CN115113205B - Holographic image method and device for road, electronic equipment and storage medium - Google Patents
Holographic image method and device for road, electronic equipment and storage medium Download PDFInfo
- Publication number
- CN115113205B CN115113205B CN202210794069.0A CN202210794069A CN115113205B CN 115113205 B CN115113205 B CN 115113205B CN 202210794069 A CN202210794069 A CN 202210794069A CN 115113205 B CN115113205 B CN 115113205B
- Authority
- CN
- China
- Prior art keywords
- point cloud
- cloud data
- track information
- track
- radar
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Active
Links
Classifications
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
- G01S13/00—Systems using the reflection or reradiation of radio waves, e.g. radar systems; Analogous systems using reflection or reradiation of waves whose nature or wavelength is irrelevant or unspecified
- G01S13/88—Radar or analogous systems specially adapted for specific applications
- G01S13/91—Radar or analogous systems specially adapted for specific applications for traffic control
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
- G01S13/00—Systems using the reflection or reradiation of radio waves, e.g. radar systems; Analogous systems using reflection or reradiation of waves whose nature or wavelength is irrelevant or unspecified
- G01S13/87—Combinations of radar systems, e.g. primary radar and secondary radar
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T17/00—Three dimensional [3D] modelling, e.g. data description of 3D objects
- G06T17/20—Finite element generation, e.g. wire-frame surface description, tesselation
Landscapes
- Engineering & Computer Science (AREA)
- Radar, Positioning & Navigation (AREA)
- Remote Sensing (AREA)
- Physics & Mathematics (AREA)
- General Physics & Mathematics (AREA)
- Computer Networks & Wireless Communication (AREA)
- Electromagnetism (AREA)
- Computer Graphics (AREA)
- Geometry (AREA)
- Software Systems (AREA)
- Theoretical Computer Science (AREA)
- Radar Systems Or Details Thereof (AREA)
Abstract
The application discloses a holographic portrait method, a holographic portrait device, electronic equipment and a storage medium of a road, wherein the method and the holographic portrait device are applied to the electronic equipment, and particularly acquire target point cloud data of a moving target on the road, wherein the point cloud data comprises output point cloud data of at least two radars; state prediction is carried out based on the cloud data of the target point, so that effective track information of the moving target is obtained; and confirming each piece of effective track information according to a preset strategy to obtain and output target track information. Through the target track information, an upper computer or a control center can effectively count according to traffic flow and queuing information of the crossing.
Description
Technical Field
The present application relates to the field of radar technology, and more particularly, to a road hologram method, apparatus, electronic device, and storage medium.
Background
As the amount of automobile maintenance increases year by year, road congestion becomes increasingly serious, and traffic efficiency is reduced. In order to solve the problem of traffic jam, accurate statistics is required for traffic flow of an intersection serving as a traffic node and queuing information of each lane so as to adopt corresponding traffic control strategies according to the vehicle statistics data, such as correcting traffic signal lamp timing and the like, so that the traffic jam is relieved, and the travel efficiency of people is improved.
Because video based on visible light is easy to be disturbed by weather, radar is generally adopted as vehicle detection equipment, so that effective detection of track information of vehicles passing through an intersection is required to be realized based on radar signals, and effective statistics of traffic flow and queuing information of the intersection can be realized according to the track information.
Disclosure of Invention
In view of the above, the present application provides a holographic image method, apparatus, electronic device and storage medium for detecting track information of vehicles in an intersection.
In order to achieve the above object, the following solutions have been proposed:
a holographic representation method of a road, applied to an electronic device, said holographic representation method comprising the steps of:
acquiring target point cloud data of a moving target on a road, wherein the point cloud data comprise output point cloud data of at least two radars;
performing state prediction based on the target point cloud data to obtain effective track information of the moving target;
and confirming each piece of effective track information according to a preset strategy to obtain and output target track information.
Optionally, the step of obtaining the point cloud data of the moving object on the road includes the steps of:
acquiring point cloud data of each radar
And summarizing the point cloud data of each radar based on the coordinates of each radar in a preset coordinate system to obtain the target point cloud data.
Optionally, the step of obtaining the point cloud data of the moving object on the road further includes the steps of:
recording track data of moving targets on the road within a period of time based on each radar;
processing the track data to obtain the rotation angle of a line segment formed by the track data relative to the road;
and calibrating the coordinates of each radar according to the rotation angle to obtain the coordinates of the radar.
Optionally, the performing state prediction based on the target point cloud data to obtain effective track information of the moving target includes the steps of:
performing prediction processing on the target point cloud data based on a motion model to obtain first track information, wherein the effective track information at least comprises the first track information;
performing initial processing according to the newly-appearing target point cloud data to obtain second track information, wherein the effective track information also comprises the second track information;
and deleting the corresponding track information when the first track information or the second track information has no point cloud data in the current frame.
