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황준연(Junyeon Hwang),허건수(Kunsoo Huh),조동일(Dong-il Cho),박장현(Jahng-hyun Park) 한국자동차공학회 2006 한국자동차공학회 춘 추계 학술대회 논문집 Vol.- No.-
Obstacle detection is a crucial issue for driver assistance system as well as for autonomous vehicle guidance function and it has to be performed with high reliability to avoid any potential collision. The vision-based obstacle detection systems are regarded promising for this purpose because they require little infrastructure on a highway. However, the feasibility of these systems in passenger car requires accurate and robust sensing performance. In this paper, an obstacle detection system using stereo vision sensors is developed. This system utilizes ROI setup, feature extraction, feature clustering and feature matching regarded epipoplar constraint in order to robustly detect the initial corresponding pairs. After the initial detection, the system executes the tracking algorithm for the obstacles. Then, the position parameters of the obstacles or leading vehicles can be obtained. The proposed obstacle detection system is implemented on a passenger car and its performances are verified experimentally.
MeanShift 기법을 이용한 차량 종횡방향 통합 궤적 생성 시스템 개발
황준연(Junyeon Hwang),이동휘(Donghwi Lee),허건수(Kunsoo Huh),나혁민(Hyuckmin Na),강형진(Hyungjin Kang) 한국자동차공학회 2009 한국자동차공학회 학술대회 및 전시회 Vol.2009 No.11
The longitudinal-lateral vehicle control techniques have been widely used in several active driver assistance systems. The lane keeping assistant control, vehicle platooning and adaptive cruise control are typical examples of their. In this study, a path planning method is proposed considering the driving environment such as road shape, ego vehicle and surrounding vehicles’ movement. The relative velocity and distance between the ego vehicle and surrounding vehicles are recognized with respect to the predicted lane shape in front of the ego vehicle. Based on the recognized information, the road shape and surrounding vehicles are mapped into the 8-bit gray scale image and the desired vector for the ego vehicle’s movement is determined by the maximum intensity density tracing. The desired vehicle path is followed by the acceleration/ deceleration control and the steering assist control, respectively. In order to evaluate the performance of the proposed system, simulations are conducted with typical LKAS and ACC systems.
레이더와 카메라 정보 융합을 이용한 목표차량 선정 기법의 개발
황준연(Junyeon Hwang),박승범(Seungbum Park) 한국자동차공학회 2010 한국자동차공학회 학술대회 및 전시회 Vol.2010 No.11
In the Advanced Driver Assistance System research field, the importance of the sensor fusion systems is rapidly focusing in recent years. With the increasing of the supplement of radar and camera sensors, the systems of ADAS such as LKAS, SCC and CDM are integrated and newly developed by adding brand new functions. Therefore, the sensor fusion system is almost a necessity for car industry. In this paper, the path from radar and lane information from camera is developed. The path-lane data fusion system is organized with three trajectories, the path only range, path-lane range and lane only range. Each range is designed to consider the driver physical time delay and lateral variation from extrapolated lane model. And the proposed system is verified by real vehicle experiments.
황준연(Junyeon Hwang),허건수(Kunsoo Huh),나혁민(Hyukmin Na),강형진(Hyungjin Kang),윤팔주(Paljoo Yoon) 한국자동차공학회 2007 한국자동차공학회 춘 추계 학술대회 논문집 Vol.- No.-
Lane keeping assistant system(LKAS) could save thousands of lives each year by maintaining lane position and is regarde3d as a promising active safety system. the LKAS is expected to reduce the driver workload and to assist the driver during driving. This paper proposes model based predictive controller for the LKAS which requires cooperative driving between driver and the assistance system. The control performance is evaluated by HILS system using Carsim and Matlab Simulink. The simulation is conducted to stabilize a vehicle along a desired path with side wind and curved road scenarios.
황준연(Junyeon Hwang),홍대건(Daegun Hong),허건수(Kunsoo Huh),조동일(Dong-il Cho),박장현(Jahng-hyon Pakr) 한국자동차공학회 2005 한국자동차공학회 춘 추계 학술대회 논문집 Vol.2005 No.11_2
Obstacle detection is a crucial issue for driver assistance system as well as for autonomous vehicle guidance function and it has to be performed with high reliability to avoid any potential collision. The vision-based obstacle detection systems are regarded promising for this purpose because they require little infrastructure on a highway. However, the feasibility of these systems in passenger car requires accurate and robust sensing performance. In this paper, an obstacle detection system using stereo vision sensors is developed. This system utilizes feature matching, epipoplar constraint and feature aggregation in order to robustly detect the initial corresponding pairs. After the initial detection, the system executes the tracking algorithm for the obstacles. The proposed system can detect a front obstacle, a leading vehicle. Then, the position parameters of the obstacles or leading vehicles can be obtained. The proposed obstacle detection system is implemented on a passenger car and its performances is verified experimentally.