CN117269963A - Tunnel target point cloud detection method based on traffic radar - Google Patents
Tunnel target point cloud detection method based on traffic radar Download PDFInfo
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- CN117269963A CN117269963A CN202310854557.0A CN202310854557A CN117269963A CN 117269963 A CN117269963 A CN 117269963A CN 202310854557 A CN202310854557 A CN 202310854557A CN 117269963 A CN117269963 A CN 117269963A
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- 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
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- 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
- G01S7/00—Details of systems according to groups G01S13/00, G01S15/00, G01S17/00
- G01S7/02—Details of systems according to groups G01S13/00, G01S15/00, G01S17/00 of systems according to group G01S13/00
- G01S7/36—Means for anti-jamming, e.g. ECCM, i.e. electronic counter-counter measures
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Abstract
The invention provides a tunnel target point cloud detection method based on traffic radar, which comprises the following steps: acquiring initial target point cloud information detected by a radar; constructing a lane model; processing the target point cloud to form a stable track; matching the stable track with the cloud target of the external point of the lane; converting the coordinates of the successfully matched out-of-lane point cloud targets into lanes according to the point cloud information characteristics; deleting the cloud targets outside the lane with failed matching; and outputting the point cloud target information. The tunnel target point cloud detection method, the tunnel target point cloud detection device and the computer readable storage medium based on the traffic radar can effectively identify and position targets in a tunnel environment, reduce the influence of multipath interference on target detection and provide stable and accurate point cloud data.
Description
Technical Field
The invention relates to the technical field of radar data processing, in particular to a tunnel target point cloud detection method, equipment and a computer readable storage medium based on traffic radar.
Background
The description of the background art to which the present invention pertains is merely for illustrating and facilitating understanding of the summary of the invention, and should not be construed as an explicit recognition or presumption by the applicant that the applicant regards the prior art as the filing date of the first filed application.
The application of the traffic radar in the tunnel is to realize the detection and tracking of traffic targets such as vehicles, pedestrians and the like. However, due to the particularities of the tunnel environment, traffic radars face serious multipath interference problems in tunnels. Multipath interference means that radar signals reach a receiver after being reflected and scattered by multiple paths in the propagation process, so that multiple echoes exist in the received signals, and detection and positioning of targets are interfered.
In tunnels, the multipath interference problem is particularly serious, mainly for the following reasons: (1) reflection of inner wall surfaces of tunnels: the surfaces of walls, ceilings and the like in the tunnel reflect radar signals to form additional echo signals, and the echo signals of the targets are interfered. (2) a plurality of reflection points: structures and obstructions within the tunnel may present multiple reflection points, causing the signal to propagate through multiple paths, increasing the complexity of multipath interference.
Due to the existence of multipath interference, the detection result of the target point cloud in the tunnel may be unstable, the angle deviation is large, and the condition that the detected point cloud is outside the lane may occur. This also makes it difficult for the subsequent tracking filter processing of the point cloud to form a stable trajectory.
In order to solve the technical problems, the invention provides a tunnel target point cloud detection method, equipment and a computer readable storage medium based on traffic radar, which can effectively identify and position targets in a tunnel environment, reduce the influence of multipath interference on target detection and provide stable and accurate point cloud data.
Disclosure of Invention
The invention provides a tunnel target point cloud detection method, equipment and a computer readable storage medium based on traffic radar, which can effectively identify and position targets in a tunnel environment, reduce the influence of multipath interference on target detection and provide stable and accurate point cloud data.
The embodiment of the first aspect of the invention provides a tunnel target point cloud detection method based on traffic radar, which comprises the following steps: acquiring initial target point cloud information detected by a radar; constructing a lane model; processing the target point cloud to form a stable track; matching the stable track with the cloud target of the external point of the lane; converting the coordinates of the successfully matched out-of-lane point cloud targets into lanes according to the point cloud information characteristics; deleting the cloud targets outside the lane with failed matching; and outputting the point cloud target information.
Preferably, in the step of acquiring the initial target point cloud information detected by the radar, the initial target point cloud information includes distance information, angle information and speed information.
Preferably, in the step of constructing the lane model, the lane model is constructed according to the actual lane environment, including straight road, curve, and combination of straight road and curve.
Preferably, in constructing the lane model according to the actual lane environment, for the combination situation of the straight road and the curve, parameters for constructing the lane model include lane width, straight road length, curve angle and curve length.
