CN110570664B - Automatic detection system for highway traffic incident - Google Patents
Automatic detection system for highway traffic incident Download PDFInfo
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Abstract
The invention discloses an automatic detection system for highway traffic incidents, which comprises: the data acquisition unit comprises a millimeter wave radar and a video acquisition device and is used for acquiring the motion track of the vehicle and the vehicle information; the data analysis and event judgment unit is used for judging whether the vehicle state belongs to an abnormal driving state or not according to the vehicle motion track and the vehicle information acquired by the data acquisition unit; the field alarm unit is used for alarming the event information on the expressway field and sending an alarm to the driver when the judgment result obtained by the data analysis and event judgment unit is in an abnormal driving state; the background management unit is used for coordinating and controlling the data acquisition unit, the data analysis and event judgment unit and the field alarm unit, and displaying the acquired data information, the judgment result and the alarm information. The invention improves the event detection range by using a mode of fusing the millimeter wave radar and the video, has more comprehensive event detection types, and realizes all-weather automatic detection and early warning reminding of highway events.
Description
Technical Field
The invention relates to the technical field of road traffic state detection and management, in particular to an automatic detection system for highway traffic events based on the fusion of millimeter wave radars and videos.
Background
With the rapid development of national economy, a four-way and eight-reach highway network is formed in China, and the total mileage of the highway is increased year by year. Due to the characteristics of large flow, high speed, various types of vehicles and the like, highway accidents are often high in loss degree and casualty rate, and become the key point of traffic management. In an accident of a highway, the driver's unlawful driving behavior is the main cause of the accident, and mainly comprises overspeed, low speed, retrograde motion, violation of parking, illegal occupation of emergency lanes, frequent lane change and the like. The abnormal driving behaviors are timely and accurately detected, the driver is reminded, and the abnormal driving behaviors are uploaded to a traffic management department for law enforcement and supervision, so that the accident occurrence probability can be effectively reduced, and the highway management level is improved.
The existing highway traffic incident automatic monitoring is mainly realized by using video equipment, including fixed-point detection equipment and high-point video monitoring equipment. The fixed-point detection mainly adopts a scheme of combining a microwave radar or a ground induction coil with a video device, the microwave radar or the ground induction coil is used for detecting the running speed of the vehicle, and the associated video device is used for carrying out illegal evidence collection. The traditional microwave radar can not detect low-speed and static targets, and has small detection range and small number of detected targets. By arranging speed measuring and video equipment for each lane, the system can only realize automatic monitoring of less types of events such as overspeed, low speed, emergency lane occupation and the like, and is only suitable for collecting section traffic events in a small range.
Although the high-point video monitoring equipment can acquire comprehensive traffic incident information, the high-point video monitoring equipment is mainly realized in a manual inspection mode. The states of the vehicles in the video range, such as the states of the vehicles in the video range, the states of the vehicles in the video range and the like are manually judged by managers, and the highway event management is realized. This method consumes a lot of manpower, and can not realize all-weather full-automatic event monitoring. In addition, in poor lighting scenarios such as fog, rain, backlighting, and glare, the accuracy and reliability of event collection can be severely compromised.
Disclosure of Invention
The invention aims to provide an automatic detection system for highway traffic incidents, so as to more comprehensively and accurately automatically monitor the highway traffic incidents.
In order to achieve the above object, the present invention provides an automatic detection system for highway traffic incident, comprising:
the data acquisition unit comprises a millimeter wave radar and a video acquisition device and is used for acquiring a vehicle motion track and vehicle information, wherein the vehicle information comprises a license plate and vehicle body characteristics;
the data analysis and event judgment unit is used for judging whether the vehicle state belongs to an abnormal driving state according to the vehicle motion track and the vehicle information acquired by the data acquisition unit, wherein the abnormal driving state comprises overspeed, low speed, illegal parking, retrograde motion, emergency lane occupation or frequent lane change;
the field alarm unit is used for alarming the event information carried out on the expressway field and sending an alarm to a driver when the judgment result obtained by the data analysis and event judgment unit is in an abnormal driving state;
and the background management unit is used for coordinating and controlling the data acquisition unit, the data analysis and event judgment unit and the field alarm unit and displaying the acquired data information, the judgment result and the alarm information.