A holographic image device for a roadway, for use in an electronic device, the holographic image device comprising:
the data acquisition module is configured to acquire target point cloud data of a moving target on a road, wherein the point cloud data comprise output point cloud data of at least two radars;
the state prediction module is configured to perform state prediction based on the target point cloud data to obtain effective track information of the moving target;
and the portrait output module is configured to confirm each piece of effective track information according to a preset strategy to obtain and output target track information.
Optionally, the data acquisition module includes:
a point cloud acquisition unit configured to acquire point cloud data of each of the radars
And the point cloud summarizing unit is configured to summarize the point cloud data of each radar based on the coordinates of each radar in a preset coordinate system to obtain the target point cloud data.
Optionally, the data acquisition module further includes:
a track recording unit configured to record track data of moving objects on the road for a period of time based on each radar;
the track processing unit is configured to process the track data to obtain the rotation angle of a line segment formed by the track data relative to the road;
and the radar calibration unit is configured to calibrate the coordinates of each radar according to the rotation angle to obtain the coordinates of the radar.
Optionally, the state prediction module includes:
the track prediction unit is configured to predict the target point cloud data based on a motion model to obtain first track information, wherein the effective track information at least comprises the first track information;
the track starting unit is configured to perform starting processing according to the newly-appearing target point cloud data to obtain second track information, wherein the effective track information further comprises the second track information;
and the track deleting unit is configured to delete the corresponding track information when the first track information or the second track information has no point cloud data in the current frame.
An electronic device comprising at least one processor and a memory coupled to the processor, wherein:
the memory is used for storing a computer program or instructions;
the processor is configured to execute the computer program or instructions to cause the electronic device to implement a holographic representation of a road as described above.
A storage medium for application to an electronic device, the storage medium carrying one or more computer programs which, when executed by the electronic device, enable the electronic device to implement a holographic representation of a road as described above.
From the above technical scheme, the application discloses a holographic image method, a holographic image device, electronic equipment and a storage medium of a road, wherein the method and the device are applied to the electronic equipment, and particularly acquire target point cloud data of a moving target on the road, wherein the point cloud data comprise output point cloud data of at least two radars; state prediction is carried out based on the cloud data of the target point, so that effective track information of the moving target is obtained; and confirming each piece of effective track information according to a preset strategy to obtain and output target track information. Through the target track information, an upper computer or a control center can effectively count according to traffic flow and queuing information of the crossing.
Drawings
In order to more clearly illustrate the embodiments of the application or the technical solutions in the prior art, the drawings that are required in the embodiments or the description of the prior art will be briefly described, it being obvious that the drawings in the following description are only some embodiments of the application, and that other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
FIG. 1 is a flow chart of a method for holographic representation of a roadway in accordance with an embodiment of the present application;
FIG. 2 is a schematic view of the location of multiple radars in the present application;
FIG. 3 is a block diagram of a holographic image device for a roadway in accordance with an embodiment of the present application;
fig. 4 is a block diagram of an electronic device according to an embodiment of the present application.
Detailed Description
The following description of the embodiments of the present application will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present application, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the application without making any inventive effort, are intended to be within the scope of the application.
Example 1
FIG. 1 is a flow chart of a method for holographic representation of a roadway in accordance with an embodiment of the present application.
As shown in fig. 1, the holographic representation method provided in the present embodiment is applied to an electronic device for representing a track of a moving object such as a vehicle at a road such as an intersection, and finally outputting track information of the moving object, and the electronic device may be understood as a computer or a server having information processing capability and data computing capability, and is connected to a radar provided on the road, or is provided in the radar by an embedded manner. The holographic image-drawing method comprises the following steps:
s1, acquiring target point cloud data of a moving target on a road.
The road refers to a road segment or an intersection to be measured, and specifically refers to acquiring output point cloud data of radars arranged at different positions of the road or at different positions of the intersection. The target point cloud data here includes output point cloud data of at least two radars.
For convenience of description, we describe the plurality of radars as a first radar and at least one second radar as a reference, respectively, and if they are disposed at an intersection, include a first radar 101 and three second radars 102, as shown in fig. 2.
The specific scheme is as follows:
first, point cloud data of each radar is acquired.
And then, summarizing the point cloud data of each radar based on the coordinates of each radar in a preset coordinate system, so as to obtain the target point cloud data. The coordinates here may be a geodetic coordinate system or a relative coordinate system with reference to the position of the first radar, the purpose of which is to combine the point cloud data of all the second radars to the point cloud data of the first radar.
