CN112529955B - Road normalization and speed recovery method and device for expressway - Google Patents
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Abstract
The application provides a method and a device for road normalization and speed recovery of a highway, wherein the method comprises the following steps: data accumulation is carried out on radar data, and accumulated data is in a t1-t2 time period S (x, y) represents a velocity accumulation value at a coordinate (x, y) position in the coordinate system; binarizing the accumulated data S (x, y) by using a binarization method, wherein the result is Sw (x, y), when Sw (x, y) =1, the place is a road, and when Sw (x, y) =0, the place is a non-road; skeletonizing the Sw (x, y) in mathematical morphology to obtain Sg (x, y); performing curve fitting on the Sg (x, y) by using a least square method to obtain a fitted curve y=f (x); and restoring the speed of the moving object according to the fitted curve y=f (x). According to the application, the included angle between the radar and any point of the road is calculated through the learning of the trend of the road, so that a basis is provided for more accurate speed measurement.
Description
Technical Field
The application belongs to the technical field of radar speed measurement, and particularly relates to a method and a device for road normalization and speed recovery of a highway.
Background
Speed measuring radar is the main means of measuring the speed of a moving object on a road. But due to radar principle limitations it measures the radial speed of the target and radar, not the actual speed. The reason for this is that, in addition to the lateral movement of part of the target, more is derived from the fact that the road itself is not perfectly perpendicular to the radar apparatus, resulting in an inaccurate speed measurement.
Disclosure of Invention
In order to solve the problem that the conventional speed measuring radar is not accurate enough in speed measurement of a moving object on a road, the embodiment of the application provides a method and a device for road normalization and speed recovery of a highway.
In a first aspect, an embodiment of the present application provides a method for normalizing and recovering speed of a highway, including:
data accumulation of radar data, at t 1 -t 2 The accumulated data in the time period is S (x, y) represents a speed accumulation value at a coordinate (x, y) position in a coordinate system, S (t) is a traffic condition at a time t, S (t) comprises speed and position information of a moving object, and road full coverage information is obtained through accumulation; wherein, the origin (0, 0) of the coordinate system is the radar placement position;
binarizing the accumulated data S (x, y) by using a binarization method, wherein the result is Sw (x, y), when Sw (x, y) =1, the place is a road, and when Sw (x, y) =0, the place is a non-road;
skeletonizing the Sw (x, y) in mathematical morphology to obtain Sg (x, y);
performing curve fitting on the Sg (x, y) by using a least square method to obtain a fitted curve y=f (x);
and restoring the speed of the moving object according to the fitted curve y=f (x).
Wherein, the restoring the speed of the moving object according to the fitted curve y=f (x) includes:
calculating a tangential slope angle θ (y) at any point on the curve y=f (x);
when the radial velocity of the moving object is detected as v within the y-point preset range, the true velocity of the moving object is vr=v/abs (cos (θ (y)).
Wherein the skeletonizing operation in mathematical morphology of Sw (x, y) to obtain Sg (x, y) includes:
performing morphological closing operation on Sw (x, y) to remove the influence of noise;
skeletonizing operation in mathematical morphology is performed to obtain Sg (x, y).
Wherein the parameter t 1 And t 2 Is a preset parameter.
The coordinate system is a Cartesian coordinate system or a polar coordinate system, and the y-axis of the coordinate system is the radar detection direction.
In a second aspect, the present application provides a road normalization and speed restoration device for an expressway, including:
a data accumulation unit configured to: data accumulation of radar data, at t 1 -t 2 The accumulated data in the time period isS (x, y) represents a speed accumulation value at a coordinate (x, y) position in a coordinate system, S (t) is a traffic condition at a time t, S (t) comprises speed and position information of a moving object, and road full coverage information is obtained through accumulation; wherein, the origin (0, 0) of the coordinate system is the radar placement position;
a binarization unit for: binarizing the accumulated data S (x, y) by using a binarization method, wherein the result is Sw (x, y), when Sw (x, y) =1, the place is a road, and when Sw (x, y) =0, the place is a non-road;
skeletonizing unit for: skeletonizing the Sw (x, y) in mathematical morphology to obtain Sg (x, y);
fitting unit, is used for: performing curve fitting on the Sg (x, y) by using a least square method to obtain a fitted curve y=f (x);
a recovery unit for: and restoring the speed of the moving object according to the fitted curve y=f (x).
