• Journal of Satellite, Information and Communications
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• v.7 no.2
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• pp.18-24
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• 2012

High current behaviors of the extended drain n-type metal-oxide-semiconductor field effects transistor (EDNMOSFET) for electrostatic discharge (ESD) protection of high voltage operating LDI (LCD Driver IC) chip are analyzed. Both the transmission line pulse (TLP) data and the thermal incorporated 2-dimensional simulation analysis demonstrate a characteristic double snapback phenomenon after triggering of biploar junction transistor (BJT) operation. Also, background doping concentration (BDC) is proven to be a critical factor to affect the high current behavior of the EDNMOS devices. The EDNMOS device with low BDC suffers from strong snapback in the high current region, which results in poor ESD protection performance and high latchup risk. However, the strong snapback can be avoided in the EDNMOS device with high BDC. This implies that both the good ESD protection performance and the latchup immunity can be realized in terms of the EDNMOS by properly controlling its BDC.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.20 no.1
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• pp.174-192
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• 2021

The LIDAR sensor, which provides higher cognitive performance than cameras and radar, is difficult to apply to ADAS or autonomous driving because of its high price. On the other hand, as the price is decreasing rapidly, expectations are rising to improve existing autonomous driving functions by taking advantage of the LIDAR sensor. In level 3 autonomous vehicles, when a dangerous situation in the cognitive module occurs due to a sensor defect or sensor limit, the driver must take control of the vehicle for manual driving. If the driver does not respond to the request, the system must automatically kick in and implement a minimum risk maneuver to maintain the risk within a tolerable level. In this study, based on this background, a LIDAR-based LKS MRM algorithm was developed for the case when the normal operation of LKS was not possible due to troubles in the cognitive system. From point cloud data collected by LIDAR, the algorithm generates the trajectory of the vehicle in front through object clustering and converts it to the target waypoints of its own. Hence, if the camera-based LKS is not operating normally, LIDAR-based path tracking control is performed as MRM. The HAZOP method was used to identify the risk sources in the LKS cognitive systems. B, and based on this, test scenarios were derived and used in the validation process by simulation. The simulation results indicated that the LIDAR-based LKS MRM algorithm of this study prevents lane departure in dangerous situations caused by various problems or difficulties in the LKS cognitive systems and could prevent possible traffic accidents.

• Journal of the Korean Society of Safety
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• v.32 no.6
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• pp.22-28
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• 2017

According to the National Fire Data System (NFDS), more than 5,000 vehicle fires have occurred every year for the last 10 years. Vehicle fires are primarily caused by mechanical (breaking system and engine), electrical (wiring and battery), and chemical (oil and fuel gas leakage) problems. The electrical factor has increased with the installation of driver convenience equipment. For example, today, the black box is widely used to provide video data recording of motor vehicle accidents. The black box consists of a front camera, rear camera, and wires. The black box wires are directly connected to the junction box or fuse box from the start battery that operates to provide normal on power supplying for engine stop. It is extremely dangerous when the wires short circuit due to insulation aging, mechanical and electrical stress, etc. In this study, the black box wiring fire risk have been analyzed and investigated when the steady state and abnormal operations, and under the following conditions: wiring arrangements with a high temperature condition, insulation aging, poor contact, and short circuits. The results showed that black box wiring short circuits had a higher fire risk than the other fire hazard elements. To prevent fire hazards caused by black box wiring, the black boxes must be installed by qualified service personnel. Do not modify the wiring, remove the fuse and secure the wiring using cable ties or insulation tape.

• Proceedings of the KSR Conference
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• 2007.11a
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• pp.1817-1823
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• 2007

In this study, we have studied rail infrastructure related to emergency action to manage the risk when emergency caused by faults of facility or rail vehicle during operation happens. Especially we have compared the effect of emergency action with examining the structure of vehicle, tunnel, bridge and access road, etc which are related to emergency action. Also, we have tried to analyze effects of radio and communication equipment, lifesaving and refuge which could be used for rolling stock, station, control room, tunnel, bridge and etc, and we have presented the way of reporting the emergency to the train driver or crew, control room, outside networks which could be used by passengers in vehicle, station, railroad line. Based on these, we have analyzed the conduct of emergency action in length of time when emergency happens in railway and high-speed railway, and studied the method of which passengers could be guided safely and escape from the scene of the accident.

• Korean Journal of Occupational Health Nursing
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• v.29 no.2
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• pp.106-113
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• 2020

Purpose: This study aimed to analyze industrial accident compensation insurance coverage and industrial accidents among concrete mixer truck drivers. Methods: Original data on industrial accidents from 2012 to 2017 were analyzed through descriptive statistics. Results: Industrial accident compensation insurance coverage was 44.6% in 2017. Most concrete mixer truck drivers were affiliated with small businesses. A total of 61 industrial accidents occurred in 2012, 65 in 2014, and 80 in 2017. The major types of industrial accident were falls, slips, and crushes. Conclusion: Because concrete mixer truck drivers are at high risk for industrial accidents, industrial accident compensation insurance coverage and industrial accident prevention should be strongly enforced.

