KR20200055492A - A vehicle driving behavior analysis system using images by uav - Google Patents

Abstract

The present invention relates to a vehicle driving behavior analysis system capable of providing a microscopic and empirical way which can clearly diagnose problem occurring spots, occurring causes, etc. with respect to traffic problems occurring on a road by using aerial images of an unmanned aerial vehicle.

Description

Vehicle driving behavior analysis system using unmanned aerial vehicle image {A VEHICLE DRIVING BEHAVIOR ANALYSIS SYSTEM USING IMAGES BY UAV}

The present invention relates to a technology capable of analyzing a vehicle's driving behavior through an image taken by an unmanned aerial vehicle, and more specifically, driving a vehicle driving on the road by analyzing a road image captured by an unmanned aerial vehicle. It is about technology that analyzes behavior and analyzes and improves the cause of traffic problems based on the analyzed results.

Currently, the concentration of traffic demand and the continuous increase have been made compared to the limited road capacity. Due to this, various traffic problems are continuously occurring, and in order to solve these traffic problems, it is used for traffic analysis based on traffic data collected from various vehicle detection devices installed on the road.

In general, a vehicle detection system (VDS) is installed on the main line of the highway at approximately 1 to 2 km intervals, and traffic data such as the number of vehicles passing through the installation point and the passing speed are collected. In addition, a DSRC (DSRC, Dedicated Short Range Communication) -based high-pass traffic information system is installed to collect the travel time based on the wireless communication time between the roadside antenna installed on the highway main line at intervals of 3 to 5 km and the high-pass terminal installed in the vehicle. The vehicle is collecting traffic communication (flow) time of travel.

As described above, the existing traffic analysis method is a traffic information collection system installed mainly on the highway main line (eg, vehicle detection system (VDS), DSRC (DSRC, Dedicated Short Range Communication) based vehicle travel time collection system, etc.) It was a method of estimating the traffic situation on the road by temporally and temporally collecting the data collected through or analyzing the cause based on the theoretical model (including simulation) and solving the traffic problem.

However, these traffic data are collected by spatiotemporally aggregated or averaged the number of vehicles passing through the corresponding point at the detection equipment installation point, rather than the driving behavior of the individual vehicle, so that the traffic problems are clear and empirical. Because it relies on traffic theory and model rather than phosphorus analysis, there was a limit that can only be approached by analyzing the cause by estimation, and there was a limit that microscopic traffic analysis that analyzes the driving behavior of individual vehicles is virtually impossible.

In addition, in the existing vehicle detection system, the analysis of the driving behavior of individual vehicles appearing in various sections, such as the intersection / classification section of the highway IC (Interchange) / JCT (Junction), the cross section, the change section of the vertical (upward / downward) slope, and the construction section Empirical and microscopic traffic analysis based on data is very difficult.

Because of these limitations, there is a problem that the improvement effect is lowered as well as limitations in improving the traffic problem because the problem is not clearly solved when solving the traffic problem.

Therefore, in order to improve the traffic problem, it is important to clearly derive and diagnose the exact point and cause of the problem through microscopic and empirical traffic analysis. .

The present invention has been created in view of the above circumstances, and the object to be reached in the present invention is to generate on the road by extracting and analyzing the driving behavior data of a vehicle through analysis of aerial images photographed through an unmanned aerial vehicle. The aim is to provide a method for clearly and empirically diagnosing the point of occurrence and the cause of traffic problem factors.

A vehicle driving behavior analysis system using an unmanned flying vehicle image for extracting and analyzing a driving behavior of a vehicle according to an embodiment of the present invention for achieving the above object, uses an unmanned flying apparatus to drive a lane driving the vehicle. For analyzing the driving behavior of the vehicle driving the road and the driving behavior of the vehicle on the lane based on the image output from the image input / output unit and the image input / output unit in which the captured image is input and output. And an information setting unit, an extracting unit for extracting the driving behavior of the vehicle based on the driving behavior of the vehicle set in the information setting unit, and a storage unit for storing the driving behavior of the vehicle extracted by the extraction unit. Is done.

