CN111699403A - Method, apparatus and storage medium for detecting ice and snow covered road surface - Google Patents
Method, apparatus and storage medium for detecting ice and snow covered road surface Download PDFInfo
- Publication number
- CN111699403A CN111699403A CN201980012195.2A CN201980012195A CN111699403A CN 111699403 A CN111699403 A CN 111699403A CN 201980012195 A CN201980012195 A CN 201980012195A CN 111699403 A CN111699403 A CN 111699403A
- Authority
- CN
- China
- Prior art keywords
- road surface
- ice
- snow
- value
- reflection
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
- 238000000034 method Methods 0.000 title claims abstract description 48
- 239000011159 matrix material Substances 0.000 claims abstract description 64
- 238000011156 evaluation Methods 0.000 claims description 73
- 108091006146 Channels Proteins 0.000 claims description 27
- 239000013598 vector Substances 0.000 claims description 13
- 238000004590 computer program Methods 0.000 claims description 11
- 238000001514 detection method Methods 0.000 claims description 11
- 238000000354 decomposition reaction Methods 0.000 claims description 7
- 238000005070 sampling Methods 0.000 claims description 7
- 238000010586 diagram Methods 0.000 description 2
- 239000002131 composite material Substances 0.000 description 1
- 238000002592 echocardiography Methods 0.000 description 1
- 230000000694 effects Effects 0.000 description 1
- 230000003993 interaction Effects 0.000 description 1
- 239000003550 marker Substances 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 230000003287 optical effect Effects 0.000 description 1
- 238000012216 screening Methods 0.000 description 1
- 238000006467 substitution reaction Methods 0.000 description 1
Images
Classifications
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
- G01S13/00—Systems using the reflection or reradiation of radio waves, e.g. radar systems; Analogous systems using reflection or reradiation of waves whose nature or wavelength is irrelevant or unspecified
- G01S13/88—Radar or analogous systems specially adapted for specific applications
- G01S13/885—Radar or analogous systems specially adapted for specific applications for ground probing
Landscapes
- Physics & Mathematics (AREA)
- Engineering & Computer Science (AREA)
- Radar, Positioning & Navigation (AREA)
- Remote Sensing (AREA)
- Electromagnetism (AREA)
- Computer Networks & Wireless Communication (AREA)
- General Physics & Mathematics (AREA)
- Radar Systems Or Details Thereof (AREA)
Abstract
A method, apparatus and storage medium for detecting ice and snow covered road surfaces. The method is applied to the millimeter wave radar, and comprises the following steps: determining at least one reflection point (101) with the reflection intensity exceeding a preset intensity threshold value on a road surface to be detected; obtaining a first autocorrelation matrix of each reflection point according to reflection signals received by n receiving channels of the radar receiving antenna corresponding to each reflection point, wherein n is an integer greater than 0 (102); and determining whether the road surface to be detected is an ice and snow covered road surface (103) according to the characteristic values corresponding to the noise space and the signal space of the first autocorrelation matrix of each reflection point, so that the recognition rate of the ice and snow covered road surface is improved.
Description
Technical Field
The embodiment of the invention relates to the technical field of intelligent traffic, in particular to a method, equipment and a storage medium for detecting an ice and snow covered road surface.
Background
Under the condition that the road surface is heavily snowed or icy, the diffuse reflection of the ground to radar waves is greatly increased, so that the clutter and noise in radar echoes are greatly increased. In other words, under the condition of ice and snow covered road surface, the false detection rate of the vehicle-mounted millimeter wave radar is greatly increased, and the recognition rate for recognizing the ice and snow covered road surface is low.
Currently, the detection of ice and snow covered surfaces generally relies on the intervention of a vision system. However, the addition of the vision system can increase the cost and complexity of the whole set of detection system, and the vision system has certain limitations, for example, under the working conditions with low visibility (such as heavy rain, night, heavy fog, etc.), the vision system cannot effectively identify whether the road surface is covered by ice and snow.
Disclosure of Invention
The invention provides a method, equipment and a storage medium for detecting an ice and snow covered road surface, which improve the recognition rate of the ice and snow covered road surface.
In a first aspect, the present invention provides a method for detecting an ice-snow covered road surface, which is applied to a millimeter wave radar, and the method includes:
determining at least one reflection point with the reflection intensity exceeding a preset intensity threshold value on a road surface to be detected;
obtaining a first autocorrelation matrix of each reflection point according to reflection signals received by n receiving channels of the radar receiving antenna corresponding to each reflection point, wherein n is an integer greater than 0;
and determining whether the road surface to be detected is an ice and snow covered road surface or not according to the characteristic value corresponding to the noise space and the signal space of the first autocorrelation matrix of each reflection point.
In a second aspect, the present invention provides a millimeter wave radar comprising: a processor and a receive antenna; the radar receiving antenna is used for receiving a reflected signal reflected by a reflecting point on a road surface to be detected; the processor is electrically connected with the radar receiving antenna, and the processor is configured to:
determining at least one reflection point with the reflection intensity exceeding a preset intensity threshold value on a road surface to be detected;
obtaining a first autocorrelation matrix of each reflection point according to reflection signals received by n receiving channels of the radar receiving antenna corresponding to each reflection point, wherein n is an integer greater than 0;
and determining whether the road surface to be detected is an ice and snow covered road surface or not according to the characteristic value corresponding to the noise space and the signal space of the first autocorrelation matrix of each reflection point.
In a third aspect, the present invention provides a vehicle comprising:
a vehicle body; and
the millimeter wave radar according to any one of the second aspects, which is mounted on the vehicle body.
