KR101677035B1 - Mobile data collection device - Google Patents

Mobile data collection device Download PDF

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Publication number
KR101677035B1
KR101677035B1 KR1020150131505A KR20150131505A KR101677035B1 KR 101677035 B1 KR101677035 B1 KR 101677035B1 KR 1020150131505 A KR1020150131505 A KR 1020150131505A KR 20150131505 A KR20150131505 A KR 20150131505A KR 101677035 B1 KR101677035 B1 KR 101677035B1
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South Korea
Prior art keywords
vehicle
pattern
accident
sensor
type
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KR1020150131505A
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Korean (ko)
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권수한
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권수한
주식회사 스타넷시스템
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Priority to KR1020150131505A priority Critical patent/KR101677035B1/en
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    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B62—LAND VEHICLES FOR TRAVELLING OTHERWISE THAN ON RAILS
    • B62D—MOTOR VEHICLES; TRAILERS
    • B62D41/00—Fittings for identifying vehicles in case of collision; Fittings for marking or recording collision areas
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B60—VEHICLES IN GENERAL
    • B60R—VEHICLES, VEHICLE FITTINGS, OR VEHICLE PARTS, NOT OTHERWISE PROVIDED FOR
    • B60R21/00—Arrangements or fittings on vehicles for protecting or preventing injuries to occupants or pedestrians in case of accidents or other traffic risks
    • B60R21/01—Electrical circuits for triggering passive safety arrangements, e.g. airbags, safety belt tighteners, in case of vehicle accidents or impending vehicle accidents
    • B60R21/013—Electrical circuits for triggering passive safety arrangements, e.g. airbags, safety belt tighteners, in case of vehicle accidents or impending vehicle accidents including means for detecting collisions, impending collisions or roll-over
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B60—VEHICLES IN GENERAL
    • B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
    • B60W40/00—Estimation or calculation of non-directly measurable driving parameters for road vehicle drive control systems not related to the control of a particular sub unit, e.g. by using mathematical models
    • B60W40/08—Estimation or calculation of non-directly measurable driving parameters for road vehicle drive control systems not related to the control of a particular sub unit, e.g. by using mathematical models related to drivers or passengers
    • B60W40/09—Driving style or behaviour
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q50/00—Systems or methods specially adapted for specific business sectors, e.g. utilities or tourism
    • G06Q50/30—Transportation; Communications
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B60—VEHICLES IN GENERAL
    • B60R—VEHICLES, VEHICLE FITTINGS, OR VEHICLE PARTS, NOT OTHERWISE PROVIDED FOR
    • B60R21/00—Arrangements or fittings on vehicles for protecting or preventing injuries to occupants or pedestrians in case of accidents or other traffic risks
    • B60R2021/0002—Type of accident

Abstract

The present invention relates to a mobile data collection device which generates a recognition pattern by combining a sensor value of a sensor attached to a vehicle, and matches the recognition pattern with a pattern for each accident type, to determine an accident type for each driving pattern of an accident vehicle driver. The mobile data collection device according to the present invention including a camera, a GPS sensor, and an acceleration sensor comprises: a recognition pattern setting unit which sets a recognition pattern about acceleration, deceleration, left turn, right turn, lifting, and deflection of a vehicle with reference to a three-axis change rate of the acceleration sensor, and maps the recognition pattern on an electronic map with reference to GPS information; a type determination unit which determines an accident type for a vehicle in accordance with pattern similarity between the recognition pattern and the reference pattern by considering an output pattern of the acceleration sensor at the time of accident as the reference pattern, to generate accident type information; and a communication unit which couples an image of a camera to the accident type information to be provided to a data center connected through a wireless network.

Description

[0001] Mobile data collection device [0002]

BACKGROUND OF THE INVENTION 1. Field of the Invention The present invention relates to a mobile data collecting apparatus, and more particularly, to a mobile data collecting apparatus that judges whether or not an accident has occurred in a vehicle by matching pattern recognition patterns of acceleration, deceleration, left turn, right turn, And provides it to a data center for incident handling and management.

In order to automate vehicle accident handling, techniques have been proposed in which an image of a vehicle mounted in a vehicle is captured, or an accident occurs in a vehicle, and the accident information is transmitted to a management server.

Many of these proposed technologies are based on mounting a collision sensor on a terminal or a black box mounted on a vehicle and judging whether or not an accident has occurred in the vehicle by means of a collision detection sensor. And notifies the management server that processes a vehicle accident. These proposals mainly focus on mounting a GPS sensor and a collision detection sensor on a vehicle and mapping the detection result of the collision detection sensor to GPS position information.