Wherein, the recovery unit is used for:
calculating a tangential slope angle θ (y) at any point on the curve y=f (x);
when the radial velocity of the moving object is detected as v within the y-point preset range, the true velocity of the moving object is vr=v/abs (cos (θ (y)).
Wherein the skeletonizing unit is used for:
performing morphological closing operation on Sw (x, y) to remove the influence of noise;
skeletonizing operation in mathematical morphology is performed to obtain Sg (x, y).
In a third aspect, embodiments of the present application provide a computer readable storage medium having stored thereon a computer program which when executed by a processor performs the steps of any of the methods described above.
In a fourth aspect, embodiments of the present application provide a computer device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, the processor implementing the steps of any one of the methods described above when the program is executed.
The method and the device for road normalization and speed recovery of the expressway have the following beneficial effects:
the application relates to a road normalization and speed recovery method for a highway, which comprises the following steps: data accumulation of radar data, at t 1 -t 2 The accumulated data in the time period isS (x, y) represents a speed accumulation value at a coordinate (x, y) position in a coordinate system, S (t) is a traffic condition at a time t, S (t) comprises speed and position information of a moving object, and road full coverage information is obtained through accumulation; wherein, the origin (0, 0) of the coordinate system is the radar placement position; binarizing the accumulated data S (x, y) by using a binarization method, wherein the result is Sw (x, y), when Sw (x, y) =1, the place is a road, and when Sw (x, y) =0, the place is a non-road; skeletonizing the Sw (x, y) in mathematical morphology to obtain Sg (x, y); performing curve fitting on the Sg (x, y) by using a least square method to obtain a fitted curve y=f (x); and restoring the speed of the moving object according to the fitted curve y=f (x).
According to the application, the included angle between the radar and any point of the road is calculated through the learning of the trend of the road, so that a basis is provided for more accurate speed measurement.
Drawings
FIG. 1 is a flow chart of a method for road normalization and speed restoration of an expressway according to an embodiment of the application;
FIG. 2 is a schematic diagram of the relationship between coordinates and radar in the method for normalizing and recovering speed of highway according to the embodiment of the present application;
FIG. 3 is a schematic diagram of speed recovery in the method for road normalization and speed recovery of expressway according to an embodiment of the application;
FIG. 4 is a schematic diagram of a road normalization and speed restoration apparatus for expressways according to an embodiment of the application;
fig. 5 is a schematic structural diagram of a computer device according to an embodiment of the present application.
Detailed Description
The application is further described below with reference to the drawings and examples.
In the following description, the terms "first," "second," and "first," are used for descriptive purposes only and are not to be construed as indicating or implying relative importance. The following description provides various embodiments of the application that may be substituted or combined between different embodiments, and thus the application is also to be considered as embracing all possible combinations of the same and/or different embodiments described. Thus, if one embodiment includes feature A, B, C and another embodiment includes feature B, D, then the present application should also be considered to include embodiments that include one or more of all other possible combinations including A, B, C, D, although such an embodiment may not be explicitly recited in the following.
The following description provides examples and does not limit the scope, applicability, or examples set forth in the claims. Changes may be made in the function and arrangement of elements described without departing from the scope of the application. Various examples may omit, replace, or add various procedures or components as appropriate. For example, the described methods may be performed in a different order than described, and various steps may be added, omitted, or combined. Furthermore, features described with respect to some examples may be combined into other examples.
Speed measuring radar is the main means of measuring the speed of a moving object on a road. But due to radar principle limitations it measures the radial speed of the target and radar, not the actual speed. The reason for this is that more comes from the road itself than from the radar apparatus being perfectly perpendicular, in addition to the lateral movement of part of the target. The application relates to a road normalization and speed recovery method suitable for radar speed measurement, which can calculate the included angle between a radar and any point of a road through learning the trend of the road so as to provide a basis for more accurate speed measurement.
The application discloses a road normalization and speed recovery method suitable for a highway speed measuring radar, which can correct speed deviation caused by that the radar can only perform radial speed measurement.
As shown in fig. 1 to 3, the method for normalizing and recovering speed of the highway according to the present application comprises the steps of: s101, accumulating radar data, at t 1 -t 2 The accumulated data in the time period is S (x, y) represents a speed accumulation value at a coordinate (x, y) position in a coordinate system, S (t) is a traffic condition at a time t, S (t) comprises speed and position information of a moving object, and road full coverage information is obtained through accumulation; wherein, the origin (0, 0) of the coordinate system is the radar placement position; s103, performing binarization processing on the accumulated data S (x, y) by using a binarization method, where Sw (x, y) is a road when Sw (x, y) =1, and Sw (x, y) =0 is a non-road; s105, skeletonizing the Sw (x, y) in mathematical morphology to obtain Sg (x, y); s107, performing curve fitting on the Sg (x, y) by using a least square method to obtain a fitted curve y=f (x); and S109, recovering the speed of the moving object according to the fitted curve y=f (x). Each step is described below.
