• Proceedings of the KIEE Conference
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• 1998.07b
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• pp.465-467
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• 1998

A lane change maneuver is a part of lateral control of an automated highway system. Assuming no direct measurement of its position during transition from one lane to another. A vehicle is controlled to follow the virtual desired trajectory using only on-board sensors. This paper investigates the development of a fuzzy controller for automated lateral control during emergencies. The performance of the fuzzy controller is presented at 20m/s for a step lane change and a double lane change. The robustness of fuzzy controller is investigated with respect to change in tire parameters and the number of passengers.

• 제어로봇시스템학회:학술대회논문집
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• 1998.10a
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• pp.226-231
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• 1998

This paper describes how a priori road geometry and its estimation may be used to detect road boundaries and lane markings in road scene images. We assume flat road and road boundaries and lane markings are all Bertrand curves which have common principal normal vectors. An active contour is used for the detection of road boundary, and we reconstruct its geometric property and make use of it to detect lane markings. Our approach to detect road boundary is based on minimizing energy function including edge related term and geometric constraint term. Lane position is estimated by pixel intensity statistics along the parallel curve shifted properly from boundary of the road. We will show the validity of our algorithm by processing real road images.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.65 no.4
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• pp.659-665
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• 2016

In this paper, we propose a novel position accuracy enhancement method of a low-end GPS using digital map information. The latest digital map has various kinds of information on geographical features. The proposed method uses position information of lane marks among the geographical features. We define the position information of lane marks as the reference points. The position information of a low-end GPS acquired for a period of time is defined as the source points. In the proposed method, rotation and translation matrices between the reference and the source points are calculated by using an Iterative Closest Point(ICP) algorithm. The source points are transformed by the obtained rotation and translation matrices. Finally, the transformed source points are projected on the reference points. Through these processes, the position accuracy of a low-end GPS is ultimately enhanced. To verify the proposed method, the various real experimental results are presented.

• Journal of Auto-vehicle Safety Association
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• v.6 no.2
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• pp.29-35
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• 2014

Lane Keeping Assistance System(LKAS) is a kind of Advanced Driver Assistance Systems(ADAS) which are developed to automate/ adapt/ enhance vehicle systems for safety and better driving. The main system function of LKAS is to support the driver in keeping the vehicle within the current lane. LKAS acquires information on the position of the vehicle within the lane and, when required, sends commands to actuators to influence the lateral movement of the vehicle. Recently, the vehicles equipped with LKAS are commercially available in a few vehicle-advanced countries and the installation of LKAS increases for safety enhancement. The test procedures for LKAS evaluations are being discussed and developed in international committees such as ISO(the International Organization for Standardization). In Korea, the evaluations of LKAS for vehicle safety are planned to be introduced in 2016 KNCAP(Korean New Car Assessment Program). Therefore, the test procedures of LKAS suitable for domestic road and traffic conditions, which accommodate international standards, should be developed. In this paper, some bullet points of the test procedures for LKAS are discussed by extensive researches of previous documents and reports, which are released in public in regard to lateral test procedures including LKAS and Lane Departure Warning System(LDWS). Later, it can be helpful to make a draft considering domestic traffic situations for test procedures of LKAS.

• Journal of the Korean Society of Environmental Restoration Technology
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• v.9 no.3
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• pp.51-58
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• 2006

The purpose of this research presents a method to position and makes the structure for eco-corridors reasonably with collectable analysing results of various effects shown in mammals' road-kill at 429 points. Target animals of this research are Leopard cat, Siberian weasel, Raccoon dog, Korean hare, Eurasian red squirrel, Siberian chipmunk and Water deer. The results derived from the empirical analysis on the contents above are followed. First, according to the results as for Leopard cat road kill analysis, which is designated as Endangered Species Class II, the eco-corridor might be located at near village having stead food in order to decrease the frequencies of road-kill, because its road kill points were mainly collected at 4 lane hilly road with mountain-road-farm area geological type of. Second, because Siberian weasel's road kill was detected at 2 lane hilly road with mountain-road-stream geological type, the eco-corridor might be located at near a mill to decrease road-kill frequencies. Third, the road-kill frequency of Eurasian red squirrel can be reduced when the eco-corridor is located at the area across coniferous tree near 4 lane west sea freeway with mountain-road-mountain. Fourth, the road-kill of Raccoon dog can be reduced when the eco-corridor is located at 4 lane mountain road or hilly road with the geological type having farm land-road-mountain(stream). Fifth, Korean hare's road-kill can be reduced when the eco-corridor is located at grass land across ridge line of mountain, because wild rabbit road kill was happened at 4 lane mountain road or 2 lane mountain road(mountain-road-mountain). Sixth, As for Siberian chipmunk, the eco-corridor might be located at the side slope of mountain road at 2 lane mountain road under the speed of 60km/h with mountain-road-mountain. Seventh, For Water deer, the eco-corridor might be located at 4 lane hilly road with mountain-road-farm land. As for Common otter, Amur hedgehog, Yellow-throated marten, Weasel, it is difficult to specify the proper site of eco-corridor due to the lack of data. Eco-corridors for carnivores might be well located at 4 lane hilly road or 2 lane hilly road with mountain-road-farm land, and the track for herbivores might be well located as a overhead bridge on mountain-road-mountain type across mountains. In order to position eco-corridors for wildlife properly, we have to research animal's behavior with ecological background, and to consider the local uniqueness and regularly collect the empirical road-kill data in long term 3 to 5 year, which can be the foundation for the more suitable place of wild life eco-corridors.

