CN111488421B - Data fusion method of traditional map and high-precision map - Google Patents
Data fusion method of traditional map and high-precision map Download PDFInfo
- Publication number
- CN111488421B CN111488421B CN202010342652.9A CN202010342652A CN111488421B CN 111488421 B CN111488421 B CN 111488421B CN 202010342652 A CN202010342652 A CN 202010342652A CN 111488421 B CN111488421 B CN 111488421B
- Authority
- CN
- China
- Prior art keywords
- map
- data
- traditional
- road
- precision
- 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.)
- Active
Links
- 238000007500 overflow downdraw method Methods 0.000 title claims abstract description 20
- 238000012545 processing Methods 0.000 claims abstract description 19
- 230000004927 fusion Effects 0.000 claims abstract description 9
- 238000007499 fusion processing Methods 0.000 claims description 10
- 238000013500 data storage Methods 0.000 claims description 7
- 238000000605 extraction Methods 0.000 claims description 4
- 238000000034 method Methods 0.000 claims description 4
- 238000004458 analytical method Methods 0.000 claims description 3
- 238000013507 mapping Methods 0.000 abstract description 3
- 238000005516 engineering process Methods 0.000 description 5
- 238000010586 diagram Methods 0.000 description 4
- 230000000694 effects Effects 0.000 description 4
- 238000011161 development Methods 0.000 description 3
- 238000012986 modification Methods 0.000 description 2
- 230000004048 modification Effects 0.000 description 2
- 230000001502 supplementing effect Effects 0.000 description 2
- 238000004364 calculation method Methods 0.000 description 1
- 238000006243 chemical reaction Methods 0.000 description 1
- 238000004891 communication Methods 0.000 description 1
- 238000007418 data mining Methods 0.000 description 1
- 238000013499 data model Methods 0.000 description 1
Classifications
-
- 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
- G01—MEASURING; TESTING
- G01C—MEASURING DISTANCES, LEVELS OR BEARINGS; SURVEYING; NAVIGATION; GYROSCOPIC INSTRUMENTS; PHOTOGRAMMETRY OR VIDEOGRAMMETRY
- G01C21/00—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00
- G01C21/26—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00 specially adapted for navigation in a road network
- G01C21/28—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00 specially adapted for navigation in a road network with correlation of data from several navigational instruments
- G01C21/30—Map- or contour-matching
- G01C21/32—Structuring or formatting of map data
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F18/00—Pattern recognition
- G06F18/20—Analysing
- G06F18/22—Matching criteria, e.g. proximity measures
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F18/00—Pattern recognition
- G06F18/20—Analysing
- G06F18/25—Fusion techniques
Landscapes
- Engineering & Computer Science (AREA)
- Theoretical Computer Science (AREA)
- Remote Sensing (AREA)
- Data Mining & Analysis (AREA)
- Radar, Positioning & Navigation (AREA)
- General Physics & Mathematics (AREA)
- Physics & Mathematics (AREA)
- General Engineering & Computer Science (AREA)
- Artificial Intelligence (AREA)
- Life Sciences & Earth Sciences (AREA)
- Databases & Information Systems (AREA)
- Bioinformatics & Cheminformatics (AREA)
- Bioinformatics & Computational Biology (AREA)
- Computer Vision & Pattern Recognition (AREA)
- Evolutionary Biology (AREA)
- Evolutionary Computation (AREA)
- Automation & Control Theory (AREA)
- Processing Or Creating Images (AREA)
- Instructional Devices (AREA)
Abstract
The invention provides a data fusion method of a traditional map and a high-precision map, which comprises the following steps: map information acquisition step: acquiring data information corresponding to a traditional map and a high-precision map in a preset area range; and a data matching processing step: the data are organized through the similarity of the data related attributes, and the data with different dimensions in the traditional map and the high-precision map are further matched; step of hierarchical fusion treatment: fusing and storing the data of each dimension according to the matching result of the data matching processing step; the map data are hierarchically associated by projecting the traditional map and the high-precision map under the same coordinate system through the same projection algorithm, and then the mapping relation between the traditional road and the high-precision lane is realized by carrying out data fusion.