In a fourth aspect, the present invention provides a storage medium comprising: a readable storage medium and a computer program for implementing the method for detecting an icy or snowy covered road surface according to any one of the embodiments of the first aspect.
In a fifth aspect, the present invention provides a program product comprising a computer program (i.e., executing instructions), the computer program being stored in a readable storage medium. The processor may read the computer program from the readable storage medium, and execute the computer program to execute the method for detecting the snow-covered road surface according to any one of the embodiments of the first aspect.
The invention provides a method, equipment and a storage medium for detecting an ice and snow covered road surface, which are used for determining at least one reflection point with the reflection intensity exceeding a preset intensity threshold value on the road surface to be detected; obtaining a first autocorrelation matrix of each reflection point according to reflection signals received by n receiving channels of the radar receiving antenna corresponding to each reflection point, wherein n is an integer greater than 0; and determining whether the road surface to be detected is an ice and snow covered road surface according to the characteristic values corresponding to the noise space and the signal space of the first autocorrelation matrix of each reflection point, decomposing the autocorrelation matrix of the reflection signal received by the radar receiving antenna to obtain the characteristic values corresponding to the signal space and the noise space, and recognizing the ice and snow covered road surface by using the characteristic values of the signal space and the noise space, wherein the recognition rate is high.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings needed to be used in the description of the embodiments or the prior art will be briefly introduced below, and it is obvious that the drawings in the following description are 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 flowchart of a method for detecting an ice-snow covered road surface according to an embodiment of the present invention;
fig. 2 is a schematic structural diagram of a millimeter wave radar according to an embodiment of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly 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 some, but not all, embodiments of the present invention. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
Firstly, the application scene related to the invention is introduced:
the method for detecting the ice and snow covered road surface provided by the embodiment of the invention is applied to a millimeter wave radar and an ice and snow covered road surface detection scene to improve the recognition rate of the ice and snow covered road surface.
Wherein the method may be performed by a millimeter wave radar, which may be provided on a vehicle; or may be performed by an in-vehicle control apparatus including the millimeter wave radar. The vehicle may be an autonomous vehicle or a general vehicle.
The method provided by the embodiment of the invention can be realized by a millimeter wave radar such as a processor of the millimeter wave radar executing corresponding software codes, and can also be realized by the millimeter wave radar executing corresponding software codes and performing data interaction with control equipment, for example, the control equipment executes partial operation to control the millimeter wave radar to execute the method for detecting the ice-snow covered road surface.
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. 1 is a schematic flow chart of an embodiment of a method for detecting an ice-snow covered road surface according to the present invention. As shown in fig. 1, the method provided by this embodiment includes:
Specifically, receiving the reflection signal of the reflection point on the road surface to be detected through the receiving antenna of the millimeter wave radar, determining the reflection intensity according to the reflection signal, wherein the reflection intensities corresponding to different reflection points are possibly different, and screening out a plurality of reflection points of which the reflection intensities exceed a preset intensity threshold value according to the received reflection signals.
And 103, determining whether the road surface to be detected is an ice and snow covered road surface or not according to the characteristic values corresponding to the noise space and the signal space of the first autocorrelation matrix of each reflection point.
Specifically, under the working condition that the ice and snow cover the road surface, the diffuse reflection of the radar waves is increased due to the fact that the object and the road surface are covered with the ice and snow, so that the signal-to-noise ratio and the signal-to-interference ratio of the reflection signals of the reflection points are reduced, the noise level is increased, and the false target detection probability is greatly increased.
According to the method provided by the embodiment of the invention, the first autocorrelation matrix of each receiving channel of the radar receiving antenna is subjected to eigenvalue decomposition, a subspace formed by eigenvectors with larger eigenvalues represents a signal space, and a subspace formed by eigenvectors with smaller eigenvalues represents a noise space.
Since the signal-to-noise ratio and the signal-to-interference ratio decrease, the ratio of the minimum eigenvalue (corresponding to the noise space) to the maximum eigenvalue (corresponding to the signal space) of the first autocorrelation matrix also increases significantly, and therefore, according to the eigenvalues of the first autocorrelation matrix of the plurality of reflection points, it can be determined whether the road surface to be detected is an ice-snow covered road surface.
The method of the embodiment comprises the steps of determining at least one reflection point with the reflection intensity exceeding a preset intensity threshold value on a road surface to be detected; obtaining a first autocorrelation matrix of each reflection point according to reflection signals received by n receiving channels of the radar receiving antenna corresponding to each reflection point, wherein n is an integer greater than 0; and determining whether the road surface to be detected is an ice and snow covered road surface according to the characteristic values corresponding to the noise space and the signal space of the first autocorrelation matrix of each reflection point, decomposing the autocorrelation matrix of the reflection signal received by the radar receiving antenna to obtain the characteristic values corresponding to the signal space and the noise space, and recognizing the ice and snow covered road surface by using the characteristic values of the signal space and the noise space, wherein the recognition rate is high.
On the basis of the foregoing embodiment, further, step 102 may specifically be implemented by the following method:
aiming at the reflection signals of n receiving channels sampled at any time in multiple times of sampling in the current updating period, forming a receiving vector by the result of performing complex Fast Fourier Transform (FFT) on the reflection signals of the n receiving channels according to a preset channel sequence;
determining a plurality of second autocorrelation matrixes corresponding to the reflection signals according to the receiving vectors corresponding to the multiple sampled reflection signals;
and taking the expectation of a plurality of second autocorrelation matrixes corresponding to the reflection points as the first autocorrelation matrix of the reflection points.
