CN112233416A - Traffic flow detection method and device - Google Patents
Traffic flow detection method and device Download PDFInfo
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- CN112233416A CN112233416A CN202010979557.XA CN202010979557A CN112233416A CN 112233416 A CN112233416 A CN 112233416A CN 202010979557 A CN202010979557 A CN 202010979557A CN 112233416 A CN112233416 A CN 112233416A
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- G—PHYSICS
- G08—SIGNALLING
- G08G—TRAFFIC CONTROL SYSTEMS
- G08G1/00—Traffic control systems for road vehicles
- G08G1/01—Detecting movement of traffic to be counted or controlled
- G08G1/0104—Measuring and analyzing of parameters relative to traffic conditions
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- G—PHYSICS
- G08—SIGNALLING
- G08G—TRAFFIC CONTROL SYSTEMS
- G08G1/00—Traffic control systems for road vehicles
- G08G1/01—Detecting movement of traffic to be counted or controlled
- G08G1/052—Detecting movement of traffic to be counted or controlled with provision for determining speed or overspeed
Abstract
The embodiment of the invention provides a traffic flow detection method and a device, wherein the method comprises the following steps: the method comprises the steps of carrying out Fourier transform processing on echo signals in a region to be detected to obtain a two-dimensional matrix, determining distance information and speed information of a plurality of detection points according to the two-dimensional matrix, carrying out constant false alarm detection on the two-dimensional matrix, determining effective point tracks from the detection points, determining angle information of the effective point tracks, determining vehicle target point tracks in the region to be detected according to the distance information, the speed information and the angle information of the effective point tracks, tracking the vehicle target point tracks to obtain tracking tracks, determining traffic flow according to the tracking tracks, determining vehicle target information by obtaining the distance, the speed and the angle information of each vehicle, further accurately obtaining traffic flow information, enabling radar signals not to be influenced by weather and light, and improving the traffic flow detection precision.
Description
Technical Field
The embodiment of the invention relates to the technical field of intelligent traffic, in particular to a traffic flow detection method and device.
Background
With the continuous progress and development of modern science and technology, vehicles are increasing, so that the traffic flow in the road needs to be detected, and corresponding measures are taken to avoid the road congestion.
In the prior art, a camera is installed above a road section to be measured, and a traffic flow is determined according to video data acquired by the camera, for example, the traffic flow is determined by a manual monitoring method or a video processing method. However, in the method, the traffic flow is determined based on the acquired video data, and a large error exists in the statistical result of the traffic flow in severe weather conditions such as rain, snow, haze and the like, or in the conditions of weak light and poor lighting conditions.
Therefore, the conventional traffic flow rate detection method has a problem of large detection error.
Disclosure of Invention
The embodiment of the invention provides a traffic flow detection method and device, which aim to improve the accuracy of traffic flow detection.
In a first aspect, an embodiment of the present invention provides a traffic flow detection method, including:
carrying out Fourier transform processing on echo signals in a region to be detected to obtain a two-dimensional matrix, and determining distance information and speed information of a plurality of detection points according to the two-dimensional matrix;
performing constant false alarm detection on the two-dimensional matrix, determining an effective point trace from the plurality of detection points, and determining angle information of the effective point trace;
determining a vehicle target point track in the area to be detected according to the distance information, the speed information and the angle information of the effective point track;
and tracking the target point trace of the vehicle to obtain a tracking trace, and determining the traffic flow according to the tracking trace.
Optionally, the fourier transform processing is performed on the echo signal in the region to be detected to obtain a two-dimensional matrix, including:
windowing the distance dimension of the echo signal, and performing fast Fourier transform on the windowed distance dimension to obtain a one-dimensional frequency spectrum containing distance information;
and windowing the velocity dimension of the one-dimensional frequency spectrum, and performing fast Fourier transform on the windowed velocity dimension to obtain a two-dimensional matrix containing velocity information.
Optionally, performing constant false alarm detection on the two-dimensional matrix, and determining an effective point trace from the multiple detection points, includes:
performing non-coherent accumulation on the two-dimensional matrix to obtain a matrix after the non-coherent accumulation;
performing constant false alarm detection on the non-coherent accumulated matrix to obtain environmental noise information;
and filtering information corresponding to the detection points in the non-coherent accumulated matrix according to the environmental noise information to obtain an effective point trace.
Optionally, determining the angle information of the valid dot trace includes:
performing fast Fourier transform on antenna dimensions of a two-dimensional matrix formed by the effective point traces, and determining the phase difference of adjacent receiving antennas;
and determining the angle information of the effective point trace according to the phase difference of the adjacent receiving antennas.
Optionally, determining a vehicle target point track in the area to be detected according to the distance information, the speed information, and the angle information of the effective point track includes:
dividing a plurality of effective point traces belonging to the same vehicle into a cluster according to the distance information, the speed information and the angle information of each effective point trace and a preset clustering condition;
and determining the distance information, the speed information and the angle information of each vehicle target point track according to the distance information, the speed information and the angle information of a plurality of effective point tracks in each cluster.
Optionally, the tracking the vehicle target point trace to obtain a tracking trace, and determining a traffic flow according to the tracking trace, including:
for each vehicle target point track, tracking the vehicle target point track according to the distance information, the speed information and the angle information of the vehicle target point track by a Kalman filtering method, and determining the tracking track of the vehicle target point track;
counting the number of tracking tracks appearing in a preset time period, and determining the traffic flow of the area to be detected according to the number of the tracking tracks.
Optionally, the echo signal is a signal obtained by reflecting a transmission signal of the millimeter wave radar by a target, and the frequency of the transmission signal is 92GHZ to 96 GHZ.
