• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.21 no.6
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• pp.299-310
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• 2022

The ability of an automated driving system is based on vehicle sensors, judgment and control algorithms, etc. The safety of automated driving system is highly related to the operational status of the road network and compliant road infrastructure. The safe operation of automated driving necessitates continuous monitoring to determine if the road and traffic conditions are suitable and safe. This paper presents a node and link system to build a road monitoring system by considering the ODD(Operational Design Domain) characteristics. Considering scalability, the design is based on the existing ITS standard node-link system, and a method for expressing the monitoring target as a node and a link is presented. We further present a technique to classify and manage hazard risk into five levels, and a method to utilize node and link information when searching for and controlling the optimal route. Furthermore, we introduce an example of system implementation based on the proposed node and link system for Sejong City.

• 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.

• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
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• v.18 no.6
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• pp.14-21
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• 2004

This automotive headlight control system is newly proposed that, under my slope degree of the driving mad(flat up-hill, and down-hill) at night driving, the reflecting mirror of the headlight can be automatically controlled for safe driving. At first whether or not any vehicle is driven near is checked by photo sensor. Secondly, using the slope degree of the automotive feedbacked from the tilt sensor, the servo motor with the headlight is controlled to be turned right or down to the suitable angle. The servo motor is appropriately controlled according to road conditions. The proposed headlight control system is designed on the basis of the tested illumination intensity obtained according to any slope degree of roads. Finally, it is confirmed that the test model works very well in the given road conditions and environments.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.40 no.1
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• pp.65-78
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• 2017

Even though cars have a good effect on modern society, traffic accidents do not. There are traffic laws that define the regulations and aim to reduce accidents from happening; nevertheless, it is hard to determine all accident causes such as road and traffic conditions, and human related factors. If a traffic accident occurs, the traffic law classifies it as 'Negligence of Safe Driving' for cases that are not defined by specific regulations. Meanwhile, as Korea is already growing rapidly elderly population with more than 65 years, so are the number of traffic accidents caused by this group. Therefore, we studied predictive and comparative analysis of the number of traffic accidents caused by 'Negligence of Safe Driving' by dividing it into two groups : All-ages and Elderly. In this paper, we used empirical monthly data from 2007 to 2015 collected by TAAS (Traffic Accident Analysis System), identified the most suitable ARIMA forecasting model by using the four steps of the Box-Jenkins method : Identification, Estimation, Diagnostics, Forecasting. The results of this study indicate that ARIMA $(1, 1, 0)(0, 1, 1)_{12}$ is the most suitable forecasting model in the group of All-ages; and ARIMA $(0, 1, 1)(0, 1, 1)_{12}$ is the most suitable in the group of Elderly. Then, with this fitted model, we forecasted the number of traffic accidents for 2 years of both groups. There is no large fluctuation in the group of All-ages, but the group of Elderly shows a gradual increase trend. Finally, we compared two groups in terms of the forecast, suggested a countermeasure plan to reduce traffic accidents for both groups.

• International journal of advanced smart convergence
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• v.12 no.4
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• pp.190-201
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• 2023

Securing transportation safety infrastructure technology for Lv.4 connected autonomous driving is very important for the spread of autonomous vehicles, and the safe operation of level 4 autonomous vehicles in adverse weather has limitations due to the development of vehicle-only technology. We developed the radar-enabled AI convergence transportation entities detection system. This system is mounted on fixed and mobile supports on the road, and provides excellent autonomous driving situation recognition/determination results by converging transportation entities information collected from various monitoring sensors such as 60GHz radar and EO/IR based on artificial intelligence. By installing such a radar-enabled AI convergence transportation entities detection system on an autonomous road, it is possible to increase driving efficiency and ensure safety in adverse weather. To secure competitive technologies in the global market, the development of four key technologies such as ① AI-enabled transportation situation recognition/determination algorithm, ② 60GHz radar development technology, ③ multi-sensor data convergence technology, and ④ AI data framework technology is required.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.43 no.4
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• pp.485-492
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• 2023

