CN110502797B - Lane acquisition modeling system and method based on GNSS - Google Patents
Lane acquisition modeling system and method based on GNSS Download PDFInfo
- Publication number
- CN110502797B CN110502797B CN201910672368.5A CN201910672368A CN110502797B CN 110502797 B CN110502797 B CN 110502797B CN 201910672368 A CN201910672368 A CN 201910672368A CN 110502797 B CN110502797 B CN 110502797B
- Authority
- CN
- China
- Prior art keywords
- lane
- unit
- module
- information
- gnss
- 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
Images
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
- G06V20/00—Scenes; Scene-specific elements
- G06V20/50—Context or environment of the image
- G06V20/56—Context or environment of the image exterior to a vehicle by using sensors mounted on the vehicle
- G06V20/588—Recognition of the road, e.g. of lane markings; Recognition of the vehicle driving pattern in relation to the road
Landscapes
- Engineering & Computer Science (AREA)
- Physics & Mathematics (AREA)
- General Physics & Mathematics (AREA)
- Multimedia (AREA)
- Theoretical Computer Science (AREA)
- Traffic Control Systems (AREA)
Abstract
The invention relates to a lane acquisition modeling system and method based on GNSS, the system comprises a GNSS position acquisition unit, a dynamic coordinate calibration unit, a local lane modeling unit and an environment calibration unit, the GNSS position acquisition unit is respectively connected with the dynamic coordinate calibration unit, the local lane modeling unit and the environment calibration unit, the system also comprises a lane information fusion unit and a lane additional information unit connected with the local lane modeling unit, and the lane information fusion unit is respectively connected with the dynamic coordinate calibration unit, the lane additional information unit and the environment calibration unit. Compared with the prior art, the method can acquire lane information through GNSS equipment under the condition of not using IMU, laser radar, cameras and other equipment, and simultaneously establish a complete lane model by utilizing lane lines, road surface dynamic coordinates and surrounding environment coordinates.
Description
Technical Field
The invention relates to a lane acquisition modeling system and method, in particular to a lane acquisition modeling system and method based on a GNSS.
Background
The unmanned vehicle can run according to a fixed route in a designated area, which requires a vehicle positioning system to provide accurate position information for the vehicle, a Global Navigation Satellite System (GNSS) can provide services such as positioning, speed measurement, time service and the like globally and all-weather in real time, a general positioning system adopts GNSS positioning equipment to realize accurate positioning of the vehicle, but the GNSS signals are weakened or invalid due to tree or building shielding, and accurate positioning cannot be realized.
The current high-precision map usually adopts GNSS, IMU, laser radar, camera and the like to realize the collection of the lane data, and the data comprises the precise three-dimensional representation of the road network and a plurality of semantic information. However, these lane modeling devices are expensive and do not take into account factors such as signal occlusion, lighting conditions, and road surface irregularities.
Disclosure of Invention
The present invention is directed to a lane acquisition modeling system and method based on GNSS, which overcome the above-mentioned drawbacks of the prior art.
The purpose of the invention can be realized by the following technical scheme:
a lane acquisition modeling system based on GNSS comprises a GNSS position acquisition unit, a dynamic coordinate calibration unit, a local lane modeling unit and an environment calibration unit, wherein the GNSS position acquisition unit is respectively connected with the dynamic coordinate calibration unit, the local lane modeling unit and the environment calibration unit;
the system comprises a GNSS position acquisition unit, a local lane modeling unit, a lane additional information unit, a dynamic coordinate calibration unit, an environment calibration unit and a lane information fusion unit, wherein the GNSS position acquisition unit is used for acquiring relative position coordinates of lane lines, the local lane modeling unit is used for performing fitting modeling on local lanes, the lane additional information unit is used for calibrating lane line type information and lane line quality information on a local lane model, the dynamic coordinate calibration unit is used for calibrating lane rugged region information and deceleration strip information, the environment calibration unit is used for calibrating weak GNSS region information around a road and weak illumination region information of night lanes, and the lane information fusion unit is used for integrating the local lane model into an integral lane model, adding information calibrated by the dynamic coordinate calibration unit and information calibrated by the environment calibration unit on the integral lane model and outputting the integral lane model.
