• Journal of the Korean Society of Safety
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• v.21 no.2 s.74
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• pp.128-137
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• 2006

Traffic accidents occur due to complex influences of transportation companies, drivers, and road environment. This study found that education and surroundings for transportation companies, driving habits of drivers, and road safety facilities and accessory facilities were main factors that affected strongly traffic accidents. Also, it found that driving habits of drivers could affect accidents heavily more than two other factors. Road safety and accessory facilities were analysed that they affected traffic accidents independently with transportation companies and their own drivers. Therefore, in order to achieve a traffic accident prevention as our main target, those companies need to produce atmosphere that their own drivers can have safety awareness, and related institutions for the above target should run parallel with policy assistance and strict traffic enforcement. In the end, this study suggests that transportation companies should secure manpower wholly being charged with traffic safety and financial resources investing in it.

• Journal of Sensor Science and Technology
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• v.24 no.3
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• pp.175-180
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• 2015

Worldwide, studies on intelligent vehicles for the convenience of drivers have been actively conducted as the number of cars has increased. However, vehicle convenience enabled by buttons lowers the concentration on driving and hence poses as a huge threat to the safety of the driver. The use of one of the convenient features, impaired driving auxiliary equipment, is limited because of its complex usage, and this device also hinders the front view of the driver. This paper proposes a vehicle-control device for controlling the convenient features as well as changes in speed and direction using gestures and motions of the driver. This device consists of an ultrasonic sensor for recognizing movement, an arduino for accepting signal control functions and servo and DC motors apply to various vehicle parts. Firstly, the vehicle-control device was designed using a 3D CAD program known as Solid-works based on the size of the steering wheel. Then, through simulations, a suitable length for minimizing the absorbent between ultrasonic sensors was confirmed using a program known as COMSOL Multiphysics. Finally, simulation results were verified through experiments, and the optimal size of the device was identified through the number of errors.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.16 no.5
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• pp.3060-3070
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• 2015

Recently, novice driver or weak drivers was difficult to understand the movement characteristics of the car and are immature sense of width and length of the car according to various each driver's sex and age, model. To complement this problem, the use of rear sensor and the camera is increased. And the parking assistance system that improves the convenience of parking the driver is being developed. Accordingly, parking guide system is needed to reflect the difference in the steering angle and correct the error distance. In this study, it is proposed that the turning radius during backward by complementing the existing Ackerman Jentaud type. And it develops more accurate parking guideline to be able to generat algorithm by applying the formula to propose a steering wheel angle sensor value derived through the handle.

• IEIE Transactions on Smart Processing and Computing
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• v.5 no.6
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• pp.390-394
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• 2016

This paper presents a new comparison method for haze-removal algorithms in next-generation automotive systems. Compared to previous peak signal-to-noise ratio-based comparisons, which measure similarity, the proposed modulation transfer function-based method checks sharpness to select a more suitable haze-removal algorithm for lane detection. Among the practical filtering schemes used for a haze-removal algorithm, experimental results show that Gaussian filtering effectively preserves the sharpness of road images, enhancing lane detection accuracy.

• Information and Communications Magazine
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• v.32 no.8
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• pp.38-44
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• 2015

본고에서는 자동차의 자율주행이라는 최종의 목표를 위해 필요한 여러 가지 필수 기술 중 하나라고 할 수 있는 자동차의 자기 위치 인식을 위한 측위 기술에 대해 소개하고 그와 관련된 여러 연구 개발 동향을 살펴보고자 한다. 또한, 지능형 운전보조 시스템(ADAS, Advanced Driver Assistance System)에서 필수적인 고정밀 전자 지도(High Precision Map)가 자동차의 자기 위치 인식 정확도 향상에 어떤 방법으로 활용되는 지에 대해서도 알아 보고자 한다.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2006.11a
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• pp.87-90
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• 2006

생활의 일부라 할 수 있는 교통시스템은 도시화, 산업화가 진행됨에 따라 더욱 복잡해지고 있다. 이를 보완하기 위해 내비게이션, 텔레메틱스 와 같은 다양한 보조 수단이 개발되고 있다. 하지만 이러한 운전자 보조 시스템은 개별화된 특성을 반영하지 않으며, 가장 일반적인 경우에 치중되어 있다. 본 논문에서는 개별화되고 사용자 중심적인 운전자 보조 시스템을 제안하며, 어떠한 정보가 이에 활용될 수 있는지를 고찰해 보았다. 또한 이런 정보를 해결하기 위한 소프트 컴퓨팅 기법을 제안하고자 한다.

