CN113554881A - Artificial intelligence road event monitoring method, device, system and storage medium - Google Patents
Artificial intelligence road event monitoring method, device, system and storage medium Download PDFInfo
- Publication number
- CN113554881A CN113554881A CN202110821479.5A CN202110821479A CN113554881A CN 113554881 A CN113554881 A CN 113554881A CN 202110821479 A CN202110821479 A CN 202110821479A CN 113554881 A CN113554881 A CN 113554881A
- Authority
- CN
- China
- Prior art keywords
- monitoring
- information
- traffic accident
- event
- traffic
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
Images
Classifications
-
- G—PHYSICS
- G08—SIGNALLING
- G08G—TRAFFIC CONTROL SYSTEMS
- G08G1/00—Traffic control systems for road vehicles
- G08G1/01—Detecting movement of traffic to be counted or controlled
- G08G1/04—Detecting movement of traffic to be counted or controlled using optical or ultrasonic detectors
-
- G—PHYSICS
- G08—SIGNALLING
- G08G—TRAFFIC CONTROL SYSTEMS
- G08G1/00—Traffic control systems for road vehicles
- G08G1/01—Detecting movement of traffic to be counted or controlled
- G08G1/0104—Measuring and analyzing of parameters relative to traffic conditions
-
- G—PHYSICS
- G08—SIGNALLING
- G08G—TRAFFIC CONTROL SYSTEMS
- G08G1/00—Traffic control systems for road vehicles
- G08G1/01—Detecting movement of traffic to be counted or controlled
- G08G1/017—Detecting movement of traffic to be counted or controlled identifying vehicles
- G08G1/0175—Detecting movement of traffic to be counted or controlled identifying vehicles by photographing vehicles, e.g. when violating traffic rules
-
- G—PHYSICS
- G08—SIGNALLING
- G08G—TRAFFIC CONTROL SYSTEMS
- G08G1/00—Traffic control systems for road vehicles
- G08G1/01—Detecting movement of traffic to be counted or controlled
- G08G1/052—Detecting movement of traffic to be counted or controlled with provision for determining speed or overspeed
Landscapes
- Physics & Mathematics (AREA)
- General Physics & Mathematics (AREA)
- Chemical & Material Sciences (AREA)
- Analytical Chemistry (AREA)
- Traffic Control Systems (AREA)
Abstract
The invention discloses an artificial intelligent road event monitoring method, a device, a system and a storage medium, which comprises the steps of establishing a traffic accident characteristic identification model, and judging whether a traffic accident event occurs in a current expressway monitoring area; if the event is judged to occur, sending the monitoring information to a control center for secondary judgment of the event; and if the confirmation instruction sent by the control center is received or the instruction sent by the control center is not received within the preset time, broadcasting the information of the accident event. Intelligent identification is carried out on the site through the traffic accident identification model, and default treatment is carried out under the condition that manual treatment is not carried out; when the result is sent to the management and control center, the prompt information is sent to the management and control center, and the staff can judge whether to rescue or evacuate in time according to the prompt information and the monitoring information; in this way, the congestion condition of the expressway or the continuous accident rate of the expressway can be reduced.
Description
Technical Field
The invention belongs to the field of highway traffic supervision, and particularly relates to an artificial intelligent road event monitoring method, device and system and a storage medium.
Background
The rapid development of the expressway and the large-scale primary molding of the expressway network really bring convenience and quickness for the people to go out. However, the congestion of the highway frequently occurs, which greatly reduces the superiority of the highway in 'fast clear', and also provides a serious challenge for the traffic safety management work of the highway.
Causes of highway traffic congestion include the following:
1. traffic jam caused by traffic accidents. The accident caused by single or multiple vehicle collision, especially the accident caused by large or heavy vehicle, can result in the accident vehicle occupying the great road surface or whole road surface of the highway, and the traffic jam. Some traffic accidents can cause serious consequences such as leakage and explosion of vehicles or dangerous chemicals, and cause traffic interruption or form traffic bottlenecks, thereby causing serious congestion.
2. Traffic congestion caused by bad weather. The ice-snow day and the fog day have great influence on traffic, the ice-snow day is easy to cause the road surface of the expressway to be iced, particularly, the bridge of the expressway is easy to be iced, and running vehicles are easy to slip and cannot normally and safely pass. Fog weather causes reduced visibility over large areas and long distances on highways, and vehicles already traveling on highways have to reduce their speed, causing congestion. In addition, traffic accidents are often caused by ice, snow and fog weather, even secondary accidents or tail forcing of multiple vehicles are often caused, and severe highway blockage is caused.
3. Traffic jam caused by road construction operation. The rapid increase of traffic volume generally causes the heavy burden of the highway, the fatigue of the road surface is accelerated, the aging is advanced, the highway operation and management unit needs to maintain, transform and upgrade the damaged road or other facilities and needs to occupy a certain lane, so that the lane becomes narrow, the speed of the vehicle is reduced, and the traffic jam is caused.
4. Traffic congestion caused by a surge in traffic volume. The rapid increase of the traffic flow becomes the most direct factor of congestion, along with the continuous and rapid development of the economic society of China, the motor vehicles grow rapidly, particularly after the country implements a freeway holiday free policy, the traffic flow during holidays is increased rapidly and far exceeds the design flow of the expressway at first, and the traffic congestion is easily caused after the traffic flow reaches a saturated state.
5. More severe congestion due to human factors. The traffic accident or other reasons are that the driver does not obey the traffic rules, the cracks are disordered when seeing the cracks, and the water cannot be drained when the rescue channel at the very least is blocked, so that the traffic accident is not conducive to traffic dispersion, the collision and scraping are easy to happen, and the work is more difficult to recover the traffic for a longer time.
At present, a plurality of cameras are arranged on a highway, are mainly used for monitoring the traffic condition on site and mainly depend on artificial identification; increasing the artificial workload.
Disclosure of Invention
The invention aims to provide an artificial intelligent road event monitoring method, device, system and storage medium, which solve the problem of intelligent monitoring of traffic conditions in the existing high-speed kilometers.
In order to achieve the purpose, the invention adopts the following technical scheme:
in a first aspect, the present invention provides an artificial intelligence road event monitoring method, comprising the following steps:
establishing a traffic accident video stream big database, acquiring a pattern when a traffic accident occurs in a video stream, marking traffic accident characteristics, and establishing a first traffic accident identification model according to the traffic accident characteristics;
judging whether a traffic accident event occurs in the current expressway monitoring area according to the first traffic accident identification model;
if the traffic accident event is judged to occur, sending the monitoring information to a control center for secondary judgment of the event;
if a confirmation instruction sent by the control center is received or an instruction sent by the control center is not received within a preset time, broadcasting the information of the accident event;
and if a negative confirmation instruction sent by the control center is received within the preset time, stopping the secondary judgment of the event.
According to the technology, a traffic accident recognition model is established, intelligent recognition is carried out on the site through the traffic accident recognition model, recognized monitoring information and results are sent to a control center for secondary manual confirmation, on the basis of manual confirmation, the results of the manual confirmation are used as a first priority, and default treatment is carried out under the condition that manual treatment is not carried out; therefore, the human interference is considered, and the intelligent equipment side can perform autonomous identification; therefore, the monitoring and checking can be carried out without the need of human 24 hours; when the result is sent to the management and control center, the prompt information is sent to the management and control center, and the staff can judge whether to rescue or evacuate in time according to the prompt information and the monitoring information; in this way, the congestion condition of the expressway or the continuous accident rate of the expressway can be reduced.
In one possible design, the method of building a large database of traffic accident video streams is as follows: acquiring monitoring information of vehicle running at the current position, judging whether the vehicle in the current monitoring information is in a normal driving state by the system, if not, judging that a traffic accident event occurs, and uploading a video stream of the event which is judged to occur at the current time of the traffic accident event to a traffic accident video stream big database. According to the invention, whether the vehicle stops in the driving process is monitored, and if the vehicle stops, traffic abnormality is possibly caused according to the driving characteristics of the highway; thus, it is possible to estimate that congestion may be caused by an accident event.
In one possible design, the monitoring information includes video stream information and radar monitoring information. Through looking screen control and radar and monitoring, video monitoring can be more directly perceived, and the radar control can avoid the condition that screen control can not be seen in the snow day fog, and in addition, the distance of radar control is farther, can reduce the installation quantity of looking screen monitoring device.
In one possible design, whether a traffic accident event occurs in a current expressway monitoring area is judged according to the video stream information, if yes, the video stream information and radar monitoring information of the monitoring area corresponding to the video stream information are uploaded, a radar monitoring information database is established, traffic accident characteristics in the radar monitoring information are marked, and a second traffic accident identification model is established;
and judging whether the traffic accident event happens in the expressway monitoring area according to the first traffic accident identification model and/or the second traffic accident identification model.
Through on the basis of using video stream information and the first traffic accident identification model of figure analysis, establish radar identification model, during the discernment, can use some regions to use to look screen monitoring and radar monitoring cooperation according to the demand, improve the discernment precision, in some regions, can use exclusive use radar control, reduce the equipment and use, realize wider control.
In a second aspect, the present invention provides an artificial intelligence road event monitoring device, comprising
The monitoring information receiving unit is used for receiving monitoring information transmitted by monitoring equipment in the current expressway monitoring area;
the traffic accident identification unit is used for acquiring a traffic accident characteristic identification model and judging whether a traffic accident event occurs in the current expressway monitoring area according to the traffic accident identification model;
the monitoring information sending unit is used for sending the monitoring information to the control center for secondary judgment of the accident if the accident identification unit judges that the accident event occurs;
the instruction receiving unit is used for receiving the confirmation instruction and the denial instruction sent by the control center;
the information broadcasting unit is used for broadcasting the information of the car accident event if the confirmation instruction sent by the control center is received or the instruction sent by the control center is not received within the preset time; if a denial instruction sent by the control center is received within a preset time, the information of the traffic accident possibly occurring is broadcasted.
In a third aspect, the invention provides an artificial intelligence road event monitoring system, comprising the road event monitoring device according to the second aspect, a plurality of view screen monitoring devices, a plurality of tracking monitoring radars and a management and control platform, wherein,
the video monitoring device is used for monitoring the highway to obtain video stream information and transmitting the video stream information to the road event monitoring device;
the plurality of tracking monitoring radars are used for tracking and monitoring the vehicles running on the highway to obtain radar monitoring information; transmitting the radar monitoring information to a road event monitoring device;
the road event monitoring device is used for receiving monitoring information, wherein the monitoring information comprises video stream information transmitted by a plurality of video monitoring devices and radar monitoring information transmitted by a plurality of tracking monitoring radars, transmitting the monitoring information to the control platform and broadcasting a car accident to a related platform when the car accident happens;
the road event monitoring device is communicated with the screen monitoring device through 5G;
and the control platform is used for receiving the monitoring information from the road event monitoring device and sending the information whether the traffic accident happens to the road event monitoring device.
According to the technical content, the tracking monitoring can be carried out on the vehicles on the detected road section by adopting the tracking monitoring radar, hundreds of vehicles can be monitored simultaneously, the traffic flow speed and the stopping condition of the vehicles can be obtained from the monitoring information, and the accident event can be judged to occur at the place where the vehicles stop, so that the system can quickly respond to the accident; less time for traffic congestion.
Through above technique, because highway is apart from long, video information is capacious, and in order to keep stable information transmission, carry out information communication through adopting 5G, can guarantee the speed of its transmission and upload the screen of looking of high definition form, make things convenient for the management and control center to look over original information.
In one possible design, the related platform comprises a traffic condition electronic display notice board arranged on a highway, a navigation system, a traffic broadcast server and an automobile terminal which is in communication connection with the traffic broadcast server.
In one possible design, after the automobile terminal enters a monitored highway section, a vehicle license number is identified through the video screen monitoring device and is transmitted to the traffic broadcast server, and the traffic broadcast server identifies vehicle information of an owner and a vehicle machine corresponding to the vehicle information from a filing database according to the license number; when the traffic broadcast server broadcasts traffic accident information, the traffic broadcast server sends the information to a vehicle machine corresponding to a vehicle terminal entering a monitored highway section; and broadcasting in real time through the vehicle machine.
Through the technology, information acquisition is carried out on vehicles entering a monitoring area, information matching is carried out, and through an intelligent vehicle machine system with information record and matching value, the vehicle machine carries out broadcasting of road conditions to remind running vehicles, so that continuous traffic accidents can be caused on less highways due to high speed.
In a fourth aspect, the present invention provides an artificial intelligence road event monitoring apparatus, comprising a memory, a processor and a transceiver connected in sequence, wherein the memory is used for storing a computer program, the transceiver is used for transmitting and receiving messages, and the processor is used for reading the computer program and executing the artificial intelligence road event monitoring method as described in the first aspect or any one of the possible designs of the first aspect.
In a fifth aspect, the present invention provides a computer readable storage medium having stored thereon instructions which, when run on a computer, perform an artificial intelligence road event monitoring method as described above in the first aspect or any one of the possible designs of the first aspect.
Has the advantages that:
1. the traffic accident recognition method comprises the steps that a traffic accident recognition model is established, intelligent recognition is conducted on a site through the traffic accident recognition model, recognized monitoring information and results are sent to a control center to conduct secondary manual confirmation, on the basis of manual confirmation, the results of the manual confirmation are used as a first priority, and default processing is conducted under the condition that manual processing is not conducted; therefore, the human interference is considered, and the intelligent equipment side can perform autonomous identification; therefore, the monitoring and checking can be carried out without the need of human 24 hours; when the result is sent to the management and control center, the prompt information is sent to the management and control center, and the staff can judge whether to rescue or evacuate in time according to the prompt information and the monitoring information; in this way, the congestion condition of the expressway or the continuous accident rate of the expressway can be reduced.
2. According to the driving characteristics of the highway, if the vehicle stops, traffic abnormality is possibly caused; thus, it is possible to estimate that congestion may be caused by an accident event.
3. The tracking monitoring radar can track and monitor vehicles on a detected road section, hundreds of vehicles can be monitored simultaneously, the traffic flow speed and the stopping condition of the vehicles can be obtained from monitoring information, and a traffic accident event can be determined to occur at the place where the vehicles stop, so that the system can quickly respond to the occurrence of the traffic accident; times of less traffic congestion; because the distance of the highway is long, the capacity of video information is large, and in order to keep stable information transmission, 5G is adopted for information communication, so that the transmission speed of the highway and the uploading of a high-definition format video screen can be ensured, and the original information can be conveniently checked by a management and control center; through carrying out information acquisition to the vehicle that gets into the control area, carry out the information matching again, through information record, matching value intelligence car machine system, implement by the car and report the road conditions and remind the vehicle that traveles, can be greatly less on the highway because the condition of the continuous traffic accident that causes fast.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, it is obvious that the drawings in the following description are only some embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to the drawings without creative efforts.
FIG. 1 is a flow diagram of an artificial intelligence road event monitoring method provided by the present invention.
Fig. 2 is a schematic diagram of the unit modules of the artificial intelligence road event monitoring device provided by the invention.
FIG. 3 is a block diagram of an artificial intelligence road event monitoring system provided by the present invention.
Detailed Description
The invention is further described with reference to the following figures and specific embodiments. It should be noted that the description of the embodiments is provided to help understanding of the present invention, but the present invention is not limited thereto. Specific structural and functional details disclosed herein are merely illustrative of example embodiments of the invention. This invention may, however, be embodied in many alternate forms and should not be construed as limited to the embodiments set forth herein.
As shown in fig. 1, the method for monitoring an artificial intelligence road event according to the first aspect of the present invention includes the following steps:
establishing a traffic accident video stream big database, acquiring a pattern when a traffic accident occurs in a video stream, marking traffic accident characteristics, and establishing a first traffic accident identification model according to the traffic accident characteristics;
judging whether a traffic accident event occurs in the current expressway monitoring area according to the first traffic accident identification model;
if the traffic accident event is judged to occur, sending the monitoring information to a control center for secondary judgment of the event;
if a confirmation instruction sent by the control center is received or an instruction sent by the control center is not received within a preset time, broadcasting the information of the accident event; in specific implementation, if the command sent by the control center is not received, the information of the possible car accident event is broadcasted; for example, the prompt message is: at 2 km ahead, a suspected car accident may cause a continuous accident or congestion, please drive cautiously.
And if a negative confirmation instruction sent by the control center is received within the preset time, stopping the secondary judgment of the event.
In one possible implementation, the method of building a large database of traffic accident video streams is as follows: acquiring monitoring information of vehicle running at the current position, judging whether the vehicle in the current monitoring information is in a normal driving state by the system, if not, judging that a traffic accident event occurs, and uploading a video stream of the event which is judged to occur at the current time of the traffic accident event to a traffic accident video stream big database.
In one possible embodiment, the monitoring information includes video stream information and/or radar monitoring information.
In one possible implementation mode, whether a traffic accident event occurs in a current expressway monitoring area is judged according to the video stream information, if yes, the video stream information and radar monitoring information of the monitoring area corresponding to the video stream information are uploaded, a radar monitoring information database is established, traffic accident characteristics in the radar monitoring information are marked, and a second traffic accident identification model is established;
and judging whether the traffic accident event happens in the expressway monitoring area according to the first traffic accident identification model and/or the second traffic accident identification model.
In specific implementation, when the vehicle is monitored to stop and exceed a preset time threshold, the vehicle is determined to be abnormally driven; the model also comprises the steps of carrying out simulation prediction on the motion track of the automobile according to the running speed and the running track of the automobile, and if the running track of the automobile does not accord with the predicted track, detecting whether the automobile stops or not by combining radar detection signals; if the driving track and the simulated track do not accord with the condition that the moving target stops, a major car accident can be judged to happen, and a high-level reminding signal is sent to the control center.
A second aspect of the present embodiment provides an artificial intelligence road event monitoring device, as shown in FIG. 2, comprising
The monitoring information receiving unit is used for receiving monitoring information transmitted by monitoring equipment in the current expressway monitoring area;
the traffic accident identification unit is used for establishing a traffic accident characteristic identification model and judging whether a traffic accident event occurs in the current expressway monitoring area according to the traffic accident identification model;
the monitoring information sending unit is used for sending the monitoring information to the control center for secondary judgment of the accident if the accident identification unit judges that the accident event occurs;
the instruction receiving unit is used for receiving the confirmation instruction and the denial instruction sent by the control center;
the information broadcasting unit is used for broadcasting the information of the car accident event if the confirmation instruction sent by the control center is received or the instruction sent by the control center is not received within the preset time; if a denial instruction sent by the control center is received within a preset time, the information of the traffic accident possibly occurring is broadcasted.
A third aspect of the present embodiment provides an artificial intelligence road event monitoring system, as shown in fig. 3, comprising a road event monitoring device according to the second aspect, a plurality of view screen monitoring devices, a plurality of tracking monitoring radars, and a management and control platform, wherein,
the video monitoring device is used for monitoring the highway to obtain video stream information and transmitting the video stream information to the road event monitoring device;
the plurality of tracking monitoring radars are used for tracking and monitoring the vehicles running on the highway to obtain radar monitoring information; transmitting the radar monitoring information to a road event monitoring device;
the road event monitoring device is used for receiving monitoring information, wherein the monitoring information comprises video stream information transmitted by a plurality of video monitoring devices and radar monitoring information transmitted by a plurality of tracking monitoring radars, transmitting the monitoring information to the control platform and broadcasting a car accident to a related platform when the car accident happens;
the road event monitoring device is communicated with the screen monitoring device through 5G;
and the control platform is used for receiving the monitoring information from the road event monitoring device and sending the information whether the traffic accident happens to the road event monitoring device.
When the radar is specifically implemented, the tracking and monitoring radar adopts multi-element omnibearing tracking and detecting radar sensor equipment, all moving vehicles or pedestrians in a radar area are tracked and positioned in real time and the real-time position of each target and the original data information of the radar are obtained in a 360-degree omnibearing scanning mode, the radar adopts a high-frequency transmitting unit with the main frequency of 77GHz, a signal receiving unit, a data processing unit, a communication unit and the like, and a core data processing unit adopts a multi-thread high-speed processor and can simultaneously track and position not less than 1000 target objects. The radar detector can track and position at least 1000 target objects in a whole area with the radius of 500 meters by taking a radar as a center in a 360-degree high-speed scanning mode, the target tracking and positioning accuracy error is less than 17.5 cm, the moving speed range of a target detected by the radar is 0-250Km/h, the positioning requirement of the vehicle for realizing full-speed intelligent driving is completely met by data interaction with the tracked target for 800 times per second, and in addition, the radar can also provide important information such as the timely speed, the moving direction, the longitude and latitude, the target size, the ID number, the direction angle and the like of each vehicle within one kilometer. The radar sensor adopts an integrated design, and the whole equipment adopts IP67 safety protection level for ensuring the service life of the radar. All parts of the radar are selected and adopt low-power-consumption designs and devices. The radar adopts a 100M network port to carry out data communication with the outside.
In one possible implementation mode, the related platform comprises a traffic condition electronic display notice board arranged on a highway, a navigation system, a traffic broadcast server and a vehicle terminal which is in communication connection with the traffic broadcast server.
In one possible implementation mode, after the automobile terminal enters a monitored highway section, a vehicle license number is identified through the video screen monitoring device, the license number is transmitted to the traffic broadcast server, and the traffic broadcast server identifies vehicle information of an owner and a vehicle machine corresponding to the vehicle information from a filing library according to the license number; when the traffic broadcast server broadcasts traffic accident information, the traffic broadcast server sends the information to a vehicle machine corresponding to a vehicle terminal entering a monitored highway section; and broadcasting in real time through the vehicle machine.
In a fourth aspect, the present invention provides an artificial intelligence road event monitoring apparatus, comprising a memory, a processor and a transceiver connected in sequence, wherein the memory is used for storing a computer program, the transceiver is used for transmitting and receiving messages, and the processor is used for reading the computer program and executing the artificial intelligence road event monitoring method as described in the first aspect or any one of the possible designs of the first aspect.
For example, the Memory may include, but is not limited to, a Random-Access Memory (RAM), a Read-Only Memory (ROM), a Flash Memory (Flash Memory), a First-in First-out (FIFO), and/or a First-in Last-out (FILO), and the like; the processor may not be limited to the use of a microprocessor model number STM32F105 family; the transceiver may be, but is not limited to, a Wireless Fidelity (WiFi) Wireless transceiver, a bluetooth Wireless transceiver, a General Packet Radio Service (GPRS) Wireless transceiver, a ZigBee Wireless transceiver (ieee 802.15.4 standard-based low power local area network protocol), and/or a ZigBee Wireless transceiver. In addition, the artificial intelligence road event monitoring device can also comprise, but is not limited to, a power supply module, a display screen and other necessary components.
In a fifth aspect, the present invention provides a computer readable storage medium having stored thereon instructions which, when run on a computer, perform an artificial intelligence road event monitoring method as described above in the first aspect or any one of the possible designs of the first aspect.
The computer-readable storage medium refers to a carrier for storing data, and may include, but is not limited to, floppy disks, optical disks, hard disks, flash memories, flash disks and/or Memory sticks (Memory sticks), etc., and the computer may be a general-purpose computer, a special-purpose computer, a computer network, or other programmable devices.
For the working process, working details and technical effects of the foregoing computer-readable storage medium provided in the fifth aspect of this embodiment, reference may be made to the interaction method described in the first aspect or any one of the possible designs of the first aspect, which is not described herein again.
A sixth aspect of the present embodiments provides a computer program product comprising instructions which, when run on a computer, cause the computer to perform a method of artificial intelligence road event monitoring as described in the first aspect or any one of the possible designs of the first aspect. The computer may be a general purpose computer, a special purpose computer, a network of computers, or other programmable devices.
The embodiments described above are merely illustrative, and may or may not be physically separate, if referring to units illustrated as separate components; if reference is made to a component displayed as a unit, it may or may not be a physical unit, and may be located in one place or distributed over a plurality of network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of the embodiment. One of ordinary skill in the art can understand and implement it without inventive effort.
Finally, it should be noted that the present invention is not limited to the above alternative embodiments, and that various other forms of products can be obtained by anyone in light of the present invention. The above detailed description should not be taken as limiting the scope of the invention, which is defined in the claims, and which the description is intended to be interpreted accordingly.
Claims (10)
1. The artificial intelligent road event monitoring method is characterized by comprising the following steps:
establishing a traffic accident video stream big database, acquiring a pattern when a traffic accident occurs in a video stream, marking traffic accident characteristics, and establishing a first traffic accident identification model according to the traffic accident characteristics;
judging whether a traffic accident event occurs in the current expressway monitoring area according to the first traffic accident identification model;
if the traffic accident event is judged to occur, sending the monitoring information to a control center for secondary judgment of the event;
if a confirmation instruction sent by the control center is received or an instruction sent by the control center is not received within a preset time, broadcasting the information of the accident event;
and if a negative confirmation instruction sent by the control center is received within the preset time, stopping the secondary judgment of the event.
2. The artificial intelligence road event monitoring method as claimed in claim 1, wherein the method of building a large database of traffic accident video streams is as follows:
acquiring monitoring information of vehicle running at the current position, judging whether the vehicle in the current monitoring information is in a normal driving state by the system, if not, judging that a traffic accident event occurs, and uploading a video stream of the event which is judged to occur at the current time of the traffic accident event to a traffic accident video stream big database.
3. The artificial intelligence road event monitoring method of claim 1, wherein the monitoring information includes video stream information and radar monitoring information.
4. The artificial intelligence road event monitoring method of claim 3, further comprising: judging whether a traffic accident event occurs in the current expressway monitoring area or not according to the video stream information, if so, uploading the video stream information and radar monitoring information of the monitoring area corresponding to the video stream information, establishing a radar monitoring information database, marking traffic accident characteristics in the radar monitoring information, and establishing a second traffic accident identification model;
and judging whether the traffic accident event happens in the expressway monitoring area according to the first traffic accident identification model and the second traffic accident identification model.
5. An artificial intelligence road event monitoring device, comprising
The monitoring information receiving unit is used for receiving monitoring information transmitted by monitoring equipment in the current expressway monitoring area;
the traffic accident identification unit is used for acquiring a traffic accident characteristic identification model and judging whether a traffic accident event occurs in the current expressway monitoring area according to the traffic accident identification model;
the monitoring information sending unit is used for sending the monitoring information to the control center for secondary judgment of the accident if the accident identification unit judges that the accident event occurs;
the instruction receiving unit is used for receiving the confirmation instruction and the denial instruction sent by the control center;
the information broadcasting unit is used for broadcasting the information of the car accident event if the confirmation instruction sent by the control center is received or the instruction sent by the control center is not received within the preset time; if a denial instruction sent by the control center is received within a preset time, the information of the traffic accident possibly occurring is broadcasted.
6. An artificial intelligence road event monitoring system, comprising the road event monitoring device of claim 5, a plurality of view monitoring devices, a plurality of tracking monitoring radars, and a management and control platform, wherein,
the video monitoring device is used for monitoring the highway to obtain video stream information and transmitting the video stream information to the road event monitoring device;
the plurality of tracking monitoring radars are used for tracking and monitoring the vehicles running on the highway to obtain radar monitoring information; transmitting the radar monitoring information to a road event monitoring device;
the road event monitoring device is used for receiving monitoring information, wherein the monitoring information comprises video stream information transmitted by a plurality of video monitoring devices and radar monitoring information transmitted by a plurality of tracking monitoring radars, transmitting the monitoring information to the control platform and broadcasting a car accident to a related platform when the car accident happens;
the road event monitoring device is communicated with the screen monitoring device through 5G;
and the control platform is used for receiving the monitoring information from the road event monitoring device and sending the information whether the traffic accident happens to the road event monitoring device.
7. The artificial intelligence road event monitoring system as claimed in claim 6, wherein the related platform comprises a traffic condition electronic display notice board arranged on a highway, a navigation system, a traffic broadcast server and a car terminal in communication connection with the traffic broadcast server.
8. The artificial intelligent road event monitoring system according to claim 7, wherein after the automobile terminal enters a monitored highway section, a vehicle license number is identified through the video monitoring device and is transmitted to the traffic broadcasting server, and the traffic broadcasting server identifies vehicle information of an owner and a vehicle machine corresponding to the vehicle information from a record library according to the license number; when the traffic broadcast server broadcasts traffic accident information, the traffic broadcast server sends the information to a vehicle machine corresponding to a vehicle terminal entering a monitored highway section; and broadcasting in real time through the vehicle machine.
9. The artificial intelligence road event monitoring device is characterized by comprising a memory, a processor and a transceiver which are connected in sequence, wherein the memory is used for storing a computer program, the transceiver is used for transmitting and receiving messages, and the processor is used for reading the computer program and executing the artificial intelligence road event monitoring method as claimed in any one of claims 1-4.
10. A storage medium, characterized by: the storage medium has stored thereon instructions for performing the artificial intelligence road event monitoring method according to any one of claims 1 to 4 when the instructions are run on a computer.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202110821479.5A CN113554881A (en) | 2021-07-20 | 2021-07-20 | Artificial intelligence road event monitoring method, device, system and storage medium |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202110821479.5A CN113554881A (en) | 2021-07-20 | 2021-07-20 | Artificial intelligence road event monitoring method, device, system and storage medium |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN113554881A true CN113554881A (en) | 2021-10-26 |
Family
ID=78103569
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202110821479.5A Pending CN113554881A (en) | 2021-07-20 | 2021-07-20 | Artificial intelligence road event monitoring method, device, system and storage medium |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN113554881A (en) |
Citations (13)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP2005056441A (en) * | 2004-11-10 | 2005-03-03 | Mitsubishi Electric Corp | Traffic flow measurement device |
| CN101710446A (en) * | 2009-12-11 | 2010-05-19 | 同济大学 | Expressway serious lawbreaking action monitoring and early warning system and method |
| CN102568190A (en) * | 2011-11-18 | 2012-07-11 | 厦门市鼎朔信息技术有限公司 | System and method for handling traffic accident |
| CN108538087A (en) * | 2018-06-01 | 2018-09-14 | 青岛理工大学 | Tunnel traffic accident monitoring based on laser scanning location technology and early warning system |
| CN207946936U (en) * | 2018-02-05 | 2018-10-09 | 南京南邮信息产业技术研究院有限公司 | The emergent leading of intelligent transportation based on Internet of Things and monitoring system |
| CN110097753A (en) * | 2019-04-04 | 2019-08-06 | 安徽中交汇能网络科技有限公司 | Method for early warning, device, system and the road monitoring device of second accident |
| CN110473402A (en) * | 2019-08-20 | 2019-11-19 | 河北德冠隆电子科技有限公司 | A kind of accident detection early warning system based on target abnormal behaviour trajectory analysis |
| CN110722574A (en) * | 2019-09-26 | 2020-01-24 | 山西省交通科技研发有限公司 | Intelligent monitoring and early warning robot for expressway and working method thereof |
| CN110796868A (en) * | 2019-12-02 | 2020-02-14 | 江苏中路工程技术研究院有限公司 | Video and microwave integrated traffic incident monitoring system and method |
| CN110942629A (en) * | 2019-11-29 | 2020-03-31 | 中核第四研究设计工程有限公司 | Road traffic accident management method and device and terminal equipment |
| CN111583719A (en) * | 2020-06-05 | 2020-08-25 | 安徽达尔智能控制系统股份有限公司 | Intelligent expressway early warning research and judgment method and system based on radar detection |
| CN112241974A (en) * | 2020-05-29 | 2021-01-19 | 北京新能源汽车技术创新中心有限公司 | Traffic accident detection method, processing method, system and storage medium |
| CN113076821A (en) * | 2021-03-18 | 2021-07-06 | 视云融聚（广州）科技有限公司 | Event detection method and device |
-
2021
- 2021-07-20 CN CN202110821479.5A patent/CN113554881A/en active Pending
Patent Citations (13)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP2005056441A (en) * | 2004-11-10 | 2005-03-03 | Mitsubishi Electric Corp | Traffic flow measurement device |
| CN101710446A (en) * | 2009-12-11 | 2010-05-19 | 同济大学 | Expressway serious lawbreaking action monitoring and early warning system and method |
| CN102568190A (en) * | 2011-11-18 | 2012-07-11 | 厦门市鼎朔信息技术有限公司 | System and method for handling traffic accident |
| CN207946936U (en) * | 2018-02-05 | 2018-10-09 | 南京南邮信息产业技术研究院有限公司 | The emergent leading of intelligent transportation based on Internet of Things and monitoring system |
| CN108538087A (en) * | 2018-06-01 | 2018-09-14 | 青岛理工大学 | Tunnel traffic accident monitoring based on laser scanning location technology and early warning system |
| CN110097753A (en) * | 2019-04-04 | 2019-08-06 | 安徽中交汇能网络科技有限公司 | Method for early warning, device, system and the road monitoring device of second accident |
| CN110473402A (en) * | 2019-08-20 | 2019-11-19 | 河北德冠隆电子科技有限公司 | A kind of accident detection early warning system based on target abnormal behaviour trajectory analysis |
| CN110722574A (en) * | 2019-09-26 | 2020-01-24 | 山西省交通科技研发有限公司 | Intelligent monitoring and early warning robot for expressway and working method thereof |
| CN110942629A (en) * | 2019-11-29 | 2020-03-31 | 中核第四研究设计工程有限公司 | Road traffic accident management method and device and terminal equipment |
| CN110796868A (en) * | 2019-12-02 | 2020-02-14 | 江苏中路工程技术研究院有限公司 | Video and microwave integrated traffic incident monitoring system and method |
| CN112241974A (en) * | 2020-05-29 | 2021-01-19 | 北京新能源汽车技术创新中心有限公司 | Traffic accident detection method, processing method, system and storage medium |
| CN111583719A (en) * | 2020-06-05 | 2020-08-25 | 安徽达尔智能控制系统股份有限公司 | Intelligent expressway early warning research and judgment method and system based on radar detection |
| CN113076821A (en) * | 2021-03-18 | 2021-07-06 | 视云融聚（广州）科技有限公司 | Event detection method and device |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN110853357B (en) | Early warning control system and method for dangerous highway sections and construction areas | |
| CN111223302B (en) | External coordinate real-time three-dimensional road condition auxiliary device for mobile carrier and system | |
| CN107742432B (en) | Expressway operation speed active early warning system based on vehicle-road cooperation and control method | |
| CN104794898B (en) | Special-region band-type private network transportation communication navigation monitoring and warning device and working method | |
| CN108550262B (en) | Urban traffic sensing system based on millimeter wave radar | |
| CN107331191B (en) | Abnormal running vehicle positioning method, cloud server and system | |
| CN111325945A (en) | Road construction area safety early warning device and method | |
| CN106781485B (en) | Road congestion identification method, V2X vehicle-mounted terminal and Internet of vehicles system | |
| CN110264742B (en) | Vehicle-mounted information monitoring device suitable for expressway and warning method thereof | |
| WO2022042098A1 (en) | Remote driving method, apparatus, and system, device, and medium | |
| CN201732479U (en) | Vehicle and vessel collision early warning control system | |
| CN104751629A (en) | Method and system for detecting traffic accidents | |
| US9135824B1 (en) | Red light violator warning | |
| CN113724531B (en) | Intersection human-vehicle road cooperation early warning system and method under Internet of vehicles environment | |
| CN106448263B (en) | Vehicle driving safety management system and method | |
| CN105390025A (en) | Intelligent traffic safety management system based on location information | |
| CN110164145A (en) | A kind of high-speed road conditions intelligence warning system and its working method | |
| CN112258833A (en) | Variable lane control method, edge calculation apparatus, and system | |
| CN107564336B (en) | Signalized intersection left turn conflict early warning system and early warning method | |
| CN110166946B (en) | Highway vehicle information monitoring method and system based on wireless communication | |
| CN110310517A (en) | A kind of anti-alarming method for power to knock into the back of vehicle on highway and system | |
| CN111427063B (en) | Mobile device traffic control method, device, equipment, system and medium | |
| CN108202683A (en) | Travel condition of vehicle monitoring method and device | |
| CN113554881A (en) | Artificial intelligence road event monitoring method, device, system and storage medium | |
| CN213241427U (en) | RSSI-based positioning and early warning device for two-passenger and one-dangerous vehicles |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| RJ01 | Rejection of invention patent application after publication | ||
| RJ01 | Rejection of invention patent application after publication |
Application publication date: 20211026 |