Preferably, the step of processing the target point cloud to form a stable trajectory specifically includes the following operations: performing DBSCAN algorithm clustering on the target point cloud in the lane; tracking and filtering the clustered target point cloud by adopting an extended Kalman algorithm; a stable trajectory is obtained.
Preferably, in the step of matching the stable trajectories with the point cloud targets outside the lane, matching condition calculation is performed on each stable trajectory and each point cloud target outside the lane, and the matching conditions include: the difference between the track speed and the point cloud speed is smaller than a first preset value; the difference between the Y-direction coordinate of the track and the Y-direction coordinate of the point cloud is smaller than a second preset value, wherein the Y-direction is the direction right in front of the radar, the Y-direction is perpendicular to the X-direction, and the X-direction is the left-right transverse direction of the radar; the SNR signal-to-noise ratio of the trace differs from the SNR signal-to-noise ratio of the point cloud by less than a third preset value.
Preferably, for the successfully matched out-of-lane point cloud targets, converting coordinates of the out-of-lane point cloud targets into the lane according to the point cloud information characteristics, wherein the converting comprises the following operations: acquiring position information of the target point cloud; judging whether the target point cloud is outside a left boundary or a right boundary according to the position relation between the position information and the lane model; calculating a first transverse distance difference ghostDist according to the boundary position relation between the position information of the target point cloud and the lane model; calculating a second transverse distance difference referDist according to the coordinate information of the stable track and the boundary position relation of the lane model; carrying out coordinate transformation according to the result of subtracting the absolute value from the first transverse distance difference ghostDist and the second transverse distance difference referty; and outputting the target point cloud information after coordinate transformation.
Preferably, the coordinate transformation is performed according to the result of subtracting the absolute value from the first lateral distance difference ghostDist and the second lateral distance difference refdist. The method specifically comprises the following operations: if the result of subtracting the absolute value from the first transverse distance difference ghostDist and the second transverse distance difference refdist is larger than a fourth preset value; when the target point cloud is outside the left boundary, the transverse coordinate posX needs to be converted into posX, posx=posx+sumlanwidth; when the target point cloud is outside the right boundary, the transverse coordinate posX needs to be converted into posX, posx=posx-sumlanwidth; wherein sumLane width represents the total width of the lane; if the result of subtracting the absolute value from the first transverse distance difference ghostDist and the second transverse distance difference refdist is smaller than a fourth preset value; when the target point cloud is outside the left boundary, the lateral coordinate posX needs to be converted into posX, posx=reftbound+ghostdist, reftbound represents the lateral coordinate of the left boundary of the lane; when the target point cloud is outside the right boundary, the lateral coordinate posX needs to be converted into posX, posx=lightbond-ghostDist, lightbond representing the lateral coordinate of the right boundary of the lane.
The embodiment of the second aspect of the invention also provides a tunnel target point cloud detection device based on traffic radar, which comprises a memory and a processor; wherein the memory is for storing executable program code; the processor is configured to read executable program code stored in the memory to perform a traffic radar based tunnel target point cloud detection method.
Embodiments of the third aspect of the present invention also provide a computer-readable storage medium storing a computer program, which when executed by a processor, implements a method for detecting a tunnel target point cloud based on traffic radar.
The tunnel target point cloud detection method, the tunnel target point cloud detection device and the computer readable storage medium based on the traffic radar can effectively identify and position targets in a tunnel environment, reduce the influence of multipath interference on target detection and provide stable and accurate point cloud data.
Additional aspects and advantages of the invention will be set forth in part in the description which follows, or may be learned by practice of the invention.
Drawings
The foregoing and/or additional aspects and advantages of the invention will become apparent and may be better understood from the following description of embodiments taken in conjunction with the accompanying drawings in which:
FIG. 1 shows a flow chart of an embodiment of a traffic radar-based tunnel target point cloud detection method of the present invention;
FIG. 2 shows a lane model diagram of an embodiment of a traffic radar-based tunnel target point cloud detection method of the present invention;
FIG. 3 shows a flow chart of a point cloud position conversion method of an embodiment of a traffic radar-based tunnel target point cloud detection method of the present invention;
FIG. 4 is a block diagram of one embodiment of a traffic radar-based tunnel target point cloud detection apparatus of the present description;
fig. 5 is a block diagram of one embodiment of a computer-readable storage medium of the traffic radar-based tunnel target point cloud detection method of the present specification.
