KR102223324B1 - Apparatus and method for determining status of vehicle LED lighting based on average grayscale ratio - Google Patents

Abstract

Hereinafter, a method and an apparatus for determining an LED lighting state between vehicles based on the lighting of the target LED region are provided.

Description

{Apparatus and method for determining status of vehicle LED lighting based on average grayscale ratio}

Hereinafter, a technology regarding a method and an apparatus for determining a state of LED lighting between vehicles is provided.

Vehicle communication may relate to a short- to medium-range communication system capable of exchanging safety warnings and traffic information between vehicles. For example, a vehicle may signal to change lanes and wait for a confirmation signal from another vehicle. In case of emergency braking, an emergency braking signal can be sent to another vehicle. When a vehicle speeds up or slows down, a warning signal is also sent to other vehicles through vehicle communication. In addition, if the vehicle tracking function is used, the ID of the vehicle can be sent to another vehicle to create a map of the vehicle driving on the roadway. Vehicle communication, along with vehicle tracking and other detection technologies such as vehicle tracking, lane detection, pedestrian detection, etc., will lead to a safer and more coordinated transportation network and ultimately enable intelligent transportation systems and autonomous vehicle networks. For this reason, vehicle communication has been intensively studied, and recently, a new technology called optical camera communication (OCC) has emerged as a promising candidate technology for vehicle communication because of its many advantages.

Korean Patent Registration Publication No. 10-0736356 (Registration date: June 29, 2007) Korean Patent Publication No. 10-2014-0096880 (Publication date: August 06, 2014)

The LED lighting state determination method according to an embodiment includes the steps of generating an LED image frame based on reception of an external LED visible light signal, dividing the LED image frame into a plurality of LED unit regions, and Calculating an overall grayscale level representing the whole, calculating a target grayscale level representing a target area among LED unit areas, the target area based on the total grayscale level and the target grayscale level It may include determining a related target grayscale ratio, and determining an illumination state of the target area based on the target grayscale ratio.

The calculating of the total grayscale level according to another embodiment includes extracting the grayscale levels of the unit regions, calculating an average value of the extracted grayscale levels, and setting the average value as the total grayscale level. It may include steps.

In addition, determining the lighting state of the target region may include determining that the lighting of the target region is turned on in response to a case where the ratio is greater than a preset threshold ratio.

The calculating of the grayscale level of the target area according to one side includes extracting grayscale levels of pixels existing in the target area, calculating an average value of the extracted grayscale levels, and calculating the average value of the target area. It may include the step of setting the grayscale level.

1 is a flowchart illustrating a method of LED communication between vehicles according to an embodiment.
2 is a diagram illustrating an LED panel and detecting an LED bit according to an embodiment.
3 is a diagram illustrating detecting an LED bit based on a grayscale level according to an embodiment.
4 is a diagram illustrating a difference in grayscale levels of an LED panel according to an exemplary embodiment.
5 is a diagram illustrating that a blooming effect appears in an LED panel according to an exemplary embodiment.
6 is a diagram illustrating a method for determining an illumination state of an LED according to an exemplary embodiment.
7 is a diagram illustrating a flow chart of a method for determining an illumination state of an LED according to an exemplary embodiment.
8 is a diagram illustrating calculating a target grayscale level representing a target area according to an exemplary embodiment.
9 is a view showing a result of determining the LED lighting state according to an embodiment.
10 is a diagram illustrating a method of classifying result values according to an embodiment.
11 is a graph showing a plurality of LED lighting state determination result values according to an embodiment.
12 is a block diagram showing the configuration of an apparatus for determining an LED lighting state according to an embodiment.

Specific structural or functional descriptions of the embodiments are disclosed for the purpose of illustration only, and may be changed and implemented in various forms. Accordingly, the embodiments are not limited to a specific disclosure form, and the scope of the present specification includes changes, equivalents, or substitutes included in the technical idea.

Although terms such as first or second may be used to describe various components, these terms should be interpreted only for the purpose of distinguishing one component from other components. For example, a first component may be referred to as a second component, and similarly, a second component may be referred to as a first component.

When a component is referred to as being "connected" to another component, it is to be understood that it may be directly connected or connected to the other component, but other components may exist in the middle.

Singular expressions include plural expressions unless the context clearly indicates otherwise. In the present specification, terms such as "comprise" or "have" are intended to designate that the described feature, number, step, action, component, part, or combination thereof is present, but one or more other features or numbers, It is to be understood that the presence or addition of steps, actions, components, parts, or combinations thereof, does not preclude the possibility of preliminary exclusion.

