KR20160070656A - Accident Type Judgment Method And System for Vehicle Accident Pattern Using A 3-Axis Acceleration Sensor, Apparatus - Google Patents

Abstract

The present invention relates to a method for determining the type of a vehicle accident based on the accident pattern using a three-axis acceleration sensor of collecting gravity acceleration data of a running vehicle using a three-axis acceleration sensor and, if an accident type determination occurs, determining and providing the type in advance based on the gravity acceleration data, a system thereof, and a device for the same. The system includes: a three-axis acceleration sensor which senses and outputs the gravity acceleration of a vehicle; and an accident determination unit which includes a Kalman filter, receives the gravity acceleration data of the vehicle from the three-axis acceleration sensor, filters the data through the Kalman filter, checks the current state of the vehicle based on the filtered data, and determines if the vehicle is in a normal state, an accident state, a rollover state, or a fallen state, and provides the determination result. By identifying the current state of a vehicle accurately, the present invention can prevent accidents such as rollover and falling in advance and help identify the existence of an accident and the type of the accident if any.

Description

Technical Field [0001] The present invention relates to a three-axis accelerometer, and more particularly,

In particular, the present invention relates to a method, system, and apparatus for determining an accident pattern based on an accident pattern using a three-axis acceleration sensor. More particularly, the present invention relates to a three-axis acceleration sensor, A method, system and apparatus for determining a type of vehicle accident based on an accident pattern using a three-axis acceleration sensor that discriminates in advance when a type of accident is determined (for example, an overturning accident, a falling accident, a contact accident, a collision accident, .

Generally, a vehicle black box is a system that records signals of various sensors installed in a vehicle in a black box internal memory to be used for an accident investigation in case of an accident. In addition, the mobile terminal receives signals of various sensors installed on the vehicle by modifying the signals, and transmits the signals to the server wirelessly to be used for investigation of an accident when an accident occurs. Such a conventional black box system for a vehicle can be used for the investigation of the occurrence of an accident at the time of an accident, but it is determined that an accident has occurred in a situation where the consciousness of the driver (user) is unclear due to a sudden accident, There is not enough ability to ask for help.

In order to compensate for this, there is a technique of detecting an accident location using a GPS-based system. In this case, however, resolution and integration of the GPS information are not high enough, so that an accident can not be efficiently determined.

Korean Patent No. 10-1428008 Korean Patent Publication No. 10-2007-0056537

In order to solve such a problem, the present invention provides a three-axis acceleration sensor to collect gravitational acceleration data for a vehicle under running, and to estimate the type of accidents (eg, rollover, crash, Collision accident, collision accident, etc.) occurs in advance.

A three-axis acceleration sensor for sensing and outputting acceleration of a vehicle based on an accident pattern based on a three-axis acceleration sensor according to an embodiment of the present invention; And a Kalman filter. The vehicle receives the gravitational acceleration data of the vehicle from the three-axis acceleration sensor, filters it through a Kalman filter, checks the current state of the vehicle on the basis of the filtered data, An overturned state, or a falled state, and provides an accident determination unit.

As an embodiment related to the present invention, it is possible to further include a GPS module that provides position information of a vehicle, and the accident determination unit may determine whether the vehicle is in an abnormal state based on the positional information and the azimuth information in the gravitational acceleration data, have.

As an embodiment related to the present invention, the Kalman filter of the accident judgment unit can correct the abnormal value from the gravitational acceleration data.

As an embodiment related to the present invention, the ideal value may be data generated by any one of the bump, the unevenness, and the directional running module.

In an embodiment related to the present invention, the accident determination unit analyzes the change of the Y axis provided from the three-axis acceleration sensor to recognize the left-turn state or the right-turn state, analyzes the change of the X- Deceleration state, and can recognize the state of the altitude change by analyzing the change of the Z axis provided from the 3-axis acceleration sensor.

As an embodiment related to the present invention, the accident judgment unit recognizes an accidental rollover when there are few changes in the X axis and a lot of changes in the Y axis and the Z axis, and when the changes in the X axis and the Y axis are small, It can be recognized as a fall accident.

