CN111260924A - Traffic intelligent control and service release strategy method adapting to edge calculation - Google Patents
Traffic intelligent control and service release strategy method adapting to edge calculation Download PDFInfo
- Publication number
- CN111260924A CN111260924A CN202010085308.6A CN202010085308A CN111260924A CN 111260924 A CN111260924 A CN 111260924A CN 202010085308 A CN202010085308 A CN 202010085308A CN 111260924 A CN111260924 A CN 111260924A
- Authority
- CN
- China
- Prior art keywords
- data
- intelligent
- facility
- road side
- event
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Granted
Links
- 238000004364 calculation method Methods 0.000 title claims abstract description 60
- 238000000034 method Methods 0.000 title claims abstract description 47
- 230000008520 organization Effects 0.000 claims abstract description 21
- 230000004069 differentiation Effects 0.000 claims abstract description 14
- 230000008447 perception Effects 0.000 claims abstract description 12
- 230000004807 localization Effects 0.000 claims abstract description 8
- 230000006855 networking Effects 0.000 claims abstract description 5
- 238000012545 processing Methods 0.000 claims description 73
- 238000004458 analytical method Methods 0.000 claims description 46
- 238000004891 communication Methods 0.000 claims description 40
- 238000007726 management method Methods 0.000 claims description 36
- 238000012544 monitoring process Methods 0.000 claims description 29
- 230000005540 biological transmission Effects 0.000 claims description 25
- 230000011218 segmentation Effects 0.000 claims description 16
- 230000002159 abnormal effect Effects 0.000 claims description 15
- 230000003044 adaptive effect Effects 0.000 claims description 10
- 230000003068 static effect Effects 0.000 claims description 10
- 230000007613 environmental effect Effects 0.000 claims description 8
- 230000003993 interaction Effects 0.000 claims description 8
- 238000004422 calculation algorithm Methods 0.000 claims description 7
- 230000004927 fusion Effects 0.000 claims description 7
- 238000011217 control strategy Methods 0.000 claims description 6
- 238000005516 engineering process Methods 0.000 claims description 5
- 241000497429 Obus Species 0.000 claims description 3
- 238000010219 correlation analysis Methods 0.000 claims description 3
- 238000007405 data analysis Methods 0.000 claims description 3
- 238000013480 data collection Methods 0.000 claims description 3
- 238000011156 evaluation Methods 0.000 claims description 3
- 238000007499 fusion processing Methods 0.000 claims description 3
- 238000012821 model calculation Methods 0.000 claims description 3
- 230000000737 periodic effect Effects 0.000 claims description 3
- 230000008569 process Effects 0.000 claims description 3
- 238000011144 upstream manufacturing Methods 0.000 claims description 3
- 230000000007 visual effect Effects 0.000 claims description 3
- 238000012800 visualization Methods 0.000 claims description 3
- 230000009471 action Effects 0.000 claims description 2
- 230000008859 change Effects 0.000 claims description 2
- 238000013461 design Methods 0.000 claims description 2
- 238000009792 diffusion process Methods 0.000 claims description 2
- 230000001502 supplementing effect Effects 0.000 claims 1
- 230000000694 effects Effects 0.000 abstract description 6
- 230000002452 interceptive effect Effects 0.000 description 5
- 230000004044 response Effects 0.000 description 4
- 238000012360 testing method Methods 0.000 description 4
- 238000011161 development Methods 0.000 description 3
- 230000018109 developmental process Effects 0.000 description 3
- 238000012986 modification Methods 0.000 description 2
- 230000004048 modification Effects 0.000 description 2
- 210000004258 portal system Anatomy 0.000 description 2
- 101001137642 Homo sapiens Kinase suppressor of Ras 1 Proteins 0.000 description 1
- 206010063385 Intellectualisation Diseases 0.000 description 1
- 102100021001 Kinase suppressor of Ras 1 Human genes 0.000 description 1
- 206010024769 Local reaction Diseases 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 230000008901 benefit Effects 0.000 description 1
- 230000007547 defect Effects 0.000 description 1
- 238000010586 diagram Methods 0.000 description 1
- 230000006870 function Effects 0.000 description 1
- 230000006698 induction Effects 0.000 description 1
- PXUQTDZNOHRWLI-OXUVVOBNSA-O malvidin 3-O-beta-D-glucoside Chemical compound COC1=C(O)C(OC)=CC(C=2C(=CC=3C(O)=CC(O)=CC=3[O+]=2)O[C@H]2[C@@H]([C@@H](O)[C@H](O)[C@@H](CO)O2)O)=C1 PXUQTDZNOHRWLI-OXUVVOBNSA-O 0.000 description 1
Images
Classifications
-
- G—PHYSICS
- G08—SIGNALLING
- G08G—TRAFFIC CONTROL SYSTEMS
- G08G1/00—Traffic control systems for road vehicles
- G08G1/01—Detecting movement of traffic to be counted or controlled
- G08G1/0104—Measuring and analyzing of parameters relative to traffic conditions
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/20—Information retrieval; Database structures therefor; File system structures therefor of structured data, e.g. relational data
- G06F16/23—Updating
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/20—Information retrieval; Database structures therefor; File system structures therefor of structured data, e.g. relational data
- G06F16/27—Replication, distribution or synchronisation of data between databases or within a distributed database system; Distributed database system architectures therefor
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/20—Information retrieval; Database structures therefor; File system structures therefor of structured data, e.g. relational data
- G06F16/29—Geographical information databases
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q10/00—Administration; Management
- G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
- G06Q10/063—Operations research, analysis or management
- G06Q10/0637—Strategic management or analysis, e.g. setting a goal or target of an organisation; Planning actions based on goals; Analysis or evaluation of effectiveness of goals
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q50/00—Information and communication technology [ICT] specially adapted for implementation of business processes of specific business sectors, e.g. utilities or tourism
- G06Q50/10—Services
- G06Q50/26—Government or public services
-
- G—PHYSICS
- G08—SIGNALLING
- G08G—TRAFFIC CONTROL SYSTEMS
- G08G1/00—Traffic control systems for road vehicles
- G08G1/01—Detecting movement of traffic to be counted or controlled
- G08G1/0104—Measuring and analyzing of parameters relative to traffic conditions
- G08G1/0125—Traffic data processing
-
- G—PHYSICS
- G08—SIGNALLING
- G08G—TRAFFIC CONTROL SYSTEMS
- G08G1/00—Traffic control systems for road vehicles
- G08G1/07—Controlling traffic signals
-
- G—PHYSICS
- G08—SIGNALLING
- G08G—TRAFFIC CONTROL SYSTEMS
- G08G1/00—Traffic control systems for road vehicles
- G08G1/09—Arrangements for giving variable traffic instructions
Landscapes
- Engineering & Computer Science (AREA)
- Business, Economics & Management (AREA)
- Physics & Mathematics (AREA)
- General Physics & Mathematics (AREA)
- Theoretical Computer Science (AREA)
- Human Resources & Organizations (AREA)
- Databases & Information Systems (AREA)
- Educational Administration (AREA)
- General Engineering & Computer Science (AREA)
- Economics (AREA)
- Data Mining & Analysis (AREA)
- Strategic Management (AREA)
- Tourism & Hospitality (AREA)
- Analytical Chemistry (AREA)
- Entrepreneurship & Innovation (AREA)
- Development Economics (AREA)
- General Business, Economics & Management (AREA)
- Marketing (AREA)
- Chemical & Material Sciences (AREA)
- Primary Health Care (AREA)
- General Health & Medical Sciences (AREA)
- Health & Medical Sciences (AREA)
- Game Theory and Decision Science (AREA)
- Operations Research (AREA)
- Quality & Reliability (AREA)
- Remote Sensing (AREA)
- Computing Systems (AREA)
- Traffic Control Systems (AREA)
Abstract
The intelligent traffic control and service release strategy method suitable for edge calculation realizes organization and distribution of high-precision characteristic identification map data of cross-intelligent road side facility nodes under the condition of beyond-visual-range linearity/networking, multi-source space-time traffic data adaptability perception and scene intelligent identification of the cross-intelligent road side facility nodes, and a localization effect domain calculation and differentiation strategy generation method of the cross-intelligent road side facility nodes, and provides service capability of a traffic facility supply side suitable for intelligent traffic, particularly for vehicle-road cooperative application and future automatic driving application.
Description
Technical Field
The invention belongs to the technical field of intelligent traffic, and particularly relates to a traffic intelligent control and service release strategy method adaptive to edge computing.
Background
With the development of new-generation information technologies such as cloud computing, internet of things, big data, block chains and the like, information computing is migrated from single independent computing to logic centralized big data processing with cloud computing as a core. However, with the high popularization of mobile terminal devices and the rapid development of cooperative driving and automatic driving of vehicles and roads, the more intelligent, safe, convenient and efficient travel demand puts higher requirements on the intellectualization, timeliness and refinement of roadside services, and thus a calculation mode with lower time delay, less broadband occupation and higher efficiency, namely an edge calculation mode, is required.
The intelligent road side facility is an intelligent infrastructure taking edge calculation as a core application, and is a key node for vehicle and road cooperative application. At present, intelligent road side facilities are mostly used in a closed test area, a semi-closed test area and the like to carry out single test application of vehicle-road interaction, and a scene strategy function test is carried out in a mode of simulating an event or manually inputting; some of the intelligent facilities are applied to signal lamp intersections and are associated with signal lamps to carry out green wave passing of intelligent facilities on one side of the intersections; some are applied to parking ETC charging and information service; the map or spatial position data of the intelligent road side facility is mainly acquired by sensing and acquiring high-precision map data, pre-installing, updating dynamic increment/full amount and processing and releasing cloud platform centralization, and the method is lack of space-time strategy data generation and application for fusing traffic events, environmental conditions, vehicle dynamic operation conditions and the like, and also lack of generation of lane-level traffic space-time strategies of the intelligent road side facility under the environment of over-the-horizon road side linear channels, and does not realize multi-unit unloading switching application managed by taking a data set in the coverage range of the intelligent road side facility as a unit.
The invention is based on an effective organization and distribution method of roadside high-precision map data, adapts to and fuses multi-source space-time traffic data of cross-intelligent roadside facility nodes, realizes a generation method of local effect domain calculation and differentiation strategies of the cross-intelligent roadside facility nodes, provides low-delay, fine, high-timeliness and intelligent control decision support for application of vehicle and road cooperative information service, intelligent driving and the like, and improves comprehensive interaction service capability and quick response capability of intelligent roadside to single and group travel.
In addition, with the development of information communication technology and network technology and the application of numerous intelligent facilities, including a large number of ETC portal systems and related field monitoring facilities built in the projects of canceling expressway provincial toll stations, how to ensure the safety of the information is particularly important. Information safety of application fields such as vehicle-road cooperative auxiliary driving and automatic driving related to the intelligent road-side facilities is more and more important, and the safety certification of intercommunication linkage and the safety distribution and transmission guarantee of information of the intelligent road-side facilities in the aspects of high-precision maps, management and control strategies, facility access and the like are lacked at present.