Unless otherwise defined, all terms used herein, including technical or scientific terms, have the same meaning as commonly understood by one of ordinary skill in the relevant technical field. Terms as defined in a commonly used dictionary should be construed as having a meaning consistent with the meaning of the related technology, and should not be interpreted as an ideal or excessively formal meaning unless explicitly defined in the present specification. Does not. Hereinafter, embodiments will be described in detail with reference to the accompanying drawings. The same reference numerals shown in each drawing indicate the same members.

1 is a flowchart illustrating a method of LED communication between vehicles according to an embodiment.

A system structure of a vehicle optical camera communication (VOCC) system according to an embodiment is shown in FIG. 1. At the transmitting side, digital bits 1 and 0 may be transmitted according to the On and Off status of the LED chip in the LED panel after error correction coding and modulation. The receiving side can process the image of the LED panel to extract the embedded data, and then decode it to obtain the original data. As shown in Fig. 1, the image processing component includes two components: LED panel detection and LED bit detection. The LED panel detection and LED bit detection have different purposes, and different classes of algorithms can be used. LED panel detection may have to be handled separately from LED bit detection. LED panel detection and LED bit detection will be described with reference to FIG. 2.

2 is a diagram illustrating an LED panel and detecting an LED bit according to an embodiment.

2 shows the difference between LED panel and LED bit detection. The left drawing of FIG. 2 shows that the entire image is processed in LED panel detection to detect the LED panel coordinates of the image. The right diagram of FIG. 2 is a diagram illustrating finding the on or off state of each LED chip of the panel by closely inspecting the LED panel detected in the LED bit detection. The on or off state of the LED chip can be converted to logic bits 1 and 0. Thereafter, decoding to correct errors can be performed, and original transmission data can be obtained. The embodiments to be described later are embodiments related to an LED bit detection algorithm required to achieve a high bit error rate (BER) in VOCC. In the embodiments described later, it is assumed that the coordinates of the LED panel are obtained in advance. In the simulation, the coordinates of the LED panel can be obtained using a simple LED panel detection algorithm.

3 is a diagram illustrating detecting an LED bit based on a grayscale level according to an embodiment.

In principle, after the LED panel is positioned, it is easy to detect the on and off status of the LED in a clear image. As described in FIG. 3, the state of each LED chip can be determined simply by comparing the grayscale level of each LED chip with a threshold value. With the image sensor and comparator circuit, the LED panel image can be binarized. The image may consist of a black and white image in which the LED is turned on in white and the LED is turned off in black. The brightness of the LED must be much higher than that of other objects in the background, so the image can be binarized into black and white.

The output grayscale image can be binarized using a predetermined threshold. The threshold value may be the lowest grayscale value at which the LED is determined to be turned on in the test image. To reduce the effect of grayscale level changes between frames, the grayscale level of an LED in the range 0 to 255 can be normalized to a range of -1 to 1 using a fixed standard value. This standard value can be determined by the maximum grayscale level of the test white LED panel with all LEDs on. The gray scale is scaled from -1 to 1, so the threshold to differentiate between on and off LEDs can be zero. The effect of the grayscale change between frames can be effectively eliminated by repeatedly measuring the grayscale of the on and off LEDs through the preamble frame. Therefore, the threshold value can be updated every 1000 frames.

4 is a diagram illustrating a difference in grayscale levels of an LED panel according to an exemplary embodiment.

The gray scale value G in the pixel in the image sensor at a given ISO speed S set by the camera may be determined according to Equation 1.

Here, H is the amount of light emission corresponding to the amount of light reaching the pixel,

는 그레이스케일 레벨을 얻기 위해 ISO 속도 S에서 요구되는 표시된 노광량일 수 있다.

May be the indicated exposure amount required at ISO speed S to obtain the grayscale level.

The light entering the image sensor from the Off LED may be ambient light reflected off the surface of the LED. Accordingly, the light emission exposure of the Off LED pixel can be calculated through Equation 2.

Here, Ev is the illuminance of the ambient light, R is the reflectance of the LED surface, t is the exposure time of the camera, N is the F-number of the lens, and K is a reflection light meter correction constant determined by.

The light coming from the On LED to the image sensor may be a combination of the light emitted by the LED and the ambient light reflected off the LED surface. Accordingly, the light emission exposure of the On LED pixel can be calculated through Equation 3.

Lv may be the illuminance of the LED.

