• 대한기계학회:학술대회논문집
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• 대한기계학회 2008년도 추계학술대회A
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• pp.795-800
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• 2008

The paper presents research for the established experiment environment of multi vehicle robot, localization algorithm that is based on vehicle control, and path tracking. The established experiment environment consists of ultrasonic positioning system, vehicle robot, server and wireless module. Ultrasonic positioning system measures positioning for using ultrasonic sensor and generates many errors because of the influence of environment such as a reflection of wall. For a solution of this fact, localization algorithm is proposed to determine a location using vehicle kinematics and selection of a reliable location data. And path tracking algorithm is proposed to apply localization algorithm and LOS, finally, that algorithms are verified via simulation and experimental

• 디지털산업정보학회논문지
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• 제17권4호
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• pp.85-94
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• 2021

The LPR system's trigger sensor makes problem occasionally due to the heave weight of vehicle or the obsolescence equipment. If we replace the hardware sensor to the deep-learning based software sensor in order to generate the trigger signal, LPR system maintenance would be a lot easier. In this paper we proposed the deep-learning sliding window based object detection and tracking algorithm for the LPR system's trigger signal generation. The gate passing vehicle's license plate recognition results are combined into the normal tracking algorithm to catch the position of the vehicle on the trigger line. The experimental results show that the deep learning sliding window based trigger signal generating performance was 100% for the gate passing vehicles including the 5.5% trigger signal position errors due to the minimum bounding box location errors in the vehicle detection process.

• 디지털융복합연구
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• 제13권12호
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• pp.209-215
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• 2015

차량 등 객체를 추적하기 위한 많은 알고리즘들이 있지만 본 논문에서 제안하는 특징점 정합 알고리즘 분야는 지수 복잡도의 시간이 걸리는 작업이다. 더구나, 차량을 추적하기 위해 기존에 제안되었던 객체 추출 등 영상 전처리 알고리즘 또한 상당한 시간을 요구한다. 따라서 본 논문에서는 도로상에서 많은 차량들의 이동 궤적을 빠르고 효율적으로 추적하기 위한 방법을 2단계로 제안한다. 1단계로 객체 탐지가 아닌 번호판 영역을 먼저 탐지한 후 특징점을 추출하는 단계하고, 2단계로 특징점들을 정합하기 위한 비용산정식을 구한 후 동적계획법을 이용하여 효율적으로 차량을 추적할 수 있는 방법을 제안한다.

• 전자공학회논문지S
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• 제34S권1호
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• pp.16-25
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• 1997

This paper shows the development of the high accuracy vehicle positioning algorithm using the differential technique in vehicle tracking systems form the existing vehicle position which is acquired from the global positioning system (GPS). The control center receives the satellite ephemerise data and pseudorange correction from the reference station, and vehicle position from the moving vehicle. The pseudorange is calculated with the satellite position and the vehicle position, and corrected by pseudorange correction. Using this corrected pseudorange and kalman filter, more improved vehicle positioning data were obtained.

• 제어로봇시스템학회논문지
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• 제13권9호
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• pp.875-882
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• 2007

In order for a vehicle to follow a predetermined trajectory accurately, its position must be estimated accurately and reliably. In this thesis, we propose trajectory tracking control methods for unmanned vehicle and a positioning system using ultrasonic wave. The positioning problem is an important part of control problem for unmanned navigation of a vehicle. Dead Reckoning is widely used for positioning of vehicle. However this method has problems because it accumulates estimation errors. We propose a new method to increase the accuracy of position estimation using the Ultrasonic Satellite System (USAT). It is shown that we will be able to estimate the position of vehicle precisely, in which errors are not accumulated. And proposed trajectory tracking control methods include both a new path planning method and a lateral control method for vehicle. The experimental results show that the proposed methods enables exact vehicle trajectory tracking even under various environmental factors.

