• Annual Conference of KIPS
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• 2024.05a
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• pp.650-651
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• 2024

자율주행 차량의 차선 인식에는 다양한 기법이 존재한다. 전통적인 방법은 주로 이미지 처리 기술을 기반으로 하며 정교한 손실 함수와 특징 추출 알고리즘을 사용한다. 본 연구에서는 딥러닝의 적용을 통해 차선 검출 성능을 확인하고, 이를 이미지 처리를 통한 방식과 비교하고자 한다.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.24 no.7
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• pp.834-841
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• 2020

Among the autonomous driving systems based on visual sensors, the control method using a vanishing point is the most general method for autonomous driving. However, if the lane is lost or does not exist, it is very difficult to detect this and estimate the vanishing point. In this paper, we predict the vanishing point of the road and the vanishing point lines on the left and right sides using CNN for the camera image and design the steering controller for autonomous driving from the predicted results. As a result of the simulation, it was confirmed that the proposed method well tracked the center of the road regardless of the presence or absence of a solid lane, and was superior to the control method using a general method using the vanishing point.

• The Journal of the Korea institute of electronic communication sciences
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• v.16 no.4
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• pp.689-698
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• 2021

In this paper, lane recognition, steering control and speed control algorithms were developed using Bluetooth wireless communication and image processing techniques. Instead of recognizing road traffic signals based on image processing techniques, a methodology for recognizing the permissible road speed by receiving speed codes from electronic traffic signals using Bluetooth wireless communication was developed. In addition, a steering control algorithm based on PWM control that tracks the lanes using the Canny algorithm and Hough transform was developed. A vehicle prototype and a driving test track were developed to prove the accuracy of the developed algorithm. Raspberry Pi and Arduino were applied as main control devices for steering control and speed control, respectively. Also, Python and OpenCV were used as implementation languages. The effectiveness of the proposed methodology was confirmed by demonstrating effectiveness in the lane tracking and driving control evaluation experiments using a vehicle prototypes and a test track.

• Proceedings of the KOR-KST Conference
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• 2007.11a
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• pp.773-779
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• 2007

• Transactions of the Korean Society of Mechanical Engineers A
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• v.39 no.2
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• pp.235-240
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• 2015

Active Safety system is requested on the market of the medium and heavy duty commercial vehicle over 4.5ton beside the market of passenger car with advancement of the digital equipment proportionally. Unlike the passenger car, the mounting position of camera in case of the medium and heavy duty commercial vehicle is relatively high, it is disadvantaged conditions for lane recognition in contradiction to passenger car. In this work, we show the method of lane recognition through the Sobel edge, based on the spatial domain processing, Hough transform and color conversion correction. Also we suggest the low error method of front vehicles recognition in order to reduce the detection error through Haar-like, Adaboost, SVM and Template matching, etc., which are the object recognition methods by frontal camera vision. It is verified that the reliability over 98% on lane recognition is obtained through the vehicle test.

• Journal of Korea Society of Industrial Information Systems
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• v.28 no.1
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• pp.1-8
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• 2023

In this paper, we developed a mid-range automotive radar systems based on the performance requirements and test procedures of the intelligent transport systems, that is lane change decision aid systems (LCDAS). The mid-range automotive radar has the maximum detection range up to 80m and an update time within 50ms. The computational loads of a signal processing were reduced by using ROI preprocessing technique. Considering actual driving environments, radar performance evaluations were conducted in two driving scenarios at an automotive proving ground.

• Proceedings of the Korea Water Resources Association Conference
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• 2015.05a
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• pp.375-375
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• 2015

