KR102205534B1 - Weather information analysis method and system based on driving vehicle information - Google Patents

Abstract

The present invention relates to a method of analyzing weather information based on driving vehicle information, capable of estimating weather information of a corresponding position by obtaining information from a vehicle driving on a road, and a system thereof and, more specifically, to a method of accurately estimating a weather condition by obtaining information about the condition of a driving vehicle and a sensor of the driving vehicle, and processing data so as to estimate the weather condition from the obtained information through artificial intelligence learning, and a system thereof. The method includes the following steps of: collecting driving information of a driving vehicle at a short period interval; analyzing the collected driving information at a long period interval; and determining a weather condition of a driving position of the driving vehicle based on the analysis result. The weather condition determination step includes the following steps of: learning a correlative information amount between the weather condition and an attribute of the collected driving information; determining an attribute of the weather condition of the driving position in accordance with the attribute of the driving information based on the correlative information amount; and determining the degree of the weather condition of the driving position based on a value of the driving information.

Description

Weather information analysis method and system based on driving vehicle information {WEATHER INFORMATION ANALYSIS METHOD AND SYSTEM BASED ON DRIVING VEHICLE INFORMATION}

The present invention relates to a driving vehicle information-based meteorological information analysis method and system capable of obtaining information from a vehicle traveling on a road and estimating the weather information of a corresponding location, and acquiring a sensor of a driving vehicle and information on the state of a driving vehicle And, the present invention relates to a method and system for accurately estimating the weather condition by processing data to estimate the weather condition from information acquired through artificial intelligence learning.

Today, weather forecasts are easily accessed through the Internet, newspapers, or television. However, these weather forecasts are reported incorrectly, and there are many cases of discomfort or damage in life. In the past, weather changes have had a lot of influence on our lives. Even in the battlefield, the change of weather became an important factor in determining victory or defeat. It is said that Zhuge Gong-myeong, an excellent intellect, used the southeastern wind in Jeokbyeok Daejeon to burn Jojo's large army with fire. This was not a magical power of the Zhuge Resonance, but rather the use of weather changes that were rare at that time, and predicting the weather can be a great force.

It is no wonder that in today's more human social activity than in the past, there is more interest in weather forecasts. Today, even in the marketing of companies that are silent battlegrounds, the weather unfortunately shows its power. In particular, for companies whose sales are significantly affected by the season, the weather change may become an extremely important sales strategy. Manufacturers of ice cream and air conditioners, for example, could make sales from the summer heat. For these companies, accurate weather forecasts are valuable information for increasing sales. In addition, the weather forecast has a very important influence on the events of the surrounding area.

Weather forecasting is also important, but since forecasts are always based on forecasts, they may not exactly match actual weather changes. Therefore, when a weather change different from the weather forecast occurs, in order to prepare for a disaster caused by the weather change, it is necessary to be able to grasp the instantaneous weather change, and the infrastructure of a system that can grasp the weather at various locations must be equipped.

However, installing sensors in all areas to predict immediate weather changes is not easy because there is a cost burden for facility installation and maintenance. Accordingly, there is a need for a method and system capable of detecting weather changes simply while using an existing system.

Accordingly, the present invention is to solve the above-described problems, and an object of the present invention is to provide a method and system capable of detecting a wake-up state of a driving position using information of a driving vehicle.

An object of the present invention is to provide a method and system capable of detecting a weather condition of a driving position by minimizing the amount of computation according to the amount of information correlated with the weather condition according to the attribute of information obtained from a driving vehicle.

A method for analyzing weather information based on driving vehicle information according to an embodiment of the present invention for solving the above problem includes: collecting driving information of a driving vehicle at short periodic intervals; Analyzing the collected driving information at long periodic intervals; And determining a wake-up state of the driving position of the driving vehicle based on the analysis result, wherein the determining of the wake-up state includes: learning an amount of correlation information between the collected driving information attribute and the wake-up state; Determining an attribute of a wake-up state of the driving position according to an attribute of the driving information based on the amount of correlation information; And determining a degree of a wake-up state at the driving position based on the value of the driving information.

