• Journal of the Korea Institute of Information and Communication Engineering
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• v.17 no.9
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• pp.2153-2159
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• 2013

In order to drive a vehicle safely, driver needs to check status of the car. Many moderns are having trouble to spare time to visit auto mechanic and have car mechanics to check their car other than their office hours. If the car status cannot be inspected regularly, it is likely to cause a big accident threatening the surroundings as well as driver's life. Inspection tool and system help driver to check their own vehicle status personally are required for preventing it. In this paper, it designed and realized system that records driving information based on changing data of vehicle (location and automotive internal data) and allows driver can check the vehicle status easily and further, driver can share the driving information with repair shop via the Internet to receive detailed inspection service for car status.

• The Journal of Information Systems
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• v.31 no.3
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• pp.47-65
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• 2022

Purpose With the recent development of Big Data and Artificial Intelligence technology, self-driving technology has developed into three stages (partial self-driving) or four stages (conditional self-driving), it is expected to bring a new paradigm to transportation in the city. Although many researchers are researching related technologies, there is no research on self-driving for disabled persons. In this study, the basic research was conducted based on the assumption that the shared self-driving car used by the disabled person is similar to the special transportation currently driving. Design In this study, data analysis and machine learning techniques were utilized to analyze the mobility patterns of disabled persons by type and to search for leading factors affecting the traffic volume of special transportation. Findings The study found that external physical disorders and developmental disorders often visit general welfare centers, internal organ disorders often visit general hospitals, and the elderly and mental disorders have various destinations. In addition, machine learning analysis showed that the main transportation routes for the disabled person use arterial roads and auxiliary arterial roads and that the ratio of building usage-related variables affecting the use of special transportation for a disabled person is high. In addition, the distance to the subway and bus stops was also mentioned as a meaningful variable. Based on these analysis results, it is expected that the necessary infrastructure for shared self-driving cars for disability person traffic will be used as meaningful research data in the future.

• International Journal of Internet, Broadcasting and Communication
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• v.13 no.1
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• pp.210-218
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• 2021

CNN (Convolutional Neural Network), a type of deep learning algorithm, is a type of artificial neural network used to analyze visual images. In deep learning, it is classified as a deep neural network and is most commonly used for visual image analysis. Accordingly, an AI autonomous driving model was constructed through real-time image processing, and a crosswalk image of a road was used as an obstacle. In this paper, we proposed a low-cost model that can actually implement autonomous driving based on the CNN model. The most well-known deep neural network technique for autonomous driving is investigated and an end-to-end model is applied. In particular, it was shown that training and self-driving on a simulated road is possible through a practical approach to realizing lane detection and keeping.

• Proceedings of the Korean Society of Computer Information Conference
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• 2019.01a
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• pp.135-136
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• 2019

센서 데이터의 발전에 따라 자율주행 자동차 산업도 급격히 성장하고 있다. 미국 우버(UBER)는 2015년부터 자율주행 자동차 산업에 뛰어들었고, 국내에서도 '판교 자율주행 셔틀'이 시범운행 되었다. 따라서 자율주행 자동차는 앞으로 우리 삶에 보다 많은 영향을 끼칠 것이 분명하나, 아직 자율주행 자동차가 완벽하게 개발되지 않은 만큼 우리가 예상하지 못한 교통사고 등 새로운 문제가 나타날 위험이 있다. 따라서 본 논문은 자율주행 자동차에 대해 살펴보고 사고 사례를 분석하여 앞으로 나타날 수 있는 사고 유형을 예측하는 것에 목적이 있다.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.7 no.6
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• pp.48-56
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• 2008

This study provides to car driver with car exhaust gas and sensor information which are car trouble code in engine and many sensors when the car has some problems. This is to provide car manager with many information of car sensors when we go to vehicle maintenance. For example, information of engine RPM, fuel system, intake air temperature, air flow sensors and oxygen sensors can provide to owner or garage, and also add to multimedia system for mp3 files and video files. This system consists of embedded linux system of low power while driving the car which uses OBD-II protocols and zigbee communication interface from CAN communication of car system to self-diagnosis embedded system of car. Finally, low power embedded system has a lot of application and OBD-II protocols for embedded linux system and CAN communication which get sensor informations of car control sensor system while driving the car.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.33 no.6
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• pp.907-917
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• 2023

