CN112257772A - Road increase and decrease interval segmentation method and device, electronic equipment and storage medium - Google Patents
Road increase and decrease interval segmentation method and device, electronic equipment and storage medium Download PDFInfo
- Publication number
- CN112257772A CN112257772A CN202011120357.5A CN202011120357A CN112257772A CN 112257772 A CN112257772 A CN 112257772A CN 202011120357 A CN202011120357 A CN 202011120357A CN 112257772 A CN112257772 A CN 112257772A
- Authority
- CN
- China
- Prior art keywords
- track
- coverage width
- road
- segment
- track coverage
- 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
Images
Classifications
-
- G—PHYSICS
- G06—COMPUTING OR CALCULATING; COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F18/00—Pattern recognition
- G06F18/20—Analysing
- G06F18/21—Design or setup of recognition systems or techniques; Extraction of features in feature space; Blind source separation
- G06F18/213—Feature extraction, e.g. by transforming the feature space; Summarisation; Mappings, e.g. subspace methods
- G06F18/2135—Feature extraction, e.g. by transforming the feature space; Summarisation; Mappings, e.g. subspace methods based on approximation criteria, e.g. principal component analysis
-
- G—PHYSICS
- G06—COMPUTING OR CALCULATING; 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 OR CALCULATING; COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T7/00—Image analysis
- G06T7/10—Segmentation; Edge detection
- G06T7/149—Segmentation; Edge detection involving deformable models, e.g. active contour models
-
- G—PHYSICS
- G06—COMPUTING OR CALCULATING; COUNTING
- G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
- G06V10/00—Arrangements for image or video recognition or understanding
- G06V10/20—Image preprocessing
- G06V10/26—Segmentation of patterns in the image field; Cutting or merging of image elements to establish the pattern region, e.g. clustering-based techniques; Detection of occlusion
- G06V10/267—Segmentation of patterns in the image field; Cutting or merging of image elements to establish the pattern region, e.g. clustering-based techniques; Detection of occlusion by performing operations on regions, e.g. growing, shrinking or watersheds
-
- G—PHYSICS
- G06—COMPUTING OR CALCULATING; COUNTING
- G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
- G06V10/00—Arrangements for image or video recognition or understanding
- G06V10/20—Image preprocessing
- G06V10/30—Noise filtering
-
- 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
- G08G1/0129—Traffic data processing for creating historical data or processing based on historical data
-
- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04W—WIRELESS COMMUNICATION NETWORKS
- H04W4/00—Services specially adapted for wireless communication networks; Facilities therefor
- H04W4/02—Services making use of location information
- H04W4/029—Location-based management or tracking services
-
- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04W—WIRELESS COMMUNICATION NETWORKS
- H04W4/00—Services specially adapted for wireless communication networks; Facilities therefor
- H04W4/30—Services specially adapted for particular environments, situations or purposes
- H04W4/40—Services specially adapted for particular environments, situations or purposes for vehicles, e.g. vehicle-to-pedestrians [V2P]
Landscapes
- Engineering & Computer Science (AREA)
- Theoretical Computer Science (AREA)
- Physics & Mathematics (AREA)
- General Physics & Mathematics (AREA)
- Computer Vision & Pattern Recognition (AREA)
- Data Mining & Analysis (AREA)
- General Engineering & Computer Science (AREA)
- Multimedia (AREA)
- Evolutionary Computation (AREA)
- Bioinformatics & Computational Biology (AREA)
- Bioinformatics & Cheminformatics (AREA)
- Artificial Intelligence (AREA)
- Life Sciences & Earth Sciences (AREA)
- Evolutionary Biology (AREA)
- Signal Processing (AREA)
- Computer Networks & Wireless Communication (AREA)
- Analytical Chemistry (AREA)
- Chemical & Material Sciences (AREA)
- Software Systems (AREA)
- Traffic Control Systems (AREA)
Abstract
本发明提供一种道路增减区间切分方法、装置、电子设备及存储介质，该方法包括：对众包车辆轨迹数据进行预处理，剔除偏离道路路面的稀疏轨迹；将轨迹数据分段，通过主成分分析法计算各分段内主方向轴，拼接对应的主方向轴得到行驶方向上的参考线；作参考线上相邻形点构成线段的垂线段，计算与车辆轨迹线的交点，统计当前道路位置的轨迹覆盖宽度；根据街景或历史底图数据，标记各车道对应的轨迹覆盖宽度，并计算各车道轨迹覆盖宽度的浮动范围；将其作为轨迹覆盖宽度核，根据轨迹覆盖宽度核，对众包车辆轨迹数据进行截断得到道路增减区间。该方案基于众包轨迹数据的处理可以降低数据采集成本，并能提高道路增减区间切分的精度，方便数据及时更新。