According to an embodiment of the present invention, in the driving information collecting step, the driving information includes speed, acceleration, external light, internal lighting, ABS state, curvature, internal temperature, external temperature, angular velocity, brake pedal level, ESP (Electronic Stability Program) status, excel level, rain or not, wiper level, and at least one of TCS (Traction Control System) status.

According to an embodiment of the present invention, the determining of the weather condition attribute may include extracting at least one attribute of a weather condition with a low amount of the collected driving information and the correlation information; And determining an attribute of a wake-up state in which the average of the plurality of collected driving information and the amount of the correlation information is the lowest as the attribute of the wake-up state of the driving position.

According to an embodiment of the present invention, the learning step of the amount of correlation information may be calculated by Equation 1 below.

[Equation 1]

(Here, IG(S, A) is the amount of correlation information, E(S) is an entropy value, S is an attribute of a weather condition, and A is an attribute of driving information.)

According to an embodiment of the present invention, the step of analyzing the driving information may include measuring a change amount of the driving information; Detecting a change in weather condition when the amount of change in the driving information is greater than or equal to a threshold value; And requesting determination of a change in the weather condition.

The meteorological information analysis system based on driving vehicle information according to an embodiment of the present invention for solving the above problem includes: an information collecting unit that collects driving information of a driving vehicle at short periodic intervals; An analysis unit that analyzes the collected driving information at long periodic intervals; And a wake-up state determination unit determining a wake-up state of the driving position of the driving vehicle based on the analysis result. Including, the wake-up state determination unit learns a correlation information amount between the collected attribute of the driving information and the wake-up state, and determines the attribute of the wake-up state of the driving position according to the attribute of the driving information based on the amount of the correlation information And, based on the value of the driving information, it is possible to determine the degree of the wake-up state of the driving position.

According to an embodiment of the present invention, the driving information includes speed, acceleration, external light, internal lighting, ABS state, curvature, internal temperature, external temperature, angular velocity, brake pedal level, ESP (Electronic Stability Program) state, and excel level. , Rainfall, a wiper level, and at least one of a TCS (Traction Control System) state may be included.

According to an embodiment of the present invention, the weather condition determination unit extracts at least one attribute of a weather condition with a low correlation information amount and a plurality of collected driving information, and extracts the plurality of collected driving information and the correlation. An attribute of the wake-up state having the lowest average amount of information may be determined as the attribute of the wake-up state of the driving position.

According to an embodiment of the present invention, the wake-up state determination unit may calculate the correlation information amount by Equation 1 below.

[Equation 1]

(Here, IG(S, A) is the amount of correlation information, E(S) is an entropy value, S is an attribute of a weather condition, and A is an attribute of driving information.)

According to an embodiment of the present invention, the analysis unit measures a change amount of the driving information, detects a change in a weather condition when the change amount of the driving information is more than a threshold value, and You can ask for judgment.

According to the present invention, it is possible to determine the wake-up state of the driving position of the vehicle by using information acquired from a driving vehicle without having to provide a separate infrastructure.

In addition, it is possible to reduce the amount of computation by learning the relationship with the property of the weather condition according to the property of information acquired from the driving vehicle.

In addition, it is possible to quickly and accurately detect a change in the weather condition at the driving position by calculating the amount of information correlated with the property of the weather condition according to the property of information obtained from the driving vehicle.

Meanwhile, the effects of the present invention are not limited to the above-mentioned effects, and various effects may be included within a range that will be apparent to a person skilled in the art from the contents to be described below.

1 is a configuration diagram of a weather information analysis system based on driving vehicle information according to an embodiment of the present invention.
2 is a block diagram of a weather analysis unit of a weather information analysis system based on driving vehicle information according to an embodiment of the present invention.
3 is an example of a weather condition determination criterion in a method for analyzing weather information based on driving vehicle information according to an embodiment of the present invention.
4 is a flowchart of a method for analyzing weather information based on driving vehicle information according to an embodiment of the present invention.
5 is a flowchart of a method for determining a weather condition according to an embodiment of the present invention.
6 is a flowchart illustrating a method of determining a weather condition attribute of a driving position of a driving vehicle according to an exemplary embodiment of the present invention.
7 is a flowchart of a method of analyzing driving information at long periodic intervals according to an embodiment of the present invention.

