadway characteristics use hierarchical design.First layer is the static nature of road, and static nature packet contains：Road track
The Back ground Information such as position, traffic signals and the traffic sign position of line, road ID, shape, the gradient, width.The second layer is
The semi-static nature on road：Traffic rule information (such as tide section), extensively road construction information, regional Weather information (rain
Snowy day gas) etc.；Third layer is half multidate information of roadway characteristic：Traffic accident position, traffic congestion position, traffic ponding position
Put, road hollow position, road barricade object location etc.；4th layer of roadway characteristic is multidate information：Pedestrian, automobile, bicycle, rub
The target such as motorcycle changing coordinates, movement locus.The result of roadway characteristic design module is by all link characteristic informations of design
Abstract data entity and object in high-precision map, as shown in Figure 4, Figure 5.
6), road feature extraction.First layer static state roadway characteristic can carry out the feature that becomes more meticulous according to existing navigation map
Modeling, obtains each feature entity, finally using suitable data structure storage in the table of database.The road of second and third layer
Road feature can finally be taken out by doing simple pretreatment (data screening checking) to the road condition data that map datum collection terminal is reported
Take corresponding semi-static and half behavioral characteristics.
7), the 4th layer of road behavioral characteristics use deep learning, the related AI technologies of image recognition from map image number
According to the pedestrian in middle identification road, automobile, bicycle, motorcycle.It is extensive first against one that map datum handles service end
The image library comprising the object type such as pedestrian, automobile, bicycle, motorcycle carry out deep learning training, trained and set up one
It is individual to accurately identify the model of road behavioral characteristics., can be quickly effective when map data collecting end uploads present road image
The traffic object feature that ground identification present road is included, the dynamic road feature that these are identified is updated to accurately
The 4th layer of corresponding database of feature of figure, then quickly can truly feed back current condition of road surface.
8), road feature extraction is complete, and all features are stored in database in a complete data entity form, the number
Roadway characteristic incremental update is supported according to storehouse.
9), multidimensional roadway characteristic anastomosing and splicing.Sequence of operations is carried out based on accurate roadway characteristic database, road is completed
The anastomosing and splicing of road feature, the anastomosing and splicing of roadway characteristic by it is above-mentioned 6), 7) in all roadway characteristics for extracting according to feature point
Layer is spliced, and is first spliced static nature, is finally merged behavioral characteristics.
10), the trackside figure anastomosing and splicing after Image space transformation in 5) is finally existed into a basic downward projection figure
Road information is marked on this image, including：Road edge, lane line, crossing point etc., the information marked collectively constitutes high-precision
Spend the map datum of map；
11) data in map data base, are subjected to visual edit, can be observed the high-precision road-map of track level and
Road behavioral characteristics, wherein data precision is up to Centimeter Level.Map data base once has the renewal of dynamic road feature, can quick body
On present visual map.
Embodiment 2
As shown in figure 5, also a kind of Dynamic High-accuracy map data processing method based on trackside sensor of the present invention, bag
Include：
S1, the original road image data of map and the original road conditions data of map for gathering magnanimity；
S2, the original road image data of map are carried out to image rectification, image coordinate conversion, the pre- place of image projection transformation
Reason, the image overlay region of the image data acquiring module collection of adjacent position can align after pretreatment；
S3, set up using deep learning, the related AI technologies of image recognition a deep learning road behavioral characteristics identification
Model, recognizes that road dynamic is special using deep learning road behavioral characteristics identification model from the original road image data of map
Levy；
The feature modeling that becomes more meticulous is carried out according to existing navigation map, road static nature is obtained；
Data screening checking is done by primitive beginning road conditions data over the ground, corresponding road semi-static nature and road is extracted
The behavioral characteristics of road half；
S4, from content all roadway characteristics are carried out with hierarchical design, first layer is road static nature, and the second layer is
Road semi-static nature, third layer is the behavioral characteristics of road half, and the 4th layer is road behavioral characteristics；
S5, same road information spliced, first complete roadway characteristic data fusion in roadway characteristic aspect, finally will
Pretreated image completes image mosaic in image aspect, and the result of splicing generates the downward projection figure of road；
S6, on downward projection figure road information is marked, the road information and downward projection figure marked collectively constitutes height
The map datum of precision map；
S7, the road information progress calibration verification to having marked；
S8, map datum carries out to visual edit, generate Dynamic High-accuracy map.
