KR102467517B1 - A method for predicting the occurrence of black ice using acoustic images - Google Patents

Abstract

The present invention relates to a method for predicting the occurrence of black ice using an acoustic image. The method for predicting occurrence of black ice using an acoustic image according to an embodiment of the present invention includes receiving road surface environment information and an acoustic image of a road surface, and receiving Determining whether at least one occurrence condition for generating black ice is satisfied based on road surface environment information, analyzing a received acoustic image, and determining a cause of noise measured on the acoustic image, and and determining a probability level of occurrence of black ice based on the type of the occurrence condition determined to be satisfied and the cause of the noise. According to the present invention, it is possible to accurately predict the occurrence of black ice even in environmental conditions (fog, nighttime, etc.) in which it is difficult to secure a view by determining whether black ice has occurred on the road surface using the acoustic image acquired by the acoustic camera installed around the road. Therefore, highly reliable black ice generation information can be provided regardless of environmental conditions.

Description

A method for predicting the occurrence of black ice using acoustic images}

The present invention relates to a method for predicting the occurrence of black ice using acoustic images, and more particularly, by determining whether or not black ice occurs on a road surface using acoustic images acquired by acoustic cameras installed around roads, under environmental conditions in which it is difficult to secure visibility. It relates to a black ice occurrence prediction method using acoustic images that can accurately predict black ice occurrence even in fog, nighttime, etc.

When the temperature suddenly drops, snow or rain that fell during the day seeps into the cracks of the asphalt road, and then mixes with oil and dust on the road overnight to form road icing, which freezes thinly on the road.

In particular, in such a road icing phenomenon, since the ice is formed so thin and transparent that the color of the black asphalt of the road can be seen as it is, it has been named black ice, which means black ice.

Such black ice is a major cause of vehicle skid accidents because it is not easily noticeable when driving on the road and it is easy to think that the road is simply a little wet. In particular, black ice is easy to occur in places with large temperature differences, such as on bridges, on roads near lakesides, or on shaded curved roads, even if it is not snowing or raining, so drivers are required to be careful about black ice regardless of the climate .

In order to detect such black ice, conventionally, a method of detecting black ice by photographing the road surface and using image analysis technology has been used. The black ice detection method using such image analysis has relatively large restrictions due to weather and brightness. As a function, there is a problem in that the accuracy of judgment is reduced because it is difficult to secure a view at night when there is cloud or fog or when the day is dark.

Therefore, it is required to develop a method for predicting the occurrence of black ice that can accurately predict the occurrence of black ice even at night when it is cloudy or foggy or when the day is dark.

Republic of Korea Patent Publication No. 10-2019-0140272

The present invention is to solve the problems of the prior art mentioned above, and an object of the present invention is to determine whether black ice is generated on the road surface using an acoustic image obtained by an acoustic camera installed around the road, thereby securing visibility. To provide a black ice occurrence prediction method using acoustic images that can accurately predict black ice occurrence even in difficult environmental conditions (fog, night, etc.).

Another object to be achieved by the present invention is to prepare for the occurrence of black ice according to the determined level by determining not only whether or not black ice occurs, but also to determine the probability level of black ice based on various environmental information, or to prepare for the occurrence of black ice or to obtain an appropriate level according to the determined level. It is to provide a black ice occurrence prediction method using an acoustic image that enables the correspondence of

Another object of the present invention is to provide a black ice generation prediction method using acoustic images capable of accurately predicting the possibility of black ice occurrence by diversifying situations in which conditions for occurrence of black ice are satisfied.

Another object of the present invention is to provide a method for predicting the occurrence of black ice using an acoustic image that determines traffic flow or an accident on a road through an acoustic image obtained to predict occurrence of black ice.

A black ice occurrence prediction method using an acoustic image according to an embodiment of the present invention includes receiving road surface environment information and a road acoustic image, and at least one method for generating black ice based on the received road surface environment information. Determining whether each generation condition is met, analyzing the received acoustic image and determining the cause of the noise measured on the acoustic image, and based on the type of the generation condition determined to be satisfied and the cause of the noise and determining a grade of possibility of occurrence of black ice.

In this case, the black ice generation prediction method may further include determining the traffic flow according to the determined cause of the noise.

In addition, the method for predicting occurrence of black ice may further include determining whether an accident occurs or a victim is generated according to the determined cause of noise.

In addition, in the black ice occurrence prediction method, occurrence conditions include road temperature conditions, road surface radiant heat conditions, and road moisture conditions, and the step of determining whether each occurrence condition is met includes determining whether the temperature conditions are met, and It may include determining whether a condition is satisfied, and determining whether a radiant heat condition is satisfied.

In addition, the road surface environment information received in the black ice occurrence prediction method includes the temperature of the road surface, and in the step of determining whether the temperature condition is satisfied, it can be determined that the temperature condition is satisfied if the received road surface temperature is 0 ° C or less. have.

In addition, the road surface environment information received in the black ice occurrence prediction method includes the humidity of the road surface, the amount of precipitation around the road surface, and the dew point temperature of the road surface. , or if the amount of precipitation around the received road surface is greater than or equal to the threshold amount of precipitation, or if the temperature of the received road surface is less than or equal to the dew point temperature of the received road surface, it may be determined that the wet condition is satisfied.

In addition, the road surface environment information received in the black ice occurrence prediction method includes the radiant heat of the road surface, and the step of determining whether the radiant heat condition is satisfied is that the received road surface temperature is equal to or less than the first threshold temperature and at the same time, the received radiant heat of the road surface. If it is less than this critical radiant heat condition, it is determined that the radiant heat condition is satisfied, and the first critical temperature may be a temperature set higher than 0°C.

In addition, in the step of determining whether the radiant heat condition is satisfied, if the temperature of the received road surface is less than or equal to the second critical temperature, it is determined that the radiant heat condition is satisfied even if the radiant heat of the received road surface exceeds the critical radiant heat, and the second critical temperature is 0°C. It may be set to a lower temperature.

In addition, in the black ice occurrence prediction method, the occurrence probability class includes a caution class, and the step of determining the occurrence probability class satisfies only one of the temperature condition, the water condition, and the radiant heat condition, or only the water condition and the radiant heat condition at the same time. If it is satisfied, the probability of occurrence can be determined as a caution level.

In addition, in the black ice occurrence prediction method, the occurrence probability grade includes a warning grade, and the step of determining the occurrence probability grade satisfies the temperature condition and at the same time satisfies any one of the water condition and the radiant heat condition, the occurrence probability grade can be determined as a warning level.

In addition, in the black ice occurrence prediction method, the occurrence probability class includes a risk class, and the step of determining the occurrence probability class satisfies all temperature conditions, wet conditions, and radiant heat conditions, and among the noise measured on the acoustic image, the tire and If there is no noise determined to be the cause of friction between black ice, the probability of occurrence may be determined as a risk level.