Specifically, for any reflection point, for any sampling of multiple times (for example, m times, m is greater than 1), performing a complex Fast Fourier Transform (FFT) on reflection signals received by n receiving channels, and forming a receiving vector Rx (a complex vector of n rows and 1 columns) from results of the complex FFT in a channel order; wherein Rx is [ R ]1，R2，...，Rn]T(ii) a Suppose thatR1Is the result of the complex FFT of the 1 st receiving channel from left to right, R2 is the result of the complex FFT of the 2 nd receiving channel from left to right, RnIs the result of the complex FFT transform of the nth receive channel from left to right.
According to the receiving vector corresponding to the multi-sampling reflection signal, a plurality of second autocorrelation matrixes Rxx (Rx) corresponding to the reflection point are determined, wherein Rxx is Rx × RxH(ii) a Where H denotes a conjugate transpose.
Obtaining the expected E [ Rxx ] of the second autocorrelation matrix of the m-times sampling of the reflection point according to the following formula (1)]Record as
Wherein, RxxiRepresenting a second autocorrelation matrix corresponding to the ith sample.
Will be provided with
As a first autocorrelation matrix corresponding to the reflection point.
On the basis of the foregoing embodiment, further, singular value decomposition is performed on the first autocorrelation matrix to obtain a characteristic value of the first autocorrelation matrix;
determining the ratio of the minimum characteristic value to the maximum characteristic value in the characteristic values, and taking the ratio as the ratio of the noise space of the reflection point to the characteristic value corresponding to the signal space; the minimum eigenvalue is an eigenvalue corresponding to the noise space, and the maximum eigenvalue is an eigenvalue corresponding to the signal space.
In particular, because
Is a square matrix so
The Singular Value Decomposition (SVD) is performed to obtain its eigenvalue (Singular value is an eigenvalue), and the diagonal elements in the diagonal matrix ∑ in the following formula (2) are the elements on the diagonal line
The characteristic value of (2).
Wherein U is a matrix
V is a matrix of left singular vectors of
A matrix composed of right singular vectors of (a);
the first autocorrelation matrix, ∑ is a diagonal matrix composed of eigenvalues, ∑ is a matrix of n rows and n columns.
Further, step 103 may be specifically implemented by the following steps:
determining an evaluation value of the ice and snow road surface of the road surface to be detected according to the ratio of the noise space of the first autocorrelation matrix of each reflection point to the corresponding characteristic value of the signal space;
and determining whether the road surface to be detected is an ice and snow covered road surface according to the ice and snow road surface evaluation value.
Specifically, according to the feature values obtained by singular value decomposition in the foregoing embodiment, the minimum feature value in the feature values is used as the feature value corresponding to the noise space, the maximum feature value in the feature values is used as the feature value corresponding to the signal space, the ratio between the minimum feature value and the maximum feature value is further calculated, and the evaluation value of the icy and snowy road surface of the road surface to be detected is determined according to the ratios corresponding to the multiple reflection points; and determining whether the road surface to be detected is an ice and snow covered road surface according to the ice and snow road surface evaluation value. The larger the evaluation value of the ice and snow road surface is, the more likely the ice and snow cover the road surface.
Further, how to calculate the icy and snowy road surface evaluation value specifically is described below:
if the number of the reflection points is at least two, the following method is adopted:
if the sum of the ratios of the noise spaces of the first autocorrelation matrixes of all the reflection points to the characteristic values corresponding to the signal spaces is greater than a first preset threshold value, increasing a first preset value to the ice and snow road surface evaluation value determined in the previous updating period to serve as the ice and snow road surface evaluation value of the road surface to be detected;
and if the sum of the ratios of the noise spaces of the first autocorrelation matrixes of all the reflection points to the characteristic values corresponding to the signal spaces is smaller than a second preset threshold value, subtracting a second preset value from the ice and snow road surface evaluation value determined in the previous updating period to serve as the ice and snow road surface evaluation value of the road surface to be detected.
In particular, the method comprises the following steps of,
is a Hermitian matrix and therefore its eigenvalues are all non-negative real numbers. Directly calculated according to the following formula (3)
The ratio of the minimum eigenvalue to the maximum eigenvalue in the equation is denoted as λratio。
The foregoing operation is performed for all reflection points in one radar update period, i.e. λ is calculated for all reflection pointsratioThen, the ratio λ of the maximum eigenvalues of the minimum eigenvalues of all reflection points is found as the following formula (4)ratioMean value of E [ lambda ]ratio]Record as
Where K represents the number of reflection points.
When in use
Is greater than a first preset threshold lambdaadd_cri(for example, an empirical value of 0.1) as follows, the evaluation value Ω of the icy and snowy road surface at the present update cycle is calculated by the following formula (5)nUpdate (omega)n-1An evaluation value of the icy and snowy road surface for the previous update period);
Ωn＝Ωn-1+Ωadd(5)；
wherein omegaaddMay be 3.
When in use
Less than a second predetermined threshold lambdaminus_cri(for example, an empirical value of 0.03) is obtained by applying the following equation (6) to the evaluation value Ω of the icy and snowy road surface at the present update cyclenUpdate (omega)n-1An evaluation value of the icy and snowy road surface for the previous update period);
Ωn＝Ωn-1-Ωminus(6)；
wherein omegaminusMay be 2.
The first preset value and the second preset value may be the same or different, and the first preset threshold and the second preset threshold may be the same or different, which is not limited in the embodiment of the present invention.
