CN111811526A - Electronic map path planning method of intelligent traffic system - Google Patents

Electronic map path planning method of intelligent traffic system Download PDF

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CN111811526A
CN111811526A CN202010710395.XA CN202010710395A CN111811526A CN 111811526 A CN111811526 A CN 111811526A CN 202010710395 A CN202010710395 A CN 202010710395A CN 111811526 A CN111811526 A CN 111811526A
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vehicle
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coordinate system
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electronic map
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龙亚梅
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Guangzhou Xinwa Technology Co ltd
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    • 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
    • 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
    • G01C21/34—Route searching; Route guidance
    • G01C21/3407—Route searching; Route guidance specially adapted for specific applications
    • G01C21/343—Calculating itineraries, i.e. routes leading from a starting point to a series of categorical destinations using a global route restraint, round trips, touristic trips
    • 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
    • G01C21/34—Route searching; Route guidance
    • G01C21/3446—Details of route searching algorithms, e.g. Dijkstra, A*, arc-flags, using precalculated routes
    • 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
    • G01C21/34—Route searching; Route guidance
    • G01C21/3453—Special cost functions, i.e. other than distance or default speed limit of road segments
    • 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
    • G01C21/34—Route searching; Route guidance
    • G01C21/3453—Special cost functions, i.e. other than distance or default speed limit of road segments
    • G01C21/3492—Special cost functions, i.e. other than distance or default speed limit of road segments employing speed data or traffic data, e.g. real-time or historical

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Abstract

The invention belongs to the technical field of electronic maps, and particularly relates to an electronic map path planning method of an intelligent traffic system. The invention comprises the following steps: (1) the traffic system server collects navigation information of a navigation system and sends the navigation information to the data processing center; (2) the data processing center smoothes the pseudo range of the navigation information, and finally generates a vehicle running attitude matrix by utilizing a linearization technology; (3) and calculating to obtain navigation parameters and floating ambiguity resolution parameters based on a least square parameter estimation method. The invention fully utilizes the dynamic characteristics of the operation in the electronic map, estimates and predicts the path parameters of the vehicles in the intelligent traffic system through the observation information, generates the real-time high-precision vehicle path information, and broadcasts the real-time high-precision vehicle path information to the user, thereby assisting the user to realize real-time high-precision navigation.

Description

Electronic map path planning method of intelligent traffic system
Technical Field
The invention belongs to the technical field of electronic maps, and particularly relates to an electronic map path planning method of an intelligent traffic system.
Background
In the history of human development, especially in the modern society, traffic has been a life line that maintains human survival and development. However, the rapid expansion of the number of urban road networks and private automobiles even in towns and towns makes road network traffic planning impossible to be performed with a linear development trend, particularly in recent years, with the rapid development of social economy, the popularization rate of vehicles in China is higher and higher, and the private quantity of automobiles is rapidly increased, statistics show that in 2009 the private quantity of automobiles in China is 4575 ten thousands, in 2016 the private quantity of automobiles in China is up to 1.65 hundred million, and the quantity of automobiles is increased by about 20% per year, so that the increase speed of road traffic infrastructure cannot meet the increase of vehicles required by the increase of vehicles, which leads to increasingly prominent problems of traffic jam, traffic accidents and the like, the traffic demand is higher and the contradiction between vehicles and roads is more and more sharp. Such problems have seriously affected the development of socio-economic and the quality of life of people.
In the early stage of the 21 st century, most countries around the world are troubled by the problem of traffic, and in order to actually solve the traffic problem, a traffic-based research field, namely an Intelligent Transportation System (ITS), is proposed. The concept of ITS was first proposed by the american society for intelligent transportation in 1990, and since this concept was proposed, countries around the world have been developing research related to the field of ITS. By applying research results in various fields, intelligent traffic management can be effectively realized, so that the probability of traffic jam is greatly reduced, the reliability in the traffic transportation process is improved, and a basis is provided for finally providing an intelligent traffic transportation system. Various electronic map data fusion technologies and application of the electronic map technologies in combination with the improved intelligent optimization algorithm in logistics vehicle path planning are researched.
