CN104575075B - A kind of city road network vehicle coordinate bearing calibration based on the Big Dipper and device - Google Patents

Abstract

The present invention provides a kind of city road network vehicle coordinate bearing calibration based on the Big Dipper, including: obtain vehicle Big Dipper information；Gridding processes city road network；Read vehicle coordinate information；Determine vehicle coordinate place grid；Determine vehicle coordinate place nine grids；Determine candidate road section collection；Computed range and deflection deviation；Calculate match index；Judge coupling section；Update correction vehicle coordinate.The present invention also provides for a kind of city road network vehicle coordinate correcting unit based on the Big Dipper.The present invention is by building nine grids and coordinate matching exponential model, it is capable of the quick and precisely location of vehicle coordinate in city road network, reduce under high buildings and large mansions traffic environment, the deviation of city road network Big Dipper location, improve the speed of mass data coupling, promote operational efficiency and the service level of floating car traffic information acquisition system.

Description

A kind of city road network vehicle coordinate bearing calibration based on the Big Dipper and device

Technical field

The present invention relates to city road network vehicle positioning technology field based on Big Dipper data, a kind of based on the Big Dipper City road network vehicle coordinate bearing calibration and device.

Background technology

Floating car traffic information acquisition technique is by installing the device such as the Big Dipper, GPS on vehicle, utilizing the dynamic of vehicle Change in location information carries out the technology of real-time road extraction, based on Floating Car displacement data, is sat by seasonal effect in time series vehicle location Mark mates with map, calculates average speed and the section travelling speed of all points that floats, and then can extract the friendship of road Logical state, this technology includes that data prediction, map match, path culculating and historical speed such as supplement at the crucial processing routine, respectively The process model of individual program is also diversified, and precision also exists difference.

City road network environment is different from rural area and rural road, due to the impact of a large amount of high-rise building things, the location of the Big Dipper There is deviation, at present normal deviation is at about 10 meters, and the more local effect of building is worse, it is impossible to vehicle is carried out essence Certainly position.In Floating Car information acquisition system, owing to the object scale of Floating Car map match is the hugest, particularly towards Metropolitan application, will be completed in a relatively short time mating, to matching speed of up to ten thousand Floating Car and up to ten thousand sections Require higher.

Summary of the invention

It is an object of the invention to provide a kind of city road network vehicle coordinate bearing calibration based on the Big Dipper and device, it is possible to Under high buildings and large mansions traffic environment, it is achieved the quick and precisely location of vehicle coordinate in city road network, promote Floating Car information gathering The operational efficiency of system and service level.

The technical scheme is that

A kind of city road network vehicle coordinate bearing calibration based on the Big Dipper, comprises the following steps:

(1) dynamic parameter of all Big Dipper Floating Car in city road network is obtained, including time, coordinate and deflection data；

(2) city road network map is carried out gridding process, by city road network all sections numbering and grid number into Row association binding；

(3) changing coordinates based on Big Dipper Floating Car, determines the grid at its place, centered by this grid, with around nine Palace lattice are radius, using being in the section in the range of Big Dipper position error in nine grids as candidate road section, obtain candidate road section collection；

(4) changing coordinates and the candidate road section that calculate Big Dipper Floating Car concentrate the distance between each candidate road section, knot Current deflection and the candidate road section of closing Big Dipper Floating Car concentrate the deviation between each candidate road section deflection, build city road Net vehicle coordinate match index model, obtains the section matched with the changing coordinates of Big Dipper Floating Car；

(5) use distance between beeline and dot formula, calculate the changing coordinates of Big Dipper Floating Car in the section matched with it On intersection point coordinate, and using this intersection point coordinate as the calibration coordinate of the changing coordinates of Big Dipper Floating Car.

Described city road network vehicle coordinate bearing calibration based on the Big Dipper, described step (2) specifically includes:

(21) on the basis of city road network, nerve of a covering is formatted process layer, determines numbering and the bounds of each grid, net The ultimate unit of lattice size is 25 meters * 25 meters；

(22) beginning and end coordinate informations based on sections all in city road network, are associated tying up to section and grid Fixed.