The GNSS position acquisition unit comprises a GNSS absolute position acquisition module, an acquisition point processing module and a coordinate conversion module, wherein the acquisition point processing module is connected with the GNSS absolute position acquisition module, and the GNSS absolute position acquisition module is connected with the coordinate conversion module;
the GNSS absolute position acquisition module is used for selecting acquisition points and acquiring latitude and longitude information on the acquisition points, the acquisition point processing module is used for screening acquisition point data, the coordinate conversion module is used for converting GNSS absolute coordinates into relative position coordinates, and the coordinate conversion module specifically comprises:
the distance rho from the acquisition point to the earth centroid is:
the local east-west coordinate X is:
X＝ρ×cos(L)×dλ
the local north-south coordinate Y is:
Y＝ρ×dL
wherein R _0 is the equatorial radius, R _ p is the pole radius, L is the local latitude, d λ is the relative longitude, and dL is the relative latitude.
The local lane modeling unit comprises a lane fitting module and a threshold judging module, and the lane fitting module is connected with the threshold judging module;
the lane fitting module is used for performing fitting modeling on a local lane according to the relative position coordinates, meanwhile, the threshold judging module judges whether the standard deviation of the fitting result is smaller than the threshold, if the standard deviation is smaller than the threshold, the fitting modeling is completed, and if the standard deviation is larger than or equal to the threshold, the fitting modeling is performed again until the standard deviation is smaller than the threshold.
The lane additional information unit comprises a lane line type calibration module and a lane line quality evaluation module, and the lane line type calibration module is connected with the lane line quality evaluation module;
the lane line type calibration module is used for classifying lane types and calibrating lane type information, and the lane line quality evaluation module is used for evaluating lane line quality and calibrating lane quality information.
The dynamic coordinate calibration unit comprises a lane rugged region calibration module and a speed bump position calibration module, and the lane rugged region calibration module is connected with the speed bump position calibration module;
the speed bump location calibration module is used for classifying the road bump locations and calibrating the starting location and the ending location of each road bump location according to the actual road surface condition, and the speed bump location calibration module is used for calibrating the position of a speed bump and performing fitting modeling on the speed bump.
The environment calibration unit comprises a weak illumination position calibration module and a weak GNSS position calibration module, and the weak illumination position calibration module is connected with the weak GNSS position calibration module;
the weak GNSS position calibration module is used for evaluating the illumination condition of a road surface with illumination at night and calibrating the information of a weak illumination area, and the weak GNSS position calibration module is used for evaluating the grade of the weak GNSS area and calibrating the information of the weak GNSS area.
The lane information fusion unit comprises an integral lane modeling module and a lane information output module, and the integral lane modeling module is connected with the lane information output module;
the overall lane modeling module is used for integrating the local lane model into an overall lane model, information calibrated by the dynamic coordinate calibration unit and information calibrated by the environment calibration unit are added to the overall lane model, and the lane information output module is used for outputting the overall lane model.
A lane acquisition modeling method based on GNSS comprises the following steps:
s1: a GNSS position acquisition unit acquires relative position coordinates;
s2: the local lane modeling unit performs fitting modeling on the local lane data;
s3: the lane additional information unit calibrates lane quality information and lane type information on the local lane model;
s4: the dynamic coordinate calibration unit is used for calibrating the information of the uneven area of the lane and the information of the speed bump by combining the relative position coordinates;
s5: the environment calibration unit calibrates weak GNSS area information and weak illumination area information by combining relative position coordinates;
s6: the lane information fusion unit integrates the local lane model into an integral lane model, adds information calibrated by the dynamic coordinate calibration module and the environment calibration module to the integral lane model, and then outputs the integral lane model.
