2013 IEEE Intelligent Vehicles Symposium (IV)最新文献_第7页

In-vehicle displays: Driving information prioritization and visualization 车载显示:驾驶信息优先化和可视化

2013 IEEE Intelligent Vehicles Symposium (IV) Pub Date : 2013-06-23 DOI: 10.1109/IVS.2013.6629542

C. Olaverri-Monreal, C. Lehsing, Nicole Trubswetter, Cheree Anne Schepp, K. Bengler

引用次数: 17

Partially occluded vehicle recognition and tracking in 3D 部分遮挡的3D车辆识别与跟踪

2013 IEEE Intelligent Vehicles Symposium (IV) Pub Date : 2013-06-23 DOI: 10.1109/IVS.2013.6629654

Eshed Ohn-Bar, Sayanan Sivaraman, M. Trivedi

引用次数: 20

Stereo-based motion detection and tracking from a moving platform 基于立体的运动检测和跟踪从一个移动平台

2013 IEEE Intelligent Vehicles Symposium (IV) Pub Date : 2013-06-23 DOI: 10.1109/IVS.2013.6629517

Victor A. Romero-Cano, Juan I. Nieto

引用次数: 19

Probabilistic non-line-of-sight detection in reliable urban GNSS vehicle localization based on an empirical sensor model 基于经验传感器模型的城市GNSS车辆可靠定位概率非视距检测

2013 IEEE Intelligent Vehicles Symposium (IV) Pub Date : 2013-06-23 DOI: 10.1109/IVS.2013.6629496

Marcus Obst, G. Wanielik

引用次数: 15

Inferring driver intentions using a driver model based on queuing network 使用基于排队网络的驱动程序模型推断驱动程序意图

2013 IEEE Intelligent Vehicles Symposium (IV) Pub Date : 2013-06-23 DOI: 10.1109/IVS.2013.6629660

Luzheng Bi, Xuerui Yang, Cuie Wang

引用次数: 11

Next generation wired intra-vehicle networks, a review 下一代有线车载网络，回顾

2013 IEEE Intelligent Vehicles Symposium (IV) Pub Date : 2013-06-23 DOI: 10.1109/IVS.2013.6629561

Shane Tuohy, M. Glavin, E. Jones, M. Trivedi, L. Kilmartin

引用次数: 27

Object tracking from medium level stereo camera data providing detailed shape estimation using local grid maps 利用局部网格图提供详细形状估计的中等水平立体相机数据跟踪目标

2013 IEEE Intelligent Vehicles Symposium (IV) Pub Date : 2013-06-23 DOI: 10.1109/IVS.2013.6629651

J. Aué, M. Schmid, T. Graf, J. Effertz, Peter Mühlfellner

引用次数: 8

The use of spatial memory for advanced driver assistance systems: Preventing stationary ACC false alarms 先进驾驶辅助系统空间记忆的使用:防止固定ACC误报

2013 IEEE Intelligent Vehicles Symposium (IV) Pub Date : 2013-06-23 DOI: 10.1109/IVS.2013.6629644

H. Deusch, R. Graf, M. Fritzsche, K. Dietmayer

引用次数: 1

Short-term traffic volume prediction using classification and regression trees 基于分类树和回归树的短期交通量预测

2013 IEEE Intelligent Vehicles Symposium (IV) Pub Date : 2013-06-23 DOI: 10.1109/IVS.2013.6629516

Yanyan Xu, Qingjie Kong, Yuncai Liu

{"title":"Short-term traffic volume prediction using classification and regression trees","authors":"Yanyan Xu, Qingjie Kong, Yuncai Liu","doi":"10.1109/IVS.2013.6629516","DOIUrl":"https://doi.org/10.1109/IVS.2013.6629516","url":null,"abstract":"Accurate short-term traffic flow prediction plays a fundamental role in intelligent transportation systems (ITS), e.g. advanced traffic management systems (ATMS). To generate accurate short-term traffic volume, nonparametric models have gained credit from quantities of researchers. On the basis of the common thought that future traffic states can be predicted according to the similar states in the historical traffic data, this paper presents a novel nonparametric-model-based method to predict the short-term traffic volume. The applied nonparametric model is the classification and regression trees (CART) model. In the application, the CART model first classifies the historical traffic states into plentiful categories. Afterwards, the linear regression model is built corresponding to each traffic state pattern. Finally, the model predicts the short-term traffic state through clustering the current state vector into the most congenial historical pattern and regression model. In the experiments, the proposed method is tested by using the 15 minutes average traffic volumes on freeways and is compared with the classic nonparametric methods k-nearest neighbors (k-NN) model, and the parametric method Kalman filter model. The results indicate that the CART-based prediction method outperforms the k-NN and Kalman filter methods in both the mean absolute percentage error and the mean absolute scaled error.","PeriodicalId":251198,"journal":{"name":"2013 IEEE Intelligent Vehicles Symposium (IV)","volume":"238 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-06-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132746311","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: 33

Cooperative multi-robot navigation, exploration, mapping and object detection with ROS 基于ROS的协同多机器人导航、探索、测绘和目标检测

2013 IEEE Intelligent Vehicles Symposium (IV) Pub Date : 2013-06-23 DOI: 10.1109/IVS.2013.6629610

R. Reid, Andrew Cann, C. Meiklejohn, L. Poli, A. Boeing, T. Bräunl

引用次数: 52