Described city road network vehicle coordinate bearing calibration based on the Big Dipper, in described step (3), based on Big Dipper Floating Car Changing coordinates, determine the grid at its place, if particularly as follows: the changing coordinates of Big Dipper Floating Car fall into certain grid upper left sit In the range of mark, upper right coordinate, lower-left coordinate, lower right coordinate define, then judge that the changing coordinates of Big Dipper Floating Car belongs to this net Lattice.

Described city road network vehicle coordinate bearing calibration based on the Big Dipper, described step (4) specifically includes:

(41) obtain candidate road section and concentrate each candidate road section information, sit including candidate road section numbering, candidate road section starting point Mark, candidate road section terminal point coordinate and candidate road section deflection；

(42) according to candidate road section starting point coordinate and candidate road section terminal point coordinate, candidate road section linear function is obtained；

(43) changing coordinates of Big Dipper Floating Car is set as (x₀, y₀, z₀), candidate road section P_iLinear equation be A_ix+B_iy+ C_iz+D_i=0, then use below equation to calculate (x₀, y₀, z₀) and P_iBetween distance di:

$d_i=\frac{|A_i{x}_0+B_i{y}_0+C_i{z}_0+D_i|}{\sqrt{A_i^2+B_i^2+C_i^2}}$

(44) city road network vehicle coordinate match index model is built:

${\mathrm{MI}}_i=\frac{0.65}{1+d_i/d}+\frac{0.35}{1+{\mathrm{\θ}}_i/\mathrm{\θ}}$

Wherein, MI_iRepresent changing coordinates and candidate road section P of Big Dipper Floating Car_iMatch index, d_iRepresent changing coordinates With candidate road section P_iBetween distance, d represents Big Dipper data range deviation threshold value, θ_iRepresent the current deflection of Big Dipper Floating Car With candidate road section P_iDeflection between deviation, θ represents Big Dipper data direction angular displacement threshold value；

(45) section that the maximum candidate road section of match index matches is chosen as the changing coordinates with Big Dipper Floating Car.

Described city road network vehicle coordinate bearing calibration based on the Big Dipper, described step (5) specifically includes:

(51) changing coordinates of Big Dipper Floating Car is set as (x₀, y₀, z₀), with (x₀, y₀, z₀) straight line of section P that matches Equation is Ax+By+Cz+D=0, then use below equation to calculate (x₀, y₀, e0) and intersection point coordinate (x ' on P₀, y '₀, z '₀):

$x_0^{\′}=x_0-\frac{A({\mathrm{Ax}}_0+{\mathrm{By}}_0+{\mathrm{Cz}}_0+D)}{A^2+B^2+C^2}$

$y_0^{\′}=y_0-\frac{B({\mathrm{Ax}}_0+{\mathrm{By}}_0+{\mathrm{Cz}}_0+D)}{A^2+B^2+C^2}$

$z_0^{\′}=z_0-\frac{C({\mathrm{Ax}}_0+{\mathrm{By}}_0+{\mathrm{Cz}}_0+D)}{A^2+B^2+C^2}$

(52) by the changing coordinates (x of Big Dipper Floating Car₀, y₀, z₀) it is updated to (x '₀, y '₀, z '₀)。

A kind of city road network vehicle coordinate correcting unit based on the Big Dipper, including data communication and storage server, backstage Processing server, issue terminal and some Big Dipper Floating Car, the outfan of described Big Dipper Floating Car passes through data communication and storage Server is connected with the input of background process server, the outfan of described background process server and the input of issue terminal End connects.

The present invention, by the mining analysis to Big Dipper data, carries out gridding process to city road network map, builds nine palaces Lattice search model and coordinate matching exponential model, by the coupling section of search vehicle coordinate, determine that it is in city road network Calibration coordinate, it is achieved the fast accurate location of vehicle, reduces under high buildings and large mansions traffic environment, and it is inclined that the city road network Big Dipper positions Difference, improves the speed of mass data coupling, promotes operational efficiency and the service level of traffic information acquisition system.

