CN108645420B - Method for creating multipath map of automatic driving vehicle based on differential navigation - Google Patents
Method for creating multipath map of automatic driving vehicle based on differential navigation Download PDFInfo
- Publication number
- CN108645420B CN108645420B CN201810387336.6A CN201810387336A CN108645420B CN 108645420 B CN108645420 B CN 108645420B CN 201810387336 A CN201810387336 A CN 201810387336A CN 108645420 B CN108645420 B CN 108645420B
- Authority
- CN
- China
- Prior art keywords
- map
- path
- terrain
- point
- points
- 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
- 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
Abstract
The invention provides a method for creating a multipath map of an automatic driving vehicle based on differential navigation, which comprises the following steps of defining automatic driving map information: collecting a multi-track map; generating a plurality of tracks of the automatic driving vehicle; the multi-track map includes regular terrain and/or irregular terrain. The invention provides a method for creating a multipath map of an automatic driving vehicle based on differential navigation, which can simultaneously generate a plurality of map paths for a plurality of automatic driving vehicles in a regular automatic driving area; for irregular paths, multiple pieces of lane position information may be collected simultaneously.
Description
Technical Field
The invention relates to the technical field of computer vision and image processing, in particular to a method for creating a multipath map of an automatic driving vehicle based on differential navigation.
Background
The technology of automatic driving vehicles is mature day by day, and a navigation map becomes a foundation stone for automatic driving. Generally, an autonomous vehicle acquires real-time positioning information through a positioning device and performs path planning by matching a navigation map with the information. At present, navigation maps are basically acquired by a team developing automatic driving before automatic driving, and main stored path information is latitude and longitude information, so that the method has no problem on small-range automatic driving or the operation of only one automatic driving vehicle in the same scene, and the map acquisition workload is within a controllable range. However, if multiple automatic driving vehicles are operated simultaneously and the running routes are different, or the positioning information of multiple lanes needs to be collected once, multiple times of collection work needs to be carried out when the map is collected in the early stage, and especially for large-scale automatic driving, a large amount of time cost and labor cost are consumed.
The invention with the application number of CN201610846436.1 discloses a system and a method for generating a lane-level navigation map of an unmanned vehicle, wherein the system comprises an offline global map and an online local map, and an offline module is that in a target area where the unmanned vehicle runs, original road data are obtained by using a satellite photo (or an aerial photo), a vehicle-mounted sensor (a laser radar and a camera) and a high-precision combined positioning system (a satellite positioning system and an inertial navigation system), then the original road data are processed offline to extract various road information, and finally the road information extraction results are fused to generate the offline global map. The offline global map is stored in a layered structure. The online module is used for extracting road data in an offline global map according to real-time positioning information and drawing an online local map which takes the vehicle as a center and is within a fixed distance range when the unmanned vehicle automatically drives in a target area. The system cannot distinguish regular terrain from irregular terrain, and the obtained global coordinates are not accurate.
The invention patent with application number CN201410202876.4 discloses a GPS navigation map accurate matching system of an unmanned automobile and an operation method thereof, wherein the method comprises the following steps: acquiring road information; determining a starting point; acquiring vehicle positioning information; matching and screening information; and repeating the steps until the matching is successful. The invention can reduce the navigation error to within two meters, and can adjust in time when the navigation error exceeds two meters, thereby greatly reducing the navigation error. A GPS navigation map accurate matching method of an unmanned automobile, map matching in a vehicle navigation and positioning system, in summary, a GPS track position with error obtained by a vehicle-mounted GPS receiver is matched to a corresponding position on a traffic vector map road with error, the map matching method comprises the following steps: a, acquiring road information; b, determining a starting point and an end point; c, acquiring vehicle positioning information; d, matching and screening information; the map accurate matching method is characterized by further comprising the following steps: a, collecting information, storing the information, extracting the information and making a map; b, determining a starting point and an end point by utilizing the orderly arranged longitude and latitude points; c, acquiring data of a GPS and data of an Inertial Navigation System (INS); d, filtering out incorrect information by using a GPS and a KML; and e, repeating the steps 1 to 4 to obtain the optimal driving route. The method can only collect one piece of map information for automatic driving.
