• 자동차안전학회지
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• 제15권1호
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• pp.16-26
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• 2023

Recently, the importance of simulation in a virtual driving environment as well as real road-based tests for autonomous vehicle testing is increasing. Real road tests are being actively conducted at K-City, an autonomous driving test bed located at the Korea Automobile Safety Test & Research Institute of the Transportation Safety Authority. In addition, the need to advance the K-City virtual driving environment and build a virtual environment similar to the autonomous driving system test environment in real road tests is increasing. In this study, for K-City of Korea Automobile Safety Test & Research Institute, using detailed drawings and actual field data, K-City virtual driving environment was advanced, and similarity verification was verified through comparative analysis with actual K-City.

• 제어로봇시스템학회논문지
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• 제21권2호
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• pp.87-94
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• 2015

This paper proposes a situation awareness method based on data fusion and independent objects for autonomous driving in on-road environment. The proposed method, designed to achieve an accurate analysis of driving situations in on-road environment, executes preprocessing tasks that include coordinate transformations, data filtering, and data fusion and independent object based situation assessment to evaluate the collision risks of driving situations and calculate a desired velocity. The method was implemented in an open-source robot operating system called ROS and tested on a closed road with other vehicles. It performed successfully in several scenarios similar to a real road environment.

• 한국측량학회지
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• 제33권6호
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• pp.589-594
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• 2015

In reflection of road expansion and increasing use rates, interest has blossomed in predicting driving environment. In addition, a gigantic scale of big data is applied to almost every area around the world. Recently, technology development is being promoted in the area of road traffic particularly for traffic information service and analysis system in utilization of big data. This study examines actual cases of road management systems and road information analysis technologies, home and abroad. Based on the result, the limitations of existing technologies and road management systems are analyzed. In this study, a development direction and expected effort of the prediction of road information are presented. This study also examines regression analysis about relationship between guide name and traffic volume. According to the development of driving environment prediction platform, it will be possible to serve more reliable road information and also it will make safe and smart road infrastructures.

• 대한인간공학회지
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• 제29권1호
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• pp.17-24
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• 2010

There has been recent interest in intelligent vehicle technologies, such as advanced driver assistance systems (ADASs) or in-vehicle information systems (IVISs) that offer a significant enhancement of safety and convenience to drivers and passengers. However, unsuitable design of HMI (Human Machine Interface) must increase driver distraction and workload, which in turn increase the chance of traffic accidents. Distraction in particular often occurs under a heavy driving workload due to multitasking with various electronic devices like a cell phone or a navigation system while driving. According to the 2005 road traffic accidents in Korea report published by the ROad Traffic Authority (ROTA), more than 60% of the traffic accidents are related to driver error caused by distraction. This paper suggests the structure of vehicle environment for real-time driving behavior monitoring system while driving which is can be used the driver workload management systems (DWMS). On-road experiment results showed the feasibility of the suggested vehicle environment for driving behavior monitoring system.

• 한국멀티미디어학회논문지
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• 제16권4호
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• pp.430-443
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• 2013

운전자에게 차량의 현재 위치 및 도로 주변 상황 인지는 안전하고 쾌적한 운전 환경 조성을 위해 반드시 필요한 정보들이다. 본 논문에서는 도로시설물 및 도로표지 등의 도로정보를 모니터링 하고 시각적으로 알리기 위해 도로주행영상 내 좌표정보 결합 및 시점변환 기법을 이용한 도로주행환경 자동인식 기술을 제안한다. 제안한 방법은 차량 내 탑재된 카메라와 GPS를 이용하여 공간정보가 반영된 도로주행영상을 생성한 후, 생성 영상의 시점 변환 및 정합, 도로정보 검출을 수행하여 사용자에게 도로정보를 시각적으로 제공할 수 있도록 한다. 제안한 방법을 도로 주행 영상에서 실험한 결과, 도로주행영상 내 GPS 좌표정보의 결합 시간은 66.5ms, 교통 표지판 검출율은 95.83%, 프레임당 표지판 검출 처리 시간은 평균 227.45ms 이었다. 따라서 15프레임/초 이하의 입력 동영상에 대하여 효과적으로 도로주행환경을 자동으로 인식하는 것이 가능함을 확인하였다.