최근 도시화에 따른 도로구역의 증가는 도심지의 불투수면적의 확대 및 도로의 수로화를 야기하여 집중호우 발생 시 노면 및 저지대의 침수를 가중시키고 있으며, 이로 인한 인명피해 및 재산 피해는 점차 증가하는 추세이다. 이에 기후변화에 따른 강우량의 변화 및 국지성 집중호우 등을 고려하고 도심지 침수 방지 및 방재성능 강화를 위하여 과거에 간선 10년, 지선 5년에서 간선 30년, 지선 10년으로 관거의 설계빈도가 상향조정 되었다. 도심지의 도로 및 유역 유출량의 배수에 상당한 영향을 미치는 빗물받이 및 빗물받이 유입구는 도심지의 방재성능강화 목적에 맞는 설계가 이루어져야 하나 현재 국내의 빗물받이 설치기준은 하수도시설기준(2009, 환경부)에서 '빗물받이 크기별, 도로 차선별 적정 빗물받이 설치간격' 으로 설계빈도 5년을 반영하여 제시된 설치기준이므로 이에 대한 수정 및 개정이 필요한 실정이다. 이에 본 연구에서는 관거시설의 설계빈도 상향을 고려한 도로 빗물받이의 유입량 산정을 위해 빗물받이 유입구 크기별 유입량을 도로의 차선, 종경사, 측구 횡경사 및 설계빈도의 변화를 고려하여 도로에서의 유출량을 산정하였으며, Froude 상사법칙을 이용하여 수리실험모형을 제작하여 실험을 실시하였다. 빗물받이 제원은 현재 대부분의 국도에 설치되는 크기인 $40{\times}50cm$, $40{\times}100cm$ 및 $40{\times}150cm$를 축소 제작하였다. 측구의 유량은 도로의 차선(2~4차선), 경사(도로 종경사 2~10%, 측구 횡경사 2~10%) 및 설계빈도(최대 30년)을 고려하였다. 실험 결과 측구의 횡경사가 커질수록 빗물받이로 유입되는 유량은 증가하였으며, 빗물받이 유입부의 길이가 증가함에 따라 유입부 측면부를 통한 횡유입량을 증가시켜 빗물받이 유입부의 차집율을 증가시켰다. 또한, 도로의 조건 변화 및 유입량의 변화에 따라서 흐름폭이 감소할수록 차집율이 증가하였다. 수리실험결과를 토대로 도로의 조건변화(차선, 도로 종경사, 측구 횡경사) 및 설계빈도 변화를 고려한 빗물받이의 차집율을 제시하였고 이를 회귀분석 하여 빗물받이 유입구 크기별 유입량 산정식을 도출하였다. 이는 설계빈도 상향에 따른 국내 빗물받이 유입부의 설계 기준 제시에 기초자료로 활용이 가능할 것으로 판단된다.

• Journal of Auto-vehicle Safety Association
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• v.6 no.2
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• pp.29-35
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• 2014

Lane Keeping Assistance System(LKAS) is a kind of Advanced Driver Assistance Systems(ADAS) which are developed to automate/ adapt/ enhance vehicle systems for safety and better driving. The main system function of LKAS is to support the driver in keeping the vehicle within the current lane. LKAS acquires information on the position of the vehicle within the lane and, when required, sends commands to actuators to influence the lateral movement of the vehicle. Recently, the vehicles equipped with LKAS are commercially available in a few vehicle-advanced countries and the installation of LKAS increases for safety enhancement. The test procedures for LKAS evaluations are being discussed and developed in international committees such as ISO(the International Organization for Standardization). In Korea, the evaluations of LKAS for vehicle safety are planned to be introduced in 2016 KNCAP(Korean New Car Assessment Program). Therefore, the test procedures of LKAS suitable for domestic road and traffic conditions, which accommodate international standards, should be developed. In this paper, some bullet points of the test procedures for LKAS are discussed by extensive researches of previous documents and reports, which are released in public in regard to lateral test procedures including LKAS and Lane Departure Warning System(LDWS). Later, it can be helpful to make a draft considering domestic traffic situations for test procedures of LKAS.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.50 no.9
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• pp.617-625
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• 2022

This study investigates the use of movable calibration target for gyroscopic and accelerometer bias compensation of inertial measurement units for firing lane alignment. Calibration source is detected with the help of vision sensor and its information in fused with other sensors on launcher for error correction. An algorithm is proposed and tested in simulation. It has been shown that it is possible to compensate sensor biases in firing launcher in few seconds by accurately estimating the location of calibration target in inertial frame of reference.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.7 no.3
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• pp.132-136
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• 2014

We introduce a new architecture of TSK-based fuzzy inference system. The proposed model used fuzzy c-means clustering method(FCM) for efficient disposal of data. The premise part of fuzzy rules don't assume any membership function such as triangular, gaussian, ellipsoidal because we construct the premise part of fuzzy rules using FCM. As a result, we can reduce to architecture of model. In this paper, we are able to use four types of polynomials as consequence part of fuzzy rules such as simplified, linear, quadratic, modified quadratic. Weighed Least Square Estimator are used to estimates the coefficients of polynomial. The proposed model is evaluated with the use of Boston housing data called Machine Learning dataset.