• 한국HCI학회:학술대회논문집
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• 2008.02c
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• pp.150-151
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• 2008

With the proliferation of vehicles and advancement of Information Technology, the technology of Telematics, which provides valuable services to people by collecting and analyzing the information from drivers, vehicles and Telematics environments (e.g. traffic information, road condition, weather information, etc.), has been a hot research area in IT and automotive recently. As the information technology revolution brings more and more assistance for driver information processing becomes increasing important. Therefore, drivers' workload is very essential factor for safety driving in Telematics environment. For managing drivers' workload effectively, ETRI haven been developing vehicle and driver management system which can collect data from drivers and vehicle in realtime and analyze the data to manage drivers' and vehcles' status since 2007. This technology will apply to commercial vehicle telematics such as texi or truck management system in the future for increasing driving safety. In this presentation, I would like to explain what we had developed so far and discuss future direction.

• Journal of Internet Computing and Services
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• v.24 no.1
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• pp.39-47
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• 2023

The development of autonomous driving and Advanced Driver Assistance System (ADAS) technology has grown rapidly in recent years. As most traffic accidents occur due to human error, self-driving vehicles can drastically reduce the number of accidents and crashes that occur on the roads today. Obviously, technical advancements in autonomous driving can lead to improved public driving safety. However, due to the current limitations in technology and lack of public trust in self-driving cars (and drones), the actual use of Autonomous Vehicles (AVs) is still significantly low. According to prior studies, people's acceptance of an AV is mainly determined by trust. It is proven that people still feel much more comfortable in personalized ADAS, designed with the way people drive. Based on such needs, a new attempt for a customized ADAS considering each driver's driving style is proposed in this paper. Each driver's behavior is divided into two categories: assertive and defensive. In this paper, a novel customized ADAS algorithm with high classification accuracy is designed, which divides each driver based on their driving style. Each driver's driving data is collected and simulated using CARLA, which is an open-source autonomous driving simulator. In addition, Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) machine learning algorithms are used to optimize the ADAS parameters. The proposed scheme results in a high classification accuracy of time series driving data. Furthermore, among the vast amount of CARLA-based feature data extracted from the drivers, distinguishable driving features are collected selectively using Support Vector Machine (SVM) technology by comparing the amount of influence on the classification of the two categories. Therefore, by extracting distinguishable features and eliminating outliers using SVM, the classification accuracy is significantly improved. Based on this classification, the ADAS sensors can be made more sensitive for the case of assertive drivers, enabling more advanced driving safety support. The proposed technology of this paper is especially important because currently, the state-of-the-art level of autonomous driving is at level 3 (based on the SAE International driving automation standards), which requires advanced functions that can assist drivers using ADAS technology.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.18 no.1
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• pp.14-26
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• 2019

For bus drivers' safe driving, a policy that analyzes the causes of the drivers' traffic accidents and then assists their safe driving is required. Therefore, the Ministry of Land, Infrastructure and Transport set up its plan to gradually expand the equipping of commercial vehicles with FCWS (Forward Collision Warning System) and LDWS(Lane Departure Warning System), from the driver-supporting ADAS(Advanced Driver Assistance Systems). However, there is not much basic research on the analysis of bus drivers' traffic accidents in Korea. As such, the time is appropriate to research what is the most necessary ADAS for bus drivers going forward to prevent bus accidents. The purpose of this research is to analyze how serious the accidents were in the different bus routes and whether the accidents were repetitive, and to give recommendations on how to support ADAS for buses, as an improvement. A model of ordered logit was used to analyze how serious the accidents were and as a result, vehicle to pedestrian accidents which directly affected individuals were statistically significant in all of the models, and violations of regulations, such as speeding, traffic signal violation and violation of safeguards for passengers, were indicated in common in several models. Therefore, the pedestrian-sensor system and automatic emergency control device for pedestrian should be installed to reduce bus accidents directly affecting persons in the future, and education for drivers and ADAS are to be offered to reduce the violations of regulations.

• Journal of the Institute of Electronics Engineers of Korea SC
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• v.43 no.5 s.311
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• pp.1-9
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• 2006

Because most applications of vision-based object tracking demonstrate satisfactory operations only under very constrained environments that have simplifying assumptions or specific visual attributes, these approaches can't track target objects for the highly variable, unstructured, and dynamic environments like a traffic scene. An adaptive fusion framework is essential that takes advantage of the richness of visual information such as color, appearance shape and so on, especially at cluttered and dynamically changing scenes with partial occlusion[1]. This paper develops a particle filter based adaptive fusion framework and improves the robustness and adaptation of this framework by adding a new distinctive visual attribute, an image feature descriptor using SIFT (Scale Invariant Feature Transform)[2] and adding an automatic teaming scheme of the SIFT feature library according to viewpoint, illumination, and background change. The proposed algorithm is applied to track various traffic objects like vehicles, pedestrians, and bikes in a driver assistance system as an important component of the Intelligent Transportation System.