CN110647877A - Three-dimensional traffic facility positioning and deviation rectifying method and device based on neural network - Google Patents
Three-dimensional traffic facility positioning and deviation rectifying method and device based on neural network Download PDFInfo
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Abstract
The embodiment of the invention provides a positioning and deviation rectifying method and a positioning and deviation rectifying device for a three-dimensional traffic facility based on a neural network, wherein the method comprises the following steps: inputting data to be corrected into a trained multilayer feedforward neural network model, wherein the multilayer feedforward neural network model is obtained by training a large amount of traffic signal lamp and traffic sign coordinate data in an acquisition area as training samples; and calculating the difference between the horizontal axis and the vertical axis of the real traffic facility coordinate and the vehicle observation coordinate, and outputting the traffic facility coordinate data after deviation rectification after inverse transformation of the normalization factor. According to the method provided by the embodiment of the invention, the normalized vehicle speed, the normalized course angle, the normalized vertical movement distance, the normalized transverse distance with the lane line reference object and the normalized observation position coordinate difference are selected as calculation input, so that the difference between the measured vehicles, the movement error in single-vehicle measurement, the observation angle influence and the test noise can be fully reduced, and the identification accuracy and the positioning accuracy of the traffic facilities can be greatly improved.
Description
Technical Field
The invention relates to the technical field of map services, in particular to a positioning and deviation rectifying method and device for a three-dimensional traffic facility based on a neural network.
Background
In various map service technologies, the accurate position of traffic facilities (including traffic lights, traffic signboards, and the like) on a map has important significance for map navigation and driving assistance.
The existing three-dimensional traffic facility positioning method mainly comprises the steps of identifying a traffic sign through a camera from a vehicle-mounted end, coupling a vehicle-mounted radar to detect the relative position of the traffic sign and a vehicle, and finally obtaining the absolute position of the vehicle through a vehicle-mounted GPS or an inertial navigation device to be comprehensively converted.
However, the single identification of the traffic facilities is easily affected by the current traffic state or weather state, the accuracy rate is less than 100%, and the absolute position accuracy rate of the three-dimensional traffic facilities is poor due to the distance conversion difference caused by the dynamic position of the vehicle, the GPS positioning difference of the single vehicle and the elevation information of the traffic facilities. Therefore, a new method for positioning and correcting the deviation of the three-dimensional transportation facility is needed to solve the above problems.
Disclosure of Invention
In order to solve the above problems, embodiments of the present invention provide a method and an apparatus for correcting a position of a three-dimensional transportation facility based on a neural network, which overcome or at least partially solve the above problems.
In a first aspect, an embodiment of the present invention provides a method for positioning and correcting a deviation of a three-dimensional transportation facility based on a neural network, including:
inputting data to be corrected into a trained multilayer feedforward neural network model, wherein the multilayer feedforward neural network model is obtained by training a large amount of traffic signal lamp and traffic sign coordinate data in an acquisition area as training samples;
and calculating the difference between the horizontal axis and the vertical axis of the real traffic facility coordinate and the vehicle observation coordinate, and outputting the traffic facility coordinate data after deviation rectification after inverse transformation of the normalization factor.
Further, before the inputting the data to be rectified into the trained multi-layer feedforward neural network model, the method further includes:
constructing a training sample set and a multi-layer feedforward neural network model;
training the multi-layer feedforward neural network model based on the training sample set.
Further, the constructing a training sample set includes:
acquiring a large amount of traffic signal lamps and traffic sign coordinate data of a specific area acquired by a vehicle;
according to the large amount of traffic signal lamps and the coordinate data of the traffic sign boards in the specific area, feature extraction is carried out and true values are marked;
and selecting the observation point data matched with the real transportation facility coordinates as the training sample set.
Further, the constructing a training sample set further includes:
the method comprises the steps of preprocessing data, wherein the preprocessing comprises the steps of converting coordinates of traffic facilities into Gauss-Lugmi coordinates, dividing the data into a traffic signal lamp set, a traffic sign set and coupling vehicle track points according to the types of the traffic facilities, calculating motion vectors of each coordinate corresponding to an observation position of a collection vehicle and coupling lane line identification data, and calculating the observation position of the collection vehicle and the transverse distance between the position of the traffic facilities and the lane lines.