In addition, the method also comprises the following scheme for unifying the coordinates of the multiple radars under the condition that the unified coordinate system is not available among the multiple radars so as to provide a basis for processing the cloud data of the target point:
firstly, 2 points of a certain lane line are recorded by the first radar and the second radar collected through the RTK, and track data with a certain duration, such as 5 minutes, are obtained.
Then, the rotation angle of the radar normal relative to the lane line is calculated through an algorithm.
Finally, collecting geographic coordinates of all the radars and 2 points of a certain lane line by a satellite positioning method, and converting the geographic coordinates into coordinates under a geodetic coordinate system, wherein the calculation formula is as follows:
x: horizontal rectangular coordinates, wherein the unit is meter;
y: longitudinal rectangular coordinates, wherein the unit is meter;
b: the dimension is radian;
l: longitude, in radians;
bo: projection reference dimension, bo=0, in radians;
lo: longitude of origin of coordinates, lo=0, in radian;
a: the major half axis of the earth ellipsoid, a= 6378137.0000, in meters;
b: the shorter half axis of the earth ellipsoid, b= 6356752.3142, in meters;
e: a first eccentricity;
let the XY coordinates of the origin conversion be [ x ] 0 ,y 0 ]The coordinates after the conversion of the remaining 2 points are respectively [ x ] 1 ,y 1 ],[x 2 ,y 2 ]Setting the linear equation as y=ax+b, fitting the optimal solution a, b by least squares,
collecting 5-minute track data, selecting points meeting certain conditions according to the following criteria, wherein the selected rules are as follows:
1. the variance is chosen at the threshold α in order to ensure that the chosen vehicle does not exhibit severe jitter.
2. The selected track speed meets a certain speed threshold beta, so that the possibility of changing the track of the vehicle is small.
3. The life cycle of the selected track is larger than a certain threshold value gamma, so that the selected track is stable and reliable.
The trajectory meeting the conditions is selected, the straight line y=cx+d is also fitted by utilizing the least square, and the actual calculation is only carried out on two angle differences with the origin as the center, so that the minimum mean square error is calculated, and the steps are as follows:
1. two straight lines are selected at n points (l 1 ,h 1 ),(l 2 ,h 2 )…(l n ,h n ) And (f) 1 ,d 1 ),(f 2 ,d 2 )…(f n ,d n )
2. Traversing the range of 0 to 90 degrees by theta, stepping to 0.1 degrees, and calculating the minimum mean square error value as the final calculated value by the following calculation formula:
selecting the first radar as a standard radar, and respectively calculating to obtain a rotation matrix of each second radar relative to the first radar through the calculation, wherein the rotation matrix is recorded as:θ and->
The translation matrix is recorded asTransferring all coordinate systems to a first radar, and taking the first radar as a center point, wherein the calculation formula is as follows:
assume thatThe point cloud data of the other three second radars can be obtained through the following coordinate transformation:
the coordinate points of the four radars are transferred to the origin of the coordinate system by taking the first radar as the origin of the coordinate system, whereinIs a holographic display of the matrix after conversion.
S2, carrying out state prediction based on the cloud data of the target point.
And obtaining effective track information of the moving target of the road or the intersection through state prediction based on the cloud data of the target point. The specific scheme is as follows:
firstly, predicting target point cloud data based on a motion model to obtain first track information. In addition, it is noted here that the effective track information described above includes the first track information. The motion model can select a uniform motion module or an acceleration motion model, so that the track state is predicted to a measurement time point, and the method is concretely as follows:
P(k|k-1)＝FP(k-1|k-1)F T +Q
wherein F is a transfer matrix, Q is a process noise matrix,for the current track state, +.>To predict the post-track state, P (k-1|k-1) is the current track covariance and P (k|k-1) is the predicted track covariance.
Then, performing initial processing according to the newly-appearing target point cloud data to obtain second track information, wherein the effective track information also comprises the second track information, and the method specifically comprises the following steps:
and (3) according to the line scanning incidence matrix theta, if the ith line is 0, the measurement i is not the radar echo of the existing track, a new track needs to be generated for the measurement i, and the generation mode is the same as the generation of the Kalman filtering initial track.
And finally, deleting the corresponding track information when the first track information or the second track information has no point cloud data in the current frame. The method comprises the following steps:
scanning an association matrix theta according to columns, if the j-th columns are all 0, indicating that the frame of the track j has no radar echo, adding one to a non-measuring association counter of the track j; and then judging whether to delete the track according to the track non-measurement correlation counter value and a set strategy (if the number of frames of the track non-measurement correlation in a frame near a is b, the track needs to be deleted).
And S3, confirming the mailbox track information according to a preset strategy.
