• 자동차안전학회지
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• 제5권1호
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• pp.69-74
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• 2013

LKAS(Lane Keeping Assistance System) main function is to support the driver in keeping the vehicle within the current lane. Therefore, this system is able to reduce the driver workload with assisting the driver during driving. In this paper, we presented on study for test procedures and evaluation methods of the LKAS. The vehicle test conducted on straight road, left curve, right curve and four different types of lane under various vehicle speeds. This study proposed the LKAS system test procedures and methods that we are able to identify LKAS driving mechanism and performance.

• 한국컴퓨터정보학회논문지
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• 제22권8호
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• pp.33-39
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• 2017

In this paper, we propose a method which implements warning to drivers through real-time analysis of risky and unexpected driver and vehicle behavior using only a smartphone without using data from digital tachograph and vehicle internal sensors. We performed the evaluation of our system that demonstrates the effectiveness and usefulness of our method for risky and unexpected driver and vehicle behavior using three information such as vehicle speed, azimuth and GPS data which are acquired from a smartphone sensors. We confirmed the results and developed the smartphone application for validate and conducted simulation using actual driving data. This novel functionality of the smartphone application enhances drivers' situational awareness, increasing safety and effectiveness of driving.

• 로봇학회논문지
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• 제5권4호
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• pp.286-293
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• 2010

The trailer system offers efficiency of transportation capability. However, it is difficult to control the backward motion. It is an open loop unstable problem. To solve this problem, we are proposed the driver assistance system. Driver assistance system assists a driver to control the backward motion of trailer system as if forward motion. A driver only secure the rear view of last passive trailer, and select the control input to drive the last passive trailer. The driver assistance system converts the control input of the driver into velocity and steering angle of the vehicle using the inverse kinematics. It is possible by electronic control input devices and the rear view camera. Effectiveness of driving assistance system is verified by the simulation and the experiments.

• 제어로봇시스템학회논문지
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• 제13권12호
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• pp.1186-1191
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• 2007

Human driver monitoring system is one of the most important systems for the safety in driving vehicles, and therefore driver assistance system has gained much attention during the last decade. This paper proposed an intelligent driver assistance system which monitors human driver's states from bio-signals such as ECG and Blood Pressure. The proposed system used mamdani fuzzy inference to evaluate the driver's mental strain and generated warning signals to the driver. The approach using bio-signals in driver assistance system is the main issue of the related systems and the preliminary results showed the possibility of further research topics in developing more intelligent embedded systems with bio-signal feedback.

• 한국ITS학회 논문지
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• 제8권6호
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• pp.149-161
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• 2009

차량의 전자제어 시스템은 운전자의 안전을 확보하려는 법률적, 사회적 요구에 발맞추어 빠르게 발달하고 있으며, 하드웨어의 가격하락과 센서 및 프로세서의 고성능화에 따라 레이더, 카메라, 레이저와 같은 다양한 센서를 적용한 다양한 운전자 지원 시스템 (Driver Assistance System)이 실용화되고 있다. 이에 본 연구에서는 CCD 카메라로부터 취득되는 영상을 이용하여 실험차량의 주행 차선 및 주변에 위치하거나 접근하는 차량을 인식할 수 있는 프로그램을 개발하였으며, 선행 연구에서 개발된 위험운전 판단 알고리즘과 통합하여 위험운전에 대한 원인 및 결과를 분석 할 수 있는 Vision 시스템 기반 위험운전 분석 프로그램을 개발하였다. 본 연구에서 개발한 위험운전 분석 프로그램은 위험운전판단 알고리즘의 판단변수인 차량 거동 데이터와 차선 및 차량인식 프로그램에서 획득된 정보와 융합하여 위험운전 행위의 원인 및 결과를 효과적으로 분석할 수 있을 것으로 판단된다.

