• 한국분무공학회지
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• 제22권2호
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• pp.47-54
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• 2017

$CO_2$ emission regulation will be prescribed and main issue in automotive industry. Mostly, vehicle's fuel efficiency deeply related to $CO_2$ emission is regulated by qualified driving test cycle by using chassis dynamometer and exhaust gas analyser. But, real driving fuel consumption rate depends so much on the individual usage profile and where it is being driven: city traffic, road conditions. In this study, vehicle model of fuel consumption rate for ICEV and PHEV was developed through co-simulation with CRUISE model and Simulink based on driving control model. The simulation results of fuel consumption rate were analysed with on-road vehicle data and compared with its official level.

• 한국자동차공학회논문집
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• 제18권4호
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• pp.115-120
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• 2010

The reduction rate of fuel consumption by showing the fuel injection data for driver was measured in this study. The fuel injection data are composed of injection period, real time fuel economy and average fuel economy. The fuel consumption was measured by processing the voltage signal of injector and driven distance by GPS sensor. The fuel consumption was reduced by driving more carefully, i.e driving more steady without sudden acceleration and deceleration watching these fuel injection data. The reduction rate was up to 37% and the rate increased as the driver is customed to this driving pattern.

• 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.

• 전력전자학회논문지
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• 제24권3호
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• pp.139-146
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• 2019

As the number of electric vehicles (EVs) in Jejudo Island increases, the secondary use of EV batteries is becoming increasingly mandatory not only in reducing greenhouse gas emissions but also in promoting resource conservation. For the secondary use of EV batteries, their capacity and performance at the end of automotive service should be evaluated properly. In this study, the battery state information from the on-board diagnostics or OBD2 port was acquired in real time while driving three distinct routes in Jejudo Island, and then the battery operating characteristics were assessed with the driving routes. The route with higher altitude led to higher current output, i.e., higher C-rate, which would reportedly deteriorate state of health (SOH) faster. In addition, the SOH obtained from the battery management system (BMS) of a 2017 Kia Soul EV with a mileage of 55,000 km was 100.2%, which was unexpectedly high. This finding was confirmed by the SOH estimation based on the ratio of the current integral to the change in state of charge. The SOH larger than 100% can be attributed to the rated capacity that was lower than the nominal capacity in EV application. Therefore, considering the driving environment and understanding the SOH estimation process will be beneficial and necessary in evaluating the capacity and performance of retired batteries for post-vehicle applications.

• 로봇학회논문지
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• 제14권2호
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• pp.122-130
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• 2019

Collecting a rich but meaningful training data plays a key role in machine learning and deep learning researches for a self-driving vehicle. This paper introduces a detailed overview of existing open-source simulators which could be used for training self-driving vehicles. After reviewing the simulators, we propose a new effective approach to make a synthetic autonomous vehicle simulation platform suitable for learning and training artificial intelligence algorithms. Specially, we develop a synthetic simulator with various realistic situations and weather conditions which make the autonomous shuttle to learn more realistic situations and handle some unexpected events. The virtual environment is the mimics of the activity of a genuine shuttle vehicle on a physical world. Instead of doing the whole experiment of training in the real physical world, scenarios in 3D virtual worlds are made to calculate the parameters and training the model. From the simulator, the user can obtain data for the various situation and utilize it for the training purpose. Flexible options are available to choose sensors, monitor the output and implement any autonomous driving algorithm. Finally, we verify the effectiveness of the developed simulator by implementing an end-to-end CNN algorithm for training a self-driving shuttle.

• 한국철도학회:학술대회논문집
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• 한국철도학회 2011년도 정기총회 및 추계학술대회 논문집
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• pp.1405-1411
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• 2011

It is very often for the experienced diesel locomotive drivers to identify the proper replacing time for the fuel adjustment tube only based on their experience. Because of that, sometimes the locomotive's fuel is burned out due to the unnecessary torque. Or sometimes, the locomotive does not operate with its accelerating performance because the fuel is not supplied at the appropriate moment. Meanwhile, recent typical auto vehicles provide drivers with the average fuel efficiency and the instant fuel efficiency in real-time. By providing the real time display mentioned above, it is one of the good examples that those drivers, who had driven their cars not properly and used a lot of fuel with their bad driving habits, obtain the efficient driving pattern by continuous educating effect. Similarly, if the diesel locomotive provides the train driver with the optimal driving pattern within a certain driving section, it will be effective for fuel saving. It is possible to make the most effective driving pattern by performing the repeated trial running especially for the railway because the track's operating routes, its grades, and etc are relatively precise. This research analyzes the result data which was obtained by many times trial running on the identical section after equipping the fuel use measuring device to a certain test vehicle, and confirms the fuel saving effect depending on the driving pattern along the test section. At the same time, the research to establish the optimal driving pattern was progressed.