For the unacknowledged track (such as generating a track in the near future), track confirmation is performed according to a set strategy (such as confirming a track if the number of frames with measurement association in a frame near a is b). Thus, the determined target track information is obtained, and the target track information is output, so that the upper computer or the control center can effectively count according to the traffic flow and queuing information of the crossing.
As can be seen from the above technical solution, the present embodiment provides a holographic image method of a road, where the method is applied to an electronic device, specifically, the method includes obtaining target point cloud data of a moving target on the road, where the target point cloud data includes output point cloud data of at least two radars; state prediction is carried out based on the cloud data of the target point, so that effective track information of the moving target is obtained; and confirming each piece of effective track information according to a preset strategy to obtain and output target track information. Through the target track information, an upper computer or a control center can effectively count according to traffic flow and queuing information of the crossing.
The flowcharts and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods and computer program products according to various embodiments of the present disclosure. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of code, which comprises one or more executable instructions for implementing the specified logical function(s). It should also be noted that, in some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams and/or flowchart illustration, and combinations of blocks in the block diagrams and/or flowchart illustration, can be implemented by special purpose hardware-based systems which perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.
Although operations are depicted in a particular order, this should not be understood as requiring that such operations be performed in the particular order shown or in sequential order. In certain circumstances, multitasking and parallel processing may be advantageous.
It should be understood that the various steps recited in the method embodiments of the present disclosure may be performed in a different order and/or performed in parallel. Furthermore, method embodiments may include additional steps and/or omit performing the illustrated steps. The scope of the present disclosure is not limited in this respect.
Computer program code for carrying out operations of the present disclosure may be written in one or more programming languages, including, but not limited to, an object oriented programming language such as Java, smalltalk, C ++ and conventional procedural programming languages, such as the C-language or similar programming languages. The program code may execute entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer or server. In the case of remote computers, the remote computer may be connected to the user computer through any kind of network, including a Local Area Network (LAN) or a Wide Area Network (WAN), or may be connected to an external computer.
Example two
FIG. 3 is a block diagram of a holographic image device for a roadway in accordance with an embodiment of the present application.
As shown in fig. 3, the holographic image device provided in this embodiment is applied to an electronic device for imaging a moving object such as a track of a vehicle on a road such as an intersection, and finally outputting track information of the moving object, and the electronic device may be understood as a computer or a server having information processing capability and data computing capability, and is connected to a radar provided on the road, or is provided in the radar by an embedded manner. The holographic image device comprises a data acquisition module 10, a state prediction module 20 and an image output module 30.
The data acquisition module is used for acquiring target point cloud data of a moving target on a road.
The road refers to a road segment or an intersection to be measured, and specifically refers to acquiring output point cloud data of radars arranged at different positions of the road or at different positions of the intersection. The target point cloud data here includes output point cloud data of at least two radars.
For convenience of description, we describe the plurality of radars as a first radar and at least one second radar as a reference, respectively, and if they are disposed at an intersection, include a first radar 101 and three second radars 102, as shown in fig. 2.
The module specifically comprises a point cloud acquisition unit and a point cloud summarizing unit.
The point cloud acquisition unit is used for acquiring point cloud data of each radar.
The point cloud summarizing unit is used for summarizing the point cloud data of each radar based on the coordinates of each radar in a preset coordinate system, so as to obtain target point cloud data. The coordinates here may be a geodetic coordinate system or a relative coordinate system with reference to the position of the first radar, the purpose of which is to combine the point cloud data of all the second radars to the point cloud data of the first radar.
In addition, the data acquisition module further comprises a track recording unit, a track processing unit and a radar calibration unit, wherein the track recording unit, the track processing unit and the radar calibration unit are used for unifying the coordinates of the plurality of radars under the condition that a unified coordinate system is not arranged among the plurality of radars so as to process the cloud data of the target points to provide a basis:
the track recording unit is used for acquiring 2 points of a certain lane line through the RTK and recording the 2 points by the first radar and the second radar, so as to obtain track data with a certain duration, such as 5 minutes.
The track processing unit is used for calculating the rotation angle of the radar normal relative to the lane line through an algorithm.