The S1 specifically includes: the GNSS position acquisition unit selects an acquisition point, acquires longitude and latitude information of the acquisition point, screens the longitude and latitude information, and then converts the acquired longitude and latitude information into relative position coordinates;
the S2 specifically includes: the local lane modeling unit performs fitting modeling on the local lane according to the relative position coordinates, judges whether the standard deviation of the fitting result is smaller than a threshold value or not, completes fitting modeling if the standard deviation is smaller than the threshold value, and performs fitting modeling again until the standard deviation is smaller than the threshold value if the standard deviation is larger than or equal to the threshold value;
the S3 specifically includes: the lane additional information unit judges the quality grade and the type of the lane line, and calibrates the lane line quality information and the lane line type information by combining the relative position coordinates;
the S4 specifically includes: the dynamic coordinate calibration unit evaluates the type of the rough road surface, models the deceleration strip, and calibrates the information of the rough area of the lane and the information of the deceleration strip by combining the relative position coordinates;
the S5 specifically includes: the environment calibration unit judges the shielding condition of GNSS signals around the road, evaluates the illumination condition of the road surface illuminated by the street lamps at night, and calibrates the weak GNSS area information and the weak illumination area information by combining the relative position coordinates.
Compared with the prior art, the invention has the following advantages:
(1) according to the method, lane information is acquired only through GNSS equipment, and accurate acquisition of lane information is achieved under the condition that IMU, laser radar, cameras and other equipment are not used;
(2) the invention establishes complete lane modeling by utilizing lane lines, road surface dynamic coordinates and surrounding environment coordinates, and can better match the combined positioning scheme of GNSS, IMU and vision used by the existing unmanned vehicle compared with the existing high-precision map acquisition scheme.
Drawings
FIG. 1 is a schematic structural diagram of a GNSS-based lane acquisition modeling system;
FIG. 2 is a flowchart of a GNSS-based lane-acquisition modeling method.
The system comprises a GNSS position acquisition unit, a GNSS 11 acquisition point processing module, a GNSS absolute position acquisition module, a GNSS 13 coordinate conversion module, a dynamic coordinate calibration unit, a lane rugged region calibration module, a speed bump position calibration module, a local lane modeling unit, a threshold judgment module, a lane fitting module, a lane additional information unit, a lane line type calibration module, a lane line quality evaluation module, a lane information fusion unit, a lane information.
Detailed Description
The invention is described in detail below with reference to the figures and specific embodiments. Note that the following description of the embodiments is merely a substantial example, and the present invention is not intended to be limited to the application or the use thereof, and is not limited to the following embodiments.
Examples
As shown in fig. 1, a lane collection modeling system based on GNSS includes a GNSS position collection unit 1, a dynamic coordinate calibration unit 2, a local lane modeling unit 3, an environment calibration unit 5, a lane additional information unit 4, and a lane information fusion unit 6, where the GNSS position collection unit 1 is connected to the dynamic coordinate calibration unit 2, the local lane modeling unit 3, and the environment calibration unit 5, the lane additional information unit 4 is connected to the local lane modeling unit 3, the lane information fusion unit 6 is connected to the dynamic coordinate calibration unit 2, the lane additional information unit 4, and the environment calibration unit 5, and the dynamic coordinate calibration unit 2, the environment calibration unit 5, and the lane additional information unit 4 calibrate pre-collected data.
The GNSS position acquisition unit 1 comprises a GNSS absolute position acquisition module 12, an acquisition point processing module 11 and a coordinate conversion module 13, wherein the acquisition point processing module 11 is connected with the GNSS absolute position acquisition module 12, and the GNSS absolute position acquisition module 12 is connected with the coordinate conversion module 13.
