CN110515942B - Storage and retrieval method of serialized lane line map - Google Patents
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- G01C21/32—Structuring or formatting of map data
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- 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
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- G01C21/34—Route searching; Route guidance
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Abstract
The invention relates to a storage and retrieval method of a serialized lane line map, which comprises the following steps: s1, lane line data of lane lines are stored in a grid sub-map mode, wherein the lane line data comprise lane line number data and coordinate data; s2, acquiring a corresponding sub-map according to the vehicle positioning information, and searching lane line data in the corresponding sub-map to obtain complete lane line information. Compared with the prior art, in the map storage stage, the method utilizes the grid sub-map mode to count the effective sub-map and store the lane line data, and encodes the attribute value according to the lane line number and the coordinate data; in the map using stage, the lane line data attribute value is obtained from the sub map through the retrieval of the positioning information of the vehicle, the lane line number and the coordinate data are obtained through decoding, and the lane line data with the same number are fitted to obtain the complete lane line information.
Description
Technical Field
The invention relates to the field of map mapping, in particular to a storage and retrieval method of a serialized lane line map.
Background
For intelligent vehicles, accurately acquiring road information of the current environment is a key and basic function, and the accurate road information determines whether the vehicle can complete corresponding tasks according to the expected planning so as to ensure that the vehicle can safely, comfortably and quickly reach a destination. In general, abundant environmental information can be acquired by constructing a high-precision map in advance, so that the intelligent vehicle can reduce the requirement for real-time perception during driving and ensure more abundant environmental information at the same time, so as to ensure the safety of the intelligent vehicle, but no mature and standard scheme exists at present for how to acquire and maintain such a high-precision map, which also becomes a bottleneck that the high-precision map cannot serve the intelligent vehicle.
Under complex traffic environments such as cities, priori information in a high-precision lane line map plays an important role in intelligent horizontal improvement of vehicles: in the aspect of guaranteeing safety, the road traffic safety system is not influenced by weather reasons and day and night such as illumination, haze, thunderstorm and the like, and can acquire complete road information even under severe weather conditions; in terms of adhering to urban traffic rules, the complete lane line information is utilized to obtain lane-level path planning, so that orderly running of vehicles is realized. Therefore, the high-precision lane line map is an important research content in an intelligent vehicle system, is also a research hotspot in the current automatic driving field, adopts a grid storage mode to accelerate the retrieval speed, however, the high grid precision leads to large map storage space, the low grid precision is difficult to ensure data precision, and the problem of how to balance the storage space and the data precision is difficult.
Disclosure of Invention
The invention aims to overcome the defects of the prior art and provide a storage and retrieval method of a serialized lane line map.
The aim of the invention can be achieved by the following technical scheme: a storage and retrieval method of a serialized lane line map comprises the following steps:
s1, lane line data of lane lines are stored in a grid sub-map mode, wherein the lane line data comprise lane line number data and coordinate data;
s2, acquiring a corresponding sub-map according to the vehicle positioning information, and searching lane line data in the corresponding sub-map to obtain complete lane line information.
Preferably, the step S1 specifically includes the following steps:
s11, automatically splitting the constructed large map through rasterization to obtain a sub-map;
s12, counting the effective number of the sub-maps, and numbering the sub-maps to obtain the storage space of the sub-maps;
s13, encoding attribute values according to lane line data in the sub-map, wherein the attribute values comprise high-order codes and low-order codes.
