CN109657031A - A kind of generation of Dynamic High-accuracy map and application method based on intelligent network connection automobile - Google Patents
A kind of generation of Dynamic High-accuracy map and application method based on intelligent network connection automobile Download PDFInfo
- Publication number
- CN109657031A CN109657031A CN201811621329.4A CN201811621329A CN109657031A CN 109657031 A CN109657031 A CN 109657031A CN 201811621329 A CN201811621329 A CN 201811621329A CN 109657031 A CN109657031 A CN 109657031A
- Authority
- CN
- China
- Prior art keywords
- information
- dynamic
- data
- map
- intelligent network
- 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
Classifications
-
- G—PHYSICS
- G09—EDUCATION; CRYPTOGRAPHY; DISPLAY; ADVERTISING; SEALS
- G09B—EDUCATIONAL OR DEMONSTRATION APPLIANCES; APPLIANCES FOR TEACHING, OR COMMUNICATING WITH, THE BLIND, DEAF OR MUTE; MODELS; PLANETARIA; GLOBES; MAPS; DIAGRAMS
- G09B29/00—Maps; Plans; Charts; Diagrams, e.g. route diagram
- G09B29/003—Maps
- G09B29/005—Map projections or methods associated specifically therewith
Landscapes
- Engineering & Computer Science (AREA)
- Physics & Mathematics (AREA)
- Theoretical Computer Science (AREA)
- Mathematical Physics (AREA)
- Business, Economics & Management (AREA)
- Educational Administration (AREA)
- Educational Technology (AREA)
- General Physics & Mathematics (AREA)
- Navigation (AREA)
- Traffic Control Systems (AREA)
Abstract
The present invention discloses a kind of generation of Dynamic High-accuracy map and application method for joining automobile based on intelligent network, it is using cloud platform and map basic data, by dynamic information collection, processing, publication, the Dynamic High-accuracy map for having difference information is formed, is used for vehicle and other users.The multidate information of vehicle perception simultaneously uploads to cloud platform again, and circulation is updated, and is thusly-formed operation with closed ring.This method is suitable for intelligent network connection automobile application, while intelligent network connection automobile is also the supplier of map datum.
Description
Technical field
The present invention relates to a kind of Dynamic High-accuracy map platforms, especially with respect to the dynamic map based on intelligent network connection automobile
It generates and applies.
Background technique
The basis that Dynamic High-accuracy map is applied as intelligent transportation, intelligent city, is the hair with artificial intelligence technology
The technology for opening up and gradualling mature and applying, each ring that artificial intelligence technology acquires high-precision map, updates, produces, applies
Section development plays an important role, and same Dynamic High-accuracy map is based on artificial intelligence technology as data content also application
On the intelligent network connection automobile operation system of core.
The basic map of existing automatic Pilot application, one is what conventional navigation figure quotient was widely used to be combined by mapping
The map vector of Mapping Technology production, one is AI enterprises to pass through the sense that sensor (laser, vision) combination algorithm crowdsourcing produces
Know map.Map vector technology path is stored as one completely from data acquisition, working process to data publication, by map datum
Spatial database, but it is a static map.Corresponding perception map is generated based on different sensors, it is dynamic comprising dynamic half
State information can quickly be understood by automobile, but contain much information, and have garbage.
Automatic driving vehicle also asks for map information update to cloud center, and Dynamic High-accuracy map datum capacity is huge, because
This user terminal (automatic driving vehicle) needs to take certain strategy in the update upgrading for obtaining map cloud center, reduces data
Transmission quantity shortens the time that vehicle obtains data.Current several modes have:
(1) incremental update mode is pre-installed
Pre-installing incremental update mode is highly developed conventional map data renewal model.In order to reduce the biography of map datum
It is defeated, it needs for map datum to be preloaded onto equipment, data longer for period of change can be paid the utmost attention to pre-install.When map number
When according to changing, the increment information of variation is only sent to vehicle by map cloud center, vehicle after receiving the data, by data
Fusion enters in map datum.When map cloud center finds that great data variation may cause traffic safety, hair of taking the initiative
Pressure is sent to update.When other general updates, vehicle makes requests update according to demand.