The GNSS absolute position acquisition module 12 is used for selecting acquisition points, the selection points comprise lane line end points, lane line middle points with one end starting at every 0.2m, start points and end points of uneven sections of a road surface, deceleration strip end points, start points and end points of a weak GNSS area and a weak illumination area, the GNSS absolute position acquisition device is also used for acquiring longitude and latitude, road surface dynamics coordinate longitude and latitude and longitude and latitude of an environment position coordinate on the acquisition points, the acquisition point processing module 11 is used for screening the data of the acquisition points, the acquired longitude and latitude information of the acquisition points fluctuates at the initial stage, 100 point averaging values with standard deviation less than 5cm are selected as the coordinates of the acquisition points, and if the requirements of threshold value and quantity are not met, the point data is discarded or the data is collected again, the coordinate conversion module 13 is configured to convert the collected point data into the relative position coordinates, and the coordinate conversion module 13 specifically includes:
the distance rho from the acquisition point to the earth centroid is:
the local east-west coordinate X is:
X＝ρ×cos(L)×dλ
the local north-south coordinate Y is:
Y＝ρ×dL
wherein R _0 is the equatorial radius, R _ p is the pole radius, L is the local latitude, d λ is the relative longitude, and dL is the relative latitude.
The local lane modeling unit 3 comprises a lane fitting module 32 and a threshold judging module 31, the lane fitting module 32 is connected with the threshold judging module 31, the lane fitting module 32 is combined with the threshold judging module 31 to group and fit the coordinate data of the relative position, when the local lane modeling unit 3 is used, the local lane modeling unit 3 performs least square fitting on each group of data and sets a standard deviation threshold, if the standard deviation of a first-degree polynomial fitting function is smaller than the threshold, the first-degree polynomial function is adopted, otherwise, second-degree polynomial fitting is performed, if the standard deviation of the second-degree polynomial function is smaller than the threshold, the second-degree polynomial function is adopted, otherwise, the group of data is divided into two groups of data according to the coordinate position, and then each group of data is refitted as described above.
The lane additional information unit 4 comprises a lane line type calibration module 41 and a lane line quality evaluation module 42, wherein the lane line type calibration module 41 is connected with the lane line quality evaluation module 42, the lane line type calibration module 41 is used for dividing lane types into broken lines, solid lines, double broken lines, double solid lines and other types and calibrating lane line type information, the lane line quality evaluation module 42 is used for dividing lane lines into four grades of Z1, Z2, Z3 and Z4 according to the definition of the lane lines and the boundary of a road surface and the integrity of the shape of the lane lines, and Z1 represents that the boundary of the lane lines is clear, the shape is complete and the quality is the best, Z2 represents that a mark has a small amount of blurring or defect, Z3 represents that a mark has a large amount of blurring or defect, Z4 represents that a mark is difficult to recognize and calibrates the lane line quality information.
The dynamic coordinate calibration unit 2 comprises a lane uneven area calibration module 21 and a speed bump position calibration module 22, the lane uneven area calibration module 21 is connected with the speed bump position calibration module 22, the lane uneven area calibration module 21 is used for classifying uneven road areas according to actual road conditions, the uneven road types comprise uneven road surfaces including pits, bulges and continuous bumpy road surfaces, the initial position and the end position of each uneven road surface are calibrated, the speed bump position calibration module 22 is used for calibrating the position of a speed bump and performing fitting modeling on the speed bump, the position of the speed bump needs to acquire the positions of two ends of the speed bump, and the position data is fitted into a linear function y being kx + b, wherein y represents a relative position in the north-south direction, x represents a relative position in the east-west direction, and the position information of the speed bump calibrated by the speed bump position calibration module 22 comprises position information of the two ends, Slope k, intercept b, and slowdown band width.