Conventionally, the techniques of mapping the collision detection sensor and the GPS sensor value are gradually tending to prevent collision or to combine image information around the vehicle using a black box.

In the case of a technique using a black box rule, a technique has been proposed in which a captured image is transmitted to a management server when an accident occurs in a vehicle, beyond the function of capturing the surroundings of the vehicle and storing the captured image in a memory. In the case of an accident, it is notified of the occurrence of an accident in a nearby vehicle or notified to a police agency server to automate an accident process.

As one of such accident handling systems, the registered patent KR10-1065327 (R1) shows the distance and the azimuth of the other vehicle and the vehicle approaching the approaching vehicle when standing on the road around the vehicle, We have proposed a vehicle collision avoidance system that notifies the accident risk,

The registration patent KR10-1190835 (R2) confirms the position and the speed information of each vehicle by using the ID assigned to each vehicle, and refers to the information and transmits a warning message to the vehicle at risk of an accident. .

The preceding documents R1 and R2 judge the possibility of collision between vehicles by referring to the GPS position information of the vehicle. However, when the possibility of collision between vehicles is judged only by the positional relationship between the vehicles and the distance, it may be difficult to judge when the vehicle is crowded.

However, since there is a blind spot depending on the angle at which the image is captured in the vehicle, the image information may not be judged whether or not an accident has occurred, and the GPS-based vehicle collision prevention system may acquire location information of an accident, There is a limitation in expressing the positional relationship between the vehicles and the sensor based collision detection device may cause an error in the detection result depending on the sensor state or sensor sensitivity.

The Applicant hereby wishes to note that these prior art documents overlook one important point.

Using a vehicle to cause an accident is a vehicle driver, a person,

There is a lack of logic to determine an accident or risk situation based on a person's behavior pattern.

The driver of the vehicle has a behavior pattern in which, when a risk of a collision occurs in the front of the vehicle, the vehicle suddenly decelerates or the handle suddenly turns on to avoid the collision,

In the case of a hit-and-run driver, it is difficult to tolerate work after the accident, so that the driver is suddenly decelerating and suddenly driving.

That is, considering that the subject causing the accident using the vehicle and the subject handling the accident after the occurrence of an accident are also people (vehicle driver), it is preferable to use a sensor that excludes behavior patterns of the vehicle driver, We can not help but point out that there are many ways to understand and understand the situation that occurred in the vehicle using GPS location information.

According to the existing prior literature, it is difficult to clearly distinguish whether an accident occurred in a vehicle, a vehicle came into contact with a ground structure, or an accident, depending on the state or sensitivity of the collision avoidance sensor. This is because it is based on only the sensor value of the sensor, without considering the behavior pattern of the person, to determine the state such as an accident.

SUMMARY OF THE INVENTION It is an object of the present invention to provide a mobile data collection system capable of determining a type of an accident according to a behavior pattern of a vehicle driver by generating a recognition pattern based on a sensor value of a sensor installed in a vehicle, Device.

According to an embodiment of the present invention, there is provided an acceleration sensor comprising a camera, a GPS sensor, and an acceleration sensor. The acceleration sensor detects acceleration, deceleration, leftward rotation, rightward rotation, vehicle lifting, A recognition pattern setting section for mapping the recognition pattern with reference to the GPS information on the electronic map, an output pattern setting section for setting the output pattern of the acceleration sensor at the occurrence of the accident as a reference pattern, And a communication unit for combining the captured image of the camera with the type determination unit and the type-of-accident information for generating the type-of-accident information by judging the type of the type of incident, and providing the combined image to the data center connected to the wireless network.

According to the present invention, it is possible to generate a recognition pattern by combining sensor values of sensors attached to a vehicle, and to match the patterns to patterns of the accident types, so that an accident type for each driving pattern of an accident vehicle driver can be grasped.

1 shows a conceptual diagram of a mobile data collection device according to an embodiment of the present invention.
Figs. 2 to 4 show reference figures according to an example of the measured values of the acceleration sensor.
Figure 5 shows a reference diagram of a method by which a type determination unit uses GPS location information when a vehicle passes a shadow area such as a tunnel.
Fig. 6 shows a reference diagram for explaining the occurrence situation of the recognition pattern.