Further, constructing a multi-layer feedforward neural network model, comprising:
selecting model input and model output from original data and calculation indexes, wherein the number of neurons in an input layer is m, the number of neurons in an output layer is n, the number of neurons in a hidden layer is k, and the number of neurons in the hidden layer is m, wherein the number of neurons in the hidden layer is k, and m, n and k are preset natural numbers.
Further, before the inputting the data to be rectified into the trained multi-layer feedforward neural network model, the method further includes:
and carrying out data normalization on the data to be rectified.
Further, the characteristics of the data include at least:
the normalized vehicle speed, course angle, vertical movement distance, transverse distance with a lane line reference object and observation position coordinate difference.
In a second aspect, an embodiment of the present invention further provides a positioning and deviation-correcting device for a three-dimensional transportation facility based on a neural network, including:
the input module is used for inputting data to be rectified into a trained multilayer feedforward neural network model, and the multilayer feedforward neural network model is obtained by training a large amount of traffic signal lamps and traffic sign coordinate data in an acquisition area as training samples;
and the output module is used for calculating the difference between the transverse axis and the longitudinal axis of the real traffic facility coordinate and the vehicle observation coordinate, and outputting the traffic facility coordinate data after deviation rectification after inverse transformation of the normalization factor.
In a third aspect, an embodiment of the present invention provides an electronic device, including:
a processor, a memory, a communication interface, and a bus; the processor, the memory and the communication interface complete mutual communication through the bus; the memory stores program instructions which can be executed by the processor, and the processor calls the program instructions to be capable of executing the three-dimensional transportation facility positioning and deviation rectifying method based on the neural network.
In a fourth aspect, an embodiment of the present invention provides a non-transitory computer-readable storage medium, which stores computer instructions, where the computer instructions cause the computer to execute the above-mentioned neural network-based three-dimensional transportation facility location rectification method.
According to the method and the device for positioning and rectifying the three-dimensional traffic facilities based on the neural network, provided by the embodiment of the invention, the normalized vehicle speed, the normalized course angle, the normalized vertical movement distance, the normalized transverse distance from the lane line reference object and the normalized observation position coordinate difference are selected as calculation input, so that the difference between the measured vehicles, the movement error in single-vehicle measurement, the observation angle influence and the test noise can be fully reduced, and the identification accuracy and the positioning accuracy of the traffic facilities are greatly improved.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, and it is obvious that the drawings in the following description are some embodiments of the present invention, and those skilled in the art can also obtain other drawings according to the drawings without creative efforts.
Fig. 1 is a schematic flow chart of a positioning and deviation-correcting method for a three-dimensional transportation facility based on a neural network according to an embodiment of the present invention;
fig. 2 is a schematic structural diagram of a positioning and deviation-correcting device for a three-dimensional transportation facility based on a neural network according to an embodiment of the present invention;
fig. 3 is a block diagram of an electronic device according to an embodiment of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are some embodiments, but not all embodiments, of the present invention. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
It should be noted that the position correction of the current transportation facility mainly depends on the calibration of the camera on the collection vehicle and the elimination of accidental errors by using the same targeted rule for collecting results for multiple times, or the error is reduced by the form of obtaining the center through clustering.
However, in a crowdsourcing acquisition mode, the calibration state of the vehicle-mounted camera is poor, the acquisition process presents randomness characteristics, the deviation of the result is greater than 10m, the traffic facility point sets acquired for multiple times are clustered and averaged directly, only accidental errors caused by randomness can be relieved, and effective deviation rectification cannot be achieved. Then the current error essentially originates mainly from three aspects:
1. compared with marks such as ground lane lines and the like, the vehicle-mounted camera has a certain height when recognizing traffic signal lamps and traffic signboards. When the camera predicts the relative position, the relative distance error is large due to the coupling influence of the elevation and the distance;
2. compared with the traffic facilities, the traffic facilities generally have larger transverse distance with the vehicle-mounted camera, have larger observation angle and certain distortion, and part of the traffic facilities have smaller observable area and more serious distortion due to special positions;
3. when the vehicle-mounted camera identifies the traffic facility, the vehicle-mounted camera is obviously influenced by the motion state of the vehicle, and the identification and the positioning of the single three-dimensional traffic facility are influenced by the running speed, the course angle and the installation height of the camera of the vehicle.