Disclosure of Invention
The invention is oriented to the related services such as vehicle-road cooperative application and the like, and realizes vehicle-mounted-road-side-center multi-stage and low-delay interactive application by deploying intelligent road-side facility nodes at the road side. Aiming at poor driving sight distance, poor driving road surface condition, traffic operation events and the like, the method can effectively and timely issue strategies such as early warning prompt, traffic control and the like through linkage of continuously deployed intelligent road side facility nodes based on vehicle-road cooperative management and control scheduling; aiming at timely discovery and early warning of dangerous vehicle fault events (or goods dangerous abnormal events), collection and processing of vehicle real-time positioning information, judgment processing and early warning of localized traffic events and the like, traffic operation scene characteristics can be adaptively sensed based on interactive shared data of cross-intelligent road side facility nodes, and meanwhile, processing, generation and distribution of road side differentiation strategies are achieved by combining vehicle road cooperative management and control scheduling.
The technical problem to be solved by the invention is as follows: the method overcomes the defects of the prior art, provides a traffic intelligent control and service release strategy generation and distribution method adaptive to edge calculation, and realizes the organization and distribution of high-precision characteristic identification map data of cross-intelligent roadside facility nodes under the condition of beyond-the-horizon linearity/networking; multi-source space-time traffic data adaptability perception and scene intelligent identification of cross-intelligent roadside facility nodes; the method for generating the localization domain calculation and the differentiation strategy of the cross-intelligent road side facility node provides the service capability of a traffic facility supply side suitable for intelligent traffic, particularly vehicle and road cooperative application and future intelligent driving application.
The technical solution of the invention is as follows: the method for generating and distributing the traffic intelligent control and service release strategy adapting to the edge computing comprises the following steps:
(1) under the road section environment of a newly-built intelligent road side facility, lane grade high-precision map data is obtained by utilizing a plurality of collection and combination modes, and three-dimensional measurable live-action image data collection, remote sensing image data analysis processing, radar collection, unmanned aerial vehicle collection and other modes can be adopted; the data result should have the attributes of object type, logical relationship, timeliness identification, etc.; the intelligent road side facilities comprise intelligent base stations controlled by taking edge calculation as nodes and lane distribution facilities;
(2) according to the high-precision map data result obtained in the step (1), performing physical segmentation processing on a high-precision map data object according to the traffic structure feature points and the uplink and downlink directions, wherein the high-precision map data set is as follows:
Amap＝{Snmz|(n,m,z)}
wherein z is road network code, m is route code in road network code z, n is physically divided small road segment sequence number in route m, SnmzRepresenting the nth split physical segment data set in the route with the route code of m in the road network z; the high-precision map data is identification map data with time characteristics in a certain spatial range of the intelligent road side facility, comprises physical data and logic data, and is used for road side traffic control and service strategy generation and distribution range and timeliness determination; the identification map data is used for spatial objects required by roadside intelligent facilities for providing local traffic control and service strategy distribution, and comprises road marking lines, lanes, road structures, monitoring facilities, safety facilities, related facilities along the lines and the like in the forms of points, lines, surfaces and the like under different time periods, and the attribute data of the spatial objects comprises object types, intelligent roadside facility identifications, spatial relationships and the like; the identification map is segmented according to the layout position of the intelligent road side facility and the space-time range calculated by the intelligent road side facility, and meanwhile, the high-precision data of the road are processed to form a minimum physical segment divided by feature units; physical data is a visual object existing in the real world and is logicalEditing data into non-physical entity objects required for providing localized processing or policy distribution;
(3) setting an Intelligent roadside facility EiSpace coverage Cr ofijAnd is combined with the high-precision map data set AmapPerforming overlay analysis to ensure AmapAll or part of CrijIn the spatial domain, thereby determining the intelligent road side facility EiPosition pile number K capable of being laidiAnd a communication unit position stake number set K ═ Kij|(i，j)}，CrijFor the ith intelligent road side facility EiThe spatial coverage range of (a) can be 360-degree coverage range, a circular area or an elliptical area, wherein the circular area Cr isijIs a radial distance, if it is an elliptical region, CrijThe distance from the center of the ellipse to the farthest point of the ellipse, i is the serial number of the node of the intelligent road-side facility preset in the road network z, j is the intelligent road-side facility EiThe number of the accessed communication unit;
(4) the position stake number K is organized and processed through dataiIntelligent road side facility EiLoading with Crij，0>j>r, where r is access to an intelligent roadside facility EiNumber of communication antennas, high-precision physical map data in the coverage area, E for data encodingiSnmzIndicates and will successfully load to EiHigh-precision map data assignment identification attribute Ei(ii) a If EiAnd Ei+1The coverage area of the map data is overlapped, and the high-precision map physical data codes of the overlapped areas are respectively used as EiSnmz&Ei+qSnmzRepresents, respectively, loading into EiAnd Ei+1At the same time will successfully load to EiAnd Ei+1The high-precision map data are respectively assigned with the identification attributes EiAnd Ei+1。
Wherein q represents EiAnd Ei+1The number of overlapping regions of (a);
(5) after the processing in the step (4), performing space-time data organization according to the hierarchy of road side-road section-region; the spatio-temporal data organization comprises object types, storage forms, spatial logic relations and transmission data forms of spatio-temporal data, wherein the storage forms comprise storage environments of intelligent road side facilities deployed on roads and data formats, data distribution nodes, data node relations and application interfaces of the spatio-temporal data; the transmission data form comprises data coding forms such as ASN.1, GML, GeoJSON and the like;
(6) after the processing of the step (5), establishing a roadside-level high-precision position map E1To EnThe spatial logic correlation is carried out between the intelligent roadside facility and the intelligent roadside facility, and the second-level data is updated according to the position update, so that the authenticity, effectiveness and accuracy of the second-level data are maintained; establishing road section/route/channel level data R1To RuThe spatial logic corresponding relation between the position and the position data is realized, and the fusion processing with the roadside high-precision position data is realized according to the cycle time T1Carrying out data synchronization and updating; establishing corresponding relation between center/area level and road section/route/channel level data, carrying out data convergence processing and visualization processing, and processing according to period time T2Carrying out data synchronization and updating;
(7) with outfield monitoring facility and intelligent roadside facility E1To EnLocal network/wireless network communication is carried out, and high-bandwidth, stable and reliable data transmission is guaranteed; the background management platform facility of the external field monitoring facility is interconnected with the road section/route/channel management platform facility, and high-bandwidth, stable and reliable data transmission is guaranteed; intelligent road side facility E1To EnInterconnection and intercommunication with an external field monitoring facility, a background management platform facility of the external field monitoring facility, a road section/route/channel management platform and the like can be realized based on a cloud platform mode;
(8) the method comprises the steps of sensing and accessing multi-source traffic data in real time, classifying according to mobile terminal facilities, different types of outfield road side facilities, different types of software platforms and network information security facilities, respectively establishing protocol interfaces with intelligent road side facilities, and respectively using M to respectively establish protocol interfacesin、Fin、Pin、SinCarrying out access protocol category identification, wherein the heartbeat periods of protocol access are respectively TM、TF、TP、SP；
(9) When a newly-added mobile terminal facility, an external field road side facility and a software platform are accessed to an intelligent road side facility, adaptive perception of multi-source traffic data can be realized only by configuring a protocol type identifier, and edge storage or online transmission of traffic operation data, traffic event data, environment data, vehicle driving data, vehicle operation data, road surface condition data, infrastructure condition data, emergency control, dynamic traffic control and the like can be performed according to the type of an access protocol; the specific data of the adaptive perception of the multi-source traffic data also comprise ETC charging data, delivery data, external system intelligent access data and other protocol access, and heartbeat data can be automatically adapted to a data interface according to protocol identification and established according to preconfiguration;
(10) based on the edge computing capability and the space-time computing analysis of the intelligent road side facility, calculating and calculatingiCr in coverage areaijBy TmPeriodically connected average speed of vehicle
Below threshold speed VlimitCalculating to generate a congestion event, and real-timely connecting the intelligent road side facility EiMap data A of coveragemapDistributing the congestion information to a vehicle-mounted terminal or a software platform, identifying the coordinate position of the start point and the stop point of the congestion information, calculating the position of the start point and the stop point pile number of the congestion information through a reference point space-time calculation model, and meanwhile, calculating by the intelligent road side facility according to EiDetecting the speed change of the jammed vehicle, and calculating whether to compare the E value with the E value in real time by using methods such as event influence analysis, duration estimation and the likeiCongestion information with stake number position calculated within coverage upstream Ei-1Is transmitted and is in Ei-1Issue congestion information if
Above threshold speed VlimitWhen the congestion information is received, the congestion information is released, and the congestion state mark of the corresponding section is released; wherein
(m is the present EiThe number of vehicles in a coverage area) loaded with special vehicle-mounted terminals such as OBUs and the like are accessed in a special short-range communication mode, and the position and speed information of other vehicles can be accessed in a sensing mode through an external field monitoring facility;
(11) calculating the intelligent road side facility E in real time based on the edge calculation capability and the space-time calculation analysis of the intelligent road side facilityiBy TM、TF、TP、TsProtocol class of time period access is Min、Fin、Pin、SinAfter the abnormal data are removed and the network information security authentication is carried out, the traffic parameters, the traffic incident information, the environmental information and the like are calculated in real time, and EiIdentified traffic operation data, traffic event data, environmental data, vehicle travel data, vehicle operation data, road condition data, infrastructure condition data, and data for emergency management and control, dynamic traffic control, etc., and intelligent road side facility EiThe stored space physical unit data and the identified bad road section data are subjected to fusion superposition analysis, and the serial number E of the cross-road side intelligent facility issued by the space-time reference calculation model and the event analysis model calculation strategy is utilizedi～EnMeanwhile, distributed association processing of the cross-intelligent roadside facility is carried out, and the position or the range of the dynamic pile number under the abnormal traffic condition is calculated;
(12) step (11) processing and generating traffic event information, environmental data, abnormal infrastructure data information and the like, wherein the traffic event information, the environmental data, the abnormal infrastructure data and the like are generated by the influenced intelligent road side facility Ei～EnGiving the corresponding event category identification and the intelligent road side facility identification to the data generated by the processing of the step (11);
(13) setting a maintainable scene category library W on the basis of industry standard specificationsxCombining the class identifier generated in step (12) with WxThe scene identification in (1) is matched, if there is matched scene type WxAccording to W in the scene libraryxIf no scene category which can be matched exists, the scene category can be automatically supplemented through a self-learning algorithm or increased through a man-machine input mode;
(14) step (12) is analyzed by correlation analysis method) And (4) comparing and analyzing the data generated in the step (13), and performing superposition analysis on the data and the spatial position of the intelligent road side facility node, wherein the intelligent identification is performed at the time Ei～EnThe type of scene to be executed;
(15) e is calculated through the processing of the steps (10) to (14)i～EnBy Tm、Tf、Tp、TsM of periodic accessin、Fin、Pin、SinMulti-source traffic data of agreement type, noted { EDtmi，EDtfi，EDtpi，EDtsiAnd calculating and identifying an event data set { WED) under the scene conditiontmi，WEDtfi，WEDtpi，WEDtsi|WEDtmi∈EDtmi，WEDtfi∈EDtfi，WEDtpi∈EDtpi，WEDtsi∈EDtsiThen calculate the event position { K ] in the event data setmi，Kfi，KpiAnd its dynamic range of influence DR { { DK { }mi，SPEEDmi}，{DKfi，SPEEDfi}，{DKpi，SPEEDpiE is calculated through the superposition analysis of DR elementsiMileage set A from event Range boundaryL＝{Lmi，Lfi，Lpi}; distributed policy calculation is carried out by adopting a space-time differentiation policy calculation model, and dynamic unloading and management are based on an E-based data volume, calculation resources and service distance three-element modeli～EnProcessing and generating the differentiation strategy;
(16) processed in step (15) to generate ALAccording to the intelligent roadside facility EiSensing the real-time position of the accessed vehicle, and calculating and generating a STRATEGY set STRATEGY { STRATEGY }mi,STRATEGYfi,STRATEGYpiI belongs to N, and uses intelligent road side facility EiThe communication unit realizes communication interaction with the vehicle, simultaneously receives strategy requests of the vehicle/vehicle-mounted terminal and passively sends EiService information or event information. Wherein the intelligent road side facility EiAnd the communication unit to which it belongs is authorized by secure trustIncluding an intelligent roadside facility EiThe method comprises the steps of security authentication, encryption processing of generated strategy information, security distribution of information under a hybrid communication network, and credible evaluation and security early warning in the aspects of identity, behavior and capability in the transmission and distribution processes of the strategy information.