For data of geographic space, a map can be well displayed, and the map is a foundation stone and an innovative source in the field of intelligent transportation. In the internet era of rapid development nowadays, electronic Maps are developed rapidly and in various forms, such as foreign Maps including Google Maps, Google Earth, Open Street Maps, Open Science Maps and the like, domestic electronic Maps are rapidly developed since the 90 th century, and domestic electronic Maps are in various forms, such as hundred degree Maps, high-grade Maps, sky Maps, Tencent Maps and the like. The electronic map technology is taken as a key technology in the field of intelligent transportation, and plays a vital role in the harmonious development of the society. The electronic map has wide application fields, and is mainly used in the aspects of city management, city emergency command, electronic navigation, information Point (POI) position inquiry, vehicle track inquiry, path planning and the like. In addition, each map uses the map data with the format corresponding to the map data, so that different maps cannot well share the data on the geographic information, and therefore, how to accurately and efficiently realize the interaction and sharing of the data among the maps is very important.
The use of the electronic map in the path planning can greatly improve the efficiency of a driver in the driving process, so that the driver can quickly arrange a driving route and avoid a traffic jam road section under the condition of unfamiliarity with the path. The electronic map technology and the intelligent optimization algorithm are combined, and a detailed path planning scheme is given, so that the vehicle can spend short time and low cost to reasonably arrange the driving route of the vehicle.
Disclosure of Invention
The invention aims to provide an electronic map path planning method of an intelligent traffic system, which realizes short-time planning by utilizing an intelligent optimization algorithm.
The purpose of the invention is realized as follows:
an electronic map path planning method of an intelligent transportation system comprises the following steps:
(1) the traffic system server collects navigation information of a navigation system and sends the navigation information to the data processing center;
(2) the data processing center smoothes the pseudo range of the navigation information, and finally generates a vehicle running attitude matrix by utilizing a linearization technology;
(3) calculating to obtain navigation parameters and floating ambiguity resolution parameters based on a least square parameter estimation method;
(4) fixing the floating solution ambiguity parameter, and verifying the correctness of the ambiguity fixation;
(5) the correctly fixed floating solution ambiguity parameters are brought into an observation equation, and the optimal solution of the vehicle motion parameters in the traffic system is obtained through repeated resolving;
(6) the optimal solution of the navigation parameters, the floating solution ambiguity parameters, the vehicle position information and the vehicle motion parameters is broadcasted to a navigation user by satellite communication, and the navigation user is assisted to obtain a real-time high-precision positioning result;
(7) and obtaining the path planning path of the electronic map according to the vehicle position information.
The step (1) comprises the following steps:
(1.1) acquiring and denoising map images:
collecting the environment of the path through an electronic map, and performing Gaussian filtering processing;
(1.2) carrying out edge detection on roads in the map:
sobel operator calculation is carried out on the gray value of the adjacent point around each pixel point in the map, and the threshold value tau is selected according to the brightness acquired by the map when
Figure BDA0002596322710000031
fx＝(2f(a-1，b-1)+3f(a-1，b)+4f(a-1，b+1)) -(3f(a+1，b+1)+5f(a-1，b)+2f(a+1，b+1))，
fy＝(3f(a-1，b-1)+3f(a，b-1)+2f(a+1，b-1)) -(4f(a-1，b-1)+3f(a，b-1)+2f(a+1，b-1))，
When So (a, b) > tau, the map midpoint (a, b) is an edge point, a and b are edge point coordinates, and f is a gray value; f. ofxIs a gray value based on the abscissa x; f. ofyIs a gray value based on the ordinate y; so (a, b) represents a calculation result based on a Sobel operator;
(1.3) planning a path according to the edge points:
collecting included angle theta between distance sensor and horizontal directionaCollecting data l returned by the distance sensoraAcquiring the measurement value z' of the height sensor, wherein the coordinate of the reference system in the horizontal direction is Dn0＝(la× cosθa，la×sinθa，z′)；
Determining a planned path according to the iterative evaluation value of nodes in the path channel, wherein the distance between two adjacent nodes is ln0，ln0＝Dn0-Dn0-1N0 is the index of the current node, starting point I0To the current nodeIn0The cost function of (a) is:
Figure BDA0002596322710000032
wherein n0-1 is the label of the previous node adjacent to the current node;
and scanning two neighbor nodes with the maximum G (n0) near each node for connection to form a planned path channel.