• 제어로봇시스템학회:학술대회논문집
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• 2005.06a
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• pp.1460-1463
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• 2005

In this paper, analysis of magnetic marker for autonomous vehicle guidance system using 3-axis magnetic sensor propose. Position sensing is an important an estimation system of vehicle position and orientation on magnetic lane, which is a parameter of the steering controller for automated lane following is described. To verify that the magnetic dipole model could be applied to a magnetic unit paved in roadway, the analysis of the data 3-axis magnetic field measured experimentally.

• Journal of Drive and Control
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• v.17 no.4
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• pp.72-79
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• 2020

To ensure the safety of Advanced Driver Assistance Systems (ADAS) or autonomous vehicles, it is important to recognize the vehicle position, and specifically, the increased accuracy of the lateral position of the vehicle is required. In recent years, the quality of GPS signals has improved a lot and the price has decreased significantly, but extreme urban environments such as tunnels still pose a critical challenge. In this study, we proposed stable and precise lane recognition and tracking methods to solve these two issues via fusion of AVM images and vehicle sensor data using an extended Kalman filter. In addition, the vehicle's lateral position recognition and the abnormal state of RTK GPS were determined using this approach. The proposed method was validated via actual vehicle experiments in urban areas reporting multipath and signal disconnections.

• Transactions of the Korean Society of Automotive Engineers
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• v.17 no.3
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• pp.54-61
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• 2009

Lane keeping assistant system (LKAS) could save thousands of lives each year by maintaining lane position and is regarded as a promising active safety system. The LKAS is expected to reduce the driver workload and to assist the driver during driving. This paper proposes a model based predictive controller for the LKAS which requires cooperative driving between the driver and the assistance system. A Hardware-In-the-Loop-Simulator (HILS) is constructed for its evaluation and includes Carsim, Matlab Simulink and a lane detection algorithm. The single camera is mounted with the HILS to acquire the monitor images and to detect the lane markers. The simulation is conducted to validate the LKAS control performance in various road scenario.

• Journal of Institute of Control, Robotics and Systems
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• v.21 no.7
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• pp.621-626
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• 2015

This paper presents a camera calibration method in order to estimate the lane detection and inter-vehicle distance estimation system for an automotive safety driving system. In order to implement the lane detection and vision-based inter-vehicle distance estimation to the embedded navigations or black box systems, it is necessary to consider the computation time and algorithm complexity. The process of camera calibration estimates the horizon, the position of the car's hood and the lane width for extraction of region of interest (ROI) from input image sequences. The precision of the calibration method is very important to the lane detection and inter-vehicle distance estimation. The proposed calibration method consists of three main steps: 1) horizon area determination; 2) estimation of the car's hood area; and 3) estimation of initial lane width. Various experimental results show the effectiveness of the proposed method.

• International Journal of Automotive Technology
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• v.8 no.4
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• pp.513-520
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• 2007

In this paper, we propose an approach to identify the driving environment for intelligent highway vehicles by means of image processing and computer vision techniques. The proposed approach mainly consists of two consecutive computational steps. The first step is the lane marking detection, which is used to identify the location of the host vehicle and road geometry. In this step, related standard image processing techniques are adapted for lane-related information. In the second step, by using the output from the first step, a four-stage algorithm for vehicle detection is proposed to provide information on the relative position and speed between the host vehicle and each preceding vehicle. The proposed approach has been validated in several real-world scenarios. Herein, experimental results indicate low false alarm and low false dismissal and have demonstrated the robustness of the proposed detection approach.