• 제어로봇시스템학회논문지
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• 제21권2호
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• pp.87-94
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• 2015

This paper proposes a situation awareness method based on data fusion and independent objects for autonomous driving in on-road environment. The proposed method, designed to achieve an accurate analysis of driving situations in on-road environment, executes preprocessing tasks that include coordinate transformations, data filtering, and data fusion and independent object based situation assessment to evaluate the collision risks of driving situations and calculate a desired velocity. The method was implemented in an open-source robot operating system called ROS and tested on a closed road with other vehicles. It performed successfully in several scenarios similar to a real road environment.

• 드라이브 ㆍ 컨트롤
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• 제16권4호
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• pp.48-55
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• 2019

The Electronic Stability Program (ESP), a system that improves vehicle safety, also known as YMC (Yaw Motion Controller) or VDC (Vehicle Dynamics Control), is a system that operates in unstable or sudden driving and braking situations. Developing conditions such as unstable or sudden driving and braking situations in a vehicle are very dangerous unless you are an experienced professional driver. Additionally, many repetitive tests are required to collect reliable data, and there are many variables to consider such as changes in the weather, road surface, and tire condition. To overcome this problem, in this paper, hardware and control software such as the ESP controller, vehicle engine, ABS, and TCS module, composed of three control zones, are modeled using MATLAB/SIMULINK, and the vehicle, climate, and road surface. Various environmental variables such as the driving course were modeled and studied for the real-time ESP real-time simulator that can be repeatedly tested under the same conditions.

• 한국컴퓨터정보학회논문지
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• 제26권1호
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• pp.93-101
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• 2021

본 논문에서는 실제 도로의 다양한 상황에서도 차량 번호판을 정확하게 탐지하기 위해 차량 번호판의 후보 지역을 공간 집중 영역으로 사용하는 차량 번호판 탐지 모델을 제안하였다. 먼저, 기존의 WPOD-NET이 전처리 과정에서 검출된 차량 영역을 이용하기 때문에 넓은 탐지 후보 영역으로 인해 불필요한 노이즈가 포함되어 탐지 정확도가 낮아짐을 확인하였다. 이를 개선하기 위해 차량 번호판의 후보 지역을 공간 집중 영역으로 사용하는 차량 번호판 탐지 모델을 제안하였고, 제안한 방법이 기존 WPOD-NET보다 탐지 정확도를 어느 정도 개선하는지 분석하기 위해 GT 데이터를 기반으로 최적의 공간 집중 영역을 설정한 경우와 함께 탐지 정확도를 비교하였다. 실험에 따르면 제안된 모델이 기존 WPOD-NET에 비해 타이트한 탐지 후보 영역을 갖기 때문에 약 20% 더 높은 탐지 정확도를 보임을 확인하였다.

• 대한임베디드공학회논문지
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• 제11권5호
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• pp.313-323
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• 2016

With the development of autonomous vehicle, there has been active research on advanced driver assistance system for road marking detection using vision sensor and 3D Laser scanner. However, vision sensor has the weak points that detection is difficult in situations involving severe illumination variance, such as at night, inside a tunnel or in a shaded area; and that processing time is long because of a large amount of data from both vision sensor and 3D Laser scanner. Accordingly, this paper proposes a road marking detection and classification method using single 2D Laser scanner. This method road marking detection and classification based on accumulation distance data and intensity data acquired through 2D Laser scanner. Experiments using a real autonomous vehicle in a real environment showed that calculation time decreased in comparison with 3D Laser scanner-based method, thus demonstrating the possibility of road marking type classification using single 2D Laser scanner.

• 자동차안전학회지
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• 제7권2호
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• pp.25-31
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• 2015

This paper presents a performance evaluation procedure for advanced emergency braking (AEB) system. To guarantee the performance of AEB system, AEB test scenario should contains various driving conditions which can be occurred in real driving condition. Also, performances of each elements of AEB system, such as sensor, decision, human machine interface (HMI) and control, should be evaluated in various situations. For this, driving conditions, road types, environment, and elements of AEB system were introduced. Test scenario has been designed to represent the real driving condition and to evaluate the safety performance of AEB system in various situations. To confirm that the proposed AEB test scenario is realistic and physically meaningful, vehicle test have been conducted in two cases of proposed AEB test scenario: subject vehicle cut-out scenario and narrow street turn left scenario.

• ETRI Journal
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• 제37권5호
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• pp.1032-1043
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• 2015

ETRI's Co-Pilot project is aimed at the development of an automated vehicle that cooperates with a driver and interacts with other vehicles on the road while obeying traffic rules without collisions. This paper presents a core block within the Co-Pilot system; the block is named "Co-Pilot agent" and consists of several main modules, such as road map generation, decision-making, and trajectory generation. The road map generation builds road map data to provide enhanced and detailed map data. The decision-making, designed to serve situation assessment and behavior planning, evaluates a collision risk of traffic situations and determines maneuvers to follow a global path as well as to avoid collisions. The trajectory generation generates a trajectory to achieve the given maneuver by the decision-making module. The system is implemented in an open-source robot operating system to provide a reusable, hardware-independent software platform; it is then tested on a closed road with other vehicles in several scenarios similar to real road environments to verify that it works properly for cooperative driving with a driver and automated driving.

