• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2017.05a
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• pp.255-257
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• 2017

These days automotive markets are launching models that include a variety of IT technologies. Tesla's Tesla model S and Google's unmanned automobiles are emerging one after another. This type of automobile with IT technology provides various convenience to the driver and the driver is getting benefit by various conveience services. on the contrary, it is also true that defects for errors in electronic components cause accidents that threaten the safety of drivers. There is a sudden unintended acceleration among these accidents. The cause of the accident is not clear yet, but the claim that the ECU device caused by the magnetic field causes accident of the car due is the most reliable. But, in Korea, when occur a car sudden unintended acceleration accident, the char maker often claims that an accident occurred due to driver's pedal malfunction. Also most drivers are responsible for the lack of grounds to refute. In this paper, the pedal operation image of the driver is acquired and the sensor is attached to the control part such as the excel and brake so as to discriminate whether the vehicle sudden unintended acceleration accident is the driver's pedal operation error or the fault of. i have implemented a system that can do this.

• Asia-pacific Journal of Multimedia Services Convergent with Art, Humanities, and Sociology
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• v.6 no.12
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• pp.469-481
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• 2016

Freight car accidents occur frequently and have a high mortality. In this reason, freight can insurance fee has been raised drastically. But speeding and overloading of trucks are still not decreased. We need to consider a measure about voluntary safe driving of truck drivers. We select UBI(Usage-Based insurance) as a measure for safe driving of truck drivers. UBI is a car insurance system and insurance fee is flexible. If vehicle drivers drive safely, insurance fee is discounted. The other way, if vehicle drivers drive dangerously, insurance fee is increased. In now, very high insurance fee for truck drivers, UBI is a effective way for leading a truck driver to safe driving. The most important thing in UBI is to evaluate truck driver rationally and accurately. In this paper, we select the reasons of truck accidents and develop driving evaluation model from multiple regression analysis and correlation analysis with accident reasons and truck accidents.

• Proceedings of the Korea Information Processing Society Conference
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• 2010.04a
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• pp.1115-1117
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• 2010

안전운전 지원 시스템 개발을 위해서 고려되어야 할 요소는 차량정보, 운전자정보, 외부 환경정보가 있다. 기존의 안전운전 지원 시스템 개발은 주로 차량의 종방향 제어, 횡방향 제어, 조향각 제어 등 차량으로부터 직접 추출한 주행정보를 이용하여 운전자의 안전유무를 평가하였다. 그러나 최근의 조사결과에 따르면 교통사고의 90%이상이 운전자 실수에 의해서 발생한다는 것을 알 수 있다. 이와 관련하여 차량의 주행 정보뿐만 아니라 실제 운전자가 주행 중에 행하게 되는 행동정보기반의 안전운전지원시스템 개발이 활발히 연구되어 지고 있다. 본 논문에서는 운전자의 행동정보를 이용한 안전운전 평가시스템의 설계 개념과 안전운전 평가시스템의 핵심 요소인 표준모델 구축 방법에 대해서 논의하고자 한다.

• Proceedings of the KOR-KST Conference
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• 2000.02a
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• pp.73-86
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• 2000

• Journal of the Korea Institute of Information Security & Cryptology
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• v.23 no.5
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• pp.919-929
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• 2013

Recently, a variety of IT technologies are applied to the vehicle. However, some vehicle-IT technologies without security considerations may cause security problems. Specially, some researches about a smart key system applied to automobiles for authentication show that the system is insecure from replay attacks and modification attacks using a wireless signal of the smart key. Thus, in this paper, we propose an authentication method for the driver by using driving patterns. Nowadays, we can obtain driving patterns using the In-vehicle network data. In our authentication model, we make driving ppatterns of car owner using standard normal distribution and apply these patterns to driver authentication. To validate our model, we perform an k-fold cross validation test using In-vehicle network data and obtain the result(true positive rate 0.7/false positive rate is 0.35). Considering to our result, it turns out that our model is more secure than existing 'what you have' authentication models such as the smart key if the authentication result is sent to the car owner through mobile networks.

• Proceedings of the Korea Information Processing Society Conference
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• 2022.11a
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• pp.851-853
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• 2022

고령화로 인해 65세 이상 운전자가 급증하며 고령운전자의 교통사고 비율이 증가함에 따라 시급한 사회 문제로 떠오르고 있다. 이에 본 연구에서는 객체 검출, 인식 모델을 결합하고 신호등을 인식하여 Text-To-Speech(TTS)로 알리는 쿠버네티스 기반의 프레임워크를 제안한다. 객체 검출 단계에서는 YOLOv5 모델들의 성능을 비교하여 활용하였으며 객체 인식 단계에서는 C-RNN 기반의 attention-OCR 모델을 활용하였다. 이는 신호등의 내부 LED 영역이 아닌 이미지 전체를 인식하는 방식으로 오탐지 요소를 낮춰 인식률을 높였다. 결과적으로 1,628장의 테스트 데이터에서 accuracy 0.997, F1-score 0.991의 성능 평가를 얻어 제안한 프레임워크의 타당성을 입증하였다. 본 연구는 후속 연구에서 특정 도메인에 딥러닝 모델을 한정하지 않고 다양한 분야의 모델을 접목할 수 있도록 하며 고령 운전자 및 신호 위반으로 인한 교통사고 문제를 예방할 수 있다.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 1995.10a
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• pp.123-129
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• 1995