Hereinafter, a'method and system for analyzing weather information based on driving vehicle information' according to the present invention will be described in detail with reference to the accompanying drawings. The described embodiments are provided so that those skilled in the art can easily understand the technical idea of the present invention, and the present invention is not limited thereto. In addition, matters expressed in the accompanying drawings are schematic drawings for easy explanation of embodiments of the present invention and may be different from the actual implementation form.

Meanwhile, each component expressed below is only an example for implementing the present invention. Accordingly, in other implementations of the present invention, other components may be used without departing from the spirit and scope of the present invention.

In addition, each component may be implemented with purely hardware or software, but may be implemented with a combination of various hardware and software components that perform the same function. In addition, two or more components may be implemented together by one piece of hardware or software.

In addition, the expression “including” certain elements simply refers to the existence of the corresponding elements as an expression of “open type”, and should not be understood as excluding additional elements.

1 is a configuration diagram of a weather information analysis system based on driving vehicle information according to an embodiment of the present invention.

Referring to FIG. 1, a weather information analysis system based on driving vehicle information according to an embodiment of the present invention includes a driving vehicle 100, a GPS satellite 200, a weather analysis unit 300, and a control server 400. I can.

The driving vehicle 100 may be a general vehicle driving on a road. The driving vehicle 100 may include all vehicles capable of driving on a road regardless of the type of vehicle. The driving vehicle 100 may check location information through the GPS satellite 200. The driving vehicle 100 may transmit driving information to the meteorological analysis unit 300. The weather analysis unit 300 receiving the driving information from the driving vehicle 100 may analyze and determine a wake-up state of the driving position of the driving vehicle 100. The meteorological analysis unit 300 may transmit a wake-up state of the driving position to the control server 400. The weather analysis unit 300 may learn a correlation between the driving information and the weather condition. The correlation may be calculated by the amount of correlation information. The meteorological analysis unit 300 may request calculation or learning with a high load from the control server 400. The meteorological analysis unit 300 may be a system included in the driving vehicle 100. The meteorological analysis unit 300 may be a system included in an external server of the driving vehicle 100.

The control server 400 may provide information to an external system using the weather conditions analyzed by the weather analysis unit 300. According to an embodiment of the present invention, the control server 400 may request a disaster notification from a disaster notification system using the weather condition analyzed by the weather analysis unit 300. The control server 400 may review a disaster that may occur according to the weather condition analyzed by the weather analysis unit 300 and request a disaster notification from the disaster notification system. The above disasters may include disasters such as landslides, heatwave warnings and advisories, heavy rain warnings and advisories, typhoon warnings and advisories, traffic accidents due to heavy rains, heavy snow warnings and advisories, and ice roads, but are not limited thereto. It can include any disaster that could have an impact.

2 is a block diagram of a weather analysis unit of a weather information analysis system based on driving vehicle information according to an embodiment of the present invention.

Referring to FIG. 2, the weather analysis unit 300 of the weather information analysis system based on driving vehicle information according to an embodiment of the present invention includes an information collection unit 310, an analysis unit 320, and a weather condition determination unit 330. It may include.

The information collection unit 310 may acquire driving information from the driving vehicle 100. The information collection unit 310 may collect driving information of the driving vehicle 100 at short periodic intervals. According to an embodiment of the present invention, the short period may be set to less than 1 second.

The driving information includes speed, acceleration, external light, internal lighting, ABS status, curvature, internal temperature, external temperature, angular velocity, brake pedal level, ESP (Electronic Stability Program) status, excel level, rainfall, wiper level, TCS ( Traction Control System) status, time, location (longitude, latitude), rain sensor level, rear and front fog lights status, downward light status, atmospheric pressure, and at least one of relative humidity.

The driving vehicle 100 includes various sensors in the latest vehicle. The sensors include speed, acceleration, external light, internal lighting, ABS status, curvature, internal temperature, external temperature, angular velocity, brake pedal level, ESP (Electronic Stability Program) status, excel level, rainfall, wiper level, TCS (Traction Control System) status, time, location (longitude, latitude), rain sensor level, rear and front fog lights status, downlight status, atmospheric pressure, and relative humidity status can be detected and provided as driving information.

