CN113936456B - Street-crossing traffic identification and feature analysis method based on millimeter wave radar - Google Patents
Street-crossing traffic identification and feature analysis method based on millimeter wave radar Download PDFInfo
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Abstract
The invention belongs to the field of traffic engineering, and provides a radar-based street crossing traffic identification and characteristic analysis method, which comprises the following steps: the method comprises the following steps that firstly, millimeter wave radar equipment is used for collecting street crossing traffic data, and the data collected by a radar is uploaded to a server by means of a wireless communication module; compiling and running a program for receiving, processing and forwarding data at a server side to obtain track data and storing the track data in a database; step three, solving the confusion problem caused by the overflow of the timestamp; step four, converting the track data into a track under a rectangular coordinate system; step five, reducing measurement noise caused by the strongest reflection criterion by using a moving smoothing method; sixthly, calculating characteristic indexes of all tracks; step seven, screening and eliminating non-street tracks to obtain street tracks; step eight, identifying the track type according to the characteristic indexes obtained in the step six; and step nine, counting the traffic volume and the average speed operation condition of various street traffics.
Description
Technical Field
The invention belongs to the field of traffic engineering, and particularly relates to a street-crossing traffic identification and feature analysis method based on a millimeter wave radar.
Background
The slow traffic of walking and non-motor vehicles is the most basic mode of human traffic, but the space of slow traffic such as walking is continuously compressed due to the rapid development of motorization, and the problems of the walking traffic such as insufficient space facilities and conflict with motor vehicles are increasingly highlighted. With the introduction of concepts such as 'low carbon' and 'sustainable' in the development of social economy, the safety and quality of slow traffic are more and more emphasized. The management of the slow traffic is concerned and paid attention, and managers and scholars study 'people oriented' from various angles to ensure the safety of pedestrians and non-motor vehicles.
Street crossing facilities are the most critical components of a slow traffic system and are the places most prone to traffic conflicts and accidents. The street crossing facilities not only directly influence the safety and quality of slow traffic, but also influence the operating efficiency of a main traffic system. For pedestrian crossings without signal control, experts develop various radar-based pedestrian crossing safety warning systems, and flash lamps or characters are used for reminding vehicles of crossing pedestrians. For crossing or road section crossing under the control of signal lamps, students develop auxiliary lamps laid on two sides of pedestrian crosswalk marking lines to synchronously display the colors of the signal lamps, and prompt the crossing of slow-speed traffic to accelerate traffic and avoid conflict with motor vehicles; even the intelligent pedestrian crossing system developed by the students is matched with a video system to shoot the pedestrians running the red light.
However, these facilities usually only have a warning or warning function, and do not analyze or acquire the street-crossing traffic characteristics of pedestrians and non-motor vehicles, such as the street-crossing traffic volume, the street-crossing speed, and the like. The pedestrian street-crossing traffic characteristics are extremely important for the slow traffic management and the improvement of slow traffic facilities. For example, only the pedestrian crossing traffic is obtained, and whether the width of the pedestrian crosswalk line is appropriate or not can be calculated or evaluated. If the pedestrian cross street traffic is more and the pedestrian crosswalk width is too narrow, the pedestrian cross street right can not be fully protected. Only the street-crossing traffic characteristics based on data analysis are obtained, and the optimization of the street-crossing traffic organization and the improvement of facilities can be purposefully carried out. The existing acquisition method of the slow traffic street data usually carries out targeted investigation, usually mainly adopts manpower, not only wastes time and labor, but also has incomplete acquired data and poor accuracy. There are also video-based surveys, but they are affected by weather or light, resulting in poor accuracy of the results. Therefore, if the existing radar equipment can be utilized to remind or warn pedestrians crossing streets and simultaneously further provide traffic characteristics for a traffic manager to make decisions, slow traffic can be better served. The invention not only can give full play to the benefits of the radar, but also can improve the fine management level of slow traffic.
The invention utilizes the existing millimeter wave radar facilities to collect the street traffic data, utilizes data mining to identify the street traffic, further obtains the street traffic characteristics such as traffic flow and the like, and provides data support for relevant scientific research and policy making.
