• 자동차안전학회지
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• 제3권2호
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• pp.28-33
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• 2011

This paper presents driving path estimation algorithm for adaptive cruise control system and advanced emergency braking system using multi-sensor fusion. Through data collection, yaw rate filtering based road curvature and vision sensor road curvature characteristics are analyzed. Yaw rate filtering based road curvature and vision sensor road curvature are fused into the one curvature by weighting factor which are considering characteristics of each curvature data. The proposed driving path estimation algorithm has been investigated via simulation performed on a vehicle package Carsim and Matlab/Simulink. It has been shown via simulation that the proposed driving path estimation algorithm improves primary target detection rate.

• 자동차안전학회지
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• 제13권4호
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• pp.123-128
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• 2021

This paper present box feature estimation from LiDAR point cluster using maximum likelihood Method. Previous LiDAR tracking method for autonomous driving shows high accuracy about velocity and heading of point cluster. However, Assuming the average position of a point cluster as the vehicle position has a lower accuracy than ground truth. Therefore, the box feature estimation algorithm to improve position accuracy of autonomous driving perception consists of two procedures. Firstly, proposed algorithm calculates vehicle candidate position based on relative position of point cluster. Secondly, to reflect the features of the point cluster in estimation, the likelihood of the particle scattered around the candidate position is used. The proposed estimation method has been implemented in robot operating system (ROS) environment, and investigated via simulation and actual vehicle test. The test result show that proposed cluster position estimation enhances perception and path planning performance in autonomous driving.

• 한국군사과학기술학회지
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• 제16권6호
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• pp.726-732
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• 2013

It is required for UGV(Unmanned Ground Vehicle) to have a LPP(Local Path Plan) component which generate a local path via the center of road by analyzing binary map to travel autonomously unpaved road in rough environment. In this paper, we present the method of boundary estimation for unpaved road and a local path planning method based on RANGER algorithm using the estimated boundary. In specially, the paper presents an approach to estimate road boundary and the selection method of candidate path to minimize the problem of zigzag driving based on Bayesian probability reasoning. Field test is conducted with scenarios in rough environment in which bush, tree and unpaved road are included and the performance of proposed method is validated.

• 한국소음진동공학회논문집
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• 제20권11호
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• pp.1071-1077
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• 2010

The reduction of the vehicle interior noise has been the main interest of noise and vibration harshness(NVH) engineers. A passenger vehicle has various and complicated transmission paths of sound and vibration. In order to identify the mechanism of transfer path, estimation of excitation force and exact modeling of transfer path are required. This paper presents method for estimating the noise source contribution on the road noise of the vehicle in a multiple input system where the input sources may be coherent with each other. And vector synthesis technique is employed to identify the characteristics of road noise and its transmission to vehicle compartment through noise and vibration analysis. Vibration reduction efficiency of each transfer path is evaluated by comparing individual vector components obtained virtual simulation.

• 제어로봇시스템학회논문지
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• 제19권7호
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• pp.599-604
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• 2013

Path planning of mobile robots has a purpose to design an optimal path from an initial position to a target point. Minimum driving time, minimum driving distance and minimum driving error might be considered in choosing the optimal path and are correlated to each other. In this paper, an efficient driving trajectory is planned in a real situation where a mobile robot follows a moving object. Position and distance of the moving object are obtained using a web camera, and the rotation angular and linear velocities are estimated using Kalman filters to predict the trajectory of the moving object. Finally, the mobile robot follows the moving object using a single curvature trajectory by estimating the trajectory of the moving object. Using the estimation by Kalman filters and the single curvature in the trajectory planning, the total tracking distance and time saved amounts to about 7%. The effectiveness of the proposed algorithm has been verified through real tracking experiments.

