• The Journal of Korea Robotics Society
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• v.5 no.4
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• pp.286-293
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• 2010

The trailer system offers efficiency of transportation capability. However, it is difficult to control the backward motion. It is an open loop unstable problem. To solve this problem, we are proposed the driver assistance system. Driver assistance system assists a driver to control the backward motion of trailer system as if forward motion. A driver only secure the rear view of last passive trailer, and select the control input to drive the last passive trailer. The driver assistance system converts the control input of the driver into velocity and steering angle of the vehicle using the inverse kinematics. It is possible by electronic control input devices and the rear view camera. Effectiveness of driving assistance system is verified by the simulation and the experiments.

• Journal of Institute of Control, Robotics and Systems
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• v.13 no.12
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• pp.1186-1191
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• 2007

Human driver monitoring system is one of the most important systems for the safety in driving vehicles, and therefore driver assistance system has gained much attention during the last decade. This paper proposed an intelligent driver assistance system which monitors human driver's states from bio-signals such as ECG and Blood Pressure. The proposed system used mamdani fuzzy inference to evaluate the driver's mental strain and generated warning signals to the driver. The approach using bio-signals in driver assistance system is the main issue of the related systems and the preliminary results showed the possibility of further research topics in developing more intelligent embedded systems with bio-signal feedback.

• Journal of Drive and Control
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• v.19 no.3
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• pp.47-52
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• 2022

The C-ITS (Cooperative-intelligent Transport System) is a driver assistance system that prevents car accidents and enhances traffic conditions, via sharing traffic information between vehicles and roadway infrastructures. A CLAS (changing lane assistance system) for unprotected left-turn, is a C-ITS that assists a driver with safely changing lanes. This system addresses a driver's critical gap, that enables the system to express a driver's uncertainty. A driver's critical gap is a time that can be used in a threshold, to change a lane or not. Unfortunately, a driver's critical gap is difficult to use in a CLAS directly. This paper addresses a driver's critical gap, and how it can be applied in a CLAS for an unprotected left-turn.

• Electronics and Telecommunications Trends
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• v.33 no.4
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• pp.61-69
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• 2018

Driver-specific technology in the automotive field has been commercialized for vehicle accessories, driver memory sheets, and side mirrors. In recent years, the demand for customized technology has expanded to include the user interface of an infotainment system (Infotainment System) and advanced driver support system (Advanced Driver Assistance System), and customized technologies for drivers have been studied. Therefore, this article describes the driver-tailored technology trends being studied in these fields, and examines the major research issues related to future driver-tailored technologies in the automotive field.

• The Journal of Korea Robotics Society
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• v.13 no.1
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• pp.38-44
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• 2018

In this paper, we propose Intelligent Driver Assistance System (I-DAS) for driver safety. The proposed system recognizes safety and danger status by analyzing blind spots that the driver cannot see because of a large angle of head movement from the front. Most studies use image pre-processing such as face detection for collecting information about the driver's head movement. This not only increases the computational complexity of the system, but also decreases the accuracy of the recognition because the image processing system dose not use the entire image of the driver's upper body while seated on the driver's seat and when the head moves at a large angle from the front. The proposed system uses a convolutional neural network to replace the face detection system and uses the entire image of the driver's upper body. Therefore, high accuracy can be maintained even when the driver performs head movement at a large angle from the frontal gaze position without image pre-processing. Experimental result shows that the proposed system can accurately recognize the dangerous conditions in the blind zone during operation and performs with 95% accuracy of recognition for five drivers.

• International Journal of Automotive Technology
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• v.5 no.2
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• pp.95-108
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• 2004

Lanekeeping assistance has the potential to save thousands of lives every year by preventing accidental road departure. This paper presents experimental validation of a potential field lanekeeping assistance system with quantitative performance guarantees. The lanekeeping system is implemented on a 1997 Corvette modified for steer-by-wire capability. With no mechanical connection between the hand wheel and road wheels the lanekeeping system can add steering inputs independently from the driver. Implementation of the lanekeeping system uses a novel combination of a multi-antenna Global Positioning System (GPS) and precision road maps. Preliminary experimental data shows that this control scheme performs extremely well for driver assistance and closely matches simulation results, verifying previous theoretical guarantees for safety. These results also motivate future work which will focus on interaction with the driver.

• Journal of Auto-vehicle Safety Association
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• v.5 no.1
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• pp.69-74
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• 2013

LKAS(Lane Keeping Assistance System) main function is to support the driver in keeping the vehicle within the current lane. Therefore, this system is able to reduce the driver workload with assisting the driver during driving. In this paper, we presented on study for test procedures and evaluation methods of the LKAS. The vehicle test conducted on straight road, left curve, right curve and four different types of lane under various vehicle speeds. This study proposed the LKAS system test procedures and methods that we are able to identify LKAS driving mechanism and performance.

• Journal of Auto-vehicle Safety Association
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• v.4 no.1
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• pp.12-17
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• 2012

ADAS(Advanced Driver Assistance System) which is developed for alleviating driver's load has become improved with extending it's role. Previously, ADAS offered simple function just to make driver's convenience. However, nowadays ADAS also acts as Active Safety system which is made to release and/or prevent accidents. Longitudinal control system, as one of major parts of Active Safety System, is assessed as doing direct effect on avoiding accidents. Therefore, many countries such as Europe and America has pushed longitudinal control system as a government-wide project. In this paper, it covers the result of evaluation system and vehicle evaluation for development study in FCW, ACC and AEB.

• 제어로봇시스템학회:학술대회논문집
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• 1999.10a
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• pp.161-164
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• 1999

An effective approach to lane detection in driver assistance system (DAS) is proposed, based on the decomposition of lane markings. The properties of the decomposed lane markings are discussed, and analyses on lane curvature are given. The current lane on road is detected quickly, the neighboring lane regions are also extracted for lane planning of the vehicle, and the parameters of lane structure are accurately estimated.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.20 no.2
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• pp.149-155
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• 2020

The Korea Highway Traffic Authority provides statistics that analyze the causes of traffic accidents that occurred since 2015 using the Traffic Accident Analysis System (TAAS). it was reported Through TAAS that the driver's forward carelessness was the main cause of traffic accidents in 2018. As statistics on the cause of traffic accidents, 51.2 percent used mobile phones and watched DMB while driving, 14 percent did not secure safe distance, and 3.6 percent violated their duty to protect pedestrians, representing a total of 68.8 percent. In this paper, we propose a system that has improved the advanced driver assistance system ADAS (Advanced Driver Assistance Systems) by utilizing CNN (Convolutional Neural Network) among the algorithms of Deep Learning. The proposed system learns a model that classifies the movement of the driver's face and eyes using Conv2D techniques which are mainly used for Image processing, while recognizing and detecting objects around the vehicle with cameras attached to the front of the vehicle to recognize the driving environment. Then, using the learned visual steering model and driving environment data, the hazard is classified and detected in three stages, depending on the driver's view and driving environment to assist the driver with the forward and blind spots.