• Journal of the Ergonomics Society of Korea
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• v.35 no.3
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• pp.135-142
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• 2016

Objective: Objective of this study is to provide characteristics of injury frequency and severity by driving condition in large truck-related traffic collisions. Background: Traffic accidents involving large trucks draw a lot of attention in accident prevention and management policies since they bring about severe human and financial damages. Method: In order to identify the major risk factors of accidents by driving condition, 255 recognized traffic accidents by large truck drivers were analyzed in terms of time of the day, road type, and shape of the road. Results: The driving conditions in the results are represented by the following form of combination, "Road Type (Non-expressway or Express) - Shape of Roads (Straight, Curved, Downhill, or Intersection) - Time of Accidents (Day or Night)". In the analysis of injury frequency, Non-expressway-Straight-Day condition was the most frequent one. Meanwhile, Expressway-Curved-Day, Non-expressway-Curved-Night and Non-expressway-Intersection-Night were evaluated as high level in view of injury severity. Also, Expressway-Straight-Night is the driving condition that is the highest in risk among the conditions that have to be managed as grade "High". Non-expressway-Straight-Night, Non-expressway-Downhill-Day, and Non-expressway-Curved-Day are also categorized as grade "High". Conclusion and Application: Safety managers in the fields require basic information on accident prevention that can be easily understood. The research findings will serve as a practical guideline for establishing preventive measures for traffic accidents.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.23 no.1
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• pp.134-145
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• 2024

The purpose of this study was to propose a framework for developing test scenarios for issuance of conditional driver's licenses. The framework was composed of five stages. Initially, we reviewed the literature on traffic crash characteristics in terms of accident frequency and severity regarding the main factors of crashes caused by older drivers. In the second stage, the characteristics of crashes attributed to non-elderly, early elderly, and late elderly drivers were analyzed using data obtained from the Traffic Accident Analysis System (TAAS), and crash types for elderly drivers were derived. In the third stage, black box videos of high-risk crash types were analyzed to derive crash stories that described the circumstances in which crashes occurred. In the fourth step, crash situations were classified by rating the types of crash stories derived to develop various scenarios. Step 5 involved creating a scenario by applying the PEGASUS 5-Layer format, which has recently been used to develop test scenarios for autonomous vehicles. The results of this study are expected to be used as a basis for developing driving ability evaluation scenarios for the issuance of conditional driver's licenses.

• The Journal of the Korea institute of electronic communication sciences
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• v.11 no.5
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• pp.531-538
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• 2016

Driving behaviors such as speeding and illegal u-turn which violate traffic rules are main causes of car accidents, and they can lead to serious accidents. Bus drivers are less aware of dangers of illegal u-turn, and infrastructures such as traffic enforcement equipment and watchmen are deficient. This research aims to develop technology for estimating driving behaviors based on map-matching in order to prevent illegal u-turns. For this research, 23,782 of u-turn permit data and 146,000 of speed limit data are collected nationwide, and an estimation algorithm is built with these data. Then, an application based on android is developed, and finally, tests are conducted to assess the accuracy in data computations and GPS data map-matching, and to extrapolate driving behavior. As a result of the tests, the accuracy results in the map-matching is 86% and the assessment of driving behavior is 83%, while the display of the data output yielded 100% accuracy. Additional research should focus on improvement in accuracy through the development of a robust monitoring system, and study of service scenarios for technology application.

• Safety and Health at Work
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• v.9 no.2
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• pp.159-163
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• 2018

Background: Fatigue and sleepiness are inter-related and common among road transport drivers. In this study, sleep deprivation and fatigue among chemical transportation drivers were examined. Methods: A cross-sectional study surveying 107 drivers from three hazardous types of chemical production and transportation industries (nonflammable gases, flammable gases, and flammable liquids) was conducted. Data on sleep deprivation were collected using questionnaires of the Stanford Sleeping Scale and the Groningen Sleep Quality Scale. Fatigue was assessed using an interview questionnaire and a flicker fusion instrument. Results: Chemical drivers had a mean sleeping scale (Stanford Sleeping Scale) of 1.98 (standard deviation 1.00) and had a mean score of 1.89 (standard deviation 2.06) on the Groningen Sleep Quality Scale. High-risk drivers had higher scores in both the Stanford Sleeping Scale and the Groningen Sleep Quality Scale with a mean score of 2.59 and 4.62, respectively, and those differences reached statistical significance (p < 0.05). The prevalence of fatigue, as assessed through a critical flicker fusion analyzer, subjective fatigue question, and either of the instruments, was 32.32%, 16.16%, and 43.43%, respectively. Drivers who slept <7 hours and had poor sleep quality were found to have more fatigue than those who slept enough and well. Drivers who had a more sleepiness score resulted in significantly more objective fatigue than those who had a less sleepiness score. Conclusion: Sleep quality and sleeping hour can affect a driver's fatigue. Optimization of work-rest model should be considered to improve productivity, driver retention, and road safety.

• The Journal of Bigdata
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• v.2 no.2
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• pp.129-140
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• 2017

Weather acts through low visibility, precipitation, high winds, and temperature extremes to affect driver capabilities, vehicle performance (i.e., traction, stability and maneuverability), pavement friction, roadway infrastructure, crash risk, traffic flow, and agency productivity. Recently a variety of road weather big data sources such as CCTV, road sensor/systems, car sensor have been developed to solve the weather-related problems, This study identifies and defines the types and characteristics of these sources to suggest how to utilize them for car safety and efficiency as well as road management through analyzing domestic and oversea cases of road weather big data applications.