• International Journal of Automotive Technology
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• 제7권2호
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• pp.227-235
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• 2006

In this paper a robust image processing method with modified Otsu algorithm to recognize the road lane for a real-time controlled autonomous vehicle is presented. The main objective of a proposed method is to drive an autonomous vehicle safely irrespective of road image qualities. For the steering of real-time controlled autonomous vehicle, a detection area is predefined by lane segment, with previously obtained frame data, and the edges are detected on the basis of a lane width. For stable as well as psudo-robust autonomous driving with "good", "shady" or even "bad" road profiles, the variable threshold with modified Otsu algorithm in the image histogram, is utilized to obtain a binary image from each frame. Also Hough transform is utilized to extract the lane segment. Whether the image is "good", "shady" or "bad", always robust and reliable edges are obtained from the algorithms applied in this paper in a real-time basis. For verifying the adaptability of the proposed algorithm, a miniature vehicle with a camera is constructed and tested with various road conditions. Also, various highway road images are analyzed with proposed algorithm to prove its usefulness.

• 한국군사과학기술학회지
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• 제22권5호
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• pp.687-696
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• 2019

To identify the driving dynamic characteristics of the Tractor-Trailer Type Transporter for mounting a large scale precision equipment, real vehicle driving tests on the 3 inch-bump-space-road were performed. And using general Dynamics Analysis Program - RecurDyn(V8R5), Dynamics M&S were carried out assuming the similar condition with real tests. Then the acceleration data obtained from real tests and M&S were analyzed and compared with each other in the part of root-mean-square-acceleration($g_{rms}$), peak-acceleration($g_{peak}$) and frequencies. In simple view of the $g_{rms}$ & $g_{peak}$, although the results of MRBD are more similar to ones of the real vehicle driving tests, but the results of RFlex have more information to get various useful dynamic characteristics.

• 한국ITS학회 논문지
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• 제7권3호
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• pp.15-28
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• 2008

교통사고의 여러 요인 중 대부분의 사고가 운전자의 특성과 운전행태가 교통사고에 가장 큰 영향을 미치고 있음을 2006년 경찰청 사고건수 자료를 통하여 파악 할 수 있다. 현재 교통사고 감소 및 안전운전에 대한 사회적 요구에 부응하기 위하여 디지털 주행기록계, 차량용 블랙박스 등이 출시되고 있으나 위험운전 유형에 대한 명확한 분류가 이루어지지 않아 그 효율성이 매우 떨어지고 있다. 이에 본 연구에서는 운전자로부터 발생할 수 있는 위험운전 유형을 발생 원인을 중심으로 7가지의 대분류와 이를 좀더 구체화한 16가지의 소분류로 재정의 하였다. 또한 재정의 된 위험운전 유형에 대한 분석을 위하여 차량거동상태에 따른 모든 차량데이터를 취득 분석할 수 있는 Data-logger를 개발하였다. 개발된 Data-logger는 시험차량으로부터 실시간으로 전송되는 가속, 감속, Yaw rate, 영상데이터 등을 이용하여 운전자로부터 발생 할 수 있는 위험운전 유형을 검출하여 실시간으로 위험운전에 대한 경보를 제공할 수 있는 시스템 및 향후 안전운전 관리 시스템을 구축할 수 있다.

• 한국자동차공학회논문집
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• 제24권4호
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• pp.454-462
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• 2016

This study was conducted in order to estimate the exhaust emissions analysis method of the real driving emission(RDE). The Association for Emissions Control by Catalyst(AECC) has developed a test procedure by using a random cycle method based on the chassis dynamometer. In order to confirm this approach in Korea, Euro 5(DPF), Euro 6(DPF + LNT), and Euro 6(DPF + SCR) were performed on three different vehicles to determine the exhaust gas characteristics of the random cycle, real-road driving test(PEMS), and emission certification driving mode(NEDC). Six different random cycle driving modes were generated by the vehicle specifications(e.g. curb weight, engine power, gear ratio, and maximum acceleration). The NOx emissions were increased in the NEDC, random cycle, and PEMS order in this study regardless of the test vehicles. The random cycle method has the advantage because it utilizes a chassis dynamometer in the laboratories for a repeatable data collection, and it allows any eminent emission improvement checked prior to a real-road driving test with PEMS.