In view of the above problems in the prior art, an embodiment of the present invention provides a method for positioning and correcting a deviation of a three-dimensional transportation facility based on a neural network, and fig. 1 is a schematic flow chart of the method for positioning and correcting a deviation of a three-dimensional transportation facility based on a neural network provided in the embodiment of the present invention, as shown in fig. 1, the method includes:
101. inputting data to be corrected into a trained multilayer feedforward neural network model, wherein the multilayer feedforward neural network model is obtained by training a large amount of traffic signal lamp and traffic sign coordinate data in an acquisition area as training samples;
102. and calculating the difference between the horizontal axis and the vertical axis of the real traffic facility coordinate and the vehicle observation coordinate, and outputting the traffic facility coordinate data after deviation rectification after inverse transformation of the normalization factor.
It should be noted that, the embodiment of the present invention provides a neural network-based traffic facility positioning and deviation rectifying method, for solving the problem of large positioning error of a traffic facility. In the crowdsourcing mode, a large amount of lower precision transportation facility coordinate data can be obtained. Firstly, dividing the obtained traffic facilities into two categories of traffic signal lamps and traffic signboards through vehicle end identification categories; then, coupling track information and lane line information of the measuring vehicles, and calculating corresponding observation indexes of the three-dimensional traffic facilities; respectively training a multilayer feedforward neural network model aiming at two main classes of traffic signal lamps and traffic signboards through pre-marked real positions of part of traffic facilities and test data of sensors; and finally, inputting the test results of other sensors into the trained multilayer feedforward neural network for conversion to obtain the output positioning result of the model, thereby realizing the positioning and deviation rectification of the low-precision three-dimensional traffic sign.
Then, in step 101, the embodiment of the present invention inputs the data to be corrected into the trained multi-layer feedforward neural network model, and since the model is trained, the calculation process can be automatically completed in step 102, mainly calculating the difference between the horizontal axis and the vertical axis of the real transportation facility coordinate and the vehicle observation coordinate, and outputting the corrected transportation facility coordinate data after inverse transformation of the normalization factor, and the correction accuracy is very high after result detection.
According to the three-dimensional traffic facility positioning and deviation rectifying method based on the neural network, provided by the embodiment of the invention, the normalized vehicle speed, the normalized course angle, the normalized movement vertical distance, the normalized transverse distance with a lane line reference object and the normalized observation position coordinate difference are selected as calculation input, so that the difference between the measured vehicles, the movement error in single-vehicle measurement, the observation angle influence and the test noise can be fully reduced, and the identification accuracy and the positioning accuracy of the traffic facility are greatly improved.
On the basis of the above embodiment, before the inputting the data to be corrected into the trained multi-layer feedforward neural network model, the method further includes:
constructing a training sample set and a multi-layer feedforward neural network model;
training the multi-layer feedforward neural network model based on the training sample set.
It can be understood that the multi-layer feedforward neural network model provided by the embodiment of the present invention is a pre-trained model, and then before actual use, the model needs to be constructed and a corresponding training sample set needs to be constructed.
On the basis of the above embodiment, the constructing a training sample set includes:
acquiring a large amount of traffic signal lamps and traffic sign coordinate data of a specific area acquired by a vehicle;
according to the large amount of traffic signal lamps and the coordinate data of the traffic sign boards in the specific area, feature extraction is carried out and true values are marked;
and selecting the observation point data matched with the real transportation facility coordinates as the training sample set.
The specific training sample set construction process comprises the following steps:
firstly, acquiring data, wherein the data comprise a large number of traffic signal lamps and traffic sign coordinate data in a specific area acquired by a vehicle: the vehicle-mounted traffic facility monitoring system comprises traffic facility longitude and latitude coordinates (sign _ x, sign _ y), a traffic facility type sign _ type (traffic signal lamps or traffic signboards), a traffic facility sub-type sign _ property (sub-types for classifying speed limit boards or guide boards and the like), corresponding collected vehicle observation position coordinates (obs _ x, obs _ y), a vehicle speed vehspd, a heading angle head _ angle and a timestamp time.
The ith data is in the form: data _ i [ [ sign _ x _ i, sign _ y _ i, sign _ type _ i, sign _ property _ i, obs _ x _ i, obs _ v _ i, vehspd _ i, head _ angle _ i, time _ i ], and data set DS [ [ data _1, data _2, …, data _ n ], where DS is sorted in order of vehicle and time.