S101, accumulating radar data, at t 1 -t 2 The accumulated data in the time period is S (x, y) represents a speed accumulation value at a coordinate (x, y) position in a coordinate system, S (t) is a traffic condition at a time t, S (t) comprises speed and position information of a moving object, and road full coverage information is obtained through accumulation; wherein the origin (0, 0) of the coordinate system is the radar installation position.
In this step, the data echo is accumulated, and data accumulation is performed on the radar data of the specific point. Including but not limited to cartesian coordinates, polar coordinates, etc. The application is described below using a Cartesian coordinate system as an example, with the y-axis of the coordinate system being the radar detection direction. Fig. 2 shows the coordinate-radar mounting relationship, where the origin (0, 0) of the coordinate system is the radar placement position and the radar antenna is oriented in the positive y-axis direction. For the selection of the parameters t1 and t2, the time period required by the user is taken as the reference, and the parameters t1 and t2 are preset parameters. The cumulative echo is observed to be constant in area at plane S (x, y).
S103, binarizing the accumulated data S (x, y) by a binarization method, and obtaining Sw (x, y), where Sw (x, y) =1 indicates that the place is a road, and Sw (x, y) =0 indicates that the place is a non-road.
In this step, the binarization processing is performed on S (x, y) by using the oxford method, which is an algorithm for determining the binary segmentation threshold of an image, and is also called a maximum inter-class difference method. The method is characterized in that the image is divided into a background part and a foreground part according to the gray characteristic of the image. Since variance is a measure of the uniformity of the gray level distribution, the larger the inter-class variance between the background and the foreground, the larger the difference between the two parts constituting the image, and the smaller the difference between the two parts when the foreground is divided into the background or the background is divided into the foreground. Thus, a segmentation that maximizes the inter-class variance means that the probability of misclassification is minimal.
S105, skeletonizing the Sw (x, y) in mathematical morphology to obtain the Sg (x, y).
Mathematical morphology (Mathematical morphology) is an image analysis discipline based on lattice and topology, and is the fundamental theory of mathematical morphological image processing. The basic operation includes: corrosion and expansion, open and closed operations, skeleton extraction, extreme corrosion, hit miss transformation, morphological gradients, top-hat transformation, particle analysis, drainage basin transformation, and the like.
In some embodiments, the step comprises: performing morphological closing operation on Sw (x, y) to remove the influence of noise; skeletonizing operation in mathematical morphology is performed to obtain Sg (x, y).
And S107, performing curve fitting on the Sg (x, y) by using a least square method to obtain a fitted curve y=f (x).
The least squares method (also known as least squares) is a mathematical optimization technique. It finds the best functional match for the data by minimizing the sum of squares of the errors. The unknown data can be easily obtained by the least square method, and the sum of squares of errors between the obtained data and the actual data is minimized.
And S109, recovering the speed of the moving object according to the fitted curve y=f (x).
As shown in fig. 3, this step includes: calculating a tangential slope angle θ (y) at any point on the curve y=f (x); when the radial velocity of the moving object is detected as v within the preset range of the y point, the true velocity of the moving object is vr=v/abs (cos (θ (y)). The size of the preset range can be determined according to actual needs.
The application discloses a road normalization method suitable for radar speed measurement, which can calculate the included angle between a radar and any point of a road through learning the trend of the road, further provide a basis for more accurate speed measurement, and obtain a more accurate speed value after the original speed is recovered.
In the method, the first step is to accumulate data echo, and the purpose of the first step is to acquire the position information of a road by accumulating the data echo for a certain time; the second step is data binarization, which aims to primarily extract road information in plane coordinates; the third step is data skeletonization, which aims to abstract the binarized information to obtain line description of the road, and meanwhile, the step can filter out the influence of echo accumulation and noise in the binarization process; the fourth step is function fitting, which aims to prevent distortion formed by noise in skeletonized data, so that the road description is closer to the real situation; then, a tangential slope list is obtained, and the purpose is to obtain the relative included angle of any point of the road relative to the radar installation position (coordinate origin); and finally, for speed recovery, calculating the real speed by utilizing the geometric relation between the radial speed and the real speed of the radar.