The radar calibration unit is used for acquiring geographic coordinates of all the radars and 2 points of a certain lane line by a satellite positioning method, converting the geographic coordinates into coordinates under a geodetic coordinate system, and the calculation formula is as follows:
x: horizontal rectangular coordinates, wherein the unit is meter;
y: longitudinal rectangular coordinates, wherein the unit is meter;
b: the dimension is radian;
l: longitude, in radians;
bo: projection reference dimension, bo=0, in radians;
lo: longitude of origin of coordinates, lo=0, in radian;
a: the major half axis of the earth ellipsoid, a= 6378137.0000, in meters;
b: the shorter half axis of the earth ellipsoid, b= 6356752.3142, in meters;
e: a first eccentricity;
let the XY coordinates of the origin conversion be [ x ] 0 ,y 0 ]The coordinates after the conversion of the remaining 2 points are respectively [ x ] 1 ,y 1 ],[x 2 ,y 2 ]Setting the linear equation as y=ax+b, fitting the optimal solution a, b by least squares,
collecting 5-minute track data, selecting points meeting certain conditions according to the following criteria, wherein the selected rules are as follows:
1. the variance is chosen at the threshold α in order to ensure that the chosen vehicle does not exhibit severe jitter.
2. The selected track speed meets a certain speed threshold beta, so that the possibility of changing the track of the vehicle is small.
3. The life cycle of the selected track is larger than a certain threshold value gamma, so that the selected track is stable and reliable.
The trajectory meeting the conditions is selected, the straight line y=cx+d is also fitted by utilizing the least square, and the actual calculation is only carried out on two angle differences with the origin as the center, so that the minimum mean square error is calculated, and the steps are as follows:
1. two straight lines are selected at n points (l 1 ,h 1 ),(l 2 ,h 2 )…(l n ,h n ) And (f) 1 ,d 1 ),(f 2 ,d 2 )…(f n ,d n )
2. Traversing the range of 0 to 90 degrees by theta, stepping to 0.1 degrees, and calculating the minimum mean square error value as the final calculated value by the following calculation formula:
selecting the first radar as a standard radar, and respectively calculating to obtain a rotation matrix of each second radar relative to the first radar through the calculation, wherein the rotation matrix is recorded as:θ and->
The translation matrix is recorded asTransferring all coordinate systems to a first radar, and taking the first radar as a center point, wherein the calculation formula is as follows:
assume thatThe point cloud data of the other three second radars can be obtained through the following coordinate transformation:
the coordinate points of the four radars are transferred to the origin of the coordinate system by taking the first radar as the origin of the coordinate system, whereinIs a holographic display of the matrix after conversion.
The state prediction module is used for performing state prediction based on the cloud data of the target point.
And obtaining effective track information of the moving target of the road or the intersection through state prediction based on the cloud data of the target point. The module includes a track prediction unit, a track initiation unit, and a track deletion unit.
The track prediction unit is used for performing prediction processing on the target point cloud data based on the motion model to obtain first track information. In addition, it is noted here that the effective track information described above includes the first track information. The motion model can select a uniform motion module or an acceleration motion model, so that the track state is predicted to a measurement time point, and the method is concretely as follows:
P(k|k-1)＝FP(k-1|k-1)F T +Q
wherein F is a transfer matrix, Q is a process noise matrix,for the current track state, +.>To predict the post-track state, P (k-1|k-1) is the current track covariance and P (k|k-1) is the predicted track covariance.
The track starting unit is used for carrying out starting processing according to the newly-appearing target point cloud data to obtain second track information, and the effective track information also comprises the second track information, and specifically comprises the following steps:
and (3) according to the line scanning incidence matrix theta, if the ith line is 0, the measurement i is not the radar echo of the existing track, a new track needs to be generated for the measurement i, and the generation mode is the same as the generation of the Kalman filtering initial track.
The track deleting unit is used for deleting the corresponding track information when the first track information or the second track information has no point cloud data in the current frame. The method comprises the following steps:
scanning an association matrix theta according to columns, if the j-th columns are all 0, indicating that the frame of the track j has no radar echo, adding one to a non-measuring association counter of the track j; and then judging whether to delete the track according to the track non-measurement correlation counter value and a set strategy (if the number of frames of the track non-measurement correlation in a frame near a is b, the track needs to be deleted).
The portrait output module is used for confirming the mailbox track information according to a preset strategy.
For the unacknowledged track (such as generating a track in the near future), track confirmation is performed according to a set strategy (such as confirming a track if the number of frames with measurement association in a frame near a is b). Thus, the determined target track information is obtained, and the target track information is output, so that the upper computer or the control center can effectively count according to the traffic flow and queuing information of the crossing.
As can be seen from the above technical solution, the present embodiment provides a holographic image device of a road, where the holographic image device is applied to electronic equipment, and specifically obtains target point cloud data of a moving target on the road, where the target point cloud data includes output point cloud data of at least two radars; state prediction is carried out based on the cloud data of the target point, so that effective track information of the moving target is obtained; and confirming each piece of effective track information according to a preset strategy to obtain and output target track information. Through the target track information, an upper computer or a control center can effectively count according to traffic flow and queuing information of the crossing.