Optionally, the data analysis and event discrimination unit includes:
the vehicle motion track data preprocessing module is used for screening effective vehicle motion track data according to a preset threshold range; the preset threshold range comprises a distance threshold, an angle threshold, a speed threshold and an RCS threshold;
the vehicle target track extraction module is used for acquiring two video frames as two sampling points each time, calculating the similarity of a vehicle target in the two sampling points according to a similarity principle, determining the same vehicle target with the maximum similarity, and forming a vehicle track according to the same vehicle target obtained for multiple times;
the vehicle state judging module is used for respectively judging the vehicle states of the vehicle states in the same vehicle target track according to a highest speed limit threshold value, a lowest speed limit threshold value, zero speed, a negative speed value, an emergency lane angle range and a lane angle range according to the vehicle track to obtain a judging result, wherein the judging result comprises a normal driving state and an abnormal driving state, and the abnormal driving state comprises overspeed, low speed, illegal stop, retrograde driving, emergency lane occupation or frequent lane change;
the vehicle characteristic extraction module is used for matching the image acquired by the millimeter wave radar with the image acquired by the video acquisition equipment, unifying coordinates, identifying vehicle characteristic information in the image acquired by the millimeter wave radar, and storing and uploading the vehicle characteristic information and the vehicle track in a correlation manner;
and the video evidence obtaining module is used for recording and storing the video stream containing the vehicle characteristic information.
Optionally, the vehicle motion trajectory data preprocessing module specifically includes:
a distance threshold setting submodule for setting the average height H of the vehicle according to the installation height H of the radar, the pitch angle delta of the radar detection range, the inclination angle theta of the antenna and the average height H of the vehicle c Calculating the maximum detection distance and the minimum detection distance range [ d ] of the radar min ,d max ]Eliminating the detection distance data which are not in the range of the distance threshold; wherein, the maximum detection distance and the minimum detection distance are calculated as follows:
an angle threshold setting submodule for detecting the range [ sigma ] according to the radar horizontal angle max ,σ max ]Eliminating detection angle data which are not in the radar horizontal angle detection range; wherein σ max The maximum detection angle of the radar level is obtained;
a speed threshold value setting submodule for setting a speed detection range [ v ] according to a running characteristic of the vehicle min ,v max ]Eliminating the detected speed data which are not in the speed detection range; wherein v is min The maximum speed of the vehicle in the reverse running is a negative value v max Is 160% times of the speed limit of the expressway;
an RCS threshold setting submodule for determining an RCS distribution range [ r ] of the vehicle target min ,r max ]And eliminating the RCS data detected in the RCS distribution range, wherein r min 、r max The minimum value and the maximum value of the vehicle target RCS statistic value.
Optionally, the vehicle target trajectory extraction module specifically includes:
a predictor calculation submodule for utilizing the formula
Calculating a predicted value of sampling point data of a p frame at a q frame time, wherein the two sampling points of the p frame and the q frame are continuous frames or two frames with set intervals, and the p frame and the q frame respectively comprise M vehicle target points and N vehicle target points;
a dissimilarity measure operator module for utilizing the formula
Calculating the dissimilarity degree between any two target points in different sampling frames; wherein M is more than or equal to 1 and less than or equal to M, N is more than or equal to 1 and less than or equal to N>
For the non-dimensionalized p-th frame sample point dataA predicted value at the q-th frame time; />
For the actual detection value, mu, at the moment of the q-th frame after non-dimensionalization d ,μ v ,μ a ,μ r Is a weighting system and satisfies mu d +μ v +μ a +μ r ＝1；
The maximum dissimilarity degree calculation submodule is used for calculating the maximum dissimilarity degree of all sampling frame samples in the historical time period T according to a formula max dif = max dif (m, n);
the similarity calculation operator module is used for calculating the similarity according to a formula sim (m, n) = max dif-dif (m, n);
and the track generation submodule is used for selecting the targets with the maximum similarity from the p frame and the q frame to be classified into one type according to the similarity so as to form the track of the same vehicle.