In a second aspect, an embodiment of the present invention provides a traffic flow detection device, including:
the processing module is used for carrying out Fourier transform processing on the echo signals in the region to be detected to obtain a two-dimensional matrix, and determining distance information and speed information of a plurality of detection points according to the two-dimensional matrix;
the first determining module is used for carrying out constant false alarm detection on the two-dimensional matrix, determining an effective trace from the plurality of detection points, and carrying out angle estimation on the effective trace to determine the angle information of the effective trace;
the second determining module is used for determining a vehicle target point track in the area to be detected according to the distance information, the speed information and the angle information of the effective point track;
and the third determining module is used for tracking the target point trace of the vehicle to obtain a tracking trace and determining the traffic flow according to the tracking trace.
In a third aspect, an embodiment of the present invention provides a traffic flow detection device, including: at least one processor and memory;
the memory stores computer-executable instructions;
the at least one processor executes computer-executable instructions stored in the memory to cause the at least one processor to perform the method of detecting vehicle flow as set forth in any one of the first aspects.
In a fourth aspect, an embodiment of the present invention provides a computer-readable storage medium, where a computer executes instructions, and when a processor executes the computer to execute the instructions, the method for detecting a traffic flow according to any one of the first aspect is implemented.
The method and the device for detecting the traffic flow provided by the embodiment of the invention obtain a two-dimensional matrix by carrying out Fourier transform processing on echo signals in a region to be detected, determine the distance information and the speed information of a plurality of detection points according to the two-dimensional matrix, performing constant false alarm detection on the two-dimensional matrix, determining effective trace points from the detection points, and determining angle information of the effective trace points, determining a vehicle target point track in the area to be detected according to the distance information, the speed information and the angle information of the effective point track, the vehicle target point trace is tracked to obtain a tracking trace, the traffic flow is determined according to the tracking trace, the distance information, the speed information and the angle information of the vehicle are detected by a radar, and then, the traffic flow information is determined according to the information, so that the accuracy of traffic flow detection can be improved.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to these drawings without creative efforts.
Fig. 1 is a schematic view of an application scenario of a traffic flow detection method according to an embodiment of the present invention;
fig. 2 is a flowchart of a traffic flow detection method according to an embodiment of the present invention;
fig. 3 is a flowchart of another traffic flow detection method according to an embodiment of the present invention;
fig. 4 is a flowchart of another method for detecting traffic flow according to an embodiment of the present invention;
FIG. 5 is a schematic diagram of a transmitting signal and an echo signal according to an embodiment of the present invention;
fig. 6 is a schematic flowchart of determining distance information and speed information of a detection point according to an embodiment of the present invention;
fig. 7 is a schematic diagram of angle estimation using two receiving antennas according to an embodiment of the present invention;
fig. 8 is a schematic diagram of angle estimation using four receiving antennas according to an embodiment of the present invention;
fig. 9 is an overall flowchart of the vehicle flow detection according to the embodiment of the present invention;
fig. 10 is a schematic structural diagram of a traffic flow detection device according to an embodiment of the present invention;
fig. 11 is a schematic hardware structure diagram of a traffic flow detection device according to an embodiment of the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
The terms "first," "second," "third," "fourth," and the like in the description and in the claims, as well as in the drawings, if any, are used for distinguishing between similar elements and not necessarily for describing a particular sequential or chronological order. It is to be understood that the data so used is interchangeable under appropriate circumstances such that the embodiments of the invention described herein are, for example, capable of operation in sequences other than those illustrated or otherwise described herein. Furthermore, the terms "comprises," "comprising," and "having," and any variations thereof, are intended to cover a non-exclusive inclusion, such that a process, method, system, article, or apparatus that comprises a list of steps or elements is not necessarily limited to those steps or elements expressly listed, but may include other steps or elements not expressly listed or inherent to such process, method, article, or apparatus.
Fig. 1 is a schematic view of an application scenario of the traffic flow detection method provided by the embodiment of the present invention, and as shown in fig. 1, the radar 10 is disposed on a cross bar above a road, a gantry, or two sides of the road, and is capable of measuring information of a vehicle target passing through an area to be detected. The radar 10 includes a transmitting unit 102, a receiving unit 102, and a traffic flow detecting device 103, where the transmitting unit 101 includes a transmitter and a transmitting antenna, the transmitter can generate an electromagnetic wave signal, i.e., a transmitting signal, and transmit the transmitting signal to an area to be detected through the transmitting antenna, and when there is a target in the area to be detected, such as the vehicle 20 and the vehicle 30, the transmitting signal is reflected by the vehicle to form a reflected signal. The receiving unit of the radar receives the reflected signal, i.e. the echo signal. The receiving unit includes a receiving antenna for receiving the echo signal, and a receiver for performing preliminary processing on the received echo signal, such as generating a mixed signal, filtering and amplifying the mixed signal, and so on. The traffic flow detection device 103 receives the echo signal after the preliminary processing, obtains distance information, position information, and angle information of the vehicle included in the echo signal, and further determines traffic flow information of the road segment.
In the prior art, when traffic flow is determined, a camera is usually used to obtain a video image of a road, and traffic flow information is determined by analyzing the video image, but when weather is bad or illumination conditions are poor, the quality of a shot video is poor, so that the determined traffic flow information is not accurate enough. In addition, when the traffic flow is determined based on the camera, the lens is required to be frequently wiped for maintenance, which has a problem of troublesome operation.
The embodiment of the invention determines the traffic flow information through the radar, the radar can obtain the echo signal through transmitting the signal, determine the distance information, the speed information and the angle information of the target according to the echo signal, and can track the vehicle target on the basis of the information to obtain the track of the vehicle target and further determine the traffic flow information. In addition, the method does not need to frequently wipe the radar, and is simple to maintain.
The technical solution of the present invention will be described in detail below with specific examples. The following several specific embodiments may be combined with each other, and details of the same or similar concepts or processes may not be repeated in some embodiments.