Description
Technical Field
The invention belongs to the technical field of electronic map data processing, and particularly relates to a data fusion method of a traditional map and a high-precision map, which can perform data fusion processing on the traditional map and the high-precision map so as to obtain the electronic map more suitable for the fields of automatic driving, intelligent traffic and the like.
Background
With the rapid development of science and technology, an electronic map has completely entered the life of common people and becomes a necessary product for people to go out daily today.
The traditional map, namely the traditional electronic map, is based on roads, abstracts the roads into a road line and records the road information; the intersections are represented by nodes, and the nodes record road traffic relations; the data does not provide elevation, the road additional facilities are expressed in the form of associated attributes, the road is required to be matched before navigation, and road-level navigation can be realized. At present, the technology of the traditional map is very mature and has been widely applied.
With the rapid development of information technology, high-precision maps are gradually developed. A high-precision map, i.e., a high-definition map, refers to a high-precision, finely defined map whose precision needs to be such that individual lanes can be distinguished. With the development of positioning technology, high-precision positioning has become possible nowadays. The definition is to store various traffic elements in the traffic scene, including road network data, lane lines, traffic marks and other data of the traditional map. Based on road lane lines, intersections are represented by lane connection, data provide lane gradients, road additional facilities are stored in a layered mode, lane-level navigation can be achieved, and the accuracy error is 0.1M.
Although the high-precision map can associate various road information on roads so as to realize rapid path planning, navigation, retrieval and the like, the high-precision map is not fully applied at present, so that each large map merchant has a set of mature data storage specifications.
With the Beidou positioning system independently researched and developed in China, the higher-precision positioning application is more and more popular. With the popularization of the 5G communication technology, the accuracy requirement on map data is higher. High-precision maps based on automatic driving are generated, but how to fuse with traditional electronic maps and how to realize quick retrieval, path planning, accurate matching and quick updating are still problems to be solved in the field.
Disclosure of Invention
The present invention has been made to solve the above-mentioned problems, and an object of the present invention is to provide a data fusion method of a conventional map and a high-precision map, which can perform data fusion processing on the conventional map and the high-precision map to obtain an electronic map more suitable for the fields of automatic driving, intelligent transportation, etc.
The invention provides a data fusion method of a traditional map and a high-precision map, which is characterized by comprising the following steps of:
map information acquisition step: acquiring data information corresponding to a traditional map and a high-precision map in a preset area range;
and a data matching processing step: data are organized through the similarity of data association attributes under the same coordinate system, and data with different dimensions in a traditional map and a high-precision map are further matched;
step of hierarchical fusion treatment: and fusing and storing the data of each dimension according to the matching result of the data matching processing step.
The data fusion method of the traditional map and the high-precision map provided by the invention can also have the characteristics that the data matching processing step comprises the following steps:
road information matching: matching the corresponding traditional road according to the road name, the road passing direction and the road shape corresponding to the high-precision map; associating all lane information of the corresponding high-precision road to the road of the traditional map, and recording information of related lanes in the traditional road information; and correcting the shape and nodes of the road of the traditional map through the high-precision map road range, and keeping other additional connection attributes of the traditional map data unchanged.
The data fusion method of the traditional map and the high-precision map provided by the invention can also have the characteristics that the step of hierarchical fusion processing further comprises the following steps:
based on the road information matching result, other layer information is further matched, and the specific process is as follows:
for the layers of the traditional map and the high-precision map, firstly, according to the association relation of lanes associated with the high-precision map layer through the acquired association relation, acquiring the roads of the traditional map data, then acquiring the appointed attribute data associated with the traditional roads, carrying out contrast analysis through the information recorded by the layers and the attribute content, and converting and updating the position coordinate precision and converting and classifying attributes according to a data storage mode, and updating the position coordinate precision and converting and classifying attributes into the traditional map;
for the data attribute of other layers of the map which are not included in the traditional map but are high-precision, the road attribute corresponding to the traditional map data is directly added according to the data specification.