Optionally, the vehicle state determination module specifically includes:
the overspeed judgment sub-module is used for judging that the vehicle target with the speed exceeding the highest speed limit threshold value is overspeed in the same vehicle target track;
the low-speed judging submodule is used for judging that the vehicle target with the speed lower than the lowest speed limit threshold value exists in the same vehicle target track as the low speed;
the illegal parking judgment submodule is used for judging the vehicle target with the speed of zero as illegal parking in the same vehicle target track;
the reverse driving judging submodule is used for judging the vehicle target with a negative speed value in the same vehicle target track to be reverse driving;
the illegal occupying emergency lane judging submodule is used for judging that the vehicle target angle is positioned in an emergency lane angle range in the same vehicle target track, and the emergency lane angle range is required to be calibrated according to the installation angle of the millimeter wave radar equipment;
and the frequent lane change judging submodule is used for judging frequent lane change when the vehicle target angle is continuously changed into different lane angle ranges in the detection range in the same vehicle target track, wherein the lane angle ranges are calibrated according to the installation angle of the millimeter wave radar equipment.
Optionally, the vehicle feature extraction module specifically includes:
the image fusion sub-module is used for matching the image acquired by the millimeter wave radar with the image acquired by the video and unifying a coordinate system;
the projection submodule is used for detecting vehicle target information of the millimeter wave radar, projecting the vehicle target information in an image acquired by a video, and identifying an interest point area, wherein the interest point area represents the position of a vehicle target;
the characteristic identification submodule is used for delimiting a vehicle target extraction boundary according to the interest point region and identifying and extracting vehicle characteristics in the boundary, wherein the vehicle characteristics comprise license plate identity characteristics, vehicle body characteristics, vehicle types and vehicle body colors;
and the association matching submodule is used for performing association matching on the vehicle characteristic information identified by the video image and the track information identified by the millimeter wave radar, storing the vehicle characteristic information and the track information in the processing unit, and uploading the vehicle characteristic information and the track information to the background management unit in real time.
Optionally, the background management unit includes a data display platform, a system operation and maintenance management platform, and an abnormal event examination and management platform.
Optionally, the data display platform is configured to dynamically display system summarization, monitoring point distribution, real-time traffic event reporting conditions, traffic event statistics, traffic flow states, video monitoring states, and radar detection target track display states in combination with big data visualization and GIS application.
Optionally, the system operation and maintenance management platform is used for performing online management on the intersection radar and video detection device, the processing unit device, the broadcasting device, the display device, and the network device, including device basis information maintenance, device failure alarm, or personnel right management.
Optionally, the abnormal event review management platform is configured to perform review and law enforcement on traffic abnormal events collected on site, perform manual review according to the event type reported by the millimeter wave radar and the video evidence obtaining information collected by the video device, determine an illegal vehicle, an illegal type, time, and a penalty standard, and transmit the obtained information to the traffic control platform for law enforcement.
According to the specific embodiment provided by the invention, the invention discloses the following technical effects:
1. the invention adopts a scheme of detecting the highway events by mainly using a millimeter wave radar and secondarily using a video, so that the detection event types are more comprehensive and accurate. In addition, the invention can reduce the dependence of the traditional video mode on the light condition, can realize the event discrimination and carry out the on-site alarm prompt in time under the poor light condition, and achieves the purpose of traffic management;
2. according to the invention, the millimeter wave radar detection target is fused with the video image, so that the video analysis data processing amount can be reduced, and the vehicle feature extraction efficiency is improved. In addition, the video recording acquisition mode based on event triggering can also greatly reduce unnecessary data transmission quantity and save network bandwidth;
3. the invention not only provides an automatic detection method for highway events in the aspect of key core technology, but also provides a systematic solution for high-speed traffic management, and reduces the probability of accidents and improves the management efficiency by combining front-section alarm prompt and background law enforcement management.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required in the embodiments will be briefly described below, it is obvious that the drawings in the following description are only some embodiments of the present invention, and it is obvious for those skilled in the art that other drawings can be obtained according to the drawings without creative efforts.
Fig. 1 is a system block diagram of an automatic detection system for highway traffic events according to an embodiment of the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
The invention aims to provide an automatic detection system for highway traffic incidents, which can be used for more comprehensively and accurately automatically monitoring the highway traffic incidents.
In order to make the aforementioned objects, features and advantages of the present invention comprehensible, embodiments accompanied with figures are described in further detail below.