Grayscale levels of the On and Off LEDs calculated from Equations 1, 2, and 3 may be determined by various factors such as a vehicle and a situation. As shown in Fig. 4, the gray scale of the on LED of Fig. 4a is much lower than the gray scale of the off LED of Fig. 4b. Therefore, there is no fixed threshold that can be used to accurately distinguish the LED states of all vehicles in all situations, and a fluctuation threshold that can be used adaptively depending on the situation must be set.

5 is a diagram illustrating that a blooming effect appears in an LED panel according to an exemplary embodiment.

During image acquisition, a number of defects can cause blur in the captured LED image to invalidate the gray scale. The two main drawbacks are Airy diffraction pattern and optical aberrations. Airy diffraction is an inevitable phenomenon caused by the swirling nature of light. Airy diffraction creates a striped pattern of light that extends from the boundary of the object in the captured image. The effect can be mathematically explained by convolving the incoming image into an Airy disk diffraction pattern as shown in FIG. 5. Optical aberration is the distortion of an image formed by defects in lens production. Like Airy diffraction, optical aberrations blur the image by scattering light over an area rather than focusing it as a point.

The result that comes as the image becomes blurred is the LED Blooming interference shown in FIG. 6. Due to interference, the gray scale level of the LEDs can be severely changed by the surrounding LEDs. For example, since FIG. 4B described above is severely affected by interference from neighboring LEDs, it may be difficult to accurately determine the LED lighting state.

6 is a diagram illustrating a method for determining an illumination state of an LED according to an exemplary embodiment.

First, the LED panel detection algorithm can be used to determine the position of the entire LED panel in the image. The output according to this algorithm is a convex hull, which can consist of a smallest convex polygon covering the entire LED panel area. According to an embodiment, the LED panel may have a rectangular shape. The resulting convex hull can thus be square or, in most cases, rectangular.

Given the number of LEDs in the array, the entire area of the LED panel can be divided into an equal number of equal sub-areas. According to the embodiment shown in Fig. 6, the LED panel includes 8 x 8 LEDs, and the LED panel is divided into 64 equal partial areas.

Each of these sub-areas is considered an LED area of the array. Once the area of the LED is determined, the characteristics of the LED can be obtained from the pixels inside the LED area and the neighboring LED area. The procedure described above may require explanation in two respects.

First, because the LED area is small, it can be difficult to directly detect a single LED in the image. Also, it can be nearly impossible to detect the LED directly when the LED is off. However, due to the presence of the On LED inside the panel with a larger and higher luminance, the panel can be detected more easily compared to the background of the image. Therefore, it may be easier to detect the entire LED panel than to directly detect the LED area. Accordingly, each LED area can be obtained indirectly by dividing the detected LED panel as shown in FIG. 6. In addition, the area of each LED area may be proportional to the size of the LED panel. As a result, when the LED panel is detected, the LED area of one panel can be determined regardless of the size of the LED.

Second, it is necessary to know the number of LEDs in the array for segmentation. According to one embodiment, the number of LEDs must be a fixed number specified in the VOCC standard. In addition, the number of LEDs can be easily obtained through the preamble frame required to establish a communication channel between two vehicles.

In order to accurately detect the lighting status of the LED, it is necessary to determine the characteristics that can distinguish the difference between the LED on and off status. The selected feature should also overcome problems such as LED gray scale change and LED blooming interference. Based on this criterion, three features are proposed: average grayscale ratio (AGR), gradient inner hardness (GRI) and adjacent grayscale ratio (NGR).

Detecting the lighting condition using the average grayscale ratio (AGR) feature is to detect the LED lighting condition using the ratio of the LED area to the total LED panel. Detecting the lighting state using the gradient inner hardness (GRI) feature is to detect the LED lighting state using the directions of a plurality of gradients in the LED area. Detecting the lighting state using the neighboring grayscale ratio (NGR) feature is to detect the LED lighting state using the grayscale levels of neighboring regions around the target LED region. According to an embodiment, the LED lighting state may be detected using at least one of the three features. In the case of using a plurality of features, result values may be classified based on FIGS. 10 and 11 to be described later.

7 is a diagram illustrating a flow chart of a method for determining an illumination state of an LED according to an exemplary embodiment.

In step 710, the processor may generate an LED image frame based on reception of an external LED visible light signal. The external LED visible light signal may be a signal received by the input device by propagating the visible LED light through a medium. For example, the external LED visible light signal may be a signal detected by an optical sensor or a camera by propagating visible light of an external LED. The processor may generate the received visible light signal by converting it into an LED image frame.

In step 720, the processor may divide the LED image frame into a plurality of LED unit regions. The processor may pre-designate the LED unit area or divide it according to the number determined by the standard. For example, the processor may divide the image frame into 8 x 8 LED unit areas.

In step 730, the processor may calculate an overall grayscale level representing the entire area of the LED. The entire LED area may mean the LED panel area. According to an embodiment, the total grayscale level may be calculated by calculating an average value of the grayscale levels of the LED unit regions, and the average value may be set as the total grayscale level. For example, when an image frame is composed of 64 unit areas, the processor may calculate an average value by calculating grayscale levels of 64 unit areas. The calculated average value may be an overall grayscale level representing the entire area of the LED.

In step 740, the processor may calculate a target grayscale level representing the target area among the unit areas. According to an embodiment, the processor may sequentially select each of the divided unit areas as a target area. Since the processor sequentially selects a target area and performs steps described below for each target area, it is possible to determine the LED lighting state for all of the divided unit areas.

The processor may extract grayscale levels of pixels existing in the target area, calculate an average value of the extracted grayscale levels, and set the calculated average value as the grayscale level of the target area. The processor may detect the target area in units of a plurality of pixels, and may extract grayscale levels of the plurality of pixels.

In step 750, the processor may determine a target grayscale ratio related to the target area based on the total grayscale level and the target grayscale level. The target grayscale ratio can be calculated through Equation 4.

는 대상 그레이스케일 레벨이고,

는 전체 그레이스케일 레벨이다.In Equation 4,

Is the target grayscale level,

Is the overall grayscale level.

In step 760, the processor may determine the lighting state of the target area based on the target grayscale ratio. In response to a case where the target grayscale ratio is greater than a preset threshold ratio, the processor may determine that the illumination of the target area is turned on.

8 is a diagram illustrating comparison of a plurality of gradient vectors and comparison vectors according to an exemplary embodiment.

Referring to FIG. 8, as an example, the LED image frame may include 2×2 unit areas 610 to 613. According to the embodiment described above with reference to FIG. 7, the processor may calculate the total grayscale level and the target grayscale level.

For example, the grayscale level may mean that the higher the level, the brighter illuminance, and the target regions 610 and 613 may have a grayscale level of 3 levels. The target regions 611 and 612 may have a grayscale level of 7 levels. Since the total grayscale level may be an average value of the grayscale levels of the target regions 610 to 613, the total grayscale level may be 5 levels.

The processor may determine the target grayscale ratio based on the calculated total grayscale level and the target grayscale level. Since the total grayscale level is 5 levels, the target grayscale ratio of the target areas 610 and 613 is 0.6 (=3/5), and the target grayscale ratio of the target areas 611 and 612 is 1.4 (=7/5). ) Can be.

The processor may determine the lighting state of the target area based on a preset threshold ratio. When the target grayscale ratio is greater than the threshold ratio, the processor may determine that the illumination of the target area is turned on, whereas when the target grayscale ratio is less than the threshold ratio, the illumination of the target area is turned off. For example, when the preset threshold ratio is 1, since the target grayscale ratio of the target regions 610 and 613 is 0.6, the processor may determine that the illumination of the target regions 610 and 613 is turned off. On the other hand, since the target grayscale ratio of the target areas 611 and 612 is 1.4, the processor may determine that the illumination of the target areas 611 and 612 is turned on.

As shown in FIG. 4, the brightness of the entire image frame may be different depending on the surrounding environment. In this case, if the lighting state is determined by a fixed threshold value, the probability that the processor incorrectly determines the lighting state increases. Accordingly, when a target grayscale level with respect to the entire grayscale level is calculated to determine the lighting state of the target area, the lighting state may be determined by reflecting the effect of the brightness of the image frame itself according to the surrounding environment.

9 is a view showing a result of determining the LED lighting state according to an embodiment.

The result graphs 9b and 9c are graphs of the results of determining the LED lighting state through the plurality of test LED samples 9a. The plurality of test LED samples 9a are LED samples having different overall brightness.

The result graph 9b is a result graph of determining the LED lighting state according to the average grayscale level of the LED unit areas. There are many areas where the average grayscale level of the LED status is on and off, and the average grayscale level of the LED is not easily judged by only the average grayscale level of the LED.

On the other hand, the result graph 9c according to an exemplary embodiment is a graph of the results of determining the LED lighting state by the method previously described with reference to FIGS. 7 to 8. It can be seen that the LED status of the LED status is on and the LED average greyscale ratio (AGR) values of the off status are distinguished based on a preset threshold ratio, and the overlapped portion is less than the result graph 9b. Therefore, rather than determining the lighting state based on the LED average grayscale value, determining the lighting state using the ratio of the target grayscale level to the total grayscale level may more accurately determine the lighting state.

10 is a diagram illustrating a method of classifying result values according to an embodiment.

The method of classifying result values according to FIG. 10 requires a training data set in which all objects are classified into two or more classes as shown in FIG. 10A. Given an arbitrary n-dimensional vector w, n is the number of features of each object, and projections of all objects x in training set to w can be given by Equation (5).

Here, the result of the dot product of w and x may be extracted through Equation 5.

And can be the average values of classes 0 and 1 projected onto the w vector, respectively,

And may be standard deviation values of classes 0 and 1 projected onto the w vector, respectively.

In a method of classifying result values according to an embodiment, as illustrated in FIG. 10B, a discrimination vector w such that a variance of projected result values between classes is a maximum and a variance of projected result values within a class is a minimum may be found. That is, as a method of classifying the result values, a discriminant vector w that maximizes the ratio of Equation 7 can be found.

는 클래스들 간의 분산이고,

는 클래스 내 분산이다.here,

Is the variance between classes,

Is the intraclass variance.

가 최대가 되는 판별 벡터 w를 계산할 수 있다.Based on Equation 8,

The discriminant vector w at which is the maximum can be calculated.

을 구할 수 있다.Here, the intra-class covariance matrix using Equation 9

Can be saved.

11 is a graph showing a plurality of LED lighting state determination result values according to an embodiment.

를 얻을 수 있다.A plurality of LED panel frame sets may be used to apply the method described in FIG. 10 to the LED bit detection. The transmitted bits of each frame are known, and the actual state of each frame's LED is also known. After performing LED panel detection and LED feature extraction, a training set of 3D LED feature vectors in every frame

Can be obtained.

,

가 얻어질 수 있다. 그 후, 두 집합 S0 및 S1의 평균 벡터 m0 및 m1이 각각 계산된다.Two sets of LED characteristic vectors depending on the known state of each LED

,

Can be obtained. Then, the average vectors m0 and m1 of the two sets S0 and S1 are calculated, respectively.

은 수학식 10에 기초하여 계산될 수 있다.Covariance matrix

May be calculated based on Equation 10.

는 모든 LED 특징 벡터들로부터 계산된 3 x 3 공분산 행렬이다.here,

Is the 3 x 3 covariance matrix calculated from all LED feature vectors.

는 특징

와

사이의 분산이다.here,

Features

Wow

Is the variance between.

In the method described in FIG. 10, the discrimination vector w is obtained, and the LED lighting state may be determined based on the feature vector u extracted from the input frame.

Here, thres is the average of the projected values of all feature vectors of set S.

For example, as illustrated in FIG. 11, using 100 sample frames, a discrimination vector w capable of distinguishing the on and off states of the LED can be found.

12 is a block diagram showing the configuration of an apparatus for determining an LED lighting state according to an embodiment.

The device 1200 may include an image generator 1210, a memory 1220, and a processor 1230. The image generator 1210 may generate an LED image frame based on reception of an external LED visible light signal. The image generator 1210 is a physical module separate from the processor 1230, and may generate an image frame, but is included in the physical module of the processor 1230 and is distinguished from an algorithm that processes an operation of the processor 1230. It is also possible to generate an image frame with an algorithm.

The processor 1230 may divide the LED image frame into a plurality of LED unit regions, and calculate an overall grayscale level representing the entire LED image frame. The processor 1230 may calculate a target grayscale level representing the target area among the LED unit areas, and determine a target grayscale ratio related to the target area based on the total grayscale level and the target grayscale level. Also, the processor 1230 may determine the lighting state of the target area based on the target grayscale ratio.

The memory 1220 may temporarily store at least one of an LED image frame, a full scale level, a target scale level, and a target grayscale ratio.

The embodiments described above may be implemented as a hardware component, a software component, and/or a combination of a hardware component and a software component. For example, the devices, methods, and components described in the embodiments are, for example, a processor, a controller, an arithmetic logic unit (ALU), a digital signal processor, a microcomputer, a field programmable gate (FPGA). array), programmable logic unit (PLU), microprocessor, or any other device capable of executing and responding to instructions. The processing device may execute an operating system (OS) and one or more software applications executed on the operating system. Further, the processing device may access, store, manipulate, process, and generate data in response to the execution of software. For the convenience of understanding, although it is sometimes described that one processing device is used, one of ordinary skill in the art, the processing device is a plurality of processing elements and/or a plurality of types of processing elements. It can be seen that it may include. For example, the processing device may include a plurality of processors or one processor and one controller. In addition, other processing configurations are possible, such as a parallel processor.

The software may include a computer program, code, instructions, or a combination of one or more of these, configuring the processing unit to operate as desired or processed independently or collectively. You can command the device. Software and/or data may be interpreted by a processing device or, to provide instructions or data to a processing device, of any type of machine, component, physical device, virtual equipment, computer storage medium or device. , Or may be permanently or temporarily embodyed in a transmitted signal wave. The software may be distributed over networked computer systems and stored or executed in a distributed manner. Software and data may be stored on one or more computer-readable recording media.

The method according to the embodiment may be implemented in the form of program instructions that can be executed through various computer means and recorded in a computer-readable medium. The computer-readable medium may include program instructions, data files, data structures, and the like alone or in combination. The program instructions recorded on the medium may be specially designed and configured for the embodiment, or may be known and usable to those skilled in computer software. Examples of computer-readable recording media include magnetic media such as hard disks, floppy disks, and magnetic tapes, optical media such as CD-ROMs and DVDs, and magnetic media such as floptical disks. -A hardware device specially configured to store and execute program instructions such as magneto-optical media, and ROM, RAM, flash memory, and the like. Examples of program instructions include not only machine language codes such as those produced by a compiler, but also high-level language codes that can be executed by a computer using an interpreter or the like. The hardware device described above may be configured to operate as one or more software modules to perform the operation of the embodiment, and vice versa.

As described above, although the embodiments have been described by the limited drawings, a person of ordinary skill in the art can apply various technical modifications and variations based on the above. For example, the described techniques are performed in a different order from the described method, and/or components such as systems, structures, devices, circuits, etc. described are combined or combined in a form different from the described method, or other components Alternatively, even if substituted or substituted by an equivalent, an appropriate result can be achieved.

Claims (9)

Generating an LED image frame based on the reception of an external LED visible light signal;
Dividing the LED image frame into a plurality of LED unit areas;
Calculating an overall grayscale level representing the entire LED image frame;
Calculating a target grayscale level representing a target area in which the LED lighting of the vehicle is arranged among the LED unit areas;
Determining a target grayscale ratio related to the target area as a ratio of the target grayscale level to the total grayscale level; And
Determining the lighting state of the target area by comparing the target grayscale ratio with a preset threshold ratio
Including,
The step of calculating the overall grayscale level,
Extracting grayscale levels of the unit regions; And
Computing an average value of the extracted grayscale levels, and setting the average value to the total grayscale level,
The step of calculating the grayscale level of the target area,
Extracting grayscale levels of pixels existing in the target area;
Computing an average value of the extracted grayscale level, and setting the average value as a grayscale level of the target area,
How to determine the LED lighting status. delete The method of claim 1,
Determining the lighting state of the target area,
In response to the ratio being greater than the preset threshold ratio, determining that the illumination of the target area is turned on
LED lighting state determination method comprising a. delete A computer-readable recording medium storing one or more computer programs including instructions for performing the method of any one of claims 1 and 3. An image generator configured to generate an LED image frame based on the reception of an external LED visible light signal;
Dividing the LED image frame into a plurality of LED unit areas,
Calculating a total grayscale level representing the entire LED image frame,
Calculate a target grayscale level representing a target area where the LED lighting of the vehicle is arranged among the LED unit areas,
A processor configured to determine a target grayscale ratio related to the target area as a ratio of the target grayscale level to the total grayscale level; And
A memory for storing at least one of the total grayscale level, the grayscale level of the target area, and the target grayscale ratio
Including,
The processor,
Extracting grayscale levels of the unit regions, calculating an average value of the extracted grayscale levels, and setting the average value as the total grayscale level,
Extracting grayscale levels of pixels existing in the target area, calculating an average value of the extracted grayscale levels, and setting the average value as the grayscale level of the target area,
LED lighting state determination device for determining the lighting state of the target area by comparing the target grayscale ratio and a preset threshold ratio. delete The method of claim 6,
The processor,
In response to the ratio being greater than the preset threshold ratio, the LED lighting state determination device determines that the lighting of the target area is turned on. delete

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