The analysis unit 320 may analyze the driving information collected by the information collection unit 310 to detect a weather change. The analysis unit 320 may analyze the collected driving information at long periodic intervals. According to an embodiment of the present invention, the long period may be set to less than 20 seconds.

The analysis unit 320 may analyze the driving information at the long-period interval to detect the occurrence of a weather change in the driving position of the driving vehicle 100.

The analysis unit 320 may measure the amount of change in the driving information. The analysis unit 320 may measure a change amount of the driving information at the short periodic interval. The analysis unit 320 may measure a change amount of the driving information at the long periodic interval. The analysis unit 320 may measure a maximum value of a change amount of the driving information within the long periodic interval.

The analysis unit 320 may detect a change in the weather condition when the amount of change in the driving information is greater than or equal to a threshold value. The threshold value may be set differently according to the attribute of the driving information. According to an embodiment of the present invention, the threshold value may be one of a value of 20% of the driving information, a change in a state level, and an on/off function.

When the analysis unit 320 detects a change in the weather condition, the analysis unit 320 may request the weather condition determination unit 330 to determine the weather condition. When the analysis unit 320 detects a change in the weather condition, the analysis unit 320 may immediately request the weather condition determination unit 330 to determine the weather condition regardless of the long period.

The wake-up state determination unit 330 may determine a wake-up state of the driving position of the driving vehicle 100 based on the driving information collected by the information collecting unit 310. A wake-up state of the driving position of the driving vehicle 100 may be determined based on the analysis result of the analysis unit 320.

The weather condition determination unit 330 may learn the amount of correlation information between the collected driving information attribute and the weather condition. The correlation information amount may mean entropy when determining the weather condition according to the attribute of the driving information.

The amount of information is a value that varies depending on how much the occurrence or change of the attribute of the driving information is related to the occurrence or change of the weather condition, and may be calculated by Equation 1 below.

[Equation 1]

Where 　p(x) is the probability of occurrence 　x. As the probability of occurrence is closer to 1, the amount of information becomes closer to 0, and as the probability of occurrence is closer to 0, the amount of information can be infinitely large.

Entropy represents the expected value (average) of the amount of information, and the larger the entropy means the larger the average amount of information, and the larger the uncertainty, the more difficult it is to classify.Therefore, the smallest entropy can be placed in the upper decision node to reduce the amount of computation. . The entropy can be obtained by calculating Equation 2 below.

[Equation 2]

Here, 　S denotes a collection of weather conditions that have already occurred, and 　c may denote the number of weather conditions.

The amount of correlation information can be calculated by calculating Equation 3 below.

[Equation 3]

Here, A means an attribute, and it is possible to determine which attribute has the smallest entropy (ie, the information acquisition amount is large).

The wake-up state determination unit 330 may determine a wake-up state attribute of the driving position according to the attribute of the driving information based on the correlation information amount. The wake-up state determination unit 330 may determine a wake-up state of the driving information attribute and the amount of the correlation information as a wake-up state of the driving position of the driving vehicle 100.

The wake-up state determination unit 330 may determine a degree of a wake-up state of the driving position based on the value of the driving information. The wake-up state determination unit 330 may classify the degree of the wake-up state into stages. The degree of the wake-up state may be determined as a step.

The wake-up state determination unit 330 may extract at least one attribute of a wake-up state with a low amount of the collected driving information and the correlation information. The driving information may be driving information that the analysis unit 320 detects that the amount of change is greater than or equal to a threshold value.

The wake-up state determination unit 330 may determine an attribute of a wake-up state in which the average of the plurality of collected driving information and the correlation information amount is the lowest as the wake-up state attribute of the driving position.

3 is an example of a weather condition determination criterion in a method for analyzing weather information based on driving vehicle information according to an embodiment of the present invention.

Referring to FIG. 3, the weather condition determination unit 330 may determine rainfall or heavy rain using at least one of a wiper level, low beam on/off, external temperature, and speed. The weather condition determination unit 330 may increase or decrease the level of the rainfall or heavy rain by using the rain sensor level and speed.