The invention patent with the application number of CN201610226020.X discloses a traveling route accurate generation method suitable for an unmanned automobile, which comprises a route determination step and a first path identification step; step two, a signal detection step of a positioning device; thirdly, acquiring visual feature data and recording driving information data; step four, generating a visual characteristic coordinate library; and step five, forming a driving scheme. The method finally generates only one driving path.
Disclosure of Invention
In order to solve the technical problem, the invention provides a method for creating a multi-path map of an automatic driving vehicle based on differential navigation, which can simultaneously generate a plurality of map paths for a plurality of automatic driving vehicles in a regular automatic driving area; for irregular paths, multiple pieces of lane position information may be collected simultaneously.
The invention provides a method for creating a multipath map of an automatic driving vehicle based on differential navigation, which comprises the following steps of defining automatic driving map information:
step 1: collecting a multi-track map;
and 2, step: generating a plurality of tracks of the automatic driving vehicle;
the multi-track map includes regular terrain and/or irregular terrain.
Preferably, the map information includes at least one of a collection time, longitude, latitude, azimuth, differential status, waypoint attribute 1 and waypoint attribute 2.
In any one of the above aspects, the regular terrain is a terrain that can be equally divided according to a certain travel route.
In any of the above aspects, it is preferable that the irregular terrain refers to terrain that cannot be equally divided with a single travel route.
In any of the above schemes, preferably, the regular terrain trajectory acquisition method includes the steps of:
step 11: the collection vehicle collects the data according to a specified path;
step 12: and carrying out median filtering and excessive density point deletion on the acquired data.
In any of the above schemes, preferably, the calculation formula in step 12 is:
f (x, y) ═ med { g (x-k, y-l), (k, l ∈ w) }, where w is a two-dimensional template.
In any of the above schemes, preferably, the method for screening the over dense points uses a set point distance threshold as a criterion to reject the points whose point distance is smaller than the threshold.
In any of the above schemes, preferably, the regular terrain multi-track generation method can be performed after the reference route is acquired and offline.
In any of the above schemes, preferably, the method for generating a multi-track on a regular terrain includes the following steps:
step 21: performing geodetic subject inverse calculation on each obtained path point, sequentially taking the acquired path points as points A, taking the azimuth angle of the acquired path points plus or minus 90 degrees as the azimuth angle from the points A to the points B as theta, and taking the transverse distance of a track to be generated as the spherical distance from the points A to the points B as L;
step 22: and the longitude and latitude of the point B are obtained through geodetic subject back calculation, and the azimuth angle, the RTK state, the road attribute 1 and the road attribute 2 of the point B are consistent with the point A.
In any of the above aspects, preferably, the method for generating a multi-track of a regular terrain further comprises a method for generating a plurality of paths.
In any of the above schemes, preferably, the method for generating multiple paths includes performing multiple earth subject back calculations on the acquisition paths at sequentially equal intervals to obtain multiple paths.
In any of the above schemes, preferably, the irregular terrain multi-track generation method adopts an online real-time track generation method.
In any of the above schemes, preferably, the irregular terrain multi-track generation method is a calculation method for calibrating back calculation parameters of the geodetic theme on line when lane change or settlement spherical distance L needs to be changed.
In any of the above schemes, preferably, the method for generating the trajectory in real time includes the following steps:
step 31: collecting the M lane of the vehicle in the N lanes at the point A moment, and marking the spherical distance between the left lane and the right lane at the point A moment;
step 32: recording the left resolving angle as the current heading angle plus 90 degrees, and recording the right resolving angle as the current heading angle minus 90 degrees;
step 33: in AtPoint-time marker AtThe spherical distance between the left lane and the right lane is calculated at the moment;
step 34: the collection vehicle collects a map on a track, and generates a plurality of tracks according to road changes.
In any of the above schemes, preferably, the method for generating a trajectory in real time further includes calibrating parameters in real time along with the change of the lane, so as to obtain an accurate multi-lane navigation map.