The accident pattern-based vehicle accident type determination system using the three-axis acceleration sensor according to an embodiment of the present invention includes a three-axis acceleration sensor, and the three-axis acceleration sensor is used to collect and transmit the gravitational acceleration data of the vehicle, ; And a Kalman filter, receives gravitational acceleration data of the vehicle from the mobile terminal, filters the Kalman filter through the Kalman filter, checks the current state of the vehicle, determines whether the vehicle is in an abnormal state, And a vehicle accident management server for informing an accident state using accident notification information registered in advance, which is judged to be abnormal.

Here, the abnormal state of the vehicle may be an accident by type of vehicle, for example, an overturn accident, a fall accident, a contact accident, a collision accident, or a collision accident.

In the embodiment of the present invention, the gravitational acceleration data is composed of a header, a body, and a tail, and the body includes a message type, a number of repetitions of three-axis acceleration sensors, a sequence number, Or a change.

In an embodiment related to the present invention, the mobile terminal further includes a GPS module, and the mobile terminal links the position information and the azimuth information to the gravity acceleration data measured through the three-axis acceleration sensor, And may determine whether the vehicle is in an abnormal state based on the positional information and the azimuth information in the gravitational acceleration data.

As an embodiment related to the present invention, the vehicle accident management server can transmit the position information as the accident point information when the accident notification information is transmitted.

As an embodiment related to the present invention, the Kalman filter of the vehicle accident management server can correct an abnormal value from gravitational acceleration data.

As an embodiment related to the present invention, the ideal value may be data generated by any one of the bump, the unevenness, and the directional running module.

As an embodiment related to the present invention, the vehicle accident management server analyzes the change of the Y axis provided from the three-axis acceleration sensor to recognize the left-turn state or the right-turn state, and analyzes the change of the X- And recognizes the acceleration state or the deceleration state, and recognizes the state of the altitude change by analyzing the change of the Z axis provided from the three-axis acceleration sensor.

As an embodiment related to the present invention, a vehicle accident management server recognizes an accidental rollover when there are few changes in the X-axis and a lot of changes in the Y-axis and the Z-axis, In many cases, it can be recognized as a fall accident.

According to another aspect of the present invention, there is provided a method for determining an accident pattern based on an accident pattern using a three-axis acceleration sensor, comprising: receiving a gravity acceleration data generated by a three-axis acceleration sensor; Wherein the vehicle accident management server comprises: extracting accident detection data by analyzing gravity acceleration data; Wherein the vehicle accident management server comprises: filtering the accident detection data using a Kalman filter to correct an abnormal value; Wherein the vehicle accident management server comprises: comparing the corrected accident detection data with previously stored vehicle movement pattern information; Wherein the vehicle accident management server comprises: analyzing a movement of the vehicle through comparison with the vehicle movement pattern information; Wherein the vehicle accident management server comprises: a step of judging whether or not a suspected overturning accident is suspected as a result of a motion analysis of the vehicle; Wherein the vehicle accident management server transmits the notice of rollover accident notice information using accident notice information registered in advance if it is suspected to be rollover, and judges whether or not the rolling accident is suspected as a rollover accident; The vehicle accident management server may be configured to transmit crash warning notice information using the accident notification information if the accident is suspected.

As an embodiment related to the present invention, the ideal value may be data generated by any one of the bump, the unevenness, and the directional running module.

As an embodiment related to the present invention, the vehicle accident management server analyzes the change of the Y axis provided from the three-axis acceleration sensor to recognize the left-turn state or the right-turn state, and analyzes the change of the X- And recognizes the acceleration state or the deceleration state, and recognizes the state of the altitude change by analyzing the change of the Z axis provided from the three-axis acceleration sensor.

As an embodiment related to the present invention, a vehicle accident management server recognizes an accidental rollover when there are few changes in the X-axis and a lot of changes in the Y-axis and the Z-axis, In many cases, it can be recognized as a fall accident.

According to the present invention, a three-axis acceleration sensor is provided in a mobile terminal to collect gravitational acceleration data for a vehicle under running and discriminate and provide various accident situations such as a rollover accident or a fall accident based on gravitational acceleration data, So that it is possible to know the type of an accident and the type of an accident.