The environment calibration unit 5 comprises a weak illumination position calibration module 51 and a weak GNSS position calibration module 52, wherein the weak illumination position calibration module 51 is connected with the weak GNSS position calibration module 52, the weak illumination position calibration module 51 is used for evaluating the grade of a weak GNSS region, the weak GNSS region is divided into three grades of L1, L2 and L3, L1 is that trees exist at the periphery, L2 is that tall buildings exist at the periphery, and L3 is an overbridge or tunnel region; the weak illumination position calibration module 51 is used for evaluating the illumination condition of the road surface and recording the coordinates of a weak illumination area at night when the street lamp illuminates.
The lane information fusion unit 6 comprises an integral lane modeling module 61 and a lane information output module 62, the integral lane modeling module 61 is connected with the lane information output module 62, the integral lane modeling module 61 is used for integrating a local lane model into an integral lane model, information calibrated by the dynamic coordinate calibration unit 2 and information calibrated by the environment calibration unit 5 are added to the integral lane model, and the lane information output module 62 is used for outputting the integral lane model.
As shown in fig. 2, a method for modeling a lane acquisition system based on GNSS specifically includes the following steps:
s1: the GNSS absolute position acquisition module 12 selects an acquisition point and acquires longitude and latitude of the acquisition point, longitude and latitude of a road surface dynamic coordinate and longitude and latitude information of an environment position coordinate, the acquisition point processing module 11 screens the longitude and latitude information, and the coordinate conversion module 13 converts the acquired longitude and latitude information into a relative position coordinate;
s2: the lane fitting module 32 and the threshold value judging module 31 perform fitting modeling on the local lane according to the relative position coordinates, and simultaneously judge whether the standard deviation of the fitting result is smaller than the threshold value, if the standard deviation is smaller than the threshold value, the fitting modeling is completed, and if the standard deviation is larger than or equal to the threshold value, the fitting modeling is performed again until the standard deviation is smaller than the threshold value;
s3: the lane line type calibration module 41 judges the type of the lane line and calibrates the lane line type information by combining the relative position coordinates, and the lane line quality assessment module 42 assesses the lane line quality and calibrates the lane line quality information by combining the relative position coordinates;
s4: the lane uneven area calibration module 21 evaluates the type of an uneven area, calibrates information of the uneven area by combining relative position coordinates, and the deceleration strip position calibration module 22 models a deceleration strip and calibrates position information of the deceleration strip by combining the relative position coordinates;
s5: the weak GNSS position calibration module 52 judges the shielding condition of GNSS signals around the road, calibrates the position of a weak GNSS area by combining relative position coordinates, evaluates the illumination condition of the road surface illuminated by the street lamp at night by the weak illumination position calibration module 51, and calibrates the position of the weak illumination area by combining relative position coordinates;
s6: the lane modeling module integrates the local lane model into an overall lane model, information calibrated by the dynamic coordinate calibration module and the environment calibration module is added to the overall lane model, and the lane information output module 62 outputs the overall lane model.
The above embodiments are merely examples and do not limit the scope of the present invention. These embodiments may be implemented in other various manners, and various omissions, substitutions, and changes may be made without departing from the technical spirit of the present invention.