The invention provides a method for creating a multipath map of an automatic driving vehicle based on differential navigation, which provides a solution for the generation of the multipath map of the automatic driving, and can simultaneously generate a plurality of map paths for a plurality of automatic driving vehicles in a regular automatic driving area; for irregular paths, multiple pieces of lane position information may be collected simultaneously. The method can be applied to the actual application of agricultural automatic driving large-area map acquisition, urban road multi-lane acquisition, park multi-lane acquisition, automatic driving vehicle formation navigation and the like.
Drawings
Fig. 1 is a flowchart of a preferred embodiment of a method for creating a multi-path map of an autonomous vehicle based on differential navigation according to the present invention.
Fig. 2 is a flowchart of a method for generating a multi-path map of a vehicle based on differential navigation according to the present invention, which is based on a regular terrain and is based on the embodiment shown in fig. 1.
Fig. 2A is a single map route map under regular terrain according to the embodiment of fig. 2 of the method for creating a multi-path map of an autonomous vehicle based on differential navigation according to the present invention.
Fig. 2B is a single-route azimuth view under regular terrain according to the embodiment of fig. 2 of the method for creating a multi-path map of an autonomous vehicle based on differential navigation according to the present invention.
Fig. 2C is a schematic diagram of longitude and latitude points of a new trajectory generated according to the embodiment of fig. 2 of the method for creating a multi-path map of an autonomous vehicle based on differential navigation according to the present invention.
Fig. 2D is a schematic diagram of the generated trajectory of the method for creating a multipath map of an autonomous vehicle based on differential navigation according to the embodiment of the present invention shown in fig. 2.
Fig. 2E is a diagram of equal-spaced traces generated on both sides of the embodiment shown in fig. 2 according to the method for creating the multipath map of the automated driving vehicle based on differential navigation in the present invention.
Fig. 2F is a diagram illustrating generation of a plurality of trajectory maps on both sides of the embodiment of fig. 2 according to the method for creating a multi-path map of an autonomous vehicle based on differential navigation according to the present invention.
Fig. 3 is a flowchart of an irregular terrain multi-track generation method according to the embodiment shown in fig. 1 of the method for creating the differential navigation based autonomous vehicle multi-path map according to the present invention.
Fig. 3A is a diagram of a plurality of trajectory locus diagrams generated on both sides of the embodiment shown in fig. 3 according to the method for creating the multipath map of the automated driving vehicle based on differential navigation.
Fig. 3B is an irregular terrain on-line marking geodetic theme solver parameter graph according to the embodiment of fig. 3 of the method for creating the differential navigation-based autonomous vehicle multi-path map according to the present invention.
Fig. 4 is a schematic diagram illustrating geodetic subject back-calculated coordinates calculation of a preferred embodiment of a method for creating a differential navigation-based autonomous vehicle multi-path map according to the present invention.
Detailed Description
The invention is further illustrated by the following detailed description of embodiments in connection with the drawings.
Example one
As shown in fig. 1, step 100 is performed to define the autopilot map information. As shown in table 1, a single waypoint information in the autopilot map is formed by: the system comprises acquisition time, longitude, latitude, azimuth (the course angle of an acquired vehicle when a map is acquired), differential state (map acquisition point positioning judgment basis), waypoint attributes 1 (road section marks: longitudinal changes of main marked road sections, such as curves, straight roads, U _ turn and the like) and waypoint attributes 2 (road section marks: transverse changes of main marked road sections, such as single-row lines, multiple lanes and the like).
TABLE 1 Individual waypoint information
Step 110 is executed to determine the type of the multi-track map. Multi-track map acquisition is divided into two categories: regular terrain, irregular terrain. The regular terrain refers to terrain which can be equally divided according to a certain driving route, such as rectangular and round farmlands, multiple lanes with the same quantity and the like; the irregular terrain refers to terrain which cannot be equally divided by a single driving route, such as roads with city lane changes, non-structured roads in a garden and the like.