The present invention provides a method, device, electronic device and storage medium for segmenting road increase/decrease intervals. The method includes: preprocessing crowdsourced vehicle trajectory data to eliminate sparse trajectories that deviate from the road surface; The principal component analysis method calculates the main direction axis in each segment, and splices the corresponding main direction axis to obtain the reference line in the driving direction; as the vertical line segment of the line segment formed by the adjacent shape points on the reference line, calculates the intersection point with the vehicle trajectory line, statistics The track coverage width of the current road position; according to the street view or historical basemap data, mark the track coverage width corresponding to each lane, and calculate the floating range of the track coverage width of each lane; use it as the track coverage width kernel, according to the track coverage width kernel, The road increase or decrease interval is obtained by truncating the crowdsourced vehicle trajectory data. The solution based on crowdsourced trajectory data processing can reduce the cost of data collection, and can improve the accuracy of road increase and decrease interval segmentation, and facilitate the timely update of data.
Description
Technical Field
The invention relates to the field of high-precision map making, in particular to a road increase and decrease interval segmentation method and device, electronic equipment and a storage medium.
Background
The high-precision map is one of important bases for realizing automatic driving, can provide lane-level path planning for vehicles, is improved to a lane level from a road-level requirement compared with a traditional map, is not fixed in the number of lanes of the same road, is segmented at the position where the number of the lanes changes, and is divided into a plurality of road sections, namely road increase and decrease sections. After the road increase and decrease sections are divided, the description of the position of the automatic driving vehicle can be more accurate and quicker, such as processing the number one lane in the number section of a certain road.
At present, the division of the road increase and decrease section is based on the segmentation of lane sideline data, a collection vehicle carries a high-precision three-dimensional laser scanner and a camera to collect data, then the road sideline is drawn according to high-precision image data and point cloud data, the position of the sideline quantity change is cut off, and the road increase and decrease section is constructed. The road increase and decrease interval constructed in the mode has high accuracy, but has high requirements on operators and acquisition equipment, high actual implementation cost and limited application range. On the basis of track data and images acquired by crowdsourcing, relatively complete lane lines can be obtained through methods such as lane line classification, cutting, line supplementing and fitting, the cost is low, line supplementing reasoning and prediction exist in the process of lane line fitting drawing, and the accuracy of the segmentation position of a road increase and decrease section is not high.
Disclosure of Invention
In view of this, embodiments of the present invention provide a method and an apparatus for segmenting a road section, an electronic device, and a storage medium, so as to solve the problem that the conventional method for segmenting a road section is difficult to improve the accuracy of segmenting the road section while reducing the cost.
In a first aspect of the embodiments of the present invention, a method for dividing a road increase/decrease interval is provided, including:
preprocessing crowdsourcing vehicle track data, and eliminating sparse tracks deviating from a road surface in the crowdsourcing vehicle track data;
segmenting the preprocessed crowdsourcing vehicle track data, calculating a main direction axis in each segment through a principal component analysis method, and splicing the main direction axes corresponding to each segment track data to obtain a reference line in the vehicle driving direction;
making a vertical line segment of a line segment formed by adjacent shape points on a reference line, obtaining intersection points of the vertical line segment and the crowdsourced vehicle trajectory, and counting and calculating the track coverage width of each intersection point at the current road position;
marking the track coverage width corresponding to each lane according to street view or historical base map data, and calculating the floating range of the track coverage width of each lane;
and taking the floating range of the track coverage width of each lane as a track coverage width kernel, and performing truncation and segmentation on the crowdsourced vehicle track data according to the track coverage width kernel to obtain a road increase and decrease section.