Disclosure of Invention
The invention aims to provide a street-crossing traffic recognition and feature analysis method based on a millimeter wave radar, which aims to detect the street-crossing traffic condition of an intersection in real time at a lower cost, and when the millimeter wave radar detects that a target crosses the street, analyzes the street-crossing traffic feature of an equipment installation site, and provides data support for related scientific research and policy establishment.
For simplicity and convenience of description, the position of one target recorded by the radar once is called an event, one target can be recorded for multiple times, and then all the events recorded by the same target are arranged according to the occurrence sequence to restore the motion track of the target; hereinafter, a road in which an incoming direction is parallel to a perpendicular direction to a normal line of the radar is referred to as an along-line road, and a road perpendicular to the along-line road is referred to as a perpendicular road.
The invention is realized in such a way that the street traffic identification and feature analysis method based on the millimeter wave radar comprises the following steps:
the method comprises the following steps that firstly, millimeter wave radar equipment is used for collecting street crossing traffic data, and the data collected by a radar is uploaded to a server by means of a wireless communication module;
compiling and running a C language program for receiving, processing and forwarding data at a server side to obtain track data, and storing the track data in a database;
step three, solving the confusion problem caused by the overflow of the timestamp;
step four, converting the track data into a track under a rectangular coordinate system;
step five, reducing measurement noise caused by the strongest reflection criterion by using a moving smoothing Method (MA);
step six, calculating characteristic indexes of all tracks;
step seven, screening and eliminating non-street-crossing tracks to obtain street-crossing tracks;
step eight, identifying the track type according to the characteristic indexes obtained in the step six;
and step nine, counting the traffic volume, the average speed and other running conditions of various street traffics.
The millimeter wave radar with relatively low price is installed on one side of the cross street to be opposite to the cross street so as to collect traffic data of the cross street, and the wireless communication module is used for establishing communication between the radar and the server. In general, the facility cost is low, and because the information is organized in the form of character strings, the bandwidth requirement can be met by using a 4G network, and in areas with underdeveloped infrastructures, the embarrassment that the cost of erecting optical cables is high can be avoided by adopting the method.
Compared with the prior art, the invention has the following advantages:
1. the invention is realized by combining a millimeter wave radar with a relatively low price and a wireless communication module, is not interfered by weather and can operate in all weather;
2. in the invention, the information transmitted between the devices is a character stream instead of a video, so that high real-time performance can be achieved;
3. the invention can collect the information of the street-crossing traffic behaviors on the basis of safety warning, and can identify the street-crossing behaviors of different categories;
4. the invention can provide the traffic characteristics of different types of street-crossing traffic behaviors, and provides data support for traffic management so as to improve traffic facilities and safety.
Drawings
Sector detection range and coordinate system of radar of fig. 1
FIG. 2 transforms the front (left) and back (right) coordinate systems
Fig. 3 is a flowchart of a method for street-crossing traffic identification and feature analysis based on a millimeter-wave radar according to an embodiment of the present invention.
Fig. 4 is a schematic diagram of a deployment site according to an embodiment of the present invention.
Fig. 5 is an image of a track before coordinate smoothing according to an embodiment of the present invention.
Fig. 6 is a track image after coordinate smoothing according to an embodiment of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention is further described in detail below with reference to the accompanying drawings.
The invention discloses a street crossing traffic identification and characteristic analysis method based on a millimeter wave radar.
The system comprises the following components: measuring traffic data by using a millimeter wave radar, and establishing communication between the radar and a server; and compiling and operating an efficient point cloud data processing program at the server side, and storing the processed track data into a database.
The technical problems to be solved are as follows: the problem of timestamp overflow caused by the accuracy limit of the radar; coordinate rotation and coordinate transformation of the track points, and converting the track under a polar coordinate system into the track under a rectangular coordinate system; repairing the measurement noise caused by the strongest reflection criterion, and calculating the characteristic indexes of all tracks; and dividing the targets into four classes according to the positions of the start point and the stop point and the movement speed, and calculating characteristic indexes of various targets according to the classes.
According to the invention, the street-crossing target is detected by the millimeter wave radar with relatively low price, the street-crossing traffic behavior is recorded, the data mining is carried out, the street-crossing behavior is identified and divided into four categories, the characteristics are analyzed according to different categories, the difficulty of relevant data acquisition is reduced, and data support is provided for relevant scientific research and policy making.
