• Journal of the Korean Institute of Gas
• /
• v.15 no.1
• /
• pp.9-14
• /
• 2011

Heavy duty diesel engine has relatively small portion of whole vehicles due to long drive distance and large engine displacement, but largely influences atmosphere environment. City buses changed to CNG (Compressed Natural Gas) bus with Korea-Japan Worldcup. Heavy duty truck and intercity bus, however, were impossible to use CNG because those kinds of vehicles had long drive distance and CNG station was installed mainly at the around of the bus garage of city. Insulation container storing the natural gas as a liquid makes heavy duty truck and intercity bus possible to use the natural gas. Drive using diesel is possible where is hard to recharge the gas. With LNG (Liquefied Natural Gas), the dependence on oil is largely decreased, PM (Particulate Matter) and NOx which is chronic disadvantage of diesel is remarkably reduced and finally $CO_2$, the representative green house gas, is reduced over 10%.

• Transactions of the Korean hydrogen and new energy society
• /
• v.29 no.6
• /
• pp.661-667
• /
• 2018

Electric vehicles are taken a long time to charge and are restricted driving where charging infrastructure was not sufficiently constructed. The vehicle developed to solve these problems is a plug-in hybrid vehicle. It is possible to drive a certain distance by using electric motor and when the battery runs out, it operate the engine. Plug-in hybrid vehicle have a complicated structure and a lot of parts comparing a general vehicle because the electric parts and the internal combustion engine are installed together. Therefore, as the aging (mileage) of the plug-in hybrid vehicle, the influence which change of fuel consumption is expected to be larger than a general vehicle, but an experimental data are lacking. In this paper, we cumulate a mileage of the plug-in hybrid vehicle about 15,000 km and measured the fuel economy when the cumulated distance reached within 160 km, 6,500 km, 15,000 km respectively, by using domestic public test method. For measuring fuel economy of the vehicle, CD mode (driving distance on a single charge) which use only motor and the CS mode which operate motor and combustion engine were measured respectively. As a result, the fuel economy slightly increased at cumulated mileage of 6,500 km compared to the 160 km and the fuel economy of 15,000 km was similar to 160 km.

• 제어로봇시스템학회:학술대회논문집
• /
• 1996.10b
• /
• pp.1153-1156
• /
• 1996

본 논문은 새로운 지역 경로 계획 알고리즘으로 DPH(Distance Profile Histogram)방법과 뉴럴네트워크를 사용한 주행 방법을 제안한다. DPH방법은 격자형 환경 모델을 기반으로 장애물의 존재 유무와 거리정보와 같은 장애물의 기하학적 배치정보를 사용하게 된다. 또한 긴 장애물이나 막힘상황(Dead end)과 같이 지역 경로 계획만으로는 회피하기 어려운 상황에서는 뉴럴네트워크에 의해 학습된 정보에 의해 주행하는 방법을 사용했다.

• Proceedings of the Korean Association of Geographic Inforamtion Studies Conference
• /
• 2010.09a
• /
• pp.129-133
• /
• 2010

본 논문은 차량의 공회전시 불완전연소로 인한 일산화탄소의 배출량이 최대인 점에 착안하여 일반 네비게이션에서 제공하는 최단거리 모드로 주행했을 시와 '막힘없는 길 안내를 제공' 한다는 최신 공간정보기술인 실시간교통정보서비스를 적용했을 시의 공회전시간 및 일산화탄소 배출량을 비교 평가하여, 실시간교통정보서비스의 일산화탄소 저감효과를 추정해보기 위해 진행되었다. 대구시내의 교통정체구역인 수성구청에서 성서초등학교에 이르는 약 12km의 구간을 선정하여 2주간 동일구간을 요일별 시간대별로 주행함으로써 최단거리 안내서비스 주행시와 실시간교통정보서비스를 적용한 경로주행시의 공회전시간을 기록하고 환경부에서 제공하는 연료별 배출계수와 평균속도에 따른 일산화탄소 배출계수를 이용하여 일산화탄소 배출량을 산정하였으며, 측정결과 공회전시간이 약 28%, 일산화탄소 배출량은 약 57%의 감축효과를 보임을 확인하였다.

• Journal of the Institute of Electronics Engineers of Korea SP
• /
• v.41 no.6
• /
• pp.187-193
• /
• 2004

In this paper, we proposed the GPS position data correction method for autonomous land navigation using vanishing point property and a monocular vision system. Simulations are carried out over driving distances of approximately 60 km on the basis of realistic road data. On a straight road, the proposed method reduces GPS position error by at least 63% within 0.5 m. However, the average accuracy of the method is not presented, because it is difficult to estimate it on other than a straight road in variable conditions.

• The Journal of The Korea Institute of Intelligent Transport Systems
• /
• v.21 no.3
• /
• pp.83-95
• /
• 2022

This paper proposes a system that performs object detection and distance estimation for application to autonomous vehicles. Object detection is performed by a network that adjusts the split grid to the input image ratio using the characteristics of the recently actively used deep learning model YOLOv4, and is trained to a custom dataset. The distance to the detected object is estimated using a bounding box and homography. As a result of the experiment, the proposed method improved in overall detection performance and processing speed close to real-time. Compared to the existing YOLOv4, the total mAP of the proposed method increased by 4.03%. The accuracy of object recognition such as pedestrians, vehicles, construction sites, and PE drums, which frequently occur when driving to the city center, has been improved. The processing speed is approximately 55 FPS. The average of the distance estimation error was 5.25m in the X coordinate and 0.97m in the Y coordinate.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
• /
• 2010.10a
• /
• pp.493-496
• /
• 2010

In this paper, We introduces 3 types of methods for estimation of a moving vehicle's CO2 emissions. These estimation methods include method based on distance traveled, method according to the calculation method proposed by the IPCC and method using vehicle information & chemical reaction equations. we describe the operating principle of each estimation method and we have driven down the actual road about 5km path because we compare performance of 3 types of methods for estimation of a driving vehicle's CO2 emissions.

• Journal of Korean Tunnelling and Underground Space Association
• /
• v.20 no.1
• /
• pp.11-22
• /
• 2018

The safety of tunnels is quantified by quantitative risk assessment when planning the disaster prevention facilities of railway tunnels, and it is decided whether they are appropriate. The purpose of this study is to estimate the probability of the train stopping in the tunnels at train fire, which has a significant effect on the results of quantitative risk assessment for tunnel fires. For this purpose, a model was developed to calculate the coasting distance of the train considering the coefficient of train running resistance. The probability of stopping in case of train fire in the tunnel is predicted by the Monte Carlo simulation method with the coasting distance and the emergency braking distance as parameters of the tunnel lengths and slopes, train initial driving speeds. The kinetic equations for predicting the coasting distance were analyzed by reflecting the coefficient train running resistance of KTX II. In the case of KTX II trains, the coasting distance is reduced as the slope increases in a tunnel with an upward slope, but it is possible to continue driving without stopping in a slope downward. The probability of the train stopping in the case of train fire in tunnel decreases as the train speed increases and the slope of the tunnel decreases. If human error is not taken into account, the probability that a high-speed train traveling at a speed of 250 km/h or above will stop in a tunnel due to a fire is 0% when the slope of the tunnel is 0.5% or less, and the probability of stopping increases rapidly as the tunnel slope increases and the tunnel length increases.

• Transportation Technology and Policy
• /
• v.10 no.1
• /
• pp.27-33
• /
• 2013

• Transportation Technology and Policy
• /
• v.11 no.1
• /
• pp.24-28
• /
• 2014