The units involved in the embodiments of the present disclosure may be implemented by means of software, or may be implemented by means of hardware. The name of the unit does not in any way constitute a limitation of the unit itself, for example the first acquisition unit may also be described as "unit acquiring at least two internet protocol addresses".
The functions described above herein may be performed, at least in part, by one or more hardware logic components. For example, without limitation, exemplary types of hardware logic components that may be used include: a Field Programmable Gate Array (FPGA), an Application Specific Integrated Circuit (ASIC), an Application Specific Standard Product (ASSP), a system on a chip (SOC), a Complex Programmable Logic Device (CPLD), and the like.
Example III
The present embodiment provides an electronic device, as shown in fig. 4, which shows a schematic structural diagram of an electronic device suitable for implementing the embodiments of the present disclosure. The terminal devices in the embodiments of the present disclosure may include, but are not limited to, mobile terminals such as mobile phones, notebook computers, digital broadcast receivers, PDAs (personal digital assistants), PADs (tablet computers), PMPs (portable multimedia players), in-vehicle terminals (e.g., in-vehicle navigation terminals), and the like, and stationary terminals such as digital TVs, desktop computers, and the like. The electronic device is merely an example and should not impose any limitations on the functionality and scope of use of embodiments of the present disclosure.
The electronic device may include a processing means (e.g., a central processing unit, a graphics processor, etc.) 401, which may perform various appropriate actions and processes according to programs stored in a Read Only Memory (ROM) 402 or programs loaded from a storage means 406 into a Random Access Memory (RAM) 403. In the RAM403, various programs and data required for the operation of the electronic device are also stored. The processing device 601, the ROM 602, and the RAM403 are connected to each other by a bus 404. An input/output (I/O) interface 405 is also connected to bus 404.
In general, the following devices may be connected to the I/O interface 405: input devices 406 including, for example, a touch screen, touchpad, keyboard, mouse, camera, microphone, accelerometer, gyroscope, etc.; an output device 407 including, for example, a Liquid Crystal Display (LCD), a speaker, a vibrator, and the like; storage 406 including, for example, magnetic tape, hard disk, etc.; and a communication device 409. The communication means 409 may allow the electronic device to communicate with other devices wirelessly or by wire to exchange data. While an electronic device having various means is shown in the figures, it is to be understood that not all of the illustrated means are required to be implemented or provided. More or fewer devices may be implemented or provided instead.
Example IV
The present embodiment provides a computer-readable storage medium. The computer readable medium carries one or more computer programs which, when executed by the electronic device, enable the electronic device to obtain target point cloud data of a moving object on a road, the point cloud data including output point cloud data of at least two radars; state prediction is carried out based on the cloud data of the target point, so that effective track information of the moving target is obtained; and confirming each piece of effective track information according to a preset strategy to obtain and output target track information. Through the target track information, an upper computer or a control center can effectively count according to traffic flow and queuing information of the crossing.
It should be noted that the computer readable medium described in the present disclosure may be a computer readable signal medium or a computer readable storage medium, or any combination of the two. The computer readable storage medium can be, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or a combination of any of the foregoing. More specific examples of the computer-readable storage medium may include, but are not limited to: an electrical connection having one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing. In the context of this disclosure, a computer-readable storage medium may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device. In the present disclosure, however, the computer-readable signal medium may include a data signal propagated in baseband or as part of a carrier wave, with the computer-readable program code embodied therein. Such a propagated data signal may take any of a variety of forms, including, but not limited to, electro-magnetic, optical, or any suitable combination of the foregoing. A computer readable signal medium may also be any computer readable medium that is not a computer readable storage medium and that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device. Program code embodied on a computer readable medium may be transmitted using any appropriate medium, including but not limited to: electrical wires, fiber optic cables, RF (radio frequency), and the like, or any suitable combination of the foregoing.
In this specification, each embodiment is described in a progressive manner, and each embodiment is mainly described by differences from other embodiments, and identical and similar parts between the embodiments are all enough to be referred to each other.
While preferred embodiments of the present application have been described, additional variations and modifications in those embodiments may occur to those skilled in the art once they learn of the basic inventive concepts. It is therefore intended that the following claims be interpreted as including the preferred embodiment and all such alterations and modifications as fall within the scope of the embodiments of the application.
Finally, it is further noted that relational terms such as first and second, and the like are used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Moreover, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or terminal that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or terminal. Without further limitation, an element defined by the phrase "comprising one … …" does not exclude the presence of other like elements in a process, method, article or terminal device comprising the element.
The foregoing has outlined rather broadly the more detailed description of the application in order that the detailed description of the application that follows may be better understood, and in order that the present principles and embodiments may be better understood; meanwhile, as those skilled in the art will have variations in the specific embodiments and application scope in accordance with the ideas of the present application, the present description should not be construed as limiting the present application in view of the above.