As shown in fig. 1, the automatic detection system for highway traffic events provided by this embodiment includes a data acquisition unit 1, a data analysis and event discrimination unit 2, a field alarm unit 3, and a background management unit 4.
The data acquisition unit 1 comprises a millimeter wave radar and a video acquisition device, the data acquisition unit 1 is used for acquiring vehicle motion tracks and vehicle information, and the vehicle information comprises license plates and vehicle body characteristics.
The working frequency range of the millimeter wave radar is between light waves and centimeter waves, and the millimeter wave radar has excellent detection performance. Particularly, the FMCW frequency-adjustable continuous millimeter wave radar has the advantages of high speed measurement precision, multi-target detection, high resolution, imaging capability, wide detection range, strong anti-interference capability and the like, has good detection performance on low-speed moving objects and static objects, and well makes up the defects of the traditional microwave radar. The embodiment adopts a mode of combining a millimeter wave radar and video acquisition, mainly judges an event by tracking a vehicle motion track through the millimeter wave radar, assists the video acquisition, and provides an all-weather highway-based event detection and early warning system.
In practical application, the millimeter wave radar and the video equipment are installed on the road side upright rod, the millimeter wave radar mainly detects the motion track characteristics of a vehicle target, and the direction of the radar antenna is consistent with the direction of traffic flow. The millimeter wave radar can simultaneously realize continuous track tracking of a plurality of vehicle targets in 3-4 lanes within the range of 150m, the tracking frame rate is 50 ms/time, wherein in the k frame, the tracking information of the ith vehicle target is as follows:
＜d k (i),v k (i),a k (i),r k (i)＞
wherein the content of the first and second substances,
d k (i) Representing the linear distance between the target i and the radar in m;
v k (i) Represents the running speed of the target i in m/s;
a k (i) Representing the azimuth of the target i;
r k (i) The RCS return energy value intensity, in dB, representing target i.
The video equipment is mainly used for detecting the license plate and the body characteristics of the vehicle, and meanwhile, video recording and evidence obtaining are carried out on the judged abnormal events, so that a basis is provided for traffic law enforcement management.
The data analysis and event judgment unit 2 is used for judging whether the vehicle state belongs to an abnormal driving state according to the vehicle motion track and the vehicle information acquired by the data acquisition unit, wherein the abnormal driving state comprises overspeed, low speed, illegal parking, retrograde motion, emergency lane occupation or frequent lane change;
the data analysis and event discrimination unit 2 is mainly executed by a processing unit disposed at the roadside, and the processing unit can be an industrial personal computer, a server, an embedded processor and other computing equipment meeting the data processing performance. The processing unit receives real-time data transmitted by the millimeter wave radar and the video equipment and executes the following steps: the method comprises the steps that millimeter wave radar data are cleaned, background noise points are removed, and a vehicle motion track point set is obtained; classifying the vehicle motion track points, and extracting the motion track data of a single vehicle; according to the motion trail of a single vehicle, judging the types of events such as overspeed, low speed, frequent lane change, emergency lane occupation, illegal parking, reverse driving and the like; road data are collected by using video equipment and are fused with millimeter wave collected data to realize vehicle feature extraction, and the vehicle feature extraction mainly comprises license plate numbers, vehicle types, vehicle body colors and the like; and finally, storing the video data in the corresponding time period as law enforcement evidence through a video recording evidence obtaining sub-module when the traffic incident occurs.
Specifically, the data analysis and event judgment unit 2 comprises a vehicle motion track data preprocessing module, a vehicle target track extraction module, a vehicle state judgment module, a vehicle feature extraction module and a video evidence obtaining module.