Further, the street crossing traffic identification and feature analysis method based on the millimeter wave radar is characterized in that the millimeter wave radar with relatively low price is adopted, and a 4G wireless communication module is used for establishing communication between the radar and the server. When the radar is in a working state, millimeter waves are transmitted to a sector area with the radar as the center, and when the millimeter waves reflected by a target are captured, the radar calculates the mirror image distance between the target and the radar by calculating the phase difference between the transmission time and the receiving time of the millimeter waves. Since the radar emits millimeter waves into a sector area centered on the radar, the effective measurement range of the radar is also sector-shaped, as shown in fig. 1. When the radar is installed, the normal line of the radar is required to be parallel to the street crossing direction, and the radar is installed on one side of the coming direction of a road along the street. When the millimeter wave radar detects that a target tries to cross the street, the millimeter wave radar flickers the spike installed on the ground to warn the vehicles at the upstream to slow down and improve the safety of the pedestrian crossing. Generally, the fixed facilities are low in cost, and because the information is organized in the form of character strings, the bandwidth requirement can be met by using a 4G network, and in areas with underdeveloped infrastructure, the embarrassment that the cost of erecting optical cables is high can be avoided by adopting the method.
Further, the street crossing traffic identification and feature analysis method based on the millimeter wave radar is characterized in that a set of programs capable of efficiently preprocessing point cloud data is realized, the point cloud data are preprocessed to obtain track data, and the track data are stored in a database table named by the date of the day. Since the target has a certain volume and the millimeter waves reflected by the target cannot carry information of the reflection point, each row in the database represents the mirror image distance between the point on the target, which reflects the millimeter waves most strongly, and the radar at the recorded moment. Each piece of data has information of 5 fields in total, namely, target ID, equipment identification number, time stamp, distance and included angle, and the meanings of the fields are shown in the table 1.
TABLE 1 field interpretation of meanings
Further, the street crossing traffic identification and feature analysis method based on the millimeter wave radar is characterized in that the problem of timestamp overflow caused by the fact that the radar uses 32-bit stored data is solved. The radar uses the data from 1/1970 to the event occurrence time to the millisecond recording event occurrence time, obviously the radar cannot accurately record such a large value, and the radar itself cannot be networked to update the current accurate time, so the absolute time recorded by the radar is meaningless. The maximum number that the radar end can store is 2147483647, and the corresponding minimum number that the radar end can store is-2147483647, which indicates that the radar can record data for about 50 days without repetition, and when the time stamp reaches the maximum number 2147483647, the operation will be completed in a complementary code mode. Therefore, if the difference between the time stamps recorded for the first time and the last time of the same object is less than 2147483647, the difference between the time stamps is the difference between the time when the event occurs. If the time difference value of the recorded first time and last time of the same target is greater than 2147483647, 4294967294 is added to the negative time stamp in the target track, so that the events arranged in descending order according to the time stamps represent the real event occurrence sequence, and the time difference value of the two events is the time difference of the event occurrence, thus the restoration of the target motion track can be realized.
Further, the street traffic identification and feature analysis method based on the millimeter wave radar is characterized in that the millimeter wave radar is converted into a rectangular coordinate system which does not depend on the installation position of the radar and has consistent meaning through two steps of coordinate rotation and coordinate conversion, and the coordinate systems before and after the conversion are shown in fig. 2. Radar ranging uses polar coordinates to record the position of a target, and each event has two fields representing the distance from the radar and the angle with the normal of the radar. The included angle recorded by the radar is a polar coordinate system which takes the radar as a pole, the normal direction of the radar as a polar axis and the clockwise direction as the positive direction.
The coordinate rotation is performed according to the following formula:
θ′＝((90-θ)+360)mod 360
in the formula:
theta' -the new angle after coordinate rotation;
theta is the included angle before coordinate rotation;
mod — modulo operation.