Claims (6)
1. A holographic representation method of a road applied to an electronic device, the holographic representation method comprising the steps of:
acquiring target point cloud data of a moving target on a road, wherein the point cloud data comprise output point cloud data of at least two radars;
performing state prediction based on the target point cloud data to obtain effective track information of the moving target;
confirming each piece of effective track information according to a preset strategy to obtain and output target track information;
the method for acquiring the point cloud data of the moving object on the road comprises the following steps:
acquiring point cloud data of each radar;
summarizing the point cloud data of each radar based on the coordinates of each radar in a preset coordinate system to obtain the target point cloud data;
the step of acquiring the point cloud data of the moving object on the road further comprises the following steps:
recording track data of moving targets on the road within a period of time based on each radar;
processing the track data to obtain the rotation angle of a line segment formed by the track data relative to the road;
and calibrating the coordinates of each radar according to the rotation angle to obtain the coordinates of the radar.
2. The holographic representation method of claim 1, wherein said performing a state prediction based on said target point cloud data to obtain effective track information of said moving object comprises the steps of:
performing prediction processing on the target point cloud data based on a motion model to obtain first track information, wherein the effective track information at least comprises the first track information;
performing initial processing according to the newly-appearing target point cloud data to obtain second track information, wherein the effective track information also comprises the second track information;
and deleting the corresponding track information when the first track information or the second track information has no point cloud data in the current frame.
3. A holographic image device for a roadway, for use in an electronic device, the holographic image device comprising:
the data acquisition module is configured to acquire target point cloud data of a moving target on a road, wherein the point cloud data comprise output point cloud data of at least two radars;
the state prediction module is configured to perform state prediction based on the target point cloud data to obtain effective track information of the moving target;
the portrait output module is configured to confirm each piece of effective track information according to a preset strategy to obtain and output target track information;
wherein, the data acquisition module includes:
a point cloud acquisition unit configured to acquire point cloud data of each of the radars
The point cloud summarizing unit is configured to summarize the point cloud data of each radar based on the coordinates of each radar in a preset coordinate system to obtain the target point cloud data;
wherein, the data acquisition module further includes:
a track recording unit configured to record track data of moving objects on the road for a period of time based on each radar;
the track processing unit is configured to process the track data to obtain the rotation angle of a line segment formed by the track data relative to the road;
and the radar calibration unit is configured to calibrate the coordinates of each radar according to the rotation angle to obtain the coordinates of the radar.
4. The holographic image device of claim 3, in which the state prediction module comprises:
the track prediction unit is configured to predict the target point cloud data based on a motion model to obtain first track information, wherein the effective track information at least comprises the first track information;
the track starting unit is configured to perform starting processing according to the newly-appearing target point cloud data to obtain second track information, wherein the effective track information further comprises the second track information;
and the track deleting unit is configured to delete the corresponding track information when the first track information or the second track information has no point cloud data in the current frame.
5. An electronic device comprising at least one processor and a memory coupled to the processor, wherein:
the memory is used for storing a computer program or instructions;
the processor is configured to execute the computer program or instructions to cause the electronic device to implement the road holographic representation method of claim 1 or 2.