The pseudo range is as follows:
Figure BDA0002596322710000041
wherein
Figure BDA0002596322710000042
Represents an IURE estimate for vehicle j,
Figure BDA0002596322710000043
the satellite-ground distance for eliminating various errors is shown, N represents the number of satellites for navigation at the current moment,
Figure BDA0002596322710000044
the trend item representing the user range error can be obtained according to historical data.
The determination step of the attitude matrix comprises the following steps:
(2.1) setting an earth coordinate system as earth coordinate system, a geographic coordinate system as geo coordinate system, a carrier coordinate system as carrier system, a navigation coordinate system as pilot system, wherein the axes of the coordinate systems are X in sequence respectivelyg、Yg、Zg；Xc、Yc、Zc；Xp、Yp、Zp；
(2.2) calculating a direction cosine matrix of a geographic coordinate system of the vehicle
Figure BDA0002596322710000045
Figure BDA0002596322710000046
Wherein the longitude of the vehicle is alphaeAnd latitude ofe；αeThe value range of (1) is (-180 degrees, 180 degrees);ethe value range of (1) is (-90 degrees, 90 degrees);
(2.3) calculating a direction cosine matrix of a carrier coordinate system of the vehicle
Figure BDA0002596322710000047
Figure BDA0002596322710000048
Wherein gamma iscFor the roll angle of the carrier coordinate system relative to the geographical coordinate system, i.e. XcRelative to XgThe included angle of (A);
wherein theta iscFor the pitch angle of the carrier coordinate system relative to the geographical coordinate system, i.e. YcRelative to YgThe included angle of (A);
wherein
Figure BDA0002596322710000058
As the heading angle of the carrier coordinate system relative to the geographic coordinate system, i.e. ZcRelative to ZgThe included angle of (A);
(2.4) calculating an attitude matrix of a navigation coordinate system of the vehicle
Figure BDA0002596322710000051
Figure BDA0002596322710000052
(2.5) calculating the attitude matrix angular rate omega of the navigation coordinate system of the intelligent glassesp：
Figure BDA0002596322710000053
Wherein, ω iseFor the projection of the angular velocity of the earth in the navigation coordinate system, ωaFor the purpose of vehicle gyroscope measurements,
Figure BDA0002596322710000054
represents an estimate of IURE for the vehicle.
The navigation parameters include:
navigation parameters: y is Hx0+
Wherein
Figure RE-GDA0002623494810000047
y(tl) Represents tlObservation information of time H (t)l) Represents tlObservation coefficient matrix at time, x0Representing the quantity of state to be estimated (t)l) Represents tlObservation noise at the moment; phi (t)l,t0) Represents t0Time tlA phase difference function of the time of day.
The step floating ambiguity resolution parameters are as follows:
Figure RE-GDA0002623494810000048
wherein r and p are the acceleration of the vehicle in the inertial coordinate system, p is the kinetic parameter, GMeIs the constant of the earth's gravity, a1Is the sum of various perturbation forces acting on the vehicle, and t is a time parameter.
The method comprises the following steps of collecting the environment where a path is located through an electronic map, optimizing an electronic map image after Gaussian filtering processing, wherein the steps comprise:
step 1: expanding the low gray value part of the electronic map image subjected to Gaussian filtering, and compressing the high gray value part of the image to obtain a preliminary gray image;
step 2: adding the color contrast of the electronic map image into the preliminary grayed image to construct an error energy function, carrying out derivation on the error energy function to obtain a derivation result, and obtaining a corresponding first image according to the derivation result;
and step 3: converting the first image into an integral image, performing region domain mean value operation on the integral image to obtain a central pixel gray value of each region, and taking the central pixel gray value as a threshold value of each region;
and 4, step 4: calculating an average threshold value of an integral region corresponding to the integral image according to the threshold value of each region, and performing binarization processing on the integral image according to the average threshold value to obtain a second image;
and 5: and performing feature detection on the second image according to an SIFT algorithm to obtain image feature points of the second image, performing space coordinate transformation on the image feature points according to affine transformation, obtaining space coordinate transformation parameters corresponding to the space coordinate transformation according to a least square method, and obtaining and outputting a final image based on the space coordinate transformation parameters.