According to an embodiment of the present invention, the weather condition determination unit 330 is the rainfall step 4 when the wafer level is the highest, the low beam is on, the outside temperature is 2 degrees or more, and the speed is 100 km/h (about 28 m/s) or less. It can be judged as. The weather condition determination unit 330 may increase the rainfall step by one step when the rain sensor level is 15 mm/h or more. When the level of the rain sensor is 25 mm/h or more, the weather condition determination unit 330 may further increase the rainfall step by one step. The weather condition determination unit 330 may increase the rainfall step by one step when the speed is 80 km/h (about 22 m/s) or less. When the speed of the weather condition determination unit 330 is less than 60 km/h (about 16 m/s), the rainfall step may be further increased by one step.

According to an embodiment of the present invention, the weather condition determination unit 330 is a snowfall step 4 when the wafer level is the highest, the low beam is on, the outside temperature is 2 degrees or less, and the speed is 80 km/h (about 22 m/s) or less. It can be judged as. When the level of the rain sensor is 10 mm/h or more, the weather condition determination unit 330 may increase the rainfall step by one step. When the level of the rain sensor is 20 mm/h or more, the weather condition determination unit 330 may further increase the rainfall step by one step. When the speed of the weather condition determination unit 330 is 60 km/h (about 16 m/s) or less, the rainfall step may be increased by one step. When the speed of the weather condition determination unit 330 is less than 40 km/h (about 11 m/s), the rainfall step may be further increased by one step.

According to an embodiment of the present invention, the wake-up state determination unit 330 activates TCS while stepping on an excel, a throttle 30% or less, an external temperature of 2 degrees or less, and a speed of 30 km/h (about 8 m/s) or less. In this case, it can be judged as the second stage of the ice road. The wake-up condition determination unit 330 may increase the ice road by one step when the external temperature is 0 degrees or less. When the speed is less than 7 km/h (about 2 m/s), the weather condition determination unit 330 may increase the ice level step by one step. When the acceleration is 1.5 m/s ² or less, the wake-up condition determination unit 330 may increase the step of the ice road by one step. When the acceleration is 1.0m/s ² or less, the weather condition determination unit 330 may further increase the step of the ice road by one step. When the acceleration is 0.7 m/s ² or less, the wake-up condition determination unit 330 may further increase the ice path step by one step.

According to an embodiment of the present invention, the wake-up state determination unit 330 activates ESP while driving, a friction coefficient of 0.15 or less, a throttle 15% or less, a brake pedal 20% or more, an external temperature of 2 degrees or less, and a speed of 50 km/ If it is less than h (about 14m/s), it can be determined as the second stage of the ice road. The wake-up condition determination unit 330 may increase the ice road by one step when the external temperature is 0 degrees or less. When the coefficient of friction is 0.10 or less, the wake-up state determination unit 330 may increase the ice level step by one step. When the friction coefficient is less than 0.05, the wake-up condition determination unit 330 may increase the step of the ice road by one step. When the acceleration is 1.5 m/s ² or less, the wake-up condition determination unit 330 may increase the step of the ice road by one step.

The weather condition determination unit 330 may update the criterion through learning. The weather condition determination unit 330 may learn the driving information and actual weather information to update the determination criterion.

4 is a flowchart of a method for analyzing weather information based on driving vehicle information according to an embodiment of the present invention, FIG. 5 is a flowchart of a method for determining a weather condition according to an embodiment of the present invention, and FIG. 6 is an embodiment of the present invention A flowchart of a method of determining a weather condition attribute of a driving position of a driving vehicle according to an example, and FIG. 7 is a flowchart illustrating a method of analyzing driving information at long periodic intervals according to an embodiment of the present invention.

4 to 7, the method of analyzing weather information based on driving vehicle information according to an embodiment of the present invention may include collecting driving information of a driving vehicle at short periodic intervals (S1100).

In step S1100, the information collection unit 310 may obtain driving information from the driving vehicle 100. The information collection unit 310 may collect driving information of the driving vehicle 100 at short periodic intervals. According to an embodiment of the present invention, the short period may be set to less than 1 second.