1 is a view for explaining a configuration of an accident pattern-based vehicle accident type determining apparatus using a three-axis acceleration sensor according to the present invention.
2 is a view for explaining a configuration of an accident pattern-based vehicle accident type determination system using a three-axis acceleration sensor according to the present invention.
FIG. 3 is a flowchart illustrating a method for determining an accident pattern based on an accident pattern using a three-axis acceleration sensor according to the present invention.
FIG. 4 is a view for explaining a mass-spring system for explaining a three-axis acceleration sensor applied to FIGS. 1 and 2. FIG.
FIG. 5 is a view for explaining a structure capable of measuring acceleration of several axes using the three-axis acceleration sensor applied to FIGS. 1 and 2. FIG.
FIG. 6 is a view attached to understand the principle of the three-axis acceleration sensor of FIGS. 1 and 2. FIG.
FIG. 7 is a view for explaining a direction angle setting state of the three-axis acceleration sensor of FIGS. 1 and 2. FIG.
FIG. 8 is a view for explaining a transmission format of gravity acceleration data generated by the three-axis acceleration sensor of FIGS. 1 and 2. FIG.
9 is a diagram for explaining a transmission format of the BODY part of FIG.
FIG. 10 is a view for explaining a travel pattern which can be judged by the three-axis acceleration sensor of FIGS. 1 and 2. FIG.

It is noted that the technical terms used in the present invention are used only to describe specific embodiments and are not intended to limit the present invention. In addition, the technical terms used in the present invention should be construed in a sense generally understood by a person having ordinary skill in the art to which the present invention belongs, unless otherwise defined in the present invention, Should not be construed to mean, or be interpreted in an excessively reduced sense. In addition, when a technical term used in the present invention is an erroneous technical term that does not accurately express the concept of the present invention, it should be understood that technical terms can be understood by those skilled in the art. In addition, the general terms used in the present invention should be interpreted according to a predefined or prior context, and should not be construed as being excessively reduced.

Furthermore, the singular expressions used in the present invention include plural expressions unless the context clearly dictates otherwise. In the present invention, terms such as "comprising" or "comprising" and the like should not be construed as encompassing various elements or stages of the invention, Or may further include additional components or steps.

Hereinafter, exemplary embodiments of the present invention will be described in detail with reference to the accompanying drawings, wherein like reference numerals refer to like or similar elements throughout the several views, and redundant description thereof will be omitted.

In the following description, well-known functions or constructions are not described in detail since they would obscure the invention in unnecessary detail.

It is to be noted that the accompanying drawings are only for the purpose of facilitating understanding of the present invention, and should not be construed as limiting the scope of the present invention with reference to the accompanying drawings.

( Example  One)

1 is a view for explaining a configuration of an accident pattern-based vehicle accident type determining apparatus using a three-axis acceleration sensor according to the present invention.

As shown in FIG. 1, the mobile terminal 100 includes a three-axis acceleration sensor 110 for sensing and outputting the acceleration of the vehicle, and a Kalman filter 161. The three- The vehicle's gravitational acceleration data is received and filtered through the Kalman filter 161 to check the current state of the vehicle to determine whether it is in a normal state, an accident state, an overturned state, A GPS module 120 for providing information on the position of the vehicle and a controller 150 for controlling the entire system and outputting the vehicle state determined by the accident determination unit 160 through the output unit 150 And a control unit 130.

That is, the accident judging unit 160 judges the abnormal state of the vehicle as an accident according to the type of the vehicle. Specifically, for example, as described above, the abduction accident, the fall accident, the contact accident, the collision accident, do.

The mobile terminal may further include various vehicle sensors such as an OBD and a TPMS in addition to the GPS module 120, and the controller 130 may control the entire system based on signals sensed by the sensors.

The accident determination unit 160 determines whether the vehicle is in an abnormal state based on the position information and the azimuth information in the gravitational acceleration data.

The Kalman filter 161 of the accident determination unit 160 corrects the anomalous value from the gravitational acceleration data, and the ideal value is data generated by any one of the bouncing chin, the unevenness, and the directional running module.

The accident determination unit 160 analyzes the change of the Y axis provided from the 3-axis acceleration sensor 110 to recognize the left-turn state or the right-turn state, analyzes the change of the X-axis provided from the 3-axis acceleration sensor 110, Axis acceleration sensor 110 and recognizes the state of the altitude change by analyzing the change of the Z axis provided from the three-axis acceleration sensor 110. [

The accident determination unit 160 recognizes the occurrence of rollover when there are few changes in the X axis and a lot of changes in the Y and Z axes and recognizes it as a fall accident when there is a small change in the X and Y axes and a large change in the Z axes .

