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Autonomous robot navigation based on OpenStreetMap geodata 基于OpenStreetMap地理数据的自主机器人导航
13th International IEEE Conference on Intelligent Transportation Systems Pub Date : 2010-11-09 DOI: 10.1109/ITSC.2010.5625092
Matthias Hentschel, Bernardo Wagner
{"title":"Autonomous robot navigation based on OpenStreetMap geodata","authors":"Matthias Hentschel, Bernardo Wagner","doi":"10.1109/ITSC.2010.5625092","DOIUrl":"https://doi.org/10.1109/ITSC.2010.5625092","url":null,"abstract":"This paper introduces the appliance of standardized, free to use and globally available geodata for autonomous robot navigation. For this, data from the famous collaborative OpenStreetMap (OSM) mapping project are used. These geodata are public domain and include rich information about streets, tracks, railways, waterways, points of interest, land use, building information and much more. Beyond the spatial information, the geodata contain detailed information about the name, type and width of the streets as well as public speed limits. As a contribution of this paper, the OSM data are integrated for the first time into the robot tasks of localization, path planning and autonomous vehicle control. Following the description of the approach, experimental results in outdoor environments demonstrate the effectiveness of this approach.","PeriodicalId":176645,"journal":{"name":"13th International IEEE Conference on Intelligent Transportation Systems","volume":"46 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-11-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114964219","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: 116
Comparison of modelling approaches for short term traffic prediction under normal and abnormal conditions 正常与异常条件下短期交通预测建模方法的比较
13th International IEEE Conference on Intelligent Transportation Systems Pub Date : 2010-11-09 DOI: 10.1109/ITSC.2010.5625291
Fangce Guo, J. Polak, R. Krishnan
{"title":"Comparison of modelling approaches for short term traffic prediction under normal and abnormal conditions","authors":"Fangce Guo, J. Polak, R. Krishnan","doi":"10.1109/ITSC.2010.5625291","DOIUrl":"https://doi.org/10.1109/ITSC.2010.5625291","url":null,"abstract":"Short-term prediction of traffic flows is an integral component of proactive traffic management systems. Prediction during abnormal conditions, such as incidents, is important for such systems. In this paper, three different models with increasing information in explanatory variables are presented. Time Delay and Recurrent Neural Networks and the k-Nearest Neighbour (kNN) algorithms are chosen as the machine learning tools in these models. The models are tested during both normal and incident conditions. The results indicate that historical patterns provide less predictive information during incidents.","PeriodicalId":176645,"journal":{"name":"13th International IEEE Conference on Intelligent Transportation Systems","volume":"23 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-11-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114981948","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: 25
Fast real-time multiclass traffic sign detection based on novel shape and texture descriptors 基于新型形状和纹理描述符的快速实时多类交通标志检测
13th International IEEE Conference on Intelligent Transportation Systems Pub Date : 2010-11-09 DOI: 10.1109/ITSC.2010.5625257
Iago Landesa-Vazquez, F. Parada-Loira, J. Alba-Castro
{"title":"Fast real-time multiclass traffic sign detection based on novel shape and texture descriptors","authors":"Iago Landesa-Vazquez, F. Parada-Loira, J. Alba-Castro","doi":"10.1109/ITSC.2010.5625257","DOIUrl":"https://doi.org/10.1109/ITSC.2010.5625257","url":null,"abstract":"Detection and classification of traffic signs is one of the most studied Advanced Driver Assistance Systems (ADAS) and some solutions are already installed in vehicles. Nevertheless these systems still have room for improvement in terms of speed and performance. When driving at high speed, warning systems require very fast processing of the video stream in order to lose as few frames as possible and minimize the chance of missing a readable traffic sign. In this paper we show a sign detection system for grayscale images based on a two-stage process: A rapid shape prefiltering, that relies on a new descriptor coined as Local Contour Patterns, rejects most of the image subwindows and preclassifies the rest as one of the three main sign types. Then, a sign-dependent AdaBoost-based cascade detector that makes use of a new set of simpler texture features, coined as Quantum Features, scans the pre-fetched subwindows to fine tune candidate traffic signs. The analysis of this detector over hundreds of video sequences which were captured with a car-mounted 752×480 grayscale camera and provided by the Galician Automotive Technology Center (CTAG) shows a very good behavior for multiclass traffic sign detection running at 14 frames/sec on a 2.8 GHz processor.","PeriodicalId":176645,"journal":{"name":"13th International IEEE Conference on Intelligent Transportation Systems","volume":"18 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-11-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115471173","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: 18
Heuristic rule for truck dispatching in open-pit mines with local information-based decisions 基于局部信息决策的露天矿卡车调度启发式规则
13th International IEEE Conference on Intelligent Transportation Systems Pub Date : 2010-11-09 DOI: 10.1109/ITSC.2010.5625231