In step S1100, the driving information includes speed, acceleration, external light, internal lighting, ABS status, curvature, internal temperature, external temperature, angular velocity, brake pedal level, ESP (Electronic Stability Program) status, excel level, rainfall, wiper Level, TCS (Traction Control System) status, time, location (longitude, latitude), rain sensor level, rear and front fog lights status, downward light status, atmospheric pressure, and may include at least one or more of relative humidity.

In step S1100, the driving vehicle 100 includes various sensors in the latest vehicle. The sensors include speed, acceleration, external light, internal lighting, ABS status, curvature, internal temperature, external temperature, angular velocity, brake pedal level, ESP (Electronic Stability Program) status, excel level, rainfall, wiper level, TCS (Traction Control System) status, time, location (longitude, latitude), rain sensor level, rear and front fog lights status, downlight status, atmospheric pressure, and relative humidity status can be detected and provided as driving information.

The method of analyzing weather information based on driving vehicle information according to an embodiment of the present invention may include analyzing the collected driving information at long periodic intervals (S1200).

In step S1200, the analysis unit 320 may detect a weather change by analyzing the driving information collected by the information collection unit 310. The analysis unit 320 may analyze the collected driving information at long periodic intervals. According to an embodiment of the present invention, the long period may be set to less than 20 seconds.

In step S1200, the analysis unit 320 may analyze the driving information at the long-period interval to detect the occurrence of a weather change in the driving position of the driving vehicle 100.

In step S1210, the analysis unit 320 may measure the amount of change in the driving information. The analysis unit 320 may measure a change amount of the driving information at the short periodic interval. The analysis unit 320 may measure a change amount of the driving information at the long periodic interval. The analysis unit 320 may measure a maximum value of a change amount of the driving information within the long periodic interval.

In step S1220, the analysis unit 320 may detect a change in the weather condition when the amount of change in the driving information is greater than or equal to a threshold value. The threshold value may be set differently according to the attribute of the driving information. According to an embodiment of the present invention, the threshold value may be one of a value of 20% of the driving information, a change in a state level, and an on/off function.

In step S1230, when the analysis unit 320 detects a change in the weather condition, the analysis unit 320 may request the weather condition determination unit 330 to determine the weather condition. When the analysis unit 320 detects a change in the weather condition, the analysis unit 320 may immediately request the weather condition determination unit 330 to determine the weather condition regardless of the long period.

The method of analyzing weather information based on driving vehicle information according to an embodiment of the present invention may include determining a wake-up state of the driving position of the driving vehicle based on the analysis result (S1300).

In step S1300, the wake-up state determination unit 330 may determine a wake-up state of the driving position of the driving vehicle 100 based on the driving information collected by the information collecting unit 310. A wake-up state of the driving position of the driving vehicle 100 may be determined based on the analysis result of the analysis unit 320.

In step S1310, the weather condition determination unit 330 may learn the amount of correlation information between the collected driving information attribute and the weather condition. The correlation information amount may mean entropy when determining the weather condition according to the attribute of the driving information.

The amount of information is a value that varies depending on how much the occurrence or change of the attribute of the driving information is related to the occurrence or change of the weather condition, and may be calculated by Equation 1 below.

[Equation 1]

Where 　p(x) is the probability of occurrence 　x. As the probability of occurrence is closer to 1, the amount of information becomes closer to 0, and as the probability of occurrence is closer to 0, the amount of information can be infinitely large.

Entropy represents the expected value (average) of the amount of information, and the larger the entropy means the larger the average amount of information, and the larger the uncertainty, the more difficult it is to classify.Therefore, the smallest entropy can be placed in the upper decision node to reduce the amount of computation. . The entropy can be obtained by calculating Equation 2 below.

[Equation 2]

Here, 　S denotes a collection of weather conditions that have already occurred, and 　c may denote the number of weather conditions.

The amount of correlation information can be calculated by calculating Equation 3 below.

[Equation 3]

Here, A means an attribute, and it is possible to determine which attribute has the smallest entropy (ie, the information acquisition amount is large).

In step S1320, the wake-up state determination unit 330 may determine the property of the wake-up state of the driving position according to the attribute of the driving information based on the amount of correlation information. The wake-up state determination unit 330 may determine a wake-up state of the driving information attribute and the amount of the correlation information as a wake-up state of the driving position of the driving vehicle 100.