Then, extracting the features, specifically: calculating the difference value of the observed traffic facility coordinates and the vehicle observed coordinates:
δx＝sign_x-obs_x
δy＝sign_y-obs_y
vehicle motion vector std _ vector (x, y) and observation angle of transportation facility:
sign_vector＝(δx,δy)
θ＝acrcos(std_vector(x,y)*sign_vector/(|std_vector(x,y)||sign_vector|))；
then, mark true value: firstly, acquiring a real-value coordinate point set (real _ x, real _ y) of a traffic facility in a certain test area, selecting area data containing a traffic setting real value from all data as training data _ train, using other data as test data _ test, carrying out density-based formal clustering on predicted positions (sign _ x, sign _ y) of the traffic facility in the training data _ train through a DBSCAN, wherein the clustering radius eps is n1 meters, and classifying traffic settings with the distance less than n1 meters into a pre-estimated class by the method, thereby obtaining a traffic facility class M: { M1, M2 … Mn };
counting the total number k of traffic facility types sign _ type and traffic facility sub-types sign _ property types in traffic facility types M { M1, M2 … Mn } obtained by clustering based on density DBSCAN { k1, k2 … kn };
then, circularly traversing the M _ i category through a kMeans clustering algorithm, dividing the M _ i category into k _ i subclasses, and obtaining corresponding clustering centers (k _ mean _ x, k _ mean _ y);
traversing the clustering core point set, and obtaining the traffic facility real _ j corresponding to the shortest distance dis _ j from the traffic facility truth value coordinate point set (real _ x, real _ y). The distance is obtained according to the Euclidean distance:
Dis_j＝min(pow((k_mean_x–real_x_j)^2+(k_mean_y–real_y_i)^2,0.5))
if the shortest distance is less than m meters, the matching is good, and the real values of the guideboards of all the clustering points corresponding to the clustering center are set as (real _ x _ j, real _ y _ j) to be used as the true values of the coordinates of the training guideboards;
and finally, selecting observation point data matched with the real transportation facility coordinates, wherein the observation point data comprises a vehicle speed vehspd, a heading angle head _ angle and index data obtained in the step 2 and the step 3, and the index data comprises a vertical distance vertical _ sign of a motion vector of the data transportation facility and the observation point, a transverse distance cross _ veh of an observation position of the vehicle and a lane line, a transverse distance cross _ sign of an observation position of the transportation facility and the lane line, an observation angle theta of the vehicle and the transportation facility, and differences delta x and delta y between the observation coordinates of the observation transportation facility and the observation coordinates of the vehicle. (ii) a
Calculating the horizontal axis difference δ x _ real, δ y _ real of the real transportation facility coordinates and the vehicle observation coordinates is given as follows:
δx_real＝real_x–obs_x；
δy_real＝real_y–obs_y；
and finishing the construction of the training set.
On the basis of the foregoing embodiments, the constructing a training sample set further includes:
the method comprises the steps of preprocessing data, wherein the preprocessing comprises the steps of converting coordinates of traffic facilities into Gauss-Lugmi coordinates, dividing the data into a traffic signal lamp set, a traffic sign set and coupling vehicle track points according to the types of the traffic facilities, calculating motion vectors of each coordinate corresponding to an observation position of a collection vehicle and coupling lane line identification data, and calculating the observation position of the collection vehicle and the transverse distance between the position of the traffic facilities and the lane lines.
Preferably, the embodiment of the present invention performs preprocessing on the data of the training sample set, where the preprocessing includes:
converting longitude and latitude coordinates of the traffic facility into Gaussian Luigmi coordinates (sign _ x, sign _ y); dividing data into a traffic signal light set and a traffic sign set according to the traffic facility type sign _ type;
coupling vehicle track points, and calculating a motion vector of each coordinate corresponding to the observation position of the collection vehicle:
vector(x,y)＝((obs_x_i+1–obs_x_i-1)/2,(obs_y_i+1–obs_y_i-1)/2)；
std_vector(x,y)＝vector(x,y)/|vector(x,y)|
and sequencing the acquired vehicle track points according to time, solving the motion vector of the current point in an intermediate difference mode, and dividing the motion vector by a module of the vector for standardization. Regarding track points of cross areas of adjacent tracks, the motion vector looks at the adjacent points;
and calculating the vertical distance vertical _ sign of the motion vector of the traffic facility and the observation point.
