IEEE Proceedings. Intelligent Vehicles Symposium, 2005.最新文献_第2页

Pedestrian detection using sparse Gabor filter and support vector machine 基于稀疏Gabor滤波和支持向量机的行人检测

IEEE Proceedings. Intelligent Vehicles Symposium, 2005. Pub Date : 2005-06-06 DOI: 10.1109/IVS.2005.1505166

Hong Cheng, N. Zheng, Junjie Qin

引用次数: 93

System development and performance investigation of mobile ad-hoc networks in vehicular environments 车载环境下移动自组网系统开发与性能研究

IEEE Proceedings. Intelligent Vehicles Symposium, 2005. Pub Date : 2005-06-06 DOI: 10.1109/IVS.2005.1505119

Bing-Wen Chuang, J. Tarng, J. Lin, C. Wang

引用次数: 13

Environmental perception and situation assessment for an advanced highway assistant 高级公路辅助系统的环境感知与态势评估

IEEE Proceedings. Intelligent Vehicles Symposium, 2005. Pub Date : 2005-06-06 DOI: 10.1109/IVS.2005.1505148

K. Weiß, H. Philipps, T. To, A. Kirchner

引用次数: 6

Hybrid electric motorcycle: a master-slave mode design 混合动力摩托车:主从模式设计

IEEE Proceedings. Intelligent Vehicles Symposium, 2005. Pub Date : 2005-06-06 DOI: 10.1109/IVS.2005.1505130

Chia-Chang Tong, Wu-Shun Jwo, Kun-Li Wen

引用次数: 4

Sustainable ITS project overview: mixed reality traffic experiment space under interactive traffic environment for ITS 可持续ITS项目概述:ITS交互式交通环境下的混合现实交通实验空间

IEEE Proceedings. Intelligent Vehicles Symposium, 2005. Pub Date : 2005-06-06 DOI: 10.1109/IVS.2005.1505191

K. Ikeuchi, M. Kuwahara, Y. Suda, Y. Tanaka, E. Chung, T. Suzuki, M. Kagesawa, S. Tanaka, I. Nishikawa, Y. Takahashi, R. Horiguchi, T. Shiraishi, H. Hanabusa, H. Kawasaki, H. Ishikawa, K. Maruoka, K. Honda, M. Furukawa, M. Kano, H. Ueno, Y. Ohba, Y. Mashiyama, T. Oda, K. Kenmotsu, T. Yamamoto, M.O. Masaaki, M. Sakai, M. Tsuji

{"title":"Sustainable ITS project overview: mixed reality traffic experiment space under interactive traffic environment for ITS","authors":"K. Ikeuchi, M. Kuwahara, Y. Suda, Y. Tanaka, E. Chung, T. Suzuki, M. Kagesawa, S. Tanaka, I. Nishikawa, Y. Takahashi, R. Horiguchi, T. Shiraishi, H. Hanabusa, H. Kawasaki, H. Ishikawa, K. Maruoka, K. Honda, M. Furukawa, M. Kano, H. Ueno, Y. Ohba, Y. Mashiyama, T. Oda, K. Kenmotsu, T. Yamamoto, M.O. Masaaki, M. Sakai, M. Tsuji","doi":"10.1109/IVS.2005.1505191","DOIUrl":"https://doi.org/10.1109/IVS.2005.1505191","url":null,"abstract":"In this paper, we show the outline of our research on interactive traffic environment, under which we are developing mixed reality traffic experiment space. In order to develop sophisticated ITS applications, it is very important to analyze human factor, but there is few method to obtain human factor, thus, we have decided to create interactive traffic environment under which we can obtain human factors. As the first stage of our research, we have developed mixed reality traffic experiment space. It consists of real observation laboratory part and virtual experiment laboratory part. In the former part, we gather various real raw data to retrieve real environment model. In the later part, we present users more realistic driving environment based on the model. Based on this experiment space, we are going to proceed to the next stages, in which we design and evaluate sustainable ITS applications. For this purpose we have started sustainable ITS project at the university of Tokyo, collaborated among industry, government, and university.","PeriodicalId":386189,"journal":{"name":"IEEE Proceedings. Intelligent Vehicles Symposium, 2005.","volume":"23 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":"125947629","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: 4

Obstacle detection for start-inhibit and low speed driving 启动抑制和低速驾驶障碍检测

IEEE Proceedings. Intelligent Vehicles Symposium, 2005. Pub Date : 2005-06-06 DOI: 10.1109/IVS.2005.1505164