In a second aspect of the embodiments of the present invention, there is provided an apparatus for road addition and subtraction section splitting, including:
the preprocessing module is used for preprocessing crowdsourcing vehicle track data and eliminating sparse tracks deviating from a road surface in the crowdsourcing vehicle track data;
the segmentation splicing module is used for segmenting the preprocessed crowdsourcing vehicle track data, calculating main direction axes in each segment through a principal component analysis method, and splicing the main direction axes corresponding to each segment track data to obtain a reference line in the vehicle driving direction;
the statistical module is used for making a vertical line segment of a line segment formed by adjacent shape points on the reference line, acquiring intersection points of the vertical line segment and the crowdsourced vehicle trajectory, and statistically calculating the track coverage width of each intersection point at the current road position;
the calculation module is used for marking the track coverage width corresponding to each lane according to street view or historical base map data and calculating the floating range of the track coverage width of each lane;
and the segmentation module is used for taking the floating range of the track coverage width of each lane as a track coverage width kernel, and performing truncation and segmentation on the crowdsourced vehicle track data according to the track coverage width kernel to obtain a road increase and decrease section.
In a third aspect of the embodiments of the present invention, there is provided an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, where the processor executes the computer program to implement the steps of the method according to the first aspect of the embodiments of the present invention.
In a fourth aspect of the embodiments of the present invention, a computer-readable storage medium is provided, in which a computer program is stored, and the computer program, when executed by a processor, implements the steps of the method provided in the first aspect of the embodiments of the present invention.
In the embodiment of the invention, a reference line of a driving direction is calculated by processing crowdsourced vehicle track data, a crowdsourced vehicle track coverage range is determined according to an intersection point of a perpendicular line segment of a figure point line segment on the reference line and a vehicle track, a track width floating range corresponding to each lane is further determined, the track width floating range is used as a track width core, and the crowdsourced vehicle track data is cut off and segmented according to the track width core, so that a road increase and decrease section is obtained. Therefore, the problem that the accuracy of segmentation of the road sections is difficult to improve while the cost is reduced by the conventional road section segmentation method is solved, the implementation cost of segmentation of the road sections can be effectively reduced based on the crowdsourcing vehicle track data, the data is updated quickly, professional mapping is not needed, the crowdsourcing track data is generally clear and complete, the segmentation precision can be guaranteed based on the calculation of the track coverage, and the accuracy of division of the road sections is improved.
Drawings
In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the drawings needed to be used in the embodiments or the prior art descriptions will be briefly described below, and it is obvious that the drawings described below are only some embodiments of the present invention, and it is obvious for those skilled in the art to obtain other drawings based on these drawings without creative efforts.
Fig. 1 is a schematic flow chart of a road increase/decrease interval segmentation method according to an embodiment of the present invention;
fig. 2 is a schematic structural diagram of an apparatus for splitting a road increasing and decreasing section according to an embodiment of the present invention.
Detailed Description
In order to make the objects, features and advantages of the present invention more obvious and understandable, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention, and it is obvious that the embodiments described below are only a part of the embodiments of the present invention, and not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by persons skilled in the art without any inventive work shall fall within the protection scope of the present invention, and the principle and features of the present invention shall be described below with reference to the accompanying drawings.
The terms "comprises" and "comprising," when used in this specification and claims, and in the accompanying drawings and figures, are intended to cover non-exclusive inclusions, such that a process, method or system, or apparatus that comprises a list of steps or elements is not limited to the listed steps or elements.
Referring to fig. 1, fig. 1 is a schematic flow chart of a road section dividing method according to an embodiment of the present invention, including:
s101, preprocessing crowdsourced vehicle track data, and eliminating sparse tracks deviating from a road surface in the crowdsourced vehicle track data;
there may be some track data outside the road surface in the crowd-sourced vehicle track data, and this part of the noise track needs to be removed through preprocessing, so as to avoid the influence of the noise track on the final track coverage width.