Fig. 2 is a flowchart of a vehicle detection method according to an embodiment of the present invention, where the method according to this embodiment may be executed by a traffic flow detection device, and the traffic flow detection device may be arranged in the radar of fig. 1 in the form of software and/or hardware. As shown in fig. 2, the method of this embodiment may include:
s201: and carrying out Fourier transform processing on the echo signals in the region to be detected to obtain a two-dimensional matrix, and determining the distance information and the speed information of the plurality of detection points according to the two-dimensional matrix.
In this embodiment, the area to be detected is a detection range in which the radar finds the target object. The area to be detected of the radar is related to the wavelength of the transmitted signal, the reflection characteristic, the height of the target object, the radar performance and the like. The echo signal received by the receiving unit of the radar is a superposition combination of a plurality of echo signals. The echo signal includes distance information, speed information, and angle information of a plurality of detection points.
In this embodiment, the radar transmits the electromagnetic wave signal through the transmitting unit first, and specifically, may generate a local oscillation signal of a preset frequency through the external signal generator, and obtain a transmitting signal of a preset frequency band with the local oscillation signal through the frequency doubling circuit. When the transmitting signal touches a vehicle target, an echo signal is generated, the receiving antenna receives the echo signal, a mixing signal is generated through the receiving unit according to the echo signal, and the mixing signal is amplified and filtered to obtain an intermediate frequency signal.
In this embodiment, the traffic flow detecting device may receive the electromagnetic wave signal in real time, so as to obtain the traffic flow information in the road in real time.
It should be noted that, the resolution of the millimeter wave radar is high, a vehicle includes a plurality of detection points, such as a head portion, a tail portion, a body portion, and the like, in some cases, different motion characteristics of the vehicle may exist, such as a high speed on one side of the vehicle and a low speed on the other side of the vehicle during steering, and a plurality of portions of a vehicle may reflect radar signals, so that a vehicle may include a plurality of detection points. Wherein, the number of the detection points needs to be determined by the result of fast Fourier transform. Optionally, the number of peak values in the frequency spectrum obtained after the fast fourier transform may be regarded as the number of detection points.
In this embodiment, the echo signal may be a superposition of echo signals reflected by a plurality of vehicle targets, or may be a superposition of echo signals reflected by different parts of one vehicle target. A target may consist of a plurality of detection points for one vehicle. In order to determine information of a detection point, an echo signal needs to be processed to obtain an intermediate frequency signal, and then the intermediate frequency signal is subjected to fourier transform twice to obtain a two-dimensional matrix, wherein an abscissa of the two-dimensional matrix is represented as a distance dimension, and an ordinate of the two-dimensional matrix is represented as a velocity dimension. The center frequency information and the Doppler frequency information of the echo signals can be obtained by performing Fourier transform on the echo signals, and further the speed information and the distance information of the detection points can be determined according to the center frequency information and the Doppler frequency information.
S202: and performing constant false alarm detection on the two-dimensional matrix, determining effective point traces from the plurality of detection points, and determining angle information of the effective point traces.
In this embodiment, since the radar may be interfered during operation, such as noise in the environment, noise inside the receiver, clutter interference, and the like, it is necessary to perform constant false alarm detection on the echo signal to distinguish the echo signal from the noise, so as to obtain an effective trace point existing in a vehicle target. For example, when the echo signals are interference signals, but the radar judges the interference signals as echo signals reflected by the vehicle, a false alarm occurs; when the echo signals are signals reflected by the vehicle, but the radar judges the signals as interference signals, the false alarm occurs. Therefore, a self-adaptive threshold value can be set, and the false alarm probability is ensured to be certain.
Constant false alarm detection refers to a method for a radar system to determine whether a target exists under the condition that the false alarm probability is kept constant. The specific method for detecting the constant false alarm rate comprises the following steps: under the condition that no target appears, processing the input noise to obtain a threshold value; and then comparing the numerical value corresponding to each detection point in the input two-dimensional matrix with the threshold value, wherein the detection point with the numerical value larger than the threshold value is an effective trace.
After the effective point traces are screened out, further angle estimation needs to be performed on the two-dimensional matrix representing the effective point traces, and the angle information, namely the azimuth angle, of the effective point traces is determined. The azimuth angle is an included angle between a projection of a connecting line of the radar and the vehicle target on a horizontal plane and the driving direction of the vehicle.
In this embodiment, the effective point trace is determined by a constant false alarm detection method, and then the angle information of the effective point trace is determined, so that the calculation amount in determining the angle information can be greatly reduced.
S203: and determining a vehicle target point track in the area to be detected according to the distance information, the speed information and the angle information of the effective point track.
After the distance information, the speed information and the angle information of the effective point trace are determined, the vehicle target point trace can be determined according to the information. One vehicle target may be composed of a plurality of valid traces, for example, a trace representing a head, a trace representing a tail, a trace representing a body, and the like. The distance information, speed information and angle information of each trace forming the vehicle target may be different, and in order to determine the traffic flow, a plurality of target point traces need to be clustered into one vehicle target point trace through a clustering algorithm.
After the vehicle target point track is determined, the information of the vehicle target, for example, the length and width of the vehicle, may also be determined according to the information of a plurality of effective point tracks constituting the vehicle target point track.
S204: and tracking the target point trace of the vehicle to obtain a tracking trace, and determining the traffic flow according to the tracking trace.
In this embodiment, after the vehicle target point trajectory is determined, the vehicle target point trajectory needs to be tracked, for example, a preset area may be set, whether the vehicle target is in the target area at the next time is determined, and if the vehicle target is in the target area, the tracking trajectory is determined according to the time and the position of the vehicle target at the previous time. Correspondingly, the tracking track of each vehicle target can be determined, and the traffic flow can be determined according to the number of the tracking tracks. For example, if the number of vehicle tracking tracks passing through a certain area to be detected in 10 minutes is 80, the traffic flow rate is 8/min.