Specifically, the image input and output unit, an image capture unit for capturing any one of the information of the vehicle, lane information as an image, an image input unit for inputting an image captured by the image capture unit, and an image input to the image input unit It characterized in that it comprises an image correction unit for correcting the correction image for information analysis.

Specifically, the image capturing unit is characterized in that it is a drone for capturing any one of the information of the vehicle and the lane information as a video at a fixed position.

Specifically, the information setting unit, the lane setting unit for setting the type of the lane from the image input to the image input unit, set a specific time for extracting the driving behavior of the vehicle from the video time taken by the image taking unit It characterized in that it comprises a time setting unit, a space setting unit for setting a space for extracting the driving behavior of the vehicle among the images taken by the image capture unit.

Specifically, the time setting unit is characterized in that to set the start time and the end time derived based on the time when a certain number of vehicles on the lane is running.

Specifically, the time setting unit is characterized in that to set the extraction time unit for analyzing the driving behavior of the vehicle from the image taken by the image capturing unit.

Specifically, the space setting unit is characterized in that to set any one of the region of interest among the region in which the vehicle travels and the lane of interest among the lanes in which the vehicle runs.

Specifically, the space setting unit is characterized in that to set the actual distance of the specified lane among the lanes the vehicle travels.

Specifically, the image input and output unit, the vehicle type information of the specific individual vehicle driving the lane, the driving speed of the specific individual vehicle, the lane information of the specific individual vehicle running, the lane change position and number of times of the specific individual vehicle, An object detection unit that detects any one of distances and headway time information from different specific individual vehicles adjacent to the specific individual vehicle, and an object calculation unit that calculates driving behavior of the specific individual vehicle detected by the object detection unit It is characterized by.

According to the vehicle driving behavior analysis system using the image of the unmanned aerial vehicle of the present invention, spatial constraints on a road section in which a traffic problem occurs, for example, a highway main line, an IC (Interchange) / JCT (Junction) confluence, classification, staggering It is possible to take an image without any restrictions such as section, vertical (up, down) slope change section, construction section, etc., and analyze the captured image to extract vehicle driving behavior for the problem section.

In particular, since microscopic and empirical traffic analysis based on the driving behavior of individual vehicles is performed, it is possible to diagnose clearly problems in the target road section, and by deriving an accurate improvement plan, the traffic improvement effect can be maximized.

1 is a view schematically showing a vehicle driving behavior analysis system according to an embodiment of the present invention.
2 is a block diagram showing the configuration of an image input and output unit according to an embodiment of the present invention.
3 is a block diagram showing a configuration of an information setting unit according to an embodiment of the present invention.
4 is a block diagram showing a configuration of an individual vehicle information setting unit according to an embodiment of the present invention.
5 is a view showing an embodiment of classifying a driving behavior and an analysis progress direction of a vehicle analyzed through a vehicle driving behavior analysis system using a drone according to an embodiment of the present invention.

It should be noted that the technical terms used in this specification are only used to describe specific embodiments and are not intended to limit the present invention. In addition, technical terms used in this specification should be interpreted as meanings generally understood by those having ordinary knowledge in the technical field to which the present invention belongs, unless defined otherwise. It should not be interpreted as a meaning or an excessively reduced meaning. In addition, when the technical term used in this specification is a wrong technical term that does not accurately represent the spirit of the present invention, it should be understood as being replaced by a technical term that can be correctly understood by those skilled in the art. In addition, the general terms used in the present invention should be interpreted as defined in the dictionary or in context before and after, and should not be interpreted as an excessively reduced meaning.

Hereinafter, preferred embodiments according to the present invention will be described in detail with reference to the accompanying drawings, but the same or similar elements will be given the same reference numbers regardless of the reference numerals, and repeated descriptions thereof will be omitted. In addition, in the description of the present invention, when it is determined that detailed descriptions of related known technologies may obscure the subject matter of the present invention, detailed descriptions thereof will be omitted. Hereinafter, embodiments of the present specification will be described with reference to the accompanying drawings.