• 한국정밀공학회지
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• 제21권4호
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• pp.88-94
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• 2004

Recently, vehicle simulators are widely used to evaluate driver's responses and driver assistance systems. It needs much effort to construct the virtual driving environment for a vehicle simulator. In this study, it is described how to make effectively the roads and the driving environment for a vehicle simulator. The GIS (Geographic Information System) is used to construct the roads and the environment effectively. Because the GIS is the integrated system of geographical data, it contains useful data to make virtual driving environment. First, the outline and centerline of roads is abstracted from the GIS. From the road outline, the road width is calculated. Using the centerline, the grid model of roads is constructed. The final graphic model of roads is constructed by mapping road image to the grid model according to the number of lanes and the kind of surface. Data of buildings from the GIS are abstracted. Each shape and height of buildings is determined according to kind of buildings, the final graphic model of buildings is constructed. Then, the graphic model of roadside tree is also constructed. Finally, the driving environment for driving simulator is constructed by converting the three graphic models with the graphic format of Direct-X and by joining the three graphic models.

• 자동차안전학회지
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• 제14권4호
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• pp.60-69
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• 2022

Recently, the importance of simulation validation in a virtual environment for autonomous driving system validation is increasing. At the same time, interest in the advancement of the virtual driving environment is also increasing. To develop autonomous driving technology, a simulation environment similar to the real-world environment is needed. For this reason, not only the road model is configured in the virtual driving environment, but also the driving environment configuration that includes the surrounding environments -traffic, object, etc- is necessary. In this article, FMTC, which is a test bed for autonomous vehicles, is implemented in a virtual environment and advanced to form a virtual driving environment similar to that of real FMTC. In addition, the similarity of the virtual driving environment is verified through comparative analysis with the real FMTC.

• 한국대기환경학회지
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• 제31권6호
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• pp.562-572
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• 2015

$NO_x$ emissions from diesel vehicles have been regarded as a main cause of high $NO_2$ concentration in metropolitan area. Recent studies have shown that the on-road $NO_x$ emissions of diesel vehicles are quite higher than the emission limits specified with the pre-defined test method for emission certification. To reduce air pollutants effectively, the discrepancy of emissions in certification and real-driving conditions should be tackled. In this study, the real-driving emissions have been estimated with portable emission measurement system (PEMS). The results of this study have shown that the on-road $NO_x$ emissions from diesel vehicles have been decreased as the introduction of stricter emission regulation, EURO-6, but additional reduction should be still required and robust technologies should be applied to control $NO_x$ in real-driving conditions. RDE-LDV (Real Driving Emission - Light Duty Vehicles) test method being developed in the European Union can represent excessive on-road $NO_x$ emissions of diesel vehicles as applied emission technologies and can be a solution to remove discrepant $NO_x$ emissions between certification and Korean real-driving conditions. Among the $NO_x$ reduction technologies for EURO-6 diesel vehicles, selective catalytic reduction (SCR) system has shown the better performance than lean $NO_x$ trap (LNT) system to control on-road $NO_x$ emissions. Implementing RDE-LDV will require vehicle manufacturers to adopt the more effective $NO_x$ reduction technology in real driving conditions.

• International Journal of Automotive Technology
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• 제4권2호
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• pp.101-108
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• 2003

There are a lot of factors that influence automotive fuel economy such as average trip time per kilometer, average trip speed, the number of times of vehicle stationary, and so forth. These factors depend on road conditions and traffic environment. In this study, various driving data were measured and recorded during road tests in Seoul. The accumulated road test mileage is around 1,300 kilometers. The objective of the study is to identify the driving patterns of the Seoul metropolitan area and to analyze the fuel economy based on these driving patterns. The driving data which was acquired through road tests was analysed statistically in order to obtain the driving characteristics via modal analysis, speed analysis, and speed-acceleration analysis. Moreover, the driving data was analyzed by multivariate statistical techniques including correlation analysis, principal component analysis, and multiple linear regression analysis in order to obtain the relationships between influencing factors on fuel economy. The analyzed results show that the average speed is around 29.2 km/h, and the average fuel economy is 10.23 km/L. The vehicle speed of the Seoul metropolitan area is slower, and the stop-and-go operation is more frequent than FTP-75 test mode which is used for emission and fuel economy tests. The average trip time per kilometer is one of the most important factors in fuel consumption, and the increase of the average speed is desirable for reducing emissions and fuel consumption.

• 대한인간공학회지
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• 제21권3호
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• pp.25-34
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• 2002

The road sign in dynamic traffic system is an important element which affects on human cognitive performance on driving. Web-based vision system simulator was developed to examine the cognition time of the road sign in dynamic environment. This experiment the cognition time of the road sign in dynamic environment. This experiment was designed in with-subject design with two factors: vehicle speed and the amount of information of the traffic sign. It measured the cognition time of the road sign through two evaluation methods: the subjective test with vision system simulator and computational cognitive model. In these two evaluations of human cognitive performance under the dynamic traffic environment, it demonstrated that subject's cognition time was affected by both the amount of information of traffic sign and driving speed.