In practical application, the method provided by this embodiment may be used to perform fourier transform processing on the acquired echo signal to obtain a two-dimensional matrix, where the two-dimensional matrix reflects information of multiple detection points, then perform constant false alarm detection, determine an effective trace from the multiple detection points, filter out noise, and cluster the multiple effective traces into one target trace according to the information of the effective trace, where the target trace may be regarded as a target point where a vehicle is located, and track the vehicle target trace, thereby determining the traffic flow.
In this embodiment, a two-dimensional matrix is obtained by obtaining an echo signal of an area to be detected and performing fourier transform on the echo signal, so as to determine distance information and speed information, and a constant false alarm detection is performed on the two-dimensional matrix to determine an effective point trace, so that noise information in the echo signal can be removed, so as to determine angle information of the effective point trace, a vehicle target point trace is determined according to the distance information, the speed information and the angle information of the effective point trace, a tracking trace information is obtained by tracking the vehicle target point trace, so as to determine traffic flow information, information of a vehicle in a road is determined according to the echo signal, and traffic flow information of the area to be detected can be accurately determined according to the vehicle information.
Fig. 3 is a flowchart of another traffic flow detection method according to an embodiment of the present invention, and as shown in fig. 3, on the basis of the foregoing embodiment, a process of determining information of an effective trace includes the following steps:
s301: and windowing the distance dimension of the echo signal, and performing fast Fourier transform on the windowed distance dimension to obtain a one-dimensional frequency spectrum containing distance information.
In this embodiment, when determining the distance information of the target from the echo signal, it is necessary to determine the distance information of the detection point from the spectrum information of the fourier transform through fourier transform. After the echo signals are acquired, the echo signals are stored in a matrix form.
And windowing the distance dimension of the matrix before carrying out Fourier transform on the matrix. Where it is essential to perform the windowing operation before performing the fourier transform. Generally, when the frequency of the echo signal is not an integral multiple of the resolution of the fourier transform, the energy of the signal will be dispersed, and thus a phenomenon of spectral energy leakage occurs. In order to prevent energy leakage, which can be solved by windowing, the signal is amplitude-modulated by a window function, so that the echo signal exhibits periodicity.
The selection of the window function can be selected according to actual requirements, for example, when the requirement on the main lobe frequency is high, a rectangular window can be selected; and selecting a Hanning window, a triangular window and the like when the signal is a narrow-band signal and large interference exists. In this embodiment, the windowing function during the windowing process is not limited, and may be selected according to actual requirements.
After windowing the echo signal, fourier transform may be performed on the windowed distance dimension to obtain a one-dimensional spectrum, and the center frequency of the signal in the frequency sweep period may be determined according to the one-dimensional spectrum, where the frequency sweep period is a period in which the radar transmits the signal. For the sawtooth wave, the center frequency couples the speed information and the distance information of the detection point, so that the speed information of the detection point needs to be further determined, and then the speed information of the detection point is substituted into the center frequency to obtain the distance information of the detection point. Since there is no distance-velocity coupling relationship with respect to the triangular wave, the distance information and velocity information of the detected point can be directly obtained by fourier transform.
Step S302: and windowing the velocity dimension of the one-dimensional frequency spectrum, and performing fast Fourier transform on the windowed velocity dimension to obtain a two-dimensional matrix containing velocity information.
In this embodiment, after the one-dimensional spectrum is obtained, fourier transform is performed on the velocity dimension of the matrix formed by the one-dimensional spectrum, that is, fourier transform is performed on each column of the matrix formed by the one-dimensional spectrum, the doppler frequency of the detection point can be obtained according to the frequency corresponding to the peak point of the fourier transform, and then the velocity information of the detection point can be determined according to the relationship between the doppler frequency and the velocity of the detection point. Wherein, performing fast fourier transform on the velocity dimension may be understood as performing fast fourier transform on echo signals at the same distance received by the radar.
The operation of performing fast fourier transform on the distance dimension is the same as that described above, and before performing fast fourier transform on the velocity dimension, windowing processing needs to be performed on the velocity dimension, and then fast fourier transform is performed on the two-dimensional matrix after windowing processing.
Through the steps, speed information and distance information of each detection point can be obtained, wherein the speed information refers to the relative speed between the detection point and the radar.
Step S303: and carrying out non-coherent accumulation on the two-dimensional matrix to obtain a matrix after the non-coherent accumulation.
After the distance information and the speed information of the detection points are determined, constant false alarm detection can be carried out on the detection points to obtain effective point traces. Specifically, before constant false alarm detection, non-coherent accumulation needs to be performed on the two-dimensional matrix.
The signal-to-noise ratio of the echo signal and the noise can be improved by performing non-coherent accumulation processing on the echo signal. This is because there is noise in the echo signal, and the influence of the noise on the echo signal needs to be removed when the echo signal is analyzed, and specifically, the echo signal and the noise can be separated by a method of increasing the signal-to-noise ratio. The detection probability can be improved by increasing the signal-to-noise ratio. The echo signals can be enhanced through non-coherent accumulation, and the noise intensity can be reduced after the non-coherent accumulation because the noise is random, so that the signal-to-noise ratio is improved. Specifically, the non-coherent accumulation method is to take the logarithm of the amplitude of the data of the two-dimensional matrix and sum the logarithm; or, the magnitude of the data of the two-dimensional matrix may be squared and summed to obtain a non-coherent accumulated matrix.
Step S304: and carrying out constant false alarm detection on the matrix after the non-coherent accumulation to obtain environmental noise information.
In this embodiment, the constant false alarm detection is to distinguish the echo signal from noise to determine the valid detection point. Specifically, the constant false alarm detection method may adopt a unit average constant false alarm detection method. Specifically, the detected unit may be selected, and when there is no target point around the detected unit, the plurality of units on both sides of the detected unit are used as reference units, and the environmental noise information is determined according to the numerical value of the reference units. For example, the average value of the values in all the reference cells is taken as the environmental noise information; or, a threshold factor may be set, and the average value of the values in all the reference units is multiplied by the threshold factor to be used as the environmental noise information.