(2) part prepackage is added in line push mode
Part prepackage is added in line push mode and is also required to before factory for full release data being disposably installed in equipment,
But subsequent data update will be pushed to equipment end by wireless network.This renewal model can accomplish real-time update, avoid
Because of data incredible problem caused by updating not in time.The mode needs vehicle arrangement end to have wireless network communication ability.
(3) online full dose renewal model
Full version data without being installed in equipment by complete online access mode before factory, it is by providing online data
The mode of service allows equipment end to request all roads of passing through in specified path or a certain range to server according to current location
The corresponding data of diameter, online service center-side extract data back according to request condition to vehicle arrangement end in real time.The mode is thorough
Bottom solves the problems, such as that data update, and does not need artificially to update.It is sent out as cloud computing, the maturation of big data processing technique and 5G are communicated
Exhibition, this mode will likely become prevailing model.
Incremental update is a kind of mode of complexity, is easy error.The mode that online full dose updates may insure that data do not go out
Mistake, but this mode depends not only upon network communication, while having extremely strong want to transmission communication transmission rate and online service ability
It asks, network flow is big, higher cost.
It is present Dynamic High-accuracy map all only unidirectional applications, only by car trader, figure there are one important problem
Quotient and cloud platform are supplied to vehicle use, and without the multidate information of vehicle is fed back to map, this controls automatic Pilot
It is a barrier.
Dynamic High-accuracy map basic platform is built, traffic dynamic information is converged and vehicle sensor information forms dynamic height
Precision map publishing platform is updated application to basic map, shares to solve accurately image resource, and crowdsourcing updates,
The service of high-precision map products is provided to OEM depot, will be needed for future development.The following dynamic map operation platform will be both
It is a part of high-precision map platform for making and intelligent network connection automobile application platform.
Summary of the invention
In view of the development relationship between current manual's intelligence, high-precision map, intelligent network connection this three of automotive system, this hair
Bright to propose a kind of generation of Dynamic High-accuracy map and application method for joining automobile based on intelligent network, the method achieve high-precisions
The acquisition of map datum, establishment, update, the operation with closed ring using links, so that intelligent network connection automobile is both map datum
The producer and map datum achievement application person, constantly for map provide more new information.
The scheme that the present invention takes is as follows: a kind of generation of Dynamic High-accuracy map and application side based on intelligent network connection automobile
Method, which is characterized in that be based on cloud platform and map basic data, comprising:
S1: the process of dynamic information collection, wherein the multidate information includes traffic dynamic information and vehicle multidate information；
S2: the process of multidate information processing, wherein comprising cleaning, classification, encoding, the mistake of positioning to the multidate information
Journey；
S3: the process of Dynamic Information Publishing；
S4: multidate information data merge Difference Calculation with basic data, form the process of dynamic difference data；
S5: dynamic difference data combination basic data and additional data are compiled, and it is static differentially to form high-precision
The process of figure；
S6: the process of Dynamic High-accuracy map is formed in conjunction with the multidate information and the static difference map of high-precision.
Further, further include vehicle obtains the Dynamic High-accuracy map by engine map in real time, determines as behavior
The process of plan foundation.
Further, further include information that vehicle will be perceived newly, and make the letter of perception difference with Dynamic High-accuracy map
Breath uploads to cloud platform again, and the process of foundation is updated as next round data.
Further, the vehicle is identified based on the Intellisense of deep learning, and it is poor to do perception with Dynamic High-accuracy map
Divide and calculates.
Further, the multidate information is loaded into basic data, shape using information position corresponding in basic data
At the multidate information based on basic data.
Further, the multidate information utilizes encoding mechanism same as basic data, and combining information reference by location is built
The fusion connection relationship of vertical basic data and multidate information data.
Further, the basic data carries out fusion update using dynamic difference data, checks other in basic data
Whether synchronized update can be just compiled related information if all synchronized updates.
Further, the compiling, for full dose compiling or incremental compilation.