본 논문은 차실내 음향 특성 개선 연구로 수행중이며 정상 상태 음향 응답 특성 시험을 통하여 차실내에서 운전자가 듣게되는 음장 특성을 알게 되었고, 라우드 스피커에 대한 음향 특성 시험으로 이러한 차실내 음장의 비평탄성은 라우드 스피커 자체의 문제가 아니라 차실 공간등이 요인에 의한 것이라는 것을 추론할 수 있었다. 배플 상태 및 차량에 장착된 상태하의 라우드 스피커에 대한 임피던스 시험 결과로 임피던스의 장착 부위의 후면 공간이 임피던스 특성 변화를 가져오게 하는 것을 알 수 있었다. 임피던스 시험 결과를 이용하여 라우드 스피커에 대한 모델 매개 변수를 규명할 수 있었고, 이를 이용한 라우드 스피커의 모델 개선이 가능하게 되었다. 또한, 시간 지연 분광법을 이용하여 차실내에서 음향 전파 과정 분석을 할 수 있었고, 이로 인해 운전자가 시간에 따라 다른 음향 응답 특성으로 듣게 되는 것을 알게 되어 향후 음질 개선 연구 수행에 이용될 수 있다. 이러한 방법으로 음이 진행함에 따라 부딪치는 반사면의 흡음 또는 반사 특성을 파악할 수도 있어서 흡음재의 선정 및 라우드 스피커의 장착 위치 및 각도를 선정하는데 이용될 수도 있다. 향후의 연구 방향은 어떠한 음향 패턴이 운전자가 좋은 음향이라고 느끼게 되는지를 규명하는 것, 즉 주관적 평가와 객관적 시험 데이터 사이의 연관성을 확립하는 것과 라우드 스피커 모델링 기법과 차실 공간의 음향 특성을 이용한 차실내 최적 음향 조건을 규명하는 것이 될 것이다.

• International Journal of Highway Engineering
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• v.11 no.4
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• pp.1-7
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• 2009

Traffic accident by aging drivers is expected to be on the rise rapidly as the number of aging drivers is rising along with the aging trend being progressed. In this study, traffic accident features depending on the classification of aging population and non aging one was evaluated. As a result of this evaluation, effect factors influencing over the aging population was found to be expressed differently from that of the non aging one. Odds ratio between the aging population and non aging one was evaluated through logit model and a model with potential accident probability of the aged drivers was developed. Accident risk of the aged drivers under the condition of curved road, cutting section and moistured road was revealed to be higher than that of the non aging population.

• The Journal of the Korea institute of electronic communication sciences
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• v.15 no.2
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• pp.349-356
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• 2020

This paper introduces the implementation of a driver's condition warning system using eye aspect ratio to prevent a car accident. The proposed driver's condition warning system using eye aspect ratio consists of a camera, that is required to detect eyes, the Raspberrypie that processes information on eyes from the camera, buzzer and vibrator, that are required to warn the driver. In order to detect and recognize driver's eyes, the histogram of oriented gradients and face landmark estimation based on deep-learning are used. Initially the system calculates the eye aspect ratio of the driver from 6 coordinates around the eye and then gets each eye aspect ratio values when the eyes are opened and closed. These two different eye aspect ratio values are used to calculate the threshold value that is necessary to determine the eye state. Because the threshold value is adaptively determined according to the driver's eye aspect ratio, the system can use the optimal threshold value to determine the driver's condition. In addition, the system synthesizes an input image from the gray-scaled and LAB model images to operate in low lighting conditions.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.26 no.6
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• pp.859-864
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• 2022

Drowsy driving, one of the biggest causes of traffic accidents every year, is accompanied by various factors. As a general method to check whether or not there is drowsiness, a method of identifying a driver's expression and driving pattern, and a method of analyzing bio-signals are being studied. This paper proposes a driver fatigue detection system using deep learning technology and bio-signal measurement technology. As the first step in the proposed method, deep learning is used to detect the driver's eye shape, yawning presence, and body movement to detect drowsiness. In the second stage, it was designed to increase the accuracy of the system by identifying the driver's fatigue state using the pulse wave signal and body temperature. As a result of the experiment, it was possible to reliably determine the driver's drowsiness and fatigue in real-time images.