Step S305: and filtering information corresponding to the detection points in the non-coherent accumulated matrix according to the environmental noise information to obtain an effective point trace.
In this embodiment, after determining the environmental noise information, the environmental noise information may be used as a threshold value, and the detection point information in the two-dimensional matrix is compared with the environmental noise information to determine whether the detection point is a valid trace. Specifically, when the information corresponding to the detection point is greater than the environmental noise information, the detection point is an effective trace; and when the information corresponding to the detection point is smaller than the environmental noise information, the detection point is a noise point.
After the noise point is determined, the echo signal corresponding to the noise point can be deleted to obtain the echo signal only containing the effective point trace, the echo signal of the effective point trace contains the echo signal reflected by each part of each vehicle, the interference of the noise signal is reduced, and the accuracy rate of the traffic flow detection can be further improved
S306: and performing fast Fourier transform on the antenna dimension of the two-dimensional matrix formed by the effective point traces, and determining the phase difference of adjacent receiving antennas.
In this embodiment, after determining the valid dot trace, a two-dimensional matrix formed by the valid dot trace may be obtained, and specifically, information of the valid dot trace may be retained to obtain the two-dimensional matrix. The two-dimensional matrix is a two-dimensional matrix corresponding to an echo signal received by one receiving antenna, for a radar, at least two receiving antennas need to be adopted when angle information of a vehicle target needs to be acquired, and if the accuracy of angle detection needs to be improved, more antennas need to be used, for example, four antennas, eight antennas, and the like. And arranging the two-dimensional matrix formed by each antenna according to the sequence of the receiving antennas to obtain a target matrix, performing fast Fourier transform on the antenna dimension of the target matrix, and obtaining the phase difference of the adjacent receiving antennas according to the frequency spectrum after the fast Fourier transform.
S307: and determining the angle information of the effective point trace according to the phase difference of adjacent receiving antennas.
In this embodiment, after determining the phase difference between the two receiving antennas, the angle information of the effective point may be determined according to the distance between the two receiving antennas. Specifically, after a transmission signal is reflected by a target point, an echo signal is obtained through the receiving antenna 1, the receiving antenna 2, the receiving antenna 3 and the receiving antenna 4, wherein the wave paths received by the four receiving antennas are different, and the wave path difference between two adjacent receiving antennas is related to the distance between the receiving antennas and the angle information of the target point, so that the phase difference between two adjacent receiving antennas is also related to the distance between the receiving antennas and the angle information of the target point, and after the phase difference between the adjacent receiving antennas is determined, the angle information of an effective point trace can be determined.
Wherein the phase difference can be determined by a fast fourier transform of the antenna dimensions. In addition, the angular resolution of the radar is related to the number of receiving antennas, and the greater the number of receiving antennas, the higher the angular resolution of the radar.
In the embodiment, the echo signal in the time domain can be analyzed to the frequency domain by windowing and fourier transform, so that the speed information and the position information of the detection point can be obtained, wherein the frequency spectrum energy can be prevented from leaking by windowing, and the precision of speed detection and distance detection is improved; before constant false alarm detection is carried out on a two-dimensional matrix, non-coherent accumulation is carried out on the two-dimensional matrix, the precision of target detection can be improved, detection point information representing noise can be filtered through the constant false alarm detection, effective point traces are obtained, and the precision of traffic flow detection can be improved.
Fig. 4 is a flowchart of another traffic flow detection method according to an embodiment of the present invention, and as shown in fig. 4, on the basis of the foregoing embodiment, the method for determining a traffic flow includes:
s401: and dividing a plurality of effective point traces belonging to the same vehicle into a cluster according to the distance information, the speed information, the angle information and the preset clustering condition of each effective point trace.
In this embodiment, one vehicle target may be composed of a plurality of effective point traces, and in order to be able to determine the traffic flow information, a plurality of effective points belonging to the same vehicle need to be clustered. Specifically, the clustering condition may be set first, for example, effective point traces with a distance difference of within 3 meters, a speed difference of within 2m/s, and an angle difference of within 5 degrees are clustered into a point. The specific clustering condition needs to be set according to the performance of the radar, which is not limited in this embodiment. After the clustering condition is set, the judgment can be carried out according to the distance information, the speed information and the angle information of each effective point trace, and a plurality of effective points meeting the clustering condition are clustered into a vehicle target.
In this embodiment, a Density-Based Clustering algorithm (DBSCAN) may be used to implement Clustering. Compared with the K-means clustering method, the method does not need to know the number of clusters to be formed, and can identify noise points. The method needs to set a clustering radius and a clustering point number, counts the number of all effective point tracks within the clustering radius of the target point track aiming at each effective point track, judges the relation between the number of the effective point tracks and the clustering point number, and further determines the cluster formed by the effective point tracks.
Specifically, when the number of the effective trace points nearby is larger than the number of the clustering points, the effective trace points and the effective trace points nearby form a cluster; when the number of the nearby effective point traces is not more than the number of the clustering points, the group sets the point as a noise point. A plurality of clusters representing the vehicle targets can be obtained by the clustering method, and the clustering method can further eliminate noise points.
S402: and determining the distance information, the speed information and the angle information of each vehicle target point track according to the distance information, the speed information and the angle information of a plurality of effective point tracks in each cluster.
In the present embodiment, for a plurality of valid trajectories in one cluster, information of the vehicle target, such as distance information, speed information, and angle information, may be determined from information of each valid trajectory. Specifically, the effective trace of the peak strongest point in one cluster may be used as the trace of the vehicle target, and the position information, the speed information, and the angle information of the effective trace of the peak strongest point may be used as the information of the vehicle target. In addition, the mean value of the position information, the mean value of the speed information and the mean value of the angle information of the effective point tracks in one cluster can be used as the distance information, the speed information and the angle information of the vehicle target point track.