The invention has the beneficial effects that:
the invention fully utilizes the dynamic characteristics of the operation in the electronic map, estimates and predicts the path parameters of the vehicles in the intelligent traffic system through the observation information, generates the real-time high-precision vehicle path information, and broadcasts the real-time high-precision vehicle path information to the user, thereby assisting the user to realize real-time high-precision navigation.
The method comprises the steps of collecting the environment where a path is located through an electronic map, optimizing an image of the electronic map after Gaussian filtering processing is carried out, carrying out gray level conversion on the image to achieve the purpose of emphasizing a low gray level part of the image and obtain a preliminary gray level image, then carrying out global contrast enhancement on the preliminary gray level image to obtain a first image, converting the first image into an integral image and carrying out binarization processing to obtain a second image, further obtaining space coordinate conversion parameters corresponding to space coordinate conversion of image feature points according to a least square method, and obtaining a final image based on the space coordinate parameters; the images are registered by adopting a least square image registration method on the premise of carrying out gray level conversion and contrast enhancement on the images, so that the calculation amount can be reduced, the calculation speed is high, and the accuracy of the obtained final images is high.
Drawings
FIG. 1 is a flow chart of the method of the present invention.
Detailed Description
The invention is further described below with reference to the accompanying drawings.
An electronic map path planning method of an intelligent transportation system comprises the following steps:
(1) the traffic system server collects navigation information of a navigation system and sends the navigation information to the data processing center;
(2) the data processing center smoothes the pseudo range of the navigation information, and finally generates a vehicle running attitude matrix by utilizing a linearization technology;
(3) calculating to obtain navigation parameters and floating ambiguity resolution parameters based on a least square parameter estimation method;
(4) fixing the floating solution ambiguity parameter, and verifying the correctness of the ambiguity fixation;
(5) the correctly fixed floating solution ambiguity parameters are brought into an observation equation, and the optimal solution of the vehicle motion parameters in the traffic system is obtained through repeated resolving;
(6) the optimal solution of the navigation parameters, the floating solution ambiguity parameters, the vehicle position information and the vehicle motion parameters is broadcasted to a navigation user by satellite communication, and the navigation user is assisted to obtain a real-time high-precision positioning result;
(7) and obtaining the path planning path of the electronic map according to the vehicle position information.
The step (1) comprises the following steps:
(1.1) acquiring and denoising map images:
collecting the environment of the path through an electronic map, and performing Gaussian filtering processing;
(1.2) carrying out edge detection on roads in the map:
sobel operator calculation is carried out on the gray value of the adjacent point around each pixel point in the map, and the threshold value tau is selected according to the brightness acquired by the map when
Figure BDA0002596322710000081
fx＝(2f(a-1，b-1)+3f(a-1，b)+4f(a-1，b+1)) -(3f(a+1，b+1)+5f(a-1，b)+2f(a+1，b+1))，
fy＝(3f(a-1，b-1)+3f(a，b-1)+2f(a+1，b-1)) -(4f(a-1，b-1)+3f(a，b-1)+2f(a+1，b-1))，
When So (a, b) > tau, the map midpoint (a, b) is an edge point, a and b are edge point coordinates, and f is a gray value; f. ofxIs a gray value based on the abscissa x; f. ofyIs a gray value based on the ordinate y; so (a, b) represents a calculation result based on a Sobel operator;
(1.3) planning a path according to the edge points:
collecting included angle theta between distance sensor and horizontal directionaCollecting data l returned by the distance sensoraAcquiring the measurement value z' of the height sensor, wherein the coordinate of the reference system in the horizontal direction is Dn0＝(la× cosθa，la×sinθa，z′)；
Determining a planned path according to the iterative evaluation value of nodes in the path channel, wherein the distance between two adjacent nodes is ln0，ln0＝Dn0-Dn0-1N0 is the index of the current node, starting point I0To the current node In0The cost function of (a) is:
Figure BDA0002596322710000082
wherein n0-1 is the label of the previous node adjacent to the current node;
and scanning two neighbor nodes with the maximum G (n0) near each node for connection to form a planned path channel.