In step S1, the original road image data of map of magnanimity are gathered using camera, using GPS, temperature and humidity sensing
Device, ponding sensor locality primitive beginning road conditions data；
The camera is that carry is taken the photograph on security monitoring camera and Internet of Things wisdom light pole used in urban transportation
As head；GPS is the GPS Base Station in current smart city, and Temperature Humidity Sensor, ponding sensor are on Internet of Things wisdom light pole
Integrated Temperature Humidity Sensor, ponding sensor.
Compared with prior art, beneficial effects of the present invention are as follows：
1), the camera of present invention collection trackside image is deployed in trackside infrastructure (light pole, high hack lever), compares car
The map datum for carrying camera collection has more real-time effectiveness；
2), the present invention can gather high-precision in real time by the multiple sensors terminal device of the integrated lamp stand carry of Internet of Things
The Dynamic and Multi dimensional information (surface gathered water, humidity, weather, street sign indicator etc.) of map is spent, these information only need to backstage and carry out letter
Single ground verification process, it is possible to be published to the application platform of high-precision map, real time service is in intelligent transportation field；
3), the map data collecting in the present invention takes full advantage of infrastructure (wisdom street lamp, the road of current smart city
Side camera, GPS Base Station), by sharing the trackside approach sensor of smart city, and the automatic data collection of map datum is uploaded
Mode, accurately map generalization cost can be reduced from many aspects；
4), the present invention passes through to roadway characteristic hierarchical design, Dividing Characteristics anastomosing and splicing and the number for supporting incremental update
Stored according to form, the quick renewal of Dynamic High-accuracy map can be achieved.
Embodiment described above only expresses the several embodiments of the present invention, and it describes more specific and detailed, but simultaneously
Therefore the limitation to the scope of the claims of the present invention can not be interpreted as.It should be pointed out that for one of ordinary skill in the art
For, without departing from the inventive concept of the premise, various modifications and improvements can be made, these belong to the guarantor of the present invention
Protect scope.Therefore, the protection domain of patent of the present invention should be determined by the appended claims.
Claims (10)
1. a kind of Dynamic High-accuracy map datum processing system based on trackside sensor, it is characterised in that including：
Map data collecting end, for providing magnanimity, the map road original number of diversification for high-precision map generation service end
According to；
High-precision map generates service end, magnanimity, the map road original of diversification provided for data acquisition end according to the map
Beginning data generate Dynamic High-accuracy map；
The high-precision map generation service end includes：
Map datum processing module, for map road initial data to be carried out into road feature extraction, to all roadway characteristics
Using hierarchical design, same road information is spliced, first roadway characteristic data fusion is completed in roadway characteristic aspect, finally
Image mosaic is completed in image aspect, the result of splicing generates the downward projection figure of road；
Map generates visualization model, for marking road information on downward projection figure, the road information and vertical view marked
Perspective view collectively constitutes the map datum of high-precision map, and map datum is carried out into visual edit, with generating Dynamic High-accuracy
Figure.
2. the Dynamic High-accuracy map datum processing system according to claim 1 based on trackside sensor, its feature exists
In the map data collecting end includes：
Image data acquiring module, the original road image data of map for gathering magnanimity；
Road condition data acquisition module, the original road conditions data of map for gathering magnanimity.
3. the Dynamic High-accuracy map datum processing system according to claim 1 based on trackside sensor, its feature exists
In the map datum processing module includes：
Pre-processing image data unit, for the original road image data of map to be carried out into image rectification, image coordinate conversion, figure
As the pretreatment of projective transformation, the image overlay region of the image data acquiring module collection of adjacent position can be right after pretreatment
Together；
Road feature extraction unit, for recognizing road behavioral characteristics from the original road image data of map；Led according to existing
Boat map carries out the feature modeling that becomes more meticulous, and obtains road static nature；Data sieve is done by primitive beginning road conditions data over the ground
Choosing checking, extracts corresponding road semi-static nature and the behavioral characteristics of road half；
Roadway characteristic design cell, for from content all roadway characteristics to be carried out with hierarchical design, first layer is that road is static
Feature, the second layer is road semi-static nature, and third layer is the behavioral characteristics of road half, and the 4th layer is road behavioral characteristics；
Multidimensional road information anastomosing and splicing unit, for same road information to be spliced, is first completed in roadway characteristic aspect
Roadway characteristic data fusion, finally completes image mosaic, the result generation road of splicing by pretreated image in image aspect
The downward projection figure on road.