The data fusion method of the traditional map and the high-precision map provided by the invention can also have the characteristics that the related map data is updated based on a two-level quadtree formula, the data is divided into a road level and a lane level, the bottommost layer in the data model is three-dimensional high-precision data based on the lane level, and whether the data storage at the level depends on the corresponding attribute of the road at the last layer.
The data fusion method of the conventional map and the high-precision map provided by the invention may further have the feature that, in the map information acquisition step, if no corresponding conventional map data exists in the current data range of the high-precision map, a conventional road processing is performed based on a predetermined rule, and the extraction process is as follows:
converting a specified lane into a traditional road according to the road of the high-precision map, and extracting nodes at the same time;
further making a road extension to the intersection at the intersection of the traditional road, and converting the node into an intersection point; and converting other layers associated with the high-precision map lane according to the relationship of the road and the storage specification category, and storing the converted layers into the related attribute of the traditional road. I.e. first extracting the road and then converting the layer for association.
Effects and effects of the invention
According to the data fusion method of the traditional map and the high-precision map, the map data are subjected to hierarchical association after the projection processing and the data matching of the traditional map and the high-precision map are carried out under the same coordinate system, then the map data are fused, so that the mapping relation between the traditional road and the high-precision lane can be realized, the information fusion of the traditional map is utilized to enrich the high-precision map information, the map data can be effectively and rapidly updated, the information of the high-precision map can be subjected to abstract preview, and the information of the traditional electronic map is enriched; the map data is updated accurately by mutually supplementing two data attributes through matching the high-precision map data of the same area on the basis of the traditional electronic map data, so that the intelligence of the map is improved, and a basis is laid for subsequent applications such as path exploration, unmanned driving and the like.
Drawings
Fig. 1 is a flowchart of a data fusion method of a conventional map and a high-precision map in an embodiment of the present invention.
Fig. 2 is a schematic diagram of a map data projection result in an embodiment of the present invention.
Fig. 3 is a schematic diagram of an electronic map obtained by fusion processing in the embodiment of the invention.
Detailed Description
The invention is further illustrated by means of the following examples, which are not intended to limit the scope of the invention.
< example >
Fig. 1 is a flowchart of a data fusion method of a conventional map and a high-precision map in an embodiment of the present invention.
As shown in fig. 1, the present embodiment provides a data fusion method 100 for a conventional map and a high-precision map, which is used for implementing data fusion of the conventional map and the high-precision map, so that an electronic map obtained after fusion is more accurate, and can be widely applied to intelligent fields such as unmanned driving, intelligent traffic, electronic map navigation, map data mining, and the like. The data fusion method 100 includes the steps of:
s1, map information acquisition: and acquiring data information corresponding to the traditional map and the high-precision map in the range of the preset area according to the data storage specification.
In this map information acquisition step S1, if there is no conventional map data within the data range of the current high-precision map, the conventional road can be extracted by a certain rule. The extraction process comprises the following steps:
the specified lane is converted into a road of a conventional road according to the road of the high-precision map while the nodes are extracted.
Then, the road is further extended to the intersection at the intersection of the conventional road, where the node is converted into the intersection point.
And finally, converting other layers of the high-precision map according to the category and the storage rule, and storing the converted layers into the related attributes of the traditional road.
S2, data matching processing step: and organizing the data through the similarity of the data association attributes under the same coordinate system, and further matching the data with different dimensions in the traditional map and the high-precision map.
Fig. 2 is a schematic diagram of a map data projection result in an embodiment of the present invention.
In the data matching processing step S2, as shown in fig. 2, the data information of the conventional map and the data information of the high-precision map are first projected under the same coordinate system by a projection algorithm. Then, matching the data with different dimensions contained in the locks in the traditional map and the high-precision map under the coordinate system, wherein the data matching processing process comprises the following steps:
road information matching: matching corresponding traditional roads according to rich attribute road names, road passing directions and road shapes corresponding to the high-precision map; other layer attributes on the road association are also associated to the high-precision map; simultaneously, all lane information of the corresponding high-precision road is related to the road of the traditional map, and meanwhile, information of related lanes is recorded in the traditional road information; and after the matching is finished, the road shape and nodes of the traditional map are corrected through the high-precision map road range, and other additional connection attributes of the traditional map data are kept unchanged.
