• Journal of Auto-vehicle Safety Association
• /
• v.11 no.2
• /
• pp.29-34
• /
• 2019

This paper presents a new method for construction of a static obstacle map. A static obstacle is important since it is utilized to path planning and decision. Several established approaches generate static obstacle map by grid method and counting algorithm. However, these approaches are occasionally ineffective since the density of LiDAR layer is low. Our approach solved this problem by applying probability theory. First, we converted all LiDAR point to Gaussian distribution to considers an uncertainty of LiDAR point. This Gaussian distribution represents likelihood of obstacle. Second, we modeled dynamic transition of a static obstacle map by adopting the Hidden Markov Model. Due to the dynamic characteristics of the vehicle in relation to the conditions of the next stage only, a more accurate map of the obstacles can be obtained using the Hidden Markov Model. Experimental data obtained from test driving demonstrates that our approach is suitable for mapping static obstacles. In addition, this result shows that our algorithm has an advantage in estimating not only static obstacles but also dynamic characteristics of moving target such as driving vehicles.

• Journal of Auto-vehicle Safety Association
• /
• v.14 no.2
• /
• pp.39-44
• /
• 2022

This paper presents LiDAR static obstacle map based vehicle position correction algorithm for urban autonomous driving. Real Time Kinematic (RTK) GPS is commonly used in highway automated vehicle systems. For urban automated vehicle systems, RTK GPS have some trouble in shaded area. Therefore, this paper represents a method to estimate the position of the host vehicle using AVM camera, front camera, LiDAR and low-cost GPS based on Extended Kalman Filter (EKF). Static obstacle map (STOM) is constructed only with static object based on Bayesian rule. To run the algorithm, HD map and Static obstacle reference map (STORM) must be prepared in advance. STORM is constructed by accumulating and voxelizing the static obstacle map (STOM). The algorithm consists of three main process. The first process is to acquire sensor data from low-cost GPS, AVM camera, front camera, and LiDAR. Second, low-cost GPS data is used to define initial point. Third, AVM camera, front camera, LiDAR point cloud matching to HD map and STORM is conducted using Normal Distribution Transformation (NDT) method. Third, position of the host vehicle position is corrected based on the Extended Kalman Filter (EKF).The proposed algorithm is implemented in the Linux Robot Operating System (ROS) environment and showed better performance than only lane-detection algorithm. It is expected to be more robust and accurate than raw lidar point cloud matching algorithm in autonomous driving.

• Journal of the Korean Society of Safety
• /
• v.31 no.1
• /
• pp.41-47
• /
• 2016

The rolling stock is composed of several cars. In order to operate in combination, it is necessary to connect the device, called coupler, between the rolling stocks. When the collision occurs between cars, couplers should be able to absorb the shock. Urban railway has used only rubber absorbers. But recently, the hydraulic buffer has been considered in general railway. In order to know the performance of the buffer it should be conducted to experiments. But whenever this combination change, we should experiments to know a lot of the dynamic behavior of each coupler. These experiments are generally replaced by the simulation, since a lot of time and cost consuming. The quasi-static map of hydraulic buffer obtained by the experiments is required for the simulation. However, the experiments for obtaining such a quasi-static map is costly and time consuming. In this paper, it proposes a method for deriving the quasi-static map of hydraulic buffer from the theoretical model.

• 한국지형공간정보학회:학술대회논문집
• /
• 2005.08a
• /
• pp.61-72
• /
• 2005

The landslide risk assessment process consists of hazard risk assessment and vulnerability analysis. landslide hazard risk is location dependent. Therefore, maps and spatial technologies such as GIS are very important components of the risk assessment process. This paper discusses the advantages of using GIS technology in the risk assessment process and illustrates the benefits through case studies of live projects undertaken. The goal of this study is to generate a map of landslide vulnerability map by analysis of static natural factors with GIS. A simple and efficient algorithm is proposed to generate a landslide potentialities map from DEM and existing maps. The categories of controlling factors for landslides, aspect of slope, soil, vegetation are defined. The weight values for landslide potentialities are calculated from AHP method. Slope and slope-direction are extracted from DEM, and soil informations are extracted from digital soil map. Also, vegetation informations are extracted from digital vegetation map. Finally, as overlaying, landslide potentialities map is made out, and it is verified with landslide place.

