IEEE Proceedings. Intelligent Vehicles Symposium, 2005.最新文献_第10页

An intelligent architecture for metropolitan area parking control and toll collection 都市区停车控制和收费的智能架构

IEEE Proceedings. Intelligent Vehicles Symposium, 2005. Pub Date : 2005-06-06 DOI: 10.1109/IVS.2005.1505189

S. Shanker, S. Mahmud

引用次数: 4

An enhanced traffic simulation system for interactive traffic environment 交互式交通环境下的增强型交通模拟系统

IEEE Proceedings. Intelligent Vehicles Symposium, 2005. Pub Date : 2005-06-06 DOI: 10.1109/IVS.2005.1505192

M. Kuwahara, S. Tanaka, M. Kano, M. Furukawa, K. Honda, K. Maruoka, T. Yamamoto, T. Shiraishi, H. Hanabusa, N. Webster

引用次数: 3

Color-based detection of vehicle lights 基于颜色的车灯检测

IEEE Proceedings. Intelligent Vehicles Symposium, 2005. Pub Date : 2005-06-06 DOI: 10.1109/IVS.2005.1505115

I. Cabani, G. Toulminet, A. Bensrhair

引用次数: 60

Lane detection for a steering assistance system 转向辅助系统的车道检测

IEEE Proceedings. Intelligent Vehicles Symposium, 2005. Pub Date : 2005-06-06 DOI: 10.1109/IVS.2005.1505095

A. Watanabe, M. Nishida

引用次数: 19

Multiple-cue data fusion with particle filters for vehicle detection in night view automotive applications 多线索数据融合与粒子过滤器车辆检测在夜视汽车应用

IEEE Proceedings. Intelligent Vehicles Symposium, 2005. Pub Date : 2005-06-06 DOI: 10.1109/IVS.2005.1505195

R. Schweiger, Heiko Neumann, Werner Ritter

引用次数: 22

The study on intelligent vehicle collision-avoidance system with vision perception and fuzzy decision making 具有视觉感知和模糊决策的智能汽车避碰系统研究

IEEE Proceedings. Intelligent Vehicles Symposium, 2005. Pub Date : 2005-06-06 DOI: 10.1109/IVS.2005.1505087

Tsung-Ying Sun, Shang-Jeng Tsai, Jiun-Yuan Tseng, Yen-Chang Tseng

{"title":"The study on intelligent vehicle collision-avoidance system with vision perception and fuzzy decision making","authors":"Tsung-Ying Sun, Shang-Jeng Tsai, Jiun-Yuan Tseng, Yen-Chang Tseng","doi":"10.1109/IVS.2005.1505087","DOIUrl":"https://doi.org/10.1109/IVS.2005.1505087","url":null,"abstract":"This paper proposes a combination scenario of vision perception and fuzzy decision making for developing an intelligent vehicle collision-avoidance system (IVCAS). In IVCAS, a CCD camera is installed on the following vehicle and used to capture the image of leading vehicles and road information. The features of the leading vehicles and lane boundary are recognized by vision perception method, which derived from our previous work on histogram-based color difference fuzzy c-means (HCDFCM). HCDFCM is a robust and fast algorithm for detecting object boundary. In this paper, we adopted the coordinate mapping relationship (CMR) with HCDFCM to provide a robust vision perception for the necessary information such as relative velocity, relative distance between leading and following vehicle and absolute velocity of following vehicle, etc. The collision-avoidance strategy is based on the vision perception and implemented by a fuzzy decision making mechanism. In this paper, the necessary information is integrated as a degree of exceeding safe-distance (DESD) to estimate the possibility of collision. A safety coefficient (SC) is defined to indicate the degree of safety. Therefore, the number of fuzzy rules that based on DESD and SC could be reduced to improve the efficiency of decision making. In addition to robust image processing, abundant information are derived from recognizing image feature using the proposed algorithm in this paper. The fuzzy decision making mechanism abstract useful compact data extracted from these abundant information. Therefore, the main advantage of IVCAS is using less number of fuzzy rules than other systems, and gets more effectiveness in vehicle collision-avoidance.","PeriodicalId":386189,"journal":{"name":"IEEE Proceedings. Intelligent Vehicles Symposium, 2005.","volume":"4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2005-06-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129787640","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 17

An improved Otsu image segmentation algorithm for path mark detection under variable illumination 一种改进的变光照下Otsu图像分割路径标记检测算法

IEEE Proceedings. Intelligent Vehicles Symposium, 2005. Pub Date : 2005-06-06 DOI: 10.1109/IVS.2005.1505209

Li-sheng Jin, Tian Lei, Rong-ben Wang, Guo Lie, Jiang-wei Chu

引用次数: 36

Detection of visibility conditions through use of onboard cameras 通过机载摄像头检测能见度

IEEE Proceedings. Intelligent Vehicles Symposium, 2005. Pub Date : 2005-06-06 DOI: 10.1109/IVS.2005.1505101

N. Hautière, R. Labayrade, D. Aubert

引用次数: 21

A modular tracking system for far infrared pedestrian recognition 一种用于远红外行人识别的模块化跟踪系统

IEEE Proceedings. Intelligent Vehicles Symposium, 2005. Pub Date : 2005-06-06 DOI: 10.1109/IVS.2005.1505196

E. Binelli, A. Broggi, A. Fascioli, S. Ghidoni, P. Grisleri, Thorsten Dr. Graf, M. Meinecke

引用次数: 46

Detection of close cut-in and overtaking vehicles for driver assistance based on planar parallax 基于平面视差的近车超车辅助检测

IEEE Proceedings. Intelligent Vehicles Symposium, 2005. Pub Date : 2005-06-06 DOI: 10.1109/IVS.2005.1505117

D. Baehring, S. Simon, W. Niehsen, C. Stiller

{"title":"Detection of close cut-in and overtaking vehicles for driver assistance based on planar parallax","authors":"D. Baehring, S. Simon, W. Niehsen, C. Stiller","doi":"10.1109/IVS.2005.1505117","DOIUrl":"https://doi.org/10.1109/IVS.2005.1505117","url":null,"abstract":"Image processing is widely considered an essential part of future driver assistance systems. This paper presents a motion-based vision approach to initial detection of static and moving objects observed by a monocular camera attached to a moving observer. The underlying principle is based on parallax flow induced by all non-planar static or moving object of a 3D scene that is determined from optical flow measurements. Initial object hypotheses are created in regions containing significant parallax flow. The significance is determined from planar parallax decomposition automatically. Furthermore, we propose a separation of detected image motion into three hypotheses classes, namely coplanar, static and moving regions. To achieve a high degree of robustness and accuracy in real traffic situations some key processing steps are supported by the data of inertial sensors rigidly attached to our vehicle. The proposed method serves as a visual short-range surveillance module providing instantaneous object candidates to a driver assistance system. Our experiments and simulations confirm the feasibility and robustness of the detection method even in complex urban environment.","PeriodicalId":386189,"journal":{"name":"IEEE Proceedings. Intelligent Vehicles Symposium, 2005.","volume":"75 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2005-06-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131947630","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 41