In addition, the length and width of the vehicle target may also be determined from the plurality of valid traces in a cluster. Specifically, two boundary point traces of the plurality of effective point traces in one cluster in the abscissa direction and two boundary point traces in the ordinate direction are determined, the length of the vehicle target is the difference between the ordinate of the two boundary point traces in the ordinate direction, and the width of the vehicle target is the difference between the abscissa of the two boundary point traces in the abscissa direction.
The type of the vehicle passing through the detection area can be further determined according to the length information and the width information of the vehicle object.
S403: and for each vehicle target point track, tracking the vehicle target point track according to the distance information, the speed information and the angle information of the vehicle target point track by a Kalman filtering method, and determining the tracking track of the vehicle target point track.
In this embodiment, the kalman filtering method may be implemented by a kalman filter, and the kalman filter may obtain the position update information of the vehicle target according to the observation information of the vehicle target, such as distance information, speed information, and angle information. Specifically, when the kalman filter tracks the vehicle target, the position information at the next time can be predicted according to the current position information and experience, the position information is distance information, speed information and angle information, and the current position information and the predicted position information at the next time are weighted and averaged to obtain the position information at the next time. Wherein, the weight of the current position information and the position information at the next moment can be set according to the actual situation.
After the position information of the vehicle target at the next moment is determined, the tracking track of the vehicle target can be obtained according to the position information of the current moment and the position information of the next moment. In addition, each vehicle target can be numbered, and the position information of the interested vehicle target can be conveniently and accurately screened.
S404: counting the number of tracking tracks appearing in a preset time period, and determining the traffic flow of the area to be detected according to the number of the tracking tracks.
In this embodiment, after the tracking trajectory of each vehicle target is determined, a preset time period may be set, for example, 10 minutes, the tracking trajectories of the vehicle targets passing through the area to be detected in the time period are counted, and since the same vehicle target forms one tracking trajectory when driving, the number of the vehicle targets passing through the preset area in the preset time period may be determined according to the number of the tracking trajectories, and further, the traffic flow of the area to be detected may be determined according to the number of the vehicle targets and the preset time period. The traffic flow of the area to be detected is the quotient of the number of the tracking tracks of the area to be detected and preset time. For example, when the traffic flow is 100 and the preset time period is 10 minutes, the traffic flow is 10 vehicles/minute.
In this embodiment, the vehicle targets, distance information, speed information, angle information and the like of the vehicle targets can be obtained by clustering the effective point traces, and then the vehicle targets can be tracked by using a kalman filtering method according to the information of each vehicle target to obtain a tracking track of each vehicle target, so as to determine the traffic flow information.
Optionally, the echo signal is a signal obtained by reflecting a transmission signal of the millimeter wave radar by a target, and the frequency of the transmission signal is 92GHZ to 96 GHZ.
In this embodiment, a signal transmitted by the radar is an electromagnetic wave signal of 92GHZ to 96GHZ, and compared with a radar signal of 24GHZ or 77GHZ adopted in the prior art, the radar signal in this embodiment has the advantages of higher frequency, wider bandwidth of modulation and smaller wavelength, and the radar has a smaller volume, and can improve the detection accuracy and the range resolution of the radar on the vehicle target.
In this embodiment, the frequency modulated continuous wave signal may be generated by an external signal generator, and in a possible design, a signal generator may be used to generate a local oscillator signal of 23GHZ, and the local oscillator signal may be passed through a 4-frequency multiplier circuit to obtain an electromagnetic wave signal of 92GHZ, so as to obtain a chirp signal of 92GHZ to 96 GHZ. The present embodiment does not limit the specific generation manner of the chirp signal from 92GHZ to 96 GHZ.
In the embodiment, the chirp signals of 92GHZ to 96GHZ are adopted as the transmitting signals of the radar, so that the detection accuracy and the range resolution of the radar on the vehicle target can be improved.
In the following, an optional specific processing flow of the embodiment of the present invention is described by taking sawtooth frequency modulation as an example in combination with the above-described principles and processes.
Fig. 5 is a schematic diagram of a transmit signal and an echo signal according to an embodiment of the present invention. The difference frequency between the transmitted signal and the echo signal is a fixed value. The signal transmitted by the millimeter wave radar is a chirp continuous wave, such as a sawtooth frequency modulation wave, wherein the sweep frequency period of the sawtooth frequency modulation wave is M, and in the mth sweep frequency period, the transmitted signal can be expressed as the following formula:
wherein T is belonged to [ (m-1) T, mT]T is the frequency modulation interval of the transmitted signal, f0Is the carrier frequency of the radar and,
for the initial phase of the transmitted signal, u-B/T is the slope of the frequency modulation, B denotes the bandwidth of the modulation, A0Is the amplitude. When the transmitting signal encounters a velocity v and an initial distance R0The echo signal generated at the detection point with echo delay τ (t) can be expressed as the following formula:
wherein T is belonged to [ (m-1) T, mT]，τ(t)＝2(R0+vt)/c，krIn order to obtain the target reflection coefficient,
additional displacement caused for target reflection. After the echo signal is subjected to frequency mixing, amplification and filtering, the obtained intermediate frequency signal can be represented as the following formula:
wherein T is belonged to [ (m-1) T, mT]，AbIs the amplitude. By the above formula, the if signal is still a chirp signal, and the center frequency of the signal is: f. ofb，m＝2u(R0+mvT)/c-2vf0C, it can be seen that the center frequency couples velocity information and range information, when the range dimension Fourier transform is performed on the intermediate frequency signal, then at the center frequency fb，mWill obtain the maximum value Rb，mWherein R isb，mCan be expressed as follows:
wherein M is [0, M ]]. M R can be obtained according to the formulab，mSince the frequency of (2) can reflect the doppler frequency information at the detection point, the fourier transform of the velocity dimension (doppler dimension) can be continued on the basis of the distance dimension fourier transform to obtain velocity information.
