KR20210073880A - Detection of Rapid Vehicle Sharp Turn Using Gyroscope of Smartphone - Google Patents

Detection of Rapid Vehicle Sharp Turn Using Gyroscope of Smartphone Download PDF

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KR20210073880A
KR20210073880A KR1020190164626A KR20190164626A KR20210073880A KR 20210073880 A KR20210073880 A KR 20210073880A KR 1020190164626 A KR1020190164626 A KR 1020190164626A KR 20190164626 A KR20190164626 A KR 20190164626A KR 20210073880 A KR20210073880 A KR 20210073880A
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sudden
traffic accidents
dangerous driving
detect
driving
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이성현
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이성현
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    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04M—TELEPHONIC COMMUNICATION
    • H04M1/00—Substation equipment, e.g. for use by subscribers
    • H04M1/72—Mobile telephones; Cordless telephones, i.e. devices for establishing wireless links to base stations without route selection
    • H04M1/724—User interfaces specially adapted for cordless or mobile telephones
    • H04M1/72448—User interfaces specially adapted for cordless or mobile telephones with means for adapting the functionality of the device according to specific conditions
    • H04M1/72454—User interfaces specially adapted for cordless or mobile telephones with means for adapting the functionality of the device according to specific conditions according to context-related or environment-related conditions
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01C—MEASURING DISTANCES, LEVELS OR BEARINGS; SURVEYING; NAVIGATION; GYROSCOPIC INSTRUMENTS; PHOTOGRAMMETRY OR VIDEOGRAMMETRY
    • G01C19/00—Gyroscopes; Turn-sensitive devices using vibrating masses; Turn-sensitive devices without moving masses; Measuring angular rate using gyroscopic effects
    • G01C19/02—Rotary gyroscopes
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04M—TELEPHONIC COMMUNICATION
    • H04M2250/00—Details of telephonic subscriber devices
    • H04M2250/12—Details of telephonic subscriber devices including a sensor for measuring a physical value, e.g. temperature or motion

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  • Environmental & Geological Engineering (AREA)
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Abstract

(1) The driver evaluation of T-MAP is helpful for dangerous driving behavior improvement but inaccurate because it is incapable of containing data on sudden left, right, and U turns. (2) In South Korea, the social cost attributable to traffic accidents amounts to 40 trillion won a year. Such accidents are highly correlated with speeding, long speeding, sudden acceleration, sudden start, sudden stop, sudden course change, sudden overtaking, and sudden U turn, which constitute 11 major dangerous driving behaviors. In order to reduce traffic accidents, it is essential to detect and correct aggressive driving behaviors based on drivers' driving records. (3) An object of the present invention is to detect the rotation angle of a vehicle using the Z axis of an intra-smartphone gyroscope sensor and detect a traffic accident-related dangerous driving behavior by adding speed information obtained by navigation system or GPS. (4) The average correlation coefficient between dangerous driving behaviors and traffic accidents is as high as 0.6. In order to reduce traffic accidents, aggressive driving behaviors are detected and corrected using drivers' driving records.

Description

스마트폰 내부 자이로스코프를 이용한 차량 급회전 탐지{Detection of Rapid Vehicle Sharp Turn Using Gyroscope of Smartphone}Detection of Rapid Vehicle Sharp Turn Using Gyroscope of Smartphone

IT기술IT technology

-MAP의 운전자 평가는 위험운전행동 개선에 도움이 되지만 급 좌회전, 급 우회전, 급 U턴에 대한 데이터를 담지못해 정확하다고 볼 수 없다. -The driver evaluation of MAP helps to improve dangerous driving behavior, but it cannot be considered accurate because it does not contain data on sudden left turns, sudden right turns, and sudden U-turns.

우리나라의 교통사고로 인한 사회적 비용은 연간 40조원에 달한다. 이러한 교통사고는 11대 위험운전행동인 과속, 장시과속, 급가속, 급출발, 급정지, 급 진로변경, 급 앞지르기, 급U턴과 높은 상관관계를 가지고 있다. 이에 교통사고를 줄이기 위해서는 운전자의 운행 기록을 통한 공격적 운전 행동을 탐지하고 교정함은 교통사고를 줄이기 위해 필수적이다. The social cost of traffic accidents in Korea amounts to 40 trillion won per year. These traffic accidents have a high correlation with the 11 most dangerous driving behaviors: speeding, long speeding, sudden acceleration, sudden start, sudden stop, sudden change of course, sudden overtaking, and sudden U-turn. Therefore, in order to reduce traffic accidents, aggressive driving through the driver's driving record Detecting and correcting behavior is essential to reduce traffic accidents.