If the terrain is a regular terrain, step 120 is executed, and the regular terrain multi-track generation method is executed. The regular terrain multi-track generation can be performed offline after the reference route is acquired. As shown in fig. 2, step 200 is executed to collect a navigation map of the reference line. And (3) carrying out path acquisition by using an acquisition vehicle according to the table 1, wherein a navigation mobile station is arranged on the acquisition vehicle, and the mobile station and the base station are in normal communication. The acquired data needs to be subjected to median filtering and excessive density point deletion, and the median filtering adopts a 1 x 5 neighborhood template. f (x, y) ═ med { g (x-k, y-l), (k, l ∈ w) }, where w is a two-dimensional template. The dense point screening is to delete the dense accumulated positions of the acquisition points, which is beneficial to saving storage space and accelerating the efficiency of map indexing, and the screening mode adopts a set point distance threshold value as a standard to eliminate the points with the distance smaller than the threshold value. Through the above two steps, a smooth and equidistant map path can be obtained, as shown in fig. 2A and 2B. Step 210 is executed to calculate the number and direction of the generated tracks. For consistency of the automatic driving path, the generated path also needs to store waypoints according to the format of table 1, the longitude and latitude of the point B are obtained by inverse calculation of the geodetic subject of fig. 2C, and the azimuth angle, the RTK state, the road attribute 1, the road attribute 2 and the point of a of the point B are consistent. All points in FIG. 2A are solved for geodetic themes to obtain FIG. 2D. Step 220 is executed to traverse the obtained map path. Step 230 is executed to determine whether the end of the map is reached. If the map tail is not reached, a step 231 is executed, geodetic subject settlement is carried out on the acquired map, and a step 232 is executed, wherein the solved coordinate attribute value, the course angle and the RTK are endowed to a new waypoint. Step 220 is executed again, and the obtained map path is traversed. If the end of the map is reached, step 240 is performed to save all paths. If a plurality of paths need to be generated, only the earth subject inverse calculation needs to be performed on the acquisition paths at equal intervals in sequence, as shown in fig. 2E and 2F. FIG. 2E illustrates the generation of an inner and outer trace based on the offline acquisition trace; FIG. 2F shows the generation of an inner trace and four traces from the interest acquisition trace, illustrating that multiple trace lines may be generated in accordance with the present invention.
According to the method, under the condition that only one map is acquired, a plurality of automatic driving vehicles can run simultaneously, or one automatic driving vehicle runs for a plurality of times simultaneously, so that the time cost for acquiring the automatic driving navigation map under the regular terrain is greatly reduced.
If the terrain is irregular, step 130 is executed, and a multi-track generation method for irregular terrain is executed, and a method for generating tracks in real time on line is adopted. When the lane changes or the calculated spherical surface distance L needs to be changed, a method for calibrating the back calculation parameters of the geodetic theme on line is adopted. As shown in fig. 3, step 300 is executed to start acquiring the navigation path in real time. Step 310 is executed to calibrate the actual acquisition waypoint inverse calculation parameters. Step 320 is executed to obtain a new waypoint. Step 330 is executed to determine whether the road section isA lane change occurs. If the lane change occurs, sequentially executing step 335 and step 340, calibrating the inverse calculation parameters in real time, and performing geodetic theme calculation on the acquired map. If no lane change occurs, step 340 is executed to perform geodetic solution on the acquired map. As shown in fig. 3A, the collection vehicle at point a is located in the third lane of the 4 lanes, the spherical distance between the two left lanes and the one right lane at this time needs to be marked, and the calculation angle on the left side is observed as the addition of 90 degrees to the current collection vehicle heading angle, and the calculation angle on the right side is the addition of 90 degrees to the current heading angle. Same principle A tAnd (4) marking a left lane and a right lane by the same marking method as the point A. And executing step 350, and endowing the solved coordinate attribute value, the course angle and the RTK to a new path point. Step 360 is executed to determine whether to end the acquisition. If the acquisition is not finished, step 320 is executed to obtain a new path point. If the acquisition is finished, step 370 is executed to save all paths. According to the method, the collection vehicle carries out map collection on one track, meanwhile, multiple tracks can be generated according to the change of the road, as shown in fig. 3B, parameters are calibrated in real time along with the change of the lanes, an accurate multi-lane navigation map can be obtained, and the storage structure of each lane route point is consistent with that in table 1.