Preferably, the specific process of counting the effective number of sub-maps in step S12 is as follows:
s121, obtaining the maximum value and the minimum value of the lane line coordinate data, wherein the maximum value of the coordinate data comprises X max And Y max The minimum value of the coordinate data includes X min And Y min ；
S122, positive coordinate data: subtracting the minimum value of the coordinate data from the coordinate data of each lane line, namely moving the position of each lane line to ensure that the coordinate data of each lane line after movement is positive;
s123, creating a two-dimensional array of the sub map, and initially marking all sub maps in the two-dimensional array of the sub map as 0;
s124, calculating sub-map position data corresponding to the lane line coordinate data in the sub-map two-dimensional array;
s125, traversing all lane line coordinate data, and marking the corresponding sub map of the lane line coordinate data in a sub map two-dimensional array as 1 after acquiring the corresponding sub map position data;
and S126, counting the number of the sub-maps marked as 1 in the two-dimensional array of the sub-map, namely the effective number of the sub-maps.
Preferably, the two-dimensional array of the sub-map in the step S123 is M sub [X num ][Y num ]The method specifically comprises the following steps:
X num ＝(X max -X min )/S sub
Y num ＝(Y max -Y min )/S sub
wherein S is sub For sub-map size data, X num And Y num The maximum abscissa and the maximum ordinate in the two-dimensional array of the sub map are respectively, and are integers.
Preferably, the sub map in the step S124 and the step S125 is M sub [X ind ][Y ind ]The method specifically comprises the following steps:
X ind ＝X lane /S sub
Y ind ＝Y lane /S sub
wherein X is lane And Y lane X is the abscissa and the ordinate of the lane line coordinate data respectively ind And Y ind And the map position data is sub-map position data corresponding to the lane line coordinate data in the sub-map two-dimensional array.
Preferably, in the step S12, the two-dimensional array of sub-maps is used for storing the numbers of the sub-maps, and the numbering of the sub-maps is specifically that the numbers of each sub-map are calculated simultaneously in the process of counting the effective number of the sub-maps: determining the number according to the sequence of accessing the sub-map, wherein the number of the sub-map is N sub Starting with =1, in the course of traversing the lane line coordinate data, lane line coordinate data X is acquired lane And Y lane Corresponding sub-map position data X ind And Y ind If the sub map M is at this time sub [X ind ][Y ind ]Is marked with 0, indicating that the sub-map has not been accessed, the number N of the sub-map sub The number of (2) needs to be added with 1;
if the sub map M is at this time sub [X ind ][Y ind ]If the mark of (1) indicates that the sub map has been accessed and numbered, no new number needs to be given, sub map number N sub The value of (2) remains unchanged.
Preferably, in the step S13, the high-order code is used for storing lane line number data, and the low-order code is used for storing the loss precision of the coordinate data after rasterization.
Preferably, the encoding attribute value in step S13 is specifically:
C att ＝I lane *100+(d x /p*10)*10+(d y /p*10)*1
wherein I is lane Data representing lane line numbers, stored on a high-order code, d x And d y The missing precision of the abscissa and the ordinate in the coordinate data is respectively represented, the missing precision is respectively stored on ten bits and one bit of the low-order code, and p represents the grid precision.
Preferably, the step S2 specifically includes the following steps:
s21, acquiring vehicle positioning information, wherein the vehicle positioning information is positioning coordinate data of a vehicle;
s22, according to the vehicle positioning information, a corresponding sub map number is obtained, and a sub map to which the vehicle belongs is determined;
s23, retrieving the coding attribute value of the lane line data from the sub-map to which the vehicle belongs, and decoding the retrieved coding attribute value to obtain the loss precision of the lane line coding data and the coordinate data;
s24, recovering the coordinate data of the loss precision of the coordinate data obtained by decoding, fitting the lane line data obtained by decoding, and thus obtaining complete lane line information, wherein the complete lane line information comprises lane line numbers and global coordinate data.
Compared with the prior art, the invention has the following advantages:
1. according to the invention, the number of the effective sub-maps is counted, and the number of the sub-maps without lane line data is not counted, so that the storage space of the system is effectively reduced.
2. The method adopts the mode of coding attribute values to code and store the lane line number data and the coordinate data, and simultaneously carries out corresponding processing on the precision of the lost coordinate data during rasterization processing, thereby improving the accuracy and precision of stored data.
