{"title":"Position and orientation estimation with high accuracy for a car-like vehicle","authors":"Bo He, Danwei W. Wang, M. Phain, T. Yu","doi":"10.1109/ITSC.2002.1041273","DOIUrl":"https://doi.org/10.1109/ITSC.2002.1041273","url":null,"abstract":"In this paper, position and orientation estimation with high accuracy based on GPS and encoders for a car-like vehicle is addressed. A novel architecture of position and orientation estimation is proposed, which consists of two extended Kalman filters (EKF) and a processing unit of Runga-Kutta-based dead reckoning. The first EKF fuses data from five encoders to estimate the velocity of vehicle and sideslip angle, The second EKF is applied to estimate position and orientation based on the measurement from precise GPS data and output from first EKF. To obtain better accuracy of estimation, an arbitrator is designed to switch on or off appropriate processing unit (EKF2 or dead reckoning). The results and analysis of experiments are presented to show the effectiveness of the proposed approach.","PeriodicalId":365722,"journal":{"name":"Proceedings. The IEEE 5th International Conference on Intelligent Transportation Systems","volume":"40 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2002-09-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128090702","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}
{"title":"Nonlinear combination of travel-time prediction model based on wavelet network","authors":"Sheng Li","doi":"10.1109/ITSC.2002.1041311","DOIUrl":"https://doi.org/10.1109/ITSC.2002.1041311","url":null,"abstract":"In the paper, research is focused on a combination of artificial neural network and Kalman filtering theory with application to real-time travel-time prediction model. ANN forecasters and Kalman filtering can model the complicated relationship between travel-time and traffic volume in related links. To enhance the prediction accuracy of these models, a nonlinear combination prediction approach of these two models is proposed based on wavelet networks. The performance of the novel model is tested by real detected traffic data or the links in the urban road networks. The results indicate that combination strategies based on the wavelet network outperform the other approaches.","PeriodicalId":365722,"journal":{"name":"Proceedings. The IEEE 5th International Conference on Intelligent Transportation Systems","volume":"26 5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2002-09-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125743078","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}
{"title":"A continuous space and time representation of dynamic road traffic flows consistent with hydrodynamic traffic theory","authors":"N. Grier, I. Chabini","doi":"10.1109/ITSC.2002.1041319","DOIUrl":"https://doi.org/10.1109/ITSC.2002.1041319","url":null,"abstract":"We present an approach to solving the dynamic network loading problem (DNLP). The approach views the roadway in terms of the underlying densities, segmenting the roadway into blocks of constant density. Using hydrodynamic theory, we describe how these blocks can be used to provide a solution method to the DNLP with a continuous representation of space and time which is readily implementable. This solution method provides an exact solution with piece-wise linear link travel times. We present a pseudocode description of the algorithm and discuss a sample computer implementation.","PeriodicalId":365722,"journal":{"name":"Proceedings. The IEEE 5th International Conference on Intelligent Transportation Systems","volume":"52 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2002-09-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131589877","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}
T. Aizawa, A. Tanaka, H. Higashikage, Y. Asokawa, M. Kimachi, S. Ogata
{"title":"Road surface estimation robust against vehicles' existence for stereo-based vehicle detection","authors":"T. Aizawa, A. Tanaka, H. Higashikage, Y. Asokawa, M. Kimachi, S. Ogata","doi":"10.1109/ITSC.2002.1041186","DOIUrl":"https://doi.org/10.1109/ITSC.2002.1041186","url":null,"abstract":"In parameters estimation for stereo-based vehicle detection, it is important to estimate the three-dimensional geometrical relationship between a stereo camera and a road surface. However, vehicles' existence in a field of view sometimes has a bad influence upon the accuracy of road surface estimation. Therefore, we propose a method for road surface estimation robust against vehicles' existence in a field of view. This method considers the difference between the distribution of feature points from a road surface and those from a vehicle in the spatio-disparity space. Therefore, road surface estimation can be done using those mostly from a road surface. We also confirm its effectiveness by experiments on field images.","PeriodicalId":365722,"journal":{"name":"Proceedings. The IEEE 5th International Conference on Intelligent Transportation Systems","volume":"92 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2002-09-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133191382","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}