The invention uses the road side high-precision map data and effectively organizes the same, adapts to and fuses the multi-source space-time traffic data of the cross-intelligent roadside facility node, performs intelligent identification of the intelligent roadside execution scene and the spatial calculation and the accurate matching of the localized traffic control and the service strategy, performs the localization effect domain calculation and the generation of the differentiated accurate control strategy of the cross-intelligent roadside facility node, realizes the organization and the distribution of the high-precision characteristic identification map data of the cross-intelligent roadside facility node under the over-the-horizon linear/networked condition, the multi-source space-time traffic data adaptability perception and the scene intelligent identification of the cross-intelligent roadside facility node, the localization effect domain calculation and the differentiated strategy generation of the cross-intelligent roadside facility node, and provides the control decision support with low time delay, refinement, high timeliness and intelligence for the application of vehicle-road cooperative information service, intelligent driving and the like, the comprehensive interactive service capability and the quick response capability of the intelligent road side to the trip of the single body and the group are improved.
Drawings
FIG. 1 is a high-precision map physical segmentation coding in the method of the present invention, which is a segmentation situation of the 1 st, 2 nd, … th, n-1 th route based on the full coverage of intelligent road side facilities E1, E2, …, En-1, En in the road network number 1; as illustrated in FIG. 1, S111The subscript variable of (1) is a road section number, a route number and a road network number from left to right in sequence, wherein the road section number is the number of different road sections under the same route number; the route number is an administrative level road code specified according to the traffic industry standard, is a national unique code, and is identified by a serial number for convenience of description, wherein each serial number corresponds to a route of a different road code; the road network number is the only number of the road network in different areas;
FIG. 2 is a high-precision map physical segmentation coding in the method of the present invention, which is a segmentation situation of the route numbered 2 in the road network number 1 based on the 1 st, 2 nd, … th, n-1 th and n th segments under the full coverage of the intelligent road side facilities E1, E2, …, En-1 and En;
FIG. 3 is a high-precision map physical segmentation code in a cross-line-of-sight environment or a tunnel environment in the method of the present invention, which is a segmentation situation of 1 st, 2 nd, … th, n-1 th and n th sections of a route numbered 1 in a road network number 1 based on full coverage of RSU2, RSUn-1 and RSUn of an intelligent road side facility E1;
FIG. 4 is a high-precision map physical segmentation code in the complex road network environment such as interchange in the method of the present invention, which is the segmentation situation of the 1 st, 2 nd and … th sections of the route numbered 1 in the road network number 1 based on the intelligent road side facility E1 under the interchange ramp coverage situation;
FIG. 5 is a high-precision map physical segmentation coding in the method of the present invention, which is a segmentation situation of the 1 st, 2 nd, … th, n-1 th route based on the partial coverage of intelligent road side facilities E1, E2, …, En-1, En in the road network number 1;
FIG. 6 is a high-precision map data convergence organization in an example of the invention;
FIG. 7 is a flow chart of the collaborative management of the localized computation and differentiated policy generation across the intelligent roadside facility nodes in the present invention.
Detailed Description
The method for traffic intelligent control and service release strategy adapting to edge computing comprises the following steps:
(1) under the road section environment of a newly-built intelligent road side facility, lane grade high-precision map data is obtained by utilizing a plurality of collection and combination modes, and three-dimensional measurable live-action image data collection, remote sensing image data analysis processing, radar collection, unmanned aerial vehicle collection and other modes can be adopted; the data result should have the attributes of object type, logical relationship, timeliness identification, etc.; the intelligent road side facilities comprise intelligent base stations controlled by using edge calculation as nodes and lane distribution facilities.
(2) According to the high-precision map data result obtained in the step (1), carrying out physical segmentation processing on a high-precision map data object according to characteristic points (including terrain, geometric alignment, infrastructure setting, ramp setting, entrances and exits, intersections, three-dimensional real-scene environments and administrative boundaries) of traffic structures and uplink and downlink directions, wherein a high-precision map data set is as follows:
Amap＝{Snmz|(n,m,z)}
wherein z is road network code, m is route code in road network code z, n is physically divided small road segment sequence number in route m, S isnmzRepresenting the nth partitioned physical segment data set in the route with the route code m in the road network z. FIGS. 1 to 5 are schematic diagrams of map data physical segmentation, wherein Cr isijFor the ith intelligent road side facility EiThe spatial coverage of (a) may be 360 degrees. Circular or elliptical areas, wherein the circular area CrijIs a radial distance, if it is an elliptical region, CrijThe distance from the center of the ellipse to the farthest point of the ellipse, i is the serial number of the node of the intelligent road-side facility preset in the road network z, j is the intelligent road-side facility EiThe number of the accessed communication unit; the high-precision map data is identification map data with time characteristics in a certain spatial range of the intelligent road side facility, comprises physical data and logic data, and is used for road side traffic control and service strategy generation and distribution range and timeliness determination; the identification map data is used for spatial objects required by roadside intelligent facilities for providing local traffic control and service strategy distribution, and comprises road marking lines, lanes, road structures, monitoring facilities, safety facilities, related facilities along the lines and the like in the forms of points, lines, surfaces and the like under different time periods, and the attribute data of the spatial objects comprises object types, intelligent roadside facility identifications, spatial relationships and the like; the identification map is segmented according to the layout position of the intelligent road side facility and the space-time range calculated by the intelligent road side facility, and meanwhile, the high-precision data of the road are processed to form a minimum physical segment divided by feature units; the physical data is a visual object existing in the real world, and the logical data is a non-physical entity object required for providing localization processing or strategy distribution; by way of example, S111The subscript variable of (1) is sequentially a road section number, a route number and a road network number from left to right, wherein the road section number is the same route numberNumbering different road sections under the number; the route number is an administrative level road code specified according to the traffic industry standard, is a national unique code, and is identified by a serial number for convenience of description, wherein each serial number corresponds to a route of different road codes, and the following steps are the same; the road network number is the only number of the road network in different areas;
(3) setting an Intelligent roadside facility EiSpace coverage Cr ofijAnd is combined with the high-precision map data set AmapPerforming overlay analysis to ensure AmapAll or part of CrijIn the spatial domain, thereby determining the intelligent road side facility EiPosition pile number K capable of being laidiAnd a communication unit position stake number set K ═ Kij|(i，j)}。
(4) The position stake number K is organized and processed through dataiIntelligent road side facility EiLoading with Crij(0>j>r, where r is access to an intelligent roadside facility EiNumber of communication antennas) high-precision physical map data in the coverage area, E for data encodingiSnmzIndicates and will successfully load to EiHigh-precision map data assignment identification attribute Ei(ii) a If EiAnd Ei+1The coverage area of the map data is overlapped, and the high-precision map physical data codes of the overlapped areas are respectively used as EiSnmz&Ei+qSnmzRepresents, respectively, loading into EiAnd Ei+1At the same time will successfully load to EiAnd Ei+1The high-precision map data are respectively assigned with the identification attributes EiAnd Ei+1. Wherein q represents EiAnd Ei+1The number of overlapping areas.
(5) After the processing of the step (4), performing space-time data organization according to the hierarchy of road side-road section (route/channel) -center (area); FIG. 6 is a spatiotemporal data organization; the spatio-temporal data organization comprises object types, storage forms, spatial logic relations and transmission data forms of spatio-temporal data, wherein the storage forms comprise storage environments of intelligent road side facilities deployed on roads and data formats, data distribution nodes, data node relations and application interfaces of the spatio-temporal data; the transmission data form comprises data coding forms such as ASN.1, GML, GeoJSON and the like.
(6) After the processing of the step (5), establishing a roadside-level high-precision position map E1To EnThe spatial logic correlation is carried out between the intelligent roadside facility and the intelligent roadside facility, and the second-level data is updated according to the position update, so that the authenticity, effectiveness and accuracy of the second-level data are maintained; establishing road section/route/channel level (area level) data R1To RuThe spatial logic corresponding relation between the position and the position data is realized, and the fusion processing with the roadside high-precision position data is realized according to the cycle time T1(second level) data synchronization and updating; establishing corresponding relation between center/area level and road section/route/channel level data, carrying out data convergence processing and visualization processing, and processing according to period time T2The synchronization and updating of the data is performed (in minutes).
(7) After the processing of the steps (1) to (6), the external field monitoring facility and the intelligent road side facility E are connected1To EnLocal network/wireless network communication is carried out, and high-bandwidth, stable and reliable data transmission is guaranteed; background management platform facility (deployable in E) for external field monitoring facility1To En) The system is interconnected with road section/route/channel management platform facilities, and high-bandwidth, stable and reliable data transmission is guaranteed; intelligent road side facility E1To EnAnd the system can be interconnected with an external field monitoring facility, a background management platform facility of the external field monitoring facility, a road section/route/channel management platform and the like based on a cloud platform mode.
(8) After the processing in the step (7), sensing and accessing multi-source traffic data (including security authentication information, key information and the like) in real time, classifying according to mobile terminal facilities, different types of outfield road-side facilities (including ETC portal systems and the like), different types of software platforms and network information security facilities, respectively establishing protocol interfaces with intelligent road-side facilities, and respectively using M to respectivelyin、Fin、Pin、SinCarrying out access protocol category identification, wherein the heartbeat periods of protocol access are respectively TM、TF、TP、Ts；
(9) When a newly-added mobile terminal facility, an external field road side facility and a software platform are accessed to an intelligent road side facility, adaptive perception of multi-source traffic data can be realized only by configuring a protocol type identifier, and edge storage or online transmission of traffic operation data, traffic event data, environment data, vehicle driving data, vehicle operation data, road surface condition data, infrastructure condition data, emergency control, dynamic traffic control and the like can be performed according to the type of an access protocol; the specific data of the adaptive perception of the multi-source traffic data also comprise ETC charging data, delivery data, external system intelligent access data and other protocol access, and heartbeat data can be automatically adapted to a data interface according to protocol identification and established according to preconfiguration;
(10) based on the edge computing capability and the space-time computing analysis of the intelligent road side facility, calculating and calculatingiCr in coverage areaijAverage speed of vehicles periodically connected
Below threshold speed VlimitCalculating to generate a congestion event, and real-timely connecting the intelligent road side facility EiMap data A of coveragemapDistributing the congestion information to a vehicle-mounted terminal or a software platform (such as an information distribution platform), identifying the coordinate position of the start point and the stop point of the congestion information, calculating the position of the pile number of the start point and the stop point of the congestion information through a reference point space-time calculation model, and meanwhile, enabling the intelligent road side facility to be based on EiDetected congested vehicle speed changes (e.g., continuously maintained at V)limitThe following time is 10s), and whether to carry out real-time E calculation by using an event influence analysis and duration estimation methodiCongestion information with stake number position calculated within coverage upstream Ei-1Is transmitted and is in Ei-1Issue congestion information if
Above threshold speed VlimitAnd when the congestion information is received, the congestion information is released and the congestion state mark of the corresponding section is released.