The present invention constructs the automatic Pilot dynamic map system to form four levels, include static part, semi-static part,
Half dynamic part, dynamic part.Wherein dynamic, half dynamic part information derive from vehicle and traffic information.The traveling shape at vehicle end
State data (such as real time position, body gesture, driving behavior, various kinds of sensors data) and real time traffic data pass through operation
Net connection converges to dynamic cloud platform, in cloud platform by being analyzed dynamic data, being handled, merged, and with static basis number
According to reference by location calculate, be formed simultaneously the real-time dynamicly figure based on position reference data, and be distributed to by the network platform
Vehicle termination；Multidate information forms online high-precision difference map, again with after high-precision basic map difference fusion calculation simultaneously
It is published to vehicle and carries out programmed decision-making, form the high-precision cartographic information operation closed loop that vehicle is both application person and the producer.
Other features and advantages of the present invention will illustrate in the following description, and partial become from specification
It is clear that understand through the implementation of the invention.
Detailed description of the invention
The process of Fig. 1 for Dynamic High-accuracy drawing generating method；
Fig. 2 is that the source of multidate information is illustrated；
Fig. 3 is multidate information treatment process；
Fig. 4 is dynamic difference treatment process.
Specific embodiment
The present invention is described in detail below with reference to the accompanying drawings and embodiments, wherein and attached drawing constitutes the application a part,
And together with embodiments of the present invention for illustrating the present invention.But it will be appreciated by those skilled in the art that following embodiment is simultaneously
Be not to technical solution of the present invention make unique restriction, it is all done under technical solution of the present invention Spirit Essence it is any equivalent
Transformation or change, are regarded as belonging to the scope of protection of the present invention.
For automatic driving vehicle, the object of the present invention is to provide a kind of Dynamic High-accuracy maps to apply for it, this
Kind Dynamic High-accuracy map and traditional map are different, and Dynamic High-accuracy cartography is the ground with four levels by the present invention
Figure, most fundamental aspect is fairly static high-precision three-dimensional map, such as roading, building etc., this layer of map datum one
As in the case of will not change, the update cycle is long, so renewal time can count monthly or over day；Secondary is above semi-static map,
It is superimposed control traffic message, road construction information and weather information etc. thereon, this category information changes, but unhappy, so more
New time available hours or minute count；Above again semi-dynamically to scheme, including road surface accident information at any time, congestion information and
Weather etc., this kind of information update time can be set as less than 1 minute；It is topmost full multidate information, this category information exists in real time
It updates, including vehicle, pedestrian's walk etc., it is vehicle pair that information transmission, which needs the time shortest, during automatic driving
The information of vehicle, so this kind of information update needs are counted with the second.
Therefore, Dynamic High-accuracy map is the Ecological information stream during intelligent network connection automobile operation.Dynamic High-accuracy
Map generalization and application, it will it is a closed loop ecological circulation system, transportation industry data are added in it on the basis of static map,
Depot's back information, and traffic information, environmental information etc. in real time, formation acquisition, processing, publication, service, the ecology fed back
Circulative metabolism.In consideration of it, the present invention provides a kind of generation method of Dynamic High-accuracy map, treatment process is in intelligent cloud platform
Convergence, processing, compiling, publication etc., supply intelligent network joins automatic driving application, and is moved by intelligent network connection automatic driving
State feedback information, provides update.
Specific ground drawing generating method, including following procedure, as shown in Figure 1:
1. multidate information obtains
Cloud platform acquires real-time dynamic information, and real-time dynamic information includes two sources, and one is traffic dynamic information, and one
A is vehicle multidate information.Traffic dynamic information includes Traffic Information, Weather information, emergency information, extraordinary maintenance etc., vehicle
Multidate information includes user information, vehicle sensor information, perception difference information etc., as shown in Figure 2.Traffic dynamic information and
Vehicle multidate information is brought together, and constitutes real-time dynamic information.