현재는 이미 고성능의 스마트폰이 보급화 되었고, 어플리케이션은 가장 쉽게 사용가능한 도구이다. 때문에 본 논문에서는 값 비싼 카메라와 장비를 사용하지 않고 저렴하고 접근이 쉬운 스마트폰을 사용한다. 스마트폰 내부에는 다양한 센서들이 내장되어 있고, 이중 우리는 자이로스코프센서 Z축을 사용해 차량의 회전각을 탐지하고, 네비게이션, 혹은 GPS에서 얻어지는 속도정보를 더하여 교통사고에 관련된 위험운전행동 탐지를 목적으로 한다. Currently, high-performance smartphones have already been popularized, and applications are the most easily available tools. Therefore, in this paper, an inexpensive and easy-to-access smartphone is used without using expensive cameras and equipment. Various sensors are built in the smartphone, and among them, we use the gyroscope sensor Z-axis to detect the rotation angle of the vehicle, and add speed information obtained from navigation or GPS to detect dangerous driving behavior related to traffic accidents. .

기존의 방식보다 사용하는 센서의 개수를 줄임으로써 CPU와 메모리 사용량을 줄여 더 효율적인 활용이 가능하게 한다. By reducing the number of sensors used compared to the conventional method, CPU and memory usage are reduced, enabling more efficient utilization.

위험운전행동과 교통사고간의 상관계수는 평균 0.6으로 매우 높은 수치를 보여 준다. 이에 교통사고를 줄이기 위는 운전자의 운행 기록을 통한 공격적 운전 행동을 탐지하고 교정한다. 또한 기존 운전자 평가에서는 과속, 급가속, 급감속만 측정이 가능했으나 급회전 탐지를 가능하게한다. The correlation coefficient between dangerous driving behavior and traffic accidents is 0.6 on average, which is very high. In order to reduce traffic accidents, we detect and correct aggressive driving behavior through the driver's driving record. In addition, in the existing driver evaluation, only overspeed, rapid acceleration, and rapid deceleration could be measured, but rapid rotation detection is possible.

Figure pat00001

<그림 1> 스마트폰 자이로스코프 센서축
x축은 차량 진행방향과 수직, y축은 차량의 진행방향, z축은 지면과 수직방향이다.
Figure pat00002

<그림 2> 시스템 설계도
최초 받아온 자이로스코프 z축 데이터의 잡음을 제거한뒤 우측, 좌측의 방향을 판단한다.
이후 판단한 방향을 통해 회전각을 구하며, 위험운전행동과 일반운전을 구분한다.
Figure pat00001

<Figure 1> Smartphone gyroscope sensor axis
The x-axis is perpendicular to the vehicle traveling direction, the y-axis is the vehicle traveling direction, and the z-axis is perpendicular to the ground.
Figure pat00002

<Figure 2> System blueprint
After removing the noise from the first received gyroscope z-axis data, the right and left directions are determined.
Afterwards, the angle of rotation is obtained through the determined direction, and dangerous driving behavior and normal driving are distinguished.

z축만을 사용해 차량의 회전 각도를 찾는다. 아래 <그림 3>은 스마트폰의 샘플링 주기를 0.06ms로 정한 뒤 좌우회전, U턴, 급회전의 값을 받아온 그래프이다. 이를 통해

Figure pat00003
값의 변화를 볼 수 있으며, 시간은 샘플링 주기를 통해 알 수 있다.Find the angle of rotation of the vehicle using only the z-axis. <Figure 3> below is a graph that receives the values of left-right, U-turn, and sharp turn after setting the sampling period of the smartphone to 0.06ms. because of this
Figure pat00003
The change in value can be seen, and the time can be known through the sampling period.

Figure pat00004
Figure pat00004

<그림 3> 자이로스코프 z축의 데이터<Figure 3> Gyroscope z-axis data

차량의 회전각은 아래 식를 통해 결정된다.