Wherein
(m is the present EiNumber of vehicles in a coverage area), special vehicle-mounted terminals such as OBUs are accessed through a special short-range communication mode, and other vehicle position and speed information can be accessed through sensing of external field monitoring facilities (such as radar detectors).
(11) Calculating the intelligent road side facility E in real time based on the edge calculation capability and the space-time calculation analysis of the intelligent road side facilityiBy TM、TF、TP、TsProtocol class of time period access is Min、Fin、Pin、SinAfter abnormal data are eliminated, traffic parameters, traffic event information (including corresponding pile numbers or road mark point positions), environmental information (including low visibility, icy road and the like) and the like are calculated in real time, and network information is subjected to security certificationiIdentified traffic operation data, traffic event data, environmental data, vehicle travel data, vehicle operation data, road condition data, infrastructure condition data, and data for emergency management and control, dynamic traffic control, etc., and intelligent road side facility EiThe stored space physical unit data and the identified bad road section data are subjected to fusion superposition analysis, and the serial number E of the cross-road side intelligent facility issued by the space-time reference calculation model and the event analysis model calculation strategy is utilizedi～EnMeanwhile, distributed association processing of the cross-intelligent roadside facility is carried out, and the position or the range (the starting and stopping point position or the electronic fence) of the dynamic pile number under the abnormal traffic condition is calculated;
(12) step (11) processing and generating traffic event information (including corresponding pile numbers or positions of road marking points and influenced lanes), environment data (including low visibility, icy road and the like), abnormal infrastructure data information (positions or ranges of abnormal facilities) and the like, wherein the intelligent roadside facility E is influenced by the abnormal infrastructurei～EnGiving the corresponding event category identification and the intelligent road side facility identification to the data generated by the processing of the step (11);
(13) setting maintainable scene categories on the basis of industry standard specificationsLibrary WxCombining the class identifier generated in step (12) with WxThe scene identification in (1) is matched, if there is matched scene type WxAccording to W in the scene libraryxIf no scene category which can be matched exists, the scene category can be automatically supplemented through a self-learning algorithm or increased through a man-machine entry mode, wherein x belongs to Z.
(14) Comparing and analyzing the data generated in the step (12) and the step (13) by using a correlation analysis method, and performing superposition analysis on the data and the spatial positions of the nodes of the intelligent road side facility, wherein the intelligent identification is performed at the time Ei～EnThe type of scene that needs to be executed.
(15) E is calculated through the processing of the steps (10) to (14)i～EnBy Tm、Tf、Tp、TsM of periodic accessin、Fin、Pin、SinMulti-source traffic data of agreement type, noted { EDtmi，EDtfi，EDtpi，EDtsiAnd calculating and identifying an event data set { WED) under the scene conditiontmi，WEDtfi，WEDtpi，WEDtsi|WEDtmi∈EDtmi，WEDtfi∈EDtfi，WEDtpi∈EDtpi，WEDtsi∈EDtsiThen calculate the event position { K ] in the event data setmi，Kfi，KpiAnd its dynamic range of influence DR { { DK { }mi，SPEEDmi}，{DKfi，SPEEDfi}，{DKpi，SPEEDpiE is calculated through the superposition analysis of DR elementsiMileage set A from event Range boundaryL＝{Lmi，Lfi，Lpi}; distributed policy calculation is carried out by adopting a space-time differentiation policy calculation model, and dynamic unloading and management are based on an E-based data volume, calculation resources and service distance three-element modeli～EnProcessing and generating the differentiated strategy.
(16) Processed in step (15) to generate ALAccording to the intelligent roadside facility EiSensing the real-time position (coordinates or pile number) of the accessed vehicle, and calculating and generating a STRATEGY set STRATEGY { STRATEGY }mi,STRATEGYfi,STRATEGYpiI belongs to N, and uses intelligent road side facility EiThe communication unit realizes communication interaction with the vehicle, and can receive strategy request of the vehicle/vehicle-mounted terminal and passively send EiService information or event information. Wherein the intelligent road side facility EiAnd the communication unit to which it belongs is authorized by security trust, including an intelligent road side facility EiThe method comprises the steps of security authentication, encryption processing for generating policy information, security distribution of information in a hybrid communication network (including but not limited to various hybrid modes of wired communication and wireless communication, private short-range communication and Ethernet communication, public network communication and private network communication and the like), and credible evaluation and security early warning in the aspects of identity, behavior and capability in the transmission and distribution process of the policy information.
The invention uses the road side high-precision map data and effectively organizes the data, adapts to and fuses multi-source space-time traffic data of intelligent road side facility nodes, carries out intelligent identification of intelligent road side execution scenes, the method has the advantages that the method can be used for carrying out local effect domain calculation and differentiation accurate control strategy generation of cross-intelligent roadside facility nodes, organization and distribution of high-precision characteristic identification map data of the cross-intelligent roadside facility nodes under the over-the-horizon linear/networked condition are achieved, multi-source space-time traffic data adaptability perception and scene intelligent identification of the cross-intelligent roadside facility nodes are achieved, local effect domain calculation and differentiation strategy generation of the cross-intelligent roadside facility nodes are achieved, low-delay, fine, high-timeliness and intelligent control decision support is provided for application of vehicle-road cooperative information service, intelligent driving and the like, and comprehensive interaction service capability and quick response capability of intelligent control on single and group traveling are improved.
Preferably, the data organization processing of step (4) is road side facility storage and organization processing based on static physical data, and may also be organized according to dynamic spatiotemporal data. Calculate intelligent trackside facility EiPosition of the pile number KiCalculating the coordinate value Rd of the reference point according to GIS dynamic segmentation theoryiCoverage area ofLinear reference coordinate sequence Rc ═ { r) on road section1,r2,…,rgWherein n is EiNumber of linear reference coordinate points, r, meeting application needs in a coverage space-time rangegIndicating the value of the g-th linear reference coordinate. Will act on EiIs marked as EiRc, if EiAnd Ei+1Has an overlapping area, the dynamic space-time reference data in the overlapping area is marked as EiRc&Ei+qRc, each at EiAnd Ei+1Dynamically calling during application; wherein, intelligence road side facility EiRead with EiAnd identifying bad road section information or dangerous road sections simultaneously by the spatial data physical units in the space-time range.
Preferably, the field monitoring facility and the intelligent road side facility E in the step (7)1To EnLocal network/wireless network communication is carried out, high-bandwidth, stable and reliable data transmission is guaranteed, and meanwhile intelligent road side facility E1To EnThe local network/wireless network high-speed communication is adopted, the network characteristics of high bandwidth and low time delay are provided, the intra-group and inter-group interconnection of grouping can be carried out according to different networking modes (looped networks, star networks and the like), and E can be realized1To EnHigh-reliability data transmission among nodes of the cross-intelligent road side facility, and dynamic multi-node cooperation management is carried out according to three element models of Data Volume (DV), computing resources (PR) and service distance (SL), FIG. 7 is a cooperation management flow chart generated by local computing and differentiated strategies of the cross-intelligent road side facility nodes, the intelligent road side facility can comprise one or both of edge computing nodes and road side communication antennas, and road side information interaction is carried out with a vehicle road cooperation management and control scheduling and monitoring module. Wherein, intelligence road side facility E1To EnReal-time sensing various data from one or more of external field monitoring facilities, monitoring management software module (platform) and mobile terminal facilities, and intelligent road side facility EiIs calculated resource utilization rate lambdai＝[(DVi+δDVk-n)*fmi]/PRiCalculating the time delay ti＝(DVi+δDVk-n)/[(λi*PRi)*fi]+δDViB + δ t, intelligent road side facility EiCalculated policy service distance SLi＝Crij±δk-n。
DViFor intelligent road side facilities EiAccess data amount of (a); delta DVk-nIndicating the k-th to nth roadside Smart facilities to offload to EiThe amount of data to be calculated; fmiRepresenting roadside Intelligent facility EiThe computing resources required for the unit data of (a); PRiRepresenting roadside Intelligent facility EiComputing resource of (2), take λ0＝0.7；fiRepresents the amount of processing data per unit of computing resource; delta DViRepresenting Intelligent roadside facility EiThe amount of data offloaded to other nodes; b represents the transmission bandwidth between intelligent road side facilities; δ t denotes roadside Intelligent facility EiThe required queuing time of (2); deltak-nRepresenting the distance of the superposition area of the nodes of the cross-intelligent road side facility, and then, obtaining a cross-intelligent road side facility node cooperation management model Emod＝{Optimum(DVi,PRi,SLi) And | i belongs to N }, and is used for generating an optimized management model for the intelligent road side facility strategy.