Accompanying drawing explanation

Fig. 1 is the method flow diagram of the present invention；

Fig. 2 is that the vehicle coordinate of the present invention mates schematic diagram with section；

Fig. 3 is assembly of the invention structural representation.

Detailed description of the invention

Below, the present invention is further illustrated in conjunction with the drawings and specific embodiments.

As it is shown in figure 1, a kind of city road network vehicle coordinate bearing calibration based on the Big Dipper, use the some north being sequentially connected with Bucket Floating Car 1, data communication and storage server 2, background process server 3 and issue terminal 4 realize (as shown in Figure 3), should Method comprises the following steps:

S1, utilize the dynamic parameter of all Big Dipper Floating Car 1 in floating car technology detection city road network, including time, seat Mark and deflection data, the dynamic parameter detected is stored in background process server in real time through data communication and storage server 2 3。

S2, on the basis of city road network, nerve of a covering is formatted process layer, determines numbering and the bounds of each grid, net The ultimate unit of lattice size is 25 meters * 25 meters, is then based on the beginning and end coordinate information in all little sections in city road network, It is associated binding to little section and grid:

If section is encoded to P_i, grid coding is Q_j, grid coding Q_jP is encoded with section_iCarry out one-to-many association to match:

Q_j={ P₁, P₂..., P_i(i ∈ I, j ∈ J) (1)

I is the numbering of current road segment；J is the numbering of current grid；I is all sections number in current grid；J is city Total number of all grids in city's road network.

S3, changing coordinates based on Big Dipper Floating Car, determine the grid at its place, centered by this grid, with around nine Palace lattice are radius, the road section information in search target zone, determine candidate road section collection, specifically include:

If the changing coordinates of Big Dipper Floating Car is G=(x₀, y₀, z₀), grid Q_j={ G₁, G₂, G₃, G₄, wherein, G₁For Q_j Top-left coordinates, G₂For Q_jUpper right coordinate, G₃For Q_jLower-left coordinate, G₄For Q_jLower right coordinate, if G falls into G₁,G₂、G₃、 G₄In the range of defining, then judge that G belongs to Q_j:

Then with Q_jCentered by, search nine grids around, utilize formula (1) to extract all road section informations in nine grids, As candidate road section collection.

S4, acquisition candidate road section concentrate each candidate road section information, sit including candidate road section numbering, candidate road section starting point Mark, candidate road section terminal point coordinate and candidate road section deflection；According to candidate road section starting point coordinate and candidate road section terminal point coordinate, To candidate road section linear function；

If the changing coordinates of Big Dipper Floating Car is (x₀, y₀, z₀), candidate road section P_iLinear equation be A_ix+B_iy+C_iz+D_i =0, then use below equation to calculate (x₀, y₀, z₀) and P_iBetween distance d_i:

$d_i=\frac{|A_i{x}_0+B_i{y}_0+C_i{z}_0+D_i|}{\sqrt{A_i^2+B_i^2+C_i^2}}---\left(2\right)$

Structure city road network vehicle coordinate match index model:

${\mathrm{MI}}_i=\frac{0.65}{1+d_i/d}+\frac{0.35}{1+{\mathrm{\θ}}_i/\mathrm{\θ}}---\left(3\right)$

Wherein, MI_iRepresent changing coordinates and candidate road section P of Big Dipper Floating Car_iMatch index, d_iRepresent changing coordinates With candidate road section P_iBetween distance, d represents default Big Dipper data range deviations threshold constant, is traditionally arranged to be 10 meters, θ_iTable Show current deflection and candidate road section P of Big Dipper Floating Car_iDeflection between deviation, θ represents default Big Dipper data side To angular displacement threshold constant, it is traditionally arranged to be 30 degree；

MI_iThe biggest, represent changing coordinates and candidate road section P of Big Dipper Floating Car_iMatching degree the highest, therefore, choose coupling The section that the candidate road section of index maximum matches as the changing coordinates with Big Dipper Floating Car.