The millimeter wave radar data acquisition contains a large amount of noise information which mainly comes from radar equipment and road environment, such as side lobe interference, false alarm noise, metal object interference targets of road guardrails and the like. Threshold analysis and elimination are carried out on the noise, and effective vehicle track data are very necessary to be screened. The vehicle motion track data preprocessing module is used for screening effective vehicle motion track data according to a preset threshold range; the preset threshold range comprises a distance threshold, an angle threshold, a speed threshold and an RCS threshold. The vehicle motion trajectory data preprocessing module specifically comprises:
a distance threshold setting submodule for setting the average height H of the vehicle according to the mounting height H of the radar, the pitch angle delta of the radar detection range, the inclination angle theta of the antenna and the average height H of the vehicle c Calculating the maximum detection distance and the minimum detection distance range [ d ] of the radar min ,d max ]Eliminating the detection distance data which are not in the range of the distance threshold; wherein, the maximum detection distance and the minimum detection distance are calculated as follows:
an angle threshold setting submodule for detecting the range [ sigma ] according to the radar horizontal angle max ,σ max ]Eliminating detection angle data which are not in the radar horizontal angle detection range; wherein σ max The maximum detection angle of the radar level is obtained;
a speed threshold setting submodule for setting, based on a running characteristic of the vehicle,setting speed detection range [ v ] min ,v max ]Eliminating the detected speed data which are not in the speed detection range; wherein v is min The maximum speed of the vehicle in the reverse running is a negative value v max The speed limit is 160 percent times of the speed limit of the highway;
an RCS threshold setting submodule for determining an RCS distribution range [ r ] of the vehicle target min ,r max ]And eliminating the RCS data detected in the RCS distribution range, wherein r min 、r max The minimum value and the maximum value of the vehicle target RCS statistic value.
The vehicle target track extraction module is used for acquiring two video frames as two sampling points each time, calculating the similarity of a vehicle target in the two sampling points according to a similarity principle, determining the same vehicle target with the maximum similarity, and forming a vehicle track according to the same vehicle target obtained for multiple times; the vehicle target track extraction module specifically comprises:
a predictor calculation submodule for utilizing the formula
Calculating a predicted value of sampling point data of a p-th frame at a q-th frame moment, wherein the sampling points of the p-th frame and the q-th frame are continuous frames or two frames with set intervals, and the p-th frame and the q-th frame respectively comprise M vehicle target points and N vehicle target points;
a dissimilarity measure operator module for utilizing the formula
Calculating the dissimilarity degree between any two target points in different sampling frames; wherein M is more than or equal to 1 and less than or equal to M, N is more than or equal to 1 and less than or equal to N>
Predicting the value of sampling point data of the p frame at the moment of the q frame after non-dimensionalization; />
For the actual detection value at the moment of the q-th frame after non-dimensionalization, μ d ,μ v ,μ a ,μ r Is a weighting system and satisfies mu d +μ v +μ a +μ r ＝1；
The maximum dissimilarity degree calculation submodule is used for calculating the maximum dissimilarity degree of all sampling frame samples in the historical time period T according to a formula max dif = max dif (m, n);
the similarity calculation operator module is used for calculating the similarity according to a formula sim (m, n) = max dif-dif (m, n);
and the track generation submodule is used for selecting the targets with the maximum similarity from the p frame and the q frame to be classified into one type according to the similarity so as to form the track of the same vehicle.
The vehicle state judging module is used for respectively judging vehicle states according to a highest speed limit threshold value, a lowest speed limit threshold value, zero speed, a negative speed value, an emergency lane angle range and a lane angle range for the vehicle state in the same vehicle target track according to the vehicle track to obtain judging results, wherein the judging results comprise normal driving states and abnormal driving states, and the abnormal driving states comprise overspeed, low speed, illegal parking, retrograde driving, emergency lane occupation or frequent lane change; the vehicle state discrimination module specifically includes:
the overspeed judging submodule is used for judging that the vehicle targets with the speed exceeding the highest speed limit threshold value are overspeed in the same vehicle target track;
the low-speed judging submodule is used for judging that the vehicle target with the speed lower than the lowest speed limit threshold value exists in the same vehicle target track as the low speed;
the illegal parking judgment submodule is used for judging the vehicle target with the speed of zero as illegal parking in the same vehicle target track;
the reverse driving judging submodule is used for judging the vehicle target with a negative speed value as the reverse driving in the same vehicle target track;
the illegal occupying emergency lane judging submodule is used for judging that the vehicle target angle is positioned in an emergency lane angle range in the same vehicle target track, and the emergency lane angle range is required to be calibrated according to the installation angle of the millimeter wave radar equipment;
and the frequent lane change judging submodule is used for judging frequent lane change when the vehicle target angle is continuously changed into different lane angle ranges in the detection range in the same vehicle target track, wherein the lane angle ranges are calibrated according to the installation angle of the millimeter wave radar equipment.