Details of components included in common in the second embodiment will be described in the second embodiment.

( Example  2)

2 is a view for explaining a configuration of an accident pattern-based vehicle accident type determination system using a three-axis acceleration sensor according to the present invention.

As shown in FIG. 2, an accident pattern-based vehicle accident type determination system using a three-axis acceleration sensor includes a mobile terminal 100, a vehicle accident management server 200, and a communication network 300.

The mobile terminal 100 includes a three-axis acceleration sensor 110 for collecting and providing gravity acceleration data of the vehicle, a GPS module 120 for receiving and providing position information of the vehicle, and a three-axis acceleration sensor 110 And outputs the gravity acceleration data measured by the three-axis acceleration sensor 110 to the acceleration / deceleration sensor 120. The gravity acceleration data is transmitted through the communication unit 140, A control unit 130 that links the vehicle to the vehicle accident management server 200 through the communication unit 140 and an output unit 150 that outputs the state of the vehicle on the screen in response to the control of the control unit 130.

At this time, the gravitational acceleration data is composed of a header, a body, and a tail, and the body includes a message type, a number of triaxial acceleration sensor repetitions, order, X axis velocity change, Y axis velocity change and Z axis velocity change.

FIG. 4 is a view for explaining a mass-spring system for explaining a three-axis acceleration sensor applied to FIGS. 1 and 2. As a principle of a sensor for measuring an acceleration for measuring an acceleration using Newton's first law, The external force is proportional to the extension distance of the spring connected to the mass object, so the acceleration a is calculated as a = kx / m.

FIG. 5 is a view for explaining a structure capable of measuring acceleration of several axes using the three-axis acceleration sensor applied in FIGS. 1 and 2, and has a structural characteristic capable of measuring acceleration of several axes. Once integrated, the position is calculated when the speed is integrated twice. Therefore, by using the two-directional acceleration sensor, it can be mounted on a moving object on the plane to determine its own position.

FIG. 6 is a view attached to understand the principle of the three-axis acceleration sensor of FIGS. 1 and 2, wherein a key element of the MEMS accelerometer is to move a beam structure composed of two sets of fingers, The other set is connected to the mass of the spring itself and the piston itself which can move in response to the applied acceleration. The acceleration applied here changes the capacitance between the fixed beam fingers and the moving beam fingers. The MEMS structure, which is only a few microns, requires very high precision photolithography and etching process technology. MEMS structures are usually made of single crystal silicon, or they consist of polysilicon deposited at ultra-high temperatures on the surface of single crystal silicon wafers. This flexible technology can be used to design structures with very different mechanical properties. One of the mechanical parameters that can be controlled and modified is spring stiffness. In addition, design changes can be made through the damping of mass and structure of sensing elements.

FIG. 7 is a diagram for explaining the direction angle setting state of the three-axis acceleration sensor of FIGS. 1 and 2. The axial direction of the three-axis acceleration sensor 110 is defined as an X-axis direction of the vehicle, a Y Set the Z axis as the vertical axis.

FIG. 8 is a view for explaining a transmission format of gravity acceleration data generated by the three-axis acceleration sensor of FIGS. 1 and 2. The mobile terminal 100 provided in the vehicle includes three axes The acceleration sensor 110 generates gravity acceleration data based on the acceleration data and transmits it to the vehicle accident management server 200. To this end, a communication protocol is defined. The three-axis acceleration sensor A header and a tail are defined in a protocol specification (BODY) transmitted to the vehicle accident management server 200 from the terminal 100. [

FIG. 9 is a diagram for explaining the transmission format of the part (BODY) of FIG. 8, which is a body part to be transmitted, in which data is transmitted in units of 50 to 300 msec in units of one minute, The number of repetitions of the acceleration sensor is adjusted.