• 한국ITS학회 논문지
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• 제10권3호
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• pp.16-24
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• 2011

차량의 전자제어 시스템은 운전자의 안전을 확보하려는 법률적, 사회적 요구에 발맞추어 빠르게 발달하고 있으며, 하드웨어의 가격하락과 센서 및 프로세서의 고성능화에 따라 레이더, 카메라, 레이저와 같은 다양한 센서를 적용한 다양한 운전자 지원 시스템 (Driver Assistance System)이 실용화되고 있다. 이에 본 연구의 선행연구에서는 CCD 카메라로부터 취득되는 영상을 이용하여 실험차량의 주행 차선 및 주변에 위치 하거나 접근하는 차량을 인식하여 운전자의 위험운전에 대한 원인 및 결과를 분석 할 수 있는 Vision 시스템 기반 위험운전 분석 프로그램을 개발하였다. 그러나 선행 연구에서 개발된 Vision 시스템은 터널, 일출, 일몰과 같이 태양광이 충분치 않은 곳에서는 차선 및 차량의 인식율이 매우 떨어지는 것으로 나타났다. 이에 본 연구에서는 밝기 대응 알고리즘을 개발하여 Vision 시스템에 탑재함으로서 언제, 어느 곳에서라도 차선 및 차량에 대한 인식율을 향상시켜 운전자의 위험운전에 대한 원인을 명확하게 분석하고자 한다.

• 한국정보통신학회:학술대회논문집
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• 한국정보통신학회 2018년도 춘계학술대회
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• pp.671-674
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• 2018

본 연구에서는 모형 자동차를 이용한 YOLO 운전 보조 시스템을 구현 하였다. YOLO는 최근에 잇슈가 되고 있는 딥 러닝을 사용하는 물체 감지 및 인식 알고리즘입니다. 이 시스템은 카메라를 통해 획득한 영상에 영상처리 기술을 적용하여 차선 이탈을 경고하고, YOLO를 이용하여 객체를 인식하며 객체 유형 및 차량 사이의 거리에 따라 다양한 기능을 수행한다. 기존 물체 검출 및 인식 알고리즘 보다 우수한 YOLO는 추가 장비 없이 주행 보조 시스템 성능을 향상시킨다. YOLO를 이용한 주행 보조 시스템은 적은 비용으로 운전자의 안전성을 확보할 수 있을 것이다.

• 자동차안전학회지
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• 제15권3호
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• pp.59-65
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• 2023

As advanced driving assistance system (ADAS) and autonomous driving performance continue to improve, existing crash accidents and crash types are changing. Accordingly, the collision angle and the seating posture of the occupant are changed. It is necessary to study how the occupant injury mechanism changes according to these different crash types. In this regard, a representative crash test mode was derived when the automatic emergency braking system (AEB), one of the autonomous driving performance, was applied to the representative car-to-car crash scenario in Korea. The derived crash test mode was used to analyse the mechanisms of collision injuries according to both impact angle and the occupant seating posture (reclined seat-back angle). The results obtained through this study can be utilized as reference data for the development of new crash evaluation methods and improvements in crash restraint systems for enhancing crash safety.

• Journal of Positioning, Navigation, and Timing
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• 제11권4호
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• pp.389-395
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• 2022

In order to ensure reliability the high-level automated driving such as Advanced Driver Assistance System (ADAS) and universal robot taxi provided by autonomous driving systems, the operation with high integrity must be generated within the defined Operation Design Domain (ODD). For this, the position and posture accuracy requirements of autonomous driving systems based on the safety driving requirements for autonomous vehicles and domestic road geometry standard are necessarily demanded. This paper presents localization requirements for safe road driving of autonomous ground vehicles based on the requirements of the positioning system installed on autonomous vehicle systems, the domestic road geometry standard and the dimensions of the vehicle to be designed. Based on this, 4 Protection Levels (PLs) such as longitudinal, lateral, vertical PLs, and attitude PL are calculated. The calculated results reveal that the PLs are more strict to urban roads than highways. The defined requirements can be used as a basis for guaranteeing the minimum reliability of the designed autonomous driving system on roads.

• International Journal of Internet, Broadcasting and Communication
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• 제14권2호
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• pp.119-128
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• 2022

Recently, due to the development of related technologies for autonomous vehicles, driving work is changing more safely. However, the development of support technologies for level 5 full autonomous driving is still insufficient. That is, even in the case of an autonomous vehicle, the driver needs to drive through forward attention while driving. In this paper, we propose a method to monitor driving tasks by recognizing driver behavior. The proposed method uses pre-trained deep convolutional neural network models to recognize whether the driver's face or body has unnecessary movement. The use of pre-trained Deep Convolitional Neural Network (DCNN) models enables high accuracy in relatively short time, and has the advantage of overcoming limitations in collecting a small number of driver behavior learning data. The proposed method can be applied to an intelligent vehicle safety driving support system, such as driver drowsy driving detection and abnormal driving detection.