After the coordinates are rotated, a rectangular coordinate system which takes the radar as an original point, the reverse direction of the coming vehicle direction as the positive direction of an x axis and the polar axis direction before the coordinates are rotated as the positive direction of a y axis can be obtained through calculation by a conversion formula of polar coordinates and rectangular coordinates. The transformation formula of the polar coordinate system and the rectangular coordinate system is as follows:
x＝ρcosθ′
y＝ρsinθ′
in the formula:
x is the component of the horizontal coordinate direction under the rectangular coordinate system;
y is the component of the ordinate direction under the rectangular coordinate system;
theta' -the new value of included angle after coordinate rotation;
ρ is the polar diameter length of the target point in polar coordinates.
Further, the street crossing traffic identification and feature analysis method based on the millimeter wave radar is characterized in that in the feature analysis stage, tracks moving along the road along the line and tracks with too few recording points are eliminated. Since the radar emits millimeter waves into a sector area with the radar as the center, the effective measurement range of the radar is also sector, which means that if a target is close to the radar, the radar can hardly distinguish whether signals are reflected by the same target, so that measurement is not accurate, and tracks moving along a road along the line and tracks with too few recording points are eliminated. If the number of recording points of a track is less than 20, its reference value is low and is therefore rejected.
Further, the method for identifying and analyzing the features of the traffic crossing based on the millimeter wave radar is characterized in that the positioning drift phenomenon caused by the ranging criterion of the strongest reflecting point is relieved through a mobile smooth Model (MA). After the MA smoothing processing, the random fluctuation of the track is basically eliminated, and the real motion track of the target is more obvious. Practical experience has shown that 5 th order MA can achieve better results, so the smoothing process can be performed according to the following formula:
in the formula:
s-the s-th trace;
x s，i -the original abscissa of the ith event in the trajectory s;
y s，i -the original ordinate of the i-th event in the trajectory s;
x′ s，n -smoothed abscissa of ith event in trajectory s;
y′ s，n -smoothed ordinate of the ith event in trajectory s;
n — event n, which represents the nth recorded event in trace s.
The smoothed trajectory may be used for further feature extraction.
The method for identifying and analyzing the street crossing traffic and the characteristics based on the millimeter wave radar is characterized in that in the sixth step, characteristic indexes of all tracks are calculated, wherein the characteristic indexes comprise start and end point coordinates, a motion direction, an average motion speed and a maximum motion speed. The starting point and the ending point of a section of track are the horizontal and vertical coordinates of the head event and the tail event which are arranged according to the ascending order of the time stamps. Instantaneous velocity v at the moment of occurrence of event i in a trajectory s s，i Average velocity
And maximum speed->
Calculated according to the following formula:
in the formula:
s-the s-th trace;
n s -total number of events in trajectory s;
x′ s，n -smoothed abscissa of ith event in trajectory s;
y′ s，n -smoothed ordinate of the ith event in trajectory s;
t s，i -timestamp of the ith event in trace s;
v s，i -the instantaneous speed at the instant of occurrence of the ith event in the trajectory s;
Further, the method for identifying and analyzing the features of the street crossing traffic based on the millimeter wave radar is characterized in that the street crossing traffic is divided into four types, and the features of the street crossing traffic are analyzed according to the types. According to the actual situation and the difference of the movement speed, the targets can be divided into pedestrians (P) and electric vehicles (E), and the street crossing demands can be divided into street crossing demands (H) from roads along the street and street crossing demands (V) from vertical roads according to the starting and ending points of the street crossing behaviors. The street crossing requirement from the road line means that at least one of the starting point and the ending point of the target is located on the street crossing requirement on the road along the line, and the street crossing requirement from the vertical road means that at least one of the starting point and the ending point is not located on the cross section of the road along the line. According to the above two classification criteria, street traffic can be divided into four classes, namely pedestrians (VP) coming from or going to the vertical road, electric Vehicles (VE) coming from or going to the vertical road, electric vehicles (HE) coming from or going to the road along the line, and pedestrians (HP) coming from or going to the road along the line. According to the field investigation result, the target with the maximum motion speed less than 2.5m/s is considered as a pedestrian, the target with the average motion speed more than 3m/s is considered as an electric vehicle, and otherwise, the judgment is carried out according to the Euclidean distance between the track characteristics and the clustering centers of the two types of targets. The decision criterion of the street crossing demand category is determined by the geometric elements of the road where the equipment is installed. After the classification is completed, the traffic volume and average speed of each class of street traffic can be calculated, taking the pedestrian (VP) coming from or going to the vertical road as an example, the traffic volume N VP And v VP According to the followingCalculating by the formula:
N VP ＝∑I{s∈VP}
in the formula:
N VP -the traffic volume of pedestrians coming from or going to a vertical road;
v VP -the average speed of pedestrians coming from or going to a vertical road;
VP-set VP, which represents the set of trajectories produced by pedestrians coming from or heading to a vertical road;
s-the s-th trace;
i { cond } -an indicative function, when the condition cond is true, the function value is 1, otherwise, the function value is 0;
Other types are similar, and are not described herein, and each category may be further divided into two sub-categories according to the motion direction.