Preferably, in the step (11), based on the edge computing capability and the space-time computing analysis of the intelligent roadside facility, the identification and judgment and situation prediction of different types of traffic events/states are performed by applying an identification and analysis algorithm of the traffic events/states based on lane-level data, wherein the events/states are marked as ETi_(i _ th event calculated by the present model); generating a state event or an early warning prompting event of the bad road section, the key point section or the potential risk structure by using a key point section or the bad road section or a structure condition identification model with potential risk and the like, wherein the event is recorded as STi_(i _ th event calculated by the present model); pushing events by utilizing access or central level analysis of platform or report information, performing space-time range-based overlay analysis on the pushed events and space-time data of intelligent road side facilities, and generating information service or information events and influence ranges, wherein the events are marked as PTi_(i _ th event calculated by the present model); traffic control model using channel or road network levelPerforming region/road segment level traffic handling strategy generation, wherein the management strategy event is recorded as GTi_(i _ th event calculated by the present model); model processing is carried out by utilizing mobile terminal access data or internet of vehicles information, vehicle events (special events, fault events and the like) are generated through analysis, and the events are recorded as MTi_(event i _ th calculated by the model), the above was subjected to a spatial-temporal range overlay analysis with the spatio-temporal data of the intelligent roadside facility management to calculate EiAnd differentiating the strategy information by the intelligent road side station and distributing the strategy information in the action domain. Based on the above, the localization domain calculation and the differentiation strategy processing of the cross-intelligent road side facility node take an event as an example, and the flow is as follows:
1) scene category and spatial location identification
Scene category identification and calculation of intelligent road side facility E through event credible analysis algorithmiPerceived Event set Event ═ { ET ═ ETi,STi,PTi,GTi,MTiI belongs to N, multi-source data fusion calculation analysis is carried out on the data set Event, and Event position and E are carried out on the basis of intelligent roadside facility space informationiMatching processing of stored or associated road sections to obtain the original stake number position, K, of the Event in the EventEvent_ih(f (Ex, Ey), f '(Ex, Ey) }, wherein Ex represents the longitude value or the x coordinate value of the intelligent roadside facility, Ey represents the latitude value or the y coordinate value of the intelligent roadside facility, f (Ex, Ey) represents the pile number value of the starting point corresponding to the coordinate (Ex, Ey), f' (Ex, Ey) represents the pile number value of the stopping point corresponding to the coordinate (Ex, Ey), and K is providedEvent_ihRepresenting roadside Intelligent facility E in Event setiThe pile number value of the sensed h-th type event, h belongs to N;
2) roadside influence range generation
Calculating original spatial data of the event influence range according to the calculation result in the step 1) and by combining the initial influence range of the event or the length of the influence road section, and recording the original spatial data as EventScope { NodeList, LinkList and PolygonList }, wherein NodeList is a spatial coordinate list, LinkList is a spatial geometric line layer list, and PolygonList is a spatial geometric plane layer list; calculating the influence or propagation speed of the event in unit time by using an event influence analysis model or a diffusion modelDegree EspeedThen, the event influence range within time t from the event occurrence is tEventScope ═ eventgope + f (eventgope, t × E)speed)|t∈R}；
3) Intelligent roadside facility space-time data overlay analysis
According to the calculation result of the step 2), and the intelligent road side facility EiSignal coverage area CrijGenerating layer EiScope, using GIS space superposition analysis to generate Eventscope and EiScope overlap region EVScope;
4) differentiated policy organization and scope computation
According to the processing of the steps 1) to 3), high-precision map data and a high-precision position matching technology are utilized, and according to different intelligent road side facilities EiAnd an Event influence range tEventScope at the time t, different strategy prompts or early warning data are formulated by combining the driving position of the vehicle, and control measures or early warning data based on the lane are formed. At the same time, according to the intelligent roadside facility EiSignal coverage of, fuse tEventScope and EiScope, to derive Scope data STRATEGYScope { (tEventscope, E) of the differentiated policyiScope)|i∈N，t∈R}；
5) Intelligent policy fusion and generation
Intelligent road side facility E according to event categoryiThe space position of the Event collection is based on the scene type library W in the influence range of various events in the Event collectionx(x-th scene class) lane Speed limit Speedlimit-s[y]Lane control Lane [ e ]]And (e is 1, forbidding, e is 2, limiting speed, e is 3, changing lanes, e is 4, passing), early warning prompting, traffic control and other strategies are automatically spliced and generated. Wherein e belongs to N, the smaller the value of e, the stricter the control measure, Speedlimit-s＝min{Speedlimit-s[y]Y belongs to N }; lane control Lane [ e ]]E in the sequence of 1-4, if different e are generated for the same lane, calculating according to the minimum value; the strategy content is processed according to the sequence of bypassing, prompting, early warning and control, if different processing strategies appear in the same space-time range, the strategy content is sequentially prompted and bypassed according to priority control and secondary early warningPriority calculation of lines, etc.; if the dynamic and static traffic control or prompt strategies exist, the dynamic traffic control strategy is prioritized, and other strategy contents are the same, so that the intelligent road side facility E can be formed finallyiOrganizing and managing the content and distributing the content according to the strategy; wherein Speedlimit-s[y]And identifying a y-th group of speed limit sets of the s-th lane calculated from right to left by taking the driving direction as a reference, if a static speed limit value specified by the traffic infrastructure exists, defaulting to be the static speed limit value of the traffic infrastructure, if a dynamic traffic speed limit value exists, performing priority control on the dynamic speed limit value, and if no dynamic speed limit value and no static speed limit value exist, taking the design speed limit value as the default speed limit value.
6) In steps 1) to 5), in the intelligent road side facility Ei～EnIn each link of space-time differential strategy calculation, dynamic unloading and cooperative management E based on a data volume, calculation resources and service distance three-element model are adoptedi～EnThe differentiated data processing of (1).
Compared with the prior art, the technical scheme of the invention has the following technical characteristics and beneficial effects:
(1) the method is a traffic intelligent control and service release strategy method suitable for edge calculation, and realizes the support application of lane-level data at a roadside end based on the high-precision time-space data organization and distribution of intelligent roadside;
(2) the adaptive perception and scene intelligent identification of multi-source time-space traffic data are combined, the spatial calculation and accurate matching of local traffic control and service strategies are carried out, the data time-space perception and lane-level strategy calculation with edge calculation as a core are realized, the local reaction capability, traffic induction capability, vehicle operation monitoring service capability and the like of the road operation environment of a channel and a road section level are improved, the second-level response of local processing and operation monitoring is realized, and the traffic safety level is improved;
(3) and multi-stage and low-delay interactive application of vehicle-mounted, road side and center is realized. Aiming at poor driving sight distance, poor driving road surface condition, traffic operation events and the like, the method can effectively issue early warning prompts, traffic control and other strategies in time through linkage of continuously deployed intelligent road side facility nodes based on vehicle-road cooperative management and control scheduling; aiming at timely discovery and early warning of dangerous vehicle fault events (or goods dangerous abnormal events), acquisition and processing of vehicle real-time positioning information, judgment processing and early warning of localized traffic events and the like, traffic operation scene characteristics can be adaptively sensed based on interactive shared data of nodes of intelligent road-side facilities, and meanwhile, processing, generation and distribution of road-side differentiated strategies are realized by combining cooperative management and control scheduling of a vehicle and a road, including safe encryption processing of strategy information and safe distribution of information under a hybrid communication network, so that the service capability of a traffic facility supply side, which is adaptive to the strategy information, particularly vehicle and road cooperative application and future automatic driving application, is provided for intelligent traffic.
The above description is only a preferred embodiment of the present invention, and is not intended to limit the present invention in any way, and all simple modifications, equivalent variations and modifications made to the above embodiment according to the technical spirit of the present invention still belong to the protection scope of the technical solution of the present invention.
Claims (5)
1. The intelligent traffic control and service release strategy method adapting to edge computing is characterized in that: the method comprises the following steps:
(1) under the road section environment of a newly-built intelligent road side facility, lane grade high-precision map data is obtained by utilizing a plurality of collection and combination modes, and three-dimensional measurable live-action image data collection, remote sensing image data analysis processing, radar equipment collection and unmanned aerial vehicle collection modes are adopted; the data result has object type, logic relation and timeliness identification attribute; the intelligent road side facilities comprise intelligent base stations controlled by taking edge calculation as nodes and lane distribution facilities;
(2) according to the high-precision map data result obtained in the step (1), performing physical segmentation processing on a high-precision map data object according to the traffic structure feature points and the uplink and downlink directions, wherein the high-precision map data set is as follows:
Amap＝{Snmz|(n,m,z)}
wherein z is road network code, m is route code in road network code z, nSequence number, S, of physically divided minor segments in route mnmzRepresenting the nth split physical segment data set in the route with the route code of m in the road network z; the high-precision map data is identification map data with time characteristics in a certain spatial range of the intelligent road side facility, comprises physical data and logic data, and is used for road side traffic control and service strategy generation and distribution range and timeliness determination; the identification map data is used for the roadside intelligent facility to provide spatial objects required by the localized traffic control and service strategy distribution, and comprises road marking lines, lanes, road structures, monitoring facilities, safety facilities and related line facilities in the forms of points, lines, surfaces and the like under different time periods, and the attribute data of the spatial objects comprises object types, intelligent roadside facility identifications and spatial relationships; the identification map is segmented according to the layout position of the intelligent road side facility and the space-time range calculated by the intelligent road side facility, and meanwhile, the high-precision data of the road are processed to form a minimum physical segment divided by feature units; the physical data is a visual object existing in the real world, and the logical data is a non-physical entity object required for providing localization processing or strategy distribution;
(3) setting an Intelligent roadside facility EiSpace coverage Cr ofijAnd is combined with the high-precision map data set AmapPerforming overlay analysis to ensure AmapAll or part of CrijIn the spatial domain, thereby determining the intelligent road side facility EiPosition pile number K of layingiAnd a communication unit position stake number set K ═ Kij|(i，j)}，CrijFor the ith intelligent road side facility EiThe space coverage range of (1) is 360-degree coverage range, a circular area or an elliptical area, wherein the circular area Cr isijIs a radial distance, if it is an elliptical region, CrijThe distance from the center of the ellipse to the farthest point of the ellipse, i is the serial number of the node of the intelligent road-side facility preset in the road network z, j is the intelligent road-side facility EiThe number of the accessed communication unit;
(4) the position stake number K is organized and processed through dataiIntelligent road side facility EiLoading with Crij，0>j>r, whereinr is an access to an intelligent roadside facility EiNumber of communication antennas, high-precision physical map data in the coverage area, E for data encodingiSnmzIndicates and will successfully load to EiHigh-precision map data assignment identification attribute Ei(ii) a If EiAnd Ei+1The coverage area of the map data is overlapped, and the high-precision map physical data codes of the overlapped areas are respectively used as EiSnmz&Ei+qSnmzRepresents, respectively, loading into EiAnd Ei+1At the same time will successfully load to EiAnd Ei+1The high-precision map data are respectively assigned with the identification attributes EiAnd Ei+1，
Wherein q represents EiAnd Ei+1The number of overlapping regions of (a);
(5) after the processing in the step (4), performing space-time data organization according to the hierarchy of road side-road section-region; the spatio-temporal data organization comprises object types, storage forms, spatial logic relations and transmission data forms of spatio-temporal data, wherein the storage forms comprise storage environments of intelligent road side facilities deployed on roads and data formats, data distribution nodes, data node relations and application interfaces of the spatio-temporal data; the transmission data form comprises ASN.1, GML and GeoJSON data coding forms;