Detailed Description
In order that the above-recited objects, features and advantages of the present invention will be more clearly understood, a more particular description of the invention will be rendered by reference to the appended drawings and appended detailed description. It should be noted that, in the case of no conflict, the embodiments of the present application and the features in the embodiments may be combined with each other.
In the following description, numerous specific details are set forth in order to provide a thorough understanding of the present invention, however, the present invention may be practiced in other ways than those described herein, and therefore the scope of the present invention is not limited to the specific embodiments disclosed below.
The following discussion provides various embodiments of the invention. While each embodiment represents a single combination of the invention, different embodiments of the invention may be substituted or combined, and the invention is thus to be considered to include all possible combinations of the same and/or different embodiments described. Thus, if one embodiment comprises A, B, C and another embodiment comprises a combination of B and D, then the present invention should also be considered to include embodiments comprising one or more of all other possible combinations comprising A, B, C, D, although such an embodiment may not be explicitly recited in the following.
Fig. 1 shows a flow chart of an embodiment of the traffic radar based tunnel target point cloud detection method of the present invention. As shown in fig. 1, the tunnel target point cloud detection method based on the traffic radar provided by the embodiment of the invention comprises the following steps: acquiring initial target point cloud information detected by a radar; constructing a lane model; processing the target point cloud to form a stable track; matching the stable track with the cloud target of the external point of the lane; converting the coordinates of the successfully matched out-of-lane point cloud targets into lanes according to the point cloud information characteristics; deleting the cloud targets outside the lane with failed matching; and outputting the optimized point cloud target information. The tunnel target point cloud detection method based on the traffic radar provided by the embodiment of the invention is an effective point cloud detection method, can optimize out-of-lane false targets generated by multipath interference, and improves the accuracy and reliability of target detection. The method can effectively identify and position the target in the tunnel environment, reduce the influence of multipath interference on target detection, and provide stable and accurate point cloud data.
In the step of acquiring the initial target point cloud information detected by the radar provided by the embodiment of the invention, the initial target point cloud information comprises distance information, angle information and speed information.
Fig. 2 shows a lane model diagram of an embodiment of the traffic radar-based tunnel target point cloud detection method of the present invention. As shown in fig. 2, in the step of constructing a lane model, the method for detecting the tunnel target point cloud based on the traffic radar provided by the embodiment of the invention constructs a lane model according to an actual lane environment, including a straight lane, a curved lane, and a combination situation of the straight lane and the curved lane. For the combination of straight road and curve, the parameters for constructing the lane model comprise lane width, straight road length, curve angle and curve length.
The step of processing the target point cloud to form the stable track provided by the embodiment of the invention specifically comprises the following operations: performing DBSCAN algorithm clustering on the target point cloud in the lane; tracking and filtering the clustered target point cloud by adopting an extended Kalman algorithm; a stable trajectory is obtained.
In the step of matching the stable track with the point cloud targets outside the lane, the matching condition calculation is performed on each stable track and each point cloud target outside the lane, wherein the matching condition comprises the following steps: the difference between the track speed and the point cloud speed is smaller than a first preset value, and the first preset value can take the value of 1m/s; the difference between the Y-direction coordinate of the track and the Y-direction coordinate of the point cloud is smaller than a second preset value, the second preset value can take a value of 10 meters, wherein the Y-direction is the direction right in front of the radar, the Y-direction is perpendicular to the X-direction, and the X-direction is the left-right transverse direction of the radar; the SNR signal-to-noise ratio of the track and the SNR signal-to-noise ratio of the point cloud differ by less than a third preset value, which may take on a value of 3. When the three matching conditions are met at the same time, the current track is considered to be successfully matched with the point cloud, the point cloud is supposed to be the position of a real target which is deviated from a lane due to multipath reflection, and the point cloud is supposed to be subjected to position conversion; if the above three conditions are not satisfied, the matching is considered to be failed, and the point cloud may be a useless interference point.