The pseudo range is as follows:
Figure BDA0002596322710000083
wherein
Figure BDA0002596322710000084
Represents an IURE estimate for vehicle j,
Figure BDA0002596322710000085
the satellite-ground distance for eliminating various errors is shown, N represents the number of satellites for navigation at the current moment,
Figure BDA0002596322710000091
the trend item representing the user range error can be obtained according to historical data.
The determination step of the attitude matrix comprises the following steps:
(2.1) setting an earth coordinate system as earth coordinate system, a geographic coordinate system as geo coordinate system, a carrier coordinate system as carrier system, a navigation coordinate system as pilot system, wherein the axes of the coordinate systems are X in sequence respectivelyg、Yg、Zg；Xc、Yc、Zc；Xp、Yp、Zp；
(2.2) calculating a direction cosine matrix of a geographic coordinate system of the vehicle
Figure BDA0002596322710000092
Figure BDA0002596322710000093
Wherein the longitude of the vehicle is alphaeAnd latitude ofe；αeThe value range of (1) is (-180 degrees, 180 degrees);ethe value range of (1) is (-90 degrees, 90 degrees);
(2.3) calculating a direction cosine matrix of a carrier coordinate system of the vehicle
Figure BDA0002596322710000094
Figure BDA0002596322710000095
Wherein gamma iscFor the roll angle of the carrier coordinate system relative to the geographical coordinate system, i.e. XcRelative to XgThe included angle of (A);
wherein theta iscFor the pitch angle of the carrier coordinate system relative to the geographical coordinate system, i.e. YcRelative to YgThe included angle of (A);
wherein
Figure BDA0002596322710000098
As the heading angle of the carrier coordinate system relative to the geographic coordinate system, i.e. ZcRelative to ZgThe included angle of (A);
(2.4) calculating an attitude matrix of a navigation coordinate system of the vehicle
Figure BDA0002596322710000096
Figure BDA0002596322710000097
(2.5) calculating the attitude matrix angular rate omega of the navigation coordinate system of the intelligent glassesp：
Figure BDA0002596322710000101
Wherein, ω iseFor the projection of the angular velocity of the earth in the navigation coordinate system, ωaFor the purpose of vehicle gyroscope measurements,
Figure BDA0002596322710000102
represents an estimate of IURE for the vehicle.
The navigation parameters include:
navigation parameters: y is Hx0+
Wherein
Figure RE-GDA0002623494810000081
y(tl) Represents tlObservation information of time H (t)l) Represents tlObservation coefficient matrix at time, x0Representing the quantity of state to be estimated (t)l) Represents tlObservation noise at the moment; phi (t)l,t0) Represents t0Time tlA phase difference function of the time of day.
The step floating ambiguity resolution parameters are as follows:
Figure RE-GDA0002623494810000082
wherein r and p are the acceleration of the vehicle in the inertial coordinate system, p is the kinetic parameter, GMeIs the constant of the earth's gravity, a1Is the sum of various perturbation forces acting on the vehicle, and t is a time parameter.
