• Journal of the Korea Society of Computer and Information
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• v.19 no.1
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• pp.1-8
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• 2014

In recent years, much attention has been paid to the development of intelligent vehicles that integrate automotive technology into the information technology, with the aim of improving user friendliness and stability. The representative function is a autonomous driving and a cruise control. In designing such vehicles, it is critical to address the real-time issues (i.e., real-time vehicle control and timely response). However, previous research excluded the real-time scheduling. We develop a model car with unmanned cruise control, design the real-time scheduler using cyclic executive to easily adapt the model car, and provide some insight into potential solutions based on various experiments.

• Journal of Institute of Control, Robotics and Systems
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• v.21 no.3
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• pp.217-223
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• 2015

Autonomous driving is no longer atechnology of the future since the development of autonomous vehicles has now been realized, and many technologies have already been developed for the convenience of drivers. For example, autonomous vehicles are one of the most important drive assistant systems. Among these many drive assistant systems, Cruise Control Systems are now a typical technology. This system constantly maintains a vehicle's speed and distance from a vehicle in front by using Radar or LiDAR sensors in real time. Cruise Control Systems do not only serve their original role, but also fulfill another role as a 'Driving Safety' measure as they can detect a situation that a driver did not predict and can intervene by assuming a vehicle's longitude control. However, these systems have the limitation of only focusing on driver safety. Therefore, in this paper, an Intelligent Cruise Control System that utilizes the path planning method and GIS is proposed to overcome some existing limitations.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2004.10a
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• pp.908-911
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• 2004

In this paper, we describe a robust image processing algorithm to recognize the road lane in real-time. For the real-time processing, a detection area is decided by a lane segment of a previous frame and edges are detected on the basis of the lane width. For the robust driving, the global threshold with the Otsu algorithm is used to get a binary image in a frame. Therefore, reliable edges are obtained from the algorithms suggested in this paper in a short time. Lastly, the lane segment is found by hough transform. We made a RC(Radio Control) car equipped with a vision system and verified these algorithms using the RC Car.

• IEMEK Journal of Embedded Systems and Applications
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• v.8 no.5
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• pp.249-254
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• 2013

In this paper we propose nonlinear control system for automatic unmanned vehicle using a RC (Radio Controlled) car which is usually controlled by a remote controller. In the proposed system, a RC car is dissembled and reassembled with several parts enabling it to be controlled by an android mobile platform with Bluetooth communication. In our system, an android mobile smartphone is mounted on the RC car and plays an important role as an eye of the car. The proposed system automatically controls the RC car to follow a lane that we draw on the floor of our laboratory. Also, the proposed RC car system can also be controlled manually using the accelerometer sensor of a smartphone through a Bluetooth module. Our proposed system that has both manual mode and automatic mode consists of several components; a microprocessor unit, a Bluetooth serial interface module, a smartphone, a dual motor controller and a RC toy car. We are now in the development of a group driving system in which one car follows the front car that tracks a lane automatically.

• The Journal of Korea Robotics Society
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• v.12 no.3
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• pp.297-305
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• 2017

As the development of autonomous vehicles becomes realistic, many automobile manufacturers and components producers aim to develop 'completely autonomous driving'. ADAS (Advanced Driver Assistance Systems) which has been applied in automobile recently, supports the driver in controlling lane maintenance, speed and direction in a single lane based on limited road environment. Although technologies of obstacles avoidance on the obstacle environment have been developed, they concentrates on simple obstacle avoidances, not considering the control of the actual vehicle in the real situation which makes drivers feel unsafe from the sudden change of the wheel and the speed of the vehicle. In order to develop the 'completely autonomous driving' automobile which perceives the surrounding environment by itself and operates, ability of the vehicle should be enhanced in a way human driver does. In this sense, this paper intends to establish a strategy with which autonomous vehicles behave human-friendly based on vehicle dynamics through the reinforcement learning that is based on Q-learning, a type of machine learning. The obstacle avoidance reinforcement learning proceeded in 5 simulations. The reward rule has been set in the experiment so that the car can learn by itself with recurring events, allowing the experiment to have the similar environment to the one when humans drive. Driving Simulator has been used to verify results of the reinforcement learning. The ultimate goal of this study is to enable autonomous vehicles avoid obstacles in a human-friendly way when obstacles appear in their sight, using controlling methods that have previously been learned in various conditions through the reinforcement learning.