1 is a view schematically showing a vehicle driving behavior analysis system according to an embodiment of the present invention, and FIG. 2 is a block diagram showing a configuration of an image input / output unit according to an embodiment of the present invention, FIG. 3 is a block diagram showing the configuration of the information setting unit according to an embodiment of the present invention.

As shown in the figure, the vehicle driving behavior analysis system 10 using an image of an unmanned flight apparatus to which the present invention is applied includes an image input / output unit 120, an information setting unit 140, an extraction unit 160, and a storage unit It is configured to include 180.

The image input / output unit 120 may include an image input / output unit 120, an image capture unit 122, an image input unit 124, and an image correction unit 126.

Specifically, the image photographing unit 122 is configured to photograph a vehicle driving through a lane and a lane in the air. To this end, the image photographing unit 122 may use an unmanned flying device, and the unmanned flying device according to an embodiment of the present invention will be described as an example of a drone as shown in FIG. Of course, it can be any one of the devices that can shoot.

The image capturing unit 122 may be equipped with a camera, a video recording device, and the like in order to photograph a lane and a driving lane. The image photographing unit 122 equipped with the photographing apparatus may photograph a road, a vehicle driving on the road, and the like at a fixed position. At this time, the image captured by the image capture unit 122 may be a high-resolution image of a format such as AVI, MP4, or higher than 4K (UHD).

On the other hand, the image capturing unit 122 formed of a drone can adjust the spatial range of the road in the image according to the flight altitude (for example, the shooting height), and can shoot in various places regardless of the type, shape, and location of the target road. Therefore, there is an advantage in that it is possible to extract driving behavior of all individual vehicles through road image analysis.

The image input unit 124 is a configuration in which an image captured by the image capture unit 122 is input. In addition to the image, the image input unit 124 may store data in which the captured image is imaged.

The image correction unit 126 is a component that corrects an image to obtain various information from the image and image input to the image input unit 124.

Specifically, the image correction unit 126 may correct fine shaking of the image captured by the input image photographing unit 122.

Through the image corrected by the image correcting unit 126, detailed image information of a lane, image information in which vehicle colors are differently displayed for each vehicle speed, image information representing a vehicle's movement trajectory in a specific lane, and a driving vehicle It is possible to obtain image information, etc. indicating the point of occurrence of a change in the lane.

The acquired information may be basic data for analyzing the driving behavior of a vehicle driving on the road, and it is possible to analyze and improve the cause of a traffic problem through the driving behavior of the vehicle analyzed through the acquired information. .

Meanwhile, the image photographed and corrected by the image input / output unit 120 may set information necessary for outputting the driving behavior of the vehicle through the information setting unit 140.

To this end, the information setting unit 140 may include a lane setting unit 142, a time setting unit 144, and a space setting unit 146.

Specifically, the lane setting unit 142 may classify the lane in which the vehicle travels from the image captured and corrected by the image input / output unit 120 and set information such as the type of lane.

At this time, the classification of the lane means information on the lane of the lane in which the vehicle is running, that is, in which lane, such as 1, 2, 3, etc.

In addition, the type of lane is a type of road to which the lane belongs in the information necessary for deriving the driving behavior of the vehicle. For example, the highway main road section, the summation section in which two or more highways formed by lanes in the same direction join or separate, and the IC / JC connection road section in which two or more highways formed by lanes in the same direction connect, and roads in long sections It is information of any one of the types such as the crossing section, which is a section where traffic flows in the same direction as they follow, and the lane changes.

The time setting unit 144 may select a specific time for outputting the driving behavior of the vehicle from the image time taken by the image input / output unit 120.

Specifically, the time setting unit 144 may select an image in a time period necessary for deriving driving behavior of the vehicle, except for unnecessary images from the image captured by the image input / output unit 120. Particularly, in order to more clearly analyze the driving behavior of the vehicle from the extracted data, it is possible to set a driving start time and a driving end time required to derive the driving behavior of the vehicle from the captured image.

Alternatively, the time setting unit 144 may set a time unit for extracting the vehicle driving behavior for traffic analysis from the image captured by the image input / output unit 120.

