• International Journal of Highway Engineering
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• v.16 no.1
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• pp.41-48
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• 2014

PURPOSES : The lane width of the domestic highway is 3.5 ~ 3.6m and it has been designed nationwide. However, the distribution of the average vehicle widths, rearview mirror widths and lateral wheel paths by region appear different. Then, lane spare widths may differ by region followingly. Thus, the flexible design of freeway lane widths is required. METHODS : The methodologies of this paper are as follows. First, vehicle widths rearview mirror widths lateral wheel paths of vehicles driven four national expressways were measured. Second, lane spare widths by vehicle widths were calculated. Third, lane spare widths reflecting rearview mirror widths were calculated by using interval estimation. Additionally, lane spare widths reflecting vehicles lateral wheel paths were calculated. RESULTS : The results of this paper are as follows. First, lane spare widths by vehicle widths ranges 0.83 to 0.95m. Second, lane spare widths reflecting rearview mirror widths ranges 0.518 to 0.747m at the confidence interval 95%. Third, lane spare widths reflecting vehicles' lateral wheel paths ranges -0.022 to 0.322m at the curved sections and the confidence interval 95%. CONCLUSIONS : It may be concluded that the present lane spare widths are relatively narrow at the curved section. Thus, there is a need to consider expanded lane widths at the curved sections. Additionally, there is a need to consider flexible design of lane widths by various conditions.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.66 no.9
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• pp.1416-1423
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• 2017

Lane detection technology is one of the most important issues on car safety and self-driving capability of autonomous vehicle. This paper introduces an accurate lane detection scheme based on OCC(Optical Camera Communication) for moving vehicles. For lane detection of moving vehicles, the streetlights and the front camera of the vehicle were used for a transmitter and a receiver, respectively. Based on the angle information of multiple streetlights in a captured image, the distance from sidewalk can be calculated using non-linear regression analysis. Simulation results show that the proposed scheme shows robust performance of accurate lane detection.

• Journal of the Korean Institute of Telematics and Electronics S
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• v.34S no.11
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• pp.126-137
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• 1997

This paper describes the algorithm which can estimate road following direction and deetect obstacle using a monocular vision system. This algorithm can estimate the course of vehicle using the vanishing point properties and detect obstacle by statistical method. The proposed algorithm is composed of four steps, which are lane prediction, lane extraction, road following parameter estimation and obstacle detection. It is designed for high processing speed and high accuracy. The former is achieved by a small area named sub-windown in lane existence area, the later is realized by using connected edge points of lane. We would like to present that the new mehod can detect obstacle using the simple statistical method. The paracticalities of the processing speed, the accuracy of the algorithm and proposing obstacle detection method, have been justified through the experiment applied VTR image of the real road to the algorithm.

• Proceedings of the IEEK Conference
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• 2000.07b
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• pp.769-772
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• 2000

In this paper, a new lateral offset estimation method, based on image processing techniques, is proposed for driver assistant system. A new description on lane markings in the image plane is presented, and its properties are discussed and used to detect lane markings. Multi-frame lane detection and analysis are adopted to improve the proposed lateral control method. An algorithm for obstacle detection is also developed. Experimental results show that the proposed method performs lateral control effectively.

• ETRI Journal
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• v.41 no.4
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• pp.506-514
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• 2019

Currently, studies on autonomous driving are being actively conducted. Vehicle positioning techniques are very important in the autonomous driving area. Currently, the global positioning system (GPS) is the most widely used technology for vehicle positioning. Although technologies such as the inertial navigation system and vision are used in combination with GPS to enhance precision, there is a limitation in measuring the lane and position in shaded areas of GPS, like tunnels. To solve such problems, this paper presents the use of LED lighting for position estimation in GPS shadow areas. This paper presents simulations in the environment of three-lane tunnels with LEDs of different color temperatures, and the results show that position estimation is possible by the analyzing chromaticity of LED lights. To improve the precision of positioning, a fuzzy logic system is added to the location function in the literature [1]. The experimental results showed that the average error was 0.0619 cm, and verify that the performance of developed position estimation system is viable compared with previous works.