4. the Dynamic High-accuracy map datum processing system according to claim 3 based on trackside sensor, its feature exists
In the road feature extraction unit includes：
Behavioral characteristics extract subelement, for setting up a deep learning using deep learning, the related AI technologies of image recognition
Road behavioral characteristics identification model, using deep learning road behavioral characteristics identification model from the original road image data of map
Recognize road behavioral characteristics；
Static nature extracts subelement, for carrying out the feature modeling that becomes more meticulous according to existing navigation map, obtains road static special
Levy；
Semi-static nature extracts subelement, for doing data screening checking, extraction pair by primitive beginning road conditions data over the ground
The road semi-static nature answered；
Half behavioral characteristics extract subelement, for doing data screening checking, extraction pair by primitive beginning road conditions data over the ground
The behavioral characteristics of road half answered.
5. the Dynamic High-accuracy map datum processing system according to claim 3 based on trackside sensor, its feature exists
In the multidimensional road information anastomosing and splicing unit includes：
Roadway characteristic anastomosing and splicing subelement, for all roadway characteristics of extraction to be spliced according to Dividing Characteristics, is first spelled
Static nature is connect, behavioral characteristics are finally merged；
Image mosaic subelement, for pretreated image to be carried out into anastomosing and splicing into a basic downward projection figure.
6. the Dynamic High-accuracy map datum processing system according to claim 1 based on trackside sensor, its feature exists
In the map generation visualization model includes：
Geography information marks unit, for marking road information on downward projection figure, and the road information marked and vertical view are thrown
Shadow figure collectively constitutes the map datum of high-precision map；
Geography information calibration verification unit, for carrying out calibration verification to the road information marked；
Map generation unit, for map datum to be carried out into visual edit, generates Dynamic High-accuracy map.
7. the Dynamic High-accuracy map datum processing system according to claim 2 based on trackside sensor, its feature exists
In described image data acquisition module is camera, and the road condition data acquisition module includes GPS, Temperature Humidity Sensor, ponding
Sensor.
8. the Dynamic High-accuracy map datum processing system according to claim 7 based on trackside sensor, its feature exists
In the camera of camera carry on the security monitoring camera and Internet of Things wisdom light pole used in urban transportation；
GPS is the GPS Base Station in current smart city, and Temperature Humidity Sensor, ponding sensor are integrated on Internet of Things wisdom light pole
Temperature Humidity Sensor, ponding sensor；The original road image data of map and the original road conditions data of map pass through Internet of Things
The Common Gateway module transfer configured on net wisdom light pole generates service end to high-precision map.
9. a kind of Dynamic High-accuracy map data processing method based on trackside sensor, it is characterised in that including：
S1, the original road image data of map and the original road conditions data of map for gathering magnanimity；
S2, the original road image data of map are carried out to image rectification, image coordinate conversion, the pretreatment of image projection transformation,
The image overlay region of the image data acquiring module collection of adjacent position can align after pretreatment；
S3, using deep learning, the related AI technologies of image recognition a deep learning road behavioral characteristics identification model is set up,
Road behavioral characteristics are recognized from the original road image data of map using deep learning road behavioral characteristics identification model；
The feature modeling that becomes more meticulous is carried out according to existing navigation map, road static nature is obtained；
Data screening checking is done by primitive beginning road conditions data over the ground, corresponding road semi-static nature and road half is extracted
Behavioral characteristics；
S4, from content all roadway characteristics are carried out with hierarchical design, first layer is road static nature, and the second layer is road half
Static nature, third layer is the behavioral characteristics of road half, and the 4th layer is road behavioral characteristics；
S5, same road information spliced, first complete roadway characteristic data fusion in roadway characteristic aspect, will finally locate in advance
Image after reason completes image mosaic in image aspect, and the result of splicing generates the downward projection figure of road；
S6, on downward projection figure road information is marked, the road information and downward projection figure marked collectively constitutes high accuracy
The map datum of map；
S7, the road information progress calibration verification to having marked；
S8, map datum carries out to visual edit, generate Dynamic High-accuracy map.
10. the Dynamic High-accuracy map data processing method according to claim 9 based on trackside sensor, its feature exists
In in step S1, using the original road image data of map of camera collection magnanimity, using GPS, Temperature Humidity Sensor, product
Water sensor locality primitive beginning road conditions data；
The camera of camera carry on the security monitoring camera and Internet of Things wisdom light pole used in urban transportation；
GPS is the GPS Base Station in current smart city, and Temperature Humidity Sensor, ponding sensor are integrated on Internet of Things wisdom light pole
Temperature Humidity Sensor, ponding sensor；The original road image data of map and the original road conditions data of map pass through Internet of Things
The Common Gateway module transfer configured on net wisdom light pole generates service end to high-precision map.
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