Recently, a self-driving car have applied deep learning technology to advanced driver assistance system can provide convenience to drivers, but it is shown deep that learning technology is vulnerable to adversarial evasion attacks. In this paper, we performed five adversarial evasion attacks, including MI-FGSM(Momentum Iterative-Fast Gradient Sign Method), targeting the object detection algorithm YOLOv5 (You Only Look Once), and measured the object detection performance in terms of mAP(mean Average Precision). In particular, we present a method applying morphology operations for YOLO to detect objects normally by removing noise and extracting boundary. As a result of analyzing its performance through experiments, when an adversarial attack was performed, YOLO's mAP dropped by at least 7.9%. The YOLO applied our proposed method can detect objects up to 87.3% of mAP performance.

• Journal of Institute of Control, Robotics and Systems
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• v.20 no.12
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• pp.1181-1188
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• 2014

In this paper, we propose a novel concept foran unmanned delivery service using a cooperative heterogeneous unmanned system consisting of a self-driving car and an unmanned aerial vehicle (UAV). The proposed concept is suitable to deliver parcels in high-density and high-rise urban or residential areas. In order to achieve the proposed concept, we will develop acooperative heterogeneous unmanned system. Customers can order goods using a smartphone application and the order information, including the position of the customer and the order time, and the package is transported automatically by the unmanned systems. The system assigns the tasks suitable for each unmanned vehicle by analyzing it based on map information. Performance is validated by experiments consisting of autonomous driving and flight tests in a real environment. For more evaluation, the landing position error analysis is performed using circular error probability (CEP).

• Journal of Platform Technology
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• v.9 no.2
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• pp.46-54
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• 2021

Recently, interest in the safety of Self-driving has been increasing. Self-driving have been studied and developed by many universities, research centers, car companies, and companies of other industries around the world since the middle 1980s. In this study, we propose the automatic extraction method of the threatening obstacle on the Road for the Self-driving. A threatening obstacle is defined in this study as a comparatively large object at center of the image. First of all, an input image and its decreased resolution images are segmented. Segmented areas are classified as the outer or the inner area. The outer area is adjacent to boundaries of the image and the other is not. Each area is merged with its neighbors when adjacent areas are included by a same area in the decreased resolution image. The Obstacle area and Non Obstacle area are selected from the inner area and outer area respectively. Obstacle areas are the representative areas for the obstacle and are selected by using the information about the area size and location. The Obstacle area and Non Obstacle area consist of the threatening obstacle on the road. Through experiments, we expect that the proposed method will be able to reduce accidents and casualties in Self-driving.

• Journal of the Korea Society of Computer and Information
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• v.21 no.11
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• pp.59-64
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• 2016

In this paper, we design and implement an Application based on android platform, which can control arduino Prime Smart Car using Bluetooth communication. This Application consist of Bluetooth communication module, manual mode module, and line-tracer mode module. In the Bluetooth communication module, it checks the on/off status of Smartphone Bluetooth. If Bluetooth status is off, it activates Bluetooth, selects the corresponding device from Bluetooth device list, and connects with a pair. In order to reduce coding time, we implements Bluetooth communication using inherited class from android Bluetooth package. In the manual mode module, it implements six direction moving button and stop button, which can control arduino Prime Smart Car. In the line-tracer mode module, it implements Prime Smart Car with self-driving function using TCRT5000 sensor. And moving button and stop button is disabled.

• Korean System Dynamics Review
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• v.13 no.4
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• pp.33-55
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• 2012

The research is to identify important diffusion factors and their effects on green car diffusion process using system dynamics perspectives and a causal-loop analysis. Through a deep review on previous research, we have found the important factors of green car diffusion process. Price, driving range, network effect, recharge system, fuel cost had important facilitation on consumer attraction and green car diffusion. Based on the review, we have constructed a causal loop diagram explaining hybrid car diffusion process. We have found 3 important reinforcing loops in the causal loop diagram. Loop for learning & economies of scale(supply side), loop for network effect(consumer side), and loop for battery development(technology side) had most significant roles in the whole diffusion process. Through a deliberate analysis on the 3 causal loops, we have found meaningful results. First, there seems to exist a critical mass in the diffusion. Second, of the 3 loops, the battery technology had most significant role. Third, not consumer installed base but sales must be a standard to decide whether the critical mass is achieved or not. Based on these findings, several meaningful implications are suggested for the government and corporations related to the green car industries.