And further matching other layer information based on the road information matching result, wherein the method specifically comprises the following two cases:
1. processing of attributes (i.e., layers) possessed by both conventional and high-precision maps
Because many other information is stored in the high-precision map data according to the layers, the map data has an association relationship with roads in general; whereas the corresponding information of the conventional map data is generally stored in the road association attribute table. Therefore, for the layers of the traditional map and the high-precision map, firstly, the roads of the traditional map data are acquired according to the association relation of lanes associated with the high-precision map layer through the acquired association relation, then the appointed attribute data associated with the traditional roads are acquired, the comparison analysis is carried out through the information and the attribute content recorded by the layers, and the position coordinates and the conversion classification attributes are converted and updated into the traditional map data according to the data storage mode based on the high-precision layer information.
2. Processing data attributes in high-precision map layers without being included in conventional maps
For the data attribute of other layers of the map which are not included in the traditional map but are high-precision, the road attribute corresponding to the traditional map data is directly added.
S3, step of hierarchical fusion processing: and fusing and storing the data of each dimension according to the matching result of the data matching processing step.
In the step S3 of the hierarchical fusion processing, the hierarchical storage is to update the associated map data based on the two-level quadtree formula, and the map data can be specifically classified into a road level and a lane level, wherein the lowest layer is based on the lane-level high-precision map.
Fig. 3 is a schematic diagram of an electronic map obtained by fusion processing in the embodiment of the invention.
The fusion processing of the traditional map and the high-precision map can be realized through the three steps shown in the figure 1, the electronic map shown in the figure 3 is obtained, the information of the traditional map is utilized, the information of the high-precision map is fused, the map data is effectively and rapidly updated, and a foundation is laid for subsequent path exploration and unmanned application.
Effects and effects of the examples
According to the data fusion method of the traditional map and the high-precision map, the map data are subjected to hierarchical association after being subjected to projection processing and data matching under the same coordinate system, and then the map data are fused, so that the mapping relation between the traditional road and the high-precision lane can be realized, the information fusion of the traditional map is utilized to enrich the high-precision map information, the map data can be effectively and rapidly updated, the information of the high-precision map can be subjected to abstract preview, and the information of the traditional electronic map is enriched; the map data is updated accurately by mutually supplementing two data attributes through matching the high-precision map data of the same area on the basis of the traditional electronic map data, so that the intelligence of the map is improved, and a basis is laid for subsequent applications such as path exploration, unmanned driving and the like.
The purpose of the embodiment is to provide more accurate and targeted navigation service for realizing effective fusion between map data with different precision, and it is significant to effectively fuse conventional map data and high-precision data. Moreover, the electronic map obtained by the data fusion method of the embodiment can quickly query and search and further lays a good foundation for other advanced applications and calculation based on the path planning made by the navigator.
While specific embodiments of the invention have been described above, it will be appreciated by those skilled in the art that this is by way of example only, and the scope of the invention is defined by the appended claims. Various changes and modifications to these embodiments may be made by those skilled in the art without departing from the principles and spirit of the invention, but such changes and modifications fall within the scope of the invention.
Claims (3)
1. The data fusion method of the traditional map and the high-precision map is characterized by comprising the following steps of:
map information acquisition step: acquiring data information corresponding to a traditional map and a high-precision map in a preset area according to a data storage specification;
and a data matching processing step: data are organized through the similarity of data association attributes under the same coordinate system, and data with different dimensions in a traditional map and a high-precision map are further matched;
step of hierarchical fusion treatment: fusing and storing the data of each dimension according to the matching result of the data matching processing step;
in the map information obtaining step, if there is no corresponding conventional map data in the current data range of the high-precision map, performing conventional road extraction processing based on a predetermined rule, where the extraction process is as follows:
converting a specified lane into a traditional road according to the road of the high-precision map, and extracting nodes at the same time;
further making a road extension to the intersection at the intersection of the traditional road, and converting the node into an intersection point; and
converting other layers of the high-precision map according to categories and storage rules, storing the converted layers into related attributes of the traditional road,
the data matching processing step comprises the following steps:
road information matching: matching corresponding traditional roads according to rich attribute road names, road passing directions and road shapes corresponding to the high-precision map; simultaneously, associating all lane information of the corresponding high-precision road to the road of the traditional map, and recording information of related lanes on the traditional road information; and correcting the shape and nodes of the road of the traditional map through the high-precision map road range, and keeping other additional connection attributes of the traditional map data unchanged.