Drawings
FIG. 1 is a schematic flow chart of the method of the present invention;
fig. 2 is a schematic diagram of retrieving sub-map data of a lane line in the embodiment.
Detailed Description
The invention will now be described in detail with reference to the drawings and specific examples.
As shown in fig. 1, a method for storing and retrieving a serialized lane line map includes the following steps:
s1, lane line data of lane lines are stored in a grid sub-map mode, wherein the lane line data comprise lane line number data and coordinate data;
s2, acquiring a corresponding sub-map according to the vehicle positioning information, and searching lane line data in the corresponding sub-map to obtain complete lane line information.
The step S1 specifically includes the following steps:
s11, automatically splitting the constructed large map through rasterization to obtain a sub-map;
s12, counting the effective number of the sub-maps and numbering the sub-maps to obtain the storage space of the sub-maps, wherein the effective number of the sub-maps is specifically:
s121, obtaining the maximum value and the minimum value of the coordinate data, wherein the maximum value of the coordinate data comprises X max And Y max The minimum value of the coordinate data includes X min And Y min ；
S122, positive coordinate data: subtracting the minimum value of the coordinate data from the coordinate data of each lane line, namely moving the position of each lane line to ensure that the coordinate data of each lane line after movement is positive;
s123, creating a two-dimensional array M of the sub map sub [X num ][Y num ]To store sub map numbers, there are:
X num ＝(X max -X min )/S sub
Y num ＝(Y max -Y min )/S sub
wherein S is sub For sub-map size data, X num And Y num The maximum abscissa and the maximum ordinate in the two-dimensional array of the sub map are respectively, and are integers;
simultaneously, all sub-maps M in the two-dimensional array of the sub-map sub [X ind ][Y ind ]The initial flag is 0;
s124, calculating sub map position data corresponding to the lane line coordinate data in the sub map two-dimensional array, wherein the sub map position data comprises:
X ind ＝X lane /S sub
Y ind ＝Y lane /S sub
wherein X is lane And Y lane X is the abscissa and the ordinate of the lane line coordinate data respectively ind And Y ind The map position data are sub-map position data corresponding to the lane line coordinate data in the sub-map two-dimensional array;
s125, traversing all lane line coordinate data, and after acquiring corresponding sub-map position data, obtaining a sub-map M corresponding to the lane line coordinate data in a sub-map two-dimensional array sub [X ind ][Y ind ]Marked as 1;
s126, counting sub-map M marked as 1 in two-dimensional array of sub-map sub [X ind ][Y ind ]The number of the sub map is the effective number of the sub map;
the numbering of the sub-maps is specifically to calculate the number of each sub-map simultaneously in the process of counting the effective number of the sub-maps: determining the number according to the sequence of accessing the sub-map, wherein the number of the sub-map is N sub Starting with =1, in the course of traversing the lane line coordinate data, lane line coordinate data X is acquired lane And Y lane Sub-map position data X corresponding in sub-map two-dimensional array ind And Y ind If the sub map M is at this time sub [X ind ][Y ind ]Is marked with 0, indicating that the sub-map has not been accessed, the number N of the sub-map sub The number of (2) needs to be added with 1;
if the sub map M is at this time sub [X ind ][Y ind ]If the mark of (1) indicates that the sub map has been accessed and numbered, no new number needs to be given, sub map number N sub The value of (2) remains unchanged;
s13, encoding attribute values according to lane line data in the sub map, wherein the attribute values comprise high-order codes and low-order codes, the high-order codes are used for storing lane line number data, and the low-order codes are used for storing loss precision of coordinate data after rasterization, and specifically comprise:
C att ＝I lane *100+(d x /p*10)*10+(d y /p*10)*1
wherein C is att Representing lane line data in sub-mapEncoding attribute value, I lane Data representing lane line numbers, stored on a high-order code, d x And d y The missing precision of the abscissa and the ordinate in the coordinate data is respectively represented, the missing precision is respectively stored on ten bits and one bit of the low-order code, and p represents the grid precision.