• Journal of Auto-vehicle Safety Association
• /
• v.13 no.4
• /
• pp.14-19
• /
• 2021

This paper presents LiDAR static obstacle map based vehicle dynamic state estimation algorithm for urban autonomous driving. In an autonomous driving, state estimation of host vehicle is important for accurate prediction of ego motion and perceived object. Therefore, in a situation in which noise exists in the control input of the vehicle, state estimation using sensor such as LiDAR and vision is required. However, it is difficult to obtain a measurement for the vehicle state because the recognition sensor of autonomous vehicle perceives including a dynamic object. The proposed algorithm consists of two parts. First, a Bayesian rule-based static obstacle map is constructed using continuous LiDAR point cloud input. Second, vehicle odometry during the time interval is calculated by matching the static obstacle map using Normal Distribution Transformation (NDT) method. And the velocity and yaw rate of vehicle are estimated based on the Extended Kalman Filter (EKF) using vehicle odometry as measurement. The proposed algorithm is implemented in the Linux Robot Operating System (ROS) environment, and is verified with data obtained from actual driving on urban roads. The test results show a more robust and accurate dynamic state estimation result when there is a bias in the chassis IMU sensor.

• Journal of the Korean Society of Manufacturing Technology Engineers
• /
• v.23 no.3
• /
• pp.279-283
• /
• 2014

Recently, a topology algorithm based on the artificial bee colony algorithm (ABCA) has been proposed for static and dynamic topology optimization. From the results, the convergence rate of the algorithm was determined to be slightly slow. Therefore, we propose a new search method to improve the convergence rate of the algorithm using a chaotic map. We investigate the effect of the chaotic map on the convergence rate of the algorithm in static and dynamic topology optimization. The chaotic map has been applied to three cases, namely, employ bee search, onlooker bee search, and both employ bee as well as onlooker bee search steps. It is verified that the case in which the logistic function of the chaotic map is applied to both employ bee as well as onlooker bee search steps shows the best dynamic topology optimization, improved by 5.89% compared to ABCA. Therefore, it is expected that the proposed algorithm can effectively be applied to dynamic topology optimization to improve the convergence rate.

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

• Spatial Information Research
• /
• v.9 no.2
• /
• pp.263-275
• /
• 2001

With increasing of utilization of 1/1,000 Digital-Map being constructed with NGIS project, it is entering upon the stage that problem as the inspection of position accuracy of Digital-Map before its use. This paper evaluated position accuracy of Digital-Map being based on the construction of college facility management system into small area and presented modification/renovation of Digital-Map on area being occurred position error. With a view to evaluation of position error to building using RTK survey, position error was shown more than 0.5mm that is prescribed in survey-law. In order to acquire good position accuracy, first we carried out Static survey to college and selected control point. And, we evaluated coordinate value to important building from control point and compared these results with RTK survey results. Second, we carried out Affine transform based on the control point of building being surveyed with RTK, transformed pre-constructed Digital-Map and compared these results with RTK Survey results. We analyzed first and second method and presented improvement method of position accuracy of Digital-Map suited on small area.

• Journal of the Korean Institute of Telematics and Electronics S
• /
• v.34S no.10
• /
• pp.59-70
• /
• 1997

Until now, considerable progress has been made in the application of range finding techanique performing direct 3-D measurement from the object. However, ther are few use of the method in the area of the application of material handing. We present a range finding vision system consisting of static and dynamic range finders to automate a reclaimer used for material handling. A static range finder detects range data of the front part of the piles of material, and a height map is obtained from the proposed image processing algorithm. The height map is used to calculate the optimal job path as features for required information for material handling function. A dynamic range finder attached on the side of the arm of the reclaimer detects the change of the local properties of the material with the handling function, which is used for avoiding collision and detecting the ending point for changing direction. the developed vision systm was applied to a 1/20 simulator and the results of test show that it is appropriate to use for automating the material handling.

• Journal of the Korean Institute of Intelligent Systems
• /
• v.14 no.4
• /
• pp.396-401
• /
• 2004

Recently a framework for the cognition-based navigational planning of a mobile robot on dynamic environment has been proposed, and simulation results applied it to the static environment been presented [1]. In this paper, we propose a linguistic map-based framework for the navigational planning of mobile robots, which is applicable to the dynamic environment including not only static obstacles but also dynamic obstacles such as temporal-spatio obstacles, by extending Lee et al. 's framework, and provide computer simulation results obtained by applying to a mobile robot on the dynamic environment in order to show the validity of the proposed algorithm.