Specifically, the similarity of any two vehicle tracks is calculated through a Frechet Distance (Frechet Distance) describing the similarity of spatial paths, a similarity matrix of the vehicle tracks is constructed, and sparse tracks or outlier tracks with the coverage rate lower than a preset value are removed through a mean shift algorithm based on the kernel density.
S102, segmenting the preprocessed crowdsourcing vehicle track data, calculating main direction axes in each segment through a principal component analysis method, and splicing the main direction axes corresponding to each segment track data to obtain a reference line in the vehicle driving direction;
segmenting the preprocessed vehicle track along the driving direction, dividing track point sets at intervals of about 1m, performing principal component analysis on the track point sets in each segment to obtain a principal direction axis, and splicing track data of each segment through operations such as translation and the like to obtain a reference line in the driving direction.
Calculating the standard deviation of the heading angle of the track point in each segment according to the heading angle of the track point in each segment; and when the standard deviation of the course angle of the track point exceeds a preset threshold value, segmenting the track point corresponding to the segmentation again, and subdividing the track point into a plurality of track point sets.
Preferably, the shape points on the reference line are compressed and interpolated to make the shape points on the reference line evenly distributed at equal intervals.
S103, drawing a vertical line segment of a line segment formed by adjacent shape points on a reference line, obtaining intersection points of the vertical line segment and crowdsourced vehicle trajectory lines, and counting the track coverage width of each intersection point at the current road position;
along the direction of a reference line, calculating a perpendicular line segment of a line segment formed by two adjacent shape points in a certain road surface width range (such as 8-lane road surface width), wherein the perpendicular line segment corresponds to the subscripts of the reference line point one by one, and the distance from the intersection point of the perpendicular line segment and a plurality of vehicle tracks to the reference line is calculated. Calculating the distance between the farthest intersection points of the two sides of each vertical line segment from the reference line, and taking the distance between the farthest intersection points of the two sides of the vertical line segment as the track coverage width of the current road position
S104, marking the track coverage width corresponding to each lane according to street view or historical base map data, and calculating the floating range of the track coverage width of each lane;
according to the existing road streetscape and historical base map data, the track coverage widths corresponding to road pavements such as a single lane, a double lane, a three lane, a four lane and the like are marked respectively, and the floating range of the track coverage width of each lane is calculated.
Specifically, track coverage width mean values of various multi-lane are respectively calculated, a delta range is taken up and down based on the track coverage width mean values to serve as a track coverage width kernel, the track coverage width kernel is set to be a hyper-parameter, and an optimal hyper-parameter is obtained through training of a neural network model to serve as the track coverage width kernel.
And S105, taking the floating range of the track coverage width of each lane as a track coverage width kernel, and performing truncation and segmentation on the crowdsourced vehicle track data according to the track coverage width kernel to obtain a road increase and decrease section.
And for each road, obtaining a curve of which the track coverage width changes along with the road mileage according to the subscript of the reference line and the corresponding track width along the driving direction, and carrying out truncation processing on the track coverage width based on a track coverage width kernel.
Specifically, the range of the core of the single lane is leveled into the average width of the single lane, the range of the core of the other lanes is leveled into the corresponding average width, at this time, the original track coverage width/mileage curve becomes step-shaped, which is parallel to the x-axis and represents a certain type of lane, and the part which changes up and down along with the mileage is the process of lane increase and decrease. The starting position of the lane number which is changed from small to large is the breaking point of the lane increase, and the ending position of the lane number which is changed from small to large is the breaking point of the lane decrease. Along the reference line, the road can be divided into different road increasing and decreasing sections according to the breaking points of the change of the number of the lanes. The breaking points at the starting position and the ending position of the lane number change correspond to the shunting and confluence of the track of the collection vehicle, and are consistent with the behavior habit of lane change driving of the collection vehicle at the position.