Example two
As shown in fig. 4, the inverse geodetic problem is solved by: knowing the longitude and latitude coordinates of point A
The spherical distance from the point A to the point B is L, the azimuth angle from the point A to the point B is theta, and the longitude and latitude of the point B are obtained
The earth is treated as a regular sphere with an arc length of Δ S per degree (engineering typically divides the earth' S circumference by 360 as the arc length of each degree on the center circle, typically approximately 111.199 km). Taking the north pole N, the ANB three points form a spherical triangle. The corner relationships are as follows:
The length of AN arc between the AN two points is as follows:
and ≈ NBA and AN are a pair of edge sum angles.
The length of the arc between the BN two points is as follows:
and ═ NAB and BN are a pair of edges and angles.
The length of the arc between AB is:
and is<ANB and AB are a pair of sides and corners, wherein
Then the following can be obtained by the spherical trigonometric cosine theorem:
cos NB＝cos NA cos AB+sin NA sin AB cos∠NAB
the latitude at point B can be obtained. Then according to the spherical triangle sine theorem, the following can be obtained:
since ≈ NBA is unknown, only the first two terms are taken to solve, and the following can be obtained:
the longitude and latitude coordinates of the point B can be obtained from the above.
For a better understanding of the present invention, the foregoing detailed description has been given in conjunction with specific embodiments thereof, but not with the intention of limiting the invention thereto. Any simple modifications of the above embodiments according to the technical essence of the present invention still fall within the scope of the technical solution of the present invention. In the present specification, each embodiment is described with emphasis on differences from other embodiments, and the same or similar parts between the respective embodiments may be referred to each other. For the system embodiment, since it basically corresponds to the method embodiment, the description is relatively simple, and for the relevant points, reference may be made to the partial description of the method embodiment.
Claims (8)
1. A method for creating a multi-path map of an autonomous vehicle based on differential navigation, comprising defining autonomous map information, characterized in that: further comprising the steps of:
Step 1: collecting a multi-track map;
step 2: generating a plurality of tracks of the automatic driving vehicle;
the multi-track map comprises regular terrain and irregular terrain; the irregular terrain refers to terrain which cannot be equally divided by a single driving route;
the regular terrain multi-track generation method comprises the following steps:
step 21: performing geodetic subject inverse calculation on each obtained path point, sequentially taking the acquired path points as points A, taking the azimuth angle of the acquired path points plus or minus 90 degrees as the azimuth angle from the points A to the points B, and taking the transverse distance of a track to be generated as the spherical distance from the points A to the points B as L;
step 22: the longitude and latitude of the point B are obtained through geodetic subject back calculation, and the azimuth angle, the RTK state, the road attribute 1 and the road attribute 2 of the point B are consistent with the point A;
the irregular terrain multi-track generation method adopts an online real-time track generation method, and the real-time track generation method comprises the following steps:
step 30: starting to collect a navigation path in real time;
step 31: calibrating the back calculation parameters of the initial acquisition waypoints;
step 32: obtaining a new path point;
step 33: judging whether the road section has lane change, and if the road section has lane change, calibrating back calculation parameters in real time;
Step 34: carrying out geodetic theme calculation on the acquired map;
step 35: assigning the solved coordinate attribute value, the course angle and the RTK to a new path point;
step 36: judging whether the collection is finished or not; if the collection is not finished, the step 32 is executed again; and if the acquisition is finished, storing all paths.
2. The differential navigation-based autonomous vehicle multi-path map creation method of claim 1, wherein: the map information includes at least one of acquisition time, longitude, latitude, azimuth, differential status, waypoint attributes 1 and waypoint attributes 2.
3. The differential navigation-based autonomous vehicle multi-path map creation method of claim 1, wherein: the regular terrain refers to terrain that can be equally divided according to a certain driving route.