Fig. 10 is a view for explaining a driving pattern which can be judged by the three-axis acceleration sensor of Figs. 1 and 2. In the case of left turn, right turn, acceleration, deceleration, accident, left overturn, right overturn, Axis acceleration sensor 110 as shown in FIG. In the case of left and right rotation, it measures the change of the Y axis, which is dedicated to the axial direction. When the Y axis has positive acceleration, it recognizes that it has moved to the left side. In case of accelerated deceleration, the sensor X-axis value in the traveling direction is used and measured. If it has positive value, it is regarded as acceleration. If it has negative value, it is regarded as deceleration. In the case of the Z axis, when the altitude is changed, the altitude is decreased (+) or when the altitude is increasing (-). When the pattern is compared, the left and right overturn patterns can be determined as stabilized (stop) when the X, Y, Z axis values approach 1G, and the final state of the vehicle can be checked to determine whether to overturn.

The vehicle accident management server 200 includes a vehicle information DB 210 storing vehicle information matching the vehicle identification information, a vehicle accident information DB 220 storing vehicle accident information matching the vehicle identification information, And a Kalman filter 231. The mobile terminal 100 receives the gravitational acceleration data transmitted through the mobile terminal 100 and corrects the abnormal value, and then checks the motion state of the vehicle to determine whether the vehicle is in a normal state, an overturned state, And a vehicle accident detection unit 230 for detecting an accident state using the accident notification information registered in advance as a result of confirmation by the vehicle accident detection unit 230, And a vehicle accident notification unit 240 for registering and managing the vehicle accident information in the vehicle accident information DB 220. [

The Kalman filter 231 is a component based on a recursive operation, and estimates a current value based on a value that was estimated immediately before. Generally, the three-axis acceleration sensor includes various noises and the noise is increased unless the flat horizontal road runs at a constant speed. In the case where a car that is moving at a constant speed repeats accelerating or decelerating further, various road surface conditions such as roads with uneven road surfaces, acceleration bumps, ramps, and dividing roads increase the noise of the acceleration sensor. In addition, the 3-axis acceleration sensor can measure the change in gravity acceleration applied to a stationary object, but in real life, it is necessary to use a Kalman filter that sets the initial value periodically to increase the reliability of the sensor. Can be found.

The vehicle accident management server (200) transmits location information to the accident point information when the accident notification information is transmitted.

The Kalman filter 231 of the vehicle accident judging unit 230 can correct the abnormal value from the gravitational acceleration data. Here, the ideal value is data generated by any one of the braking, bump, and directional running modules.

Here, the principle of the Kalman filter 231 will be briefly described, and it plays an important role in correcting various errors due to the road surface state, vehicle vibration, fine irregularity, and shaking.

Kalman filter is one of the most widely known techniques in motion prediction because it provides an optimal prediction method for linear dynamic systems with Gaussian (normal distribution) noise. The Kalman filter provides a generalized regression algorithm that can be easily implemented using a computer. In general, the Kalman filter expresses the system as a system state model and a measurement model as shown in Eqs. (1) and (2).

The system state s (k) at the kth time point is linearly related to the frame at the (k-1) th time point, and the relationship between the measurement model m (k) and the system state s (k) is also linear. It is assumed that w (k) and v (k) represent state and measurement noise, respectively, and are independent of each other and have Gaussian noise. In (2), H (k) is a state transition matrix that associates the state model with the measurement model. to be. In other words, in moving objects, it is often the case that it can not measure accurately due to occlusion and malfunction due to various road surface conditions and noise. By using the Kalman filter 231, the noise removing operation can be easily performed.

The vehicle accident determination unit 230 of the vehicle accident management server 200 analyzes the change of the Y axis provided from the three axis acceleration sensor 110 corrected by the Kalman filter 231 to recognize the left turn state or the right turn state, Axis acceleration sensor 110 to recognize the acceleration state or the deceleration state and to recognize the state of the altitude change by analyzing the change of the Z axis provided from the three-axis acceleration sensor 110. [

The vehicle accident determination unit 230 of the vehicle accident management server 200 recognizes the occurrence of an overturning accident when there are few changes in the X axis and a lot of changes in the Y axis and the Z axis and the change in the X axis and the Y axis is small, If there are many changes, it is recognized as a fall accident.

A method of determining an accident pattern based on an accident pattern using the three-axis acceleration sensor constructed as above will be described below.