According to the method provided by the embodiment, the increase and decrease of the road segmentation points are determined through statistical analysis of mass crowdsourcing trajectory data, a large amount of time and labor cost consumed by surveying and mapping the ground marking by using a traditional segmentation method are avoided, the data acquisition cost is lower, and the updating period is shorter. Meanwhile, the lane side lines may be abraded, or the co-traveling vehicles are shielded, so that the data perception is incomplete, the crowdsourcing vehicle tracks are generally continuous and complete, and the precision in increasing and decreasing interval division can be guaranteed. Road increase and decrease section based on vehicle trajectory analysis and division accords with human driving behavior, also makes automatic driving behavior more fit with human driving habit.
It should be understood that, the sequence numbers of the steps in the foregoing embodiments do not imply an execution sequence, and the execution sequence of each process should be determined by its function and inherent logic, and should not constitute any limitation to the implementation process of the embodiments of the present invention.
Fig. 2 is a schematic structural diagram of an apparatus for splitting a road increase/decrease section according to an embodiment of the present invention, where the apparatus includes:
the preprocessing module 210 is configured to preprocess crowdsourced vehicle trajectory data and remove sparse trajectories, deviating from a road surface, in the crowdsourced vehicle trajectory data;
specifically, the similarity of any two vehicle tracks is calculated based on the Frechst distance, and a similarity matrix of the vehicle tracks is constructed; and eliminating sparse tracks with the coverage rate lower than a preset value through a mean shift algorithm based on the kernel density.
The segment splicing module 220 is configured to segment the preprocessed crowdsourced vehicle trajectory data, calculate a principal direction axis in each segment through a principal component analysis method, and splice the principal direction axis corresponding to each segment trajectory data to obtain a reference line in the vehicle driving direction;
optionally, the segment splicing module 220 further includes:
the calculation unit is used for calculating the standard deviation of the heading angle of the track point in each segment according to the heading angle of the track point in each segment;
and the segmentation unit is used for segmenting the track points of the corresponding segments again and subdividing the track points into a plurality of track point sets when the standard deviation of the course angles of the track points exceeds a preset threshold value.
The step of splicing the main direction axes corresponding to the segmented track data to obtain a reference line in the vehicle driving direction further comprises:
compressing and interpolating the shape points on the reference line to ensure that the shape points on the reference line are uniformly distributed at equal intervals.
The statistical module 230 is configured to take a vertical line segment of a line segment formed by adjacent shape points on the reference line, obtain intersection points of the vertical line segment and the crowd-sourced vehicle trajectory line, and statistically calculate a track coverage width of each intersection point at the current road position;
specifically, the distance between the farthest intersection points of the two sides of each vertical line segment from the reference line is calculated, and the distance between the farthest intersection points of the two sides of the vertical line segment is used as the track coverage width of the current road position
The calculation module 240 is configured to mark track coverage widths corresponding to the lanes according to street view or historical base map data, and calculate a floating range of the track coverage widths of the lanes;
and the segmentation module 250 is configured to use the floating range of the track coverage width of each lane as a track coverage width kernel, and perform truncation and segmentation on crowd-sourced vehicle track data according to the track coverage width kernel to obtain a road increase and decrease section.
Specifically, track coverage width mean values of various multi-lane are respectively calculated, a delta range is taken up and down based on the track coverage width mean values to serve as a track coverage width kernel, the track coverage width kernel is set to be a hyper-parameter, and an optimal hyper-parameter is obtained through training of a neural network model to serve as the track coverage width kernel.
Specifically, a curve of the track coverage width of the road vehicle changing along with the road mileage is constructed; and cutting the track coverage width of the crowdsourced vehicle according to the track coverage width check.
It is understood that, in one embodiment, the electronic device includes a memory, a processor, and a computer program stored in the memory and executable on the processor, and the computer program executes steps S101 to S105 in the first embodiment, and the processor implements the segmentation of the road increase and decrease section when executing the computer program.
