• Journal of ILASS-Korea
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• v.22 no.2
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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.

• Transactions of the Korean Society of Automotive Engineers
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• v.18 no.4
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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.

• Journal of Institute of Control, Robotics and Systems
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• v.21 no.2
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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.

• The Transactions of the Korean Institute of Power Electronics
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• v.24 no.3
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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.

• The Journal of Korea Robotics Society
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• v.14 no.2
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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.

• Proceedings of the KSR Conference
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• 2011.10a
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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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• v.7 no.2
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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.

• Journal of the Korea Institute of Military Science and Technology
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• v.22 no.5
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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.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.7 no.3
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• pp.15-28
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• 2008

According to the accident statistics published by the National Police Agency in 2006, it can be recognized that drivers' characteristics and driving behaviors are the most causational factors on the traffic accidents. At present, although many recording tools such as digital speedometer or black box are distributed in the market to meet social requests of decreasing traffic accidents and increasing safe driving behaviors, it is also true that it still lacks in obvious categories for dangerous driving types and then, the efficiency of the categories to be studied has been low. In this study, dangerous driving types are redefined. They are grouped into 7 classifications in the first level, and the seven classifications are regrouped into 16 in more detail. To verify the redefined dangerous driving types, a Data-logger is developed to receive and analyze the data that occur from the driving behaviors of the test vehicle. The developed Data-logger can be used to construct a real time warning system and safe driving management system with dangerous driving patterns based on acceleration, deceleration, Yaw rate, image data, etc.

• Transactions of the Korean Society of Automotive Engineers
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• v.24 no.4
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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.