• 자동차안전학회지
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• 제10권2호
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• pp.20-28
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• 2018

Currently, sharp increase of car is on the rise as a serious social problem due to loss of lives from car accident and environmental pollution. There is a study on ITS (Intelligent Transportation System) to seek coping measures. As for the commercialization of ITS, we aim for occupancy of world market through ASV (Advanced Safety Vehicle) related system development and international standardization. However, the domestic environment is very insufficient. Core factor technologies of ITS are Adaptive Cruise Control, Lane Keeping Assist System, Forward Collision Warning System, AEB (Autonomous Emergency Braking) system etc. These technologies are applied to cars to support driving of a driver. AEB system is stop the car automatically based on the result decided by the relative speed and distance with obstacle detected through sensor attached on car rather than depending on the driver. The purpose of AEB system is to measure the distance and speed of car and to prevent accident. Thus, AEB will be a system useful for prevention of accident by decreasing car accident along with the development of automobile technology. This study suggests a scenario to suggest a test evaluation method that accords with domestic environment and active response of international standard regarding the test evaluation method of AEB. Also, by setting the goal with function for distance, it suggests theoretic model according to the result. And the study aims to verify the theoretic evaluation standard per proposed scenario using car which is installed with AEB device through field car driving test on test road. It will be useful to utilize the suggested scenario and theoretical model when conducting AEB test evaluation.

• 대한임베디드공학회논문지
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• 제7권4호
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• pp.163-172
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• 2012

A driver assistance system is critical to improve a convenience and stability of vehicle driving. Several systems have been already commercialized such as adaptive cruise control system and forward collision warning system. Efficient vehicle detection is very important to improve such driver assistance systems. Most existing vehicle detection systems are based on a radar system, which measures distance between a host and leading (or oncoming) vehicles under various weather conditions. However, it requires high deployment cost and complexity overload when there are many vehicles. A camera based vehicle detection technique is also good alternative method because of low cost and simple implementation. In general, night time vehicle detection is more complicated than day time vehicle detection, because it is much more difficult to distinguish the vehicle's features such as outline and color under the dim environment. This paper proposes a method to detect vehicles at night time using analysis of a captured color space with reduction of reflection and other light sources in images. Four colors spaces, namely RGB, YCbCr, normalized RGB and Ruta-RGB, are compared each other and evaluated. A suboptimal threshold value is determined by Otsu algorithm and applied to extract candidates of taillights of leading vehicles. Statistical features such as mean, variance, skewness, kurtosis, and entropy are extracted from the candidate regions and used as feature vector for SVM(Support Vector Machine) classifier. According to our simulation results, the proposed statistical feature based SVM provides relatively high performances of leading vehicle detection with various distances in variable nighttime environments.

• 한국통신학회논문지
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• 제42권5호
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• pp.1069-1076
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• 2017

C-ITS(Cooperative-Intelligent Transportation System)는 전방 충돌 경고, 도로 안전 서비스 및 비상 정지와 같은 엄격한 실시간성이 요구되는 서비스들을 제공한다. C-ITS의 핵심 기술인 WAVE(Wireless Access in Vehicular Environments)는 고속 이동 환경을 위해 고안된 기술이지만, 실제 도로 환경에 적용하여 안정적인 통신 서비스를 제공하기 위해서는 실차 환경의 다양한 성능 시험이 요구된다. 실제 도로 환경에서 WAVE 통신 성능은 이동 중인 차량, 도로의 형상 및 지형 등의 주변 환경에 의해 영향을 받으며 특히, 차량이 고속으로 이동하는 경우 차량의 속도에 따라 주행하는 위치와 노변장치와의 접근성 등 주변 환경이 빠르게 변화한다. 이 변화는 통신 성능에 영향을 주는 요소이므로 이를 분석하기 위한 시스템과 방법이 필요하다. 본 논문에서는 고속 주행 환경에서 효과적인 성능 시험 시스템 구성과 시험 방법을 제안하고, 실차 시험을 통해 측정한 데이터를 기반으로 통신 성능을 분석한 결과를 제시한다.