Acquiring lane line vectors by processing the camera lane line data; coupling the relative positions of the vehicle coordinates and the traffic facility coordinates and the lane lines in the same frame of picture, and calculating the transverse distances cross _ veh and cross _ sign between the vehicle coordinates and the lane lines;
the distance calculation is performed by converting the vector sum determination point into a linear equation and then calculating the distance between the outer point of the straight line and the straight line, as follows: d | Ax + By + C |/pow (a × a + B |/B, 0.5).
On the basis of the above embodiments, a multi-layer feedforward neural network model is constructed, which includes:
selecting model input and model output from original data and calculation indexes, wherein the number of neurons in an input layer is m, the number of neurons in an output layer is n, the number of neurons in a hidden layer is k, and the number of neurons in the hidden layer is m, wherein the number of neurons in the hidden layer is k, and m, n and k are preset natural numbers.
Specifically, model inputs [ vehspd, head _ angle, vertical _ sign, cross _ veh, cross _ sign, δ x, δ y ] are taken; selecting a model to output [ delta x _ real ] and [ delta y _ real ] respectively; the number of input layer neurons is [7], the number of output neurons is [1], the number of hidden layer neurons is [7,21], model training errors and test errors are calculated, and the number of hidden layer neurons with the minimum error is selected to be k.
On the basis of the above embodiments, before the inputting the data to be corrected into the trained multi-layer feedforward neural network model, the method further includes:
and carrying out data normalization on the data to be rectified.
For all data signals, the training set was normalized using the following linear transformation:
y＝(x-min)/(max-min)；
and the normalization factor para for each signal is recorded for subsequent inverse transformation.
On the basis of the above embodiments, the characteristics of the data at least include:
the normalized vehicle speed, course angle, vertical movement distance, transverse distance with a lane line reference object and observation position coordinate difference.
And importing the data into the obtained neural network model, and calculating to obtain the horizontal axis difference delta x _ real and the vertical axis difference delta y _ real of the real transportation facility coordinate and the vehicle observation coordinate.
After inverse transformation is carried out by the middle normalization factor para, the vehicle observation coordinates (obs _ x, obs _ y) are superposed to obtain the corrected transportation facility coordinate data (sign _ pre _ x, sign _ pre _ y).
sign_pre_x＝obs_x+para_x*δx_real；
sign _ pre _ y is obs _ y + para _ y δ y _ real, and the final result is output.
Fig. 2 is a schematic structural diagram of a positioning and deviation-correcting device for a three-dimensional transportation facility based on a neural network according to an embodiment of the present invention, as shown in fig. 2, including: an input module 201 and an output module 202, wherein:
the input module 201 is used for inputting data to be rectified into a trained multilayer feedforward neural network model, wherein the multilayer feedforward neural network model is obtained by training a large amount of traffic signal lamps and traffic sign coordinate data in an acquisition area as training samples;
the output module 202 is configured to calculate a difference between a horizontal axis and a vertical axis of the real transportation facility coordinate and the vehicle observation coordinate, and output the corrected transportation facility coordinate data after inverse transformation of the normalization factor.
Specifically, how to implement the technical scheme of the embodiment of the positioning and deviation-correcting method for a three-dimensional transportation facility of a neural network shown in fig. 1 through the input module 201 and the output module 202 is similar to the implementation principle and the technical effect, and details are not repeated here.
According to the three-dimensional traffic facility positioning and deviation rectifying device based on the neural network, the normalized vehicle speed, the normalized course angle, the normalized vertical movement distance, the normalized transverse distance with the lane line reference object and the normalized observation position coordinate difference are selected as calculation input, so that the difference between the measured vehicles, the movement error in single-vehicle measurement, the observation angle influence and the test noise can be fully reduced, and the identification accuracy and the positioning accuracy of the traffic facility are greatly improved.
On the basis of the above embodiments, the apparatus is further configured to:
constructing a training sample set and a multi-layer feedforward neural network model;
training the multi-layer feedforward neural network model based on the training sample set.
