2010 IEEE Intelligent Vehicles Symposium最新文献_第2页

Road detection using support vector machine based on online learning and evaluation 基于在线学习与评价的支持向量机道路检测

2010 IEEE Intelligent Vehicles Symposium Pub Date : 2010-06-21 DOI: 10.1109/IVS.2010.5548086

Shengyan Zhou, Jian-wei Gong, Guang-ming Xiong, Huiyan Chen, K. Iagnemma

引用次数: 101

Probabilistic representation of the uncertainty of stereo-vision and application to obstacle detection 立体视觉不确定性的概率表示及其在障碍物检测中的应用

2010 IEEE Intelligent Vehicles Symposium Pub Date : 2010-06-21 DOI: 10.1109/IVS.2010.5548010

M. Perrollaz, A. Spalanzani, Didier Aubert

{"title":"Probabilistic representation of the uncertainty of stereo-vision and application to obstacle detection","authors":"M. Perrollaz, A. Spalanzani, Didier Aubert","doi":"10.1109/IVS.2010.5548010","DOIUrl":"https://doi.org/10.1109/IVS.2010.5548010","url":null,"abstract":"Stereo-vision is extensively used for intelligent vehicles, mainly for obstacle detection, as it provides a large amount of data. Many authors use it as a classical 3D sensor which provides a large tri-dimensional cloud of metric measurements, and apply methods usually designed for other sensors, such as clustering based on a distance. For stereo-vision, the measurement uncertainty is related to the range. For medium to long range, often necessary in the field of intelligent vehicles, this uncertainty has a significant impact, limiting the use of this kind of approaches. On the other hand, some authors consider stereo-vision more like a vision sensor and choose to directly work in the disparity space. This provides the ability to exploit the connectivity of the measurements, but roughly takes into consideration the actual size of the objects. In this paper, we propose a probabilistic representation of the specific uncertainty for stereo-vision, which takes advantage of both aspects - distance and disparity. The model is presented and then applied to obstacle detection, using the occupancy grid framework. For this purpose, a computationally-efficient implementation based on the u-disparity approach is given.","PeriodicalId":123266,"journal":{"name":"2010 IEEE Intelligent Vehicles Symposium","volume":"208 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-06-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116506864","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: 55

Extracting information from continuous naturalistic driving data: sample applications 从连续的自然驾驶数据中提取信息:示例应用程序

2010 IEEE Intelligent Vehicles Symposium Pub Date : 2010-06-21 DOI: 10.1109/IVS.2010.5547964

Miguel A. Perez, Zachary R. Doerzaph, C. Gaylord, J. Hankey

{"title":"Extracting information from continuous naturalistic driving data: sample applications","authors":"Miguel A. Perez, Zachary R. Doerzaph, C. Gaylord, J. Hankey","doi":"10.1109/IVS.2010.5547964","DOIUrl":"https://doi.org/10.1109/IVS.2010.5547964","url":null,"abstract":"The technology and tools used for naturalistic driving data collection have evolved greatly in recent years. Data collection efforts that required a trunk full of equipment and days of installation can now be achieved with data acquisition systems that are about the size of a deck of cards and can be installed in minutes. This evolution has made possible large-scale driving data collection efforts, such as the upcoming Second Safety Highway Research Program Naturalistic Driving Study (SHRP2 NDS). Data from naturalistic studies allow for an unparalleled breadth and depth of driver behavior analysis that goes beyond the quantification and description of driver distraction into a deeper understanding of how drivers interact with their vehicles. This paper describes key aspects of how such studies are designed and executed, and provides some examples of how common types of data are extracted from these naturalistic driving datasets. Specifically, the use of RADAR and speed data are discussed in detail. In addition, a sample architecture for the storage of and access to these vast quantities of driving data and video is provided. Naturalistic driving data have allowed for a transformation in the understanding of driver behavior and, as datasets are expanded to include diverse populations, they will help researchers and automotive engineers in developing novel ways to mitigate and prevent vehicular crashes and their consequences.","PeriodicalId":123266,"journal":{"name":"2010 IEEE Intelligent Vehicles Symposium","volume":"31 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-06-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114019634","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

