• Proceedings of the Korea Information Processing Society Conference
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• 2005.11a
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• pp.75-78
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• 2005

텔레매틱스 서비스를 위한 많은 어플리케이션들이 개발 됨에 인해 테스트를 위한 도로 네트워크 기반의 이동객체 궤적데이터의 필요성이 증대되고 있다. 본 논문에서는 도로 네트워크 상의 이동객체들의 실 궤적 데이터와 유사한 합성 궤적 데이터를 구축하기 위한 방법론을 제안한다. 그리고 실제 구현 결과와 실 데이터와의 속도 패턴을 비교하여 실 데이터와의 유사성을 보인다.

• Journal of Auto-vehicle Safety Association
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• v.10 no.1
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• pp.38-44
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• 2018

This paper presents an analysis on driving safety in lane change situation based on road driving data. Autonomous driving is a global trend in vehicle industry. LKAS technologies are already applied in commercial vehicle and researches about lane change maneuver have been actively studied. In autonomous vehicle, not only safety control issue but also imitating human driving maneuver is important. Driving data analysis in lane change situation has been usually dealt with ego vehicle information such as longitudinal acceleration, yaw rate, and steering angle. For this reason, developing safety index according to surrounding vehicle information based on human driving data is needed. In this research, driving data is collected from perception module using LIDAR, radar and RT-GPS sensors. By analyzing human driving pattern in lane change maneuver, safety index that considers both ego vehicle and surrounding vehicle state by using relative velocity and longitudinal clearance has been designed.

• Journal of Korean Society of Steel Construction
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• v.28 no.6
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• pp.449-458
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• 2016

The temperature data were measured for two years in a bridge specimen and the bridge in service nearby in order to calculate the effective temperature for thermal loads in steel box girder bridge. The maximum and minimum effective temperatures were calculated in the bridge specimen and the bridge according to air temperature in 2014, 2015 and 2years. The effective temperatures calculated in this study were compared the Euro code and the Highway Bridge Design Criteria. The coefficients of determination in the maximum effective temperature and the Euro code for 2 year were calculated from R = 0.927, R = 0.894 in a bridge specimen and the bridge respectively. Those of minimum temperature and the Euro code were analyzed from R = 0.992, R = 0.813 in two bridge respectively. Also, the results were evaluated as being very similar, or slightly increased as compared with the maximum temperature of the Korean Highway Bridge Design Code(Limit State Design).

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.19 no.5
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• pp.162-177
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• 2020

The perception of traffic environment based on various sensors in autonomous driving system has a direct relationship with driving safety. Recently, as the perception model based on deep neural network is used due to the development of machine learning/in-depth neural network technology, a the perception model training and high quality of a training dataset are required. However, there are several realistic difficulties to collect data on all situations that may occur in self-driving. The performance of the perception model may be deteriorated due to the difference between the overseas and domestic traffic environments, and data on bad weather where the sensors can not operate normally can not guarantee the qualitative part. Therefore, it is necessary to build a virtual road environment in the simulator rather than the actual road to collect the traning data. In this paper, a training dataset collection process is suggested by diversifying the weather, illumination, sensor position, type and counts of vehicles in the simulator environment that simulates the domestic road situation according to the domestic situation. In order to achieve better performance, the authors changed the domain of image to be closer to due diligence and diversified. And the performance evaluation was conducted on the test data collected in the actual road environment, and the performance was similar to that of the model learned only by the actual environmental data.

• Proceedings of the Korea Information Processing Society Conference
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• 2020.05a
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• pp.497-499
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• 2020

자율주행 차량이 많아지고 차량의 ECU가 고도화되면서 정확한 차량의 데이터를 획득하고 분석하여 활용하는 것이 중요해지고 있다. 현재에는 내연 기관 차량의 ECU 데이터를 얻기 위해서 OBDII 포트(규격)에 기반한 CAN동선을 주로 이용하고 있다. 하지만 OBDII 규격을 통해서 연비와 같은 중요한 차량 정보를 얻는 경우, 변환식 (MAF 센서(흡입 공기량 센서)와 공기/연료 비율을 이용)의 오차 범위가 커서 데이터의 정확도가 낮다. 본 연구에서는 머신 러닝 기법 중에 하나인 회귀 기법을 통해서 기존의 계산보디 더 정확한 연비를 구할 수 있는 모델을 개발하였다. 이러한 모델 개발을 통하여 차량의 RAW 데이터를 기반으로 필요한 차량 데이터를 정확하게 구할 수 있게 되었으며 20회가 넘는 실 도로주행을 통해서 본 모델의 정확도를 검증하였다.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.23 no.4
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• pp.94-109
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• 2024