On the basis of the above embodiment, the constructing a training sample set includes:
acquiring a large amount of traffic signal lamps and traffic sign coordinate data of a specific area acquired by a vehicle;
according to the large amount of traffic signal lamps and the coordinate data of the traffic sign boards in the specific area, feature extraction is carried out and true values are marked;
and selecting the observation point data matched with the real transportation facility coordinates as the training sample set.
On the basis of the foregoing embodiments, the constructing a training sample set further includes:
the method comprises the steps of preprocessing data, wherein the preprocessing comprises the steps of converting coordinates of traffic facilities into Gauss-Lugmi coordinates, dividing the data into a traffic signal lamp set, a traffic sign set and coupling vehicle track points according to the types of the traffic facilities, calculating motion vectors of each coordinate corresponding to an observation position of a collection vehicle and coupling lane line identification data, and calculating the observation position of the collection vehicle and the transverse distance between the position of the traffic facilities and the lane lines.
On the basis of the above embodiments, a multi-layer feedforward neural network model is constructed, which includes:
selecting model input and model output from original data and calculation indexes, wherein the number of neurons in an input layer is m, the number of neurons in an output layer is n, the number of neurons in a hidden layer is k, and the number of neurons in the hidden layer is m, wherein the number of neurons in the hidden layer is k, and m, n and k are preset natural numbers.
On the basis of the above embodiments, before the inputting the data to be corrected into the trained multi-layer feedforward neural network model, the method further includes:
and carrying out data normalization on the data to be rectified.
On the basis of the above embodiments, the characteristics of the data at least include:
the normalized vehicle speed, course angle, vertical movement distance, transverse distance with a lane line reference object and observation position coordinate difference.
Fig. 3 is a block diagram of an electronic device according to an embodiment of the present invention, and referring to fig. 3, the electronic device includes: a processor (processor)301, a communication Interface (communication Interface)302, a memory (memory)303 and a bus 304, wherein the processor 301, the communication Interface 302 and the memory 303 complete communication with each other through the bus 304. Processor 301 may call logic instructions in memory 303 to perform the following method: inputting data to be corrected into a trained multilayer feedforward neural network model, wherein the multilayer feedforward neural network model is obtained by training a large amount of traffic signal lamp and traffic sign coordinate data in an acquisition area as training samples; and calculating the difference between the horizontal axis and the vertical axis of the real traffic facility coordinate and the vehicle observation coordinate, and outputting the traffic facility coordinate data after deviation rectification after inverse transformation of the normalization factor.
The embodiment of the invention discloses a computer program product, which comprises a computer program stored on a non-transitory computer readable storage medium, wherein the computer program comprises program instructions, and when the program instructions are executed by a computer, the computer can execute the three-dimensional transportation facility positioning and deviation rectifying method based on the neural network provided by the above method embodiments, for example, the method comprises the following steps: inputting data to be corrected into a trained multilayer feedforward neural network model, wherein the multilayer feedforward neural network model is obtained by training a large amount of traffic signal lamp and traffic sign coordinate data in an acquisition area as training samples; and calculating the difference between the horizontal axis and the vertical axis of the real traffic facility coordinate and the vehicle observation coordinate, and outputting the traffic facility coordinate data after deviation rectification after inverse transformation of the normalization factor.
An embodiment of the present invention provides a non-transitory computer-readable storage medium, where the non-transitory computer-readable storage medium stores computer instructions, and the computer instructions enable the computer to execute the method for positioning and correcting a deviation of a three-dimensional transportation facility based on a neural network provided in the foregoing method embodiments, for example, the method includes: inputting data to be corrected into a trained multilayer feedforward neural network model, wherein the multilayer feedforward neural network model is obtained by training a large amount of traffic signal lamp and traffic sign coordinate data in an acquisition area as training samples; and calculating the difference between the horizontal axis and the vertical axis of the real traffic facility coordinate and the vehicle observation coordinate, and outputting the traffic facility coordinate data after deviation rectification after inverse transformation of the normalization factor.
Through the above description of the embodiments, those skilled in the art will clearly understand that each embodiment can be implemented by software plus a necessary general hardware platform, and certainly can also be implemented by hardware. With this understanding in mind, the above-described technical solutions may be embodied in the form of a software product, which can be stored in a computer-readable storage medium such as ROM/RAM, magnetic disk, optical disk, etc., and includes instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to execute the method according to each embodiment or some parts of the embodiments.