4. The differential navigation-based autonomous vehicle multi-path map creation method of claim 3, wherein: the regular terrain track acquisition method comprises the following steps:
step 11: the collection vehicle collects the data according to a specified path;
step 12: and carrying out median filtering and excessive density point deletion on the acquired data.
5. The differential navigation-based autonomous vehicle multi-path map creation method of claim 4, wherein: the calculation formula in step 12 is: 
Wherein, in the step (A),
is a two-dimensional template.
6. The differential navigation-based autonomous vehicle multi-path map creation method of claim 5, wherein: the screening method of the dense points adopts a set point interval threshold value as a standard to eliminate the points with the point interval smaller than the threshold value.
7. The differential navigation-based autonomous vehicle multi-path map creation method of claim 6, wherein: the regular terrain multi-track generation method can be carried out after the reference route is acquired and in an off-line mode.
8. The differential navigation-based autonomous vehicle multi-path map creation method of claim 7, wherein: the regular terrain multi-track generation method further comprises a method of generating a plurality of paths.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201810387336.6A CN108645420B (en) | 2018-04-26 | 2018-04-26 | Method for creating multipath map of automatic driving vehicle based on differential navigation |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201810387336.6A CN108645420B (en) | 2018-04-26 | 2018-04-26 | Method for creating multipath map of automatic driving vehicle based on differential navigation |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN108645420A CN108645420A (en) | 2018-10-12 |
| CN108645420B true CN108645420B (en) | 2022-06-14 |
Family
ID=63747974
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201810387336.6A Active CN108645420B (en) | 2018-04-26 | 2018-04-26 | Method for creating multipath map of automatic driving vehicle based on differential navigation |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN108645420B (en) |
Families Citing this family (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US11143513B2 (en) * | 2018-10-19 | 2021-10-12 | Baidu Usa Llc | Labeling scheme for labeling and generating high-definition map based on trajectories driven by vehicles |
| CN109739225B (en) | 2018-12-18 | 2021-04-20 | 北京百度网讯科技有限公司 | Positioning data processing method, device, equipment, storage medium and vehicle |
| EP3901814A4 (en) * | 2018-12-19 | 2022-09-14 | Changsha Intelligent Driving Institute Corp. Ltd | Method for generating real-time relative map, intelligent driving device and computer storage medium |
| CN109726676B (en) * | 2018-12-28 | 2020-07-07 | 苏州大学 | Planning method for automatic driving system |
| CN110187372B (en) * | 2019-06-20 | 2021-11-02 | 北京联合大学 | Combined navigation method and system in low-speed unmanned vehicle park |
| CN113405558B (en) * | 2020-02-29 | 2024-04-09 | 华为技术有限公司 | Automatic driving map construction method and related device |
| CN114783214A (en) * | 2020-04-28 | 2022-07-22 | 上海波若智能科技有限公司 | Road network dynamic data acquisition method and road network dynamic data acquisition system |
| CN112579924B (en) * | 2020-12-29 | 2022-03-25 | 武汉中海庭数据技术有限公司 | Road generation method, electronic device, and storage medium |
Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP5522785B2 (en) * | 2010-03-19 | 2014-06-18 | 株式会社日立ソリューションズ | Agricultural work vehicle operation management system |
| CN104089619A (en) * | 2014-05-14 | 2014-10-08 | 北京联合大学 | GPS navigation map accurate matching system of pilotless automobile, and its operation method |
| CN104781633A (en) * | 2014-03-06 | 2015-07-15 | 日本集奥瑟甫有限公司 | Agricultural field navigation system and agricultural field navigation method, software and software storage equipment |
| CN106441319A (en) * | 2016-09-23 | 2017-02-22 | 中国科学院合肥物质科学研究院 | System and method for generating lane-level navigation map of unmanned vehicle |
| CN107462243A (en) * | 2017-08-04 | 2017-12-12 | 浙江大学 | A kind of cloud control automatic Pilot task creating method based on high-precision map |