The recognition pattern referred to herein may correspond to a combination according to the number of cases for the sensor value. The recognition pattern is obtained by detecting the sensor value of the sensor 1 and the sensor value of the sensor 2 when a plurality of sensors such as the sensor 1, the sensor 2 and the sensor 3 are attached to the vehicle, , Which ultimately can represent the pattern of behavior of the driver of the vehicle.

The recognition pattern can also be determined according to the size of the sensor value. If the sensor value by the sensor 1 is related to the acceleration of the vehicle, it is possible to judge general acceleration, deceleration, rapid acceleration and deceleration. If a rapid deceleration occurs after a rapid acceleration referring to the behavior pattern of the driver, It can be judged that the risk of collision is detected. As described above, the recognition pattern may refer to a sensor value that combines the number of cases to map the incident type according to the size of the sensor value as well as the presence or absence of the sensor value.

The reference pattern referred to herein can be defined as an array of sensor values by type of incident.

The reference pattern refers to a behavior pattern in which a behavior pattern of a driver of the accident vehicle seen before and after an accident is transited to the vehicle. For example, when the driver finds that the vehicle is passing through the road, It may correspond to a combination of sensor values installed in the vehicle according to an operation of the driver to check the condition of the passenger.

In this case, the sensor value of the acceleration sensor may show a type of rapid deceleration-stop-rapid acceleration (hit-and-run), and when the person and the vehicle collide, the amount of the impact can be detected through the acceleration sensor among the sensors of the vehicle.

The mobile data collecting device referred to herein may refer to a type of terminal that is installed inside a vehicle or mounted near a vehicle operator's seat. When installed inside the vehicle, it can be installed so that it can not be touched by the driver of the vehicle. In the case of being mounted near the driver's seat, it can be mounted at a position such as a navigator or a black box that is not disturbed by operation. In this case, the mobile data collection device may exist as a single device or may be integrated with another device, for example, a navigator or a black box, or may be implemented as being connected to a portable terminal such as a smart phone. However, the mobile data collecting apparatus according to the embodiment can perform remote wireless communication according to the CDMA or GSM communication method even if it is not linked to the portable terminal, . ≪ / RTI >

Hereinafter, the present invention will be described in detail with reference to the drawings.

1 shows a conceptual diagram of a mobile data collection device according to an embodiment of the present invention.

The portable data collection device according to the embodiment is installed in the vicinity of the driver's vehicle or stored inside the vehicle so as not to touch the driver's hand so as to obtain a recognition pattern corresponding to the behavior pattern of the vehicle driver, Of the type of accident.

In other words, the mobile data collecting apparatus according to the embodiment does not determine whether an accident occurs based on a simple sensor value, but rather, it determines the manner in which the vehicle has moved and how the moving method is related to the type of accident Leave.

Preferably, the mobile data collecting apparatus according to the embodiment includes a GPS sensor 110, an acceleration sensor 120, a camera 130, a recognition pattern matching unit 140, a type determination unit 150, a communication unit 160, A database 170, and the like.

The GPS sensor 110 may obtain GPS position information of the portable data collection device according to the embodiment. Since the portable data collecting apparatus 100 according to the embodiment is mounted or housed in the vehicle, the GPS position information measured by the GPS sensor 110 corresponds to the position of the vehicle. Hereinafter, it is described that the GPS sensor 110 acquires position information of the vehicle and will be described.

The acceleration sensor (120)

- Acceleration and deceleration to the front compared to the vehicle movement direction - 1 axis,

- Left turn and right turn relative to vehicle movement direction - 2 axes and

- It is possible to perform 3-axis detection for the lower direction (road surface direction) and the upper direction of the vehicle. The gyro sensor is one of the acceleration sensors according to the embodiment, and the acceleration sensor is an upper concept of the gyro sensor. In this embodiment, the acceleration sensor and the gyro sensor are used as the gyro sensor. And collectively referred to as an acceleration sensor.

The acceleration sensor can derive the measured values for acceleration and deceleration based on the traveling direction of the vehicle,

Based on the traveling direction of the vehicle, measured values for the left turn and the right turn can be derived,

It is possible to derive the measured value of the vehicle ascending or descending on the ground. The type by which the recognition pattern is derived from these measured values will be described with reference to FIGS. 2 to 4 together.

2 shows a reference diagram according to an example of measured values of an acceleration sensor.

Referring to FIG. 2, the measured values measured by the acceleration sensor 120 in accordance with the movement of the vehicle can be detected for X, Y, and Z. FIG. here,

1) The X-axis corresponds to the measured value when the vehicle accelerates or decelerates while moving forward,

2) The Y axis corresponds to the measured value when the vehicle is moving left or right while moving,

3) The Z axis corresponds to the measured value when the vehicle moves up or down from the ground.