Those skilled in the art will appreciate that all or part of the steps in the method for implementing the above embodiments may be implemented by a program to instruct associated hardware, where the program may be stored in a computer-readable storage medium, and when the program is executed, the program includes steps S101 to S105, where the storage medium includes, for example: ROM/RAM, magnetic disk, optical disk, etc.
In the above embodiments, the descriptions of the respective embodiments have respective emphasis, and reference may be made to the related descriptions of other embodiments for parts that are not described or illustrated in a certain embodiment.
The above-mentioned embodiments are only used for illustrating the technical solutions of the present invention, and not for limiting the same; although the present invention has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some technical features may be equivalently replaced; and such modifications or substitutions do not depart from the spirit and scope of the corresponding technical solutions of the embodiments of the present invention.
Claims (10)
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202011120357.5A CN112257772B (en) | 2020-10-19 | 2020-10-19 | Road increase and decrease interval segmentation method and device, electronic equipment and storage medium |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202011120357.5A CN112257772B (en) | 2020-10-19 | 2020-10-19 | Road increase and decrease interval segmentation method and device, electronic equipment and storage medium |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN112257772A true CN112257772A (en) | 2021-01-22 |
| CN112257772B CN112257772B (en) | 2022-05-13 |
Family
ID=74244986
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202011120357.5A Active CN112257772B (en) | 2020-10-19 | 2020-10-19 | Road increase and decrease interval segmentation method and device, electronic equipment and storage medium |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN112257772B (en) |
Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112991726A (en) * | 2021-02-08 | 2021-06-18 | 东南大学 | Method for setting road marking in urban expressway interweaving area |
| CN113139258A (en) * | 2021-04-28 | 2021-07-20 | 北京百度网讯科技有限公司 | Road data processing method, device, equipment and storage medium |
| CN114509062A (en) * | 2021-12-31 | 2022-05-17 | 武汉中海庭数据技术有限公司 | Reverse track filtering method and device based on track big data |
| CN114595304A (en) * | 2022-03-21 | 2022-06-07 | 智道网联科技(北京)有限公司 | Lane line changing interrupting method, electronic device and storage medium |
Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20090070025A1 (en) * | 2007-09-11 | 2009-03-12 | Hitachi, Ltd. | Dynamic Prediction of Traffic Congestion by Tracing Feature-Space Trajectory of Sparse Floating-Car Data |
| CN108151751A (en) * | 2017-11-21 | 2018-06-12 | 武汉中海庭数据技术有限公司 | A kind of paths planning method and device combined based on high-precision map and traditional map |
| CN109186617A (en) * | 2018-08-13 | 2019-01-11 | 武汉中海庭数据技术有限公司 | A kind of view-based access control model crowdsourcing data automatically generate method, system and the memory of lane grade topological relation |
| CN111578964A (en) * | 2020-04-13 | 2020-08-25 | 河北德冠隆电子科技有限公司 | High-precision map road information rapid generation system and method based on space-time trajectory reconstruction |
-
2020
- 2020-10-19 CN CN202011120357.5A patent/CN112257772B/en active Active
Patent Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20090070025A1 (en) * | 2007-09-11 | 2009-03-12 | Hitachi, Ltd. | Dynamic Prediction of Traffic Congestion by Tracing Feature-Space Trajectory of Sparse Floating-Car Data |
| CN108151751A (en) * | 2017-11-21 | 2018-06-12 | 武汉中海庭数据技术有限公司 | A kind of paths planning method and device combined based on high-precision map and traditional map |
| CN109186617A (en) * | 2018-08-13 | 2019-01-11 | 武汉中海庭数据技术有限公司 | A kind of view-based access control model crowdsourcing data automatically generate method, system and the memory of lane grade topological relation |
| CN111578964A (en) * | 2020-04-13 | 2020-08-25 | 河北德冠隆电子科技有限公司 | High-precision map road information rapid generation system and method based on space-time trajectory reconstruction |
Non-Patent Citations (1)
| Title |
|---|
| 唐炉亮等: "一种基于朴素贝叶斯分类的车道数量探测", 《中国公路学报》 * |