As autonomous vehicles are allowed to drive on public roads, there is an increasing amount of on-road data available for research. It has therefore become possible to analyze impacts of autonomous vehicles on traffic safety using real-world data. It is necessary to use indicators that are well-representative of the driving behavior of autonomous vehicles to understand the implications of them on traffic safety. This study aims to derive indicators that effectively reflect the driving stability of autonomous vehicles by analyzing the driving behavior using the Waymo Open Dataset. Principal component analysis was adopted to derive indicators with high explanatory capability for the dataset. Driving stability indicators were separated into longitudinal and lateral ones. The road segments on the dataset were divided into four based on the characteristics of each, which were signalized and unsignalized intersections, tangent road section, and curved road section. The longitudinal driving stability was 35.48% higher in the curved road sections compared to the unsignalized intersections. With regard to the lateral driving stability, the driving stability was 76.08% higher in the signalized intersections than in the unsignalized intersections. The comparison between curved and tangent road segments showed that tangent roads are 146.87% higher regarding lateral driving stability. The results of this study are valuable for the further research to analyze the impact of autonomous vehicles on traffic safety using real-world data.

• Journal of the Korean Data and Information Science Society
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• v.28 no.3
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• pp.505-513
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• 2017

Recently, numerous small area estimation studies have been conducted to obtain more detailed and accurate estimation results. Most of these studies have employed spatial regression models, which require a clear definition of spatial neighborhoods. In this study, we introduce the Delaunay triangulation as a method to define spatial neighborhood, and compare this method with the k-nearest neighbor method. A simulation was conducted to determine which of the two methods is more efficient in defining spatial neighborhood, and we demonstrate the performance of the proposed method using a land price data.

• Journal of Auto-vehicle Safety Association
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• v.10 no.4
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• pp.40-49
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• 2018

Currently, a lot of researches about high risk test scenarios for autonomous vehicle and advanced driver assistance systems have been carried out to evaluate driving safety. This study proposes new type of test scenario that evaluate the driving safety for autonomous vehicle by reconstructing accident database of national automotive sampling system crashworthiness data system (NASS-CDS). NASS-CDS has a lot of detailed accident data in real fields, but there is no data of accurate velocity in accident moments. So in order to propose scenario generation method from accident database, we try to reconstruct accident moment from accident sketch diagram. At the same step, we propose an accident of occurrence frequency which is based on accident codes and road shapes. The reconstruction paths from accident database are integrated into evaluation of simulation environment. Our proposed methods and processor are applied to MILS (Model In the Loop Simulation) and VILS (Vehicle In the Loop Simulation) test environments. In this paper, a reasonable method of accident reconstruction typology for autonomous vehicle evaluation of feasibility is proposed.

• Proceedings of the Earthquake Engineering Society of Korea Conference
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• 2003.03a
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• pp.442-448
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• 2003

최근 장대교량의 건설이 증가하고 지진에 대한 관심이 고조되면서 교략의 내진설계와 면진설계가 교량설계의 주요항목으로 자리잡게 되었다. 특히 최근 ILM교량과 같은 다경간 장대교의 경우 지진시 교량의 상부구조와 하부구조를 격리시켜 주기를 변화시키는 LRB를 이용한 면진설계가 주로 이루어지고 있다. 그러나 아직까지 국내에는 Mt5의 거동과 기본성능에 대한 실험데이터가 절대적으로 부족하며, 따라서 그 특성을 엄밀히 검증하지 못한 채 고속도로 및 국도상의 교량에 LRB를 다수 사용하고 있는 현실이다. 본 연구에서는 실교량에 사용될 LRB에 대하여 기본성능 중의 하나인 피로마모시험을 수행함으로써 온도하중과 같은 반복하중에 대한 LRB의 피로거동을 모사하고 피로시험 전.후의 기본성능의 변화를 파악하였다. 이러한 LRB의 기본성능에 대한 실험온 LRB의 성능에 대한 검증과 함께 LRB의 거동에 대한 불확실성을 줄일 수 있는 기본 자료로 축적할 수 있으며, 현재 한국도로공사 도로교통기술원에서 마련중인 지진격리장치(LRB) 성능시험기준(안)의 정립에 기초 자료로참고하고자 한다.

• Transactions of the Korean Society of Automotive Engineers
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• v.23 no.6
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• pp.575-582
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• 2015

The Durability cycle is very important for the success of vehicle testing to evaluate Retarder. The purpose of this study is to develop the durability mode on performance evaluation of retarder. Commercial vehicles are equipped with an auxiliary braking device in order to increase safety. A typical device for retarder depends heavily on imports. Domestic development has now become an urgent task. But, No state has an evaluation method for performance evaluation of the auxiliary braking device. We presented the durability test mode for the performance evaluation of the retarder was verified experimentally.