• Journal of Korean Tunnelling and Underground Space Association
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• v.14 no.2
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• pp.107-116
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• 2012

The experiment was performed to analyze the intersectional ventilation efficiency by intersection structure and Jet Fan in network-form road tunnel. For this, the size of real road tunnel was reduced by 1/45. To apply traffic inertia force when driving, blower fan was used to form an airflow in model tunnel and the intersectional efficiency was also investigated by measuring the speed at local point of the tunnel. To improve the reduction of ventilation caused by the structure character, Jet Fan was installed to optimize ventilation efficiency in tunnel.

• IEMEK Journal of Embedded Systems and Applications
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• v.16 no.3
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• pp.107-111
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• 2021

A 3D point cloud map is an essential elements in various fields, including precise autonomous navigation system. However, generating a 3D point cloud map using a single sensor has limitations due to the price of expensive sensor. In order to solve this problem, we propose a precise 3D mapping system using low-cost sensor fusion. Generating a point cloud map requires the process of estimating the current position and attitude, and describing the surrounding environment. In this paper, we utilized a commercial visual-inertial odometry sensor to estimate the current position and attitude states. Based on the state value, the 2D LiDAR measurement values describe the surrounding environment to create a point cloud map. To analyze the performance of the proposed algorithm, we compared the performance of the proposed algorithm and the 3D LiDAR-based SLAM (simultaneous localization and mapping) algorithm. As a result, it was confirmed that a precise 3D point cloud map can be generated with the low-cost sensor fusion system proposed in this paper.

• The Journal of Korea Robotics Society
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• v.18 no.1
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• pp.72-81
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• 2023

We introduce tightly-coupled GNSS-LiDAR-Inertial state estimator, which is capable of SLAM (Simultaneously Localization and Mapping) and autonomous driving. Long term drift is one of the main sources of estimation error, and some LiDAR SLAM framework utilize loop closure to overcome this error. However, when loop closing event happens, one's current state could change abruptly and pose some safety issues on drivers. Directly utilizing GNSS (Global Navigation Satellite System) positioning information could help alleviating this problem, but accurate information is not always available and inaccurate vertical positioning issues still exist. We thus propose our method which tightly couples raw GNSS measurements into LiDAR-Inertial SLAM framework which can handle satellite positioning information regardless of its uncertainty. Also, with NLOS (Non-light-of-sight) satellite signal handling, we can estimate our states more smoothly and accurately. With several autonomous driving tests on AGV (Autonomous Ground Vehicle), we verified that our method can be applied to real-world problem.

• Journal of Institute of Control, Robotics and Systems
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• v.7 no.8
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• pp.703-710
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• 2001

The nonlinear observers are proposed for a nonlinear system. To improve the characteristics such as stability, convergence, and $H^{\infty}$ filter performance criterion, we utilize an $H^{\infty}$ filter Riccati equation or a modified $H^{\infty}$ filter Riccati equation with a freedom parameter. Using the Lyapunov function method, the characteristics of the observers are analyzed. Then the in-flight alignment for a strapdown inertial navigation system(SDINS) is designed using the proposed observer. And the additive quaternion error model is especially used to reduce the uncertainty of the SDINS error model. Simulation results show that the observer with the modified $H^{\infty}$ filter Riccati equation effectively improves the performance of the in-flight alignment.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.36 no.6
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• pp.673-679
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• 2012

Vehicle rollover is a serious kind of accident, particularly for sport utility vehicles, and its occurrence can be minimized by utilizing active rollover prevention systems. The performance of these protection systems is very sensitive to vehicle inertial parameters such as the vehicle's mass and center of mass. These parameters vary with the number of passengers and in different load situations. In this paper, a unified method for vehicle mass estimation is proposed that takes into account the available driving conditions. Three estimation algorithms are developed based on longitudinal, lateral, and vertical vehicle motion, respectively. Then, the three algorithms are combined to extract information on the vehicle's mass during arbitrary vehicle maneuvering. The performance of the proposed vehicle mass estimation method is demonstrated through real-time experiments.