In addition, in the black ice occurrence prediction method, the occurrence probability class includes the occurrence class, and the step of determining the occurrence probability class satisfies all temperature conditions, moisture conditions, and radiant heat conditions, and among the noise measured on the acoustic image, the tire and If noise determined to be caused by friction between black ice exists, an occurrence possibility grade may be determined as an occurrence grade.

In addition, the black ice occurrence prediction method includes the steps of spraying salt water toward the generated black ice when the occurrence probability level is determined as the occurrence level, and when the spraying of salt water is completed, after a predetermined delay time from the time when the salt water spraying is completed, additional The method may further include determining whether friction noise between the tire and the black ice is measured by analyzing the received acoustic image, and adjusting a probability level according to the determined result.

According to the present invention, it is possible to accurately predict the occurrence of black ice even in environmental conditions (fog, nighttime, etc.) in which it is difficult to secure a view by determining whether black ice has occurred on the road surface using the acoustic image acquired by the acoustic camera installed around the road. Therefore, highly reliable black ice generation information can be provided regardless of environmental conditions.

In addition, the present invention determines not only the occurrence of black ice, but also the occurrence possibility grade of black ice based on various environmental information, so that it is possible to prepare for the occurrence of black ice according to the determined grade or to respond at an appropriate level according to the determined grade. let it

In addition, the present invention diversifies situations in which conditions for generating black ice are satisfied, so that the possibility of occurrence of black ice can be more accurately predicted.

In addition, the present invention can determine the traffic flow on the road or the occurrence of an accident through the acoustic image obtained to predict the occurrence of black ice, thereby saving the cost of building a separate infrastructure for detecting or determining the traffic flow. and can respond immediately in the event of an accident.

1 is a diagram conceptually illustrating a black ice occurrence prediction system to which a black ice occurrence prediction method using a sound image according to an embodiment of the present invention is applied.
2 is a flowchart illustrating a black ice prediction method using acoustic images according to an embodiment of the present invention.
3 is a functional block diagram of a server according to an embodiment of the present invention.
4 is a diagram illustrating a noise analysis process in a noise analyzer according to an embodiment of the present invention.
5 is a diagram showing an example of an acoustic image obtained by an acoustic camera according to an embodiment of the present invention.
6 is a diagram showing an example of a screen output from a display device according to an embodiment of the present invention.
7 is a flowchart illustrating a process of determining whether black ice occurrence conditions are satisfied in a condition satisfaction determination unit according to an embodiment of the present invention.
8 is a flowchart illustrating a process of determining a black ice occurrence possibility grade in a grade determination unit according to an embodiment of the present invention.
9 (a) and (b) are diagrams illustrating an operation example of a salt water injection device and a compressed air injection device according to an embodiment of the present invention.
10 is a diagram showing a timeline from the time of determining the grade of the grade determination unit to the time of adjusting the grade according to an embodiment of the present invention.

It should be noted that technical terms used in the present invention are only used to describe specific embodiments and are not intended to limit the present invention. In addition, technical terms used in the present invention should be interpreted in terms commonly understood by those of ordinary skill in the art to which the present invention belongs, unless specifically defined otherwise in the present invention, and are excessively inclusive. It should not be interpreted in a positive sense or in an excessively reduced sense. In addition, when the technical terms used in the present invention are incorrect technical terms that do not accurately express the spirit of the present invention, they should be replaced with technical terms that those skilled in the art can correctly understand.

Also, singular expressions used in the present invention include plural expressions unless the context clearly dictates otherwise. In the present invention, terms such as "consisting of" or "comprising" should not be construed as necessarily including all of the various elements or steps described in the invention, and some of the elements or steps are included. It should be construed that it may not be, or may further include additional components or steps.

In addition, it should be noted that the accompanying drawings are only for easily understanding the spirit of the present invention, and should not be construed as limiting the spirit of the present invention by the accompanying drawings.

Hereinafter, a black ice prediction method using acoustic images according to the present invention will be described in more detail with reference to the accompanying drawings.

1 is a diagram conceptually illustrating a black ice occurrence prediction system to which a black ice occurrence prediction method using a sound image according to an embodiment of the present invention is applied.

Hereinafter, a black ice generation prediction system (hereinafter referred to as "black ice prediction method") to which a black ice generation prediction method (hereinafter referred to as "black ice prediction method") using a sound image according to an embodiment of the present invention is applied with reference to FIG. An ice prediction system) will be schematically described.

The system for predicting black ice according to the present embodiment may determine whether black ice has occurred on the road surface by using the sound image of the road surface in order to accurately predict the occurrence of black ice even in environmental conditions (fog, nighttime, etc.) in which visibility is difficult to secure.

The black ice prediction system according to the present embodiment includes an information acquisition unit 10 that acquires road surface environment information and sound images, and a server 30 that predicts the possibility of occurrence of black ice on the road surface based on the road surface environment information and sound images. In this case, the information obtaining unit 10 may include a sound camera 11 to obtain an sound image of the road surface.

A sound camera/acoustic camera is a camera that converts sound and resonance from an object into a visual screen, and includes a camera module that acquires a visible light image and a plurality of microphones spaced apart from each other. The acoustic camera generates an acoustic image by detecting a location where noise is generated using a time difference in which sound is input to a plurality of microphones, and displaying the location of the detected noise on a visible light image acquired through a camera module. At this time, the acoustic camera generates acoustic images in which the arrangement of waves generated according to the vibration of the measured noise is expressed in different colors, and the generated acoustic images include noise wavelength information, noise frequency information, and noise amplitude information. can

Preferably, in this embodiment, an acoustic camera 11 capable of measuring a wide frequency range from a low frequency to a high frequency and selectively measuring noise corresponding to a frequency within a specific range may be used.

The information acquisition unit 10 may obtain information on the road surface or road surface environment in the vicinity of the road surface, in addition to the sound image. Here, the road surface environment information may include various information such as temperature, humidity, and dew point temperature of the road surface or its surroundings. In order to acquire such road surface environment information, the information obtaining unit 10 may include a thermometer, a hygrometer, a dew point gauge, etc. installed on or near the road surface.

Here, a plurality of sound cameras 11 of the information acquisition unit, a thermometer, a hygrometer, and a dew point meter may be respectively provided. In addition, a plurality of sound cameras 11, thermometers, hygrometers, and dew point gauges may be installed at predetermined distances along major roads, or may be installed around roads where black ice is habitually generated.

Road surface environment information and sound images obtained by the information acquisition unit 10 may be transmitted to the server 30 through a network. In addition, the server 30 may determine whether or not black ice may occur on the road surface based on the received environmental information and the sound image, and may determine an occurrence possibility grade.