(6) after the processing of the step (5), establishing a roadside-level high-precision position map E1To EnThe spatial logic correlation is carried out between the intelligent roadside facility and the intelligent roadside facility, and the second-level data is updated according to the position update, so that the authenticity, effectiveness and accuracy of the second-level data are maintained; establishing road section/route/channel level data R1To RuThe spatial logic corresponding relation between the position and the position data is realized, and the fusion processing with the roadside high-precision position data is realized according to the cycle time T1Carrying out data synchronization and updating; establishing corresponding relation between center/area level and road section/route/channel level data, carrying out data convergence processing and visualization processing, and processing according to period time T2Carrying out data synchronization and updating;
(7) with outfield monitoring facility and intelligent roadside facility E1To EnPerforming local network/wirelessThe network is connected, and high-bandwidth, stable and reliable data transmission is guaranteed; the background management platform facility of the external field monitoring facility is interconnected with the road section/route/channel management platform facility, and high-bandwidth, stable and reliable data transmission is guaranteed; intelligent road side facility E1To EnInterconnection and intercommunication with an external field monitoring facility, a background management platform facility of the external field monitoring facility, a road section/route/channel management platform and the like can be realized based on a cloud platform mode;
(8) the method comprises the steps of sensing and accessing multi-source traffic data in real time, classifying according to mobile terminal facilities, different types of outfield road side facilities, different types of software platforms and network information security facilities, respectively establishing protocol interfaces with intelligent road side facilities, and respectively using M to respectively establish protocol interfacesin、Fin、Pin、SinCarrying out access protocol category identification, wherein the heartbeat periods of protocol access are respectively TM、TF、TP、Ts；
(9) When a newly-added mobile terminal facility, an outfield road side facility and a software platform are accessed to an intelligent road side facility, adaptive perception of multisource traffic data can be realized only by configuring a protocol type identifier, and traffic operation data, traffic event data, environment data, vehicle running data, vehicle operation data, road surface condition data, infrastructure condition data, emergency control and edge storage or online transmission of dynamic traffic control can be performed according to the type of an access protocol; the specific data of the adaptive perception of the multi-source traffic data further comprise ETC charging data, delivery data and the protocol access of external system intelligent access data, and heartbeat data are automatically adapted to a data interface according to protocol identification and established according to preconfiguration;
(10) based on the edge computing capability and the space-time computing analysis of the intelligent road side facility, calculating and calculatingiCr in coverage areaijAverage speed of vehicles periodically connected
Below threshold speed VlimitCalculating to generate a congestion event, and real-timely connecting intelligent road sidesFacility EiMap data A of coveragemapDistributing the congestion information to a vehicle-mounted terminal or a software platform, identifying the coordinate position of the start point and the stop point of the congestion information, calculating the position of the start point and the stop point pile number of the congestion information through a reference point space-time calculation model, and meanwhile, calculating by the intelligent road side facility according to EiDetecting the speed change of the jammed vehicle, and calculating whether to compare E with E in real time by using an event influence analysis and duration estimation methodiCongestion information with stake number position calculated within coverage upstream Ei-1Is transmitted and is in Ei-1Issue congestion information if
Above threshold speed VlimitWhen the congestion information is received, the congestion information is released, and the congestion state mark of the corresponding section is released; wherein
m is the current EiThe number of vehicles in a coverage area is loaded with special vehicle-mounted terminals such as OBUs and the like, and the special vehicle-mounted terminals are accessed in a special short-range communication mode, and the position and speed information of other vehicles can be accessed in a sensing mode through an external field monitoring facility;
(11) calculating the intelligent road side facility E in real time based on the edge calculation capability and the space-time calculation analysis of the intelligent road side facilityiBy TM、TF、TP、TsProtocol class of time period access is Min、Fin、Pin、SinAfter the abnormal data are removed and the network information security authentication is carried out, the traffic parameters, the traffic incident information and the environment information are calculated in real time, and E which is subjected to the network information security authentication is carried outiIdentified traffic operational data, traffic event data, environmental data, vehicle travel data, vehicle operational data, road condition data, infrastructure condition data, and emergency management and control, dynamic traffic control data, and intelligent road side facility EiThe stored space physical unit data and the identified bad road section data are subjected to fusion superposition analysis, and a cross road issued by a space-time reference calculation model and an event analysis model calculation strategy is utilizedNumber E of side intelligent facilityi～EnMeanwhile, distributed association processing of the cross-intelligent roadside facility is carried out, and the position or the range of the dynamic pile number under the abnormal traffic condition is calculated;
(12) step (11) processing and generating traffic event information, environment data and abnormal infrastructure data information, wherein the traffic event information, the environment data and the abnormal infrastructure data information are influenced by the intelligent road side facility Ei～EnGiving the corresponding event category identification and the intelligent road side facility identification to the data generated by the processing of the step (11);
(13) setting a maintainable scene category library W on the basis of industry standard specificationsiCombining the class identifier generated in step (12) with WiThe scene identification in (1) is matched, if there is matched scene type WiAccording to W in the scene libraryiIf no scene category which can be matched exists, automatically supplementing the scene category through a self-learning algorithm or increasing the scene category through a man-machine input mode;
(14) comparing and analyzing the data generated in the step (12) and the step (13) by using a correlation analysis method, and performing superposition analysis on the data and the spatial positions of the nodes of the intelligent road side facility, wherein the intelligent identification is performed at the time Ei～EnThe type of scene to be executed;
(15) e is calculated through the processing of the steps (10) to (14)i～EnBy Tm、Tf、Tp、TsM of periodic accessin、Fin、Pin、SinMulti-source traffic data of agreement type, noted { EDtmi，EDtfi，EDtpi，EDtsiAnd calculating and identifying an event data set { WED) under the scene conditiontmi，WEDtfi，WEDtpi，WEDtsi|WEDtmi∈EDtmi，WEDtfi∈EDtfi，WEDtpi∈EDtpi，WEDtsi∈EDtsiThen calculate the event position { K ] in the event data setmi，Kfi，KpiAnd its dynamic range of influence DR { { DK { }mi，SPEEDmi}，{DKfi，SPEEDfi}，{DKpi，SPEEDpiE is calculated through the superposition analysis of DR elementsiMileage set A from event Range boundaryL＝{Lmi，Lfi，Lpi}; performing distributed policy calculation by adopting a space-time differentiation policy calculation model, and dynamically unloading and managing the E after passing the network information security authentication based on a three-element model of data volume, calculation resources and service distancei～EnProcessing and generating the differentiation strategy;
(16) processed in step (15) to generate ALAccording to the intelligent roadside facility EiSensing the real-time position of the accessed vehicle, and calculating and generating a STRATEGY set STRATEGY { STRATEGY }mi,STRATEGYfi,STRATEGYpiI belongs to N, and uses intelligent road side facility EiThe communication unit realizes communication interaction with the vehicle, simultaneously receives strategy requests of the vehicle/vehicle-mounted terminal and passively sends EiService information or event information, wherein the Intelligent road side facility EiAnd the communication unit to which it belongs is authorized by security trust, including an intelligent road side facility EiThe method comprises the steps of security authentication, encryption processing of generated strategy information, security distribution of information under a hybrid communication network, and credible evaluation and security early warning in the aspects of identity, behavior and capability in the transmission and distribution processes of the strategy information.
2. The method for intelligent traffic control and service release strategy adapting to edge computing according to claim 1, characterized in that: the data organization processing of the step (4) is road side end facility storage and organization processing based on static physical data, or organization according to dynamic space-time data; calculate intelligent trackside facility EiPosition of the pile number KiCalculating the coordinate value Rd of the reference point according to GIS dynamic segmentation theoryiLinear reference coordinate sequence Rc ═ { r) of road section in coverage area1,r2,…,rgWherein n is EiNumber of linear reference coordinate points, r, meeting application needs in a coverage space-time rangegRepresenting the value of the g-th linear reference coordinate; will act on EiDynamic space-time ofReference data mark EiRc, if EiAnd Ei+1Has an overlapping area, the dynamic space-time reference data in the overlapping area is marked as EiRc&Ei+qRc, each at EiAnd Ei+1Dynamically calling during application; wherein, intelligence road side facility EiRead with EiAnd identifying bad road section information or dangerous road sections simultaneously by the spatial data physical units in the space-time range.
3. The method for intelligent traffic control and service release strategy adapting to edge computing according to claim 2, characterized in that: the field monitoring facility and the intelligent road side facility E in the step (7)1To EnLocal network/wireless network communication is carried out, high-bandwidth, stable and reliable data transmission is guaranteed, and meanwhile intelligent road side facility E1To EnThe local network/wireless network high-speed communication is adopted, the network characteristics of high bandwidth and low time delay are provided, the intra-group and inter-group interconnection of grouping is carried out according to different networking modes, and E is realized1To EnHigh-reliability data transmission among nodes of the intelligent road side facility is crossed, dynamic multi-node cooperation management is carried out according to a three-element model of data volume, calculation resources and service distance, the intelligent road side facility comprises one or both of edge calculation nodes and road side communication antennas, and road side level information interaction is carried out with a vehicle road cooperation management and control scheduling and monitoring module; wherein, intelligence road side facility E1To EnThe intelligent road side facility E senses various data from one or more of an external field monitoring facility, a monitoring management software module and a mobile terminal facility in real timeiIs calculated resource utilization rate lambdai＝[(DVi+δDVk-n)*fmi]/PRiCalculating the time delay ti＝(DVi+δDVk-n)/[(λi*PRi)*fi]+δDViB + δ t, intelligent road side facility EiCalculated policy service distance SLi＝Crij±δk-n；DViFor intelligent road side facilities EiAccess data amount of (a); delta DVk-nIndicating the k-th to nth roadside Smart facilities to offload to EiThe amount of data to be calculated; fmiRepresenting roadside Intelligent facility EiThe computing resources required for the unit data of (a); PRiRepresenting roadside Intelligent facility EiComputing resource of (2), take λ0＝0.7；fiRepresents the amount of processing data per unit of computing resource; delta DViRepresenting Intelligent roadside facility EiThe amount of data offloaded to other nodes; b represents the transmission bandwidth between intelligent road side facilities; δ t denotes roadside Intelligent facility EiThe required queuing time of (2); deltak-nRepresenting the distance of the superposition area of the nodes of the cross-intelligent road side facility, and then, obtaining a cross-intelligent road side facility node cooperation management model Emod＝{Optimum(DVi,PRi,SLi) And | i belongs to N }, and is used for generating an optimized management model for the intelligent road side facility strategy.
4. The method for intelligent traffic control and service release strategy adapting to edge computing according to claim 3, characterized in that: in the step (11), based on the edge computing capability and the space-time computing analysis of the intelligent road side facility, the identification and judgment and the situation prediction of different types of traffic events/states are carried out by applying an identification and analysis algorithm of the traffic events/states based on lane-level data, wherein the events/states are marked as ETi_(ii) a Generating a state event or an early warning prompting event of the bad road section, the key point section or the potential risk structure by using a key point section or the bad road section or the potential risk structure condition identification model method, wherein the event is recorded as STi_(ii) a Pushing events by utilizing access or central level analysis of platform or report information, performing space-time range-based overlay analysis on the pushed events and space-time data of intelligent road side facilities, and generating information service or information events and influence ranges, wherein the events are marked as PTi_(ii) a Utilizing a channel or road network level traffic control model to generate an area/road segment level traffic handling strategy, wherein a control strategy event is recorded as GTi_I th calculated by the model_An event; model processing is carried out by utilizing mobile terminal access data or vehicle networking information, vehicle events are generated through analysis, and the events are recorded as MTi_Above, inPerforming space-time range superposition analysis with the space-time data managed by the intelligent road side facilities to calculate EiAnd differentiating the strategy information by the intelligent road side station and distributing the strategy information in the action domain.