Fig. 3 shows a flow chart of a point cloud position conversion method of an embodiment of a tunnel target point cloud detection method based on traffic radar. As shown in fig. 3, in the method for detecting a tunnel target point cloud based on traffic radar according to the embodiment of the present invention, the track and the point cloud are successfully matched in the foregoing step, which indicates that the point cloud is an effective point cloud, and the position of the point cloud needs to be converted into a lane according to the position information of the track and the point cloud. And converting the coordinates of the successfully matched out-of-lane point cloud targets into the in-lane point cloud targets according to the point cloud information characteristics, wherein the conversion comprises the following operations: acquiring position information of the target point cloud, and acquiring a transverse direction coordinate and a longitudinal direction coordinate; judging whether the target point cloud is outside a left boundary or a right boundary according to the position relation between the target point cloud coordinate information and the lane model; calculating a first transverse distance difference ghostDist according to the boundary position relation between the position information of the target point cloud and the lane model; calculating a second transverse distance difference referDist according to the coordinate information of the stable track and the boundary position relation of the lane model; carrying out coordinate transformation according to the result of subtracting the absolute value from the first transverse distance difference ghostDist and the second transverse distance difference referty; and outputting the target point cloud information after coordinate transformation.
As shown in fig. 3, the method for detecting a tunnel target point cloud based on a traffic radar according to the embodiment of the present invention performs coordinate transformation according to a result of subtracting an absolute value from a first lateral distance difference ghostDist and a second lateral distance difference refemdist, and specifically includes the following operations: if the result of subtracting the absolute value from the first lateral distance difference ghostDist and the second lateral distance difference refdist is greater than a fourth preset value, the target point cloud may be reflected by two side walls for multiple times, and the fourth preset value may be 3.5; when the target point cloud is outside the left boundary, the transverse coordinate posX needs to be converted into posX, posx=posx+sumlanwidth; when the target point cloud is outside the right boundary, the transverse coordinate posX needs to be converted into posX, posx=posx-sumlanwidth; wherein sumLane width represents the total width of the lane; if the result of subtracting the absolute value from the first lateral distance difference ghostDist and the second lateral distance difference refdist is smaller than a fourth preset value, the target point cloud may be subjected to primary specular reflection; when the target point cloud is outside the left boundary, the lateral coordinate posX needs to be converted into posX, posx=reftbound+ghostdist, reftbound represents the lateral coordinate of the left boundary of the lane; when the target point cloud is outside the right boundary, the lateral coordinate posX needs to be converted into posX, posx=lightbond-ghostDist, lightbond representing the lateral coordinate of the right boundary of the lane.
According to the tunnel target point cloud detection method based on the traffic radar, in the process of detecting and tracking the traffic radar, stable target point cloud can be obtained in the tunnel, so that vehicle targets in the tunnel can be accurately and stably tracked, and the application effect of the traffic radar in the tunnel can be improved.
Fig. 4 is a block diagram of one embodiment of a traffic radar-based tunnel target point cloud detection apparatus of the present specification. Referring now to fig. 4, a schematic diagram of a configuration of a traffic radar-based tunnel target point cloud detection apparatus 300 suitable for use in implementing embodiments of the present disclosure is shown. The electronic 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 shown in fig. 4 is merely an example and should not be construed to limit the functionality and scope of use of the disclosed embodiments.
As shown in fig. 4, the electronic device 300 may include a processing means (e.g., a central processing unit, a graphics processor, etc.) 301 that may perform various appropriate actions and processes according to a program stored in a Read Only Memory (ROM) 302 or a program loaded from a storage means 308 into a Random Access Memory (RAM) 303. In the RAM303, various programs and data required for the operation of the electronic apparatus 300 are also stored. The processing device 301, the ROM 302, and the RAM303 are connected to each other via a bus 304. An input/output (I/O) interface 305 is also connected to bus 304.
In general, the following devices may be connected to the I/O interface 305: input devices 306 including, for example, a touch screen, touchpad, keyboard, mouse, image sensor, microphone, accelerometer, gyroscope, etc.; an output device 307 including, for example, a Liquid Crystal Display (LCD), a speaker, a vibrator, and the like; storage 308 including, for example, magnetic tape, hard disk, etc.; and communication means 309. The communication means 309 may allow the electronic device 300 to communicate with other devices wirelessly or by wire to exchange data. While fig. 4 shows an electronic device 300 having various means, 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.