In detail, the time setting unit 144 may set a time unit for storing an image necessary for deriving driving behavior of the vehicle from the captured image. For example, assuming that the captured image is 24 frames per second (24 frames / sec), the image captured by the image capture unit 122 may be set to 6 frames, and the captured image may be stored in 0.25 second increments. Can be. The set extraction time unit may be used as information capable of extracting vehicle speed and acceleration.

Meanwhile, the space setting unit 146 may set a space required for deriving a vehicle driving behavior among the images captured by the image input / output unit 120.

In detail, the space setting unit 146 may set any one of the lane of interest and the lane of interest among the lanes in which the vehicle travels. The region of interest and the lane of interest may be a road section or a lane where traffic problems frequently occur, and the set region of interest and the lane of interest may be changed according to a user's condition. The set region of interest and the lane of interest may be used as information to analyze and extract the density relationship of the vehicle.

Also, the space setting unit 146 may set an actual distance of a specified lane among driving lanes.

Among the driving behavior of the vehicle extracted from the captured image, it may be a traffic speed for each vehicle, a degree of acceleration and deceleration of the vehicle, a movement trajectory, a distance between vehicles, and a density of roads. To extract this information, the user designates two points that know the actual distance (m), and then converts and sets the specified two points into the actual distance (m) through pixels / distance (m).

When the spatiotemporal region is set in the information setting unit 140 as described above, the driving behavior of the vehicle may be extracted based on the spatiotemporal region set in the extraction unit 160.

The driving behavior of the extracted vehicle is, for example, a vehicle type, a vehicle speed / acceleration, a driving lane, and the like, and through these data, traffic flow characteristics, a specific individual vehicle trajectory, and mutual driving characteristics between two or more specific individual vehicles. Etc. can be extracted.

In detail, in the case of a vehicle model, small, medium, and large can be classified and extracted based on the vehicle length according to the number of pixels, and the vehicle speed is based on the information set by the information setting unit 140. Can calculate the driving speed and acceleration. The driving lane distinguishes the lane on which the vehicle is driving, and the driving lane position can be calculated using the location information of the vehicle and the user's set lane identification information (1, 2, 3 lanes).

On the other hand, when extracting the driving behavior of the vehicle, it is also possible to extract the driving behavior of the vehicle of a specific individual vehicle selected from among the vehicles driving the road. In detail, a plurality of individual vehicles may be selected, and the vehicle density of the road may be extracted by the selected specific individual vehicles. At this time, the calculation formula for extracting the vehicle density is as follows.

Vehicle density (k) = traffic volume (q) / average speed (u)

That is, based on the conditions set by the information setting unit 140, the average speed of the passing vehicle within the time and space range is calculated, and the number of passing vehicles passing per unit time is calculated from the information necessary to derive the driving behavior of the vehicle. By doing so, the vehicle density can be calculated from information necessary to derive the driving behavior of the vehicle.

Meanwhile, the driving behavior of the individual vehicle may be extracted based on the driving behavior of the vehicle set in the information setting unit 140. To this end, the extraction unit 160 may include an object detection unit 162 and an object calculation unit 164.

The driving behavior of individual vehicles detected by the object detection unit 162 may be driving behavior of a plurality of individual vehicles selected from images captured by the image input / output unit 120. In detail, it may be the vehicle type of the individual vehicle, the driving speed of the individual vehicle and the driving lane, the number and location of changes of the lane during the driving of the individual vehicle, the distance between the individual vehicles and the adjacent individual vehicles.

When the object detection unit 162 detects the information of the individual vehicle, the driving behavior of the individual vehicle may be calculated based on the information detected by the object calculation unit 164. Individuals according to the vehicle type of the selected individual vehicle, the driving speed of the individual vehicle and the driving lane, the number and location of changes of the individual vehicle while driving, and the distance between the individual vehicles and other individual vehicles adjacent to each other, as described above. The driving behavior of the vehicle can be detected.

The driving behavior of the vehicle thus calculated may be stored in the storage unit 180, and the stored data may be used as basic data to derive a traffic problem on the road and analyze a cause to establish a solution.

Hereinafter, with reference to FIG. 5, the driving behavior of the vehicle analyzed through the vehicle driving behavior analysis system 10 of the embodiment of the present invention and the direction of analysis progress will be described.

5 is a view showing an embodiment of classifying a driving behavior and an analysis progress direction of a vehicle analyzed through a vehicle driving behavior analysis system using a drone according to an embodiment of the present invention.

Referring to the drawings, a road is photographed through the image input / output unit 120. It is possible to correct fine shaking caused in the process of photographing the captured image. Through the corrected image, detailed image information of the lane, image information that displays the vehicle color differently according to the speed of the driving vehicle, image information that expresses the trajectory of the vehicle in a specific lane, and image showing the lane change point of the driving vehicle Information, etc. can be obtained.

When an image is captured, information necessary for outputting a driving behavior of the vehicle may be set. For example, a reference point, which is one of a region of interest and a lane based on a user's preference, may be preferentially selected and divided into sections according to the selected reference point. At this time, the length of the divided section can be divided evenly, but differently, the length can be set differently according to the purpose and need, and the number of divided sections can also be changed according to conditions.

From the information required to derive the driving behavior of the divided vehicle, data can be extracted according to the type of lane.

First, as an example of FIG. 5 (a), a confluence part lane in which the confluence part connecting path 2, which is different roads formed in the same direction as the main line 1 of the road and the main line 1, is joined will be taken as an example.

In the confluence lane, it is a section in which the confluence connection path 2 at the point of confluence is connected to the main line 1, so as to secure the driving rights of the vehicle on the road main line 1 and the inflow vehicle at the confluence connection path 2. It is a road for interaction to occur. Traffic congestion may be caused in these confluence lanes due to conflicts between vehicles, and the traffic analysis items to solve this are as follows.

First, it is possible to compare the traffic volume of the main line (1) and the confluence connection road (2). To this end, the driving behavior of the vehicle may be derived by setting a congestion time with a lot of vehicle traffic and a non-congestion time with little vehicle traffic on a road (a confluence lane) photographed by the image photographing unit 122. At this time, in order to set a specific time zone, the time setting unit 144 sets the start time and the end time.

In addition, the traffic variables affecting the confluence of the vehicle according to the characteristics of the confluence of the confluence include traffic volume between the main line 1 and the confluence connection path 2, the number of times the vehicle has changed lanes, the vehicle's running speed, density, and the like. .

On the other hand, it is possible to analyze the traffic volume affecting the formation of congestion in the confluence section of the main line (1) and the confluence connection path (2) according to the driving behavior of the derived vehicle. Based on the analyzed results, it is possible to derive a solution to problems such as adjusting the position of the section in which the lane decreases.

Meanwhile, the lane extracted from the image photographed by the image input / output unit 120 may have a structure of a classifier lane.

That is, in the classifier lane shown in FIG. 5 (b), a section in which the classifier connecting path 3 is discharged from the road main line 1 to the outside of the main line 1 is attempted to escape to the classifier connecting path 3 Is a road where many vehicles have frequent lane changes, resulting in traffic jams.

It is possible to analyze the driving behavior of the vehicle in the classification lane and derive a solution to the traffic problem based on the analyzed result.

Meanwhile, the lane extracted from the image photographed by the image input / output unit 120 may be a staggered section.

That is, as shown in Fig. 5 (c), the crossing section means a section in which traffic flows in the same direction are alternating and a lane is changed while following a long section of a road without the aid of a traffic control facility.

For example, in the staggered section, both the confluence connection path 2 and the classification connection path 3 may be formed along the main line 1. In this staggered section, since both the vehicle flowing in from the confluence connection path 2 and the vehicle flowing out into the classification connection path 3 are traveling, congestion may occur due to frequent lane changes.

The traffic analysis items for solving this staggered section are as follows, because traffic stagnation, which is a traffic problem, occurs due to the staggering of the vehicle.

First, it is possible to compare the traffic volume of the confluence of the main line (1) and the confluence section (2). To this end, the driving behavior of the vehicle may be analyzed by setting a congestion time and a non-congestion time with a lot of vehicle traffic on a road (a confluence lane) photographed by the image photographing unit 122. At this time, in order to set a specific time zone, the time setting unit 144 sets the start time and the end time.

In addition, it is possible to analyze other traffic problems in the number of vehicle changes by comparing the number of lane changes between the vehicle introduced from the confluence connection path 2 and the vehicle discharged from the sorting portion connection path 3.

In addition, by analyzing the vehicle speed, density, and acceleration for each section of the lane, it is possible to analyze a section where traffic jams and accidents frequently occur in a staggered lane.

Depending on the traffic problems analyzed in the crossing lanes, for example, the width of the road for entry into the sorting section connecting path (3) from the main line (1) is expanded, or from the main line (1) to the sorting section connecting path (3). When entering, it is possible to solve the traffic problem by improving roads, such as in advance that the dotted road surface can be displayed in advance.

Through this problem solving solution, it is possible to derive an effect of securing visibility of driving and preventing safety accidents when driving in a crossing lane.

Meanwhile, the lane extracted from the image photographed by the image input / output unit 120 may be a lane of a slope.

Referring to FIG. 5 (d), a road in a horizontal direction is cut in a horizontal direction based on an inclined vertical slope section and a horizontal line of a road in a direction in which the road is cut in the vertical direction based on the horizontal line of the road. It can be said to be a lane consisting of a lateral section that slopes from the side.

Vehicle driving is greatly affected by the terrain conditions in these sloping lanes. In particular, heavy vehicles (e.g., heavy vehicles, vans, etc.) can be decelerated due to the driving speed rather than passenger cars. Can cause

That is, it can be seen that the deceleration of the vehicle appears as the inclination and the inclination length increase in the inclined section lane. Specifically, when a vehicle moves by a length of 1000m on a road having a different 4% inclination angle, when the vehicle moves by a length of 1000m on a road with a speed reduction width (A) of the vehicle and a 9% inclination angle When comparing the speed deceleration width (B) of the vehicle, it appears that the reduction width is greater in the case of (B) than in the case of (A). Accordingly, it can be seen that the driving speed of the vehicle, such as when the weight of the vehicle increases or the road slope is high in the lane of the slope section, affects the vehicle driving behavior.

As described above, when the driving behavior of the vehicle in the lane of the slope section is derived and the traffic problem is analyzed based on the derived result, for example, the installation of the slope display panel causes the vehicle to decelerate in advance, or advances from before the slope point. It is judged that an improvement plan such as installing a lane change guidance system informing the lane change of the vehicle will be necessary.

According to this improvement plan, it is possible to minimize the occurrence of accidents and congestion caused by the change of the lane of the vehicle in the lane of the slope.

Meanwhile, the lane extracted from the image photographed by the image input / output unit 120 may be a non-repetitive congestion-inducing section road.

Referring to (e) of FIG. 5, a non-repetitive congestion-inducing section in which traffic congestion does not occur repeatedly, or where congestion is predictable or impossible, occupies a lane for a specific reason, such as a construction section or an accident section. It can be said to be a section that limits the traffic of a vehicle. Hereinafter, in the embodiment of the present invention, an example in which a non-repetitive congestion-inducing section occurs due to a construction section will be described.

When the non-repetitive congestion-inducing section 4 occurs on the road, a vehicle driving at a high speed and a constant speed decelerates due to the non-repetitive congestion-inducing section 4 and secures a right to travel, such as frequent lane changes. Congestion for will occur.

In order to solve the problem, analysis of traffic volume in the construction section, lane change distribution in the main line (1), and general vehicle information (speed, density, acceleration, etc.) in the lane blocking section analyzes information such as non-repetitive congestion causing sections ( 4) can provide a method for solving the traffic problem.

In addition to the roads exemplified above, it is possible to analyze vehicle driving behavior in various sections, such as the tunnel entry section, drowsiness shelter section, and speed camera installation section, and solve problems such as vehicle stability and traffic jam reduction based on the analyzed data. have.

By using the vehicle driving behavior analysis system, it is possible to selectively photograph an image of a road where a traffic problem occurs, and through the photographed image, it is possible to check the microscopic driving behavior of the vehicle at a point where the problem occurs.

As described above, according to the vehicle driving behavior analysis system using the image of the unmanned aerial vehicle of the present invention, spatial constraints on a road section in which a traffic problem occurs, for example, the consolidation of an expressway main line, IC (Interchange) / JCT (Junction), The image can be taken without restrictions such as a classification section, a staggered section, a vertical (upward, downward) slope change section, a construction section, etc., and an analysis of the captured image can be used to extract vehicle driving behavior for a problem-prone section.

In particular, since microscopic and empirical traffic analysis based on the driving behavior of the vehicle is performed, it is possible to diagnose problems clearly in the target road section, and by deriving an accurate improvement plan, the traffic improvement effect can be maximized.

On the other hand, the functional operations and subject implementations described herein are implemented as digital electronic circuits, or computer software, firmware, or hardware including the structures disclosed herein and their structural equivalents, or one or more of them. It can be implemented in combination. Implementations of the subject matter described herein are one or more modules for computer program instructions encoded on a tangible program storage medium to control or thereby execute operations of one or more computer program products, ie control systems. Can be implemented.

The computer-readable medium can be a machine-readable storage device, a machine-readable storage substrate, a memory device, a composition of materials affecting a machine-readable propagated signal, or a combination of one or more of these.

As used herein, "system" or "device" encompasses all mechanisms, devices, and machines for controlling data, including, for example, programmable processors, computers, or multiple processors or computers. The control system may include, in addition to hardware, code that forms an execution environment for a computer program upon request, such as code constituting the processor firmware, protocol stack, database management system, operating system, or a combination of one or more of them. .

A computer program (also known as a program, software, software application, script, or code) can be written in any form of a compiled or interpreted language or a programming language, including a priori or procedural languages. It can be deployed in any form, including components, subroutines or other units suitable for use in a computer environment. Computer programs do not necessarily correspond to files in the file system. The program is in a single file provided to the requested program, or in multiple interactive files (e.g., one or more modules, files storing subprograms or parts of code), or part of a file holding other programs or data (Eg, one or more scripts stored in a markup language document). The computer program can be deployed to be executed on one computer or on multiple computers that are located at one site or distributed across multiple sites and interconnected by a communication network.

On the other hand, computer-readable media suitable for storing computer program instructions and data include, for example, semiconductor memory devices such as EPROM, EEPROM and flash memory devices, such as magnetic disks such as internal hard disks or external disks, magneto-optical disks and CDs. It can include any form of non-volatile memory, media and memory devices, including -ROM and DVD-ROM disks. The processor and memory may be supplemented by, or incorporated in, special purpose logic circuitry.

Implementations of the subject matter described herein include, for example, a back-end component, such as a vehicle's odometer server, or, for example, a middleware component, such as an application server, or, for example, a web browser that allows a user to interact with implementations of the subject matter described herein. Or a front-end component such as a client computer with a graphical user interface, or a computational system including any combination of one or more of such back-end, middleware or front-end components. The components of the system can be interconnected by any form or medium of driving behavior communication of a digital vehicle, such as a communication network.

This specification includes details of many specific implementations, but these should not be understood as limiting on the scope of any invention or claim, but rather as a description of features that may be specific to a particular embodiment of a particular invention. It should be understood. Likewise, certain features described herein in the context of individual embodiments may be implemented in combination in a single embodiment. Conversely, various features described in the context of a single embodiment can also be implemented in multiple embodiments individually or in any suitable subcombination. Further, although features may operate in a particular combination and may be initially depicted as so claimed, one or more features from the claimed combination may in some cases be excluded from the combination, and the claimed combination subcombined. Or sub-combinations.

Also, although the descriptions describe the operations in the drawings in a specific order, it should be understood that such operations must be performed in the specific order or sequential order shown in order to obtain a desired result, or that all illustrated actions should be performed. Can not be done. In certain cases, multitasking and parallel processing may be advantageous. In addition, the separation of various system components of the above-described embodiments should not be understood as requiring such separation in all embodiments, and the described program components and systems will generally be integrated together into a single software product or packaged in multiple software products. Understand that you can

As such, this specification is not intended to limit the invention to the specific terms presented. Therefore, although the present invention has been described in detail with reference to the above-mentioned examples, those skilled in the art can make modifications, alterations and modifications to the examples without departing from the scope of the present invention. The scope of the present invention is indicated by the following claims rather than the above detailed description, and it should be interpreted that all changes or modifications derived from the meaning and scope of the claims and equivalent concepts thereof are included in the scope of the present invention. do.

According to the vehicle driving behavior analysis system of the present invention, it is possible to analyze a road traffic problem using an unmanned flight apparatus and realize a technology that can provide a service capable of clearly diagnosing a problem cause and a cause of trouble. In addition, as it surpasses the limitations of the existing technology, it is not only the use of the related technology, but also the possibility of commercially available or sales of the applied device, as well as the degree to which it can be practiced clearly and realistically.

10: Vehicle driving behavior analysis system
120: video input and output unit
140: information setting unit
160: extraction unit
180: storage unit

Claims (9)

In the vehicle driving behavior analysis system using the image of the unmanned flight apparatus for outputting the driving behavior of the vehicle,
An image input / output unit for photographing a lane on which the vehicle travels as an image using an unmanned flying device, and inputting and outputting the photographed image;
An information setting unit configured to set information for outputting driving behavior of the vehicle driving the lane based on the image output from the image input / output unit;
An extraction unit that extracts the driving behavior of the vehicle based on the information set by the information setting unit; And
A storage unit which stores the driving behavior of the vehicle extracted from the extraction unit;
Vehicle driving behavior analysis system using an unmanned aerial vehicle image, characterized in that it comprises a. According to claim 1,
The video input and output unit,
An image photographing unit for photographing any one of the vehicle information and lane information as an image;
An image input unit to which an image photographed by the image photographing unit is input; And
An image correction unit that corrects an image input to the image input unit to extract data to output the driving behavior of the vehicle; Vehicle driving behavior analysis system using an unmanned aerial vehicle image, characterized in that it comprises a. According to claim 2,
The image capture unit,
A vehicle driving behavior analysis system using an image of an unmanned aerial vehicle, characterized in that it is a drone for capturing either information of the vehicle information or lane information as a video at a fixed location. According to claim 1,
The information setting unit,
A lane setting unit for setting the type of the lane required to derive driving behavior of the vehicle from the image taken by the image input / output unit;
A time setting unit for setting a specific time required for deriving driving behavior of the vehicle from the image taken by the image input / output unit;
A space setting unit for setting a space required for deriving driving behavior of the vehicle among the images captured by the image capturing unit; Vehicle driving behavior analysis system using an unmanned aerial vehicle image, characterized in that it comprises a. The method of claim 4,
The time setting unit,
A vehicle driving behavior analysis system using an image of an unmanned flight apparatus, characterized in that an analysis start time and an analysis end time are set based on a time point when a certain number of vehicles are running on the lane. The method of claim 4,
The time setting unit
Vehicle driving behavior analysis system using the image of the unmanned flight apparatus, characterized in that for setting the time unit for extracting the driving behavior of the vehicle from the image taken by the image input and output unit. The method of claim 4,
The space setting unit,
Vehicle driving behavior analysis system using an image of an unmanned flight apparatus, characterized in that to set one of the region of interest and the lane of interest among the lane in which the vehicle is driven. The method of claim 4,
The space setting unit,
A vehicle driving behavior analysis system using an unmanned flight apparatus image, characterized in that an actual distance of a designated lane is set among the lanes on which the vehicle travels. According to claim 1,
The extraction unit,
Vehicle type information of a specific individual vehicle selected among vehicles driving the lane, driving speed of the specific individual vehicle, lane information driven by the specific individual vehicle, lane change position and number of times of the specific individual vehicle, adjacent to the specific individual vehicle An object detection unit detecting any one of distance and headway time information from different specific individual vehicles; And
And an object calculator configured to calculate driving behavior of the specific individual vehicle detected by the object detecting unit.

Images