16th International IEEE Conference on Intelligent Transportation Systems (ITSC 2013)最新文献_第10页

Vehicle-sharing: Technological infrastructure, vehicles, and user-side devices - Technological review 车辆共享:技术基础设施、车辆和用户端设备-技术回顾

16th International IEEE Conference on Intelligent Transportation Systems (ITSC 2013) Pub Date : 2013-10-01 DOI: 10.1109/ITSC.2013.6728458

Andrea G. Bianchessi, Carlo Ongini, G. Alli, Emanuele Panigati, S. Savaresi

引用次数: 7

Adaptive side slip angle observer using simple combined vehicle dynamics 基于简单组合车辆动力学的自适应侧滑角观测器

16th International IEEE Conference on Intelligent Transportation Systems (ITSC 2013) Pub Date : 2013-10-01 DOI: 10.1109/ITSC.2013.6728215

C. Kang, Wonhee Kim, Bo-Ah Kim, Youngseop Son, Seung-Hi Lee, C. Chung

引用次数: 7

Infrastructure-based vehicle maneuver estimation at urban intersections 基于基础设施的城市交叉口车辆机动估计

16th International IEEE Conference on Intelligent Transportation Systems (ITSC 2013) Pub Date : 2013-10-01 DOI: 10.1109/ITSC.2013.6728433

T. Schendzielorz, P. Mathias, F. Busch

引用次数: 12

Lessons from proving ground experiments to investigate junction control 研究结控制的试验经验

16th International IEEE Conference on Intelligent Transportation Systems (ITSC 2013) Pub Date : 2013-10-01 DOI: 10.1109/ITSC.2013.6728320

S. Box, John D. Lees-Miller, James R. Snowdon, James Hammond, Andrew Hamilton, S. Gupta, R. E. Wilson, B. Waterson

{"title":"Lessons from proving ground experiments to investigate junction control","authors":"S. Box, John D. Lees-Miller, James R. Snowdon, James Hammond, Andrew Hamilton, S. Gupta, R. E. Wilson, B. Waterson","doi":"10.1109/ITSC.2013.6728320","DOIUrl":"https://doi.org/10.1109/ITSC.2013.6728320","url":null,"abstract":"An experiment was conducted using the InnovITS proving ground in Nuneaton. Thirty cars with volunteer drivers were asked to drive around a tight closed road circuit causing them to pass repeatedly through a cross-roads junction from all directions. The junction was signalized. In different test-runs of the experiment the traffic lights were controlled by either an automated fixed-time system or by a human using remote control. All vehicles in the test were instrumented using GPS and bluetooth. Video footage from two cameras was also recorded.recorded. The goal of the experiment was to collect data on the performance of human junction controllers. This was motivated by earlier work indicated that human controllers could perform well at this task in a simulated `computer game' environment. In particular this paper examines some of the issues that arise when trying to simulate an urban road junction in this manner. For example results are presented indicating differences in network performance depending on whether the drivers were instructed to follow a fixed route or a random route of their choice. Thus providing some guidance for maximizing the fidelity of this type of simulation in the future. The paper also presents a detailed analysis of the sensor data and video footage to measure the performance of the junction under the different modes of control.","PeriodicalId":275768,"journal":{"name":"16th International IEEE Conference on Intelligent Transportation Systems (ITSC 2013)","volume":"18 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114979798","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: 1

Vehicular network rerouting autonomy with a V2V, I2V, and V2I communication matrix classification 基于V2V、I2V和V2I通信矩阵分类的车辆网络重路由自治

16th International IEEE Conference on Intelligent Transportation Systems (ITSC 2013) Pub Date : 2013-10-01 DOI: 10.1109/ITSC.2013.6728229