• 한국콘텐츠학회논문지
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• 제21권12호
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• pp.190-201
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• 2021

도로전광표지판(VMS : Variable Message Sign)은 도로이용자에게 교통, 도로, 기상상황 및 공사로 인한 통제 등에 대한 실시간 정보를 제공함으로써 교통흐름의 효율화와 통행의 안전성을 향상시키기 위한 장비이다. 일반국도 VMS의 대부분을 차지하는 문자식 VMS에서 표출 및 제공되는 소통정보 메시지 구성은 일반적으로 구간, 구간에 대한 통행시간, 소통상황에 대한 정보로 이루어진다. 본 연구에서는 수도권 일반국도에서 운영 중인 문자식 VMS를 대상으로 도로이용자 선호도 분석을 통해 도로관리자 위주의 기존 표출문안을 도로이용자 중심으로 개선안을 마련하였다. 아울러 도로이용자가 가장 선호하는 돌발상황에 대한 정보를 신속하고 효율적으로 제공할 수 있는 시스템 개선에 대한 방안을 제시함으로써 도로이용자 중심의 VMS 교통정보 제공 효용성을 향상시키고자 한다.

• 자동차안전학회지
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• 제8권4호
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• pp.31-37
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• 2016

Since real road experiments have many restrictions, a multi-vehicle traffic simulator can be an effective tool to develop and evaluate fully automated driving systems. This paper presents multi-vehicle environment simulation tool to develop and evaluate motorway automated driving systems. The proposed simulation tool consists of following two main parts: surrounding vehicle model and environment sensor model. The surrounding vehicle model is designed to quickly generate rational complex traffic situations of motorway. The environment sensor model depicts uncertainty of environment sensor. As a result, various traffic situations with uncertainty of environment sensor can be proposed by the multi-vehicle environment simulation tool. An application to automated driving system has been conducted. A lane changing algorithm is evaluated by performance indexes from the multi-vehicle environment simulation tool.

• Journal of Mechanical Science and Technology
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• 제15권9호
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• pp.1248-1256
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• 2001

In this study, a test bed for vehicle longitudinal control is developed using a chassis dynamometer and real time 3-D graphics. The proposed test bed system consists of a chassis dynamometer on which test vehicle can run longitudinally, a video system that shows virtual driver view, and computers that control the test vehicle and realize the real time 3-D graphics. The purpose of the proposed system is to test vehicle longitudinal control and warning algorithms such as Adaptive Cruise Control(ACC), stop and go systems, and collision warning systems. For acceleration and deceleration situations which only need throttle movements, a vehicle longitudinal spacing control algorithm has been tested on the test bed. The spacing control algorithm has been designed based on sliding mode control and road grade estimation scheme which utilizes the vehicle engine torque map and gear shift information.

• 정보처리학회논문지B
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• 제18B권5호
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• pp.255-264
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• 2011

본 논문에서는 스테레오 비전기반의 컬럼 검출과 조감도 맵핑을 이용한 전방 차량 검출 알고리즘을 제안한다. 제안된 알고리즘은 실제 복잡한 도로 환경에서 전방 차량을 강건하게 검출할 수 있다. 전체적인 알고리즘은 도로 특징기반의 컬럼 검출, 조감도 기반의 장애물체 세그멘테이션, 차량 특징기반의 영역 재결합, 차량 검증으로 크게 네 단계로 구성되어 있다. 먼저 v-시차맵상에서 최대 빈도값을 이용하여 도로 특징 정보만을 추출한 후, 이를 기반으로 컬럼 검출을 수행한다. 도로 특징 정보는 기존의 중앙값과 달리 도로 환경에 영향을 받지 않아 도로상의 장애물체 유무를 판단하는 기준으로 적절하다. 그러나 다수의 장애물체가 동일한 장애물체로 검출되는 것을 해결하기 위하여 조감도 기반의 세그멘테이션을 수행한다. 조감도는 시차맵과 카메라 정보를 기반으로 계산된 장애물체들의 위치를 평면상에 표시함으로써 장애물체를 쉽게 분리할 수 있다. 그러나 분리된 장애물체 중에는 동일한 장애물체인 경우도 있으므로, 도로상의 차량 특징을 기반으로 장애물체가 동일한지를 판단하여 재결합하는 과정을 수행한다. 마지막으로 시차맵과 그레이 영상기반의 차량 검증 단계를 수행하여 차량만 검출한다. 제안된 알고리즘을 실제 복잡한 도로 영상에 적용함으로써 차량 검증 성능을 검증한다.