Among the methods to improve traffic congestion by providing real-time traffic information and solving problems like traffic congestion and traffic crashes, private enterprise is implementing policies to lower insurance premiums like compensation for drivers' driving safety scores. Despite the emergence of various incentive policies, a study on the level of incentive payment for safe/eco-friendly driving is insufficient. The research analyzed the satisfactory factors that affect the scale of incentives through questionnaires and the applicable scale of incentives that enable safe/eco-friendly driving using a binary logistic regression model. As a result of analyzing the incentive scale of the appropriate payment amount for each driving score increase, 0.4% of the toll fee was derived when the driving score increased by 20 points, and 0.5% of the toll fee was derived when the driving score increased by 30 points. This study on calculating the appropriate incentive payment scale for driver information sharing and driving score increase will help optimize incentives and prepare system implementation plans.

• Journal of Advanced Navigation Technology
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• v.17 no.2
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• pp.165-170
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• 2013

In this paper, we propose the information acquisition and analysis system for a vehicle driver in order to provide the safe driving environments. We first define the list of reckless driving behaviors and propose the recognition system, which recognizes the reckless behaviors, by using the acquired information. The collaboration among the information acquisition, the analysis, and the behavior comparison modules increases the accuracy of the recognition rate. Our system alarms to a vehicle driver in order to notify the potential to confront the dangerous situation due to the abnormal or reckless driving behaviors.

• Proceedings of the KSME Conference
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• 2007.05a
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• pp.849-854
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• 2007

This paper describes design and tuning of a full-range Adaptive Cruise Control (ACC) with collision avoidance. The control scheme is designed to control the vehicle so that it would feel natural to the human driver and passengers during normal safe driving situations and to avoid rear-end collision in vehicle following situations. In this study, driving situations are determined using a non-dimensional warning index and time-to-collision (TTC). A confusion matrix method based on natural driving data sets was used to tune control parameters in the proposed ACC System. An ECU-Brake Hardware-in-the-loop Simulation (HiLS) was developed and used for an evaluation of ACC System. The ECU-Brake HiLS results for alternative driving situation are compared to manual driving data measured on actual traffic way. The ACC/CA control logic implemented in an ECU was tested using the ECU-Brake HiLS in a real vehicle environment.

• 제어로봇시스템학회:학술대회논문집
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• 2005.06a
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• pp.543-547
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• 2005

In this paper, we designed a intelligent autonomous driving robot by using Fuzzy algorithm. The object of designed robot is recognition of obstacle, avoidance of obstacle and safe arrival. We append a suspension system to auxiliary wheel for improvement in stability and movement. The designed robot can arrive at destination where is wanted to go by the old and the weak and the handicapped at indoor hospital and building.

• Journal of Korea Society of Digital Industry and Information Management
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• v.19 no.2
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• pp.47-55
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• 2023

Recently, Sensor technologies are emerging to prevent traffic accidents and support safe driving in complex environments where human perception may be limited. The UWS is a technology that uses an ultrasonic sensor to detect objects at short distances. While it has the advantage of being simple to use, it also has the disadvantage of having a limited detection distance. The LDWS, on the other hand, is a technology that uses front image processing to detect lane departure and ensure the safety of the driving path. However, it may not be sufficient for determining the driving environment around the vehicle. To overcome these limitations, a system that utilizes FMCW radar is being used. The BSD radar system using FMCW continuously emits signals while driving, and the emitted signals bounce off nearby objects and return to the radar. The key technologies involved in designing the BSD radar system are tracking algorithms for detecting the surrounding situation of the vehicle. This paper presents a tracking algorithm for designing a BSD radar system, while explaining the principles of FMCW radar technology and signal types. Additionally, this paper presents the target tracking procedure and target filter to design an accurate tracking system and performance is verified through simulation.