2. converging information
Cloud platform storage has map basic data, and (basic data refers to a number of recent renewal in dynamic circulation update
According to), it include about classification informations such as element, relationship, attributes in map basic data, initial data can be by scheming quotient, car trader, friendship
Guan Deng department provides.
After traffic dynamic information and vehicle multidate information to cloud platform, using information position corresponding in basic data, add
It is downloaded in basic data, forms the multidate information based on basic data.
3. intelligence cleaning
Dynamic map information based on basic data, some may be the information useless for intelligent transportation or have repetition
Information, it is therefore desirable to useless invalid information is rejected.According to the source of information (vehicle, manufacturer, figure quotient or country's instruction),
The confidence level for judging information, screens information；Then pass through the relevance to traffic historical information and current information, judgement
Whether information is useful, is filtered cleaning.
4. polymerization classification
Data after over cleaning are the data of effective use, these information are closed according to basic data framework relationship
And classify, form multidate information data.
5. data encoding
Using encoding mechanism, information reference by location of the multidate information in map and road ID (are arranged in basic data
Have data original coding and various ID), establish the fusion connection relationship of basic data Yu multidate information data.
6. fusion connection calculates
Fusion connection calculating is that multidate information data and basic data are carried out Difference Calculation, obtains differential data, and do
Label, while also by differential data storage into basic data.Difference Calculation can use existing any feasible method.
7. publication
Differential data is divided into both direction publication.
7.1 on the one hand, and multidate information data and basic data carry out Difference Calculation, and dynamic difference data are transmitted to basic number
According to basic data carries out fusion update using these dynamic difference information, checks other corresponding related informations in basic data
Whether update simultaneously, if all synchronized updates, basic data updates requisite quality and generates；Then by basic data along with attached
Addend is compiled after, and additional data includes car trader, schemes quotient, the individuation data that vehicle is temporarily added herein, with base
Plinth data are unrelated, unrelated with multidate information, for increasing some individuation datas of service support.
Compiling, by the related additional data of updated basic data, the physical format based on high-precision map application is carried out
Compiling.More new information is compiled into basic map, then is released, forms the difference high-precision based on basic map statically
Figure.It is small for information update amount, requirement of real-time it is high incremental compilation can be used, it is big for changing, the update cycle ask it is long can
It is compiled using full dose.After compiling forms digital map navigation information simultaneously, cloud platform is also uploaded to, for this use of other application.
7.2 on the other hand, and multidate information data and basic data establish fusion connection relationship, will be dynamic with reference by location
State information data real-time release is gone out, and real-time dynamic information is formed.
8. with driving Dynamic High-accuracy map generalization
Real-time dynamic information is loaded into high-precision static map, Dynamic High-accuracy map is formed, is perceived according to vehicle
Perception difference information is had with what Dynamic High-accuracy map match generated.Dynamic High-accuracy map is provided to vehicle use, together
When perception difference and vehicle sensor information also upload to cloud platform for other use.
9. driving being cyclically updated and applying for Dynamic High-accuracy map
Intelligent network connection automobile obtains the Dynamic High-accuracy map by updating by engine map in real time, dynamically using this
Foundation of the figure information as next step behaviour decision making.
At the same time, the multidate information of update is uploaded to cloud platform again in terms of vehicle and traffic, as next round
Scheme the foundation updated, so recycle, constitutes the operation with closed ring that dynamic map updates.Renewal time can be set to second grade, such as week
The personage on side and information of vehicles；It can be set to minute grade to update, such as traffic events, situation, flow, first aid information, weather；
Hour grade can be set to update, such as control traffic message, maintenance information, traffic information, a wide range of weather；Settable day several levels are more
Newly, such as traffic mark, road reformation；Settable month grade or time grade update, as road is planned again.
Further, intelligent network connection automobile can also do perception Difference Calculation, and difference information is uploaded in real time, constitute vehicle
Multidate information is uploaded to cloud platform, and the perception information of new perception information and difference is uploaded to cloud platform together, for next round
Figure, which updates, to be used with Dynamic Information Publishing to all vehicles.Still further, perception Difference Calculation, is based on based on deep learning
Perceptual computing, with high precision after engine map real-time three-dimensional matching intelligent recognition, update differential sense based on map is calculated,
It is standardized based on Feature Compression.