If the above
Less, indicating less likelihood of ice or snow covering the road surface, and therefore less a second predetermined value, if
Since the probability of the ice and snow covering the road surface is high, a first preset value is subtracted as the evaluation value of the ice and snow road surface in the current update cycle. Final root ofAnd determining whether the road surface to be detected is an ice and snow covered road surface according to the ice and snow road surface evaluation value.
If the number of the reflection points is one, adopting the following mode two:
if the ratio of the noise space of the first autocorrelation matrix of the reflection point to the characteristic value corresponding to the signal space is greater than a first preset threshold value, increasing the ice and snow road surface evaluation value determined in the previous updating period by a first preset value to serve as the ice and snow road surface evaluation value of the road surface to be detected;
and if the ratio of the noise space of the first autocorrelation matrix of the reflection point to the characteristic value corresponding to the signal space is smaller than a second preset threshold value, subtracting a second preset value from the ice and snow road surface evaluation value determined in the previous updating period to serve as the ice and snow road surface evaluation value of the road surface to be detected.
Specifically, it is directly calculated according to the following formula (3)
The ratio of the minimum eigenvalue to the maximum eigenvalue in the equation is denoted as λratio。
If the lambda isratioIf the value is larger than the first preset threshold value, increasing the ice and snow road surface evaluation value determined in the previous updating period by a first preset value to be used as the ice and snow road surface evaluation value of the current updating period of the road surface to be detected;
if the lambda isratioAnd if the value is smaller than the second preset threshold value, subtracting the second preset value from the evaluation value of the ice and snow road surface determined in the previous updating period to serve as the evaluation value of the ice and snow road surface of the current updating period of the road surface to be detected.
See, in particular, the foregoing equations (5) and (6).
The first preset value and the second preset value may be the same or different, and the first preset threshold and the second preset threshold may be the same or different, which is not limited in the embodiment of the present invention.
Above-mentioned lambdaratioThe size of the composite material is small,indicating that the snow or ice is less likely to cover the road surface, a second predetermined value is subtracted ifratioSince the probability of the ice and snow covering the road surface is high, a first preset value is subtracted as the evaluation value of the ice and snow road surface in the current update cycle. And finally, determining whether the road surface to be detected is the ice and snow covered road surface according to the ice and snow road surface evaluation value.
Further, in an embodiment of the present invention, if the evaluation value of the icy and snowy road surface is greater than a third preset threshold value, it is determined that the road surface to be detected is an icy and snowy covered road surface.
Further, if the evaluation value of the ice and snow road surface is greater than a third preset threshold value, updating the ice and snow road surface mark to be effective;
and if the ice and snow road surface mark position is effective, determining that the road surface to be detected is an ice and snow covered road surface.
In another embodiment of the present invention, the icy or snowy road surface flag is updated to be invalid if the icy or snowy road surface evaluation value is smaller than a fourth preset threshold value.
Specifically, as shown in the following equation (7), the evaluation value Ω of the icy and snowy road surface at the present update cyclenGreater than a threshold value omegaflag_on(empirical value 300) updating the snow and ice road surface marker to True (i.e., indicating detection of snow covered road surface); evaluation value omega of ice and snow road surface in the updating periodnLess than threshold Ωflag_off(empirical value 100) Flag of ice and snow road surfacesnowUpdated to invalid False (i.e., indicating that ice and snow covering the road surface is not detected)
The third preset threshold and the fourth preset threshold may be the same or different, and are not limited in the embodiment of the present invention.
According to the method, the autocorrelation matrix of the reflected signal received by the radar receiving antenna is decomposed to obtain the characteristic values corresponding to the signal space and the noise space, the recognition of the ice and snow covered road surface is achieved by utilizing the statistical characteristic of the ratio of the characteristic values of the signal space and the noise space, and the recognition rate is high. Meanwhile, the automatic driving system has the capability of judging the ice and snow covered road surface at night through the identification of the ice and snow covered road surface by the millimeter wave radar.
On the basis of the foregoing embodiment, if it is determined that the road surface to be detected is an ice and snow covered road surface, the method of this embodiment further includes:
increasing the preset intensity threshold by a third preset value, and taking the increased preset intensity threshold as a new preset intensity threshold;
and detecting the ice and snow covered road surface according to the new preset intensity threshold value.
Specifically, if it is determined that the road surface to be detected is an ice-snow covered road surface, FlagsnowON, the preset intensity threshold value PowernormalIncreasing the preset value (e.g. empirical value of 15dB) as the new preset intensity threshold Powersnow. I.e. only the reflection intensity exceeds PowersnowIs considered to reliably detect the target with a reflection intensity lower than PowersnowWill be deleted. This reduces false target detection.
According to the method, the energy threshold value (namely the preset intensity threshold value) of the target object detection is dynamically adjusted according to the recognition result of the ice and snow covered road surface, so that the false detection rate of the millimeter wave radar under the working condition of the ice and snow covered road surface can be greatly reduced.
In summary, in the method according to the embodiment of the present invention, the vehicle-mounted radar not only has the capability of recognizing the ice and snow covered road surface, but also can effectively reduce the false detection rate of the radar under the ice and snow covered road surface by adjusting the energy threshold (i.e. the preset intensity threshold) detected by the target object when the ice and snow covered road surface is determined.