Fig. 2 is that city road network vehicle coordinate mates schematic diagram, if having trunk section and three gps coordinates point P, Q, R, with coordinate Being analyzed as a example by some Q, its place nine grids are as shown in Fig. 2 bend region, and candidate road section includes b, e, m, c, g, coordinate points Q Distance relation to each candidate road section is d_m＜ d_b=d_c＜ d_g＜ d_e, the deflection deviation between section m and coordinate points Q is relatively Greatly, further according to the match index calculated, it is determined that coordinate points Q is on the b of section.In like manner obtain coordinate points P on the l of section.And coordinate Point R is positioned near crossing, and its deflection has relatively large deviation, and once coupling is it is difficult to ensure that correctly, can determine when route searching Its position, if next coordinate points is on a of section, then can determine that coordinate points R is on the b of section.

S5, set the changing coordinates (x with Big Dipper Floating Car₀, y₀, z₀) linear equation of section P that matches is Ax+By+Cz + D=0, then use below equation to calculate (x₀, y₀, z₀) intersection point coordinate (x ' on P₀, y '₀, z '₀):

$x_0^{\′}=x_0-\frac{A({\mathrm{Ax}}_0+{\mathrm{By}}_0+{\mathrm{Cz}}_0+D)}{A^2+B^2+C^2}---\left(4\right)$

$y_0^{\′}=y_0-\frac{B({\mathrm{Ax}}_0+{\mathrm{By}}_0+{\mathrm{Cz}}_0+D)}{A^2+B^2+C^2}---\left(5\right)$

$z_0^{\′}=z_0-\frac{C({\mathrm{Ax}}_0+{\mathrm{By}}_0+{\mathrm{Cz}}_0+D)}{A^2+B^2+C^2}---\left(6\right)$

By the changing coordinates (x of Big Dipper Floating Car₀, y₀, z₀) it is updated to (x '₀, y '₀, z '₀) realize city road network vehicle coordinate Correction.

Vehicle coordinate after S6, correction is stored in data base, and issue terminal 4 is by calling data base interface service acquisition vehicle Calibration coordinate information, it is achieved city road network vehicle coordinate be accurately positioned and information issue.

The present invention is by building nine grids and coordinate matching exponential model, it is possible to realize the fast of vehicle coordinate in city road network Speed is accurately positioned, and reduces under high buildings and large mansions traffic environment, and the deviation of city road network Big Dipper location improves the speed of mass data coupling Degree, promotes operational efficiency and the service level of floating car traffic information acquisition system.

The above embodiment is only to be described the preferred embodiment of the present invention, the not model to the present invention Enclose and be defined, on the premise of designing spirit without departing from the present invention, the those of ordinary skill in the art technical side to the present invention Various deformation that case is made and improvement, all should fall in the protection domain that claims of the present invention determines.

Claims (5)

1. a city road network vehicle coordinate bearing calibration based on the Big Dipper, it is characterised in that comprise the following steps:

(1) dynamic parameter of all Big Dipper Floating Car in city road network is obtained, including time, coordinate and deflection data；

(2) city road network map is carried out gridding process, all sections numbering in city road network is closed with grid numbering Connection binding；

(3) changing coordinates based on Big Dipper Floating Car, determines the grid at its place, centered by this grid, with nine grids around For radius, using nine grids being in the section in the range of Big Dipper position error as candidate road section, obtain candidate road section collection；

(4) changing coordinates and the candidate road section that calculate Big Dipper Floating Car concentrate the distance between each candidate road section, in conjunction with north Current deflection and the candidate road section of bucket Floating Car concentrate the deviation between each candidate road section deflection, build city road network car Coordinate matching exponential model, obtains the section matched with the changing coordinates of Big Dipper Floating Car；

(5) use distance between beeline and dot formula, calculate the changing coordinates of Big Dipper Floating Car on the section matched with it Intersection point coordinate, and using this intersection point coordinate as the calibration coordinate of the changing coordinates of Big Dipper Floating Car；