Cited By (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112991726A (en) * | 2021-02-08 | 2021-06-18 | 东南大学 | Method for setting road marking in urban expressway interweaving area |
| CN113139258A (en) * | 2021-04-28 | 2021-07-20 | 北京百度网讯科技有限公司 | Road data processing method, device, equipment and storage medium |
| WO2022227487A1 (en) * | 2021-04-28 | 2022-11-03 | 北京百度网讯科技有限公司 | Road data processing method and apparatus, and device and storage medium |
| CN113139258B (en) * | 2021-04-28 | 2024-01-09 | 北京百度网讯科技有限公司 | Road data processing method, device, equipment and storage medium |
| CN114509062A (en) * | 2021-12-31 | 2022-05-17 | 武汉中海庭数据技术有限公司 | Reverse track filtering method and device based on track big data |
| CN114509062B (en) * | 2021-12-31 | 2023-10-13 | 武汉中海庭数据技术有限公司 | Retrograde trajectory filtering method and device based on large trajectory data |
| CN114595304A (en) * | 2022-03-21 | 2022-06-07 | 智道网联科技(北京)有限公司 | Lane line changing interrupting method, electronic device and storage medium |
| CN114595304B (en) * | 2022-03-21 | 2025-01-03 | 智道网联科技(北京)有限公司 | Lane changing lane line interruption method, electronic device and storage medium |
Also Published As
| Publication number | Publication date |
|---|---|
| CN112257772B (en) | 2022-05-13 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN112257772B (en) | Road increase and decrease interval segmentation method and device, electronic equipment and storage medium | |
| CN110749329B (en) | A lane-level topology construction method and device based on structured road | |
| EP4120123A1 (en) | Scan line-based road point cloud extraction method | |
| EP3631364A1 (en) | Method and apparatus for producing a lane-accurate road map | |
| CN102564431A (en) | Multi-sensor-fusion-based unstructured environment understanding method | |
| CN114663804A (en) | Driving area detection method, device, mobile equipment and storage medium | |
| CN115690773B (en) | DEM partitioning and rebuilding method, computing device and storage medium | |
| CN114758096A (en) | Road edge detection method, device, terminal equipment and storage medium | |
| CN118918182A (en) | Ground element labeling method, storage medium, vehicle-mounted equipment and vehicle | |
| CN113095164A (en) | Lane line detection and positioning method based on reinforcement learning and mark point characterization | |
| CN112435336A (en) | Curve type identification method and device, electronic equipment and storage medium | |
| CN116994086A (en) | Road model generation method, device, equipment and medium | |
| CN119180156B (en) | Mapping method, mapping device, computer equipment, storage medium and program product | |
| CN109492454B (en) | Object identification method and device | |
| CN119169807B (en) | Road traffic control method, device, equipment and storage medium | |
| CN119824773A (en) | Automatic scribing method, device, equipment and storage medium for scribing robot | |
| CN113778102A (en) | AVP global path planning system, method, vehicle and storage medium | |
| CN112685517B (en) | Method and apparatus for identifying diverging/converging regions | |
| CN117423000B (en) | MARS-based intelligent road longitudinal section linear reconstruction method | |
| CN109726728A (en) | A kind of training data generation method and device | |
| CN115795626B (en) | Digital road model analysis method, device, computing equipment and storage medium | |
| CN114067562B (en) | Lane effectiveness prediction method and prediction system based on multi-dimensional features | |
| EP4116930B1 (en) | Method and device for object tracking | |
| CN113569782B (en) | Free flow speed estimation method and system based on artificial intelligence and laser radar | |
| CN117292562A (en) | Signal control time period division method based on intersection accurate flow direction identification |
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 | ||
| PE01 | Entry into force of the registration of the contract for pledge of patent right | ||
| PE01 | Entry into force of the registration of the contract for pledge of patent right |
Denomination of invention: A method for road interval division and merging, a device, an electronic equipment, and a storage medium Granted publication date: 20220513 Pledgee: CITIC Bank Limited by Share Ltd. Wuhan branch Pledgor: WUHHAN KOTEL BIG DATE Corp. Registration number: Y2025980038914 |