Fig. 2 is a schematic structural diagram of a millimeter wave radar according to an embodiment of the present invention. The millimeter wave radar provided by the embodiment is used for executing the method for detecting the ice and snow covered road surface provided by any one of the foregoing embodiments. As shown in fig. 2, the millimeter wave radar provided in the present embodiment may include: a processor 201 and a radar receiving antenna 202. The radar receiving antenna is used for receiving a reflected signal reflected by a reflecting point on a road surface to be detected;
the processor is electrically connected with the radar receiving antenna, and the processor is configured to:
determining at least one reflection point with the reflection intensity exceeding a preset intensity threshold value on a road surface to be detected;
obtaining a first autocorrelation matrix of each reflection point according to reflection signals received by n receiving channels of the radar receiving antenna corresponding to each reflection point, wherein n is an integer greater than 0;
and determining whether the road surface to be detected is an ice and snow covered road surface or not according to the characteristic value corresponding to the noise space and the signal space of the first autocorrelation matrix of each reflection point.
In one possible implementation, the processor is configured to:
determining an evaluation value of the ice and snow road surface of the road surface to be detected according to the ratio of the noise space of the first autocorrelation matrix of each reflection point to the corresponding characteristic value of the signal space;
and determining whether the road surface to be detected is an ice and snow covered road surface according to the ice and snow road surface evaluation value.
In a possible implementation manner, if the number of the reflection points is at least two, the processor is configured to:
if the sum of the ratios of the noise spaces of the first autocorrelation matrixes of all the reflection points to the characteristic values corresponding to the signal spaces is greater than a first preset threshold value, increasing a first preset value to the ice and snow road surface evaluation value determined in the previous updating period to serve as the ice and snow road surface evaluation value of the road surface to be detected;
and if the sum of the ratios of the noise spaces of the first autocorrelation matrixes of all the reflection points to the characteristic values corresponding to the signal spaces is smaller than a second preset threshold value, subtracting a second preset value from the ice and snow road surface evaluation value determined in the previous updating period to serve as the ice and snow road surface evaluation value of the road surface to be detected.
In a possible implementation manner, if the number of the reflection points is one, the processor is configured to:
if the ratio of the noise space of the first autocorrelation matrix of the reflection point to the characteristic value corresponding to the signal space is greater than a first preset threshold value, increasing the ice and snow road surface evaluation value determined in the previous updating period by a first preset value to serve as the ice and snow road surface evaluation value of the road surface to be detected;
and if the ratio of the noise space of the first autocorrelation matrix of the reflection point to the characteristic value corresponding to the signal space is smaller than a second preset threshold value, subtracting a second preset value from the ice and snow road surface evaluation value determined in the previous updating period to serve as the ice and snow road surface evaluation value of the road surface to be detected.
In one possible implementation, the processor is configured to:
aiming at the reflection signals of n receiving channels sampled at any time in multiple times of sampling in the current updating period, forming a receiving vector by the result of performing complex Fast Fourier Transform (FFT) on the reflection signals of the n receiving channels according to a preset channel sequence;
determining a plurality of second autocorrelation matrixes corresponding to the reflection signals according to the receiving vectors corresponding to the multiple sampled reflection signals;
and taking the expectation of a plurality of second autocorrelation matrixes corresponding to the reflection points as the first autocorrelation matrix of the reflection points.
In one possible implementation, the processor is configured to:
singular value decomposition is carried out on the first autocorrelation matrix to obtain a characteristic value of the first autocorrelation matrix;
determining the ratio of the minimum characteristic value to the maximum characteristic value in the characteristic values, and taking the ratio as the ratio of the noise space of the reflection point to the characteristic value corresponding to the signal space; the minimum eigenvalue is an eigenvalue corresponding to the noise space, and the maximum eigenvalue is an eigenvalue corresponding to the signal space.
In one possible implementation, the processor is configured to:
and if the evaluation value of the ice and snow road surface is greater than a third preset threshold value, determining that the road surface to be detected is an ice and snow covered road surface.
In one possible implementation, the processor is configured to:
if the evaluation value of the ice and snow road surface is larger than a third preset threshold value, updating the ice and snow road surface mark to be effective;
and if the ice and snow road surface mark position is effective, determining that the road surface to be detected is an ice and snow covered road surface.
In one possible implementation, the processor is configured to:
increasing the preset intensity threshold by a third preset value, and taking the increased preset intensity threshold as a new preset intensity threshold;
and detecting the ice and snow covered road surface according to the new preset intensity threshold value.
In one possible implementation, the processor is configured to:
and if the evaluation value of the ice and snow road surface is smaller than a fourth preset threshold value, updating the ice and snow road surface mark to be invalid.
The millimeter wave radar provided by this embodiment is used for executing the method for detecting an ice and snow covered road surface provided by any one of the foregoing embodiments, and the technical principle and the technical effect are similar, and are not described herein again.
The embodiment of the present invention further provides a computer-readable storage medium, where a computer program is stored, and when the computer program is executed by a processor, the method in the foregoing method embodiment is implemented.
Also provided in an embodiment of the present invention is a program product including a computer program (i.e., execution instructions) stored in a readable storage medium. The processor may read the computer program from a readable storage medium, and execute the computer program to perform the method for detecting an ice and snow covered road surface provided by any one of the foregoing method embodiments.
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 for illustrating the technical solutions of the embodiments of the present invention, and are not limited thereto; although embodiments of the present invention have been described in detail with reference to the foregoing embodiments, those skilled in the art will understand 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 (22)
1. A detection method for ice and snow covered road surfaces is applied to a millimeter wave radar, and is characterized by comprising the following steps:
determining at least one reflection point with the reflection intensity exceeding a preset intensity threshold value on a road surface to be detected;
obtaining a first autocorrelation matrix of each reflection point according to reflection signals received by n receiving channels of the radar receiving antenna corresponding to each reflection point, wherein n is an integer greater than 0;
and determining whether the road surface to be detected is an ice and snow covered road surface or not according to the characteristic value corresponding to the noise space and the signal space of the first autocorrelation matrix of each reflection point.