M. Bertozzi, A. Broggi, P. Medici, P. Porta, R. Vitulli

引用次数: 10

Practical automotive applications of Cramer-Rao bound analysis Cramer-Rao界分析在汽车中的实际应用

IEEE Proceedings. Intelligent Vehicles Symposium, 2005. Pub Date : 2005-06-06 DOI: 10.1109/IVS.2005.1505078

M. Rydstrom, E. Strom, A. Svensson, A. Urruela

{"title":"Practical automotive applications of Cramer-Rao bound analysis","authors":"M. Rydstrom, E. Strom, A. Svensson, A. Urruela","doi":"10.1109/IVS.2005.1505078","DOIUrl":"https://doi.org/10.1109/IVS.2005.1505078","url":null,"abstract":"The Cramer-Rao lower bound places a bound, in a mean squared sense, on the performance of all unbiased estimators. In this paper, as a base for discussion, we provide a straight forward derivation of such bounds for estimators of mobile node positions, operating on observations of distances between entities in an asynchronous network. While Cramer-Rao bound analysis is very common in the positioning community, it is mostly used for analytical evaluation of various estimators, for comparison purposes and for sensor information fusion. In this work, we present some more commercial and practical applications of these tools for performance evaluation. We first discuss the deployment of beacons throughout a transportation infrastructure as a first step towards providing global automotive, possibly GPS augmented, positioning services with applications such as collision warning and collision avoidance. We then move on to describe how the cost associated with the deployment of such beacons can be drastically reduced through relying on inter-node range measurements and a dynamic beaconing scheme we call the lighthouse scheme. We also present a method for complexity reduction in the estimation of relative node coordinates and evaluation of Cramer-Rao performance measures.","PeriodicalId":386189,"journal":{"name":"IEEE Proceedings. Intelligent Vehicles Symposium, 2005.","volume":"48 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":"129514317","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: 3

A photo-realistic driving simulation system for mixed-reality traffic experiment space 一种用于混合现实交通实验空间的逼真驾驶模拟系统

IEEE Proceedings. Intelligent Vehicles Symposium, 2005. Pub Date : 2005-06-06 DOI: 10.1109/IVS.2005.1505194

Shintaro Ono, †. KoichiOgawara, M. Kagesawa, Hiroshi Kawasaki, M. Onuki, Jun-ichi Abeki, ¶. ToruYano, ¶. MasamiNerio, K. Honda, K. Ikeuchi, Ltd. Chodai Co.

引用次数: 17

Learning based symmetric features selection for vehicle detection 基于学习的车辆检测对称特征选择

IEEE Proceedings. Intelligent Vehicles Symposium, 2005. Pub Date : 2005-06-06 DOI: 10.1109/IVS.2005.1505089

T. Liu, N. Zheng, L. Zhao, H. Cheng

{"title":"Learning based symmetric features selection for vehicle detection","authors":"T. Liu, N. Zheng, L. Zhao, H. Cheng","doi":"10.1109/IVS.2005.1505089","DOIUrl":"https://doi.org/10.1109/IVS.2005.1505089","url":null,"abstract":"This paper describes a symmetric features selection strategy based on statistical learning method for detecting vehicles with a single moving camera for autonomous driving. Symmetry is a good class of feature for vehicle detection, but the areas with high symmetry and threshold for segmentation is hard to be decided. Usually, the additional supposition is added artificially, and this will decrease the robustness of algorithms. In this paper, we focus on the problem of symmetric features selection using learning method for autonomous driving environment. Global symmetry and local symmetry are defined and used to construct a cascaded structure with a one-class classifier followed by a two-class classifier. Especially for local symmetric features, different symmetric areas in the rear view image of vehicles are searched through Adaboost based learning, and most useful symmetric features are extracted. The threshold for classification is also found through learning. The effective features selection strategy shows that the integration of global symmetry and local symmetry helps to improve the robustness of algorithms. Experimental results indicate the robustness and real-time performance of the algorithm.","PeriodicalId":386189,"journal":{"name":"IEEE Proceedings. Intelligent Vehicles Symposium, 2005.","volume":"1 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":"129568405","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: 56

A multiple detector approach to low-resolution FIR pedestrian recognition 低分辨率FIR行人识别的多检测器方法

IEEE Proceedings. Intelligent Vehicles Symposium, 2005. Pub Date : 2005-06-06 DOI: 10.1109/IVS.2005.1505123

Mirko Miihlisch, M. Oberlander, O. Lohlein, D. Gavrila, Werner Ritter

引用次数: 49