Figure pat00005
는 자이로스코프 z값 평균,
Figure pat00006
는 파형의길이이다. 이렇게 얻어진
Figure pat00007
를 통해 차량의 조향을 판단하고, 이후 속도를 더해 위험 운전행동을 판단한다.The rotation angle of the vehicle is determined by the following equation.
Figure pat00005
is the gyroscope z-value average,
Figure pat00006
is the length of the waveform. thus obtained
Figure pat00007
It judges the steering of the vehicle through , and then adds the speed to determine dangerous driving behavior.

Figure pat00008
Figure pat00008

식을 통해 계산한

Figure pat00009
를 통해 우리는 좌회전, 우회전, U턴과 같은 차량의 조향을 판단할 수 있었다. 조향을 판단한 이후
Figure pat00010
와 함께 차량의 속도 통해 한국교통안전공단에서 제공하는 11대 위험운전행동 기준에 따라 판단한다. 아래 <표 1>는 11대 위험운전행동 중 급 차로변경, 급좌회전, 급우회전, 급U턴의 기준을 보여준다.calculated by the formula
Figure pat00009
Through this, we were able to judge the steering of the vehicle such as left turn, right turn, and U-turn. After judging the steering
Figure pat00010
It is judged according to the 11 dangerous driving behavior standards provided by the Korea Transportation Safety Authority through the speed of the vehicle. <Table 1> below shows the criteria for sudden lane change, sudden left turn, sharp right turn, and sudden U-turn among the 11 dangerous driving behaviors.

Figure pat00011
Figure pat00011

<표 1> 11대 위험운전행동 기준<Table 1> 11 Criteria for Dangerous Driving Behavior

위 표를 기준으로

Figure pat00012
와
Figure pat00013
를 이용하여 위험 운전행동을 판단할 수 있다. based on the table above
Figure pat00012
Wow
Figure pat00013
can be used to determine dangerous driving behavior.

교통사고와 11대 위험운전행동 간의 상관계수는 0.6으 로 위험운전행동 탐지를 통한 교정은 매우 필수적으로 보인다. 기존의 탐지기법은 과속, 급가속, 급감속의 측정만이 가능했으나, 급회전의 데이터를 추가하여 보다 정확한 평가가 가능해진다. 또한 IoT의 발전과 더불어 자율주행의 관심도가 높아짐과 동시에 안정성 검사에 관한 문제 역시 필수적으로 보인다. 자율주행의 안정성을 검사하고 모니터링을 위한 방법으로 자이로스코프를 통한 운전행동 탐지는 적합하다고 보인다. The correlation coefficient between traffic accidents and the 11 most dangerous driving behaviors is 0.6, so correction through the detection of dangerous driving behaviors is very essential. Existing detection methods were only capable of measuring overspeed, rapid acceleration, and rapid deceleration, but more accurate evaluation is possible by adding data on rapid rotation. In addition, with the development of IoT, interest in autonomous driving increases, and at the same time, the problem of stability inspection seems to be essential. Inspecting the safety of autonomous driving As a monitoring method, the detection of driving behavior through a gyroscope seems appropriate.

Claims (2)

운행 데이터로 받아지는 자이로스코프의 Z축 데이터 잡음을 필터링하는 기술과,

Z축 데이터 만을 가지고 방위각을 판단하는 기술과,

방위각과 시간을 이용하여 좌우회전과 U턴을 탐지하는 기술.A technology for filtering the noise of the Z-axis data of the gyroscope received as driving data,

A technology for judging the azimuth using only the Z-axis data,

A technology that detects left and right turns and U-turns using azimuth and time. 청구항 1에 있어서,

방위각과 시간을 이용하여 위험운전행동을 구분하는 기술.The method according to claim 1,

Technology to classify dangerous driving behaviors using azimuth and time.
KR1020190164626A 2019-12-11 2019-12-11 Detection of Rapid Vehicle Sharp Turn Using Gyroscope of Smartphone KR20210073880A (en)

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Cited By (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
EP4101357A1 (en) 2021-06-08 2022-12-14 LG Electronics Inc. Blender

Cited By (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
EP4101357A1 (en) 2021-06-08 2022-12-14 LG Electronics Inc. Blender

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