FIG. 3 is a flowchart illustrating a method for determining an accident pattern based on an accident pattern using a three-axis acceleration sensor according to the present invention.

3, the vehicle accident determination unit 230 of the vehicle accident management server 200 receives the gravity acceleration data generated by the three-axis acceleration sensor 110 (S110), analyzes the gravity acceleration data And the incident detection data is extracted (S120).

The vehicle accident determination unit 230 of the vehicle accident management server 200 corrects the abnormal value by filtering the accident detection data using the Kalman filter 231 (S130), and transmits the corrected accident detection data to the vehicle movement And compares it with the pattern information (S140). Here, it is a noise generated by the bump, the unevenness, and the directional running module.

The vehicle accident determination unit 230 of the vehicle accident management server 200 analyzes the movement of the vehicle through comparison with the vehicle movement pattern information (S150), and determines whether or not it is suspected of a rollover accident as a result of the motion analysis of the vehicle S160).

If it is determined in step S160 that there is a rollover accident, the vehicle accident determination unit 230 of the vehicle accident management server 200 transmits the rollover accident notice information using the accident notification information registered in advance (S170) It is judged whether or not there is a suspicion of a fall accident (S180).

As a result of the determination in step S180, if the vehicle accident determination unit 230 of the vehicle accident management server 200 suspects a fall accident, the crash incident prediction information is transmitted using the incident notification information (S190).

The vehicle accident determination unit 230 of the vehicle accident management server 200 analyzes the change of the Y axis provided from the 3-axis acceleration sensor 110 to recognize the left turn state or the right turn state and provides the 3-axis acceleration sensor 110 Axis acceleration sensor 110, recognizes the acceleration state or the deceleration state, and analyzes the change in the Z axis provided from the three-axis acceleration sensor 110 to recognize the state of the high-level change.

The vehicle accident determination unit 230 of the vehicle accident management server 200 recognizes the occurrence of an overturning accident when there are few changes in the X axis and a lot of changes in the Y axis and the Z axis and the change in the X axis and the Y axis is small, If there are many changes, it is recognized as a fall accident.

It will be apparent to those skilled in the art that various modifications and variations can be made in the present invention without departing from the spirit or essential characteristics thereof. Therefore, the embodiments disclosed in the present invention are intended to illustrate rather than limit the scope of the present invention, and the scope of the technical idea of the present invention is not limited by these embodiments. The scope of protection of the present invention should be construed according to the following claims, and all technical ideas within the scope of equivalents thereof should be construed as falling within the scope of the present invention.

100: mobile terminal
110: 3-axis acceleration sensor 120: GPS module
130: control unit 140:
150:
200: vehicle accident management server
210: vehicle information DB 220: vehicle accident information DB
230: vehicle accident determination unit 231: Kalman filter
240: Vehicle accident notification unit

Claims (18)