In particular, according to embodiments of the present disclosure, the processes described above with reference to flowcharts may be implemented as computer software programs. For example, embodiments of the present disclosure include a computer program product comprising a computer program embodied on a computer readable medium, the computer program comprising program code for performing the method shown in the flowcharts. In such an embodiment, the computer program may be downloaded and installed from a network via a communication device 309, or installed from a storage device 308, or installed from a ROM 302. The above-described functions defined in the methods of the embodiments of the present disclosure are performed when the computer program is executed by the processing means 301.
Fig. 5 is a block diagram of one embodiment of a computer-readable storage medium of the traffic radar-based tunnel target point cloud detection method of the present specification. As shown in fig. 5, a computer-readable storage medium 40 according to an embodiment of the present disclosure has stored thereon non-transitory computer-readable instructions 41. When the non-transitory computer readable instructions 41 are executed by the processor, all or part of the steps of the traffic radar-based tunnel target point cloud detection method of the embodiments of the present disclosure described above are performed.
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.
The computer readable medium may be contained in the electronic device; or may exist alone without being incorporated into the electronic device.
The computer readable medium carries one or more programs which, when executed by the electronic device, cause the electronic device to: constructing a basic page, wherein the page code of the basic page is used for constructing an environment required by the operation of the service page and/or realizing the same abstract workflow in the similar service scene; constructing one or more page templates, wherein the page templates are used for providing code templates for realizing service functions in service scenes; based on the corresponding page template, generating a final page code of each page of the service scene through code conversion of a specific function of each page of the service scene; and merging the generated final page code of each page into the page code of the basic page to generate the code of the service page.
Alternatively, the computer-readable medium carries one or more programs that, when executed by the electronic device, cause the electronic device to: constructing a basic page, wherein the page code of the basic page is used for constructing an environment required by the operation of the service page and/or realizing the same abstract workflow in the similar service scene; constructing one or more page templates, wherein the page templates are used for providing code templates for realizing service functions in service scenes; based on the corresponding page template, generating a final page code of each page of the service scene through code conversion of a specific function of each page of the service scene; and merging the generated final page code of each page into the page code of the basic page to generate the code of the service page.
Computer program code for carrying out operations of the present disclosure may be written in one or more programming languages, including an object oriented programming language such as Java, smalltalk, C ++ and conventional procedural programming languages, such as the "C" programming 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 a remote computer, the remote computer may be connected to the user's 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 (for example, through the Internet using an Internet service provider).
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.
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. Wherein the names of the units do not constitute a limitation of the units themselves in some cases.
The tunnel target point cloud detection method, the tunnel target point cloud detection device and the computer readable storage medium based on the traffic radar can effectively identify and position targets in a tunnel environment, reduce the influence of multipath interference on target detection and provide stable and accurate point cloud data.
In the present invention, the terms "first," "second," "third," and the like are used for descriptive purposes only and are not to be construed as indicating or implying relative importance; the term "plurality" means two or more, unless expressly defined otherwise. The terms "mounted," "connected," "secured," and the like are to be construed broadly, and may be, for example, fixedly connected, detachably connected, or integrally connected; "coupled" may be directly coupled or indirectly coupled through intermediaries. The specific meaning of the above terms in the present invention can be understood by those of ordinary skill in the art according to the specific circumstances.
In the description of the present invention, it should be understood that the directions or positional relationships indicated by the terms "upper", "lower", etc. are based on the directions or positional relationships shown in the drawings, are merely for convenience of description and simplification of the description, and do not indicate or imply that the apparatus or unit referred to must have a specific direction, be constructed and operated in a specific direction, and therefore, should not be construed as limiting the present invention.
In the description of the present specification, the terms "one embodiment," "some embodiments," "particular embodiments," and the like, mean that a particular feature, structure, material, or characteristic described in connection with the embodiment or example is included in at least one embodiment or example of the present invention. In this specification, schematic representations of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the particular features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples.
The foregoing is merely illustrative of certain embodiments of the present invention and is not intended to limit the invention so that various modifications and changes may be made to the invention by those skilled in the art. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention should be included in the protection scope of the present invention.
Claims (10)
1. The tunnel target point cloud detection method based on the traffic radar is characterized by comprising the following steps of:
acquiring initial target point cloud information detected by a radar;
constructing a lane model;
processing the target point cloud to form a stable track;
matching the stable track with the out-of-lane point cloud target;
converting the coordinates of the successfully matched out-of-lane point cloud targets into lanes according to the point cloud information characteristics; deleting the cloud targets outside the lane with failed matching;
and outputting the point cloud target information.