Fig. 6 is a schematic flowchart of determining distance information and speed information of a detection point according to an embodiment of the present invention. As shown in fig. 6, the process of determining the distance information and the speed information of the detection point may include the steps of:
s601: and respectively carrying out N-point Fourier transform on the echo signals of the M scanning frequency bands to obtain an M-N matrix.
S602: and performing Fourier transform processing on each column of the M-N matrix to obtain a two-dimensional matrix.
S603: and determining frequency information corresponding to the distance and frequency information corresponding to the speed according to the amplitude information in the two-dimensional matrix.
S604: and determining distance information and speed information according to the frequency information corresponding to the distance and the frequency information corresponding to the speed.
When the distance information and the speed information of the detection point are determined according to the echo signal, the distance information and the speed information of the detection point can be determined through a method of Fourier transform twice, firstly, Fourier transform of a distance dimension is carried out on the intermediate frequency signal to obtain a peak value of each sweep frequency period, Fourier transform is carried out on all the peak values to obtain the position of a second-dimension peak value, Doppler frequency of the detection point can be obtained according to the position, therefore, the speed information of the detection point is determined, and after the speed information is determined, the speed information can be substituted into the center frequency of the intermediate frequency signal, and then the distance information of the detection.
After the distance information and the speed information of the detection points are determined, non-coherent accumulation can be carried out on the two-dimensional Fourier transform result, the signal to noise ratio of echo signals and noise is favorably improved, constant false alarm detection is carried out on the signals subjected to the non-coherent accumulation, noise points in the detection points are removed, and only effective point traces, namely the point traces forming the vehicle target, are reserved. After the effective point trace is determined, angle estimation can be continuously carried out on the effective point trace, so that the angle information of the effective point trace is obtained.
Fig. 7 is a schematic diagram of angle estimation using two receiving antennas according to an embodiment of the present invention. Fig. 8 is a schematic diagram of angle estimation using four receiving antennas according to an embodiment of the present invention.
As shown in fig. 7 and 8, at least two receiving antennas are required for angle estimation, and when there are two receiving antennas, the signals received by the two receiving antennas have a wave path difference, and when the distance between the two receiving antennas is d and the angle is θ, the wave path difference is d × sin θ, so that the angle can be determined by calculating the wave path difference.
When there are four or more receiving antennas, the phase difference between each two receiving antennas is the same, and may be expressed as ω ═ (2 pi/λ) × d × sin θ. Therefore, the phase of the receiving antenna is linearly increased, so that the phase difference between the two receiving antennas can be determined by performing fourier transform on the antenna dimension, and the angle information of the detection point can be determined according to the relation that the phase difference is an angle.
After the angle information of the effective point tracks is determined, the effective point tracks belonging to the target point track of the same vehicle can be clustered according to the distance information, the speed information and the angle information of the effective point tracks, the clustered target point tracks of the vehicle are tracked to obtain tracking tracks, and finally the traffic flow is determined.
Fig. 9 shows an overall flowchart of vehicle traffic flow detection according to an embodiment of the present invention, which mainly includes obtaining an echo signal, performing fourier transform on a distance dimension of the echo signal, performing fourier transform on a speed dimension based on the distance dimension fourier transform to obtain distance information and speed information of a detection point, performing constant false alarm detection based on the speed dimension fourier transform to obtain an effective trace, performing angle estimation on the effective trace, determining angle information of the effective trace, and finally performing cluster tracking according to the distance information, the speed information, and the angle information of the effective trace to obtain a tracking trace of a target trace of a vehicle, thereby determining vehicle traffic flow.
Fig. 10 is a schematic structural diagram of a traffic flow rate detection device according to an embodiment of the present invention, and as shown in fig. 10, the traffic flow rate detection device 100 according to this embodiment may include: a processing module 1001, a first determining module 1002, a second determining module 1003 and a third determining module 1004.
The processing module 1001 is configured to perform fourier transform processing on an echo signal in a region to be detected to obtain a two-dimensional matrix, and determine distance information and speed information of a plurality of detection points according to the two-dimensional matrix.
A first determining module 1002, configured to perform constant false alarm detection on the two-dimensional matrix, determine an effective trace from the multiple detection points, and perform angle estimation on the effective trace to determine angle information of the effective trace.
A second determining module 1003, configured to determine a vehicle target track in the area to be detected according to the distance information, the speed information, and the angle information of the effective track.
And a third determining module 1004, configured to track the vehicle target point trace to obtain a tracking trace, and determine a traffic flow according to the tracking trace.
The traffic flow detection device provided in the embodiment of the present invention can implement the traffic flow detection method according to the embodiment shown in fig. 2, and the implementation principle and the technical effect are similar, which are not described herein again.
Fig. 11 is a schematic hardware structure diagram of a traffic flow detection device according to an embodiment of the present invention. As shown in fig. 11, the traffic flow detecting device 110 provided in the present embodiment includes: at least one processor 1101 and memory 1102. The processor 1101 and the memory 1102 are connected by a bus 1103.
In a specific implementation process, the at least one processor 1101 executes the computer-executable instructions stored in the memory 1102, so that the at least one processor 1101 executes the traffic flow detection method in the above-described method embodiment.
For a specific implementation process of the processor 1101, reference may be made to the above method embodiments, which implement similar principles and technical effects, and details of this embodiment are not described herein again.
In the embodiment shown in fig. 11, it should be understood that the Processor may be a Central Processing Unit (CPU), other general purpose processors, a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), etc. A general purpose processor may be a microprocessor or the processor may be any conventional processor or the like. The steps of a method disclosed in connection with the present invention may be embodied directly in a hardware processor, or in a combination of the hardware and software modules within the processor.
The memory may comprise high speed RAM memory and may also include non-volatile storage NVM, such as at least one disk memory.