Ariel Arelovich, F. Masson, O. Agamennoni, Stewart Worrall, E. Nebot
{"title":"Heuristic rule for truck dispatching in open-pit mines with local information-based decisions","authors":"Ariel Arelovich, F. Masson, O. Agamennoni, Stewart Worrall, E. Nebot","doi":"10.1109/ITSC.2010.5625231","DOIUrl":"https://doi.org/10.1109/ITSC.2010.5625231","url":null,"abstract":"This paper proposes a new algorithm to make real time dispatching decisions in open-pit mines based on discrete position information. New methods are presented to estimate the probability density function for the position of each vehicle across the mine. New heuristic rules are then presented that use current local data gathered by peer to peer communication systems and vehicle position estimates to select the optimal destination and travel plan for each vehicle. A comparison of the algorithm with the existing approaches based on global information of truck position is presented. The results show that the performance improves using the discrete information, and there is significant improvements in the event of accidents or queuing.","PeriodicalId":176645,"journal":{"name":"13th International IEEE Conference on Intelligent Transportation Systems","volume":"315 4","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-11-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"120984586","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: 14
Visual state estimation of traffic lights using hidden Markov models 基于隐马尔可夫模型的交通灯视觉状态估计
13th International IEEE Conference on Intelligent Transportation Systems Pub Date : 2010-11-09 DOI: 10.1109/ITSC.2010.5625241
Dennis Nienhüser, M. Drescher, Johann Marius Zöllner
{"title":"Visual state estimation of traffic lights using hidden Markov models","authors":"Dennis Nienhüser, M. Drescher, Johann Marius Zöllner","doi":"10.1109/ITSC.2010.5625241","DOIUrl":"https://doi.org/10.1109/ITSC.2010.5625241","url":null,"abstract":"The comprehension of dynamic objects in the environment is a major concern of prospective assistance systems. Among the relevant dynamic objects are not only road users, but also parts of the traffic infrastructure: Traffic lights switch between different light colors to manage traffic at intersections. We propose a camera-based approach to incorporate the visual information of traffic lights. Assistance systems can use it to realize comfort, fuel economy and safety functions. We focus on the classification and state estimation using support vector machines and hidden Markov models. Our system is able to distinguish different types of traffic lights - even blinking lights - in real-time.","PeriodicalId":176645,"journal":{"name":"13th International IEEE Conference on Intelligent Transportation Systems","volume":"13 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-11-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127493220","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: 36
Improving localization accuracy based on Lightweight Visual Odometry 基于轻量级视觉里程计的定位精度提高
13th International IEEE Conference on Intelligent Transportation Systems Pub Date : 2010-11-09 DOI: 10.1109/ITSC.2010.5625176
D. Pojar, P. Jeong, S. Nedevschi
{"title":"Improving localization accuracy based on Lightweight Visual Odometry","authors":"D. Pojar, P. Jeong, S. Nedevschi","doi":"10.1109/ITSC.2010.5625176","DOIUrl":"https://doi.org/10.1109/ITSC.2010.5625176","url":null,"abstract":"New methods based on vision have emerged in the area of mobile vehicle localization. Such methods offer an improved alternative in terms of accuracy to traditional localization methods like wheel odometry. In this paper we propose such a method that does not compromise precision and can run in real time. Depending on environment, feature numbers are sometimes insufficient. To solve this, our algorithm allows using slower feature detectors like SURF for frame keypoints, together with Shi-Tomasi corners for increasing points number. We show how accuracy is further improved by using a Kalman filter to enhance the computation of pose to pose relative motion variation.","PeriodicalId":176645,"journal":{"name":"13th International IEEE Conference on Intelligent Transportation Systems","volume":"9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-11-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125372459","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: 2
A reliability-based dynamic re-routing algorithm for in-vehicle navigation 基于可靠性的车载导航动态重路由算法
13th International IEEE Conference on Intelligent Transportation Systems Pub Date : 2010-11-09 DOI: 10.1109/ITSC.2010.5625060
I. Kaparias, M. Bell
{"title":"A reliability-based dynamic re-routing algorithm for in-vehicle navigation","authors":"I. Kaparias, M. Bell","doi":"10.1109/ITSC.2010.5625060","DOIUrl":"https://doi.org/10.1109/ITSC.2010.5625060","url":null,"abstract":"This paper presents a new algorithm for a car navigation system, whose purpose is to offer a reliable re-route to the driver in case he/she deviates from the route he/she has been following, or if a traffic incident is reported en route. A reliable route is defined as one that has a low probability of being congested. The new method makes use of the A* route finding algorithm and introduces a link penalizing procedure to avoid unreliable (i.e. potentially congested) and incident-affected links in order to re-route the driver from his/her current position to his/her destination, while constraints are imposed on the route output by the algorithm so as to ensure driver acceptability. The new algorithm, called RDIN-R, is first described and then tested through a simulation experiment on the road network of Munich, Germany.","PeriodicalId":176645,"journal":{"name":"13th International IEEE Conference on Intelligent Transportation Systems","volume":"26 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-11-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126618189","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: 8