As shown in fig. 4, the road normalization and speed restoration device for expressways of the present application includes: a data accumulation unit 201 for: data accumulation of radar data, at t 1 -t 2 The accumulated data in the time period isS (x, y) represents a speed accumulation value at a coordinate (x, y) position in a coordinate system, S (t) is a traffic condition at a time t, S (t) comprises speed and position information of a moving object, and road full coverage information is obtained through accumulation; wherein, the origin (0, 0) of the coordinate system is the radar placement position;
a binarization unit 202 for: binarizing the accumulated data S (x, y) by using a binarization method, wherein the result is Sw (x, y), when Sw (x, y) =1, the place is a road, and when Sw (x, y) =0, the place is a non-road;
a skeletonizing unit 203 for: skeletonizing the Sw (x, y) in mathematical morphology to obtain Sg (x, y);
fitting unit 204, for: performing curve fitting on the Sg (x, y) by using a least square method to obtain a fitted curve y=f (x);
a recovery unit 205 for: and restoring the speed of the moving object according to the fitted curve y=f (x).
Wherein, the recovery unit is used for:
calculating a tangential slope angle θ (y) at any point on the curve y=f (x);
when the radial velocity of the moving object is detected as v within the y-point preset range, the true velocity of the moving object is vr=v/abs (cos (θ (y)).
Wherein the skeletonizing unit is used for:
performing morphological closing operation on Sw (x, y) to remove the influence of noise;
skeletonizing operation in mathematical morphology is performed to obtain Sg (x, y).
In the present application, the embodiment of the device for normalizing and recovering the road of the expressway is basically similar to the embodiment of the method for normalizing and recovering the road of the expressway, and the related points are referred to the description of the embodiment of the method for normalizing and recovering the road of the expressway.
It will be clear to those skilled in the art that the technical solutions of the embodiments of the present application may be implemented by means of software and/or hardware. "Unit" and "module" in this specification refer to software and/or hardware capable of performing a specific function, either alone or in combination with other components, such as an FPGA (Field-Programmable Gate Array, field programmable gate array), an IC (Integrated Circuit ), etc.
The processing units and/or modules of the embodiments of the present application may be implemented by an analog circuit that implements the functions described in the embodiments of the present application, or may be implemented by software that executes the functions described in the embodiments of the present application.
The embodiment of the application also provides a computer readable storage medium, on which a computer program is stored, which when being executed by a processor, realizes the steps of the road normalization and speed recovery method of the expressway. The computer readable storage medium may include, among other things, any type of disk including floppy disks, optical disks, DVDs, CD-ROMs, micro-drives, and magneto-optical disks, ROM, RAM, EPROM, EEPROM, DRAM, VRAM, flash memory devices, magnetic or optical cards, nanosystems (including molecular memory ICs), or any type of media or device suitable for storing instructions and/or data.
Fig. 5 is a schematic diagram of a computer device according to an embodiment of the present application, such as a laptop computer, a desktop computer, a workstation, a personal digital assistant, a server, a blade server, a mainframe computer, and other suitable computers, as shown in fig. 5. The computer device may also represent various forms of mobile apparatuses, such as personal digital processing, cellular telephones, smartphones, wearable devices, and other similar computing apparatuses. The inventive computer device comprises a processor 401, a memory 402, input means 403 and output means 404. The processor 401, memory 402, input device 403, and output device 404 may be connected by a bus 405 or otherwise. The memory 402 stores a computer program which can be run on the processor 401, and which, when executed by the processor 401, implements the above-mentioned road normalization and speed restoration method steps of the expressway.
The input device 403 may receive input numeric or character information and generate key signal inputs related to user settings and function control of the data processing computer device, such as a touch screen, keypad, mouse, trackpad, touchpad, pointer stick, one or more mouse buttons, trackball, joystick, and like input devices. The output device 404 may include a display apparatus, auxiliary lighting devices (e.g., LEDs), and haptic feedback devices (e.g., vibration motors), among others. Display devices may include, but are not limited to, liquid Crystal Displays (LCDs), light Emitting Diode (LED) displays, plasma displays, and touch screens.
In the several embodiments provided by the present application, it should be understood that the disclosed apparatus and method may be implemented in other manners. The above-described embodiment of the apparatus is merely illustrative, and for example, the division of the units is merely a logic function division, and there may be other division manners in actual implementation, such as: multiple units or components may be combined or may be integrated into another system, or some features may be omitted, or not performed. In addition, the various components shown or discussed may be coupled or directly coupled or communicatively coupled to each other via some interface, whether indirectly coupled or communicatively coupled to devices or units, whether electrically, mechanically, or otherwise.