The present invention provides an embodiment: an electronic map path planning method for an intelligent transportation system collects the environment of a path through an electronic map, optimizes an electronic map image after Gaussian filtering processing, and comprises the following steps:
step 1: expanding the low gray value part of the electronic map image subjected to Gaussian filtering, and compressing the high gray value part of the image to obtain a preliminary gray image;
step 2: adding the color contrast of the electronic map image into the preliminary grayed image to construct an error energy function, carrying out derivation on the error energy function to obtain a derivation result, and obtaining a corresponding first image according to the derivation result;
and step 3: converting the first image into an integral image, performing region domain mean value operation on the integral image to obtain a central pixel gray value of each region, and taking the central pixel gray value as a threshold value of each region;
and 4, step 4: calculating an average threshold value of an integral region corresponding to the integral image according to the threshold value of each region, and performing binarization processing on the integral image according to the average threshold value to obtain a second image;
and 5: and performing feature detection on the second image according to an SIFT algorithm to obtain image feature points of the second image, performing space coordinate transformation on the image feature points according to affine transformation, obtaining space coordinate transformation parameters corresponding to the space coordinate transformation according to a least square method, and obtaining and outputting a final image based on the space coordinate transformation parameters.
The working principle and the beneficial effects of the design scheme are as follows: acquiring the environment of a path through an electronic map, optimizing an electronic map image after Gaussian filtering processing, performing gray level conversion on the image to achieve the purpose of emphasizing a low gray level part of the image and obtain a primary gray level image, then performing global contrast enhancement on the primary gray level image to obtain a first image, converting the first image into an integral image and performing binarization processing to obtain a second image, further obtaining a spatial coordinate transformation parameter corresponding to the spatial coordinate transformation of the image feature point according to minimum two-time multiplication, and obtaining a final image based on the spatial coordinate parameter; the images are registered by adopting a least square image registration method on the premise of carrying out gray level conversion and contrast enhancement on the images, so that the calculation amount can be reduced, the calculation speed is high, and the accuracy of the obtained final images is high.
The medium adopts the road planning method of the intelligent glasses under the artificial intelligence big data to plan the road. The road planning equipment for the intelligent glasses under the artificial intelligence big data adopts the road planning medium of the intelligent glasses under the artificial intelligence big data to plan the road. The invention fully utilizes the dynamic characteristics of the operation in the electronic map, estimates and predicts the path parameters of the vehicles in the intelligent traffic system through the observation information, generates the real-time high-precision vehicle path information, and broadcasts the real-time high-precision vehicle path information to the user, thereby assisting the user to realize real-time high-precision navigation.
The present invention may be embodied in other specific forms without departing from the spirit or essential attributes thereof, and it should be understood that various changes and modifications can be made therein by those skilled in the art without departing from the spirit and scope of the invention as defined in the appended claims.

Claims (9)

1. An electronic map path planning method of an intelligent transportation system is characterized by comprising the following steps:
(1) the traffic system server collects navigation information of a navigation system and sends the navigation information to the data processing center;
(2) the data processing center smoothes the pseudo range of the navigation information, and finally generates a vehicle running attitude matrix by utilizing a linearization technology;
(3) navigation float parameter and ambiguity resolution parameter calculated and obtained based on least square parameter estimation method
(4) Fixing the floating solution ambiguity parameter, and verifying the correctness of the ambiguity fixation;
(5) the correctly fixed floating solution ambiguity parameters are brought into an observation equation, and the optimal solution of the vehicle motion parameters in the traffic system is obtained through repeated resolving;
(6) the optimal solution of the navigation parameters, the floating solution ambiguity parameters, the vehicle position information and the vehicle motion parameters is broadcasted to a navigation user by satellite communication, so that the navigation user is assisted to obtain a real-time high-precision positioning result;
(7) and obtaining the path planning path of the electronic map according to the vehicle position information.