It can be seen that the X-axis measurement value increases at point P1 in FIG. This means that the vehicle accelerates. In Fig. 2, the vehicle accelerates at P1, indicating that the acceleration of the vehicle is decreasing at P2. At P2, the vehicle can see that the Z-axis measurement values are moving up and down in an "M" shape.

It can be seen that the measurement value is shown in the form shown in P2 by the process of the vehicle being driven up and down by the traffic structure while the vehicle meets the traffic structure such as the speed limit bite and accelerates while the vehicle is accelerating. Then, at the point P3, the Y-axis measurement value is detected to be +0.6, which corresponds to the meaning that the vehicle makes a left turn. On the other hand, if the Y-axis measured value is a negative value, the vehicle can be identified as turning right. For example, if the Y-axis measured value at the point P3 is detected at the point P5, the vehicle can be regarded as having made a right turn.

According to the Z-axis measurement at point P4, the vehicle passes a parallel road through a traffic structure, such as an overspeed protector, and the X-axis measurement at point P5 indicates that the speed of the vehicle is decreasing.

P3, P4 and P5, it can be interpreted that after the vehicle has passed a traffic structure such as a speed bump, the speed of the vehicle is reduced and then left-handed.

In the present embodiment, a combination of the above measured values for the X-axis, the Y-axis, and the Z-axis is referred to as a recognition pattern.

If the recognition pattern is interpreted at the same point in time along the X axis, the Y axis and the Z axis, the type of movement of the vehicle can be grasped. When the movement type is matched with the movement type of the accident vehicle, . The type of incident corresponds to one of the movement types.

The recognition pattern can be mapped on the electronic map to specify the type of movement of the vehicle. In addition, it is possible to understand how the vehicle caused the accident through the movement type mapped on the electronic map.

Next, the measurement values for the X-axis Y-axis and the Z-axis of FIG. 3 will be described.

Looking at the X-axis, the vehicle repeatedly decelerates and accelerates in T1 section, then accelerates to "0" in T2 section and accelerates rapidly in T3 section.

Similarly, looking at the Y-axis, the vehicle is making a sudden left turn in the T3 section, and the Z-axis shows no significant variation. When interpreted, it can be seen that the vehicle stopped while driving, and after the stop, it made a left turn.

However, if it is not known where the vehicle stopped, why is it not understood why the vehicle stopped at the T2 section?

It is necessary to map the behavior pattern of the vehicle driver obtained through the measurement value of the acceleration sensor to the electronic map in order to grasp the reason for stopping the vehicle in the T2 section and to understand why the vehicle should stop at the corresponding section.

Assuming that the area where the T2 section occurs is an intersection, the measured value in Fig. 3 can be interpreted as follows.

When the vehicle goes straight ahead, it stops to wait for a signal at the intersection, and after it stops, it receives a left turn signal and can judge that it made a left turn at the intersection.

As such, when the recognition pattern is linked to the road structure, the behavior pattern of the driver of the vehicle can be grasped. The recognition pattern setting unit 140 maps the recognition pattern to the electronic map so as to grasp the behavior pattern of the driver of the vehicle, and obtains the GPS position information at the point where the behavior pattern occurs In combination A recognition pattern can be generated.

The type determination unit 150 compares the recognition pattern with the incident type information stored in the database 160 to inquire the correspondence between the recognition pattern and the recognition pattern, and if there is a similar one among them, the recognition pattern may correspond to the incident type information have.

Meanwhile, in order to determine the similarity between the recognition pattern and the incident type information, the type determination unit 150 may calculate a correlation value for the recognition pattern and the incident type information. If the correlation value between the recognition pattern mapping information and the incident type information is equal to or greater than a preset reference value, the type determination unit 150 can recognize the recognition pattern as the same as the incident type information and determine the type of the incident corresponding to the recognition pattern. In the opposite case, the type determination unit 150 may determine that the recognition pattern does not correspond to the accident type information.

The calculation of the correlation value can be calculated for each of the measured values of the X axis, the Y axis, and the Z axis, but instead of using the correlation value, the correlation pattern may be calculated in the form of a graph, After configuring the information, pattern matching for both graphs can be used to determine the relevance of the recognition pattern and the type of thinking information.