• 대한전자공학회:학술대회논문집
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• 대한전자공학회 2004년도 학술대회지
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• pp.729-734
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• 2004

The e-Logistics means the virtual business activity and service architecture among the logistics companies based on the Internet technology. The management of vehicles' location in most conventional vehicle tracking system has some critical defects when it deals with data which are continuously changed. It means the conventional vehicle tracking system based on the conventional database is unable eventually to cope with the environment that should manage the frequently changed location of vehicles. The important things in the evaluation of the vehicle tracking system is to determine the threshold of cost of database ,update period and communication period between vehicles and the system. In other words, the difference between the reallocation of vehicle and the data in database can evaluate the overall performance of vehicle tracking systems. Most of the previous works considers only the information that is valid at the current time, and is hard to manage efficiently the past and future information. To overcome this problem, the efforts on moving objects management system(MOMS) and uncertainty processing have been started from a few years ago. In this paper, we propose an uncertainty processing model and system implementation of moving object that tracks the location of the vehicles. We adopted both linear-interpolation method and trigonometric function to chase up the location of vehicles for the past time as well as future time, respectively. We also explain the comprehensive examples of MOMS and uncertainty processing in parcel application that is one of major application of e-Logistics domain.

• 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.

• 대한기계학회논문집A
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• 제29권1호
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• pp.139-144
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• 2005

A vehicle cruise control algorithm using an Interacting Multiple Model (IMM)-based Multi-Target Tracking (MTT) method has been presented in this paper. The vehicle cruise control algorithm consists of three parts; track estimator using IMM-Probabilistic Data Association Filter (PDAF), a primary target vehicle determination algorithm and a single-target adaptive cruise control algorithm. Three motion models; uniform motion, lane-change motion and acceleration motion. have been adopted to distinguish large lateral motions from longitudinal motions. The models have been validated using simulated and experimental data. The improvement in the state estimation performance when using three models is verified in target tracking simulations. The performance and safety benefits of a multi-model-based MTT-ACC system is investigated via simulations using real driving radar sensor data. These simulations show system response that is more realistic and reflective of actual human driving behavior.

• 한국정밀공학회지
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• 제17권4호
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• pp.121-128
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• 2000

In this paper, the control algorithm fur an autonomous vehicle is studied and applied to an actual 2 wheel-driven vehicle system. In order to control a nonholonomic system, the kinematic model for an autonomous vehicle is constructed by relative velocity relationship about the virtual point at distance from the vehicle's frame. And the optimal controller that based on the kinematic model is operated on purpose to track a reference vehicle's path. The actual system is designed with named 'HYAVI' and the system controller is applied. Because all the results of simulation don't satisfy the driving conditions of HYAVI, a reformed control algorithm that satisfies an actual autonomous vehicle is applied at HYAVI. At the results of actual experiments, the path tracking works very well by the reformed control algorithm. An autonomous vehicle that applied this control algorithm can be easily used for a path generation algorithm.

• 한국통신학회논문지
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• 제39A권1호
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• pp.1-11
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• 2014

본 논문에서는 추적중인 차량의 외형 변화에 대해 온라인 학습 능력이 있는 비전 기반의 차량 검출 및 추적 시스템을 제안한다. 제안하는 시스템은 새로 검출된 차량의 연속된 프레임 간 움직임을 빠르고 강건하게 추정하기 위해 특징점 기반 추적 방법을 사용한다. 동시에 추적중인 차량에 대해 온라인 차량 검출기를 훈련시키고, 일시적인 차량 추적 실패 시 검출기의 결과를 이용해 추적기를 재초기화하여 강건한 추적을 가능하게 한다. 특히 차량 외형 모델의 업데이트 방법을 개선하여 시스템의 추적 성능을 높이고 처리시간을 단축시켰다. 다양한 주행환경에서 획득한 데이터세트를 사용하여 제안하는 시스템의 차량 검출 및 추적 성능을 평가하였다. 특히 우천 및 터널통과와 같은 악조건에서 기존의 방법에 비해 차량 추적 성능이 상당히 개선된 것을 증명하였다.