S. Boskovich, M. Barth

{"title":"Vehicular network rerouting autonomy with a V2V, I2V, and V2I communication matrix classification","authors":"S. Boskovich, M. Barth","doi":"10.1109/ITSC.2013.6728229","DOIUrl":"https://doi.org/10.1109/ITSC.2013.6728229","url":null,"abstract":"Intelligent Transportation Systems allow for broad implementation approaches regarding the distribution of data and the processing of that data for use in a particular framework. In this paper, we provide an analysis and approach in describing methods of vehicular network autonomy incorporating concepts that include V2I, I2V and V2V communications. Specifically, we provide validity to concepts that are taken for granted by providing simulations to support these considerations. We also provide a discussion of the architecture implementations that define these concepts that capture classification methods negating the problem with open interpretation as to where the data are being collected from as well as being processed. We provide a refinement within the scope of ITS by systematically classifying the roles and interaction between processing and data collection. Furthermore, we then utilize key classifications of processing and data collection that includes either a centralized or distributed framework approaches. These approaches illustrate how vehicles can be used to accomplish dynamic rerouting by applying knowledge of network state but also network composition to achieve lower travel times when incidents occur.","PeriodicalId":275768,"journal":{"name":"16th International IEEE Conference on Intelligent Transportation Systems (ITSC 2013)","volume":"116 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123201449","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: 9

A receding-horizon planning approach for rail operations in seaport container terminals 海港集装箱码头铁路运营的后退地平线规划方法

16th International IEEE Conference on Intelligent Transportation Systems (ITSC 2013) Pub Date : 2013-10-01 DOI: 10.1109/ITSC.2013.6728548

C. Caballini, C. Pasquale, S. Sacone, S. Siri

引用次数: 2

The definition of a model framework for managing rail systems in the case of breakdowns 在故障情况下管理铁路系统的模型框架的定义

16th International IEEE Conference on Intelligent Transportation Systems (ITSC 2013) Pub Date : 2013-10-01 DOI: 10.1109/ITSC.2013.6728372

L. D’Acierno, M. Gallo, B. Montella, A. Placido

引用次数: 34

ChangiNOW: A mobile application for efficient taxi allocation at airports ChangiNOW:一个在机场高效分配出租车的移动应用程序

16th International IEEE Conference on Intelligent Transportation Systems (ITSC 2013) Pub Date : 2013-10-01 DOI: 10.1109/ITSC.2013.6728312

Afian Anwar, M. Volkov, D. Rus

引用次数: 28

Driver assistance via optical information with spatial reference 驾驶员辅助通过光学信息与空间参考

16th International IEEE Conference on Intelligent Transportation Systems (ITSC 2013) Pub Date : 2013-10-01 DOI: 10.1109/ITSC.2013.6728524

Matthias Pfromm, S. Cieler, Ralph Bruder

引用次数: 37

Riding patterns recognition for Powered two-wheelers users' behaviors analysis 基于电动两轮车用户行为分析的骑行模式识别

16th International IEEE Conference on Intelligent Transportation Systems (ITSC 2013) Pub Date : 2013-10-01 DOI: 10.1109/ITSC.2013.6728528

F. Attal, Abderrahmane Boubezoul, L. Oukhellou, S. Espié

{"title":"Riding patterns recognition for Powered two-wheelers users' behaviors analysis","authors":"F. Attal, Abderrahmane Boubezoul, L. Oukhellou, S. Espié","doi":"10.1109/ITSC.2013.6728528","DOIUrl":"https://doi.org/10.1109/ITSC.2013.6728528","url":null,"abstract":"In this paper, we develop a simple and efficient methodology for riding patterns recognition based on a machine learning framework. The riding pattern recognition problem is formulated as a classification problem aiming to identify the class of the riding situation by using data collected from three-accelerometer and three-gyroscope sensors mounted on the motorcycle. These measurements constitute experimental database which is valuable to analyze Powered Two Wheelers (PTW) rider behavior. Five well known machine learning techniques are used: the Gaussian mixture models (GMMs), k-Nearest Neighbors (k-NN), Support Vector Machines (SVMs), Random Forests (RFs) and the Hidden Markov Models (HMMs) in both (discrete and continuous) cases. The HMMs are widely applied for studying time series data which is the case of our problem. The data preprocessing consists of filtering, normalizing and manual labeling in order to create the training and testing sets. The experimental study carried out on a real dataset shows the effectiveness of the proposed methodology and more particularly of the HMM approach to perform such riding pattern recognition. These encouraging results work in favor of developing such methodologies in the naturalistic riding studies (NRS).","PeriodicalId":275768,"journal":{"name":"16th International IEEE Conference on Intelligent Transportation Systems (ITSC 2013)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128069812","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: 13