Further, in the dynamic processes of information, as shown in Figure 3:
User information, vehicle sensor information etc., wherein carrying out anonymous processing before upload about the information of privacy.
About Weather information, Traffic Information, grid dimerization processing can be done.
Polymerization classification is divided by region, road information, dotted event etc. by point and face.
Data encoding based on reference by location, the building for having topological relation of formation, the building of attribute information and element are closed
The building of system.
Further, during multidate information difference processing, as shown in Figure 4:
The fusion connection of static basis data and multidate information data calculates, be accurately figure at the mistake of dynamic map
Journey is the process that multidate information reference by location building dynamic difference calculates, and when forming difference information, multidate information is based on base
The data encoding of plinth data will be matched with basic data coding, and formation is also topological relation differential data, attribute information difference
Data and element relationship differential data.After basic data is added, in following more new data, the more new data of publication is also opened up
The update of the update of relationship, the update of attribute information and element relationship is flutterred, the basic difference update data of high-precision is constituted, simultaneously will
It is configured to according to information update frequency by the real-time dynamic information as unit of hour, minute, millisecond.
There are two aspect effects by the multidate information that position constructs, first is that real-time dynamic information is supplied directly to vehicle ginseng
With decision calculate, first is that by with form differential data after static basis data difference, for basic map update use.
The present invention provides the map content that dynamic updates using intelligent network connection automobile, is then using updated map again
Automobile navigation, the vehicle environmental dynamic change after navigation play after uploading platform, as nearby vehicle real-time dynamic information decision
Information, and the foundation updated in next step as map so in cycles form closed loop application person, that is, data of vehicle and map
The closed loop of the producer.
The foregoing is only a preferred embodiment of the present invention, but scope of protection of the present invention is not limited thereto,
In the technical scope disclosed by the present invention, any changes or substitutions that can be easily thought of by anyone skilled in the art,
It should be covered by the protection scope of the present invention.
Claims (8)
1. a kind of Dynamic High-accuracy map based on intelligent network connection automobile generates and application method, which is characterized in that flat based on cloud
Platform and map basic data, comprising:
S1: the process of dynamic information collection, wherein the multidate information includes traffic dynamic information and vehicle multidate information；
S2: the process of multidate information processing, wherein comprising cleaning, classification, encoding, the process of positioning to the multidate information；
S3: the process of Dynamic Information Publishing；
S4: multidate information data merge Difference Calculation with basic data, form the process of dynamic difference data；
S5: dynamic difference data combination basic data and additional data are compiled, and form the static difference map of high-precision
Process；
S6: the process of Dynamic High-accuracy map is formed in conjunction with the multidate information and the static difference map of high-precision.
2. the Dynamic High-accuracy map according to claim 1 based on intelligent network connection automobile generates and application method, special
Sign is, further includes that vehicle by engine map obtains the Dynamic High-accuracy map, the mistake as behaviour decision making foundation in real time
Journey.
3. the Dynamic High-accuracy map according to claim 1 or 2 based on intelligent network connection automobile generates and application method,
Be characterized in that, further include the information that vehicle will be perceived newly, and with Dynamic High-accuracy map do perception difference information again on
Cloud platform is passed to, the process of foundation is updated as next round data.
4. the Dynamic High-accuracy map according to claim 3 based on intelligent network connection automobile generates and application method, special
Sign is that the vehicle is identified based on the Intellisense of deep learning, does perception Difference Calculation with Dynamic High-accuracy map.
5. the Dynamic High-accuracy map according to claim 1 based on intelligent network connection automobile generates and application method, special
Sign is that the multidate information is loaded into basic data, is formed based on basis using information position corresponding in basic data
The multidate information of data.
6. the Dynamic High-accuracy map according to claim 1 or 5 based on intelligent network connection automobile generates and application method,
It is characterized in that, the multidate information utilizes encoding mechanism same as basic data, and basic number is established in combining information reference by location
According to the fusion connection relationship with multidate information data.