5. The method for intelligent traffic control and service release strategy adapting to edge computing according to claim 4, characterized in that: the method comprises the following steps of calculating a localization domain of a cross-intelligent road side facility node and processing a differentiation strategy, taking an event as an example:
1) scene category and spatial location identification
Scene category identification and calculation of intelligent road side facility E through event credible analysis algorithmiPerceived Event set Event ═ { ET ═ ETi,STi,PTi,GTi,MTiI belongs to N, multi-source data fusion calculation analysis is carried out on the data set Event, and Event position and E are carried out on the basis of intelligent roadside facility space informationiMatching processing of stored or associated road sections to obtain the original stake number position, K, of the Event in the EventEvent_ih(f (Ex, Ey), f '(Ex, Ey) }, wherein Ex represents the longitude value or the x coordinate value of the intelligent roadside facility, Ey represents the latitude value or the y coordinate value of the intelligent roadside facility, f (Ex, Ey) represents the pile number value of the starting point corresponding to the coordinate (Ex, Ey), f' (Ex, Ey) represents the pile number value of the stopping point corresponding to the coordinate (Ex, Ey), and K is providedEvent_ihRepresenting roadside Intelligent facility E in Event setiThe pile number value of the sensed h-th type event, h belongs to N;
2) roadside influence range generation
Calculating original spatial data of the event influence range according to the calculation result in the step 1) and by combining the initial influence range of the event or the length of the influence road section, and recording the original spatial data as EventScope { NodeList, LinkList and PolygonList }, wherein NodeList is a spatial coordinate list, LinkList is a spatial geometric line layer list, and PolygonList is a spatial geometric plane layer list; calculating the event influence or propagation speed E in unit time by using an event influence analysis model or a diffusion model methodspeedThen there is a range of event impact within time t from event occurrence
tEventScope＝{EventScope+f(EventScope，t*Espeed)|t∈R}；
3) Intelligent roadside facility space-time data overlay analysis
According to the calculation result of the step 2), and the intelligent road side facility EiSignal coverage area CrijGenerating layer EiScope, using GIS space superposition analysis to generate Eventscope and EiScope overlap region EVScope;
4) differentiated policy organization and scope computation
According to the processing of the steps 1) to 3), high-precision map data and a high-precision position matching technology are utilized, and according to different intelligent road side facilities EiSetting different strategy prompts or early warning data by combining the vehicle driving position and forming a lane-based control measure or early warning data; at the same time, according to the intelligent roadside facility EiSignal coverage of, fuse tEventScope and EiScope, to derive Scope data STRATEGYScope { (tEventscope, E) of the differentiated policyiScope)|i∈N，t∈R}；
5) Intelligent policy fusion and generation
Intelligent road side facility E according to event categoryiThe space position of the Event collection is based on the scene type library W in the influence range of various events in the Event collectionx(x-th scene class) lane Speed limit Speedlimit-s[y]Lane control Lane [ e ]]And (e is 1, forbidding, e is 2, limiting Speed, e is 3, changing lanes, e is 4, passing), early warning prompting, traffic control and other strategies are automatically spliced and generated, wherein e belongs to N, the smaller the e value is, the stricter the control measure is, and Speed islimit-s＝min{Speedlimit-s[y]Y belongs to N }; lane control Lane [ e ]]E in the sequence of 1-4, if different e are generated for the same lane, calculating according to the minimum value; processing the strategy content according to the sequence of detour, prompt, early warning and control, and if different processing strategies appear in the same space-time range, calculating the priority of priority control, secondary early warning, sequential prompt, detour and the like; if the dynamic and static traffic control or prompt strategies exist, the dynamic traffic control strategy takes precedence,the other strategy contents are similar to each other, and finally the intelligent road side facility E can be formed according to the cross-intelligent wayiOrganizing and managing the content and distributing the content according to the strategy; wherein Speedlimit-s[y]Identifying a y-th group of speed limit sets of an s-th lane calculated from right to left by taking the driving direction as a reference, if a static speed limit value specified by a traffic infrastructure exists, defaulting to the static speed limit value of the traffic infrastructure, if a dynamic traffic speed limit value exists, performing priority control on the dynamic speed limit value, and if no dynamic speed limit value and no static speed limit value exist, taking a design speed limit value as a default speed limit value;
6) in the steps 1) to 5), each link of space-time differentiation strategy calculation is carried out in the intelligent road side facilities Ei-En, and dynamic unloading and cooperative management E based on three element models of data volume, calculation resources and service distance are adoptedi～EnThe differentiated data processing of (1).
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202010085308.6A CN111260924B (en) | 2020-02-10 | 2020-02-10 | Traffic intelligent control and service release strategy method adapting to edge calculation |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202010085308.6A CN111260924B (en) | 2020-02-10 | 2020-02-10 | Traffic intelligent control and service release strategy method adapting to edge calculation |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN111260924A true CN111260924A (en) | 2020-06-09 |
| CN111260924B CN111260924B (en) | 2021-01-26 |
Family
ID=70949231
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202010085308.6A Active CN111260924B (en) | 2020-02-10 | 2020-02-10 | Traffic intelligent control and service release strategy method adapting to edge calculation |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN111260924B (en) |
Cited By (27)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN111818189A (en) * | 2020-09-09 | 2020-10-23 | 浙江吉利控股集团有限公司 | Vehicle road cooperative control system, method and medium |
| CN111899541A (en) * | 2020-08-18 | 2020-11-06 | 河南中天高新智能科技股份有限公司 | Intelligent expressway traffic protection scheme issuing system and method |
| CN111899515A (en) * | 2020-09-29 | 2020-11-06 | 深圳市城市交通规划设计研究中心股份有限公司 | Vehicle detection system based on wisdom road edge calculates gateway |
| CN111915907A (en) * | 2020-08-18 | 2020-11-10 | 河南中天高新智能科技股份有限公司 | Multi-scale traffic information publishing system and method based on vehicle-road cooperation |
| CN112053562A (en) * | 2020-09-15 | 2020-12-08 | 黑龙江省交投千方科技有限公司 | Intelligent service open platform based on edge calculation |
| CN112203290A (en) * | 2020-09-30 | 2021-01-08 | 中国联合网络通信集团有限公司 | MEC node deployment position determining method and MEC node deployment device |
| CN112232679A (en) * | 2020-10-19 | 2021-01-15 | 杭州世创电子技术股份有限公司 | Electric vehicle and charging equipment dynamic intelligent matching method based on edge calculation |
| CN112243239A (en) * | 2020-12-21 | 2021-01-19 | 长沙理工大学 | Unmanned aerial vehicle deployment method based on overpass and related device |
| CN112330159A (en) * | 2020-11-06 | 2021-02-05 | 盐城郅联空间科技有限公司 | 3DGIS information platform management method and system based on block chain |
| CN112434924A (en) * | 2020-11-18 | 2021-03-02 | 刘凤 | Risk inspection monitoring platform based on cloud platform under full-electric-network multi-source data |
| CN112435485A (en) * | 2020-11-02 | 2021-03-02 | 南京莱斯网信技术研究院有限公司 | System for vehicle-road information cooperation |
| CN112637264A (en) * | 2020-11-23 | 2021-04-09 | 北京百度网讯科技有限公司 | Information interaction method and device, electronic equipment and storage medium |
| CN113409179A (en) * | 2021-01-26 | 2021-09-17 | 陕西交通电子工程科技有限公司 | ETC portal operation state monitoring system for expressway |
| CN113741485A (en) * | 2021-06-23 | 2021-12-03 | 阿波罗智联(北京)科技有限公司 | Control method and device for cooperative automatic driving of vehicle and road, electronic equipment and vehicle |
| CN113888871A (en) * | 2021-10-20 | 2022-01-04 | 上海电科智能系统股份有限公司 | Automatic handling linkage system and method for highway traffic incident |
| CN113936469A (en) * | 2021-12-09 | 2022-01-14 | 安徽交控信息产业有限公司 | Traffic information interaction system and method based on highway lane sensing equipment |
| CN114155447A (en) * | 2021-12-02 | 2022-03-08 | 北京中科智易科技有限公司 | Artificial intelligence big data acquisition system |
| CN114374952A (en) * | 2021-12-24 | 2022-04-19 | 联通智网科技股份有限公司 | Traffic event information sending method and device and edge cloud server |
| CN114430415A (en) * | 2022-01-17 | 2022-05-03 | 中国电子科技集团公司第十五研究所 | Intelligent control system |
| CN114812578A (en) * | 2021-01-29 | 2022-07-29 | 中交宇科（北京）空间信息技术有限公司 | Dynamic map providing method and device and service platform |
| CN114863701A (en) * | 2022-04-26 | 2022-08-05 | 北京百度网讯科技有限公司 | Traffic signal lamp control method, device, electronic equipment and medium |
| CN115713866A (en) * | 2022-10-11 | 2023-02-24 | 悉地（苏州）勘察设计顾问有限公司 | Road static information active service method based on vehicle running characteristics |
| TWI794713B (en) * | 2020-12-28 | 2023-03-01 | 鴻海精密工業股份有限公司 | Blockchain construction method and related devices |
| CN116258829A (en) * | 2023-05-15 | 2023-06-13 | 深圳市中科云科技开发有限公司 | Method and device for constructing map and vision robot |
| CN117870651A (en) * | 2024-03-11 | 2024-04-12 | 北京理工大学前沿技术研究院 | Map high-precision acquisition method, memory and storage medium based on RTK-SLAM technology |
| CN117975736A (en) * | 2024-03-29 | 2024-05-03 | 北京市计量检测科学研究院 | Unmanned vehicle road cooperative application scene test method and system |
| CN117975736B (en) * | 2024-03-29 | 2024-06-07 | 北京市计量检测科学研究院 | Unmanned vehicle road cooperative application scene test method and system |
Citations (11)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN106251620A (en) * | 2016-09-26 | 2016-12-21 | 北京东土科技股份有限公司 | Centring system based on intelligent transportation cloud control system |
| CN108022435A (en) * | 2016-11-01 | 2018-05-11 | 中国移动通信有限公司研究院 | A kind of traffic control method and system |
| CN109034578A (en) * | 2018-07-13 | 2018-12-18 | 交通运输部公路科学研究所 | A kind of composite communications transport network node different degree appraisal procedure |
| CN110176153A (en) * | 2019-05-20 | 2019-08-27 | 重庆大学 | A kind of blind area vehicle collision prewarning method based on edge calculations |
| CN110213716A (en) * | 2019-05-20 | 2019-09-06 | 北京邮电大学 | A kind of vehicle connection network-building method based on mist Radio Access Network |
| CN110288846A (en) * | 2019-06-25 | 2019-09-27 | 江西省高速公路联网管理中心 | A kind of covering system of V2X roadside unit |