In step S1330, the wake-up state determination unit 330 may determine a degree of a wake-up state of the driving position based on the value of the driving information. The wake-up state determination unit 330 may classify the degree of the wake-up state into stages. The degree of the wake-up state may be determined as a step.

In step S1321, the wake-up state determination unit 330 may extract at least one attribute of a wake-up state with a low amount of the collected driving information and the correlation information. The driving information may be driving information that the analysis unit 320 detects that the amount of change is greater than or equal to a threshold value.

In step S1322, the wake-up state determination unit 330 may determine an attribute of a wake-up state in which the average of the plurality of collected driving information and the amount of correlation information is the lowest as the wake-up state attribute of the driving position.

In step S1300, the weather condition determination unit 330 may determine rainfall or heavy rain using at least one of a wiper level, low beam on/off, external temperature, and speed. The weather condition determination unit 330 may increase or decrease the level of the rainfall or heavy rain by using the rain sensor level and speed.

In step S1300, according to an embodiment of the present invention, according to an embodiment of the present invention, the wake-up state determination unit 330 has a wafer level of the highest, low beam on, an external temperature of 2 degrees or more, and a speed of 100 km/h (about 28 m/s) or less. In this case, it can be judged as rainfall stage 4. The weather condition determination unit 330 may increase the rainfall step by one step when the rain sensor level is 15 mm/h or more. When the level of the rain sensor is 25 mm/h or more, the weather condition determination unit 330 may further increase the rainfall step by one step. The weather condition determination unit 330 may increase the rainfall step by one step when the speed is 80 km/h (about 22 m/s) or less. When the speed of the weather condition determination unit 330 is less than 60 km/h (about 16 m/s), the rainfall step may be further increased by one step.

In step S1300, according to an embodiment of the present invention, according to an embodiment of the present invention, the wake-up condition determination unit 330 has the highest wiper level, low beam on, outside temperature 2 degrees or less, and speed of 80 km/h (about 22 m/s) or less. If it is, it can be judged as snowfall stage 4. When the level of the rain sensor is 10 mm/h or more, the weather condition determination unit 330 may increase the rainfall step by one step. When the level of the rain sensor is 20 mm/h or more, the weather condition determination unit 330 may further increase the rainfall step by one step. When the speed of the weather condition determination unit 330 is 60 km/h (about 16 m/s) or less, the rainfall step may be increased by one step. When the speed of the weather condition determination unit 330 is less than 40 km/h (about 11 m/s), the rainfall step may be further increased by one step.

In step S1300, according to an embodiment of the present invention, the wake-up state determination unit 330 activates the TCS while stepping on the excel, the throttle is 30% or less, the external temperature is 2 degrees or less, and the speed is 30 km/h (about 8 m). If it is below /s), it can be judged as the second stage of the ice road. The wake-up condition determination unit 330 may increase the ice road by one step when the external temperature is 0 degrees or less. When the speed is less than 7 km/h (about 2 m/s), the weather condition determination unit 330 may increase the ice level step by one step. When the acceleration is 1.5 m/s ² or less, the wake-up condition determination unit 330 may increase the step of the ice road by one step. When the acceleration is 1.0m/s ² or less, the weather condition determination unit 330 may further increase the step of the ice road by one step. When the acceleration is 0.7 m/s ² or less, the wake-up condition determination unit 330 may further increase the ice path step by one step.

In step S1300, according to an embodiment of the present invention, the wake-up state determination unit 330 activates ESP during driving, a friction coefficient of 0.15 or less, a throttle 15% or less, a brake pedal 20% or more, an external temperature of 2 degrees or less, and a speed If the speed is less than 50km/h (about 14m/s), it can be judged as the second stage of the ice road. The wake-up condition determination unit 330 may increase the ice road by one step when the external temperature is 0 degrees or less. When the coefficient of friction is 0.10 or less, the wake-up state determination unit 330 may increase the ice level step by one step. When the friction coefficient is less than 0.05, the wake-up condition determination unit 330 may increase the step of the ice road by one step. When the acceleration is 1.5 m/s ² or less, the wake-up condition determination unit 330 may increase the step of the ice road by one step.