6. A storage medium for use in an electronic device, wherein the storage medium carries one or more computer programs which, when executed by the electronic device, enable the electronic device to implement the holographic representation of a roadway as claimed in claim 1 or 2.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202210794069.0A CN115113205B (en) | 2022-07-07 | 2022-07-07 | Holographic image method and device for road, electronic equipment and storage medium |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202210794069.0A CN115113205B (en) | 2022-07-07 | 2022-07-07 | Holographic image method and device for road, electronic equipment and storage medium |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN115113205A CN115113205A (en) | 2022-09-27 |
| CN115113205B true CN115113205B (en) | 2023-10-20 |
Family
ID=83331828
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202210794069.0A Active CN115113205B (en) | 2022-07-07 | 2022-07-07 | Holographic image method and device for road, electronic equipment and storage medium |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN115113205B (en) |
Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110750612A (en) * | 2019-10-23 | 2020-02-04 | 福建汉特云智能科技有限公司 | Laser radar-based flight path management method and system |
| CN112017432A (en) * | 2020-08-05 | 2020-12-01 | 中交第一公路勘察设计研究院有限公司 | Method for laying expressway traffic running state perception radar |
| CN113777600A (en) * | 2021-09-09 | 2021-12-10 | 北京航空航天大学杭州创新研究院 | Multi-millimeter-wave radar cooperative positioning tracking method |
| CN114463984A (en) * | 2022-03-02 | 2022-05-10 | 智道网联科技(北京)有限公司 | Vehicle track display method and related equipment |
| CN114519845A (en) * | 2020-10-30 | 2022-05-20 | 北京万集科技股份有限公司 | Multi-sensing data fusion method and device, computer equipment and storage medium |
Family Cites Families (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US9759812B2 (en) * | 2014-10-02 | 2017-09-12 | Trimble Inc. | System and methods for intersection positioning |
| CN109801508B (en) * | 2019-02-26 | 2021-06-04 | 百度在线网络技术（北京）有限公司 | Method and device for predicting movement track of obstacle at intersection |
| CN114137509B (en) * | 2021-11-30 | 2023-10-13 | 南京慧尔视智能科技有限公司 | Millimeter wave Lei Dadian cloud clustering method and device |
-
2022
- 2022-07-07 CN CN202210794069.0A patent/CN115113205B/en active Active
Patent Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110750612A (en) * | 2019-10-23 | 2020-02-04 | 福建汉特云智能科技有限公司 | Laser radar-based flight path management method and system |
| CN112017432A (en) * | 2020-08-05 | 2020-12-01 | 中交第一公路勘察设计研究院有限公司 | Method for laying expressway traffic running state perception radar |
| CN114519845A (en) * | 2020-10-30 | 2022-05-20 | 北京万集科技股份有限公司 | Multi-sensing data fusion method and device, computer equipment and storage medium |
| CN113777600A (en) * | 2021-09-09 | 2021-12-10 | 北京航空航天大学杭州创新研究院 | Multi-millimeter-wave radar cooperative positioning tracking method |
| CN114463984A (en) * | 2022-03-02 | 2022-05-10 | 智道网联科技(北京)有限公司 | Vehicle track display method and related equipment |
Non-Patent Citations (2)
| Title |
|---|
| "Point-Cloud-Based Road Course Estimation on Automotive Radar Data";Yi Jin et al;《2021 IEEE International Conference on Microwaves, Antennas, Communications and Electronic Systems (COMCAS)》;第29-34页 * |
| "一种雷达航迹融合技术的设计原理及实现";于振华等;《舰船电子对抗》;第38卷(第4期);第36-39页 * |
Also Published As
| Publication number | Publication date |
|---|---|
| CN115113205A (en) | 2022-09-27 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN111127563A (en) | Combined calibration method and device, electronic equipment and storage medium | |
| CN109188438B (en) | Yaw angle determination method, device, equipment and medium | |
| CN109435955B (en) | Performance evaluation method, device and equipment for automatic driving system and storage medium | |
| US9191992B2 (en) | System and method for participants data retrieval post accident or event | |
| WO2021254185A1 (en) | Vehicle positioning method, apparatus and device, and storage medium | |
| CN112328731B (en) | Vehicle lane level positioning method and device, electronic equipment and computer readable medium | |
| CN113537362A (en) | Perception fusion method, device, equipment and medium based on vehicle-road cooperation | |
| WO2023098001A1 (en) | Moving object position estimation and prediction method and apparatus, device, and medium | |
| WO2023142823A1 (en) | Assisted positioning method and apparatus, and electronic device and storage medium | |
| CN111121755B (en) | Multi-sensor fusion positioning method, device, equipment and storage medium | |
| CN112731565A (en) | Weather forecast data processing method and device based on multi-level grid map | |
| CN117471513B (en) | Vehicle positioning method, positioning device, electronic equipment and storage medium | |
| CN115113205B (en) | Holographic image method and device for road, electronic equipment and storage medium | |
| CN116758498B (en) | Obstacle information generation method, obstacle information generation device, electronic device, and computer-readable medium | |
| CN113256804B (en) | Three-dimensional reconstruction scale recovery method and device, electronic equipment and storage medium | |
| CN114419788A (en) | Internet of things gas meter reading method and device, electronic equipment and storage medium | |
| CN115201796B (en) | External reference correction method of vehicle sensor | |
| CN115079157B (en) | Pilot information transmission control method and device, electronic equipment and storage medium | |
| CN110263852B (en) | Data processing method and device and electronic equipment | |
| CN117948965A (en) | Point cloud track optimization method and device, electronic equipment and storage medium | |
| CN111477033B (en) | Traffic management method and device based on navigation volume, electronic equipment and storage medium | |
| CN112866628B (en) | Image collector name determining method, device, server and storage medium | |
| CN117928518A (en) | Point cloud track optimization method and device, electronic equipment and storage medium | |
| CN116311152A (en) | Evaluation method and device for parking space detection effect and related equipment | |
| CN117269963A (en) | Tunnel target point cloud detection method based on traffic radar |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |