CN113112805B - Intersection monitoring and early warning method based on base station communication and intersection camera positioning - Google Patents
Intersection monitoring and early warning method based on base station communication and intersection camera positioning Download PDFInfo
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Abstract
The invention discloses an intersection monitoring and early warning method based on base station communication and intersection camera positioning, which comprises the following steps: the method comprises the following steps: judging whether the traffic participants enter a monitoring area or not by connection; step two: establishing communication connection with more than two base stations of the intersection through 5G mobile equipment carried by traffic participants; step three: the road intersection camera synchronously sends the intersection video to the edge computing server; step four: numbering the information respectively; step five: re-numbering the traffic participants in sequence; step six: obtaining a rectangular collision model of each traffic participant; step seven: judging whether collision conflicts occur among the traffic participants; step eight: and sending an early warning message to the vehicle OBU equipment through the road side RSU equipment. Has the advantages that: the identification rate and the positioning accuracy of the traffic participants at the road intersection are further improved, the potential conflict of the traffic participants at the road intersection can be early warned all the time around the clock, and the accident rate of intelligent traffic can be effectively reduced.
Description
Technical Field
The invention relates to an intersection monitoring and early warning method, in particular to an intersection monitoring and early warning method based on base station communication and intersection camera positioning.
Background
At present, with the increase of urban and rural construction strength, urban traffic scenes are more complex, and the number of various traffic participants at road junctions is more, including: pedestrians, bicycles, two-wheeled motor vehicles, cars, transportation vehicles, passenger vehicles, etc. Therefore, when any one of the traffic participants has abnormal traffic behaviors, the abnormal traffic behaviors can affect other traffic participants at the road junction, and further traffic accidents occur. With the popularization of road infrastructure construction and 5G technology in recent years, many leading-edge subjects take human-vehicle-road integration as a research key point, and in solving the traffic conflict problem in urban road intersection scenes, traffic participants can be combined in real time through the Internet of vehicles and the 5G technology, so that the accident rate is reduced.
At present, a common road intersection monitoring method monitors information by means of an intersection camera, and although the construction cost is low and the image information content is rich, due to the characteristics of the camera, the area of a road surface, which is shielded by obstacles, cannot be monitored, and the road intersection monitoring method is easily influenced by illumination and weather, so that the all-weather real-time monitoring effect is poor.
Therefore, a monitoring device and an early warning method with wide sensing range, small blind area, high real-time performance and low construction cost are needed.
Disclosure of Invention
The invention aims to solve the problems of image perception and blind area identification caused by the characteristics of a camera in the conventional road intersection monitoring method, and provides an intersection monitoring and early warning method based on base station communication and intersection camera positioning.
The invention provides an intersection monitoring and early warning method based on base station communication and intersection camera positioning, which comprises the following steps:
the method comprises the following steps: judging whether the traffic participants enter a monitoring area or not through the connection of 5G mobile terminals carried by the traffic participants and 5G base stations at each intersection of the road, wherein the monitoring area is the coverage area of four 5G base stations at each intersection of the road;
step two: if the traffic participant enters the base station monitoring area of the road intersection, the traffic participant passes throughThe 5G mobile equipment carried by the participator establishes communication connection with more than two base stations of the intersection, wherein the position information (x) of the base stations i ,y i ) As is known, the TDOA arrival time difference algorithm is utilized
Calculating an equation set to obtain (x, y) coordinate information, namely coordinate information of a mobile terminal carried by a traffic participant, wherein i and j are base station labels (i ≠ j), and sending the coordinate information to an edge calculation server;
step three: the road intersection camera synchronously sends the intersection video to the edge calculation server, and the intersection camera fixes the known position information to perform Gaussian filtering on the two-dimensional image
Extracting and matching the characteristics to obtain the information of the category, speed and position of the current traffic participant;
step four: respectively numbering traffic participant information identified by data of a 5G mobile device terminal and a base station and traffic participant information identified by road intersection camera data;
step five: comparing the information of the two groups of traffic participants, screening out traffic participants which are not identified due to the blind areas of the visual fields of the cameras at the road intersections, the illumination conditions and the foreign matter shielding reasons, and sequencing and numbering the traffic participants again;
step six: estimating the three-dimensional size of the traffic participants by using the speed, the orientation and the position information of the traffic participants and combining a traffic database to obtain a rectangular collision model of each traffic participant;
step seven: judging whether collision conflicts occur among the traffic participants according to the identified GPS information, speed and acceleration data of the traffic participants;
step eight: and if collision and collision among the traffic participants are possible, the edge computing server sends an early warning message to the vehicle OBU equipment through the road side RSU equipment.
And monitoring traffic participants in the road intersection monitoring area in the step two, wherein the traffic participants comprise pedestrians, non-motor vehicles and motor vehicles.
The method for monitoring and sensing in the steps is based on the real-time fusion of 5G communication and road intersection camera data, and comprises the following specific steps:
(1) the 5G mobile equipment carried by the traffic participant collects the speed, the acceleration and the GPS positioning data of the traffic participant, sends the information to the intersection 5G base station in a data packet mode and then sends the information to the edge computing server;
(2) the intersection camera synchronously uploads an intersection monitoring picture with time information to the edge computing server;
(3) and the edge calculation server respectively processes the two types of monitoring data in real time to obtain the speed, acceleration, track and other prediction information of the road intersection traffic participants.
And thirdly, comparing the traffic participants identified by the two groups of monitoring data, searching the traffic participants which are not identified in the monitoring data of the camera, monitoring three data periods, screening whether the traffic participants which are not identified by the camera at the intersection are positioned in the video blind area, if the traffic participants are judged to be positioned in the blind area, listing the traffic participants as the identified traffic participants, and renumbering all the identified traffic participants.
In the steps of the method, the three-dimensional size of the traffic participants is estimated by utilizing the currently known speed, direction and position information of the traffic participants and combining a data experience base, so that a rectangular collision model of each traffic participant is obtained.
Selecting any traffic participant as a main participant, judging other traffic participants as far-direction traffic participants, judging whether collision occurs between the main and far-direction traffic participants by taking the predicted track intersection point of the main and far-direction traffic participants as a predicted collision point, and obtaining the GPS information, the predicted track, the speed and the acceleration information of the main and far-direction traffic participants, wherein the radius of the earth is 6378137m, and the latitude and longitude information Lon of the main and far-direction traffic participants h And Lat h Far-away traffic informationLatitude and longitude information Lon of the fellow t And Lat t And the difference between the longitude and latitude, Δ Lat ═ Lat t -Lat h 、ΔLongt＝Lont t -Lont h Thereby obtaining a relative distance scalar between the main traffic participant and the far traffic participant
And calculating the speed component of the far-direction vehicle on the heading angle of the main-direction vehicle, further obtaining the relative collision time TTC taking the headway as a measurement standard, and taking whether the TTC is smaller than a specific threshold value as a judgment condition.
If the judgment condition in the steps of the method determines that the collision occurs, the edge computing server sends an early warning instruction to the road side RSU equipment, and the road side RSU equipment sends BSM format data to the vehicle-mounted OBU equipment to early warn the abnormal condition of the road intersection.
The related algorithm processes in the steps of the method are all operated and processed in the edge computing server, and the road junction 5G base station, the road junction camera and the road side RSU equipment only upload and forward data.
The working principle of the invention is as follows:
the crossing monitoring and early warning method based on base station communication and crossing camera positioning provided by the invention monitors the 5G mobile terminal carried by the traffic participant at the road crossing, establishing communication connection with the mobile terminal through a plurality of 5G base stations at the road intersection, calculating the position information of a mobile terminal carrier through an arrival time difference algorithm, uploading the position information to an edge calculation server, uploading the data of the cameras together in real time, the monitoring data of the base station positioning and the camera are fused in the edge computing server, so as to identify the traffic participants at the current moment, and the traffic participants in the dead zone of the camera at the intersection are monitored and discriminated through the real-time communication between the mobile terminal and the base station, the method comprises the following steps of obtaining a predicted collision point by predicting the driving track of traffic participants, and judging whether the traffic participants collide or not according to the time difference of the traffic participants reaching the predicted collision point: if the conflict is judged not to occur, continuing monitoring; and if the conflict is judged to occur, sending an early warning prompt to the affected traffic participants through the road side RSU equipment, and finishing the early warning function based on the Internet of vehicles.
The invention has the beneficial effects that:
the crossing monitoring and early warning method based on base station communication and crossing camera positioning provided by the invention provides an intelligent terminal combined with traffic participators to further improve the identification rate and positioning accuracy of the traffic participators at the road crossing aiming at the phenomena of mistaken identification and missed identification of the traffic objects caused by the fact that the road crossing cameras are easy to generate image acquisition blind areas and the identification rate at night is insufficient, and the traffic participants in the dead zone of the camera at the intersection are identified by the real-time communication between the mobile equipment and the base station, a method for calculating the potential conflict of the road intersection by an edge calculation server and broadcasting danger information to vehicles at the road intersection by combining with internet of vehicles communication, the method can identify the positions and the tracks of the road junction vulnerable traffic participants, can early warn the potential conflicts of the road junction traffic participants all the day and all the time, and can effectively reduce the accident rate of intelligent traffic.
Drawings
Fig. 1 is a schematic diagram of a road intersection monitoring and early warning system based on 5G and internet of vehicles according to the present invention.
FIG. 2 is a schematic diagram of the interaction between traffic objects based on the 5G and the Internet of vehicles according to the present invention.
Fig. 3 is a schematic diagram of a mobile terminal positioning system based on a multi-base-station TDOA algorithm according to the present invention.
Fig. 4 is a flow chart of the road intersection monitoring and early warning system according to the invention.
FIG. 5 is a schematic diagram illustrating a traffic participant conflict scenario in accordance with an embodiment of the present invention.
Detailed Description
Please refer to fig. 1 to 5:
the invention provides a road intersection monitoring and early warning system based on a base station and a video positioning and vehicle networking technology, which comprises the following specific implementation methods:
the method comprises the following steps: when an intelligent mobile terminal carried by a traffic participant of a road intersection enters a monitoring area of the road intersection, the intelligent mobile terminal establishes communication connection with intersection base stations, and along with the movement of the traffic participant, the intelligent mobile terminal carried by the traffic participant establishes communication connection with a plurality of intersection base stations;
step two: as shown in fig. 2 and fig. 3, a pedestrian 105, a rider 106, and a motor vehicle 107 at the intersection establish communication connection with a base station 111 at the intersection through intelligent mobile devices 101, 102, and 103 carried by the pedestrian, the rider 106, and the motor vehicle 107 respectively, and then calculate to obtain specific coordinate information of the traffic participants through the position and time difference relationship between a plurality of intersection base stations and the mobile intelligent terminal, and send the calculated data information to an edge meter server 112 through a high-speed optical fiber data line:
in the formula (x) i ,y j ) Base station coordinates of the road intersection are shown, wherein i and j are base station labels (i, j is 1 … n, i is not equal to j, and n is the number of base stations); (x, y) are position coordinates of the mobile terminal; t is t i And t j Communication transfer time between the ith and jth base stations and the mobile terminal respectively;
establishing a position equation set between a base station and a mobile terminal, and solving the equation set to obtain GPS position information of the mobile terminal;
step three: the edge computing server performs data noise reduction and post-processing on the acquired position data of the traffic participants, obtains a state analysis model of the traffic participants by using a classification algorithm, analyzes the GPS position information, the orientation information and the speed information of the traffic participants in the model in the whole time period, and identifies and obtains the specific categories of the traffic participants by using a clustering algorithm;
step four: the intersection camera sends the image information with the timestamp information to the edge calculation server through a high-speed optical fiber data line;
step five: the edge calculation server carries out clustering identification on the image data with the timestamp information to obtain basic position information of traffic participants such as pedestrians, electric vehicles, motor vehicles and large vehicles based on a road intersection coordinate system, and numbers are carried out on each traffic participant.
Step six: and performing time synchronization fusion on the information acquired by the base station and the camera, matching the position information and the category information of the traffic participants obtained based on the mobile terminal with the position coordinate information and the category information obtained based on the image data by the edge calculation server, further enriching the pose information and the track information of the traffic participants obtained by image recognition, and continuously reserving the serial numbers of the traffic participants in the step five.
Step seven: numbering traffic participants which are not synchronously identified based on the image data in the 5G-based mobile terminal;
step eight: and identifying and judging the traffic participants in the step seven, if the relevant traffic participant information can be obtained by continuously calculating through the data information uploaded by the base station by the edge calculation server in the following three data acquisition periods, considering that the traffic participant exists outside the sensing range of the camera or in the blind area, and numbering the traffic participants of the type.
Step nine: and uniformly numbering the two types of traffic participants in the sixth step and the eighth step, dividing the two types of traffic participants into four groups according to the four directions from the road to the intersection, and sequentially numbering the traffic participants in each group from large to small according to the monitored real-time speed.
Step ten: matching the three-dimensional size of each traffic participant by using the information of the speed, the orientation, the position and the like of the four groups of traffic participants in the step nine and combining a data experience base to obtain a rectangular collision model of the traffic participants;
step eleven: as shown in fig. 5, based on the positions, speeds and rectangular model information of the traffic participants, it is computationally determined whether a conflict between the traffic participants will occur:
wherein the position angle of the far traffic participant B relative to the main traffic participant A is
In the formula Lon h And Lat h Latitude and longitude information, Lon, of a leading traffic participant A t And Lat t Longitude and latitude information of a far-away traffic participant B;
head in the formula A Is the heading angle of traffic participant a; α is the relative angle between projected collision point C and traffic participant B and traffic participant A;
β＝Head B -Head A ；
head in the formula B Is the heading angle of traffic participant B; beta is the difference of course angles;
wherein the radius of the earth is taken as R-6378137 m; Δ Lat ═ Lat t -Lat h 、ΔLongt＝Lont t -Lont h The longitude and latitude difference of the main traffic participant A and the far traffic participant B are respectively; x ab Is the scalar distance between traffic participant a and traffic participant B;
X ac ＝X ab ·(cosα-sinα/tanβ)；
in the formula X ac Is the scalar distance between traffic participant a and trajectory collision point C;
X bc ＝-X ab ·sinα/sinβ；
in the formula X bc Is the scalar distance between the traffic participant B and the trajectory collision point C;
step twelve: according to the speed and acceleration information of the main traffic participants and the far traffic participants, respectively calculating the time of the two vehicles reaching the track intersection, and calculating the time difference of the two vehicles reaching the intersection area:
t A ＝X ac /v A ；
t B ＝X bc /v B ；
TTC＝|t A -t B |；
in the formula v A Is the travel speed of traffic participant a; v. of B The travel speed of traffic participant B; t is t A The time when the leading traffic participant reaches the trajectory collision point; t is t B The time of the far traffic participant reaching the track collision point; TTC is the time difference between the two reaching the locus collision point.
Step thirteen: establishing a time difference database related to the speeds of the two vehicles according to expert experience, taking the time difference TTC as a judgment condition, and judging that the two vehicles conflict with each other if the time difference is smaller than a certain threshold value:
fourteen steps: if the traffic participants determine that a conflict occurs, sending the early warning message to the OBU device 104 of the vehicle through the road-side RSU device 110;
step fifteen: the OBU equipment of vehicle sends information to vehicle ECU to turn into the sound with early warning message or give motor vehicle driver through HUD image suggestion, with the unusual early warning function of final realization road crossing.
In this example, the roadside RSU device based on the internet of vehicles is specifically configured to send the sent early warning information to the vehicle-mounted OBU device in a BSM format.
Claims (1)
1. A crossing monitoring and early warning method based on base station communication and crossing camera positioning is characterized in that: the method comprises the following steps:
the method comprises the following steps: judging whether the traffic participants enter a monitoring area or not through the connection of 5G mobile terminals carried by the traffic participants and 5G base stations at each intersection of the road, wherein the monitoring area is the coverage area of four 5G base stations at each intersection of the road;
step two: if the traffic participant enters the base station monitoring area of the road intersection, the 5G mobile terminal carried by the traffic participant establishes communication connection with more than two base stations of the intersection, wherein the position information (x) of the base stations i ,y i ) As is known, the TDOA arrival time difference algorithm is utilized
Wherein i and j are base station labels, i ≠ jCalculating an equation set to obtain (x, y) coordinate information, namely coordinate information of a mobile terminal carried by a traffic participant, and sending the coordinate information to an edge calculation server; the traffic participants monitored in the road intersection monitoring area comprise pedestrians, non-motor vehicles and motor vehicles;
step three: the road intersection camera synchronously sends the intersection video to the edge calculation server, and the known position information is fixed by the intersection camera to perform Gaussian filtering on the two-dimensional image
Extracting and matching the characteristics to obtain the information of the category, speed and position of the current traffic participant; comparing the traffic participants identified by the two groups of monitoring data, searching traffic participants which are not identified in the monitoring data of the camera, monitoring three data periods, judging whether the traffic participants which are not identified by the camera at the intersection are positioned in a video blind area, if the traffic participants which are not identified by the camera at the intersection are positioned in the video blind area, listing the traffic participants as the identified traffic participants, and numbering all the identified traffic participants again;
step four: respectively numbering traffic participant information identified by the 5G mobile terminal and the base station and traffic participant information identified by the road intersection camera data;
step five: comparing the information of the two groups of traffic participants, screening out traffic participants which are not identified due to the blind areas of the visual fields of the cameras at the road intersections, the illumination conditions and the foreign matter shielding reasons, and sequencing and numbering the traffic participants again;
step six: estimating the three-dimensional size of the traffic participants by using the speed, the orientation and the position information of the traffic participants and combining a traffic database to obtain a rectangular collision model of each traffic participant;
step seven: judging whether collision conflicts occur among the traffic participants according to the identified GPS information, speed and acceleration data of the traffic participants;
step eight: if collision and collision are possible among the traffic participants, the edge computing server sends an early warning message to the vehicle OBU equipment through the road side RSU equipment;
the method for monitoring and sensing in the steps is based on the real-time fusion of 5G communication and road intersection camera data, and comprises the following specific steps:
(1) the 5G mobile terminal carried by the traffic participant collects the speed, the acceleration and the GPS positioning data of the traffic participant, sends the information to the intersection 5G base station in a data packet mode and then sends the information to the edge computing server;
(2) the intersection camera synchronously uploads an intersection monitoring picture with time information to the edge computing server;
(3) respectively processing the two types of monitoring data in real time by the edge computing server to obtain speed, acceleration and track prediction information of road intersection traffic participants;
the method comprises the steps of estimating the three-dimensional size of the traffic participants by using the currently known speed, orientation and position information of the traffic participants and combining a data experience base to obtain a rectangular collision model of each traffic participant;
selecting any traffic participant as a main participant, judging other traffic participants as far-direction traffic participants, judging whether collision occurs between the main and far-direction traffic participants by taking the predicted track intersection point of the main and far-direction traffic participants as a predicted collision point, and obtaining the GPS information, the predicted track, the speed and the acceleration information of the main and far-direction traffic participants, wherein the radius of the earth is 6378137m, and the latitude and longitude information Lon of the main and far-direction traffic participants h And Lat h Longitude and latitude information Lon of far-distance traffic participants t And Lat t And the difference between the longitude and latitude, Δ Lat ═ Lat t -Lat h 、ΔLongt＝Lont t -Lont h Thereby obtaining a relative distance scalar between the main traffic participant and the far traffic participant
And calculating the speed of the remote vehicle at the heading angle of the host vehicleComponent, further obtaining relative collision time TTC taking the headway as a measurement standard, and taking whether the TTC is smaller than a specific threshold value as a judgment condition;
if the judgment condition in the steps of the method determines that the collision occurs, the edge computing server sends an early warning instruction to the road side RSU equipment, and the road side RSU equipment sends BSM format data to the vehicle-mounted OBU equipment to early warn the abnormal condition of the road intersection;
the related algorithm processes in the steps of the method are all operated and processed in the edge computing server, and the road junction 5G base station, the road junction camera and the road side RSU equipment only upload and forward data.
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| TWI776700B (en) * | 2021-10-05 | 2022-09-01 | 財團法人車輛研究測試中心 | Traffic warning system and method based on information fusion |
| CN115131990A (en) * | 2022-05-20 | 2022-09-30 | 兆边(上海)科技有限公司 | Early warning method and system for mixed flow collision risk of non-signal control intersection |
| CN115376344A (en) * | 2022-07-20 | 2022-11-22 | 安徽电信规划设计有限责任公司 | Intelligent driving control method and system based on wireless 5G technology |
| CN115346370B (en) * | 2022-08-10 | 2023-11-03 | 重庆大学 | Intersection anti-collision system and method based on intelligent traffic |
| CN115571152B (en) * | 2022-10-12 | 2023-06-06 | 深圳市旗扬特种装备技术工程有限公司 | Safety early warning method, device, system, equipment and medium for non-motor vehicle |
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