In step S1300, the wake-up state determination unit 330 may update the criterion through learning. The weather condition determination unit 330 may learn the driving information and actual weather information to update the determination criterion.

So far, the present invention has been looked at around its preferred embodiments. Those of ordinary skill in the technical field to which the present invention pertains will be able to understand that the present invention may be implemented in a modified form without departing from the essential characteristics of the present invention. Therefore, the disclosed embodiments should be considered from an illustrative point of view rather than a limiting point of view. The scope of the present invention is shown in the claims rather than the foregoing description, and all differences within the scope equivalent thereto should be construed as being included in the present invention.

Claims (10)

Collecting driving information of the driving vehicle at short periodic intervals;
Analyzing the collected driving information at long periodic intervals; And
Determining a wake-up state of the driving position of the driving vehicle based on the analysis result; Including,
The step of determining the weather condition,
Learning an amount of correlation information between a property of the collected driving information and a weather condition;
Determining an attribute of a wake-up state of the driving position according to an attribute of the driving information based on the amount of correlation information; And
Determining a degree of a wake-up state of the driving position based on the value of the driving information; and
In the driving information collection step,
The driving information,
Speed, acceleration, external light, internal lighting, ABS status, curvature, internal temperature, external temperature, angular velocity, brake pedal level, ESP (Electronic Stability Program) status, excel level, rainfall, wiper level, TCS (Traction Control System) Includes the state,
The step of determining the weather condition attribute,
Extracting at least one attribute of a weather condition with a low amount of the plurality of collected driving information and the correlation information; And
Determining an attribute of a weather condition having the lowest average of the plurality of collected driving information and the amount of correlation information as an attribute of the weather condition of the driving position.
delete delete The method of claim 1,
The learning step of the amount of correlation information,
A method of analyzing weather information based on driving vehicle information, characterized in that it is calculated by Equation 1 below.
[Equation 1]

(Here, IG(S, A) is the amount of correlation information, E(S) is an entropy value, S is an attribute of a weather condition, and A is an attribute of driving information.)
The method of claim 4,
The driving information analysis step,
Measuring a change amount of the driving information;
Detecting a change in a weather condition when the amount of change in the driving information is greater than or equal to a threshold value; And
Requesting the determination of the change in the weather condition; and weather information analysis method based on driving vehicle information.
An information collection unit that collects driving information of a traveling vehicle at short periodic intervals;
An analysis unit that analyzes the collected driving information at long periodic intervals; And
A wake-up state determination unit determining a wake-up state of the driving position of the driving vehicle based on the analysis result; Including,
The weather condition determination unit,
Learning the amount of correlation information between the collected driving information attribute and the weather condition, determining the attribute of the weather condition of the driving position according to the attribute of the driving information based on the correlation information amount, and based on the value of the driving information Determine the degree of the wake-up state of the driving position,
The driving information,
Speed, acceleration, external light, internal lighting, ABS status, curvature, internal temperature, external temperature, angular velocity, brake pedal level, ESP (Electronic Stability Program) status, excel level, rainfall, wiper level, TCS (Traction Control System) Includes the state,
The weather condition determination unit,
At least one attribute of a weather condition having a low amount of the plurality of collected driving information and the amount of correlation information is extracted, and an attribute of the weather state having the lowest average of the plurality of collected driving information and the amount of correlation information is the driving position Weather information analysis system based on driving vehicle information that determines the property of the weather condition of.
delete delete The method of claim 6,
The weather condition determination unit,
A weather information analysis system based on driving vehicle information, characterized in that calculating the correlation information amount by Equation 1 below.
[Equation 1]

(Here, IG(S, A) is the amount of correlation information, E(S) is an entropy value, S is an attribute of a weather condition, and A is an attribute of driving information.)
The method of claim 9,
The analysis unit,
Based on driving vehicle information, characterized in that, when the amount of change in the driving information is measured, and when the amount of change in the driving information is greater than or equal to a threshold value, a change in a weather condition is detected, and a determination of the change in the weather condition is requested by the weather condition determination unit. Weather information analysis system.

Images