• Journal of the Korea Convergence Society
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• v.9 no.5
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• pp.7-14
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• 2018

The purpose of this study is to analyze the effect of commuter's departure time on transportation system in future traffic system operated autonomous vehicle using agent based model. Various scenarios have been set up, such as when all passenger choose a similar departure time, or if the passenger chooses a different departure time. Also, this study tried to analyze the effect of road capacity. It was found that although many of the scenarios had been completed in a stable manner, many commuters were significantly coordinated at the desired departure time. In particular, in the case of a reduction in road capacity or in certain scenarios, it has been shown that, despite excessive schedule adjustments, many passengers are unable to commute before 9 o'clock. As a result, it is suggested that traffic management and pricing policies are different from current ones in the era of autonomous car operation.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.22 no.10
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• pp.1294-1299
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• 2018

'SMART Police' means collecting, analyzing and utilizing the police database such as crime statistics, reflecting 'strategic management', 'analysis and research', 'science technology' do. In recent years, the number of sex offenses has more than doubled based on the 'crime analysis' of the Supreme Prosecutors' In this trend, we started to build a sex crime prevention program for women 's relief special 3.0 which is suitable for regional characteristics. However, it was not enough for the women to have low publicity and utilization results, and to show the practical effect. In the future, we propose a crime prevention system which can safely take home safeguarding with autonomous driving car or drone by applying ICT to the future police system. Although it is difficult to operate autonomous vehicles and drones in the surrounding environment such as narrow alleys and power lines for application to this system, it is thought that the artificial intelligence technology can be sufficiently overcome to operate.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.17 no.6
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• pp.84-95
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• 2018

A mathematical model of responsibility-sensitive safety (RSS) has been proposed as a way to determine whether an autonomous driving accident has occurred. Autonomous vehicles related industry and academia have shown great interest in this model. However, this mathematical model lacks a comprehensive review on whether the model can be used to clarify responsibilities of autonomous vehicles in the event of a traffic accident. In this study, we analyzed the issues that need to be solved in order to apply the RSS model. In conclusion, there is a limit in the equation and the social acceptability of the RSS model. To use the RSS model practically, it is necessary to define the response time of the autonomous vehicle and to measure and control the reaction time value according to the appropriate technology level for each autonomous vehicle.

• Journal of Korea Multimedia Society
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• v.23 no.9
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• pp.1219-1228
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• 2020

In this study, the factors affecting the acceptance intention for level 4-5 of autonomous vehicles were investigated by applying TAM(Technology Acceptance Model). To this end, 332 ordinary persons interested in autonomous vehicle and experienced in driving car were analyzed by using SEM(Structural Equation Modeling). The results showed that self-efficacy and personal innovation had a positive effect on perceived usefulness. On the other hand personal innovation has been shown to have a negative effect on perceived usefulness. Perceived ease of use has a positive effect on perceived usefulness, perceived ease of use and perceived usefulness has a positive effect on acceptance intention. Safety and Privacy has been shown to have a positive effect on trust, trust has a positive effect on acceptance intention. Lastly, autonomous vehicles have a higher impact on their 20s and 30s. The result of this study is expected to be a very useful basic research for the development of target autonomous vehicles, the selection of targets, the direction of corporate marketing strategies, and the preparation of government policies.

• Korean Journal of Remote Sensing
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• v.34 no.6_1
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• pp.1131-1142
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• 2018

The autonomous vehicles are being developed and operated widely because of the advantages of reducing the traffic accident and saving time and cost for driving. The vehicle localization is an essential component for autonomous vehicle operation. In this paper, localization algorithm based on sensor fusion is developed for cost-effective localization using in-vehicle sensors, GNSS, an image sensor and reference images that made in advance. Information of the reference images can overcome the limitation of the low positioning accuracy that occurs when only the sensor information is used. And it also can acquire estimated result of stable position even if the car is located in the satellite signal blockage area. The particle filter is used for sensor fusion that can reflect various probability density distributions of individual sensors. For evaluating the performance of the algorithm, a data acquisition system was built and the driving data and the reference image data were acquired. Finally, we can verify that the vehicle positioning can be performed with an accuracy of about 0.7 m when the route image and the reference image information are integrated with the route path having a relatively large error by the satellite sensor.