A three-axis acceleration sensor for sensing and outputting acceleration of the vehicle; And
Axis acceleration sensor, receives the gravity acceleration data of the vehicle from the three-axis acceleration sensor, filters the data through the Kalman filter, checks the current state of the vehicle based on the filtered data, An accident judging unit for judging whether or not the crane is in a rollover state,
A device for judging the type of vehicle accident based on an accident pattern using a three-axis acceleration sensor.
The method according to claim 1,
Further comprising a GPS module for providing location information of the vehicle,
Wherein the accident determination unit determines whether the vehicle is in an abnormal state based on the position information and the azimuth information, and provides the gravity acceleration data to the gravity acceleration data.
The method according to claim 1,
Wherein the Kalman filter of the accident judging unit corrects the abnormal value from the gravitational acceleration data.
The method of claim 3,
Wherein the abnormal value is data generated by any one of the braking force, the unevenness, and the directional running module.
3. The method of claim 2,
The accident determination unit recognizes a left-turn state or a right-turn state by analyzing the change of the Y-axis provided from the three-axis acceleration sensor, and recognizes an acceleration state or a deceleration state by analyzing a change of the X- Axis acceleration sensor, and recognizes an altitude change state by analyzing the change of the Z axis provided from the 3-axis acceleration sensor.
6. The method of claim 5,
The accident judging section recognizes the occurrence of rollover when there are few changes in the X-axis and a lot of changes in the Y-axis and the Z-axis. Accident pattern based vehicle accident type determination device using 3 - axis acceleration sensor.
A mobile terminal having a three-axis acceleration sensor and collecting and transmitting the gravity acceleration data of the vehicle through the three-axis acceleration sensor; And
A Kalman filter, and receives gravitational acceleration data of the vehicle from the mobile terminal, filters the Kalman filter through the Kalman filter, and checks the current state of the vehicle to determine whether the vehicle is in a normal state, an accident state, A vehicle accident management server for judging whether or not the vehicle is in an overturned state or a falled state as a result of the determination, and notifying an accident state using previously registered accident notification information;
Accident Pattern Type Judgment System Based on Accident Pattern Using 3 - Axis Acceleration Sensor.
8. The method of claim 7,
Wherein the gravity acceleration data includes at least one of a header, a body, and a tail, and the body comprises at least one of a message type, a number of triaxial acceleration sensor repetition times, a sequence number, an X axis velocity change, a Y axis velocity change, A three - axis acceleration sensor is used to detect accident patterns based on accident patterns.
8. The method of claim 7,
The mobile terminal further includes a GPS module,
The mobile terminal links the position information and the azimuth information to the gravity acceleration data measured by the three-axis acceleration sensor, and transmits the gravity acceleration data to the vehicle accident management server. And determining whether the vehicle is in an abnormal state based on the accident pattern.
10. The method of claim 9,
Wherein the vehicle accident management server sends the position information to the accident point information when the accident notification information is transmitted.
8. The method of claim 7,
Wherein the Kalman filter of the vehicle accident management server corrects an abnormal value from the gravitational acceleration data.
12. The method of claim 11,
Wherein the abnormal value is data generated by any one of a braking force, an unevenness, and a directional running module.
11. The method of claim 10,
The vehicle accident management server analyzes the change of the Y axis provided from the three-axis acceleration sensor to recognize a left-turn state or a right-turn state, analyzes the change of the X-axis provided from the three-axis acceleration sensor, Axis acceleration sensor, and recognizes the state of the altitude change by analyzing the change of the Z axis provided from the 3-axis acceleration sensor.
14. The method of claim 13,
The vehicle accident management server recognizes the occurrence of a rollover accident when there are few changes in the X-axis and a lot of changes in the Y-axis and the Z-axis. A three - axis acceleration sensor is used to detect accident patterns based on accident patterns.
The vehicle accident management server includes: receiving gravity acceleration data generated by a three-axis acceleration sensor;
Wherein the vehicle accident management server comprises: extracting accident detection data by analyzing gravity acceleration data;
Wherein the vehicle accident management server comprises: filtering the accident detection data using a Kalman filter to correct an abnormal value;
Wherein the vehicle accident management server comprises: comparing the corrected accident detection data with previously stored vehicle movement pattern information;
Wherein the vehicle accident management server comprises: analyzing a movement of the vehicle through comparison with the vehicle movement pattern information;
Wherein the vehicle accident management server comprises: a step of judging whether or not a suspected overturning accident is suspected as a result of a motion analysis of the vehicle;
Wherein the vehicle accident management server transmits the notice of rollover accident notice information using accident notice information registered in advance if it is suspected to be rollover, and judges whether or not the rolling accident is suspected as a rollover accident;
Wherein the vehicle accident management server transmits crash warning notice information using the accident notification information if it is suspected to be the fall accident;
A method of judging vehicle accident type based on accident pattern using 3 - axis acceleration sensor.
16. The method of claim 15,
Wherein the abnormal value is data generated by any one of the braking force, the unevenness, and the directional running module.
16. The method of claim 15,
The vehicle accident management server analyzes the change of the Y axis provided from the three-axis acceleration sensor to recognize a left-turn state or a right-turn state, analyzes the change of the X-axis provided from the three-axis acceleration sensor, Axis acceleration sensor, and recognizes the state of the altitude change by analyzing the change of the Z-axis provided from the 3-axis acceleration sensor.
16. The method of claim 15,
The vehicle accident management server recognizes the occurrence of a rollover accident when there are few changes in the X-axis and a lot of changes in the Y-axis and the Z-axis. A method for determining a type of vehicle accident based on an accident pattern using a three-axis acceleration sensor.

Images