7. the Dynamic High-accuracy map according to claim 1 based on intelligent network connection automobile generates and application method, special
Sign is that the basic data carries out fusion update using dynamic difference data, checks that other related informations are in basic data
No synchronized update can be just compiled if all synchronized updates.
8. the Dynamic High-accuracy map according to claim 1 based on intelligent network connection automobile generates and application method, special
Sign is that the compiling is full dose compiling or incremental compilation.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201811621329.4A CN109657031B (en) | 2018-12-28 | 2018-12-28 | High-precision dynamic map generation and application method based on intelligent networked automobile |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201811621329.4A CN109657031B (en) | 2018-12-28 | 2018-12-28 | High-precision dynamic map generation and application method based on intelligent networked automobile |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN109657031A true CN109657031A (en) | 2019-04-19 |
| CN109657031B CN109657031B (en) | 2020-08-04 |
Family
ID=66117477
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201811621329.4A Active CN109657031B (en) | 2018-12-28 | 2018-12-28 | High-precision dynamic map generation and application method based on intelligent networked automobile |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN109657031B (en) |
Cited By (14)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN111915879A (en) * | 2019-05-10 | 2020-11-10 | 丰田自动车株式会社 | Information processing apparatus, information processing method, and computer program |
| CN112562314A (en) * | 2020-11-02 | 2021-03-26 | 福瑞泰克智能系统有限公司 | Road end sensing method and device based on deep fusion, road end equipment and system |
| CN112639763A (en) * | 2020-06-24 | 2021-04-09 | 华为技术有限公司 | Method, device and system for processing map updating data |
| CN112655226A (en) * | 2020-04-09 | 2021-04-13 | 华为技术有限公司 | Vehicle sensing method, device and system |
| CN112650227A (en) * | 2020-12-14 | 2021-04-13 | 锐捷网络股份有限公司 | Automatic guided vehicle AGV scheduling method, device, equipment and medium |
| CN112833900A (en) * | 2020-12-31 | 2021-05-25 | 江苏鸿鹄电子科技有限公司 | Edge calculation unit with dynamic map algorithm |
| CN112887913A (en) * | 2019-11-30 | 2021-06-01 | 华为技术有限公司 | Dynamic information sending method and device based on Internet of vehicles |
| CN112948525A (en) * | 2021-05-13 | 2021-06-11 | 禾美(浙江)汽车股份有限公司 | High-precision dynamic map generation method, equipment and medium for intelligent networked automobile |
| CN113157832A (en) * | 2020-12-31 | 2021-07-23 | 上海淞泓智能汽车科技有限公司 | Intelligent automobile high-precision map generation method based on edge calculation |
| WO2021146921A1 (en) * | 2020-01-21 | 2021-07-29 | 深圳元戎启行科技有限公司 | Method and apparatus for updating high-precision map, and computer device, and storage medium |
| CN114236583A (en) * | 2021-12-23 | 2022-03-25 | 国汽大有时空科技(安庆)有限公司 | Space geographic range coding method and global-range GNSS differential enhanced positioning method |
| WO2022100550A1 (en) * | 2020-11-11 | 2022-05-19 | 华为技术有限公司 | Dynamic event classification method and device |
| CN115374235A (en) * | 2022-10-21 | 2022-11-22 | 天津市职业大学 | Dynamic map generation and application method based on intelligent networked automobile |
| US11626013B2 (en) | 2020-12-16 | 2023-04-11 | Apollo Intelligent Connectivity (Beijing) Technology Co., Ltd. | Traffic data analysis method, electronic device, vehicle and storage medium |
Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN105976606A (en) * | 2016-07-25 | 2016-09-28 | 吉林大学 | Intelligent urban traffic management platform |
| CN107229690A (en) * | 2017-05-19 | 2017-10-03 | 广州中国科学院软件应用技术研究所 | Dynamic High-accuracy map datum processing system and method based on trackside sensor |