2. The method according to claim 1, wherein the determining whether the road surface to be detected is an ice and snow covered road surface according to the characteristic value corresponding to the noise space and the signal space of the first autocorrelation matrix of each reflection point comprises:
determining an evaluation value of the ice and snow road surface of the road surface to be detected according to the ratio of the noise space of the first autocorrelation matrix of each reflection point to the corresponding characteristic value of the signal space;
and determining whether the road surface to be detected is an ice and snow covered road surface according to the ice and snow road surface evaluation value.
3. The method according to claim 2, wherein if the number of the reflection points is at least two, the determining the evaluation value of the icy and snowy road surface of the road surface to be detected according to the ratio of the noise space of the first autocorrelation matrix of each reflection point to the characteristic value corresponding to the signal space comprises:
if the sum of the ratios of the noise spaces of the first autocorrelation matrixes of all the reflection points to the characteristic values corresponding to the signal spaces is greater than a first preset threshold value, increasing a first preset value to the ice and snow road surface evaluation value determined in the previous updating period to serve as the ice and snow road surface evaluation value of the road surface to be detected;
and if the sum of the ratios of the noise spaces of the first autocorrelation matrixes of all the reflection points to the characteristic values corresponding to the signal spaces is smaller than a second preset threshold value, subtracting a second preset value from the ice and snow road surface evaluation value determined in the previous updating period to serve as the ice and snow road surface evaluation value of the road surface to be detected.
4. The method according to claim 2, wherein if the number of the reflection points is one, the determining the evaluation value of the icy and snowy road surface of the road surface to be detected according to the ratio of the noise space of each reflection point to the characteristic value corresponding to the signal space comprises:
if the ratio of the noise space of the first autocorrelation matrix of the reflection point to the characteristic value corresponding to the signal space is greater than a first preset threshold value, increasing the ice and snow road surface evaluation value determined in the previous updating period by a first preset value to serve as the ice and snow road surface evaluation value of the road surface to be detected;
and if the ratio of the noise space of the first autocorrelation matrix of the reflection point to the characteristic value corresponding to the signal space is smaller than a second preset threshold value, subtracting a second preset value from the ice and snow road surface evaluation value determined in the previous updating period to serve as the ice and snow road surface evaluation value of the road surface to be detected.
5. The method according to any one of claims 1 to 4, wherein obtaining a first autocorrelation matrix of each reflection point according to reflection signals received by n receiving channels of a radar receiving antenna corresponding to each reflection point comprises:
aiming at the reflection signals of n receiving channels sampled at any time in multiple times of sampling in the current updating period, forming a receiving vector by the result of performing complex Fast Fourier Transform (FFT) on the reflection signals of the n receiving channels according to a preset channel sequence;
determining a plurality of second autocorrelation matrixes corresponding to the reflection signals according to the receiving vectors corresponding to the multiple sampled reflection signals;
and taking the expectation of a plurality of second autocorrelation matrixes corresponding to the reflection points as the first autocorrelation matrix of the reflection points.
6. The method according to any one of claims 2 to 4, wherein before determining whether the road surface to be detected is an ice-covered road surface or not according to the characteristic values of the first autocorrelation matrix of each reflection point corresponding to the noise space and the signal space, the method further comprises:
singular value decomposition is carried out on the first autocorrelation matrix to obtain a characteristic value of the first autocorrelation matrix;
determining the ratio of the minimum characteristic value to the maximum characteristic value in the characteristic values, and taking the ratio as the ratio of the noise space of the reflection point to the characteristic value corresponding to the signal space; the minimum eigenvalue is an eigenvalue corresponding to the noise space, and the maximum eigenvalue is an eigenvalue corresponding to the signal space.
7. The method according to any one of claims 2 to 4, wherein the determining whether the road surface to be detected is an icy and snowy covered road surface according to the icy and snowy road surface evaluation value comprises:
and if the evaluation value of the ice and snow road surface is greater than a third preset threshold value, determining that the road surface to be detected is an ice and snow covered road surface.
8. The method according to claim 7, wherein the determining that the road surface to be detected is an ice-snow covered road surface comprises:
if the evaluation value of the ice and snow road surface is larger than a third preset threshold value, updating the ice and snow road surface mark to be effective;
and if the ice and snow road surface mark position is effective, determining that the road surface to be detected is an ice and snow covered road surface.
9. The method according to any one of claims 1 to 8, wherein if it is determined that the road surface to be detected is an ice-snow covered road surface, the method further comprises:
increasing the preset intensity threshold by a third preset value, and taking the increased preset intensity threshold as a new preset intensity threshold;
and detecting the ice and snow covered road surface according to the new preset intensity threshold value.
10. The method of claim 8, further comprising:
and if the evaluation value of the ice and snow road surface is smaller than a fourth preset threshold value, updating the ice and snow road surface mark to be invalid.
11. A millimeter wave radar, comprising: a processor and a radar receiving antenna; the radar receiving antenna is used for receiving a reflected signal reflected by a reflecting point on a road surface to be detected;
the processor is electrically connected with the radar receiving antenna, and the processor is configured to:
determining at least one reflection point with the reflection intensity exceeding a preset intensity threshold value on a road surface to be detected;
obtaining a first autocorrelation matrix of each reflection point according to reflection signals received by n receiving channels of the radar receiving antenna corresponding to each reflection point, wherein n is an integer greater than 0;
and determining whether the road surface to be detected is an ice and snow covered road surface or not according to the characteristic value corresponding to the noise space and the signal space of the first autocorrelation matrix of each reflection point.