2. The data fusion method of a conventional map and a high-precision map according to claim 1, wherein:
the step of hierarchical fusion processing further comprises:
based on the road information matching result, other layer information is further matched, and the specific process is as follows:
for layers of the traditional map and the high-precision map, firstly, acquiring roads of the traditional map data according to association relations acquired by lanes associated with the high-precision map layer, then acquiring appointed layer attribute data associated with the traditional roads, carrying out contrast analysis through information and attribute content recorded by the layers, converting and updating position coordinate precision and converting and classifying attributes according to a data storage mode, and updating the position coordinate precision and converting and classifying attributes into the traditional map data;
and the data attribute of other layers of the map which are not provided with the traditional map but have high precision is directly added with the road attribute corresponding to the traditional map data.
3. The data fusion method of the conventional map and the high-precision map according to claim 2, wherein:
the hierarchical fusion processing step comprises updating associated map data, wherein the data are divided into a road level and a lane level, and the bottommost layer is a map based on lane level high precision.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202010342652.9A CN111488421B (en) | 2020-04-27 | 2020-04-27 | Data fusion method of traditional map and high-precision map |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202010342652.9A CN111488421B (en) | 2020-04-27 | 2020-04-27 | Data fusion method of traditional map and high-precision map |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN111488421A CN111488421A (en) | 2020-08-04 |
| CN111488421B true CN111488421B (en) | 2024-04-16 |
Family
ID=71795261
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202010342652.9A Active CN111488421B (en) | 2020-04-27 | 2020-04-27 | Data fusion method of traditional map and high-precision map |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN111488421B (en) |
Families Citing this family (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112256811B (en) * | 2020-10-19 | 2022-04-29 | 武汉中海庭数据技术有限公司 | Map information representation method and device based on map structure |
| CN112380305B (en) * | 2020-11-06 | 2023-01-17 | 华南理工大学 | High-precision map data crowdsourcing method and device based on space-time similarity |
| CN113010793A (en) * | 2021-04-09 | 2021-06-22 | 阿波罗智联(北京)科技有限公司 | Method, device, equipment, storage medium and program product for map data processing |
| CN113155141A (en) * | 2021-04-09 | 2021-07-23 | 阿波罗智联(北京)科技有限公司 | Map generation method and device, electronic equipment and storage medium |
| CN113095309B (en) * | 2021-06-10 | 2021-09-14 | 立得空间信息技术股份有限公司 | Method for extracting road scene ground marker based on point cloud |
| CN113390407A (en) * | 2021-06-29 | 2021-09-14 | 北京百度网讯科技有限公司 | Method, device and equipment for constructing lane-level navigation map and storage medium |
| CN114323037B (en) * | 2021-12-29 | 2024-04-12 | 高德软件有限公司 | Road segment position matching and navigation method and device and storage medium |
| CN114413914A (en) * | 2022-01-18 | 2022-04-29 | 上汽通用五菱汽车股份有限公司 | Precision improving method and system for high-precision map and computer readable storage medium |
Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CA2480493A1 (en) * | 2002-03-29 | 2003-10-23 | Matsushita Electric Industrial Co., Ltd. | Map matching method, map matching device, database for shape matching, and shape matching device |
| CN105043403A (en) * | 2015-08-13 | 2015-11-11 | 武汉光庭信息技术有限公司 | High precision map path planning system and method |
| KR20160053201A (en) * | 2014-10-31 | 2016-05-13 | 현대엠엔소프트 주식회사 | Method for matching map of high-precision with navigation link |