When the recognition pattern acquired by the recognition pattern setting unit 140 corresponds to the incident type information, the type determination unit 150 adds the image information photographed through the camera 130 to the recognition pattern at the corresponding time point, 170, and the communication unit 170 may pack the recognition pattern, the incident type information, and the image information into the data center 200 and provide the packed data to the data center 200. The image information captured by the camera 130 may be later applied to an accident process and an insurance process and may also be used to determine whether an actual accident has occurred according to a recognition pattern determined as an accident type by the type determination unit 150 .

The communication unit 170 may include a long-distance wireless communication module such as CDMA, GSM, LTE, and LTE-A. The communication unit 170 may transmit the type of the incident, the recognition pattern information, the image information, and the GPS position information determined by the type determination unit 150 to the data center 200. At this time, the communication unit 170 may transmit the incident type, the recognition pattern information, the image information, and the GPS position information to the data center 200 using the Wi-Fi communication in the environment where the Wi-Fi network is established. In this case, the communication unit 170 needs to have a function of searching for a Wi-Fi network, and a function of determining whether sufficient signal strength is maintained when searching for Wi-Fi is required. And can provide the data center 200 with the type of accident, recognition pattern information, image information, and GPS location information using the Wi-Fi network under satisfactory conditions. Here, the communication speed means a speed at which a vehicle can form a good communication channel when the vehicle uses the Wi-Fi network, and the speed of the vehicle can be referred to as 40 Km or less per hour. Of course, if the Wi-Fi network can provide a good communication channel for an object moving at a speed of 40 Km or more at a later time, the communication speed can be set to be higher than the indicated value.

The database 160 may store position information measured at the GPS position sensor 110, three axis (X, Y and Z) measured values measured at the acceleration sensor 120, and image information captured at the camera 130 . In addition, the database 160 may include an electronic map and a reference pattern. An electronic map may refer to an electronic map to which GPS location information is to be mapped. The electronic map may include information about intersections, bridges, crosswalks, various road structures and roads, and directions of vehicle movement on the roads. have.

The reference pattern may correspond to a pattern when an accident occurs among measurement values measured for each axis (X axis, Y axis, and Z axis) in the acceleration sensor 120. [ The reference pattern can be provided for each speed of the vehicle, which is followed by the vehicle being able to cause the same accident at various speeds.

Figure 4 shows a reference diagram according to an example of a recognition pattern associated with an accident.

Referring to FIG. 4, in the section T31, the vehicle has no rapid acceleration or deceleration, the traveling direction of the vehicle is straight, the left and right, there is no rotation, and the Z axis is stable, indicating that the vehicle is normally running straight. However, in the section T32, the vehicle is rapidly accelerated, and the vehicle rapidly decelerates in the section T33.

When the vehicle is rapidly decelerating, the Z-axis measurement results indicate that the vehicle is lifted upwards, the vehicle stops at T34, and the vehicle accelerates at T35. At this time, the Y-axis measurement value is larger than "0" Value, that is, left turn.

The behavior pattern of the driver is as follows.

- next -

1) The driver of the vehicle went straight in the T32 section,

2) There was a collision with a person in the T33 section, resulting in lifting of the vehicle,

3) In the T34 section,

4) In the T35 section, it can be interpreted that the driver left the accident site while leaving the accident site.

This is a type of hit-and-run accident, and the behavior pattern of the T33 to T34 section can be seen as a typical behavior of a hit-and-run accident.

If the behavior type of the section from T31 to T35 is an accident occurring at a specific location, the accident type information may be extended to T31 T35. Depending on the road structure and traffic conditions, certain types of accident type information can be found in certain areas. For example, in a road structure that makes a left turn after a straight line, when another road joins a left-turn area, it may happen that the vehicle suddenly accelerates to make a left turn ahead of a vehicle joined on another road. In the road structure as shown in Fig. 6, the vehicle 5 may attempt to drive the left-turn road ahead of other vehicles entering from the confluent road, and depending on the road structure and traffic conditions shown in Fig. 6, T31 to T35 The behavior pattern of the driver of the vehicle according to the present invention can correspond to the accident type information. Therefore, depending on the present position of the vehicle, the accident type information according to the embodiment may be applied only to T33 - T34, or to T31 - T35. Accordingly, the accident type information needs to be determined in conjunction with the GPS position information, and the database 160 may store the accident type information classified according to the area.

In the foregoing, various algorithms for determining the type of an accident by the portable data collection device and the portable data collection device according to the embodiments have been described. Hereinafter, with reference to FIG. 5, the recognition pattern setting unit 140 will explain the deviation correction using the GPS position information and the algorithm for specifying the accident point. The description of FIG. 5 will be made with reference to FIGS. 1 to 4 together.