12. The millimeter-wave radar of claim 11, wherein the processor is configured to:
determining an evaluation value of the ice and snow road surface of the road surface to be detected according to the ratio of the noise space of the first autocorrelation matrix of each reflection point to the corresponding characteristic value of the signal space;
and determining whether the road surface to be detected is an ice and snow covered road surface according to the ice and snow road surface evaluation value.
13. The millimeter-wave radar of claim 12, wherein if the number of reflection points is at least two, the processor is configured to:
if the sum of the ratios of the noise spaces of the first autocorrelation matrixes of all the reflection points to the characteristic values corresponding to the signal spaces is greater than a first preset threshold value, increasing a first preset value to the ice and snow road surface evaluation value determined in the previous updating period to serve as the ice and snow road surface evaluation value of the road surface to be detected;
and if the sum of the ratios of the noise spaces of the first autocorrelation matrixes of all the reflection points to the characteristic values corresponding to the signal spaces is smaller than a second preset threshold value, subtracting a second preset value from the ice and snow road surface evaluation value determined in the previous updating period to serve as the ice and snow road surface evaluation value of the road surface to be detected.
14. The millimeter-wave radar of claim 12, wherein if the number of reflection points is one, the processor is configured to:
if the ratio of the noise space of the first autocorrelation matrix of the reflection point to the characteristic value corresponding to the signal space is greater than a first preset threshold value, increasing the ice and snow road surface evaluation value determined in the previous updating period by a first preset value to serve as the ice and snow road surface evaluation value of the road surface to be detected;
and if the ratio of the noise space of the first autocorrelation matrix of the reflection point to the characteristic value corresponding to the signal space is smaller than a second preset threshold value, subtracting a second preset value from the ice and snow road surface evaluation value determined in the previous updating period to serve as the ice and snow road surface evaluation value of the road surface to be detected.
15. The millimeter wave radar of any of claims 11-14, wherein the processor is configured to:
aiming at the reflection signals of n receiving channels sampled at any time in multiple times of sampling in the current updating period, forming a receiving vector by the result of performing complex Fast Fourier Transform (FFT) on the reflection signals of the n receiving channels according to a preset channel sequence;
determining a plurality of second autocorrelation matrixes corresponding to the reflection signals according to the receiving vectors corresponding to the multiple sampled reflection signals;
and taking the expectation of a plurality of second autocorrelation matrixes corresponding to the reflection points as the first autocorrelation matrix of the reflection points.
16. The millimeter wave radar of any of claims 12-14, wherein the processor is configured to:
singular value decomposition is carried out on the first autocorrelation matrix to obtain a characteristic value of the first autocorrelation matrix;
determining the ratio of the minimum characteristic value to the maximum characteristic value in the characteristic values, and taking the ratio as the ratio of the noise space of the reflection point to the characteristic value corresponding to the signal space; the minimum eigenvalue is an eigenvalue corresponding to the noise space, and the maximum eigenvalue is an eigenvalue corresponding to the signal space.
17. The millimeter wave radar of any of claims 12-14, wherein the processor is configured to:
and if the evaluation value of the ice and snow road surface is greater than a third preset threshold value, determining that the road surface to be detected is an ice and snow covered road surface.
18. The millimeter-wave radar of claim 17, wherein the processor is configured to:
if the evaluation value of the ice and snow road surface is larger than a third preset threshold value, updating the ice and snow road surface mark to be effective;
and if the ice and snow road surface mark position is effective, determining that the road surface to be detected is an ice and snow covered road surface.
19. The millimeter wave radar of any of claims 11-18, wherein the processor is configured to:
increasing the preset intensity threshold by a third preset value, and taking the increased preset intensity threshold as a new preset intensity threshold;
and detecting the ice and snow covered road surface according to the new preset intensity threshold value.
20. The millimeter-wave radar of claim 19, wherein the processor is configured to:
and if the evaluation value of the ice and snow road surface is smaller than a fourth preset threshold value, updating the ice and snow road surface mark to be invalid.
21. A vehicle, characterized by comprising:
a vehicle body; and
a millimeter wave radar according to any one of claims 11 to 20, mounted on the vehicle body.
22. A storage medium, comprising: readable storage medium and computer program for implementing the method for detecting snow-covered road surfaces according to any one of claims 1 to 10.