• Journal of Advanced Navigation Technology
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• v.20 no.4
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• pp.314-320
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• 2016

Inertial navigation system (INS) has used aided systems and sensors to compensate navigation error. Global navigation satellite system (GNSS), velocity measurement sensor (VMS), and radar are commonly used to aid INS. Land navigation system (LNS) also mainly uses VMS when GNSS cannot be used such as at tunnel or on jammed scenario. A straight drive is required when VMS-aided navigation is used, because there is only speed of straight direction whereas no crossways and vertical directions. In local environment, even an expressway has lack of straight drive which is constraint of VMS-aided navigation algorithm. This paper proposes an enhanced VMS-aided navigation algorithm for LNS with indirect drive by restricting filter update condition. Also, there is a result of vehicle test to prove performance of the proposed algorithm.

• The KIPS Transactions:PartB
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• v.14B no.3 s.113
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• pp.163-170
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• 2007

In autonomous navigation of a mobile vehicle or a mobile robot, localization calculated from recognizing its environment is most important factor. Generally, we can determine position and pose of a camera equipped mobile vehicle or mobile robot using INS and GPS but, in this case, we must use enough known ground landmark for accurate localization. hi contrast with homography method to calculate position and pose of a camera by only using the relation of two dimensional feature point between two frames, in this paper, we propose a method to calculate the position and the pose of a camera using relation between the location to predict through perspective transform of 3D feature points obtained by overlaying 3D model with previous frame using GPS and INS input and the location of corresponding feature point calculated using KLT tracking method in current frame. For the purpose of the performance evaluation, we use wireless-controlled vehicle mounted CCD camera, GPS and INS, and performed the test to calculate the location and the rotation angle of the camera with the video sequence stream obtained at 15Hz frame rate.

• Journal of the Korean Institute of Intelligent Systems
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• v.13 no.3
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• pp.300-309
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• 2003

The Japanese developed the first Car navigation system in 1981 with the advent of Honda, which was known as the car inertial navigation system. Now days, It is possible to search the shortest route to and from places and arrival time using the internet via cell phone to the driver based on GIS and GPS. However, even with a good navigation system, it losses the shortest route when there is an average speed of the vehicle being between S-15 kilometers. Therefore, in order to improve the vehicle waiting time and average vehicle speed, we are suggesting an optimal green time algorithm using fuzzy adaptive control, where there are different traffic intersection lengths, and lanes. In this paper, to be able to assist the driver and forecast the optimal traffic information with regards to the road conditions; dangerous roads, construction work and estimation of arrival time at their destination using internet.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.22 no.2
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• pp.514-521
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• 2021

When calculating an intersection collision speed using a formula, it is very difficult to grasp the degree of deceleration of a vehicle after the collision unless there is road surface trace in the entire section where each vehicle moved from the point of collision to their final positions after the collision. A vehicle's motion trajectory shows an irregular curve after a collision due to the effects of inertia based on the driving characteristics of the vehicle, the eccentric force according to the collision site, and the collision speed. Therefore, it is very important to set the appropriate departure angle after a collision for accurate collision speed analysis. In this study, based on experimental collision data using a computer simulation (PC-Crash), the correlation between an appropriate vehicle departure angle and the post-collision speed was analyzed, and then, a regression analysis model was derived. Through this, we propose a method to calculate collision speed by applying only the vehicle departure angle in some types of collisions for traffic accidents at intersections.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.24 no.11
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• pp.1417-1423
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• 2020

This paper proposes a waypoint guidance algorithm for the Autonomous Underwater Vehicle(AUV). The proposed simplified guidance algorithm is presented, which is combined LOS guidance and cross-track guidance for path following. Cross-track error is calculated using the position of the AUV and reference path. LOS guidance and cross-track guidance are appropriately changed according to cross-track error. And the stability of the system has been improved using variable cross-track control gain by cross-track error. Also, in this paper, navigation hardware in-the loop simulation(HILS) is implemented to verify navigation algorithm of AUV that performs combined navigation using inertial navigation device and doppler velocity log(DVL). Finally, we design integrated system HILS (including navigation HILS) for performance verification of guidance algorithm of the autonomous underwater vehicle. By comparing the sea test result with HILS result, the proposed guidance algorithm and HILS configuration were confirmed be correct.