5 shows a reference diagram of a method in which the type determination unit 150 uses GPS position information when a vehicle passes a shadow area such as a tunnel.

5, when the vehicle 5 passes through a road structure such as the tunnel 10, the GPS sensor 110 may not be able to obtain GPS position information for the vehicle 5. When the vehicle 5 passes through the shaded area, the recognition pattern setting unit 140 deduces the position information of the vehicle 5 based on the GPS position information finally acquired for the vehicle 5 on the electronic map. However, the type determination unit 150 according to the embodiment re-maps the position of the vehicle 5 to a road structure corresponding to a shadow area where the GPS position information of the vehicle is not obtained, for example, mapping unit 140 notifies the recognition pattern setting unit 140 that the recognition pattern setting unit 140 uses the re-mapped GPS position information.

On the other hand, when the amount of the impact to the vehicle 5 is detected while GPS position information is not measured, the type determining unit 150 determines that the position where the vehicle 5 caused the accident is the same road structure as the tunnel 10 . However, if the GPS position information measured after the vehicle 5 passes through the tunnel is in a position not related to the tunnel 10, the re-mapped GPS position information is released and the GPS sensor 110 has caused an error It can be judged.

110: GPS sensor 120: Acceleration sensor
130: camera 140: recognition pattern setting unit
150: type determination unit 160: database
170; Communication unit 200: Data center

Claims (7)

A camera, a GPS sensor, and an acceleration sensor,
Setting a recognition pattern for accelerating, decelerating, leftward turning, right turning, vehicle lifting and vehicle deflection of the vehicle with reference to the three-axis change amount of the acceleration sensor, mapping the recognition pattern on the electronic map with reference to GPS information A recognition pattern setting unit;
A type determination unit that determines an accident type for the vehicle based on a pattern similarity between the recognition pattern and the reference pattern and generates an accident type information by using an output pattern of an acceleration sensor at the time of an accident as a reference pattern; And
And a communication unit for combining the captured image of the camera with the incident type information and providing the captured image to a data center connected to a wireless network,
The type determiner,
Characterized in that it is determined as a hit-and-run accident for a vehicle or a person when the impact amount is detected after a rapid deceleration of the vehicle determined by the acceleration sensor, . The method according to claim 1,
The type determiner,
Wherein the position value of the GPS sensor is mapped to a position where the position value of the GPS sensor can be measured on the electronic map,
Maps the position value of the vehicle to a road structure in which a GPS position value is not detected when the GPS sensor is unable to measure the position value in the shadow area. 3. The method of claim 2,
The type determiner,
And when the amount of impact on the vehicle is detected by the acceleration sensor in the shaded area, it is determined that the accident location of the vehicle is a tunnel, and the GPS position value is re-mapped. delete The method according to claim 1,
The type determiner,
Wherein the GPS position information is corrected by associating the position of the vehicle with the position of the overspeed preventing structure when the recognition pattern for the lifting and deflating of the vehicle matches with the overspeed preventing structure on the electronic map, Collecting device. The method according to claim 1,
The type determiner,
A point where a rapid acceleration or deceleration of the vehicle occurs through the acceleration sensor is mapped to the electronic map,
And provides the data to the data center through the communication unit by matching the image information about the point where the rapid acceleration or the rapid deceleration occurs. The method according to claim 1,
The type determiner,
Wherein the control unit determines that the vehicle is a behavior pattern perceived by a person on the road when the vehicle travels after a rapid deceleration through the measured value by the acceleration sensor and an amount of impact is not detected, And provides the image information of the generated point to the data center.
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CN113928327A (en) * 2021-09-29 2022-01-14 深圳市麦谷科技有限公司 Three-emergency event detection method and system
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CN110223503A (en) * 2019-06-10 2019-09-10 兰州交通大学 A kind of expressway traffic accident early warning and autonomous method for seeking help
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KR20220092723A (en) * 2020-12-24 2022-07-04 주식회사 유라코퍼레이션 Method and apparatus for recording driving video
KR102547348B1 (en) 2020-12-24 2023-06-26 주식회사 유라코퍼레이션 Method and apparatus for recording driving video
CN113928327A (en) * 2021-09-29 2022-01-14 深圳市麦谷科技有限公司 Three-emergency event detection method and system
CN113928327B (en) * 2021-09-29 2024-04-05 深圳市麦谷科技有限公司 Method and system for detecting three emergency events

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