The step S2 specifically includes the following steps:
s21, acquiring vehicle positioning information, wherein the vehicle positioning information is positioning coordinate data of a vehicle;
s22, according to the vehicle positioning information, a corresponding sub map number is obtained, and a sub map to which the vehicle belongs is determined;
s23, retrieving the coding attribute value of the lane line data from the sub-map to which the vehicle belongs, and decoding the retrieved coding attribute value to obtain the lane line number data and the coordinate data loss precision;
s24, recovering the coordinate data of the loss precision of the coordinate data obtained by decoding, fitting the lane line data obtained by decoding, and thus obtaining complete lane line information, wherein the complete lane line information comprises lane line numbers and global coordinate data.
The invention adopts the format of the grid sub-map to store the lane line number data and the coordinate data, so that the retrieval speed can be increased, the storage space can be reduced in a sub-map mode, different sub-maps have unique numbers, a sub-map two-dimensional array is constructed to store the number of each sub-map, and the data of each sub-map is stored in the storage space allocated by the sub-map;
the storage space address of the sub map is obtained by adding the initial storage address of the map and the offset calculated by the serial number of the sub map, all map data need to be traversed for calculating the serial number of the sub map, the number of the sub map is obtained after the traversing is finished according to the sequence of first accessing the sub map, and the required total storage space of the map can be calculated.
In this embodiment, a grid size of 0.2m is adopted, the sub-map size is 20m x 20m, the number of the sub-map is stored by using a two-dimensional array of the sub-map, and two dimensions of the sub-map are obtainedAll sub-maps in the group are initially marked as 0, and the number variable N of the accessed sub-maps is recorded in the process of traversing the lane line coordinate data sub For each sub map, if the sub map is not accessed, the sub map number is added with 1, and the complete lane line coordinate data is traversed to obtain a sub map two-dimensional array storing the sub map number.
The invention adopts the storage lane line number data and the coordinate data to carry out attribute value coding, and the decimal part of the coordinate data lost by rasterization is coded into the units and ten bits of the attribute value, and the higher bit stores the lane line number data, and the specific coding method is as follows: the lane line data mainly comprises lane line number data and coordinate data, the coordinate data has the problem of precision loss in the rasterization process, for example, the size of a grid is 0.20 m, the change unit of the coordinate is 0.2m, the number data and the lost precision are fused together to be used as attribute values of the lane line data to be stored, by the method, the precision of the lost horizontal coordinate can be stored on ten bits of the attribute values, the precision of the lost vertical coordinate is stored on the position, and the number of the lane line is stored on the high bit of the attribute values.
When the actual lane line map retrieval is carried out, firstly, the coordinates of the vehicle in the lane line map are obtained according to the positioning information of the vehicle, and the sub-map number can be calculated according to the coordinates; then searching lane line data near the vehicle according to the serial numbers of the sub-maps; and finally, decoding the attribute value to obtain lane line numbers and lost precision information, and fitting the recovered lane line data with the same numbers to obtain curve information of the lane lines.
Fig. 2 is a schematic diagram of a lane line map retrieving process by adopting the method of the present invention in the embodiment, ten sub-maps 1 to 10 are obtained after rasterization, the accuracy of the grid is 0.2, and taking a sub-map 5 as an example: the number of the lane line is 86, the corresponding coordinate data is (1 x 0.2+5/10 x 0.2,1 x 0.2+7/10 x 0.2) when the attribute value stored in the position of the sub map (1, 1) is 8657, the coordinate data of the lane line is (0.3,0.34), the global coordinate corresponding to the origin of the sub map 5 is (20, 40), and finally the global coordinate of the lane line is (20.3,40.34), and the embodiment adopts a grid of 0.2 m.
