• Journal of Platform Technology
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• v.9 no.2
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• pp.46-54
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• 2021

Recently, interest in the safety of Self-driving has been increasing. Self-driving have been studied and developed by many universities, research centers, car companies, and companies of other industries around the world since the middle 1980s. In this study, we propose the automatic extraction method of the threatening obstacle on the Road for the Self-driving. A threatening obstacle is defined in this study as a comparatively large object at center of the image. First of all, an input image and its decreased resolution images are segmented. Segmented areas are classified as the outer or the inner area. The outer area is adjacent to boundaries of the image and the other is not. Each area is merged with its neighbors when adjacent areas are included by a same area in the decreased resolution image. The Obstacle area and Non Obstacle area are selected from the inner area and outer area respectively. Obstacle areas are the representative areas for the obstacle and are selected by using the information about the area size and location. The Obstacle area and Non Obstacle area consist of the threatening obstacle on the road. Through experiments, we expect that the proposed method will be able to reduce accidents and casualties in Self-driving.

• Atmosphere
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• v.17 no.4
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• pp.381-391
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• 2007

The characteristics of flow around a single obstacle with fixed height and varied length and width are numerically investigated using a computational fluid dynamics (CFD) model. As the obstacle length increases, flow distortion near the upwind side of the obstacle increases and the size of the recirculation zone behind the obstacle also increases. As the obstacle width increases, the size of the recirculation zone decreases, despite almost invariable flow distortion near the upwind side of the obstacle. Flow passing through an obstacle is separated, one part going around the obstacle and the other crossing over the obstacle. The size of the recirculation zone is determined by the distance between the obstacle and the point (reattachment point) at which both the flows converge. When the obstacle width is relatively large, flows are reattached at the obstacle surface and their recoveries occur. Resultant shortening of the paths of flows crossing over and going around decreases the size of the recirculation zone. To support this, the extent of flow distortion defined based on the change in wind direction is analyzed. The result shows that flow distortion is largest near the ground surface and decreases with height. An increase in obstacle length increases the frontal area fraction of flow distortion around the obstacle. In the cases of increasing the width, the frontal area fraction near the upwind side of the obstacle does not change much, but near the downwind side, it becomes larger as the width increases. The frontal area fraction is in a better correlation with the size of the recirculation zone than the building aspect ratios, suggesting that the frontal area fraction is a good indicator for explaining the variation in the size of the recirculation zone with the building aspect ratios.

• Journal of Institute of Control, Robotics and Systems
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• v.18 no.4
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• pp.321-327
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• 2012

This paper presents a comparative analysis on three different types of performance indices of obstacle detection for an overlapped ultrasonic sensor ring. Due to beam overlap, the entire sensing zone of each ultrasonic sensor can be divided into three smaller sensing subzones, which leads to significant reduction of positional uncertainty in obstacle detection. First, the positional uncertainty in obstacle detection is expressed in terms of the area of a sensing subzone, and type 1 performance index is then defined as the area ratio of side and center sensing subzones. Second, based on the area of a sensing subzone, type 2 performance index is defined taking into account the size of the entire range of obstacle detection as well as the degree of the positional uncertainty in obstacle detection. Third, the positional uncertainty in obstacle detection is now expressed in terms of the length of the uncertainty arc spanning a sensing subzone, and type 3 performance index is then defined as the average value of the uncertainty arc lengths over the entire range of obstacle detection. Fourth, using a commercial low directivity ultrasonic sensor, the changes of three different performance indices depending on the parameter of an overlapped ultrasonic sensor ring are examined and compared.

• Journal of Institute of Control, Robotics and Systems
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• v.22 no.7
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• pp.552-556
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• 2016

Obstacle detection is a key feature in the safe driving control of electric wheelchairs. The suggested obstacle detection algorithm was designed to provide obstacle avoidance direction and detect the existence of cliffs. By means of this information, the wheelchair can determine where to steer and whether to stop or go. A 3D depth camera (Microsoft KINECT) is used to scan the 3D point data of the scene, extract information on obstacles, and produce a steering direction for obstacle avoidance. To be specific, ground detection is applied to extract the obstacle candidates from the scanned data and the candidates are projected onto a 2D map. The 2D map provides discretized information of the extracted obstacles to decide on the avoidance direction (left or right) of the wheelchair. As an additional function, cliff detection is developed. By defining the "cliffband," the ratio of the predefined band area and the detected area within the band area, the cliff detection algorithm can decide if a cliff is in front of the wheelchair. Vehicle tests were carried out by applying the algorithm to the electric wheelchair. Additionally, detailed functions of obstacle detection, such as providing avoidance direction and detecting the existence of cliffs, were demonstrated.

• 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.