The application of the principles of the present invention will now be described in detail with reference to the drawings and examples. It should be understood that the specific embodiments described herein are merely illustrative of the invention and do not limit the invention.
The method for identifying street-crossing traffic and analyzing characteristics based on the millimeter wave radar is demonstrated by taking G15 sinking sea high speed and double-road intersection as an example.
As shown in fig. 3, the method for identifying and analyzing characteristics of street crossing traffic based on millimeter wave radar according to the embodiment of the present invention includes the following steps:
step one, installing a millimeter wave radar at an intersection according to a mode specified by the invention, and establishing communication with a server for receiving data, wherein two sets of facilities are installed in the embodiment, and the installation positions are shown in fig. 4;
compiling and running a C language program for receiving, processing and forwarding data at a server side, storing track data obtained after preprocessing by the server into a database, wherein the C language efficient characteristic is suitable for processing original point cloud data with large data volume, and the processed data has information of 5 fields including target ID, equipment identification number, timestamp, distance and included angle;
step three, track data are taken out from the database, and the problem of time overflow caused by the limitation of radar storage precision is solved;
step four, converting the track recorded by the polar coordinates into a track under a rectangular coordinate system through coordinate rotation and transformation;
and step five, using a 5-order MA model to relieve the positioning drift caused by the strongest reflection ranging criterion. Fig. 5 is a certain track image before coordinate smoothing, fig. 6 is a certain track image after coordinate smoothing, 4 marks at the upper left corner in fig. 5 and fig. 6 respectively represent a target ID, an equipment identification number, an average movement speed and a maximum movement speed, a red dot in the figures represents a track starting point, and an origin point is a position where a radar is located;
calculating starting and ending point coordinates, a movement direction, an average movement speed and a maximum movement speed of each track;
removing the non-street-crossing tracks to obtain street-crossing tracks;
step eight, dividing the tracks into four types according to the characteristics obtained in the step five;
and step nine, respectively counting the traffic volume and the average speed of various street-crossing traffic behaviors, and according to the geometric characteristics of roads, if the ordinate of the starting point or the ending point of a certain track is greater than 7.5 meters, considering that the demand is related to the vertical road. Table 2 shows the results of the inventive examples at 1 month, 28 days 15, 2021: 00-20:00, python implementation, which is the data analysis technique, is used from the third step.
The above description is only for the purpose of illustrating the preferred embodiments of the present invention and is not to be construed as limiting the invention, and any modifications, equivalents and improvements made within the spirit and principle of the present invention are intended to be included within the scope of the present invention.