Specifically, the server 30 may determine whether occurrence conditions required for occurrence of black ice are met using the received road surface environment information. Here, in order to generate black ice, temperature conditions, moisture conditions, and radiant heat conditions must all be satisfied. Specifically, in order for black ice to occur, the road surface temperature must fall below 0°C (temperature condition), there must be more than a certain amount of moisture on the road surface (moisture condition), and the radiant heat reflected on the road surface must be less than a certain amount (radiant heat condition). .

In addition, the server 30 may determine whether black ice has occurred on the road surface by using the received sound image. Specifically, the server 30 may analyze the received acoustic image to determine the cause of the noise measured on the acoustic image. At this time, the server 30 determines the cause of the noise based on the frequency pattern of the noise, and a more detailed description thereof will be described later with reference to FIG. 5 .

The server 30 determines the possibility of occurrence of black ice based on the type or number of occurrence conditions (temperature condition, moisture condition, radiant heat condition) determined to be satisfied and the cause of noise measured on the received acoustic image. can decide

At this time, the probability of occurrence is divided into several levels, and the server 30 according to the present embodiment may determine the probability of occurrence as one of a safety level, a caution level, a warning level, a danger level, and an occurrence level.

Here, the safety grade means a road surface condition in which the possibility of black ice occurrence is very rare, the caution grade means a road surface condition in which black ice may occur with a relatively low probability, and the warning grade means a road surface condition where the temperature of the road surface is low and black ice may occur depending on the situation. This means a road surface condition in which ice may occur at any time, and the hazard level may mean a road surface condition in which black ice is expected to occur soon because all conditions for occurrence of black ice are satisfied. Also, the occurrence grade may refer to a road surface state in which it is determined that black ice has already occurred.

As described above, the black ice prediction system according to the present embodiment determines whether or not black ice will occur as well as the level of occurrence possibility, so as to prepare for the occurrence of black ice according to the determined level or to obtain an appropriate level of black ice according to the determined level. enable response.

At this time, the safety level, caution level, warning level, and risk level are the occurrence probability levels determined when it is determined that black ice does not exist on the road surface, and the occurrence level is determined when it is determined that black ice exists on the road surface. is the probability level.

That is, if it is determined that black ice exists on the road surface, the server 30 may determine an occurrence probability grade as an occurrence grade, and the existence of black ice may be determined based on the cause of the noise measured on the acoustic image. have. Specifically, the server 30 may determine that black ice exists on the corresponding road surface when there is noise determined to be generated by friction between the tire and the black ice among the noises measured on the sound image. Accordingly, since the black ice prediction system according to the present embodiment determines whether black ice is generated by using the sound image, it is possible to accurately determine whether black ice is generated even in environmental conditions (fog, nighttime, etc.) in which visibility is difficult to secure. Therefore, the system for predicting black ice according to the present embodiment can provide highly reliable black ice occurrence information regardless of environmental conditions around the road surface.

On the other hand, the black ice prediction system according to the present embodiment does not stop at determining the probability of occurrence of black ice, but displays the probability of black ice so that the administrator can directly select and perform a response, or Responses can be performed automatically.

To this end, the black ice prediction system according to the present embodiment may further include a display device 50 for displaying the determined occurrence probability level to a manager, and a countermeasure means 70 for responding to black ice.

The output of the display device 50 is controlled by the server 30, and an occurrence possibility grade of each road surface to be monitored, an acoustic image of each road surface, and the like may be output. Preferably, the display device 50 outputs a road map including each road surface to be monitored so that a manager can see at a glance the probability level of each road surface, and the occurrence probability level of each road surface is located at each road surface location on the road map. this can be displayed.

The countermeasure unit 70 notifies the driver of the possibility or occurrence of black ice so that the driver can prepare for the risk of black ice, or remove the black ice or the cause (biting) of black ice to reduce the risk of black ice. can make it disappear

Specifically, the countermeasure unit 70 may include an electronic display board, a directional speaker, a salt spray device, and a compressed air spray device.

An electric display board and a directional speaker can be installed on the upstream road of the road surface subject to black ice monitoring to warn the driver of the occurrence or possibility of black ice. The salt spray device is installed around the road surface to be monitored and sprays salt water toward the road surface to remove black ice. In addition, the compressed air injection device is installed around the road surface to be monitored and sprays compressed air toward the road surface to remove water on the road surface that can turn into black ice or to remove salt water sprayed on the road surface by the salt water injection device. have.

2 is a flowchart illustrating a black ice prediction method using acoustic images according to an embodiment of the present invention.

Hereinafter, a process of predicting the possibility of black ice and determining a grade performed by the server 30 will be briefly described.

The server 30 receives the road surface environment information and the sound image acquired by the information obtaining unit 10 through the network (S10).

Further, the server 30 may determine whether or not each generation condition required for occurrence of black ice is satisfied based on the received road surface environment information (S20).

In addition, the server 30 may analyze the acoustic image to determine a cause of noise present in the received acoustic image (S30).

The server 30 determines an occurrence probability level based on the type and number of satisfied occurrence conditions and the cause of the noise existing on the acoustic image. At this time, the determined occurrence probability level is a safety level, a caution level, and a warning level. , is one of the risk level and occurrence level (S40).

The server 30 that determines the probability of occurrence may operate and control the response means 70 so that a response is performed according to the determined occurrence level (S50). In addition, if the corresponding means is operated and controlled, the server 30 may re-determine the occurrence possibility grade after a predetermined time and adjust the grade according to the re-determined result (S60). In this case, the server 30 may receive the newly acquired road surface environment information and the sound image, and may re-perform the process of determining whether each black ice occurrence condition is met and analyzing the sound image in order to re-determine the occurrence probability grade. This grade adjustment process is to lower the occurrence possibility grade if black ice is removed or the cause of black ice is removed by the operation of the countermeasure unit 70 .

3 is a functional block diagram of a server according to an embodiment of the present invention, FIG. 4 is a diagram for explaining a noise analysis process in a noise analyzer according to an embodiment of the present invention, and FIG. It is a diagram showing an example of an acoustic image acquired by an acoustic camera according to an embodiment of the present invention. 6 is a diagram showing an example of a screen output from a display device according to an embodiment of the present invention.

Looking at the detailed configuration of the server 30 with reference to FIG. 3, the server 30 includes a communication unit 310 for communication connection, a storage unit 320 for storing various data, and occurrence conditions for black ice to occur. A condition satisfaction determination unit 330 that determines whether the conditions are met, a noise analysis unit 340 that analyzes noise on the acoustic image to determine whether or not black ice has occurred, and a level determination unit 350 that determines a probability level of occurrence and a display unit 360 for controlling the output of the display device 50, and a corresponding controller 370 for controlling the operation of the corresponding means 70.

The communication unit 310 may be connected to the information acquisition unit 10, the display device 50, and the corresponding means 70 through wired or wireless communication through a network. The communication unit 310 may receive road environment information and sound images from the information acquisition unit 10 and provide the received road environment information to the condition satisfaction determination unit 330 and the noise analysis unit 340 . At this time, the communication unit 310 may receive road surface environment information from an outside source other than the information obtaining unit 10 . In detail, the communication unit 310 may receive road surface environment information from the Korea Meteorological Administration server, and at this time, the road surface environment information received from the Korea Meteorological Administration server may include weather information for each region.

The communication unit 310 receives the control signal generated by the display unit 360 and transmits it to the display device 50, or receives the control signal generated by the corresponding control unit 370 and transmits it to the corresponding means 70. can

Various types of data are stored in the storage unit 320 . The stored data may include frequency pattern information of various noises, radiant heat information according to the date and time of each road surface, identification information of each component constituting the information acquisition unit 10 including a sound camera, and installation location information. .

The frequency pattern information of various noises stored in the storage unit 320 includes frequency patterns of noise caused by friction between tires and black ice. Various frequency patterns may be stored according to the type of tire, the type of tire, and the degree of wear of the tire.

The condition satisfaction determining unit 330 may receive road surface environment information from the communication unit 310 and determine whether the temperature condition, moisture condition, and radiant heat condition are satisfied, respectively, based on the provided road surface environment information.

Specifically, the condition satisfaction determination unit 330 determines whether the temperature condition determination unit 331 determines whether the temperature condition is satisfied based on the provided road surface environment information, and whether the wet condition is satisfied based on the provided road surface environment information. A radiant heat condition determining unit that determines whether the radiant heat condition is satisfied based on the moisture condition determining unit 333 and the received road surface environment information and the radiant heat information according to the date and time of each road surface stored in the storage unit 320 ( 335) may be configured. Here, a detailed description of the determination of satisfaction for each condition performed by the temperature condition determining unit 331, the wet condition determining unit 333, and the radiant heat condition determining unit 335 will be described later with reference to FIG. 7 .

The noise analyzer 340 may determine the cause of noise measured on the sound image based on the sound image provided from the communication unit 310 and the frequency pattern of each noise stored in the storage 320 .

Referring to FIG. 4 as an example, the process of analyzing the cause of noise by the noise analyzer 340 is described in detail. The noise analyzer 340 extracts the frequency pattern h1 of the target noise belonging to the reference frequency range from the provided acoustic image can do. In this case, the reference frequency range may include a frequency range of noise generated when friction occurs between vehicle tires and black ice or between vehicle tires and water accumulated on the road surface.

Further, the noise analyzer 340 compares the extracted frequency pattern h1 with the stored frequency patterns stored in the storage unit 320, and among the stored frequency patterns stored in the storage unit 320, the stored frequency pattern exhibiting a predetermined or higher coincidence rate. can be selected first. If the firstly selected storage frequency pattern is one, the corresponding frequency pattern is finally selected, and if there are a plurality of firstly selected storage frequency patterns, the storage frequency pattern h2 having the highest matching rate may be finally selected. The noise analyzer 340 may determine the cause of the target noise by checking which noise frequency pattern the finally selected storage frequency pattern h2 corresponds to.

At this time, if the finally selected frequency pattern h2 is the frequency pattern of the noise generated during friction between the tire and the black ice, the noise analyzer 340 determines the cause of the noise existing on the acoustic image by the friction between the tire and the black ice. can be judged to be

However, the acoustic image will include noise caused by various causes in addition to noise caused by friction between the tire and the black ice. In addition, the noise analyzer 340 may determine various causes of noise according to the frequency range set for target noise extraction and the type of frequency pattern stored in the storage unit 320 .

Preferably, the noise analyzer 340 may determine whether the cause of the target noise is a vehicle collision accident or a rescue request by the victim.

To this end, the noise analyzer 340 sets a reference frequency range to include a frequency range of noise generated when a vehicle crashes or a rescue request by a person in need, and extracts a frequency pattern of target noise belonging to the set reference frequency range. can do. In addition, the storage unit 320 may store a frequency pattern of noise generated during a vehicle collision and a frequency pattern of noise for which a victim requests rescue.

Accordingly, the noise analyzer 340 may determine whether the cause of the target noise is a vehicle collision accident or a rescue request by a person in need. In addition, when the noise analyzer 340 determines that the cause of the target noise is a vehicle collision or a rescue request by a person in need, a rescue signal is immediately generated, and the generated rescue signal is transmitted through the communication unit 310 to the police station server and the fire station. By being transmitted to a server, a hospital server, etc., it is possible to respond immediately in a related institution in the event of a vehicle collision or a person in need.

Preferably, the noise analyzer 340 may determine whether the target noise is caused by vehicle driving, and if it is determined that the target noise is caused by vehicle driving, the driving speed of the vehicle is determined based on the target noise. can judge

To this end, the noise analyzer 340 may set a reference frequency range to include a frequency range of noise generated during vehicle driving, and extract a frequency pattern of target noise belonging to the set reference frequency range. In addition, the storage unit 320 may store a frequency pattern for noise generated while driving the vehicle (noise generated when the vehicle passes around the sound camera). The pattern may be stored by being classified according to the driving speed of the vehicle.

As such, when the frequency range of the target noise includes the frequency range of the noise generated during vehicle driving and the frequency pattern for the noise generated during vehicle driving is stored in the storage unit 320, the noise analyzer 340 generates the target noise. It is possible to determine whether the cause is due to driving of the vehicle.

At this time, if the noise analyzer 340 determines that the source of the target noise is caused by the driving of the vehicle, the driving speed of the vehicle can be determined by determining at what speed the finally selected stored frequency pattern is the frequency pattern of the vehicle traveling. have. For example, among the stored frequency patterns stored in the storage unit 320, if the last selected stored frequency pattern has the highest matching rate with the frequency pattern of the target noise and is 'the frequency pattern of noise generated when the vehicle is traveling at 80 km/h', The noise analyzer 340 may determine that the vehicle on the road is traveling at 80 km/h.

The driving speed of the vehicle determined in this way can be transmitted to the outside and used as a criterion for determining the traffic flow of the corresponding road, thereby saving costs for constructing a separate infrastructure for detecting or determining the traffic flow.

Preferably, the noise analyzer 340 normally determines the vehicle driving speed from the acoustic image, and when the condition satisfaction determination unit 330 determines that all of the temperature condition, the water condition, and the radiant heat condition are satisfied, the black ice from the sound image. occurrence can be determined.

Specifically, the acoustic camera 11 is initially set to acquire noise within a first frequency range, and the noise analyzer 340 satisfies all of the temperature condition, the moisture condition, and the radiant heat condition in the condition satisfaction determination unit 330. Noise belonging to the first frequency range may be determined as the target noise until it is determined to be the target noise. Here, the first frequency range may be a frequency range of noise generated during vehicle driving. Therefore, the acoustic camera 11 may acquire an acoustic image including noise generated during driving of the vehicle in an initial setting state, and the noise analyzer 340 may obtain the temperature condition and the moisture content from the condition satisfaction determination unit 330. Acoustic images may be analyzed to determine vehicle speed until it is determined that both conditions and radiant heat conditions are satisfied.

At this time, when the condition satisfaction determination unit 330 determines that the temperature condition, the moisture condition, and the radiant heat condition are all satisfied, the acoustic camera 11 generates a setting control signal for setting control to acquire noise within the second frequency range, The generated setting control signal may be transmitted to the sound camera 11 installed near the road surface through the communication unit 310 . Further, when the condition satisfaction determination unit 330 determines that the temperature condition, the moisture condition, and the radiant heat condition are all satisfied, the noise analyzer 340 may determine noise belonging to the second frequency range as the target noise. Here, the second frequency range may be a frequency range of noise generated by friction between the tire and the black ice. Therefore, when the temperature condition, the wet condition, and the radiant heat condition are all satisfied, the acoustic camera 11 around the road surface can obtain an acoustic image in which the noise generated by the friction between the tire and the black ice is intensively included, The noise analyzer 340 may analyze the sound image to determine whether black ice has occurred.

Preferably, the noise analyzer 340, when it is determined that the temperature condition, the moisture condition, and the radiant heat condition are all satisfied by the condition satisfaction determination unit 330, the vehicle determined up to a predetermined time from the time when all of them are satisfied The speed average value of can be calculated. Further, the noise analyzer 340 may define a stored frequency pattern to be compared when comparing the frequency pattern of the target noise with the stored frequency pattern based on the calculated speed average value.

Specifically, when the noise analyzer 340 determines that the temperature condition, the moisture condition, and the radiant heat condition are all satisfied, the average value calculated for the stored frequency pattern to be compared and the tire of the vehicle traveling at a speed within the threshold value and black It can be limited to the frequency pattern of noise generated by friction between ice. For example, if the calculated speed average value is 80 km/h, the noise analyzer 340 compares the target noise and the stored frequency pattern to be compared with the tires of a vehicle traveling at a speed of 70 km/h to 90 km/h and black ice. It can be limited to the frequency pattern of noise generated during friction between the liver.

The limitation of the noise collection frequency of the acoustic camera 11, the frequency of the target noise, and the limitation of the storage frequency pattern to be compared can improve the noise source determination accuracy of the noise analyzer 340.

Meanwhile, the noise analyzer 340 may determine actual road surface coordinates where the cause of the noise is determined. Taking FIG. 5 as an example, the noise analyzer 340 may extract a noise generation area n from the acoustic image i when the source of the target noise is determined. At this time, as described above, the acoustic camera 11 displays the arrangement state of the waves generated according to the vibration of the noise in different colors on the acoustic image, and the noise analyzer 340 uses this to display the noise generating area ( n) can be extracted.

After extracting the noise generating area n, the server 30 may determine a pixel position occupied by the extracted noise generating area n on the sound image (i). At this time, the noise analyzer 340 may identify the acoustic camera 11 that acquired the corresponding acoustic image through unique identification information included in the acoustic image i. In addition, the actual road surface coordinates for each pixel on the acoustic image acquired by the acoustic camera 11 are pre-stored in the storage unit 320, and the noise analyzer 340 is based on the actual road surface coordinates for each pixel pre-stored in the storage unit 320. It is possible to determine the actual road surface coordinates of the generated area (n) extracted by doing so.

Here, if the cause of the noise determined on the acoustic image (i) is due to friction between the tire and the black ice, the actual road surface coordinates of the generation area (n) determined by the noise analyzer 340 are the It will correspond to the coordinates of occurrence.

On the other hand, the rating determination unit 350 determines the probability level of black ice based on the type and number of conditions determined to be met by the condition satisfaction determination unit 330 and the cause of noise determined by the noise analysis unit 340. can

Specifically, the grade determination unit 350 determines the type and number of conditions determined to be satisfied by the condition satisfaction determination unit 330 among temperature conditions, moisture conditions, and radiant heat conditions, and the cause of noise determined by the noise analysis unit 340. Based on whether the occurrence of black ice is caused by friction between the tire and the black ice, the possibility of occurrence of black ice may be determined as one of a safety grade, a caution grade, a warning grade, a danger grade, and an occurrence grade. A more detailed description thereof is shown in FIG. 8 will be described later.

When the rating determination unit 350 determines the probability of occurrence, the display unit 360 maps the information on the probability of occurrence of each road surface to a road map including each road surface to be monitored and outputs the display device 50 together. output can be controlled.

Specifically, the display unit 360 maps the road surface for which the class determination unit 350 has determined the probability of occurrence to a road map, and displays the road map output from the display device 50 to the level of probability of occurrence at each road surface position. can be displayed. At this time, the occurrence probability grade of each road surface displayed on the road map may be displayed in text, but preferably, it may be displayed in a color-coded manner so that a manager can intuitively recognize the occurrence probability grade.

Referring to FIG. 6 as an example, in the road map output from the display device 50, the road surface p1 for which the probability level is determined as the safety level may be output by overlapping a green figure at the corresponding location, and the probability level may be The road surface (p2) determined as a warning level may be output by overlapping a blue figure at the corresponding location, and the road surface (p3) whose occurrence probability level is determined as a warning level may be output by overlapping a yellow figure at the corresponding position, The road surface p4 in which the probability of occurrence is determined as the risk level may be output by overlapping an orange figure at the corresponding position. In addition, the road surface p5 in which the probability of occurrence is determined as the risk level may be output by overlapping a red figure at the corresponding position.

The corresponding control unit 370 may operate and control the electronic display board, the directional speaker, the salt spray device, and the compressed air spray device. In this case, the corresponding control unit 370 may selectively operate a specific device among the electronic display board, the directional speaker, the salt spraying device, and the compressed air blowing device based on the probability of occurrence determined by the rating determining unit 350 .

Preferably, the response control unit 370 may disable all of the electronic display board, the directional speaker, the salt spraying device, and the compressed air spraying device when the occurrence probability level is determined as a safety level or a caution level. However, the electronic display board and directional speakers can be used for other purposes (provision of traffic information, etc.) other than the purpose of warning of black ice.

Preferably, if the occurrence probability level is determined to be a warning level, the response control unit 370 may control an output so that a warning about the possibility of occurrence of black ice on the road ahead is output from the electronic display board and the directional speaker.

Preferably, if the occurrence probability level is determined as a risk level, the response control unit 370 controls an output so that a warning about the possibility of black ice occurring on the road ahead is output from the electric signboard and the directional speaker, and at the same time, compressed air is directed toward the corresponding road surface. It is possible to remove moisture from the road surface by operating and controlling the compressed air injection device to spray. In this case, the corresponding control unit 370 may operate and control the salt water injection device to spray salt water toward the corresponding road surface, thereby reducing the possibility of occurrence of black ice on the corresponding road surface.

Preferably, if the occurrence possibility grade is determined as the occurrence grade, the response control unit 370 controls an output so that a warning about occurrence of black ice on the road ahead is output from the electric signboard and the directional speaker, and at the same time sprays salt water toward the corresponding road surface. The black ice generated on the road surface may be removed by operating and controlling the salt spray device. At this time, the administrator can prevent accidents caused by black ice by controlling traffic on the road surface where black ice occurs.

Preferably, if the control unit 370 operates and controls the salt spray device, the compressed air spray device is operated and controlled so that the compressed air is sprayed toward the road surface after a predetermined point in time after the operation control of the salt spray device is completed. Residual brine on the bed can be removed. At this time, the operation control for the compressed air injection device may be made regardless of the occurrence possibility grade.

7 is a flowchart illustrating a process of determining whether black ice occurrence conditions are satisfied in a condition satisfaction determination unit according to an embodiment of the present invention.

Hereinafter, with reference to FIG. 7 , a process of determining whether the temperature condition, the moisture condition, and the radiant heat condition are satisfied in the condition satisfaction determination unit 330 will be described in more detail.

The temperature condition determination unit 331 of the condition satisfaction determination unit may determine whether the road surface temperature included in the road environment information provided from the communication unit 310 is 0°C or less (S201).

The temperature condition determination unit 331 determines that the temperature condition is satisfied if the road surface temperature is 0°C or less (S201-Y), and if the road surface temperature exceeds 0°C (S201-N), the temperature condition is determined. It may be determined that it is not satisfied (S203).

Based on the meteorological information included in the road surface environment information provided from the communication unit 310, the wet condition determining unit 333 may determine whether the amount of precipitation near the road surface is greater than or equal to a critical amount of precipitation or the amount of rainfall around the road surface is greater than or equal to a critical amount of rainfall. (S204).

If the amount of precipitation around the road surface is greater than or equal to the critical amount of precipitation or if the amount of precipitation around the road surface is greater than or equal to the critical amount of rainfall (S204-Y), the wetness condition determining unit 333 may determine that the wetness condition is satisfied (S207). On the other hand, if the amount of precipitation around the road surface is less than the critical amount or if the amount of rainfall around the road surface is less than the critical amount (S204-N), the wet condition determination unit 333 determines the road surface included in the road environment information provided from the communication unit 310. It may be determined whether the temperature is equal to or less than the dew point temperature (S205).

Further, the temperature condition determining unit 331 may determine that the moisture condition is satisfied if the road surface temperature is equal to or less than the dew point temperature (S205-Y). On the other hand, if the road surface temperature exceeds the dew point temperature (S205-N), the moisture condition determining unit 333 may determine whether the road surface humidity included in the road surface environment information provided from the communication unit 310 is greater than or equal to the critical humidity (S205-N). S206).

If the road surface humidity is greater than or equal to the critical humidity (S206-Y), the moisture condition determination unit 333 may determine that the moisture condition is satisfied (S207). However, if the road surface moisture is less than the critical humidity (S206-N), the moisture condition determination unit 333 may determine that the moisture condition is not satisfied (S208).

In summary, if the amount of precipitation (or rainfall) is greater than or equal to the critical amount of precipitation, the wet condition determination unit 333 determines that there is moisture on the road surface due to rain (or snow) and determines that the wet condition is satisfied. If the road surface temperature is below the dew point temperature, it can be determined that the moisture condition is satisfied by the condensation phenomenon on the road surface. It is possible to determine that a moisture condition is satisfied by determining that a predetermined amount of moisture is present on the surface. However, if the amount of precipitation (or amount of rainfall) is less than the critical amount of precipitation (or the amount of critical rainfall), the temperature of the road surface exceeds the dew point temperature, and the humidity of the road surface is less than the critical humidity at the same time, the moisture condition determination unit 333 detects moisture on the road surface. It can be judged that it does not exist and it is determined that the bite condition is not met.

Preferably, a malfunction of such a sensor may reflect a different result from the actual one. To cope with this, the acoustic camera measures the frictional noise between the vehicle wheel and the road surface to determine whether the road surface conditions are met. .

Meanwhile, the radiant heat condition determination unit 335 may determine whether the road surface temperature included in the road environment information received from the communication unit 310 is equal to or less than a first threshold temperature (S209). If the road surface temperature exceeds the first threshold temperature (S209-N), the radiant heat condition determination unit 335 may determine that the radiant heat condition is not satisfied (S213). On the other hand, if the road surface temperature is less than or equal to the first threshold temperature (S209-Y), the radiant heat condition determination unit 335 determines whether the road surface temperature included in the road surface environment information provided from the communication unit 310 is less than or equal to the second threshold temperature. It can (S210). Here, the second threshold temperature is a temperature value set lower than the first threshold temperature.

If the road surface temperature is less than or equal to the second critical temperature (S210-Y), the radiant heat condition determination unit 335 may determine that the radiant heat condition is satisfied (S212). However, if the road surface temperature exceeds the second critical temperature (S210-N), the radiant heat condition determining unit 335 may determine whether the radiant heat of the road surface at the current time stored in the storage unit 320 is less than or equal to the critical radiant heat ( S211).

And if the radiant heat of the road surface is less than the critical radiant heat (S211-Y), the radiant heat condition determining unit 335 determines that the radiant heat condition is satisfied (S212), but if the radiant heat of the road surface exceeds the critical radiant heat (S211-N), the radiant heat The condition determination unit 335 may determine that the radiant heat condition is not met (S213).

Summarizing the determination process of the radiant heat condition determining unit 335, the radiant heat condition determining unit 335 determines that the radiant heat condition is satisfied when the radiant heat of the road surface is less than the critical radiant heat due to a cause such as shade, and the radiant heat of the road surface is the critical radiant heat. If it exceeds , it is determined that the radiant heat condition is not met. This is because it is difficult to generate black ice due to the radiant heat of the road surface if the radiant heat of the road surface exceeds the critical radiant heat.

However, if the road surface temperature is relatively high, it is difficult to generate black ice regardless of the radiant heat of the road surface. Conversely, if the road surface temperature is relatively low, black ice is highly likely to occur even if the radiant heat of the road surface is large. Therefore, the radiant heat condition determination unit 335 determines that the radiant heat condition is not satisfied regardless of the radiant heat of the road surface if the road surface temperature exceeds the first threshold temperature (eg, 10 ° C.), and the road surface temperature is the second threshold temperature. If the temperature is lower than the temperature (for example, minus 10 ° C), it can be determined that the radiant heat condition is satisfied regardless of the radiant heat of the road surface.

8 is a flowchart illustrating a process of determining a black ice occurrence possibility grade in a grade determination unit according to an embodiment of the present invention.

Hereinafter, with reference to FIG. 8 , a process of determining an occurrence possibility level in the level determining unit 350 will be described in more detail.

When the condition satisfaction determination unit 330 determines whether the temperature condition, the moisture condition, and the radiant heat condition are satisfied, the rating determination unit 350 determines whether at least one of the temperature condition, the moisture condition, and the radiant heat condition is satisfied. (S401).

If, among the temperature conditions, moisture conditions, and radiant heat conditions are not satisfied (S401-N), the grade determination unit 350 may determine the occurrence probability grade as a safety grade (S402). On the other hand, if one or more conditions are met (S401-Y), the rating determining unit 350 may determine whether the temperature condition is met and at least one of the moisture condition and the radiant heat condition is met (S403).

If the temperature condition is not met, or if the temperature condition is met but neither the moisture condition nor the radiant heat condition is met (S403-N), the rating determination unit 350 determines the occurrence possibility rating as a warning rating. It can (S404). On the other hand, if the temperature condition is met and at least one condition of the moisture condition and the radiant heat condition is satisfied at the same time (S403-Y), the rating determining unit 350 may determine whether the temperature condition, the moisture condition, and the radiant heat condition are all satisfied. Yes (S405).

If even one of the temperature condition, the moisture condition, and the radiant heat condition is not satisfied (S405-N), the grade determining unit 350 may determine the occurrence possibility grade as a warning grade (S406). On the other hand, if the temperature condition, moisture condition, and radiant heat condition are all satisfied (S405-Y), the rating determination unit 350 determines the tire and black ice among the noise measured on the acoustic image based on the determination of the noise determination unit 340. It may be determined whether there is noise generated by friction between the surfaces (S407).

If, among the noises measured on the sound image, there is no noise generated by friction between the tire and the black ice (S407-N), the rating determining unit 350 may determine the probability of occurrence as a risk rating (S408). , On the other hand, if there is noise generated by friction between the tire and the black ice among the noises measured on the acoustic image (S407-Y), the rating determination unit 350 may determine the occurrence probability grade as the occurrence grade (S409). .

In summary, the safety level is a level that is determined when none of the temperature conditions, moisture conditions, and radiant heat conditions are met, and the caution level is a level that only the temperature conditions are met, or at least one of the water conditions and radiant heat conditions is met. It is a grade determined when the temperature condition is not met, a warning grade is a grade determined when the temperature condition is met and only one of the moisture condition and the radiant heat condition is satisfied, and the hazard grade is the temperature condition, the moisture condition, and the radiant heat condition. This rating is determined when all of these are met, but black ice does not occur. And the occurrence grade is the grade determined when the temperature condition, moisture condition, and radiant heat condition are all satisfied and black ice occurs.

9 (a) and (b) are diagrams illustrating an operation example of a salt water injection device and a compressed air injection device according to an embodiment of the present invention.

Hereinafter, with reference to FIGS. 9 (a) and (b), the salt water spraying device 710 and the compressed air blowing device 730 included in the countermeasure unit 70 in the black ice prediction system according to the present embodiment are described. Let's take a more detailed look.

A plurality of salt spray devices 710 may be provided around the road surface to be monitored. Preferably, the plurality of salt spray devices 710 are spaced apart from each other along the road surface at regular intervals and alternately arranged on the left and right sides of the road surface. can The plurality of salt water spraying devices 710 may be arranged so that the spraying ranges of each other do not overlap as much as possible. Since the plurality of salt water spraying devices 710 according to the present embodiment are arranged alternately even if the spraying ranges do not overlap each other, most of the salt water spraying devices 710 are arranged alternately. of the road surface area may be included in the salt spray range.

As described above, the salt water injection device 710 may be operated and controlled by the corresponding control unit 370 when the occurrence probability level is the occurrence level. Specifically, the response control unit 370 receives the occurrence coordinates of the black ice (b) determined by the noise analysis unit 340 through the above-described process, and the salt water injection range overlapping with the provided occurrence coordinates of the black ice (b). Salt spraying devices 710-1 and 710-2 having (s) can be selected. Here, information on the salt spray range (s) of each salt spray device may be information pre-stored in the storage unit 320 .

And, as shown in (a) of FIG. 9 , the salt spraying device 710 may operate and control the selected salt spraying devices 710-1 and 710-2 to spray salt water for a predetermined time.

Therefore, the black ice prediction system according to the present embodiment can minimize the amount of salt water used by localizing the salt water injection area for removing the black ice, thereby reducing the cost of supplying salt water and damage that may occur due to the sprayed salt water. (e.g. spraying on moving vehicles) can be minimized.

Preferably, the corresponding control unit 370 may operate and control the salt water injection device 710 based on the traffic flow determined by the noise analyzer 340 . Specifically, the corresponding controller 370 receives traffic flow information determined by the noise analyzer 340 before operating and controlling the selected salt spray devices 710-1 and 710-2 to remove the black ice b. If the average vehicle speed of the corresponding road surface is less than or equal to the predetermined speed based on the provided traffic flow, the salt spray device 710 may not be operated and controlled. That is, the corresponding control unit 370 prevents the salt water spraying device 710 from operating when the vehicles on the road where the black ice (b) has occurred are moving slowly, which prevents a large amount of the sprayed salt water from being sprayed to the slow-moving vehicle. It is for

Preferably, the response control unit 370 may operate and control the salt water injection device 710 based on the occurrence of an accident determined by the noise analyzer 340 . The response control unit 370 may operate and control the salt water injection device 710 when an accident vehicle occurs even on a road surface where the probability of occurrence is not a risk level or an occurrence level. Specifically, when the noise analyzer 340 determines that an accident has occurred, the response control unit 370 can operate and control the salt water spraying device 710 disposed on a road upstream of the accident location to operate. This is to induce slow movement of vehicles located upstream of the accident vehicle by spraying salt water on the road upstream of the accident vehicle.

Meanwhile, a plurality of compressed air injection devices 730 are provided around the road surface to be monitored, and may inject compressed air toward the road surface under the control of the corresponding control unit 370 . Here, the compressed air injected from the compressed air injection device 730 evaporates the water when the amount of water on the road surface is small, and pushes the water on the road surface to one side when the amount of water on the road surface is large. can be taken out and removed. Preferably, the response control unit 370 may operate and control the compressed air injection device 730 of the corresponding road surface when it is determined that the bite condition is satisfied regardless of the degree of possibility of occurrence.

A plurality of salt water spraying devices 710 are spaced apart from each other along the road surface and may be selectively disposed on the left or right side of the road surface. As shown in (b) of FIG. 9, the compressed air injection device 730 is arranged to inject compressed air from upstream to downstream based on the drainage direction of the road surface, thereby pushing water on the road surface downward to efficiently remove water. can be removed with

10 is a diagram showing a timeline from the time of determining the grade of the grade determination unit to the time of adjusting the grade according to an embodiment of the present invention.

As described above, after the server 30 controls the operation of the countermeasure means 70 so that a response according to the level is performed, the possibility of occurrence of black ice is re-determined to re-determine the occurrence possibility grade, and the grade is determined according to the re-determined result. can be adjusted

Hereinafter, with reference to FIG. 10 , a class re-determination and adjustment process when determining an occurrence class will be described in more detail.

When the grade determination unit 350 determines the occurrence grade, as described above, the corresponding control unit 370 operates and controls the salt spray device 710 so that the salt spray device 710 sprays salt water toward the black ice for a predetermined time. can When the spraying of salt water is terminated, the control unit 370 operates the compressed air spraying device 730 to inject compressed air toward the road surface for a predetermined period of time after leaving a sufficient delay time for the black ice to be removed by the salt water. You can control it. Here, the compressed air that is injected toward the road surface removes salt water from the road surface as well as water from the black ice melted by the salt water. When the injection of compressed air is terminated, the condition satisfaction determination unit 330 determines whether each condition is satisfied again, and the level determination unit 350 may adjust the occurrence probability level according to whether each condition is met again.

Preferably, only the biting condition may be re-determined without re-determining all conditions when re-determining the satisfaction of the conditions. That is, when the compressed air injection ends, only the bite condition determining unit 333 may re-perform the condition satisfaction determination.

The above description is merely an example of the technical idea of the present invention, and those skilled in the art will be able to make various modifications and variations without departing from the essential characteristics of the present invention. Therefore, the embodiments disclosed in the present invention are not intended to limit the technical idea of the present invention, but to explain, and the scope of the technical idea of the present invention is not limited by these embodiments. The protection scope of the present invention should be construed according to the claims below, and all technical ideas within the equivalent range should be construed as being included in the scope of the present invention.

10: information acquisition unit
11: acoustic camera
30: server
50: display device
70 Countermeasures
310: communication department
320: storage unit
330: condition satisfaction determination unit
331: temperature condition determination unit
333: biting condition determination unit
335: radiant heat condition determination unit
340: noise analysis unit
350: rating determination unit
360: display unit
370: corresponding control unit
710: salt spray device
730: compressed air injection device

Claims (13)

Receiving road surface environment information and a sound image of a road surface;
determining whether at least one generating condition for generating black ice is satisfied based on the received road surface environment information;
analyzing the received acoustic image and determining a cause of noise measured on the acoustic image;
determining a probability level of the occurrence of the black ice based on the type of the generation condition determined to be satisfied and the cause of the noise;
spraying brine toward the black ice generated when the probability of occurrence is determined as an occurrence;
When the injection of the salt water is completed, spraying compressed air toward the road surface on which the salt water is sprayed after a predetermined delay time from the point in time when the spray of the salt water is completed,
When the injection of the compressed air is terminated in the step of injecting the compressed air, determining whether each generation condition is satisfied, determining the cause of the noise, and determining a probability level of the black ice. is performed again to adjust the probability of occurrence,
The step of determining whether each generation condition is satisfied includes determining whether a temperature condition is satisfied, determining whether a moisture condition is satisfied, and determining whether a radiant heat condition is satisfied,
When the step of determining whether each generation condition is satisfied after the end of the injection of the compressed air is performed again, the step of determining whether the temperature condition is satisfied and the step of determining whether the radiant heat condition are satisfied are omitted. Black ice occurrence prediction method using .
The method of claim 1, wherein the black ice occurrence prediction method
The black ice occurrence prediction method using acoustic images, further comprising determining traffic flow according to the determined cause of the noise.
The method of claim 1, wherein the black ice occurrence prediction method
Black ice prediction method using acoustic images, characterized in that it further comprises the step of determining the occurrence of an accident or a requester according to the determined cause of the noise.
delete The method of claim 1, wherein in the black ice prediction method
The received road surface environment information includes the temperature of the road surface,
Determining whether the temperature condition is satisfied,
Black ice occurrence prediction method using acoustic images, characterized in that it is determined that the temperature condition is satisfied when the received temperature of the road surface is 0 ° C or less.
The method of claim 5, in the black ice prediction method
The received road surface environment information includes the humidity of the road surface, the amount of precipitation around the road surface, and the dew point temperature of the road surface;
The step of determining whether the bite condition is satisfied,
If the received humidity of the road surface is greater than or equal to the critical humidity, or the received amount of precipitation around the road surface is greater than or equal to the critical amount of precipitation, or the received temperature of the road surface is less than or equal to the received dew point temperature of the road surface, the moisture condition is satisfied. Black ice occurrence prediction method using acoustic images, characterized in that for determining.
The method of claim 5, in the black ice prediction method
The received road surface environment information includes radiant heat of the road surface,
In the step of determining whether the radiant heat condition is satisfied, it is determined that the radiant heat condition is satisfied when the received radiant heat condition is less than or equal to a first threshold temperature and at the same time, the received radiant heat condition is less than or equal to a critical radiant heat temperature,
The first threshold temperature is a black ice occurrence prediction method using an acoustic image, characterized in that the temperature set higher than 0 ℃.
The method of claim 7, wherein determining whether the radiant heat condition is satisfied
If the received temperature of the road surface is less than or equal to a second threshold temperature, it is determined that the radiant heat condition is satisfied even if the received radiant heat of the road surface exceeds the critical radiant heat;
The second threshold temperature is a black ice occurrence prediction method using an acoustic image, characterized in that the temperature set lower than 0 ℃.
The method of claim 5, in the black ice prediction method
The probability of occurrence includes a caution level,
The step of determining the probability of occurrence may include setting the probability of occurrence to the attention level when only one of the temperature condition, the moisture condition, and the radiant heat condition is satisfied, or only the moisture condition and the radiant heat condition are simultaneously satisfied. Black ice occurrence prediction method using acoustic images, characterized in that for determining.
The method of claim 5, in the black ice prediction method
The probability of occurrence includes a warning level,
In the step of determining the probability of occurrence, if the temperature condition is satisfied and at the same time any one of the moisture condition and the radiant heat condition is satisfied, the occurrence probability grade is determined as the warning grade. Acoustic image, characterized in that Black ice occurrence prediction method using .
The method of claim 5, in the black ice prediction method
The probability of occurrence includes a risk grade,
In the step of determining the probability of occurrence, all of the temperature condition, the moisture condition, and the radiant heat condition are satisfied, and among the noises measured on the acoustic image, there is noise determined to be caused by friction between the tire and black ice. If not, the black ice occurrence prediction method using acoustic images, characterized in that the occurrence probability grade is determined as the risk grade.
The method of claim 5, in the black ice prediction method
The occurrence possibility grade includes the occurrence grade,
In the step of determining the probability of occurrence, all of the temperature condition, the moisture condition, and the radiant heat condition are satisfied, and among the noises measured on the acoustic image, there is noise determined to be caused by friction between the tire and black ice. Black ice occurrence prediction method using an acoustic image, characterized in that the occurrence probability grade is determined as the risk grade.
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