Finally, it should be noted that: the above examples are only intended to illustrate the technical solution of the present invention, but not to limit it; although the present invention has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some technical features may be equivalently replaced; and such modifications or substitutions do not depart from the spirit and scope of the corresponding technical solutions of the embodiments of the present invention.
Claims (10)
1. A three-dimensional traffic facility positioning and deviation rectifying method based on a neural network is characterized by comprising the following steps:
inputting data to be corrected into a trained multilayer feedforward neural network model, wherein the multilayer feedforward neural network model is obtained by training a large amount of traffic signal lamp and traffic sign coordinate data in an acquisition area as training samples;
and calculating the difference between the horizontal axis and the vertical axis of the real traffic facility coordinate and the vehicle observation coordinate, and outputting the traffic facility coordinate data after deviation rectification after inverse transformation of the normalization factor.
2. The method for positioning and rectifying errors of three-dimensional transportation facilities based on the neural network as claimed in claim 1, wherein before the inputting the data to be rectified into the trained multi-layer feedforward neural network model, the method further comprises:
constructing a training sample set and a multi-layer feedforward neural network model;
training the multi-layer feedforward neural network model based on the training sample set.
3. The method for positioning and rectifying deviation of three-dimensional transportation facilities based on neural network as claimed in claim 2, wherein the constructing of the training sample set comprises:
acquiring a large amount of traffic signal lamps and traffic sign coordinate data of a specific area acquired by a vehicle;
according to the large amount of traffic signal lamps and the coordinate data of the traffic sign boards in the specific area, feature extraction is carried out and true values are marked;
and selecting the observation point data matched with the real transportation facility coordinates as the training sample set.
4. The method for positioning and rectifying deviation of three-dimensional transportation facilities based on neural network as claimed in claim 3, wherein the constructing of the training sample set further comprises:
the method comprises the steps of preprocessing data, wherein the preprocessing comprises the steps of converting coordinates of traffic facilities into Gauss-Lugmi coordinates, dividing the data into a traffic signal lamp set, a traffic sign set and coupling vehicle track points according to the types of the traffic facilities, calculating motion vectors of each coordinate corresponding to an observation position of a collection vehicle and coupling lane line identification data, and calculating the observation position of the collection vehicle and the transverse distance between the position of the traffic facilities and the lane lines.
5. The method for positioning and rectifying deviation of three-dimensional transportation facilities based on the neural network as claimed in claim 3, wherein constructing a multi-layer feedforward neural network model comprises:
selecting model input and model output from original data and calculation indexes, wherein the number of neurons in an input layer is m, the number of neurons in an output layer is n, the number of neurons in a hidden layer is k, and the number of neurons in the hidden layer is m, wherein the number of neurons in the hidden layer is k, and m, n and k are preset natural numbers.
6. The method for positioning and rectifying errors of three-dimensional transportation facilities based on the neural network as claimed in claim 1, wherein before the inputting the data to be rectified into the trained multi-layer feedforward neural network model, the method further comprises:
and carrying out data normalization on the data to be rectified.
7. The method for positioning and rectifying the deviation of the three-dimensional transportation facility based on the neural network as claimed in any one of claims 1 to 6, wherein the characteristics of the data at least comprise:
the normalized vehicle speed, course angle, vertical movement distance, transverse distance with a lane line reference object and observation position coordinate difference.
8. The utility model provides a three-dimensional transportation facilities location deviation correcting device based on neural network which characterized in that includes:
the input module is used for inputting data to be rectified into a trained multilayer feedforward neural network model, and the multilayer feedforward neural network model is obtained by training a large amount of traffic signal lamps and traffic sign coordinate data in an acquisition area as training samples;
and the output module is used for calculating the difference between the transverse axis and the longitudinal axis of the real traffic facility coordinate and the vehicle observation coordinate, and outputting the traffic facility coordinate data after deviation rectification after inverse transformation of the normalization factor.
9. An electronic device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, characterized in that the processor implements the steps of the multi-service hybrid access method according to any of claims 1 to 7 when executing the program.
10. A non-transitory computer readable storage medium, having stored thereon a computer program, for implementing the steps of the multi-service hybrid access method according to any one of claims 1 to 7 when being executed by a processor.
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