Claims (5)
1. The storage and retrieval method of the serialized lane line map is characterized by comprising the following steps of:
s1, lane line data of lane lines are stored in a grid sub-map mode, wherein the lane line data comprise lane line number data and coordinate data;
s2, acquiring a corresponding sub-map according to the vehicle positioning information, and searching lane line data in the corresponding sub-map to obtain complete lane line information;
the step S1 specifically comprises the following steps:
s11, automatically splitting the constructed large map through rasterization to obtain a sub-map;
s12, counting the effective number of the sub-maps, and numbering the sub-maps to calculate the storage space of the required sub-map;
s13, encoding attribute values according to lane line data in the sub map, wherein the attribute values comprise high-order codes and low-order codes;
the specific process of counting the effective number of the sub-maps in the step S12 is as follows:
s121, obtaining the maximum value and the minimum value of the lane line coordinate data, wherein the maximum value of the coordinate data comprises X max And Y max The minimum value of the coordinate data includes X min And Y min ；
S122, positive coordinate data: subtracting the minimum value of the coordinate data from the coordinate data of each lane line, namely moving the position of each lane line to ensure that the coordinate data of each lane line after movement is positive;
s123, creating a two-dimensional array of the sub map, and initially marking all sub maps in the two-dimensional array of the sub map as 0;
s124, calculating sub-map position data corresponding to the lane line coordinate data in the sub-map two-dimensional array;
s125, traversing all lane line coordinate data, and marking the corresponding sub map of the lane line coordinate data in a sub map two-dimensional array as 1 after acquiring the corresponding sub map position data;
s126, counting the number of sub-maps marked as 1 in the two-dimensional array of the sub-map, namely the effective number of the sub-maps;
the S13 is used for storing lane line number data in a high-order code and storing loss precision of coordinate data after rasterization in a low-order code;
the step S2 specifically comprises the following steps:
s21, acquiring vehicle positioning information, wherein the vehicle positioning information is positioning coordinate data of a vehicle;
s22, according to the vehicle positioning information, a corresponding sub map number is obtained, and a sub map to which the vehicle belongs is determined;
s23, retrieving the coding attribute value of the lane line data from the sub-map to which the vehicle belongs, and decoding the retrieved coding attribute value to obtain the lane line number data and the coordinate data loss precision;
s24, recovering the coordinate data of the coordinate data loss precision obtained by decoding, and fitting the lane line data obtained by decoding, so that complete lane line information is obtained, wherein the complete lane line information comprises lane line numbers and global coordinate data.
2. The method for storing and retrieving a serialized lane line map of claim 1, wherein said S123 sub-map two-dimensional array is M sub [X num ][Y num ]The method specifically comprises the following steps:
X num ＝(X max -X min )/S sub
Y num ＝(Y max -Y min )/S sub
wherein S is sub For sub-map size data, X num And Y num Respectively the maximum abscissa and the maximum ordinate in the two-dimensional array of the sub map, and are integers。
3. The method for storing and retrieving a serialized lane line map of claim 2 wherein the sub-map in S124 and S125 is M sub [X ind ][Y ind ]The method specifically comprises the following steps:
X ind ＝X lane /S sub
Y ind ＝Y lane /S sub
wherein X is lane And Y lane X is the abscissa and the ordinate of the lane line coordinate data respectively ind And Y ind And the map position data is sub-map position data corresponding to the lane line coordinate data in the sub-map two-dimensional array.
4. The method for storing and retrieving a serialized lane line map as claimed in claim 3, wherein the two-dimensional array of sub-maps in S12 is used for storing the numbers of the sub-maps, and the numbers of the sub-maps are calculated simultaneously in the process of counting the effective number of the sub-maps: determining the number according to the sequence of accessing the sub-map, wherein the number of the sub-map is N sub Starting with =1, in the course of traversing the lane line coordinate data, lane line coordinate data X is acquired lane And Y lane Corresponding sub-map position data X ind And Y ind If the sub map M is at this time sub [X ind ][Y ind ]Is marked with 0, indicating that the sub-map has not been accessed, the number N of the sub-map sub The number of (2) needs to be added with 1;
if the sub map M is at this time sub [X ind ][Y ind ]If the mark of (1) indicates that the sub map has been accessed and numbered, no new number needs to be given, sub map number N sub The value of (2) remains unchanged.