| CN106767853A (en) * | 2016-12-30 | 2017-05-31 | 中国科学院合肥物质科学研究院 | A kind of automatic driving vehicle high-precision locating method based on Multi-information acquisition |
| CN108304559A (en) * | 2018-02-08 | 2018-07-20 | 广州地理研究所 | A kind of regional geography spatial data fusion method |
Family Cites Families (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| KR102479037B1 (en) * | 2016-05-25 | 2022-12-20 | 한국전자통신연구원 | Device for tile map service and method thereof |
-
2020
- 2020-04-27 CN CN202010342652.9A patent/CN111488421B/en active Active
Patent Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CA2480493A1 (en) * | 2002-03-29 | 2003-10-23 | Matsushita Electric Industrial Co., Ltd. | Map matching method, map matching device, database for shape matching, and shape matching device |
| KR20160053201A (en) * | 2014-10-31 | 2016-05-13 | 현대엠엔소프트 주식회사 | Method for matching map of high-precision with navigation link |
| CN105043403A (en) * | 2015-08-13 | 2015-11-11 | 武汉光庭信息技术有限公司 | High precision map path planning system and method |
| CN106767853A (en) * | 2016-12-30 | 2017-05-31 | 中国科学院合肥物质科学研究院 | A kind of automatic driving vehicle high-precision locating method based on Multi-information acquisition |
| CN108304559A (en) * | 2018-02-08 | 2018-07-20 | 广州地理研究所 | A kind of regional geography spatial data fusion method |
Non-Patent Citations (1)
| Title |
|---|
| 聂俊兵.GIS中地图数据自动融合技术的研究.《科技信息(科学教研)》.2007,(第31期),正文第102页. * |
Also Published As
| Publication number | Publication date |
|---|---|
| CN111488421A (en) | 2020-08-04 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN111488421B (en) | Data fusion method of traditional map and high-precision map | |
| CN108088448B (en) | Method and device for matching high-precision lane group with traditional road | |
| CN103150398B (en) | A kind of increment type map updating method based on national fundamental geographic information data | |
| CN109256028B (en) | Method for automatically generating unmanned high-precision road network | |
| CN106568456B (en) | Non-stop charging method based on GPS/ Beidou positioning and cloud computing platform | |
| JP2007233658A (en) | Data processing method, device, and its processing program | |
| CN101694669A (en) | Pace note making method, device thereof, pace note making and sharing system | |
| US8438186B2 (en) | Method and apparatus for creating topological features inside a database system | |
| CN109101743A (en) | A kind of construction method of high-precision road net model | |
| CN113628291B (en) | Multi-shape target grid data vectorization method based on boundary extraction and combination | |
| CN104252507B (en) | A kind of business data matching process and device | |
| CN109214314B (en) | Automatic fusion matching algorithm for lane lines | |
| CN113239107B (en) | ETL-based road vector data element matching and linkage method | |
| CN112100311B (en) | Road traffic network geographic information data management method, device and system | |
| CN111508258A (en) | Positioning method and device | |
| CN116050429B (en) | Geographic environment entity construction system and method based on multi-mode data association | |
| CN115164918B (en) | Semantic point cloud map construction method and device and electronic equipment | |
| CN114238542A (en) | Multi-level real-time fusion updating method for multi-source traffic GIS road network | |
| CN111522892A (en) | Geographic element retrieval method and device | |
| TW202146852A (en) | Route deviation quantification and vehicular route learning based thereon | |
| CN114509065B (en) | Map construction method, system, vehicle terminal, server and storage medium | |
| CN104063421B (en) | Magnanimity traffic remotely-sensed data search method and device | |
| CN113656979B (en) | Road network data generation method and device, electronic equipment and storage medium | |
| CN115031744A (en) | Cognitive map positioning method and system based on sparse point cloud-texture information | |
| Wang et al. | Lightweight map updating for highly automated driving in non-paved roads |
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 |