Described step (4) specifically includes:

(41) obtain candidate road section and concentrate each candidate road section information, including candidate road section numbering, candidate road section starting point coordinate, time Routing segment endpoint coordinate and candidate road section deflection；

(42) according to candidate road section starting point coordinate and candidate road section terminal point coordinate, candidate road section linear function is obtained；

(43) changing coordinates of Big Dipper Floating Car is set as (x₀, y₀, z₀), candidate road section P_iLinear equation be A_ix+B_iy+C_iz+D_i =0, then use below equation to calculate (x₀, y₀, z₀) and P_iBetween distance d_i:

$d_i=\frac{|A_i{x}_0+B_i{y}_0+C_i{z}_0+D_i|}{\sqrt{A_i^2+B_i^2+C_i^2}}$

(44) city road network vehicle coordinate match index model is built:

${\mathrm{MI}}_i=\frac{0.65}{1+d_i/d}+\frac{0.35}{1+{\mathrm{\θ}}_i/\mathrm{\θ}}$

Wherein, MI_iRepresent changing coordinates and candidate road section P of Big Dipper Floating Car_iMatch index, d_iRepresent changing coordinates and time Routing section P_iBetween distance, d represents Big Dipper data range deviation threshold value, θ_iRepresent current deflection and the time of Big Dipper Floating Car Routing section P_iDeflection between deviation, θ represents Big Dipper data direction angular displacement threshold value；

(45) section that the maximum candidate road section of match index matches is chosen as the changing coordinates with Big Dipper Floating Car.

City road network vehicle coordinate bearing calibration based on the Big Dipper the most according to claim 1, it is characterised in that described step Suddenly (2) specifically include:

(21) on the basis of city road network, nerve of a covering is formatted process layer, determines numbering and the bounds of each grid, and grid is big Little ultimate unit is 25 meters * 25 meters；

(22) beginning and end coordinate informations based on sections all in city road network, are associated binding to section and grid.

City road network vehicle coordinate bearing calibration based on the Big Dipper the most according to claim 1, it is characterised in that described step Suddenly in (3), changing coordinates based on Big Dipper Floating Car, determine the grid at its place, if particularly as follows: the current seat of Big Dipper Floating Car In the range of village enters the top-left coordinates of certain grid, upper right coordinate, lower-left coordinate, lower right coordinate define, then judge that the Big Dipper floats The changing coordinates of motor-car belongs to this grid.

City road network vehicle coordinate bearing calibration based on the Big Dipper the most according to claim 1, it is characterised in that described step Suddenly (5) specifically include:

(51) changing coordinates of Big Dipper Floating Car is set as (x₀,y₀, z₀), with (x₀, y₀, z₀) linear equation of section P that matches For Ax+By+Cz+D=0, then below equation is used to calculate (x₀, y₀, z₀) intersection point coordinate (x ' on P₀, y '₀, z '₀):

$x_0^{\′}=x_0-\frac{A({\mathrm{Ax}}_0+{\mathrm{By}}_0+{\mathrm{Cz}}_0+D)}{A^2+B^2+C^2}$

$y_0^{\′}=y_0-\frac{B({\mathrm{Ax}}_0+{\mathrm{By}}_0+{\mathrm{Cz}}_0+D)}{A^2+B^2+C^2}$

$z_0^{\′}=z_0-\frac{C({\mathrm{Ax}}_0+{\mathrm{By}}_0+{\mathrm{Cz}}_0+D)}{A^2+B^2+C^2}$

(52) by the changing coordinates (x of Big Dipper Floating Car₀, y₀, z₀) it is updated to (x '₀,y′₀, z '₀)。

5. realize a city road network vehicle coordinate correcting unit based on the Big Dipper for method described in any one of Claims 1-4, It is characterized in that: include data communication and storage server, background process server, issue terminal and some Big Dipper Floating Car, The outfan of described Big Dipper Floating Car is connected with the input of background process server by data communication and storage server, institute The input of the outfan and issue terminal of stating background process server is connected.