5. The method for storing and retrieving a serialized lane line map of claim 1, wherein the encoding attribute values in S13 specifically are:
C att ＝I lane *100+(d x /p*10)*10+(d y /p*10)*1
wherein I is lane Data representing lane line numbers, stored on a high-order code, d x And d y The missing precision of the abscissa and the ordinate in the coordinate data is respectively represented, the missing precision is respectively stored on ten bits and one bit of the low-order code, and p represents the grid precision.
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Citations (9)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101483779A (en) * | 2009-01-22 | 2009-07-15 | 浙江工商大学 | Compressing method for two-dimension vector map |
| CN101959121A (en) * | 2009-07-14 | 2011-01-26 | 阿孜古丽吾拉木 | Geographical position service identification coding technique |
| CN101996258B (en) * | 2010-11-30 | 2012-11-14 | 重庆大学 | Electronic map information grid processing and querying method facilitating quick query |
| CN102855322A (en) * | 2012-09-11 | 2013-01-02 | 哈尔滨工程大学 | Map data storage method based on space exploration technology |
| CN104243958A (en) * | 2014-09-29 | 2014-12-24 | 联想(北京)有限公司 | Coding method, decoding method, coding device and decoding device for three-dimensional grid data |
| CN104572755A (en) * | 2013-10-24 | 2015-04-29 | 高德软件有限公司 | Method for creating data index, data searching method and related device |
| CN107121980A (en) * | 2017-03-17 | 2017-09-01 | 北京理工大学 | A kind of automatic driving vehicle paths planning method based on virtual constraint |
| CN107270922A (en) * | 2017-06-15 | 2017-10-20 | 北京航空航天大学 | A kind of traffic accident space-location method based on POI indexes |
| CN109800281A (en) * | 2019-01-22 | 2019-05-24 | 苏州寻息电子科技有限公司 | The method of the positional relationship of Query Location point and fence in positioning system |
-
2019
- 2019-07-12 CN CN201910630486.XA patent/CN110515942B/en active Active
Patent Citations (9)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101483779A (en) * | 2009-01-22 | 2009-07-15 | 浙江工商大学 | Compressing method for two-dimension vector map |
| CN101959121A (en) * | 2009-07-14 | 2011-01-26 | 阿孜古丽吾拉木 | Geographical position service identification coding technique |
| CN101996258B (en) * | 2010-11-30 | 2012-11-14 | 重庆大学 | Electronic map information grid processing and querying method facilitating quick query |
| CN102855322A (en) * | 2012-09-11 | 2013-01-02 | 哈尔滨工程大学 | Map data storage method based on space exploration technology |
| CN104572755A (en) * | 2013-10-24 | 2015-04-29 | 高德软件有限公司 | Method for creating data index, data searching method and related device |
| CN104243958A (en) * | 2014-09-29 | 2014-12-24 | 联想(北京)有限公司 | Coding method, decoding method, coding device and decoding device for three-dimensional grid data |
| CN107121980A (en) * | 2017-03-17 | 2017-09-01 | 北京理工大学 | A kind of automatic driving vehicle paths planning method based on virtual constraint |
| CN107270922A (en) * | 2017-06-15 | 2017-10-20 | 北京航空航天大学 | A kind of traffic accident space-location method based on POI indexes |
| CN109800281A (en) * | 2019-01-22 | 2019-05-24 | 苏州寻息电子科技有限公司 | The method of the positional relationship of Query Location point and fence in positioning system |
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