Table 2 example at 2021, month 1, 28, day 15:00-20:00 results of characteristic analysis
Claims (6)
1. A street crossing traffic identification and feature analysis method based on radar is characterized by comprising the following steps:
the method comprises the following steps that firstly, millimeter wave radar equipment is used for collecting street crossing traffic data, and the data collected by a radar are uploaded to a server by means of a wireless communication module;
compiling and running a program for receiving, processing and forwarding data at a server side to obtain track data and storing the track data in a database;
step three, solving the confusion problem caused by the overflow of the timestamp;
step four, converting the track data into a track under a rectangular coordinate system;
step five, reducing the measurement noise caused by the strongest reflection criterion by using a moving smoothing Method (MA);
sixthly, calculating characteristic indexes of all tracks;
step seven, screening and eliminating non-street tracks to obtain street tracks;
step eight, identifying the track type according to the characteristic indexes obtained in the step six; distinguishing pedestrian tracks and electric vehicle tracks according to the average movement speed and the maximum movement speed;
counting the traffic volume and average speed operation conditions of various street traffics;
the application algorithm involved in the sixth step to the ninth step is as follows:
the characteristic indexes of the track comprise starting and ending point coordinates, a motion direction, an average motion speed and a maximum motion speed;
the starting point and the ending point of a section of track are horizontal and vertical coordinates of head events and tail events which are arranged according to the ascending order of the timestamps; instantaneous speed of the moment of occurrence of event i in a section of trajectory sDegree v s，i Average velocity
And maximum speed->
Calculated according to the following formula:
in the formula:
s-the s-th trace;
n s -total number of events in trajectory s;
x′ s，n -smoothed abscissa of ith event in trajectory s;
y′ s，n -smoothed ordinate of the ith event in trajectory s;
t s，i -timestamp of the ith event in trace s;
v s，i -the instantaneous speed at the instant of occurrence of the ith event in the trajectory s;
street traffic is divided into four classes and their characteristics are analyzed according to the classes:
dividing the target into a pedestrian (P) and an electric vehicle (E) according to the difference of the actual situation and the movement speed, and dividing the street crossing demand into a street crossing demand (H) from a road along the road and a street crossing demand (V) from a vertical road according to the starting point and the ending point of the street crossing; the street crossing requirement from the road line means that at least one of the starting point and the end point of the target is located on the street crossing requirement along the line, and the street crossing requirement from the vertical road means that at least one of the starting point and the end point is not located on the cross section of the road along the line;
according to the two classification criteria, the street traffic is divided into four classes, namely pedestrians (VE) coming from or going to the vertical road, electric vehicles (HE) coming from or going to the road along the line and pedestrians (HP) coming from or going to the road along the line;
the target with the maximum motion speed less than 2.5m/s is a pedestrian, the target with the average motion speed more than 3m/s is an electric vehicle, and the judgment is carried out according to the Euclidean distance between the track characteristics and the clustering centers of the two types of targets under other conditions; the judgment criterion of the street crossing demand category is determined by the geometric elements of the road of the equipment installation place;
after the classification is finished, the traffic volume and the average speed of each class of street traffic are calculated, and the traffic volume N is VP And v VP Calculated according to the following formula:
N VP ＝∑I{s∈VP}
in the formula:
N VP -the traffic volume of pedestrians coming from or going to a vertical road;
v VP -the average speed of pedestrians coming from or heading to a vertical road;
VP-set VP, which represents the set of trajectories produced by pedestrians coming from or heading to a vertical road;
s-the s-th trace;
i { cond } -an indicative function, when the condition cond is true, the function value is 1, otherwise, the function value is 0;
and respectively calculating the characteristics of each type of street traffic, including traffic volume and average speed, according to the identified track type so as to know the street crossing conditions of pedestrians and non-motor vehicles and provide data support for traffic management decisions.
2. The method for cross-street traffic recognition and characterization based on millimeter wave radar of claim 1 wherein the relatively inexpensive millimeter wave radar is mounted on one side of the cross-street, and is directed at the cross-street to collect cross-street traffic data, and the wireless communication module is used to establish communication between the radar and the server.
3. The method for street-crossing traffic recognition and feature analysis based on millimeter wave radar as claimed in claim 1, wherein a set of C language program for efficiently preprocessing point cloud data is implemented, and the processed track data has 5 fields including target ID, equipment identification number, timestamp, distance and included angle.
4. The millimeter wave radar-based street crossing traffic recognition and feature analysis method of claim 1, wherein the problem of timestamp overflow in the trajectory data is repaired.
5. The method for street-crossing traffic recognition and feature analysis based on millimeter wave radar as claimed in claim 1, wherein the track position is converted from polar coordinate recording to rectangular coordinate recording by coordinate rotation and transformation, and track samples with too few vehicle motion tracks and recording points are removed; and using the MA model to mitigate positioning drift due to the strongest reflection ranging criterion.
6. The method for identifying and analyzing features of traffic crossing based on millimeter wave radar as claimed in claim 1, wherein in step seven, the trace with the motion along the road direction as the main expression form in the trace data and other noise data are eliminated according to the geometric features of the road.
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