Claims (8)
1. The lane acquisition modeling system based on the GNSS is characterized by comprising a GNSS position acquisition unit (1), a dynamic coordinate calibration unit (2), a local lane modeling unit (3) and an environment calibration unit (5), wherein the GNSS position acquisition unit (1) is respectively connected with the dynamic coordinate calibration unit (2), the local lane modeling unit (3) and the environment calibration unit (5), the system further comprises a lane information fusion unit (6) and a lane additional information unit (4) connected with the local lane modeling unit (3), and the lane information fusion unit (6) is respectively connected with the dynamic coordinate calibration unit (2), the lane additional information unit (4) and the environment calibration unit (5);
the GNSS position acquisition unit (1) is used for acquiring the relative position coordinates of the lane line, the local lane modeling unit (3) is used for performing fitting modeling on a local lane, the lane additional information unit (4) is used for calibrating lane line type information and lane line quality information on a local lane model, the dynamic coordinate calibration unit (2) is used for calibrating the information of the uneven area of the lane and the information of the deceleration strip, the environment calibration unit (5) is used for calibrating weak GNSS area information around a road and weak illumination area information of a night lane, the lane information fusion unit (6) is used for integrating the local lane model into an integral lane model, adding information calibrated by the dynamic coordinate calibration unit (2) and information calibrated by the environment calibration unit (5) to the integral lane model, and outputting the integral lane model;
the GNSS position acquisition unit (1) comprises a GNSS absolute position acquisition module (12), an acquisition point processing module (11) and a coordinate conversion module (13), wherein the acquisition point processing module (11) is connected with the GNSS absolute position acquisition module (12), and the GNSS absolute position acquisition module (12) is connected with the coordinate conversion module (13);
the GNSS absolute position acquisition module (12) is used for selecting acquisition points and acquiring longitude and latitude information on the acquisition points, the acquisition point processing module (11) is used for screening acquisition point data, the coordinate conversion module (13) is used for converting GNSS absolute coordinates into relative position coordinates, and the coordinate conversion module (13) specifically comprises:
the distance rho from the acquisition point to the earth centroid is:
the local east-west coordinate X is:
X＝ρ×cos(L)×dλ
the local north-south coordinate Y is:
Y＝ρ×dL
wherein R _0 is the equatorial radius, R _ p is the pole radius, L is the local latitude, d λ is the relative longitude, and dL is the relative latitude.
2. The GNSS-based lane collection modeling system of claim 1, wherein the local lane modeling unit (3) comprises a lane fitting module (32) and a threshold value judging module (31), the lane fitting module (32) is connected with the threshold value judging module (31);
the lane fitting module (32) is used for performing fitting modeling on a local lane according to the relative position coordinates, meanwhile, the threshold value judging module (31) judges whether the standard deviation of the fitting result is smaller than the threshold value, if the standard deviation is smaller than the threshold value, the fitting modeling is completed, and if the standard deviation is larger than or equal to the threshold value, the fitting modeling is performed again until the standard deviation is smaller than the threshold value.
3. The GNSS-based lane collection modeling system of claim 1, wherein the lane-keeping information unit (4) comprises a lane line type calibration module (41) and a lane line quality assessment module (42), the lane line type calibration module (41) is connected with the lane line quality assessment module (42);
the lane line type calibration module (41) is used for classifying lane types and calibrating lane type information, and the lane line quality evaluation module (42) is used for evaluating lane line quality and calibrating lane quality information.
4. The GNSS-based lane collection modeling system of claim 1, wherein the dynamic coordinate calibration unit (2) comprises a lane rugged region calibration module (21) and a deceleration strip position calibration module (22), the lane rugged region calibration module (21) is connected with the deceleration strip position calibration module (22);
the lane rugged region calibration module (21) is used for classifying the lane rugged regions and calibrating the starting position and the ending position of each lane rugged region according to the actual road surface condition, and the speed reduction belt position calibration module (22) is used for calibrating the position of a speed reduction belt and performing fitting modeling on the speed reduction belt.
5. The GNSS based lane keeping track of modeling system of claim 1, wherein the environment calibration unit (5) comprises a weak GNSS position calibration module (51) and a weak GNSS position calibration module (52), the weak GNSS position calibration module (51) is connected with the weak GNSS position calibration module (52);
the weak GNSS position calibration module (52) is used for evaluating the grade of a weak GNSS area and calibrating the information of the weak GNSS area.
6. The GNSS-based lane collection modeling system of claim 1, wherein the lane information fusion unit (6) comprises an overall lane modeling module (61) and a lane information output module (62), the overall lane modeling module (61) is connected with the lane information output module (62);
the overall lane modeling module (61) is used for integrating the local lane model into the overall lane model, information calibrated by the dynamic coordinate calibration unit (2) and information calibrated by the environment calibration unit (5) are added to the overall lane model, and the lane information output module (62) is used for outputting the overall lane model.