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| PCT/CN2019/089256 WO2020237567A1 (en) | 2019-05-30 | 2019-05-30 | Method and apparatus for detecting road surface snow, and storage medium |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN111699403A true CN111699403A (en) | 2020-09-22 |
Family
ID=72476457
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201980012195.2A Pending CN111699403A (en) | 2019-05-30 | 2019-05-30 | Method, apparatus and storage medium for detecting ice and snow covered road surface |
Country Status (2)
| Country | Link |
|---|---|
| CN (1) | CN111699403A (en) |
| WO (1) | WO2020237567A1 (en) |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| KR102671985B1 (en) | 2021-08-13 | 2024-06-03 | 동아대학교 산학협력단 | Apparatus for detecting black ice on a road surface for a vehicle and method for detecting black ice therewith |
Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2007115650A1 (en) * | 2006-03-31 | 2007-10-18 | Volkswagen Aktiengesellschaft | Device and method for detecting one or more objects in the environment of a vehicle |
| CN101349752A (en) * | 2007-07-20 | 2009-01-21 | 株式会社电装 | Device and method for estimating the number of arrival signals |
| CN102608598A (en) * | 2012-03-19 | 2012-07-25 | 西安电子科技大学 | Method for imaging actual aperture foresight on basis of subspace projection |
| CN104777467A (en) * | 2015-04-03 | 2015-07-15 | 中国科学院电子学研究所 | Target detection method based on frequency scan antenna |
| CN106199547A (en) * | 2016-06-30 | 2016-12-07 | 西安电子科技大学 | Weak target detection method at a slow speed based on external illuminators-based radar |
| US20180341006A1 (en) * | 2017-05-24 | 2018-11-29 | Mitsubishi Electric Corporation | Radar signal processing device |
Family Cites Families (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102967561B (en) * | 2012-12-11 | 2015-07-15 | 河南中原光电测控技术有限公司 | Backward multi-wavelength infrared spectroscopy non-contact pavement condition detection method |
| CN104111064B (en) * | 2014-07-28 | 2017-12-05 | 西安纳兴电子科技有限公司 | Image and laser combined type remote sensing pavement monitoring device |
| CN108931945B (en) * | 2017-05-27 | 2022-01-07 | 比亚迪股份有限公司 | Vehicle control method and device |
-
2019
- 2019-05-30 WO PCT/CN2019/089256 patent/WO2020237567A1/en active Application Filing
- 2019-05-30 CN CN201980012195.2A patent/CN111699403A/en active Pending
Patent Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2007115650A1 (en) * | 2006-03-31 | 2007-10-18 | Volkswagen Aktiengesellschaft | Device and method for detecting one or more objects in the environment of a vehicle |
| CN101349752A (en) * | 2007-07-20 | 2009-01-21 | 株式会社电装 | Device and method for estimating the number of arrival signals |
| CN102608598A (en) * | 2012-03-19 | 2012-07-25 | 西安电子科技大学 | Method for imaging actual aperture foresight on basis of subspace projection |
| CN104777467A (en) * | 2015-04-03 | 2015-07-15 | 中国科学院电子学研究所 | Target detection method based on frequency scan antenna |
| CN106199547A (en) * | 2016-06-30 | 2016-12-07 | 西安电子科技大学 | Weak target detection method at a slow speed based on external illuminators-based radar |
| US20180341006A1 (en) * | 2017-05-24 | 2018-11-29 | Mitsubishi Electric Corporation | Radar signal processing device |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| KR102671985B1 (en) | 2021-08-13 | 2024-06-03 | 동아대학교 산학협력단 | Apparatus for detecting black ice on a road surface for a vehicle and method for detecting black ice therewith |
Also Published As
| Publication number | Publication date |
|---|---|
| WO2020237567A1 (en) | 2020-12-03 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| Melvin et al. | Assessment of multichannel airborne radar measurements for analysis and design of space-time processing architectures and algorithms | |
| JP4138825B2 (en) | Weight calculation method, weight calculation device, adaptive array antenna, and radar device | |
| US8912951B2 (en) | Moving target detection using a two-dimensional folding approach | |
| CA2411615A1 (en) | Surface wave radar | |
| Roos et al. | Compressed sensing based single snapshot DoA estimation for sparse MIMO radar arrays | |
| CN106707247B (en) | A kind of high frequency ocean radar target detection method based on compact antenna battle array | |
| KR102013205B1 (en) | Simulation Apparatus and Method for Radar Signal Processing | |
| CN107229040B (en) | high-frequency radar target detection method based on sparse recovery space-time spectrum estimation | |
| CN113933808A (en) | Airborne radar moving target detection method, device, equipment and storage medium | |
| JP2024508909A (en) | A method for classifying objects in automotive grade radar signals | |
| Gardill et al. | A multi-layer perceptron applied to number of target indication for direction-of-arrival estimation in automotive radar sensors | |
| Lopez-Estrada et al. | Decision tree based FPGA-architecture for texture sea state classification | |
| CN111699403A (en) | Method, apparatus and storage medium for detecting ice and snow covered road surface | |
| KR102099388B1 (en) | Method of estimating direction of arrival of radar signal based on antenna array extrapolation and apparatus for the same | |
| US10302741B2 (en) | Method and apparatus for live-object detection | |
| CN114137494A (en) | Array echo data dimension reduction processing method based on minimum redundant eigen beams | |
| JP5152949B2 (en) | Weight calculation method, weight calculation device, adaptive array antenna, and radar device | |
| Zhang et al. | Cnn based target classification in vehicular networks with millimeter-wave radar | |
| KR102047979B1 (en) | Apparatus identifying target using dual polarized channel signal reflecting physical characteristic of target in w-band millimeter wave air-to-ground radar and method thereof | |
| CN111105419B (en) | Vehicle and ship detection method and device based on polarized SAR image | |
| Dehkordi et al. | Region of interest based adaptive high resolution parameter estimation with applications in automotive radar | |
| CN113866750A (en) | Pedestrian target detection and tracking method based on millimeter wave radar | |
| US20220034995A1 (en) | Electronic device, method for controlling electronic device, and electronic device control program | |
| CN109861770B (en) | Broadband signal detection method based on beam forming output power combination | |
| CN112612007A (en) | Moving target distance ambiguity resolving method of ultra-sparse array airborne radar based on near field effect |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| WD01 | Invention patent application deemed withdrawn after publication | ||
| WD01 | Invention patent application deemed withdrawn after publication |
Application publication date: 20200922 |