| CN107613751A (en) * | 2015-09-14 | 2018-01-19 | 株式会社久保田 | Working truck support system |
Family Cites Families (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP2008072963A (en) * | 2006-09-21 | 2008-04-03 | Yanmar Co Ltd | Agricultural working vehicle |
| US8073617B2 (en) * | 2006-12-27 | 2011-12-06 | Aisin Aw Co., Ltd. | Map information generating systems, methods, and programs |
| US9542846B2 (en) * | 2011-02-28 | 2017-01-10 | GM Global Technology Operations LLC | Redundant lane sensing systems for fault-tolerant vehicular lateral controller |
-
2018
- 2018-04-26 CN CN201810387336.6A patent/CN108645420B/en active Active
Patent Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP5522785B2 (en) * | 2010-03-19 | 2014-06-18 | 株式会社日立ソリューションズ | Agricultural work vehicle operation management system |
| CN104781633A (en) * | 2014-03-06 | 2015-07-15 | 日本集奥瑟甫有限公司 | Agricultural field navigation system and agricultural field navigation method, software and software storage equipment |
| CN104089619A (en) * | 2014-05-14 | 2014-10-08 | 北京联合大学 | GPS navigation map accurate matching system of pilotless automobile, and its operation method |
| CN107613751A (en) * | 2015-09-14 | 2018-01-19 | 株式会社久保田 | Working truck support system |
| CN106441319A (en) * | 2016-09-23 | 2017-02-22 | 中国科学院合肥物质科学研究院 | System and method for generating lane-level navigation map of unmanned vehicle |
| CN107462243A (en) * | 2017-08-04 | 2017-12-12 | 浙江大学 | A kind of cloud control automatic Pilot task creating method based on high-precision map |
Non-Patent Citations (1)
| Title |
|---|
| 一种无人驾驶汽车的资产监控系统;高子萍等;《中国计算机用户协会网络应用分会2017年第二十一届网络新技术与应用年会论文集》;20171221;第199-203页 * |
Also Published As
| Publication number | Publication date |
|---|---|
| CN108645420A (en) | 2018-10-12 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN108645420B (en) | Method for creating multipath map of automatic driving vehicle based on differential navigation | |
| Wang et al. | Map-based localization method for autonomous vehicles using 3D-LIDAR | |
| AU2017300097B2 (en) | Crowdsourcing and distributing a sparse map, and lane measurements for autonomous vehicle navigation | |
| CN108303103B (en) | Method and device for determining target lane | |
| US10240934B2 (en) | Method and system for determining a position relative to a digital map | |
| JP5505723B2 (en) | Image processing system and positioning system | |
| Schreiber et al. | Laneloc: Lane marking based localization using highly accurate maps | |
| CN102208036B (en) | Vehicle position detection system | |
| CN110146910B (en) | Positioning method and device based on data fusion of GPS and laser radar | |
| Brenner | Extraction of features from mobile laser scanning data for future driver assistance systems | |
| CN102208012B (en) | Landscape coupling reference data generation system and position measuring system | |
| JP5388082B2 (en) | Stationary object map generator | |
| CN108303721A (en) | A kind of vehicle positioning method and system | |
| JP5286653B2 (en) | Stationary object map generator | |
| GB2614379A (en) | Systems and methods for vehicle navigation | |
| US20210381849A1 (en) | Map management using an electronic horizon | |
| CN102788580A (en) | Flight path synthetic method in unmanned aerial vehicle visual navigation | |
| JP2023508769A (en) | Systems and methods for optimizing map tile requests for navigation | |
| CN114114367A (en) | AGV outdoor positioning switching method, computer device and program product | |
| CN114593739B (en) | Vehicle global positioning method and device based on visual detection and reference line matching | |
| CN104063499A (en) | Space vector POI extracting method based on vehicle-mounted space information collection | |
| EP4211423A1 (en) | A method and device for determining a vehicle position | |
| CN113227713A (en) | Method and system for generating environment model for positioning | |
| CN114889606A (en) | Low-cost high-precision positioning method based on multi-sensor fusion | |
| Naumann et al. | Multi-drive road map generation on standardized high-velocity roads using low-cost sensor data |
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 |