• 한국정밀공학회지
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• 제25권10호
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• pp.58-66
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• 2008

In the development of intelligent vehicles, path tracking of unmanned vehicle is a basis of autonomous driving and automatic navigation. It is very important to find the exact position of a vehicle for the path tracking, and it is possible to get the position information from GPS. However the information of GPS is not the current position but the past position because a vehicle is moving and GPS has a time delay. In this paper, therefore, the moving distance of a vehicle is estimated using a direction sensor and a velocity sensor to compensate the position error of GPS. In the steering control, optimal fuzzy rules for the path tracking can be found through the simulation of Simulink. Real driving experiments show the fuzzy rules are good for the steering control and the position error of GPS is well compensated by the proposed estimation method.

• 로봇학회논문지
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• 제19권1호
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• pp.130-138
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• 2024

High-precision 3D Object Detection is a crucial component within autonomous driving systems, with far-reaching implications for subsequent tasks like multi-object tracking and path planning. In this paper, we propose a novel approach designed to enhance the performance of 3D Object Detection, especially in heading angle estimation by employing a moving object segmentation technique. Our method starts with extracting point-wise moving labels via a process of moving object segmentation. Subsequently, these labels are integrated into the LiDAR Pointcloud data and integrated data is used as inputs for 3D Object Detection. We conducted an extensive evaluation of our approach using the KITTI-road dataset and achieved notably superior performance, particularly in terms of AOS, a pivotal metric for assessing the precision of 3D Object Detection. Our findings not only underscore the positive impact of our proposed method on the advancement of detection performance in lidar-based 3D Object Detection methods, but also suggest substantial potential in augmenting the overall perception task capabilities of autonomous driving systems.

• 센서학회지
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• 제22권2호
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• pp.111-117
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• 2013

The autonomous driving method using magnetic sensors recognizes the position by measuring magnetic fields in autonomous robots or vehicles after installing magnetic markers in a moving path. The Position estimate method using magnetic sensors has an advantage of being affected less by variation of driving environment such as oil, water and dust due to the use of magnetic field. It also has the advantages that we can use the magnet as an indicator and there is no consideration for power and communication environment. In this paper, we propose an efficient sensor system for an autonomous driving vehicle supplemented for existing disadvantage. In order to efficiently eliminate geomagnetism, we analyze the components of the horizontal and vertical magnetic field. We propose an algorithm for position estimation and geomagnetic elimination to ease analysis, and also propose an initialization method for sensor applied in the vehicle. We measured and analyzed the developed system in various environments, and we verify the advantages of proposed methods.

• 제어로봇시스템학회논문지
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• 제22권11호
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• pp.904-911
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• 2016

This paper presents a novel driving system by the humanoid robot to drive a vehicle in disaster response situations. To enhance robot's capability for substituting human activities in responding to natural and man-made disaster, the one of prerequisite skills for the rescue robot is the mounted mobility to maneuver a vehicle safely in disaster site. Therefore, our driving system for the humanoid is developed in order to steer a vehicle through unknown obstacles even under poor communication conditions such as time-delay and black-out. Especially, the proposed system includes a tele-manipulation interface and stable navigation strategies. First, we propose a new type of path estimation method to overcome limited communication. Second, we establish navigation strategies when the operator cannot recognize obstacles based on Dynamic Window Approach. The effectiveness of the proposed developments is verified through simulation and experiments, which demonstrate suitable system for driving a vehicle in disaster response.

• 한국정보통신학회:학술대회논문집
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• 한국해양정보통신학회 2010년도 추계학술대회
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• pp.493-496
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• 2010

본 논문에서는 주행중인 차량의 CO2 배출량을 추정하기 위한 방법들로, 주행거리에 따른 CO2 배출량 추정 방법, IPCC에 따른 CO2 배출량 추정 방법, MAF와 화학반응식을 이용한 CO2 배출량 추정 방법 등에 대해서 설명하고 각각의 방법들의 성능을 비교하기 위해 실차(제네시스 3.3)를 이용하여 약 5Km의 실제 도로 경로를 주행하며 각각의 CO2 추정 방법들에 대한 성능을 실증 분석하였다.