The functional units in the embodiments of the present application may be all integrated in one processing unit, or each unit may be separately used as one unit, or two or more units may be integrated in one unit; the integrated units may be implemented in hardware or in hardware plus software functional units.
The above description is only of the preferred embodiments of the present application and is not intended to limit the present application, but various modifications and variations can be made to the present application by those skilled in the art. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application should be included in the protection scope of the present application.
Claims (10)
1. The method for normalizing and recovering the speed of the expressway is characterized by comprising the following steps of:
data accumulation of radar data, at t 1 -t 2 The accumulated data in the time period is S (x, y) represents a speed accumulation value at a coordinate (x, y) position in a coordinate system, S (t) is a traffic condition at a time t, S (t) comprises speed and position information of a moving object, and road full coverage information is obtained through accumulation; wherein, the origin (0, 0) of the coordinate system is the radar placement position;
binarizing the accumulated data S (x, y) by using a binarization method, wherein the result is Sw (x, y), when Sw (x, y) =1, the place is a road, and when Sw (x, y) =0, the place is a non-road;
skeletonizing the Sw (x, y) in mathematical morphology to obtain Sg (x, y);
performing curve fitting on the Sg (x, y) by using a least square method to obtain a fitted curve y=f (x);
and restoring the speed of the moving object according to the fitted curve y=f (x).
2. The method for normalizing and recovering speed of a highway according to claim 1, wherein recovering the speed of the moving object according to the fitted curve y=f (x) comprises:
calculating a tangential slope angle θ (y) at any point on the curve y=f (x);
when the radial velocity of the moving object is detected as v within the y-point preset range, the true velocity of the moving object is vr=v/abs (cos (θ (y)).
3. The method for road normalization and speed restoration of an expressway according to claim 1 or 2, wherein said performing a skeletonizing operation in mathematical morphology on said Sw (x, y) to obtain Sg (x, y) includes:
performing morphological closing operation on Sw (x, y) to remove the influence of noise;
skeletonizing operation in mathematical morphology is performed to obtain Sg (x, y).
4. The method for normalizing and recovering speed of highway according to claim 1 or 2, wherein the parameter t is 1 And t 2 Is a preset parameter.
5. The method for road normalization and speed restoration for expressways according to claim 1 or 2, wherein the coordinate system is a cartesian coordinate system or a polar coordinate system, and the y-axis of the coordinate system is the radar detection direction.
6. A road normalization and speed restoration device for an expressway, comprising:
a data accumulation unit configured to: data accumulation of radar data, at t 1 -t 2 The accumulated data in the time period isS (x, y) represents a speed accumulation value at a coordinate (x, y) position in a coordinate system, S (t) is a traffic condition at a time t, S (t) comprises speed and position information of a moving object, and road full coverage information is obtained through accumulation; wherein, the origin (0, 0) of the coordinate system is the radar placement position;
a binarization unit for: binarizing the accumulated data S (x, y) by using a binarization method, wherein the result is Sw (x, y), when Sw (x, y) =1, the place is a road, and when Sw (x, y) =0, the place is a non-road;
skeletonizing unit for: skeletonizing the Sw (x, y) in mathematical morphology to obtain Sg (x, y);
fitting unit, is used for: performing curve fitting on the Sg (x, y) by using a least square method to obtain a fitted curve y=f (x);
a recovery unit for: and restoring the speed of the moving object according to the fitted curve y=f (x).
7. The highway road normalization and speed restoration device according to claim 6, wherein the restoration unit is configured to:
calculating a tangential slope angle θ (y) at any point on the curve y=f (x);
when the radial velocity of the moving object is detected as v within the y-point preset range, the true velocity of the moving object is vr=v/abs (cos (θ (y)).
8. The highway road normalization and speed restoration device according to claim 6 or 7, wherein the skeletonizing unit is configured to:
performing morphological closing operation on Sw (x, y) to remove the influence of noise;
skeletonizing operation in mathematical morphology is performed to obtain Sg (x, y).
9. A computer readable storage medium, on which a computer program is stored, characterized in that the program, when being executed by a processor, implements the steps of the method according to any of the claims 1-5.
10. A computer device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, characterized in that the processor implements the steps of the method of any of claims 1-5 when the program is executed.
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