2. The electronic map path planning method of the intelligent transportation system according to claim 1, wherein the step (1) comprises the steps of:
(1.1) acquiring and denoising map images:
collecting the environment of the path through an electronic map, and performing Gaussian filtering processing;
(1.2) carrying out edge detection on roads in the map:
sobel operator calculation is carried out on the gray value of the adjacent point around each pixel point in the map, and the threshold value tau is selected according to the brightness acquired by the map when
Figure FDA0002596322700000011
fx＝(2f(a-1,b-1)+3f(a-1,b)+4f(a-1,b+1))-(3f(a+1,b+1)+5f(a-1,b)+2f(a+1,b+1))，
fy＝(3f(a-1,b-1)+3f(a,b-1)+2f(a+1,b-1))-(4f(a-1,b-1)+3f(a,b-1)+2f(a+1,b-1))，
When So (a, b)>Tau, the map midpoint (a, b) is the edge point, a, b are the edge point coordinates, f is the gray value, fxIs a gray value based on the abscissa x; f. ofyIs a gray value based on the ordinate y; so (a, b) represents a calculation result based on a Sobel operator;
(1.3) planning a path according to the edge points:
collecting included angle theta between distance sensor and horizontal directionaCollecting data l returned by the distance sensoraAcquiring the measurement value z' of the height sensor, wherein the coordinate of the reference system in the horizontal direction is Dn0＝(la×cosθa,la×sinθa,z′)；
Determining a planned path according to the iterative evaluation value of nodes in the path channel, wherein the distance between two adjacent nodes is ln0，ln0＝Dn0-Dn0-1N0 is the index of the current node, starting point I0To the current node In0The cost function G (n0) of (a) is:
Figure FDA0002596322700000021
wherein n0-1 is the label of the previous node adjacent to the current node;
and scanning two neighbor nodes with the maximum G (n0) near each node for connection to form a planned path channel.
3. The method as claimed in claim 1, wherein the pseudo-range is:
Figure FDA0002596322700000022
wherein
Figure FDA0002596322700000023
Represents an IURE estimate for vehicle j,
Figure FDA0002596322700000024
the satellite-ground distance for eliminating various errors is shown, N represents the number of the satellites for navigation at the current moment,
Figure FDA0002596322700000025
the trend item representing the user range error can be obtained according to historical data.
4. The electronic map path planning method of the intelligent transportation system according to claim 1, wherein the determining of the attitude matrix comprises:
(2.1) setting an earth coordinate system as earth coordinate system, a geographic coordinate system as geo coordinate system, a carrier coordinate system as carrier coordinate system, a navigation coordinate system as pilot coordinate system, wherein the axes of the coordinate systems are X in sequence respectivelyg、Yg、Zg；Xc、Yc、Zc；Xp、Yp、Zp；
(2.2) calculating a direction cosine matrix of a geographic coordinate system of the vehicle
Figure FDA0002596322700000031
Figure FDA0002596322700000032
Wherein the longitude of the vehicle is alphaeAnd latitude ofe；αeThe value range of (1) is (-180 degrees, 180 degrees);ethe value range of (1) is (-90 degrees, 90 degrees);
(2.3) calculating a direction cosine matrix of a carrier coordinate system of the vehicle
Figure FDA0002596322700000033
Figure FDA0002596322700000034
Wherein gamma iscFor the roll angle of the carrier coordinate system relative to the geographical coordinate system, i.e. XcRelative to XgThe included angle of (A);
wherein theta iscFor the pitch angle of the carrier coordinate system relative to the geographical coordinate system, i.e. YcRelative to YgThe included angle of (A);
wherein
Figure FDA0002596322700000035
As the heading angle of the carrier coordinate system relative to the geographic coordinate system, i.e. ZcRelative to ZgThe included angle of (A);
(2.4) calculating an attitude matrix of a navigation coordinate system of the vehicle
Figure FDA0002596322700000036
Figure FDA0002596322700000037
(2.5) calculating the attitude matrix angular rate omega of the navigation coordinate system of the intelligent glassesp：
Figure FDA0002596322700000041
Wherein, ω iseFor the projection of the angular velocity of the earth in the navigation coordinate system, ωaFor the purpose of vehicle gyroscope measurements,
Figure FDA0002596322700000042
represents an estimate of IURE for the vehicle.
5. The method according to claim 1, wherein the navigation parameters include:
navigation parameters: y is Hx0+
Wherein
Figure RE-FDA0002623494800000036
y(tl) Represents tlObservation information of time H (t)l) Represents tlObservation coefficient matrix at time, x0Representing the quantity of state to be estimated (t)l) Represents tlObservation noise at the moment; phi (t)l，t0) Represents t0Time tlA phase difference function of the time of day.