• Journal of the Institute of Electronics and Information Engineers
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• v.50 no.1
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• pp.215-224
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• 2013

This paper proposes a robust lane detection algorithm for bumpy or slope changing roads by estimating extrinsic camera parameters, which represent the pose of the camera mounted on the car. The proposed algorithm assumes that two lanes are parallel with the predefined width. The lane detection and the extrinsic camera parameter estimation are performed simultaneously by utilizing B-snake in motion compensated and merged feature map with consecutive sequences. The experimental results show the robustness of the proposed algorithm in various road environments. Furthermore, the accuracy of extrinsic camera parameter estimation is evaluated by calculating the distance to a preceding car with the estimated parameters and comparing to the radar-measured distance.

• Journal of Auto-vehicle Safety Association
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• v.14 no.4
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• pp.16-20
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• 2022

Accurate state estimation is important for autonomous driving. However, the estimation error increases in situations that a lot of longitudinal slip occurs. Therefore, this paper presents a vehicle state estimation method using an Extended Kalman Filter. The filter estimates the states of the host vehicle robust to wheel slip. It utilizes the measurements of the four-wheel rotational speeds, longitudinal acceleration, yaw-rate, and steering wheel angle. Nonlinear measurement model is represented by Ackermann Model. The main advantage of this approach is the accurate estimation of yaw rate due to the measurement of the steering wheel angle. The proposed algorithm is verified in scenarios of autonomous emergency braking (AEB), lane change (LC), lane keeping (LK) using an automated vehicle. The results show that the proposed algorithm guarantees accurate estimation in such scenarios.

• Proceedings of the IEEK Conference
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• 2001.06e
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• pp.127-130
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• 2001

In this paper, we describes a method of estimating traveling direction of a autonomous vehicle. For the development of autonomous vehicle, it is important to detect road lane and to reckon traveling direction. The object of a propose algorithm is to perform lane detection in real-time for standalone vision system. And we calculate efficent traveling direction to find steering angie for lateral control system. Therefore autonomous vehicle go forward the center of lane by adjusting the current steering angle using traveling direction.

• Journal of Auto-vehicle Safety Association
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• v.9 no.3
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• pp.24-30
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• 2017

This paper presents the development of laser scanner based static obstacle detection algorithm for vehicle localization on lane lost section. On urban autonomous driving, vehicle localization is based on lane information, GPS and digital map is required to ensure. However, in actual urban roads, the lane data may not come in due to traffic jams, intersections, weather conditions, faint lanes and so on. For lane lost section, lane based localization is limited or impossible. The proposed algorithm is designed to determine the lane existence by using reliability of front vision data and can be utilized on lane lost section. For the localization, the laser scanner is used to distinguish the static object through estimation and fusion process based on the speed information on radar data. Then, the laser scanner data are clustered to determine if the object is a static obstacle such as a fence, pole, curb and traffic light. The road boundary is extracted and localization is performed to determine the location of the ego vehicle by comparing with digital map by detection algorithm. It is shown that the localization using the proposed algorithm can contribute effectively to safe autonomous driving.

• International Journal of Highway Engineering
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• v.14 no.6
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• pp.149-158
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• 2012

PURPOSES : This research is to evaluate the effects of zig-zag marking. METHODS : Based on the literature review, This research adopt Comparison Group method which was developed by Hauer. RESULTS : The findings are as follows. Effectiveness of treatment at all site 1.06, intersection 0.92, median bus lane 1.93, school zone 0.50, below 6-lane 0.48, above 6-lane 1.19, cat vs person 0.57, car vs car 1.36, car only 1.29. CONCLUSIONS : Though the analysis, it was founded that zig-zag marking on the small scale road(below 6-lane) and school zone contribute to reduce traffic accidents(effectiveness of the treatment : 0.50, 0.48). But the accidents at large scale road and median bus lane where installed zig-zag marking demonstrated negative result.(effectiveness of the treatment indicates exceed 1.0).