The vehicle feature extraction module is used for matching the images acquired by the millimeter wave radar with the images acquired by the video acquisition equipment, unifying coordinates, identifying vehicle feature information in the images acquired by the millimeter wave radar, and storing and uploading the vehicle feature information and the vehicle track in a correlation manner; the vehicle feature extraction module specifically includes:
the image fusion submodule is used for matching the image acquired by the millimeter wave radar with the image acquired by the video and unifying a coordinate system;
the projection submodule is used for detecting vehicle target information of the millimeter wave radar, projecting the vehicle target information in an image acquired by a video, and identifying an interest point area, wherein the interest point area represents the position of a vehicle target;
the characteristic identification submodule is used for delimiting a vehicle target extraction boundary according to the interest point region and identifying and extracting vehicle characteristics in the boundary, wherein the vehicle characteristics comprise license plate identity characteristics, vehicle body characteristics, vehicle types and vehicle body colors;
and the association matching submodule is used for performing association matching on the vehicle characteristic information identified by the video image and the track information identified by the millimeter wave radar, storing the vehicle characteristic information and the track information in the processing unit, and uploading the vehicle characteristic information and the track information to the background management unit in real time.
And the video forensics module is used for recording and storing the video stream containing the vehicle characteristic information. When the millimeter wave radar detects an abnormal event, the characteristics of the vehicle are extracted by combining a video fusion method, meanwhile, on-site video stream data are recorded, and the recorded video stream data are stored locally and uploaded to a background so as to provide a basis for law enforcement management. The invention provides that the video data recording and storing function is triggered only when the abnormal event occurs on the road in real time, so that the network bandwidth resource is effectively saved, and unnecessary data uploading is reduced especially for the road section with smaller traffic flow.
The on-site warning unit 3 is used for warning the event information on the highway on site and giving a warning to a driver when the judgment result obtained by the data analysis and event judgment unit is in an abnormal driving state. And after the abnormal event in the radar coverage area is identified, alarming the event information of the expressway site to remind a driver of paying attention to safe driving. The alarm modes mainly include two types: in the broadcasting mode, sound reminding is carried out on a driver by installing sound amplification equipment on the road side; and an induction screen reminding mode: display equipment such as an LED induction screen is installed on the road side, vehicle information and illegal information are displayed, and a driver is reminded of driving safely.
The background management unit 4 is used for coordinating and controlling the data acquisition unit, the data analysis and event judgment unit and the field alarm unit, and displaying the acquired data information, the judgment result and the alarm information. The background management unit 4 comprises a data display platform, a system operation and maintenance management platform and an abnormal event examination and management platform.
The data display platform is used for dynamically displaying system summarization, monitoring point distribution, real-time traffic event reporting conditions, traffic event statistical conditions, traffic flow states, video monitoring states and radar detection target track display states by combining big data visualization and GIS application.
The system operation and maintenance management platform is used for online management of the road junction radar and video detection equipment, the processing unit equipment, the broadcasting equipment, the display equipment and the network equipment, and comprises equipment basic information maintenance, equipment fault alarm or personnel authority management.
The abnormal event examination management platform is used for auditing and enforcing the traffic abnormal events collected on site, carrying out manual auditing according to the event types reported by the millimeter wave radar and the video evidence obtaining information collected by the video equipment, determining illegal vehicles, illegal types, time and penalty standards, and transmitting the obtained information to the traffic control platform for enforcement
According to the method, the event detection range is widened by using a mode of integrating the millimeter wave radar and the video, the event detection types are more comprehensive, and all-weather automatic detection and early warning reminding of the highway event are realized. The defects that a traditional microwave radar cannot identify static vehicles and detect vehicle track information, a single video acquisition mode is influenced by light and the like are overcome, and the defects that the existing highway incident detection system is few in detection incident types, small in range and the like are overcome.
The principles and embodiments of the present invention have been described herein using specific examples, which are provided only to help understand the method and the core concept of the present invention; meanwhile, for a person skilled in the art, according to the idea of the present invention, the specific embodiments and the application range may be changed. In view of the above, the present disclosure should not be construed as limiting the invention.