| CN110349423A (en) * | 2019-06-28 | 2019-10-18 | 京东数字科技控股有限公司 | A kind of road side system based on bus or train route collaboration |
| US20190392707A1 (en) * | 2018-06-25 | 2019-12-26 | At&T Intellectual Property I, L.P. | Dynamic edge network management of vehicular traffic |
| CN110634297A (en) * | 2019-10-08 | 2019-12-31 | 交通运输部公路科学研究所 | Signal lamp state identification and passing control system based on vehicle-mounted laser radar |
| CN110647056A (en) * | 2019-10-28 | 2020-01-03 | 苏州智行众维智能科技有限公司 | Intelligent networking automobile environment simulation system based on whole automobile hardware-in-loop |
| CN110660221A (en) * | 2019-10-09 | 2020-01-07 | 浙江省交通规划设计研究院有限公司 | Information interaction method and device based on vehicle-road cooperative system |
-
2020
- 2020-02-10 CN CN202010085308.6A patent/CN111260924B/en active Active
Patent Citations (11)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN106251620A (en) * | 2016-09-26 | 2016-12-21 | 北京东土科技股份有限公司 | Centring system based on intelligent transportation cloud control system |
| CN108022435A (en) * | 2016-11-01 | 2018-05-11 | 中国移动通信有限公司研究院 | A kind of traffic control method and system |
| US20190392707A1 (en) * | 2018-06-25 | 2019-12-26 | At&T Intellectual Property I, L.P. | Dynamic edge network management of vehicular traffic |
| CN109034578A (en) * | 2018-07-13 | 2018-12-18 | 交通运输部公路科学研究所 | A kind of composite communications transport network node different degree appraisal procedure |
| CN110176153A (en) * | 2019-05-20 | 2019-08-27 | 重庆大学 | A kind of blind area vehicle collision prewarning method based on edge calculations |
| CN110213716A (en) * | 2019-05-20 | 2019-09-06 | 北京邮电大学 | A kind of vehicle connection network-building method based on mist Radio Access Network |
| CN110288846A (en) * | 2019-06-25 | 2019-09-27 | 江西省高速公路联网管理中心 | A kind of covering system of V2X roadside unit |
| CN110349423A (en) * | 2019-06-28 | 2019-10-18 | 京东数字科技控股有限公司 | A kind of road side system based on bus or train route collaboration |
| CN110634297A (en) * | 2019-10-08 | 2019-12-31 | 交通运输部公路科学研究所 | Signal lamp state identification and passing control system based on vehicle-mounted laser radar |
| CN110660221A (en) * | 2019-10-09 | 2020-01-07 | 浙江省交通规划设计研究院有限公司 | Information interaction method and device based on vehicle-road cooperative system |
| CN110647056A (en) * | 2019-10-28 | 2020-01-03 | 苏州智行众维智能科技有限公司 | Intelligent networking automobile environment simulation system based on whole automobile hardware-in-loop |
Non-Patent Citations (3)
| Title |
|---|
| HONG LIU, YAN ZHANG, AND TAO YANG: "Blockchain-Enabled Security in Electric Vehicles Cloud and Edge Computing", 《IEEE NETWORK》 * |
| 李玲琳: "基于边缘计算的动态智能交通诱导系统设计", 《电脑知识与技术》 * |
| 王 勇: "基于 5G 边缘计算技术的智慧交通分级决策系统", 《网络与通信技术》 * |
Cited By (40)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN111899541B (en) * | 2020-08-18 | 2023-01-31 | 河南中天高新智能科技股份有限公司 | Intelligent expressway traffic protection scheme issuing system and method |
| CN111899541A (en) * | 2020-08-18 | 2020-11-06 | 河南中天高新智能科技股份有限公司 | Intelligent expressway traffic protection scheme issuing system and method |
| CN111915907A (en) * | 2020-08-18 | 2020-11-10 | 河南中天高新智能科技股份有限公司 | Multi-scale traffic information publishing system and method based on vehicle-road cooperation |
| CN111818189B (en) * | 2020-09-09 | 2020-12-25 | 浙江吉利控股集团有限公司 | Vehicle road cooperative control system, method and medium |
| CN111818189A (en) * | 2020-09-09 | 2020-10-23 | 浙江吉利控股集团有限公司 | Vehicle road cooperative control system, method and medium |
| CN112053562A (en) * | 2020-09-15 | 2020-12-08 | 黑龙江省交投千方科技有限公司 | Intelligent service open platform based on edge calculation |
| CN111899515A (en) * | 2020-09-29 | 2020-11-06 | 深圳市城市交通规划设计研究中心股份有限公司 | Vehicle detection system based on wisdom road edge calculates gateway |
| CN112203290A (en) * | 2020-09-30 | 2021-01-08 | 中国联合网络通信集团有限公司 | MEC node deployment position determining method and MEC node deployment device |
| CN112203290B (en) * | 2020-09-30 | 2022-07-22 | 中国联合网络通信集团有限公司 | MEC node deployment position determining method and MEC node deployment device |
| CN112232679A (en) * | 2020-10-19 | 2021-01-15 | 杭州世创电子技术股份有限公司 | Electric vehicle and charging equipment dynamic intelligent matching method based on edge calculation |
| CN112232679B (en) * | 2020-10-19 | 2023-08-29 | 杭州世创电子技术股份有限公司 | Electric vehicle and charging equipment dynamic intelligent matching method based on edge calculation |
| CN112435485A (en) * | 2020-11-02 | 2021-03-02 | 南京莱斯网信技术研究院有限公司 | System for vehicle-road information cooperation |
| CN112435485B (en) * | 2020-11-02 | 2022-01-25 | 南京莱斯网信技术研究院有限公司 | System for vehicle-road information cooperation |
| CN112330159A (en) * | 2020-11-06 | 2021-02-05 | 盐城郅联空间科技有限公司 | 3DGIS information platform management method and system based on block chain |
| CN112434924A (en) * | 2020-11-18 | 2021-03-02 | 刘凤 | Risk inspection monitoring platform based on cloud platform under full-electric-network multi-source data |
| CN112637264A (en) * | 2020-11-23 | 2021-04-09 | 北京百度网讯科技有限公司 | Information interaction method and device, electronic equipment and storage medium |
| CN112637264B (en) * | 2020-11-23 | 2023-04-21 | 阿波罗智联(北京)科技有限公司 | Information interaction method and device, electronic equipment and storage medium |
| CN112243239B (en) * | 2020-12-21 | 2021-03-12 | 长沙理工大学 | Unmanned aerial vehicle deployment method based on overpass and related device |
| CN112243239A (en) * | 2020-12-21 | 2021-01-19 | 长沙理工大学 | Unmanned aerial vehicle deployment method based on overpass and related device |
| TWI794713B (en) * | 2020-12-28 | 2023-03-01 | 鴻海精密工業股份有限公司 | Blockchain construction method and related devices |
| CN113409179A (en) * | 2021-01-26 | 2021-09-17 | 陕西交通电子工程科技有限公司 | ETC portal operation state monitoring system for expressway |
| CN114812578A (en) * | 2021-01-29 | 2022-07-29 | 中交宇科（北京）空间信息技术有限公司 | Dynamic map providing method and device and service platform |
| CN113741485A (en) * | 2021-06-23 | 2021-12-03 | 阿波罗智联(北京)科技有限公司 | Control method and device for cooperative automatic driving of vehicle and road, electronic equipment and vehicle |
| CN113888871B (en) * | 2021-10-20 | 2023-05-05 | 上海电科智能系统股份有限公司 | Automatic handling linkage system and method for expressway traffic incidents |
| CN113888871A (en) * | 2021-10-20 | 2022-01-04 | 上海电科智能系统股份有限公司 | Automatic handling linkage system and method for highway traffic incident |
| CN114155447A (en) * | 2021-12-02 | 2022-03-08 | 北京中科智易科技有限公司 | Artificial intelligence big data acquisition system |
| CN114155447B (en) * | 2021-12-02 | 2022-06-24 | 北京中科智易科技有限公司 | Artificial intelligence big data acquisition system |
| CN113936469A (en) * | 2021-12-09 | 2022-01-14 | 安徽交控信息产业有限公司 | Traffic information interaction system and method based on highway lane sensing equipment |
| CN114374952A (en) * | 2021-12-24 | 2022-04-19 | 联通智网科技股份有限公司 | Traffic event information sending method and device and edge cloud server |
| CN114430415B (en) * | 2022-01-17 | 2024-04-16 | 中国电子科技集团公司第十五研究所 | Intelligent control system |
| CN114430415A (en) * | 2022-01-17 | 2022-05-03 | 中国电子科技集团公司第十五研究所 | Intelligent control system |
| CN114863701B (en) * | 2022-04-26 | 2024-01-16 | 北京百度网讯科技有限公司 | Traffic signal lamp control method, device, electronic equipment and medium |
| CN114863701A (en) * | 2022-04-26 | 2022-08-05 | 北京百度网讯科技有限公司 | Traffic signal lamp control method, device, electronic equipment and medium |
| CN115713866A (en) * | 2022-10-11 | 2023-02-24 | 悉地（苏州）勘察设计顾问有限公司 | Road static information active service method based on vehicle running characteristics |
| CN115713866B (en) * | 2022-10-11 | 2023-08-22 | 悉地（苏州）勘察设计顾问有限公司 | Road static information active service method based on vehicle operation characteristics |
| CN116258829A (en) * | 2023-05-15 | 2023-06-13 | 深圳市中科云科技开发有限公司 | Method and device for constructing map and vision robot |
| CN117870651A (en) * | 2024-03-11 | 2024-04-12 | 北京理工大学前沿技术研究院 | Map high-precision acquisition method, memory and storage medium based on RTK-SLAM technology |
| CN117870651B (en) * | 2024-03-11 | 2024-05-07 | 北京理工大学前沿技术研究院 | Map high-precision acquisition method, memory and storage medium based on RTK-SLAM technology |
| CN117975736A (en) * | 2024-03-29 | 2024-05-03 | 北京市计量检测科学研究院 | Unmanned vehicle road cooperative application scene test method and system |
| CN117975736B (en) * | 2024-03-29 | 2024-06-07 | 北京市计量检测科学研究院 | Unmanned vehicle road cooperative application scene test method and system |
Also Published As
| Publication number | Publication date |
|---|---|
| CN111260924B (en) | 2021-01-26 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN111260924B (en) | Traffic intelligent control and service release strategy method adapting to edge calculation | |
| CN110930747B (en) | Intelligent internet traffic service system based on cloud computing technology | |
| Kumar et al. | A novel Software-Defined Drone Network (SDDN)-based collision avoidance strategies for on-road traffic monitoring and management | |
| Astarita et al. | The use of adaptive traffic signal systems based on floating car data | |
| US20180288502A1 (en) | Information collection system and information collection apparatus | |
| Kong et al. | Multimedia data fusion method based on wireless sensor network in intelligent transportation system | |
| JP6994203B2 (en) | Highway system for connected self-driving cars and methods using it | |
| CN104751628A (en) | Method and system for monitoring vehicle | |
| CN110807917A (en) | Highway intelligent security system based on cloud calculates | |
| Rezaei et al. | ReFOCUS: A hybrid fog-cloud based intelligent traffic re-routing system | |
| CN112712729A (en) | Method and system for predicting motion trajectory | |
| Nayak et al. | PHVA: A position based high speed vehicle detection algorithm for detecting high speed vehicles using vehicular cloud | |
| CN107395757B (en) | Parallel vehicle networking system based on ACP method and social physical information system | |
| Ragavan et al. | Traffic video‐based intelligent traffic control system for smart cities using modified ant colony optimizer | |
| CN109979211A (en) | Car networking system, transportation planning method, device, mist calculate equipment and medium | |
| CN115116216A (en) | Global cooperative sensing and decision-making method and device based on vehicle-road cloud interface | |
| Chen et al. | Key technologies related to c-v2x applications | |
| CN113724499A (en) | Three-dimensional visual analysis method and system for road traffic events | |
| CN114407900A (en) | Vehicle-road cooperative automatic driving function distribution system and method | |
| Shankaran et al. | Intelligent Transport Systems and Traffic Management | |
| Yang et al. | Stochastic Parameter Identification Method for Driving Trajectory Simulation Processes Based on Mobile Edge Computing and Self-Organizing Feature Mapping | |
| Stankov et al. | Urban Intelligent Transport Management Systems | |
| Liu et al. | Roadside unit deployment of cooperative vehicle-infrastructure system based on digital measurable image method | |
| Oskarbski et al. | Multi-level transport systems model for traffic management activities | |
| Čolaković et al. | Adaptive Traffic Management Systems Based on the Internet of Things (IoT) |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |