CN103377558A - System and method for managing and controlling traffic flow - Google Patents
System and method for managing and controlling traffic flow Download PDFInfo
- Publication number
- CN103377558A CN103377558A CN2012101257131A CN201210125713A CN103377558A CN 103377558 A CN103377558 A CN 103377558A CN 2012101257131 A CN2012101257131 A CN 2012101257131A CN 201210125713 A CN201210125713 A CN 201210125713A CN 103377558 A CN103377558 A CN 103377558A
- Authority
- CN
- China
- Prior art keywords
- road
- real
- human
- vehicle
- time imaging
- 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
Landscapes
- Traffic Control Systems (AREA)
Abstract
The invention provides a system for managing and controlling traffic flow. The system is applied to a control host computer in communicating connection with a UAV and traffic signs. The UAV shoots the real-time images of each road and senses the position coordinate of the spot for photography of each real-time image and the shooting directions of the UAV in the process of shooting the real-time images, and the real-time images, the position coordinates, the shooting directions and other data are transmitted to the control host computer through a network module. The system for managing and controlling the traffic flow analyzes the real-time images to obtain the number data of people and vehicles in the real-time images, the number data of people and the vehicles in each real-time image are signed at the corresponding positions of an electronic map according to the position coordinates, the shooting directions and other information, and the managing and controlling states of the traffic signs of each road are dynamically adjusted according to the number data of people and the vehicles of each road signed on the electronic map. The invention further provides a method for managing and controlling the traffic flow.
Description
Technical field
The present invention relates to a kind of information monitoring system and method, especially is a kind of magnitude of traffic flow managing and control system and method.
Background technology
At present, the traffic control guidance method generally is when rush hour or passerby circulate a notice of road conditions, rely on traffic police and wait the manpower parent to go to on-the-spotly to confirm the situation such as highway section vehicle flowrate and commanded, or carry out the management and control operation of traffic signal to on-the-spot with manual mode by personnel, to assist the situation that relieves traffic congestion.This method not only manpower requirement to the traffic control personnel is larger, and relevant road conditions also rely on regularly patrol of personnel or passerby circulates a notice of in real time.
Summary of the invention
In view of above content, be necessary to provide a kind of magnitude of traffic flow managing and control system and method, can the Real-time Obtaining traffic flow information and dynamically adjust the management and control state of traffic signal according to traffic flow information.
A kind of magnitude of traffic flow managing and control system is applied to control the main control system of traffic sign management and control state.This magnitude of traffic flow managing and control system receives the real-time imaging that unmanned vehicle UAV utilizes each road of image capture unit shooting, utilize the position coordinate data of spot for photography of every real-time imaging of global position system GPS detecting, and the shooting directional data of image capture unit when utilizing this real-time imaging of shooting of electronic compass detecting.Afterwards, this system utilizes vehicle, the described real-time imaging of human type detection technical Analysis, obtains image information human-like in each road real-time imaging, vehicle; Add up quantity human-like in the real-time imaging of each road, vehicle, with the quantity of human-like in each real-time imaging, vehicle according to information flags such as the position coordinates of the spot for photography of this real-time imaging and described shooting directions in the corresponding position of electronic chart; And dynamically adjust the management and control state of the traffic sign of each road according to human-like, incremental data vehicle of each road of mark on the electronic chart.
A kind of magnitude of traffic flow management-control method is applied to control the main control system of traffic sign management and control state.The method comprises: (A) receive the real-time imaging that unmanned vehicle UAV utilizes each road of image capture unit shooting, utilize the position coordinate data of spot for photography of every real-time imaging of global position system GPS detecting, and the shooting directional data of image capture unit when utilizing this real-time imaging of shooting of electronic compass detecting; (B) utilize vehicle, the described real-time imaging of human type detection technical Analysis, obtain image information human-like in each road real-time imaging, vehicle; (C) add up quantity human-like in the real-time imaging of each road, vehicle, with the quantity of human-like in each real-time imaging, vehicle according to information flags such as the position coordinates of the spot for photography of this real-time imaging and described shooting directions in the corresponding position of electronic chart; And (D) dynamically adjust the management and control state of the traffic sign of each road according to human-like, incremental data vehicle of each road of mark on the electronic chart.
Compared to prior art, magnitude of traffic flow managing and control system provided by the invention and method can the Real-time Obtaining traffic flow informations and dynamically adjust the management and control state of traffic signal according to traffic flow information.
Description of drawings
Fig. 1 is the applied environment figure of magnitude of traffic flow managing and control system preferred embodiments of the present invention.
Fig. 2 is the process flow diagram of magnitude of traffic flow management-control method preferred embodiments of the present invention.
Fig. 3 is UAV and the synoptic diagram that is installed on the image capture unit on the UAV.
Fig. 4 is that the UAV shown in Fig. 3 stays in the overhead synoptic diagram of taking the real-time imaging of this road of a road.
Fig. 5 is the synoptic diagram of the image information of human-like in the road real-time imaging sometime, vehicle.
Fig. 6 is the synoptic diagram that indicates each road all directions people, car incremental data in electronic chart.
The main element symbol description
| UAV | 1 |
| Main control system | 2 |
| Storage system | 3 |
| Traffic sign | 4 |
| GPS | 11 |
| Image capture unit | 12 |
| Electronic compass | 13 |
| Mixed-media network modules mixed-media | 14、21 |
| Processor | 22 |
| Magnitude of traffic flow managing and control system | 23 |
| Analysis module | 231 |
| Mark module | 232 |
| Control module | 233 |
| Electronic chart | 24 |
Following embodiment further specifies the present invention in connection with above-mentioned accompanying drawing.
Embodiment
Consulting shown in Figure 1ly, is the applied environment figure of magnitude of traffic flow managing and control system 23 preferred embodiments of the present invention.This magnitude of traffic flow managing and control system 23 is applied to control the main control system 2 of the management and control state of traffic sign 4.This traffic sign 4 is the photochromic signals with mutual change, is arranged to hand over fork in the road or other particular location, in order to the traffic control facility that wayleave is assigned to its whereabouts of vehicle drive people and pedestrian control and turns to.This traffic sign 4 comprises the vehicular control livery, the special-purpose livery of pedestrian, and special traffic signal (for example blind prompt sound).
This main control system 2 and unmanned vehicle (Unmanned Aerial Vehicle, UAV) 1 connect by network communication.This UAV1 comprises GPS (global position system, GPS) 11, image capture unit 12, electronic compass 13 and mixed-media network modules mixed-media 14.
Consult shown in Figure 3ly, image capture unit 12 is installed on the head position of UAV1, and the camera lens of image capture unit 12 and the head of UAV1 are towards consistent.In the present embodiment, this image capture unit 12 is for having the digital camera of shooting at night function.This UAV1 utilizes image capture unit 12 to take the real-time imaging of each road.Consulting shown in Figure 4ly, is that this UAV1 stays in the overhead synoptic diagram of taking the real-time imaging of this road of road.
The coordinate information of the spot for photography of every real-time imaging of GPS11 detecting, the coordinate information of this UAV1 present position when namely image capture unit 12 is taken every real-time imaging.The direction of image capture unit 12 when every real-time imaging is taken in the electronic compass 13 detecting shooting direction of every real-time imaging (below be called).
The position coordinates of the real-time imaging of each road that UAV1 obtains shooting by mixed-media network modules mixed-media 14, the spot for photography of every image and take the data such as direction and be sent to main control system 2.
Consult shown in Figure 1ly, this main control system 2 also comprises mixed-media network modules mixed-media 21, processor 22 and Figure 24 electronically.This main control system 2 receive by mixed-media network modules mixed-media 21 real-time imaging of each road that UAV1 transmit, every image the spot for photography position coordinates and take the data such as direction, and be stored to storage system 3.This storage system 3 can be the memory storage of these main control system 2 inside, also can be the external memory that is connected with this main control system 2, such as database server etc.
This magnitude of traffic flow managing and control system 23 is analyzed the data of above-mentioned reception, utilize vehicle, the described real-time imaging of human type detection technical Analysis, obtain incremental data human-like in each road real-time imaging, vehicle, the incremental data of human-like in each real-time imaging, vehicle according to the position coordinates of the spot for photography of this real-time imaging and take the information flag such as direction in the corresponding position of electronic chart, and is dynamically adjusted the management and control state of the traffic sign 4 of each road according to human-like, incremental data vehicle of each road of mark on the electronic chart.
Consult shown in Figure 1ly, this magnitude of traffic flow managing and control system 23 comprises analysis module 231, mark module 232 and control module 233.This module 231-233 comprises the Calculator Program code, and these code storage are in storage system 3, and processor 22 is carried out these Calculator Program codes, and the above-mentioned functions of this magnitude of traffic flow managing and control system 23 is provided.The concrete function of this module 231-233 sees also hereinafter the explanation about Fig. 2.
Consulting shown in Figure 2ly, is the process flow diagram of magnitude of traffic flow management-control method preferred embodiments of the present invention.
Step S10, UAV1 utilize image capture unit 12 to take the real-time imaging of each road, and utilize position coordinates and the shooting direction of the spot for photography of GPS11 and every real-time imaging of electronic compass 13 detectings.Consulting shown in Figure 4ly, is that this UAV1 stays in the overhead synoptic diagram of taking the real-time imaging of this road of road.UAV1 is when taking the real-time imaging of this road, the longitude coordinate that the GPS11 detecting obtains UAV1 is 152.6248, latitude coordinate is 25.8214, and the direction (i.e. the shooting direction of this real-time imaging) that electronic compass 13 detectings obtain this image capture unit 12 is N-W15 °.Wherein, first English alphabet N represents that the main shooting direction of image capture unit 12 is the positive north, second English alphabet W represents that the offset direction of image capture unit 12 is the west, and 15 ° of expressions of numeral image capture unit 12 is by the angle of direct north west skew.
The position coordinates of the real-time imaging of each road that step S20, UAV1 obtain shooting by mixed-media network modules mixed-media 14, the spot for photography of every image and take directional data and be sent to main control system 2.
After step S30, main control system 2 received above-mentioned data by mixed-media network modules mixed-media 21, analysis module 231 utilized vehicle, the described real-time imaging of human type detection technical Analysis, obtains image information human-like in each road real-time imaging, vehicle.Consulting Fig. 5, is road real-time imaging sometime, and analysis module 231 analysis obtains the image information of human-like, the vehicle in this real-time imaging, and indicates the imagery zone of human-like, the vehicle in this real-time imaging in the mode of rectangle addend bit number.
Shown in the human type detection technology include, but not limited to human-like characteristic information statistic law and feature samples comparison classification (Template Matching Method).
Particularly, human-like characteristic information statistic law comprises the steps:
(1) with image processing mode with real-time imaging background simplification;
(2) the human-like characteristic point data of each posture of more than ten more than ten thousand in real time image data and the database is compared;
(3) estimate by the characteristic point data in the real-time imaging with statistical and whether have human-like information to exist in the real-time imaging.
Feature samples comparison classification comprises the steps:
(1) collect first the human-like feature samples of each posture of some and the non-human-like feature samples of some, for example, the human-like pictures such as the stance front of collection some and stance side, sitting posture;
(2) after the non-human-like feature samples of the human-like feature samples of finishing some different gestures and some is collected, beginning is further carried out continuous training (Training) with neural network (Artificial Neural Network) training patterns, continues to correct mistakes.If do not use the neural network training patterns, also can change with the Ada-Boost classification and classify, the human-like feature masterplate (Template) of finishing through training just or the Ada-Boost sorter can be for follow-up human type detection (Testing).
In the present embodiment, vehicle detection can be adopted the car plate detection technique based on Adaboost cascade, does not repeat them here.
Step S40, the quantity of human-like in the real-time imaging of each road of mark module 232 statistics, vehicle, with the incremental data of human-like in each real-time imaging, vehicle according to the position coordinates of the spot for photography of this real-time imaging and take the information flag such as direction in the corresponding position of electronic chart.Fig. 6 has shown the electronically subregion of Figure 24, this electronically Figure 24 except the sign that shows road, buildings, the spot for photography of also on Figure 24 electronically, taking every real-time imaging with UAV1 corresponding to the incremental data of human-like, vehicle of the corresponding road all directions that obtain of this real-time imaging of position labeled analysis.Supposing that UAV1 takes in each crossroad obtains one or more real-time imaging, consults shown in Figure 6ly, and each place, crossroad that shows on Figure 24 has electronically indicated the arrow of indication different directions.The shooting direction of image capture unit 12 when this arrow is used for representing to take a certain real-time imaging, each arrow is other to have indicated this road that 2 numerals represent respectively to obtain from this real-time imaging analysis to taking the human-like and vehicle fleet size of direction.In the present embodiment, place the human-like quantity of numeral of circle, do not place the numeral vehicle fleet size of circle.
Step S50, control module 233 is dynamically adjusted the management and control state of the traffic sign 4 of each road according to electronically human-like, incremental data vehicle of each road of Figure 24 subscript note.For example, when the quantity human-like, vehicle of certain current direction of road surpassed pre-set threshold value, control module 233 produced control command to the traffic sign 4 on this road and prolongs people, the car transit time that this road is somebody's turn to do current direction.
It should be noted that at last, above embodiment is only unrestricted in order to technical scheme of the present invention to be described, although with reference to preferred embodiment the present invention is had been described in detail, those of ordinary skill in the art is to be understood that, can make amendment or be equal to replacement technical scheme of the present invention, and not break away from the spirit and scope of technical solution of the present invention.
Claims (8)
1. magnitude of traffic flow managing and control system is applied to control the main control system of traffic sign management and control state, it is characterized in that this magnitude of traffic flow managing and control system comprises:
Mixed-media network modules mixed-media, be used for receiving the real-time imaging that unmanned vehicle UAV utilizes each road of image capture unit shooting, utilize the position coordinate data of spot for photography of every real-time imaging of global position system GPS detecting, and the shooting directional data of image capture unit when utilizing this real-time imaging of shooting of electronic compass detecting;
Analysis module is used for utilizing vehicle, the described real-time imaging of human type detection technical Analysis, obtains image information human-like in each road real-time imaging, vehicle;
Mark module, the real-time imaging that is used for each road of statistics is human-like, the quantity of vehicle, and the quantity of human-like in each real-time imaging, vehicle is marked on the corresponding position of electronic chart according to position coordinates and the described shooting directional information of the spot for photography of this real-time imaging; And
Control module is used for dynamically adjusting according to human-like, incremental data vehicle of each road of mark on the electronic chart management and control state of the traffic sign of each road.
2. magnitude of traffic flow managing and control system as claimed in claim 1, it is characterized in that, this traffic sign is the photochromic signal with mutual change, is arranged to hand over fork in the road or other particular location, in order to the traffic control facility that wayleave is assigned to its whereabouts of vehicle drive people and pedestrian control and turns to.
3. magnitude of traffic flow managing and control system as claimed in claim 1 is characterized in that, this human type detection technology comprises human-like characteristic information statistic law and feature samples comparison classification.
4. magnitude of traffic flow managing and control system as claimed in claim 1, it is characterized in that, the management and control state that the incremental data human-like, vehicle of described each road according to mark on the electronic chart is dynamically adjusted the traffic sign of each road comprises: when the quantity human-like, vehicle of certain current direction of road surpasses pre-set threshold value, produce control command to the traffic sign on this road and prolong people, the car transit time that this road should current direction.
5. magnitude of traffic flow management-control method is applied to control the main control system of traffic sign management and control state, it is characterized in that the method comprises:
Receive the real-time imaging that unmanned vehicle UAV utilizes each road of image capture unit shooting, utilize the position coordinate data of spot for photography of every real-time imaging of global position system GPS detecting, and the shooting directional data of image capture unit when utilizing this real-time imaging of shooting of electronic compass detecting;
Utilize vehicle, the described real-time imaging of human type detection technical Analysis, obtain image information human-like in each road real-time imaging, vehicle;
Add up quantity human-like in the real-time imaging of each road, vehicle, the quantity of human-like in each real-time imaging, vehicle is marked on the corresponding position of electronic chart according to position coordinates and the described shooting directional information of the spot for photography of this real-time imaging; And
Dynamically adjust the management and control state of the traffic sign of each road according to human-like, incremental data vehicle of each road of mark on the electronic chart.
6. magnitude of traffic flow management-control method as claimed in claim 5, it is characterized in that, this traffic sign is the photochromic signal with mutual change, is arranged to hand over fork in the road or other particular location, in order to the traffic control facility that wayleave is assigned to its whereabouts of vehicle drive people and pedestrian control and turns to.
7. magnitude of traffic flow management-control method as claimed in claim 5 is characterized in that, this human type detection technology comprises human-like characteristic information statistic law and feature samples comparison classification.
8. magnitude of traffic flow management-control method as claimed in claim 5, it is characterized in that, the management and control state that the incremental data human-like, vehicle of described each road according to mark on the electronic chart is dynamically adjusted the traffic sign of each road comprises: when the quantity human-like, vehicle of certain current direction of road surpasses pre-set threshold value, produce control command to the traffic sign on this road and prolong people, the car transit time that this road should current direction.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN2012101257131A CN103377558A (en) | 2012-04-26 | 2012-04-26 | System and method for managing and controlling traffic flow |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN2012101257131A CN103377558A (en) | 2012-04-26 | 2012-04-26 | System and method for managing and controlling traffic flow |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN103377558A true CN103377558A (en) | 2013-10-30 |
Family
ID=49462613
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN2012101257131A Pending CN103377558A (en) | 2012-04-26 | 2012-04-26 | System and method for managing and controlling traffic flow |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN103377558A (en) |
Cited By (11)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN104537861A (en) * | 2015-01-06 | 2015-04-22 | 成都智科立信科技有限公司 | Internet-of-Things traffic signal indicating system |
| CN104954393A (en) * | 2014-03-24 | 2015-09-30 | 高登智慧科技股份有限公司 | Cloud community service system |
| CN106297338A (en) * | 2016-09-14 | 2017-01-04 | 深圳市喜悦智慧数据有限公司 | A kind of traffic robot's control system and method |
| CN106548634A (en) * | 2016-10-31 | 2017-03-29 | 安徽科力信息产业有限责任公司 | The method and device of semaphore control parameter is precisely adjusted based on Internet map |
| CN106652494A (en) * | 2015-11-03 | 2017-05-10 | 中国移动通信集团公司 | Traffic light control method and device |
| CN106998451A (en) * | 2017-04-21 | 2017-08-01 | 湖北天专科技有限公司 | The area condition panorama guide system and its method monitored based on unmanned vehicle |
| CN108682151A (en) * | 2018-05-30 | 2018-10-19 | 苏州广元智感智能交通技术有限公司 | Mobile magnitude of traffic flow statistic device based on wireless signal scan technology and method |
| CN109979209A (en) * | 2017-12-28 | 2019-07-05 | 深圳市城市交通规划设计研究中心有限公司 | Traffic monitor, method, non-volatile memory medium and system |
| CN110085045A (en) * | 2019-05-05 | 2019-08-02 | 方星星 | Road and driving Real-time security monitoring, navigation system and safety monitoring method |
| US10891856B1 (en) * | 2015-12-30 | 2021-01-12 | United Services Automobile Association (Usaa) | Traffic drone system |
| CN113421442A (en) * | 2021-06-01 | 2021-09-21 | 上海大学 | Traffic signal lamp control system based on visual analysis |
Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN1945213A (en) * | 2006-11-02 | 2007-04-11 | 武汉大学 | Method for realizing visual position service based on measurable real image |
| CA2579898A1 (en) * | 2006-02-24 | 2007-08-24 | Michael Eineder | Method for the processing and representing of ground images obtained by synthetic aperture radar systems (sar) |
| CN101203894A (en) * | 2005-07-11 | 2008-06-18 | 通腾科技股份有限公司 | Method for determining traffic information and device and executing the method through setting |
| CN101799987A (en) * | 2010-03-10 | 2010-08-11 | 北京航空航天大学 | Self-adaptive intelligent traffic light and control method thereof |
| CN102117545A (en) * | 2009-12-31 | 2011-07-06 | 杨茂君 | Intelligent traffic signal lamp control system and control method thereof |
| CN202150183U (en) * | 2010-12-15 | 2012-02-22 | 黄钰峰 | Traffic management air information acquisition platform |
| CN102436678A (en) * | 2010-09-29 | 2012-05-02 | 比亚迪股份有限公司 | Method and system for generating three-dimensional road model |
-
2012
- 2012-04-26 CN CN2012101257131A patent/CN103377558A/en active Pending
Patent Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101203894A (en) * | 2005-07-11 | 2008-06-18 | 通腾科技股份有限公司 | Method for determining traffic information and device and executing the method through setting |
| CA2579898A1 (en) * | 2006-02-24 | 2007-08-24 | Michael Eineder | Method for the processing and representing of ground images obtained by synthetic aperture radar systems (sar) |
| CN1945213A (en) * | 2006-11-02 | 2007-04-11 | 武汉大学 | Method for realizing visual position service based on measurable real image |
| CN102117545A (en) * | 2009-12-31 | 2011-07-06 | 杨茂君 | Intelligent traffic signal lamp control system and control method thereof |
| CN101799987A (en) * | 2010-03-10 | 2010-08-11 | 北京航空航天大学 | Self-adaptive intelligent traffic light and control method thereof |
| CN102436678A (en) * | 2010-09-29 | 2012-05-02 | 比亚迪股份有限公司 | Method and system for generating three-dimensional road model |
| CN202150183U (en) * | 2010-12-15 | 2012-02-22 | 黄钰峰 | Traffic management air information acquisition platform |
Cited By (14)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN104954393A (en) * | 2014-03-24 | 2015-09-30 | 高登智慧科技股份有限公司 | Cloud community service system |
| CN104537861A (en) * | 2015-01-06 | 2015-04-22 | 成都智科立信科技有限公司 | Internet-of-Things traffic signal indicating system |
| CN106652494A (en) * | 2015-11-03 | 2017-05-10 | 中国移动通信集团公司 | Traffic light control method and device |
| US10891856B1 (en) * | 2015-12-30 | 2021-01-12 | United Services Automobile Association (Usaa) | Traffic drone system |
| US11688278B1 (en) | 2015-12-30 | 2023-06-27 | United Services Automobile Association (Usaa) | Traffic drone system |
| CN106297338A (en) * | 2016-09-14 | 2017-01-04 | 深圳市喜悦智慧数据有限公司 | A kind of traffic robot's control system and method |
| CN106548634A (en) * | 2016-10-31 | 2017-03-29 | 安徽科力信息产业有限责任公司 | The method and device of semaphore control parameter is precisely adjusted based on Internet map |
| CN106548634B (en) * | 2016-10-31 | 2019-05-14 | 安徽科力信息产业有限责任公司 | Method and device based on the accurate adjustment signal machine control parameter of Internet map |
| CN106998451A (en) * | 2017-04-21 | 2017-08-01 | 湖北天专科技有限公司 | The area condition panorama guide system and its method monitored based on unmanned vehicle |
| CN109979209A (en) * | 2017-12-28 | 2019-07-05 | 深圳市城市交通规划设计研究中心有限公司 | Traffic monitor, method, non-volatile memory medium and system |
| CN108682151A (en) * | 2018-05-30 | 2018-10-19 | 苏州广元智感智能交通技术有限公司 | Mobile magnitude of traffic flow statistic device based on wireless signal scan technology and method |
| CN108682151B (en) * | 2018-05-30 | 2020-11-17 | 广东矩阵流大数据科技有限公司 | Mobile traffic flow statistical device and method based on wireless signal scanning technology |
| CN110085045A (en) * | 2019-05-05 | 2019-08-02 | 方星星 | Road and driving Real-time security monitoring, navigation system and safety monitoring method |
| CN113421442A (en) * | 2021-06-01 | 2021-09-21 | 上海大学 | Traffic signal lamp control system based on visual analysis |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN103377558A (en) | System and method for managing and controlling traffic flow | |
| TWI438728B (en) | System and method for controlling traffic flow information | |
| JP6494719B2 (en) | Traffic signal map creation and detection | |
| CN107563419B (en) | Train positioning method combining image matching and two-dimensional code | |
| CN110146097B (en) | Method and system for generating automatic driving navigation map, vehicle-mounted terminal and server | |
| EP3078937B1 (en) | Vehicle position estimation system, device, method, and camera device | |
| CN111444798B (en) | Identification method and device for driving behavior of electric bicycle and computer equipment | |
| WO2015129045A1 (en) | Image acquisition system, terminal, image acquisition method, and image acquisition program | |
| CN108091141B (en) | License plate recognition system | |
| CN107923756B (en) | Method for locating an automated motor vehicle | |
| KR102558657B1 (en) | Map data updating method, apparatus, device, and readable storage medium | |
| CN113593250A (en) | Illegal parking detection system based on visual identification | |
| JP2020077414A (en) | Scene detection device and method | |
| CN112101223B (en) | Detection method, detection device, detection equipment and computer storage medium | |
| CN1577319A (en) | Position tracking system and method based on digital video processing technique | |
| US11829959B1 (en) | System and methods for fully autonomous potholes detection and road repair determination | |
| CN110869867A (en) | Method for verifying a digital map of a vehicle with a high degree of automation, corresponding device and computer program | |
| CN115294544A (en) | Driving scene classification method, device, equipment and storage medium | |
| Yang | Comprehensive visibility indicator algorithm for adaptable speed limit control in intelligent transportation systems | |
| CN111640301A (en) | Method, system and device for detecting fault vehicle, electronic equipment and storage medium | |
| JP2017188164A (en) | Image acquisition device, terminal, and image acquisition system | |
| CN114241373A (en) | End-to-end vehicle behavior detection method, system, equipment and storage medium | |
| JP6916975B2 (en) | Sign positioning system and program | |
| US11815362B2 (en) | Map data generation apparatus | |
| Pan et al. | Identifying Vehicles Dynamically on Freeway CCTV Images through the YOLO Deep Learning Model. |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| C06 | Publication | ||
| PB01 | Publication | ||
| C10 | Entry into substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| C02 | Deemed withdrawal of patent application after publication (patent law 2001) | ||
| WD01 | Invention patent application deemed withdrawn after publication |
Application publication date: 20131030 |