A confidence measure for vehicle tracking based on a generalization of Bayes estimation 基于Bayes估计泛化的车辆跟踪置信度测度

2010 IEEE Intelligent Vehicles Symposium Pub Date : 2010-06-21 DOI: 10.1109/IVS.2010.5548027

R. Altendorfer, S. Matzka

引用次数: 11

Sensor fusion-based line detection for unmanned navigation 基于传感器融合的无人导航线路检测

2010 IEEE Intelligent Vehicles Symposium Pub Date : 2010-06-21 DOI: 10.1109/IVS.2010.5547995

C. Chun, SeungBeum Suh, Chi-won Roh, Yeonsik Kang, Sungchul Kang, Jung-yup Lee, Chang-Soo Han

引用次数: 6

Driver route choice behavior: Experiences, perceptions, and choices 司机路线选择行为:经验、感知和选择

2010 IEEE Intelligent Vehicles Symposium Pub Date : 2010-06-21 DOI: 10.1109/IVS.2010.5547968

Aly M. Tawfik, Hesham A Rakha, Shadeequa D. Miller

{"title":"Driver route choice behavior: Experiences, perceptions, and choices","authors":"Aly M. Tawfik, Hesham A Rakha, Shadeequa D. Miller","doi":"10.1109/IVS.2010.5547968","DOIUrl":"https://doi.org/10.1109/IVS.2010.5547968","url":null,"abstract":"Within the context of transportation modeling, driver route choice is typically captured using mathematical programming approaches, which assume that drivers, in attempting to minimize some objective function, have full knowledge of the transportation network state. Typically, drivers are assumed to either minimize their travel time (user equilibrium) or minimize the total system travel time (system optimum). Given the dynamic and stochastic nature of the transportation system, the assumption of a driver's perfect knowledge is at best questionable. While it is well documented in psychological sciences that humans tend to minimize their cognitive efforts and follow simple heuristics to reach their decisions, especially under uncertainty and time constraints, current models assume that drivers have perfect or close to perfect knowledge of their choice set, as well as the travel characteristics associated with each of the choice elements. Only a few of the many route choice models that are described in the literature are based on observed human behavior. With this in mind the research presented in this paper monitors and analyzes actual human route choice behavior. It compares actual drivers experiences, perceptions and choices, and demonstrates that (a) drivers perceptions are significantly different from their actual experiences, and that drivers' choices are better explained by their perceptions than their experiences; (b) drivers perceive travel speeds better than travel times (c) perceived travel speeds seem to influence route choice more than perceived travel times, and (d) drivers' route choice behavior differs across different driver groups.","PeriodicalId":123266,"journal":{"name":"2010 IEEE Intelligent Vehicles Symposium","volume":"12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-06-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127782853","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: 32

Using Multiple Correspondence Analysis for large driving signals database exploration. Example with lane narrowing and curves 基于多重对应分析的大型驾驶信号数据库探索。车道变窄和曲线的例子

2010 IEEE Intelligent Vehicles Symposium Pub Date : 2010-06-21 DOI: 10.1109/IVS.2010.5547989

P. Loslever, J. Popieul, P. Simon, A. Todoskoff

引用次数: 0

A system architecture for IP-camera based driver assistance applications 基于ip摄像头的驾驶员辅助应用系统架构

2010 IEEE Intelligent Vehicles Symposium Pub Date : 2010-06-21 DOI: 10.1109/IVS.2010.5548103

W. Hintermaier, E. Steinbach

引用次数: 11

Map based road boundary estimation 基于地图的道路边界估计

2010 IEEE Intelligent Vehicles Symposium Pub Date : 2010-06-21 DOI: 10.1109/IVS.2010.5548011

M. Darms, M. Komar, S. Lüke

引用次数: 41

Research on driver experience based route planning method 基于驾驶员经验的路线规划方法研究

2010 IEEE Intelligent Vehicles Symposium Pub Date : 2010-06-21 DOI: 10.1109/IVS.2010.5548109

Man Li, Wenjia Wang, Yuhe Zhang

引用次数: 5