• Journal of the Korea Academia-Industrial cooperation Society
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• v.18 no.12
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• pp.628-637
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• 2017

The automobile industry has developed Advanced Driver Assistance Systems (ADASs) to prevent traffic accidents and reduce the burden for drivers. One example is the Lane Keeping Assistance System (LKAS), which was developed for automotive vehicle systems for safety and better driving. The main system of the LKAS supports the driver while maintaining the vehicle within a lane. LKAS uses a radar sensor and camera sensor to collect information about the vehicle's position in the lane and send commands to the actuator to influence the lateral movement of the vehicle if necessary. Recently, vehicles equipped with LKAS have become commercially available. Test procedures for international LKAS evaluation are being discussed and developed by international committees, such as the International Organization for Standardization and United Nations Economic Commission for Europe. In Korea, an evaluation of LKASs for car safety is being planned by the Korean New Car Assessment Program. Therefore, test procedures should be developed for LKASs that are suitable for the domestic road environment while accommodating international standards. We developed a test scenario for LKASs and propose a formula for obtaining the target relative distance. To validate the methods, a series of experiments were conducted using commercially available vehicles equipped with LKAS.

• Journal of IKEEE
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• v.23 no.3
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• pp.844-851
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• 2019

As wearable technology is becoming more common and a part of our lives, there have been many efforts to offer various smart services with wearable devices, such as motion recognition, safety of driving, and so on. In this paper, we present a method that exploits the 9-axis inertial sensors embedded in a smartwatch to identify whether the user is a vehicle driver or not and to estimate the steering wheel turning direction in the vehicle. The system consists of three components: (i) position recognition, (ii) driver recognition, and (iii) steering-wheel turning detection components. We have developed a prototype system for detecting user's motion with Arduino boards and IMU sensors. Our experiments show high accuracy in recognizing the driver and in estimating the wheel rotation angle. The average experimental error was $11.77^{\circ}$ which is small enough to perceiver the turning direction of steering-wheel.

• Journal of the Korean Society of Marine Environment & Safety
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• v.27 no.1
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• pp.135-144
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• 2021

In this study, we investigated the characteristics of the process of hydrogen production using boil-of gas (BOG) generated from an LNG-fueled ship and the application of hydrogen fuel cell systems as auxiliary engines. In this study, the BOG steam reformer process was designed using the UniSim R410 program, and the reformer outlet temperature, pressure, and the fraction and consumption of the product according to the steam/carbon ratio (SCR) were calculated. According to the study, the conversion rate of methane was 100 % when the temperature of the reformer was 890 ℃, and maximum hydrogen production was observed. In addition, the lower the pressure, the higher is the reaction activity. However, higher temperatures have led to a decrease in hydrogen production owing to the preponderance of adverse reactions and increased amounts of water and carbon dioxide. As SCR increased, hydrogen production increased, but the required energy consumption also increased proportionally. Although the hydrogen fraction was the highest when the SCR was 1.8, it was confirmed that the optimal operation range was for SCR to operate at 3 to prevent cocking. In addition, the lower the pressure, the higher is the amount of carbon dioxide generated. Furthermore, 42.5 % of the LNG cold energy based on carbon dioxide generation was required for cooling and liquefaction.

• Journal of the Institute of Electronics and Information Engineers
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• v.51 no.1
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• pp.116-125
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• 2014

Road conditions can provide important information for driving safety in driving assistance systems. The input images usually include unnecessary information and they need to be analyzed only in a region of interest (ROI) to reduce the amount of computation. In this paper, a vision-based road ROI determination algorithm is proposed to detect the road region using the positional information of a vanishing point and line segments. The line segments are detected using Canny's edge detection and Hough transform. The vanishing point is traced by a Kalman filter to reduce the false detection due to noises. The road ROI can be determined automatically and adaptively in every frame after initialization. The proposed method is implemented using C++ and the OpenCV library, and the road ROIs are obtained from various video images of black boxes. The results show that the proposed algorithm is robust.