• The KIPS Transactions:PartB
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• v.18B no.5
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• pp.255-264
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• 2011

In this paper, we propose a forward vehicle detection algorithm using column detection and bird's-eye view mapping based on stereo vision. The algorithm can detect forward vehicles robustly in real complex traffic situations. The algorithm consists of the three steps, namely road feature-based column detection, bird's-eye view mapping-based obstacle segmentation, obstacle area remerging and vehicle verification. First, we extract a road feature using maximum frequent values in v-disparity map. And we perform a column detection using the road feature as a new criterion. The road feature is more appropriate criterion than the median value because it is not affected by a road traffic situation, for example the changing of obstacle size or the number of obstacles. But there are still multiple obstacles in the obstacle areas. Thus, we perform a bird's-eye view mapping-based obstacle segmentation to divide obstacle accurately. We can segment obstacle easily because a bird's-eye view mapping can represent the position of obstacle on planar plane using depth map and camera information. Additionally, we perform obstacle area remerging processing because a segmented obstacle area may be same obstacle. Finally, we verify the obstacles whether those are vehicles or not using a depth map and gray image. We conduct experiments to prove the vehicle detection performance by applying our algorithm to real complex traffic situations.

• 제어로봇시스템학회:학술대회논문집
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• 2001.10a
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• pp.59.6-59
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• 2001

A virtual obstacle method for escaping local minima encountered by sonar-based mobile robot navigation used in real-time obstacle avoidance is presented. The new algorithm judges the mobile robot falls into local minima and helps the mobile robot escape from Et, which regards a concave obstacle as convex or flat one, virtual obstacle method. In the algorithm, it starts to make virtual-obstacle when the mobile robot meets a certain condition, then the robot mores back slowly taking inside area of local minima as obstacle gradually The new algorithm is simulated. The experimental results are presented to demonstrate the usefulness of the method.

• Journal of information and communication convergence engineering
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• v.14 no.2
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• pp.129-135
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• 2016

In this paper, we present a novel algorithm for image-based maritime obstacle detection using global sparsity potentials (GSPs), in which "global" refers to the entire sea area. The horizon line is detected first to segment the sea area as the region of interest (ROI). Considering the geometric relationship between the camera and the sea surface, variable-size image windows are adopted to sample patches in the ROI. Then, each patch is represented by its texture feature, and its average distance to all the other patches is taken as the value of its GSP. Thereafter, patches with a smaller GSP are clustered as the sea surface, and patches with a higher GSP are taken as the obstacle candidates. Finally, the candidates far from the mean feature of the sea surface are selected and aggregated as the obstacles. Experimental results verify that the proposed approach is highly accurate as compared to other methods, such as the traditional feature space reclustering method and a state-of-the-art saliency detection method.

• 제어로봇시스템학회:학술대회논문집
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• 2005.06a
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• pp.2325-2330
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• 2005

A potential field method for solving the problem of path planning based on global and local information for a mobile robot moving among a set of stationary obstacles is described. The concept of various method used path planning is used design a planning strategy. A real human living area is constructed by many moving and imminence obstacles. Home service mobile robot must avoid many obstacles instantly. A path that safe and attraction towards the goal is chosen. The potential function depends on distance from the goal and heuristic function relies on surrounding environments. Three additional combined methods are proposed to apply to human living area, calibration robots position by measured surrounding environment and adapted home service robots. In this work, we proposed the application of various path planning theory to real area, human living. First, we consider potential field method. Potential field method is attractive method, but that method has great problem called local minimum. So we proposed intermediate point in real area. Intermediate point was set in doorframe and between walls there is connect other room or other area. Intermediate point is very efficiency in computing path. That point is able to smaller area, area divided by intermediate point line. The important idea is intermediate point is permanent point until destruction house or apartment house. Second step is move robot with sensing on front of mobile robot. With sensing, mobile robot recognize obstacle and judge moving obstacle. If mobile robot is reach the intermediate point, robot sensing the surround of point. Mobile robot has data about intermediate point, so mobile robot is able to calibration robots position and direction. Third, we gave uncertainty to robot and obstacles. Because, mobile robot was motion and sensing ability is not enough to control. Robot and obstacle have uncertainty. So, mobile robot planed safe path planning to collision free. Finally, escape local minimum, that has possibility occur robot do not work. Local minimum problem solved by virtual obstacle method. Next is some supposition in real living area.

• The Korean Journal of Emergency Medical Services
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• v.20 no.3
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• pp.95-105
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• 2016

Purpose: The purpose of the study was to examine the career obstacle factors and career identity according to career choice type among paramedic students. Methods: A self-reported questionnaire was completed by 234 paramedic students in C area from August 25 to September 3, 2015. The questionnaire consisted of general characteristics of the subjects, career obstacle factors, career identity, and types of career choice. The data were analyzed by t test, ANOVA, post hoc $Scheff{\acute{e}}$ test, Pearson's correlation analysis, and multiple regression analysis using SPSS v. 20.0. Results: Career obstacle factors and career identity of paramedic students had significant differences on motivation of university choice, major satisfaction, and job preference. A stable type of career choice showed a significantly lower score for career obstacle factors and a higher score for career identity. Career identity had a strongly positive relationship with major satisfaction and had a negative relationship with career obstacle factors. Logistic regression analysis revealed that the main variables affecting career identity were types of career choice, motivation of university choice, major satisfaction, and career obstacle factors. The explanatory power was 58.0%. Conclusion: The development of a career integrity enhancement program can solve the career obstacle factors for paramedic students.