6. The method for planning the route of the electronic map of the intelligent transportation system according to claim 1, wherein the step-floating ambiguity resolution parameters are as follows:
Figure RE-FDA0002623494800000037
wherein r and p are the acceleration of the vehicle in the inertial coordinate system, p is the kinetic parameter, GMeIs the constant of the earth's gravity, a1Is the sum of various perturbation forces acting on the vehicle, and t is a time parameter.
7. The method for planning the route according to the electronic map of the intelligent transportation system of claim 1, wherein the optimal solution of the vehicle motion parameters is as follows:
(5.1) extracting vehicle cloud data M at the previous time point through a cloud data systemWOn point cloud data MWSet m of midpointi∈MW(ii) a Simultaneous extraction of miCorresponding real set
Figure FDA0002596322700000046
(5.2) extracting point cloud data Q of the vehicle reference target at the current previous time pointWOn point cloud data QWSet of midpoint values qi∈QWLet | qi-miObtaining a minimum value, |;
(5.3) calculating the rotation matrix RWAnd translation matrix TW；
(5.4) by rotating the matrix RWAnd translation matrix TWThe obtained real set
Figure FDA0002596322700000051
Set m 'after pose transformation'i；
Figure FDA0002596322700000052
(5.5) calculating m'iAnd q isiAverage distance of
Figure FDA0002596322700000053
(5.6) if average distance
Figure FDA0002596322700000054
Pose early warning threshold value less than or equal to
Figure FDA0002596322700000055
And (5) the target pose is normal and is the optimal solution of the vehicle motion parameters, otherwise, the step (5.1) is executed again.
8. The method as claimed in claim 7, wherein the vehicle position information includes a vehicle driving direction φ0：
Figure FDA0002596322700000056
Wherein v isOx，vOyThe speed of the vehicle on the x and y axes of the reference system, beta0Is the vehicle initial angle.
9. The electronic map path planning method of the intelligent transportation system according to claim 2, wherein the environment where the path is located is collected through the electronic map, and the electronic map image is optimized after gaussian filtering processing, and the method includes the steps of:
step 1: expanding the low gray value part of the electronic map image subjected to Gaussian filtering, and compressing the high gray value part of the image to obtain a preliminary gray image;
step 2: adding the color contrast of the electronic map image into the preliminary grayed image to construct an error energy function, carrying out derivation on the error energy function to obtain a derivation result, and obtaining a corresponding first image according to the derivation result;
and step 3: converting the first image into an integral image, performing region domain mean operation on the integral image to obtain a central pixel gray value of each region, and taking the central pixel gray value as a threshold of each region;
and 4, step 4: calculating an average threshold value of an integral region corresponding to the integral image according to the threshold value of each region, and performing binarization processing on the integral image according to the average threshold value to obtain a second image;
and 5: and performing feature detection on the second image according to an SIFT algorithm to obtain image feature points of the second image, performing space coordinate transformation on the image feature points according to affine transformation, obtaining space coordinate transformation parameters corresponding to the space coordinate transformation according to a least square method, obtaining a final image based on the space coordinate transformation parameters, and outputting the final image.
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Cited By (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN113515128A (en) * 2021-07-30 2021-10-19 华东理工大学 Unmanned vehicle real-time path planning method and storage medium
CN116456048A (en) * 2023-06-19 2023-07-18 中汽信息科技(天津)有限公司 Automobile image recording method and system based on scene adaptation

Cited By (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN113515128A (en) * 2021-07-30 2021-10-19 华东理工大学 Unmanned vehicle real-time path planning method and storage medium
CN113515128B (en) * 2021-07-30 2022-11-08 华东理工大学 Unmanned vehicle real-time path planning method and storage medium
CN116456048A (en) * 2023-06-19 2023-07-18 中汽信息科技(天津)有限公司 Automobile image recording method and system based on scene adaptation
CN116456048B (en) * 2023-06-19 2023-08-18 中汽信息科技(天津)有限公司 Automobile image recording method and system based on scene adaptation

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