2. The traffic radar-based tunnel target point cloud detection method according to claim 1, wherein in the step of acquiring initial target point cloud information of radar detection, the initial target point cloud information includes distance information, angle information, and speed information.
3. The traffic radar-based tunnel target point cloud detection method according to claim 1, wherein in the step of constructing a lane model, the lane model is constructed according to an actual lane environment, including straight road, curved road, and straight road and curved road combination.
4. The traffic radar-based tunnel target point cloud detection method according to claim 3, wherein in the constructing the lane model according to the actual lane environment, parameters for constructing the lane model for the combination of the straight road and the curve include a lane width, a straight road length, a curve angle, and a curve length.
5. The traffic radar-based tunnel target point cloud detection method according to claim 1, wherein the step of processing the target point cloud to form a stable trajectory specifically includes the following operations:
performing DBSCAN algorithm clustering on the target point cloud in the lane;
tracking and filtering the clustered target point cloud by adopting an extended Kalman algorithm;
a stable trajectory is obtained.
6. The traffic radar-based tunnel target point cloud detection method according to claim 1, wherein in the step of matching the stable trajectories with the out-of-lane point cloud targets, a matching condition calculation is performed for each stable trajectory and each out-of-lane point cloud target, and the matching condition includes:
the difference between the track speed and the point cloud speed is smaller than a first preset value;
the difference between the Y-direction coordinate of the track and the Y-direction coordinate of the point cloud is smaller than a second preset value, wherein the Y-direction is the direction right in front of the radar, the Y-direction is perpendicular to the X-direction, and the X-direction is the left-right transverse direction of the radar;
the SNR signal-to-noise ratio of the trace differs from the SNR signal-to-noise ratio of the point cloud by less than a third preset value.
7. The traffic radar-based tunnel target point cloud detection method according to any of claims 1 to 6, wherein the step of converting the coordinates of the successfully matched out-of-lane point cloud target into the inside of the lane according to the point cloud information characteristics comprises the following operations:
acquiring position information of the target point cloud;
judging whether the target point cloud is outside a left boundary or a right boundary according to the position relation between the position information and the lane model;
calculating a first transverse distance difference ghostDist according to the boundary position relation between the position information of the target point cloud and the lane model;
calculating a second transverse distance difference referDist according to the coordinate information of the stable track and the boundary position relation of the lane model;
performing coordinate transformation according to the result of subtracting the absolute value from the first transverse distance difference ghostDist and the second transverse distance difference refertdist;
and outputting the target point cloud information after coordinate transformation.
8. The traffic radar-based tunnel target point cloud detection method according to claim 7, wherein the coordinate transformation is performed according to a result of subtracting an absolute value from the first lateral distance difference ghostDist and the second lateral distance difference refemdist. The method specifically comprises the following operations:
if the result of subtracting the absolute value from the first transverse distance difference ghostDist and the second transverse distance difference refertdist is larger than a fourth preset value; when the target point cloud is outside the left boundary, the transverse coordinate posX needs to be converted into posX, posx=posx+sumlanwidth; when the target point cloud is outside the right boundary, the transverse coordinate posX needs to be converted into posX, posx=posx-sumlanwidth; wherein sumLane width represents the total width of the lane;
if the result of subtracting the absolute value from the first transverse distance difference ghostDist and the second transverse distance difference refertdist is smaller than a fourth preset value; when the target point cloud is outside the left boundary, the lateral coordinate posX needs to be converted into posX, posx=reftbound+ghostdist, reftbound represents the lateral coordinate of the left boundary of the lane; when the target point cloud is outside the right boundary, the lateral coordinate posX needs to be converted into posX, posx=lightbond-ghostDist, lightbond representing the lateral coordinate of the right boundary of the lane.
9. A tunnel target point cloud detection device based on traffic radar comprises a memory and a processor; wherein the memory is for storing executable program code; the processor is configured to read executable program code stored in the memory to perform the traffic radar based tunnel target point cloud detection method according to any of claims 1-8.
10. A computer readable storage medium storing a computer program, wherein the computer program when executed by a processor implements the traffic radar based tunnel target point cloud detection method of any of claims 1-8.
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