7. A GNSS-based lane collection modeling method based on the lane collection modeling system of any one of claims 1 to 6, the method comprising the steps of:
s1: the GNSS position acquisition unit (1) acquires relative position coordinates;
s2: the local lane modeling unit (3) performs fitting modeling on the local lane data;
s3: the lane additional information unit (4) marks lane quality information and lane type information on the local lane model;
s4: the dynamic coordinate calibration unit (2) calibrates the information of the lane rugged region and the information of the deceleration strip by combining the relative position coordinates;
s5: the environment calibration unit (5) calibrates weak GNSS region information and weak illumination region information by combining relative position coordinates;
s6: the lane information fusion unit (6) integrates the local lane model into an integral lane model, adds information calibrated by a dynamic coordinate calibration module and an environment calibration module to the integral lane model, and then outputs the integral lane model;
s1 specifically includes: firstly, selecting an acquisition point and acquiring longitude and latitude information on the acquisition point, then screening acquisition point data, finally converting a GNSS absolute coordinate into a relative position coordinate, and acquiring the distance rho from the acquisition point to the earth centroid in the process of converting the GNSS absolute coordinate into the relative position coordinate by the following formula:
the local east-west coordinate X is:
X＝ρ×cos(L)×dλ
the local north-south coordinate Y is:
Y＝ρ×dL
wherein R _0 is the equatorial radius, R _ p is the pole radius, L is the local latitude, d λ is the relative longitude, and dL is the relative latitude.
8. The GNSS based lane keeping track of claim 7 wherein,
the S2 specifically includes: the local lane modeling unit (3) performs fitting modeling on the local lane according to the relative position coordinates, judges whether the standard deviation of the fitting result is smaller than a threshold value, completes fitting modeling if the standard deviation is smaller than the threshold value, and performs fitting modeling again until the standard deviation is smaller than the threshold value if the standard deviation is larger than or equal to the threshold value;
the S3 specifically includes: the lane additional information unit (4) judges the quality grade and the type of the lane line, and calibrates the lane line quality information and the lane line type information by combining the relative position coordinates;
the S4 specifically includes: the dynamic coordinate calibration unit (2) evaluates the type of the rough road surface, models the deceleration strip, and calibrates the information of the rough area of the lane and the information of the deceleration strip by combining the relative position coordinates;
the S5 specifically includes: the environment calibration unit (5) judges the shielding condition of GNSS signals around the road, evaluates the illumination condition of the road surface illuminated by the street lamps at night, and calibrates the weak GNSS area information and the weak illumination area information by combining the relative position coordinates.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201910672368.5A CN110502797B (en) | 2019-07-24 | 2019-07-24 | Lane acquisition modeling system and method based on GNSS |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201910672368.5A CN110502797B (en) | 2019-07-24 | 2019-07-24 | Lane acquisition modeling system and method based on GNSS |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN110502797A CN110502797A (en) | 2019-11-26 |
| CN110502797B true CN110502797B (en) | 2021-06-04 |
Family
ID=68586772
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201910672368.5A Active CN110502797B (en) | 2019-07-24 | 2019-07-24 | Lane acquisition modeling system and method based on GNSS |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN110502797B (en) |
Families Citing this family (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113932800A (en) * | 2021-11-03 | 2022-01-14 | 维沃移动通信有限公司 | Navigation method and device thereof |
Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN105667518A (en) * | 2016-02-25 | 2016-06-15 | 福州华鹰重工机械有限公司 | Lane detection method and device |
| CN106324618A (en) * | 2015-06-17 | 2017-01-11 | 百利得汽车主动安全***（苏州）有限公司 | System for detecting lane line based on laser radar and realization method thereof |
| CN108573272A (en) * | 2017-12-15 | 2018-09-25 | 蔚来汽车有限公司 | Track approximating method |
| CN109059911A (en) * | 2018-07-31 | 2018-12-21 | 太原理工大学 | A kind of GNSS, INS and barometrical data fusion method |
Family Cites Families (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US9612123B1 (en) * | 2015-11-04 | 2017-04-04 | Zoox, Inc. | Adaptive mapping to navigate autonomous vehicles responsive to physical environment changes |
| CN106777811B (en) * | 2017-01-16 | 2020-09-25 | 昆明理工大学 | Traffic channelized map design and development method |
| CN108986207B (en) * | 2018-06-29 | 2023-05-23 | 广东星舆科技有限公司 | Road and along-line building simulation modeling method based on real pavement data |
-
2019
- 2019-07-24 CN CN201910672368.5A patent/CN110502797B/en active Active
Patent Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN106324618A (en) * | 2015-06-17 | 2017-01-11 | 百利得汽车主动安全***（苏州）有限公司 | System for detecting lane line based on laser radar and realization method thereof |
| CN105667518A (en) * | 2016-02-25 | 2016-06-15 | 福州华鹰重工机械有限公司 | Lane detection method and device |
| CN108573272A (en) * | 2017-12-15 | 2018-09-25 | 蔚来汽车有限公司 | Track approximating method |
| CN109059911A (en) * | 2018-07-31 | 2018-12-21 | 太原理工大学 | A kind of GNSS, INS and barometrical data fusion method |
Non-Patent Citations (1)
| Title |
|---|
| "应用于智能车的车道信息感知及增量建模技术研究";于泳;《中国博士学位论文全文数据库 工程科技Ⅱ辑》;20190115;第15-18、30-34、39-58、90-97、118页 * |
Also Published As
| Publication number | Publication date |
|---|---|
| CN110502797A (en) | 2019-11-26 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| US11131551B2 (en) | Map data providing system using coordinate transformation and reference points | |
| CN110146910B (en) | Positioning method and device based on data fusion of GPS and laser radar | |
| US20210199437A1 (en) | Vehicular component control using maps | |
| CN101334288B (en) | Public transport bus exact stop method based on standard line matching | |
| CN102645203B (en) | Power line crossover measurement method based on airborne laser radar data | |
| KR100340451B1 (en) | Method and apparatus for processing digital map data | |
| CN103743383B (en) | A kind of Automatic extraction method for road information based on a cloud | |
| CN111582079A (en) | Lane positioning method and device based on computer vision | |
| CN109556569B (en) | Topographic map surveying and mapping method and device | |
| CN104573733A (en) | High-precision map generation system and method based on high-definition ortho-photo map | |
| WO2020083103A1 (en) | Vehicle positioning method based on deep neural network image recognition | |
| CN102208013A (en) | Scene matching reference data generation system and position measurement system | |
| CN114859374B (en) | Newly-built railway cross measurement method based on unmanned aerial vehicle laser point cloud and image fusion | |
| CN114323050A (en) | Vehicle positioning method and device and electronic equipment | |
| CN110502797B (en) | Lane acquisition modeling system and method based on GNSS | |
| CN114841188A (en) | Vehicle fusion positioning method and device based on two-dimensional code | |
| CN113673386A (en) | Method for marking traffic signal lamp in prior-to-check map | |
| Walters et al. | Rural positioning challenges for connected and autonomous vehicles | |
| CN112113580A (en) | Vehicle positioning method and device and automobile | |
| CN113727434B (en) | Vehicle-road cooperative auxiliary positioning system and method based on edge computing gateway | |
| CN114791282A (en) | Road facility coordinate calibration method and device based on vehicle high-precision positioning | |
| JP7211513B2 (en) | Map data generator | |
| CN111121714B (en) | Method and system for measuring driving sight distance | |
| CN114910085A (en) | Vehicle fusion positioning method and device based on road administration facility identification | |
| Bassani et al. | Alignment data collection of highways using mobile mapping and image analysis techniques |
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 |