Study on information fusion algorithm and application based on improved SVM 基于改进支持向量机的信息融合算法及应用研究
13th International IEEE Conference on Intelligent Transportation Systems Pub Date : 2010-11-09 DOI: 10.1109/ITSC.2010.5624991
Yan-hui Wang, Chenchen Zhang, Jun Luo
{"title":"Study on information fusion algorithm and application based on improved SVM","authors":"Yan-hui Wang, Chenchen Zhang, Jun Luo","doi":"10.1109/ITSC.2010.5624991","DOIUrl":"https://doi.org/10.1109/ITSC.2010.5624991","url":null,"abstract":"Authors presented the information fusion algorithm based on improved SVM, namely, decision tree - support vector machine algorithm (Decision Tree Method-Support Vector Mechines, DTM-SVM). The algorithm overcame the limitations of the conventional SVM classification which applied only to two-classification problem by a “one to many” pattern, solved multi-classification problem and met a wider range of application requirements. Finally, based on the establishment of a freeway traffic state identification evaluation system, the DTM-SVM model was applied to solve the freeway traffic state recognition. Results show that: the algorithm can identify in a shorter time to reach higher recognition accuracy.","PeriodicalId":176645,"journal":{"name":"13th International IEEE Conference on Intelligent Transportation Systems","volume":"25 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-11-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115036855","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: 2
Fuzzy observer for state estimation of the METANET traffic model 基于模糊观测器的METANET流量模型状态估计
13th International IEEE Conference on Intelligent Transportation Systems Pub Date : 2010-11-09 DOI: 10.1109/ITSC.2010.5625223
Z. Hidayat, Z. Lendek, Robert Babuška, B. Schutter
{"title":"Fuzzy observer for state estimation of the METANET traffic model","authors":"Z. Hidayat, Z. Lendek, Robert Babuška, B. Schutter","doi":"10.1109/ITSC.2010.5625223","DOIUrl":"https://doi.org/10.1109/ITSC.2010.5625223","url":null,"abstract":"Traffic control has proven an effective measure to reduce traffic congestion on freeways. In order to determine appropriate control actions, it is necessary to have information on the current state of the traffic. However, not all traffic states can be measured (such as the traffic density) and so state estimation must be applied in order to obtain state information from the available measurements. Linear state estimation methods are not directly applicable, as traffic models are in general nonlinear. In this paper we propose a nonlinear approach to state estimation that is based on a Takagi-Sugeno (TS) fuzzy model representation of the METANET traffic model. By representing the METANET traffic model as a TS fuzzy system, a structured observer design procedure can be applied, whereby the convergence of the observer is guaranteed. Simulation results are presented to illustrate the quality of the estimate.","PeriodicalId":176645,"journal":{"name":"13th International IEEE Conference on Intelligent Transportation Systems","volume":"22 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-11-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116070458","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
Curve warning driver support systems. A sensitivity analysis to errors in the estimation of car velocity 弯道警告驾驶员支持系统。汽车速度估计误差的敏感性分析
13th International IEEE Conference on Intelligent Transportation Systems Pub Date : 2010-11-09 DOI: 10.1109/ITSC.2010.5625275
E. Bertolazzi, F. Biral, M. Lio, M. Galvani
{"title":"Curve warning driver support systems. A sensitivity analysis to errors in the estimation of car velocity","authors":"E. Bertolazzi, F. Biral, M. Lio, M. Galvani","doi":"10.1109/ITSC.2010.5625275","DOIUrl":"https://doi.org/10.1109/ITSC.2010.5625275","url":null,"abstract":"Past research projects on intelligent vehicles have already led to the development of a large number of Advanced Driver Assistance Systems. The current research focus is now shifting towards integration and adaptive automation systems that share the control between driver and the machine. Artificial co-drivers can be used for this scope, as tutors to provide holistic support to the driver. However the question of the accuracy and robustness of co-driver evaluations, with respect to perception noise, becomes critical. This work discusses the robustness to perception noise of a Curve Support function, as part of a holistic driver support system based on a co-driver concept. The main objective of the work is to respond to the following question: how accurate should the vehicle state estimation be to design a reliable system? The paper gives a general framework and preliminary results related to noise in the estimation of the vehicle velocity vector.","PeriodicalId":176645,"journal":{"name":"13th International IEEE Conference on Intelligent Transportation Systems","volume":"212 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-11-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122657073","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
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