Claims (7)
1. An automatic detection system for highway traffic events, the detection system comprising:
the data acquisition unit comprises a millimeter wave radar and a video acquisition device and is used for acquiring a vehicle motion track and vehicle information, wherein the vehicle information comprises a license plate and vehicle body characteristics;
the data analysis and event judgment unit is used for judging whether the vehicle state belongs to an abnormal driving state according to the vehicle motion track and the vehicle information acquired by the data acquisition unit, wherein the abnormal driving state comprises overspeed, low speed, illegal parking, retrograde motion, emergency lane occupation or frequent lane change;
the field alarm unit is used for alarming the event information carried out on the expressway field and sending an alarm to a driver when the judgment result obtained by the data analysis and event judgment unit is in an abnormal driving state;
the background management unit is used for coordinating and controlling the data acquisition unit, the data analysis and event judgment unit and the field alarm unit and displaying acquired data information, judgment results and alarm information;
the vehicle motion track data preprocessing module is used for screening effective vehicle motion track data according to a preset threshold range; the preset threshold range comprises a distance threshold, an angle threshold, a speed threshold and an RCS threshold; the RCS threshold represents a radar scattering sectional area threshold;
the vehicle target track extraction module is used for acquiring two video frames as two sampling points each time, calculating the similarity of a vehicle target in the two sampling points according to a similarity principle, determining the same vehicle target with the largest similarity, and forming a vehicle track according to the same vehicle target obtained for multiple times;
the vehicle state judging module is used for respectively judging the vehicle states of the vehicle states in the same vehicle target track according to a highest speed limit threshold value, a lowest speed limit threshold value, zero speed, a negative speed value, an emergency lane angle range and a lane angle range according to the vehicle track to obtain a judging result, wherein the judging result comprises a normal driving state and an abnormal driving state, and the abnormal driving state comprises overspeed, low speed, illegal stop, retrograde driving, emergency lane occupation or frequent lane change;
the vehicle characteristic extraction module is used for matching the image acquired by the millimeter wave radar with the image acquired by the video acquisition equipment, unifying coordinates, identifying vehicle characteristic information in the image acquired by the millimeter wave radar, and storing and uploading the vehicle characteristic information and the vehicle track in a correlation manner;
the video forensics module is used for recording and storing a video stream containing the vehicle characteristic information;
the vehicle motion trajectory data preprocessing module specifically comprises:
a distance threshold setting submodule for setting the average height H of the vehicle according to the installation height H of the radar, the pitch angle delta of the radar detection range, the inclination angle theta of the antenna and the average height H of the vehicle c Calculating the maximum detection distance and the minimum detection distance range [ d ] of the radar min ,d max ]Eliminating the detection distance data which are not in the range of the distance threshold; wherein, the maximum detection distance and the minimum detection distance are calculated as follows:
an angle threshold setting submodule for detecting the range [ sigma ] according to the radar horizontal angle max ,σ max ]Eliminating detection angle data which are not in the radar horizontal angle detection range; wherein σ max The maximum detection angle of the radar level is obtained;
a speed threshold value setting submodule for setting a speed detection range [ v ] according to a running characteristic of the vehicle min ,v max ]Rejecting the detected speed data which is not in the speed detection range; wherein v is min The maximum speed of the vehicle in the reverse running is a negative value v max Is 160% times of the speed limit of the expressway;
an RCS threshold setting submodule for determining an RCS distribution range [ r ] of the vehicle target min ,r max ]And eliminating the RCS data detected in the RCS distribution range, wherein r min 、r max The minimum value and the maximum value of the RCS statistic value of the vehicle target are obtained; the RCS distribution range represents a radar scattering sectional area distribution range, and the RCS data represents radar scattering sectional area data; the RCS statistic value of the vehicle target represents a radar scattering sectional area statistic value of the vehicle target;
the vehicle target track extraction module specifically comprises:
a predictor calculation sub-module for utilizing the formula
Calculating a predicted value of sampling point data of a p-th frame at a q-th frame moment, wherein the sampling points of the p-th frame and the q-th frame are continuous frames or two frames with set intervals, and the p-th frame and the q-th frame respectively comprise M vehicle target points and N vehicle target points; t is t p ,v p ,a p ,r p Respectively in the p-th frame sample point dataInterval, running speed, azimuth angle and RCS return energy value intensity; t is t q At the moment of the qth frame; d' q ,v' q ,a' q ,r' q A predicted value of the sampling point data of the p frame at the moment of the q frame is obtained; the RCS return energy value intensity represents the radar scattering cross section area return energy value intensity;
a dissimilarity measure operator module for utilizing the formula
Calculating the dissimilarity degree between any two target points in different sampling frames; wherein M is more than or equal to 1 and less than or equal to M, N is more than or equal to 1 and less than or equal to N>
Predicting the value of sampling point data of the p frame at the moment of the q frame after non-dimensionalization; />
For the actual detection value, mu, at the moment of the q-th frame after non-dimensionalization d ,μ v ,μ a ,μ r Is a weighting system and satisfies mu d +μ v +μ a +μ r ＝1；
The maximum dissimilarity degree calculation submodule is used for calculating the maximum dissimilarity degree of all sampling frame samples in the historical time period T according to a formula maxdif = maxdif (m, n);
the similarity operator module is used for calculating the similarity according to a formula sim (m, n) = maxdif-dif (m, n);
and the track generation submodule is used for selecting the targets with the maximum similarity from the p frame and the q frame to be classified into one type according to the similarity so as to form the track of the same vehicle.