The bus may be an Industry Standard Architecture (ISA) bus, a Peripheral Component Interconnect (PCI) bus, an Extended ISA (EISA) bus, or the like. The bus may be divided into an address bus, a data bus, a control bus, etc. For ease of illustration, the buses in the figures of the present application are not limited to only one bus or one type of bus.
The embodiment of the invention also provides a computer-readable storage medium, wherein a computer execution instruction is stored in the computer-readable storage medium, and when a processor executes the computer execution instruction, the traffic flow detection method of the embodiment of the method is realized.
The computer-readable storage medium may be implemented by any type of volatile or non-volatile memory device or combination thereof, such as Static Random Access Memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic memory, flash memory, magnetic or optical disk. Readable storage media can be any available media that can be accessed by a general purpose or special purpose computer.
An exemplary readable storage medium is coupled to the processor such the processor can read information from, and write information to, the readable storage medium. Of course, the readable storage medium may also be an integral part of the processor. The processor and the readable storage medium may reside in an Application Specific Integrated Circuits (ASIC). Of course, the processor and the readable storage medium may also reside as discrete components in the apparatus.
Those of ordinary skill in the art will understand that: all or a portion of the steps of implementing the above-described method embodiments may be performed by hardware associated with program instructions. The program may be stored in a computer-readable storage medium. When executed, the program performs steps comprising the method embodiments described above; and the aforementioned storage medium includes: various media that can store program codes, such as ROM, RAM, magnetic or optical disks.
Finally, it should be noted that: the above embodiments are only used to illustrate the technical solution of the present invention, and not to limit the same; while the invention has been described in detail and with reference to the foregoing embodiments, it will be understood by those skilled in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some or all of the technical features may be equivalently replaced; and the modifications or the substitutions do not make the essence of the corresponding technical solutions depart from the scope of the technical solutions of the embodiments of the present invention.
Claims (10)
1. A traffic flow detection method characterized by comprising:
carrying out Fourier transform processing on echo signals in a region to be detected to obtain a two-dimensional matrix, and determining distance information and speed information of a plurality of detection points according to the two-dimensional matrix;
performing constant false alarm detection on the two-dimensional matrix, determining an effective point trace from the plurality of detection points, and determining angle information of the effective point trace;
determining a vehicle target point track in the area to be detected according to the distance information, the speed information and the angle information of the effective point track;
and tracking the target point trace of the vehicle to obtain a tracking trace, and determining the traffic flow according to the tracking trace.
2. The method of claim 1, wherein performing fourier transform processing on the echo signals in the region to be detected to obtain a two-dimensional matrix comprises:
windowing the distance dimension of the echo signal, and performing fast Fourier transform on the windowed distance dimension to obtain a one-dimensional frequency spectrum containing distance information;
and windowing the velocity dimension of the one-dimensional frequency spectrum, and performing fast Fourier transform on the windowed velocity dimension to obtain a two-dimensional matrix containing velocity information.
3. The method of claim 2, wherein performing constant false alarm detection on the two-dimensional matrix to determine a valid trace of points from the plurality of detection points comprises:
performing non-coherent accumulation on the two-dimensional matrix to obtain a matrix after the non-coherent accumulation;
performing constant false alarm detection on the non-coherent accumulated matrix to obtain environmental noise information;
and filtering information corresponding to the detection points in the non-coherent accumulated matrix according to the environmental noise information to obtain an effective point trace.
4. The method of claim 3, wherein determining the angular information of the valid footprint comprises:
performing fast Fourier transform on antenna dimensions of a two-dimensional matrix formed by the effective point traces, and determining the phase difference of adjacent receiving antennas;
and determining the angle information of the effective point trace according to the phase difference of the adjacent receiving antennas.
5. The method according to claim 4, wherein determining a vehicle target point track in the area to be detected according to the distance information, the speed information and the angle information of the effective point track comprises:
dividing a plurality of effective point traces belonging to the same vehicle into a cluster according to the distance information, the speed information and the angle information of each effective point trace and a preset clustering condition;
and determining the distance information, the speed information and the angle information of each vehicle target point track according to the distance information, the speed information and the angle information of a plurality of effective point tracks in each cluster.
6. The method according to claim 5, wherein tracking the vehicle target point trace to obtain a tracking trace, and determining the traffic flow according to the tracking trace comprises:
for each vehicle target point track, tracking the vehicle target point track according to the distance information, the speed information and the angle information of the vehicle target point track by a Kalman filtering method, and determining the tracking track of the vehicle target point track;
counting the number of tracking tracks appearing in a preset time period, and determining the traffic flow of the area to be detected according to the number of the tracking tracks.
7. The method according to any one of claims 1 to 6, wherein the echo signal is a signal obtained by reflecting a transmission signal of a millimeter wave radar by a target, and the frequency of the transmission signal is 92GHZ to 96 GHZ.
8. A vehicle flow rate detection device characterized by comprising:
the processing module is used for carrying out Fourier transform processing on the echo signals in the region to be detected to obtain a two-dimensional matrix, and determining distance information and speed information of a plurality of detection points according to the two-dimensional matrix;
the first determining module is used for carrying out constant false alarm detection on the two-dimensional matrix, determining an effective trace from the plurality of detection points, and carrying out angle estimation on the effective trace to determine the angle information of the effective trace;
the second determining module is used for determining a vehicle target point track in the area to be detected according to the distance information, the speed information and the angle information of the effective point track;
and the third determining module is used for tracking the target point trace of the vehicle to obtain a tracking trace and determining the traffic flow according to the tracking trace.
9. A vehicle flow rate detection apparatus, characterized by comprising: at least one processor and memory;
the memory stores computer-executable instructions;
the at least one processor executing the computer-executable instructions stored by the memory causes the at least one processor to perform the method of detecting vehicle flow as recited in any of claims 1-7.
10. A computer-readable storage medium having computer-executable instructions stored therein, which when executed by a processor, implement the traffic flow detection method according to any one of claims 1 to 7.
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