• 제어로봇시스템학회논문지
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• 제16권5호
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• pp.446-455
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• 2010

The research presented covers the design and control method of unmanned ground vehicle (UGV). An electric vehicle is used and is driven by DC motor. The power system on the UGV has been adjusted and actuators have been installed for steering and brake automation. A toggle switch is implemented to easily switch between manual and autonomous states. The UGV state is monitored by a velocity sensor, as well as steering and brake position sensors. An emergency stop device was designed and installed to quickly and safely stop the UGV. Different control methods, including the PID controller, were studied for improved steering responsiveness, and results were confirmed through experimentation. Satisfactory performance was achieved and several possible areas of future research have arisen.

• 대한임베디드공학회논문지
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• 제4권4호
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• pp.180-189
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• 2009

Currently, most automobile companies are interested in research on intelligent autonomous vehicle. They are mainly focused on driver's intelligent assistant and driver replacement. In order to develop an autonomous vehicle, lateral and longitudinal control is necessary. This paper presents a lateral and longitudinal control system for autonomous vehicle that has only mono-vision camera. For lane detection, we present a new lane detection algorithm using clothoid parabolic road model. The proposed algorithm in compared with three other methods such as virtual line method, gradient method and hough transform method, in terms of lane detection ratio. For adaptive control, we apply a vanishing point estimation to fuzzy control. In order to improve handling and stability of the vehicle, the modeling errors between steering angle and predicted vanishing point are controlled to be minimized. So, we established a fuzzy rule of membership functions of inputs (vanishing point and differential vanishing point) and output (steering angle). For simulation, we developed 1/8 size robot (equipped with mono-vision system) of the actual vehicle and tested it in the athletics track of 400 meter. Through the test, we prove that our proposed method outperforms 98 % in terms of detection rate in normal condition. Compared with virtual line method, gradient method and hough transform method, our method also has good performance in the case of clear, fog and rain weather.

• 센서학회지
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• 제10권2호
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• pp.134-141
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• 2001

본 논문은 자기장을 이용한 자율주행 전기자동차의 조향 제어에 대한 연구이다. 도로의 중앙에 일정 간격으로 영구자석을 설치하고 차량에 자기 센서를 장착하여 차량의 이동에 따른 자기장의 변화를 측정하여 조향 제어가 되도록 하였다. 단일 자석에 의한 자계 분포로부터 일정한 간격으로 나열된 영구자석에 의한 자계를 분석하였고, 이를 이용하여 자석에 의한 직선 및 곡선 도로를 만들어 주행 시뮬레이션을 하였다. 차량의 조향 제어를 위해 신경회로망을 이용하여 제어기를 구성하고, 자석을 설치하여 만든 실험도의에서 실제 제작한 전기자동차를 이용하여 주행 실험을 하였다. 실제 주행 실험에서 차체와 외부 지자기에 의한 영향을 보상하여 제안된 방법에 의한 자율 주행 차량의 자율 주행이 가능함을 확인하였다.

• 사물인터넷융복합논문지
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• 제9권1호
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• pp.65-70
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• 2023

본 논문에서는 비전 센서 기반 시스템의 차선 이탈과 신호등 오인식 등을 개선하기 위해 End to End 모델을 활용한 자율주행 시스템을 제안한다. End to End 학습은 다양한 환경 조건에 대해 확장을 할 수 있다. 비전 센서 기반 모형 자동차를 이용하여 주행 데이터를 수집한다. 수집한 데이터를 이용하여 기존의 데이터와 아웃레이어를 제거한 데이터로 구성한다. 입력 데이터인 카메라 이미지 데이터, 출력 데이터인 속도와 조향 데이터로 클래스를 구성하고 End to End 모델을 활용하여 데이터 학습을 수행하였다. 학습된 모델의 신뢰성을 확인했다. 모형 자동차에 학습한 End to End 모델을 적용하여 이미지 데이터로 조향각을 예측한다. 모형 자동차의 학습 결과, 아웃레이어를 제거한 모델이 기존 모델보다 향상된 것을 볼 수 있다.

• International Journal of Automotive Technology
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• 제7권2호
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• pp.227-235
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• 2006

In this paper a robust image processing method with modified Otsu algorithm to recognize the road lane for a real-time controlled autonomous vehicle is presented. The main objective of a proposed method is to drive an autonomous vehicle safely irrespective of road image qualities. For the steering of real-time controlled autonomous vehicle, a detection area is predefined by lane segment, with previously obtained frame data, and the edges are detected on the basis of a lane width. For stable as well as psudo-robust autonomous driving with "good", "shady" or even "bad" road profiles, the variable threshold with modified Otsu algorithm in the image histogram, is utilized to obtain a binary image from each frame. Also Hough transform is utilized to extract the lane segment. Whether the image is "good", "shady" or "bad", always robust and reliable edges are obtained from the algorithms applied in this paper in a real-time basis. For verifying the adaptability of the proposed algorithm, a miniature vehicle with a camera is constructed and tested with various road conditions. Also, various highway road images are analyzed with proposed algorithm to prove its usefulness.

• 한국정밀공학회:학술대회논문집
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• 한국정밀공학회 2013년도 춘계학술대회 논문집
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• pp.401-402
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• 2013

• 한국정밀공학회:학술대회논문집
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• 한국정밀공학회 2010년도 춘계학술대회 논문집
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• pp.57-58
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• 2010

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 2005년도 ICCAS
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• pp.1460-1463
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• 2005

In this paper, analysis of magnetic marker for autonomous vehicle guidance system using 3-axis magnetic sensor propose. Position sensing is an important an estimation system of vehicle position and orientation on magnetic lane, which is a parameter of the steering controller for automated lane following is described. To verify that the magnetic dipole model could be applied to a magnetic unit paved in roadway, the analysis of the data 3-axis magnetic field measured experimentally.

• 자동차안전학회지
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• 제10권1호
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• pp.38-44
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• 2018

This paper presents an analysis on driving safety in lane change situation based on road driving data. Autonomous driving is a global trend in vehicle industry. LKAS technologies are already applied in commercial vehicle and researches about lane change maneuver have been actively studied. In autonomous vehicle, not only safety control issue but also imitating human driving maneuver is important. Driving data analysis in lane change situation has been usually dealt with ego vehicle information such as longitudinal acceleration, yaw rate, and steering angle. For this reason, developing safety index according to surrounding vehicle information based on human driving data is needed. In this research, driving data is collected from perception module using LIDAR, radar and RT-GPS sensors. By analyzing human driving pattern in lane change maneuver, safety index that considers both ego vehicle and surrounding vehicle state by using relative velocity and longitudinal clearance has been designed.

• 동력기계공학회지
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• 제9권4호
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• pp.194-201
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• 2005

In this paper, we propose an adaptive mechanism based on humoral immune algorithm and neural network identifier technique. It is also applied for an autonomous guided vehicle (AGV) system. When the immune algorithm is applied to the PID controller, there exists the case that the plant is damaged due to the abrupt change of PID parameters since the parameters are almost adjusted randomly. To slove this problem, we use the neural network identifier technique for modeling the plant humoral immune algorithm (HIA) which performs the parameter tuning of the considered model, respectively. Finally, the experimental results for control of steering and speed of AGV system illustrate the validity of the proposed control scheme. Also, these results for the proposed method show that it has better performance than other conventional controller design method such as PID controller.