2. The system according to claim 1, wherein the vehicle state discrimination module specifically comprises:
the overspeed judgment sub-module is used for judging that the vehicle target with the speed exceeding the highest speed limit threshold value is overspeed in the same vehicle target track;
the low-speed judging submodule is used for judging that the vehicle target with the speed lower than the lowest speed limit threshold exists in the same vehicle target track as the low speed;
the illegal parking judgment submodule is used for judging the vehicle target with the speed of zero in the same vehicle target track as illegal parking;
the reverse driving judging submodule is used for judging the vehicle target with a negative speed value as the reverse driving in the same vehicle target track;
the illegal emergency lane occupation judging sub-module is used for judging that the vehicle target angle is in an emergency lane angle range in the same vehicle target track, and the emergency lane is illegally occupied, wherein the emergency lane angle range is calibrated according to the installation angle of the millimeter wave radar equipment;
and the frequent lane change judging submodule is used for judging frequent lane change when the vehicle target angle is continuously changed into different lane angle ranges in the detection range in the same vehicle target track, wherein the lane angle ranges are calibrated according to the installation angle of the millimeter wave radar equipment.
3. The system according to claim 1, wherein the vehicle feature extraction module comprises:
the image fusion submodule is used for matching the image acquired by the millimeter wave radar with the image acquired by the video and unifying a coordinate system;
the projection submodule is used for detecting vehicle target information of the millimeter wave radar, projecting the vehicle target information in an image acquired by a video, and identifying an interest point area, wherein the interest point area represents the position of a vehicle target;
the characteristic identification submodule is used for delimiting a vehicle target extraction boundary according to the interest point region and identifying and extracting vehicle characteristics in the boundary, wherein the vehicle characteristics comprise license plate identity characteristics, vehicle body characteristics, vehicle types and vehicle body colors;
and the association matching submodule is used for performing association matching on the vehicle characteristic information identified by the video image and the track information identified by the millimeter wave radar, storing the vehicle characteristic information and the track information in the processing unit, and uploading the vehicle characteristic information and the track information to the background management unit in real time.
4. The system of claim 1, wherein the background management unit comprises a data display platform, a system operation and maintenance management platform, and an abnormal event review management platform.
5. The system according to claim 4, wherein the data display platform is used for dynamically displaying system summarization, monitoring point distribution, real-time traffic event reporting condition, traffic event statistical condition, traffic flow state, video monitoring state and radar detection target track display state in combination with big data visualization and GIS application; the GIS represents a geographic information system.
6. The system of claim 4, wherein the system operation and maintenance management platform is used for online management of the intersection radar and video detection device, the processing unit device, the broadcasting device, the display device and the network device, and comprises device basic information maintenance, device failure alarm or personnel authority management.
7. The system according to claim 4, wherein the abnormal event audit management platform is configured to audit and enforce the traffic abnormal events collected on site, perform manual audit according to the event types reported by the millimeter wave radar and the video evidence obtaining information collected by the video device, specify illegal vehicles, illegal types, time and penalty standards, and transmit the obtained information to the traffic control platform for enforcement.
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