| CN107845160A (en) * | 2017-10-30 | 2018-03-27 | 青岛慧拓智能机器有限公司 | A kind of automatic driving vehicle data collecting system |
| CN107941235A (en) * | 2016-10-13 | 2018-04-20 | 阿里巴巴集团控股有限公司 | A kind of methods of exhibiting of navigation map, device, the device and operating system |
| CN108010360A (en) * | 2017-12-27 | 2018-05-08 | 中电海康集团有限公司 | A kind of automatic Pilot context aware systems based on bus or train route collaboration |
| WO2018127461A1 (en) * | 2017-01-05 | 2018-07-12 | Fraunhofer-Gesellschaft zur Förderung der angewandten Forschung e.V. | Generation and use of hd maps |
| CN108765487A (en) * | 2018-06-04 | 2018-11-06 | 百度在线网络技术（北京）有限公司 | Rebuild method, apparatus, equipment and the computer readable storage medium of three-dimensional scenic |
-
2018
- 2018-12-28 CN CN201811621329.4A patent/CN109657031B/en active Active
Patent Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN105976606A (en) * | 2016-07-25 | 2016-09-28 | 吉林大学 | Intelligent urban traffic management platform |
| CN107941235A (en) * | 2016-10-13 | 2018-04-20 | 阿里巴巴集团控股有限公司 | A kind of methods of exhibiting of navigation map, device, the device and operating system |
| WO2018127461A1 (en) * | 2017-01-05 | 2018-07-12 | Fraunhofer-Gesellschaft zur Förderung der angewandten Forschung e.V. | Generation and use of hd maps |
| CN107229690A (en) * | 2017-05-19 | 2017-10-03 | 广州中国科学院软件应用技术研究所 | Dynamic High-accuracy map datum processing system and method based on trackside sensor |
| CN107845160A (en) * | 2017-10-30 | 2018-03-27 | 青岛慧拓智能机器有限公司 | A kind of automatic driving vehicle data collecting system |
| CN108010360A (en) * | 2017-12-27 | 2018-05-08 | 中电海康集团有限公司 | A kind of automatic Pilot context aware systems based on bus or train route collaboration |
| CN108765487A (en) * | 2018-06-04 | 2018-11-06 | 百度在线网络技术（北京）有限公司 | Rebuild method, apparatus, equipment and the computer readable storage medium of three-dimensional scenic |
Cited By (18)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN111915879A (en) * | 2019-05-10 | 2020-11-10 | 丰田自动车株式会社 | Information processing apparatus, information processing method, and computer program |
| CN112887913B (en) * | 2019-11-30 | 2022-10-18 | 华为技术有限公司 | Dynamic information sending method and device based on Internet of vehicles |
| CN112887913A (en) * | 2019-11-30 | 2021-06-01 | 华为技术有限公司 | Dynamic information sending method and device based on Internet of vehicles |
| WO2021146921A1 (en) * | 2020-01-21 | 2021-07-29 | 深圳元戎启行科技有限公司 | Method and apparatus for updating high-precision map, and computer device, and storage medium |
| CN112655226A (en) * | 2020-04-09 | 2021-04-13 | 华为技术有限公司 | Vehicle sensing method, device and system |
| CN112655226B (en) * | 2020-04-09 | 2022-08-26 | 华为技术有限公司 | Vehicle sensing method, device and system |
| WO2021203341A1 (en) * | 2020-04-09 | 2021-10-14 | 华为技术有限公司 | Vehicle sensing method, apparatus and system |
| CN112639763A (en) * | 2020-06-24 | 2021-04-09 | 华为技术有限公司 | Method, device and system for processing map updating data |
| CN112562314A (en) * | 2020-11-02 | 2021-03-26 | 福瑞泰克智能系统有限公司 | Road end sensing method and device based on deep fusion, road end equipment and system |
| CN112562314B (en) * | 2020-11-02 | 2022-06-24 | 福瑞泰克智能系统有限公司 | Road end sensing method and device based on deep fusion, road end equipment and system |
| WO2022100550A1 (en) * | 2020-11-11 | 2022-05-19 | 华为技术有限公司 | Dynamic event classification method and device |
| CN112650227A (en) * | 2020-12-14 | 2021-04-13 | 锐捷网络股份有限公司 | Automatic guided vehicle AGV scheduling method, device, equipment and medium |
| US11626013B2 (en) | 2020-12-16 | 2023-04-11 | Apollo Intelligent Connectivity (Beijing) Technology Co., Ltd. | Traffic data analysis method, electronic device, vehicle and storage medium |
| CN113157832A (en) * | 2020-12-31 | 2021-07-23 | 上海淞泓智能汽车科技有限公司 | Intelligent automobile high-precision map generation method based on edge calculation |
| CN112833900A (en) * | 2020-12-31 | 2021-05-25 | 江苏鸿鹄电子科技有限公司 | Edge calculation unit with dynamic map algorithm |
| CN112948525A (en) * | 2021-05-13 | 2021-06-11 | 禾美(浙江)汽车股份有限公司 | High-precision dynamic map generation method, equipment and medium for intelligent networked automobile |
| CN114236583A (en) * | 2021-12-23 | 2022-03-25 | 国汽大有时空科技(安庆)有限公司 | Space geographic range coding method and global-range GNSS differential enhanced positioning method |
| CN115374235A (en) * | 2022-10-21 | 2022-11-22 | 天津市职业大学 | Dynamic map generation and application method based on intelligent networked automobile |
Also Published As
| Publication number | Publication date |
|---|---|
| CN109657031B (en) | 2020-08-04 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN109657031A (en) | A kind of generation of Dynamic High-accuracy map and application method based on intelligent network connection automobile | |
| Xu et al. | Opencda: an open cooperative driving automation framework integrated with co-simulation | |
| US11526538B2 (en) | Autonomous vehicle relative atlas incorporating hypergraph data structure | |
| CN110861650B (en) | Vehicle path planning method and device, vehicle-mounted equipment and storage medium | |
| CN109753047A (en) | System and method for autonomous vehicle behaviour control | |
| US11760385B2 (en) | Systems and methods for vehicle-to-vehicle communications for improved autonomous vehicle operations | |
| CN112896170B (en) | Automatic driving transverse control method under vehicle-road cooperative environment | |
| CN109709593A (en) | Join automobile mounted terminal platform based on " cloud-end " tightly coupled intelligent network | |
| Mahdavian et al. | Drivers and barriers to implementation of connected, automated, shared, and electric vehicles: An agenda for future research | |
| CN109029463B (en) | Indoor multi-balance vehicle autonomous navigation and scheduling system for safe driving of vehicle | |
| WO2019191292A1 (en) | Relative atlas for autonomous vehicle and generation thereof | |
| CN110440801A (en) | A kind of location aware information acquisition method, apparatus and system | |
| Lattarulo et al. | Urban Motion Planning Framework Based on N‐Bézier Curves Considering Comfort and Safety | |
| WO2019191287A1 (en) | Use of relative atlas in an autonomous vehicle | |
| CN111721306B (en) | Road matching method and device, electronic equipment and readable storage medium | |
| WO2022083487A1 (en) | Method and apparatus for generating high definition map and computer-readable storage medium | |
| CN114363862A (en) | Intelligent information conversion system and method for serving cooperative automatic driving of vehicle and road | |
| CN116518960B (en) | Road network updating method, device, electronic equipment and storage medium | |
| Griggs et al. | Leader and leaderless multi-layer consensus with state obfuscation: An application to distributed speed advisory systems | |
| CN116129066A (en) | Automatic driving high-precision map model based on digital twin and high-precision static map manufacturing method | |
| Cheng et al. | Societal intelligence for safer and smarter transportation | |
| CN107622665B (en) | Traffic distribution method for interaction of macroscopic traffic simulation system and microscopic traffic simulation system | |
| CN104574966B (en) | Variable information identity device and variable information identification method | |
| CN110426215B (en) | Model establishing method for vehicle ride comfort test and intelligent driving system | |
| Ma | Effects of vehicles with different degrees of automation on traffic flow in urban areas |
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 |