{"title":"Highway overhead structure detection using video image sequences","authors":"Yang Chen","doi":"10.1109/ITSC.2002.1041191","DOIUrl":"https://doi.org/10.1109/ITSC.2002.1041191","url":null,"abstract":"A radar-based forward collision warning system has the problem of not being able to distinguish a stopped vehicle from the false alarms caused by highway overhead structures. We describe a system that detects the presence of overhead structures based on video images from an in-vehicle camera. The system also estimates the height and distance of the structures to the host vehicle, which can be used to help reject false alarms from radar caused by overhead structures. Our system uses a unique horizontal edge projection (HEP) as the primary feature for the detection of the overhead structures, which is computationally simple and robust to noise. Image vertical motion compensation and HEP tracking algorithms are developed to establish the HEP tracks of trajectories of the overhead structures in the image sequence. A least-squares algorithm is presented for the estimation of structure height and distance to the host vehicle using the HEP tracks. Experiments using real image sequences are presented.","PeriodicalId":365722,"journal":{"name":"Proceedings. The IEEE 5th International Conference on Intelligent Transportation Systems","volume":"4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2002-09-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128861052","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}
N. Ihata, M. Ikegami, M. Kawaguchi, H. Hasegawa, M. Ayama, M. Kasuga
{"title":"Reaction time of the secondary task while driving in various situations","authors":"N. Ihata, M. Ikegami, M. Kawaguchi, H. Hasegawa, M. Ayama, M. Kasuga","doi":"10.1109/ITSC.2002.1041228","DOIUrl":"https://doi.org/10.1109/ITSC.2002.1041228","url":null,"abstract":"In order to develop an in-vehicle information system which is adaptive to driver mental workloads, an attempt to measure the spare capacity of driver's attention was carried out in a laboratory experiment. A video movie taken from the front panel of a car was projected onto three screens in front of the subject whose main task was to operate the steering wheel and foot pedals in accordance with the movie. The subsidiary task was a simple addition task of two difficulty levels. The stimulus movies were classified into seven different types of road situation. The results were consistent with previous results obtained in field trials that the spare capacity of attention while driving changed with the road situations. In this study, the reaction time, which is another aspect of the subtask performance, was analyzed. Contrary to the results of the percentage correctness, the same degree of degradation was found for both levels of the subsidiary task.","PeriodicalId":365722,"journal":{"name":"Proceedings. The IEEE 5th International Conference on Intelligent Transportation Systems","volume":"31 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2002-09-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134456966","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}
{"title":"Towards efficient roadway network topology with pre-processing","authors":"K. H. Quek, T. Srikanthan","doi":"10.1109/ITSC.2002.1041270","DOIUrl":"https://doi.org/10.1109/ITSC.2002.1041270","url":null,"abstract":"A novel pre-processing technique has been devised to facilitate the removal of redundant links that are unlikely to be associated with the optimum path for a given O-D pair. The technique is highly suited for on-line removal of unwanted links as It relies only on simple logic and arithmetic operations. Our investigations based on simulations using Singapore roadway network show that optimum path computations on the preprocessed network are comparable to that using the entire network Moreover, pruning the network in the manner proposed will inevitably expedite the computations of optimum paths further.","PeriodicalId":365722,"journal":{"name":"Proceedings. The IEEE 5th International Conference on Intelligent Transportation Systems","volume":"33 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2002-09-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115644105","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}
{"title":"A study on the short-term prediction of traffic volume based on wavelet analysis","authors":"G. He, Shoufeng Ma","doi":"10.1109/ITSC.2002.1041309","DOIUrl":"https://doi.org/10.1109/ITSC.2002.1041309","url":null,"abstract":"An approach based on wavelet decomposition and reconstruction is put forward, this approach can be used to predict traffic volume effectively. The determination of the parameters is discussed and simulation results are given.","PeriodicalId":365722,"journal":{"name":"Proceedings. The IEEE 5th International Conference on Intelligent Transportation Systems","volume":"183 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2002-09-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115073995","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}
{"title":"Knowledge-based alarm correlation in traffic monitoring and control","authors":"S. Bandini, D. Bogni, S. Manzoni","doi":"10.1109/ITSC.2002.1041304","DOIUrl":"https://doi.org/10.1109/ITSC.2002.1041304","url":null,"abstract":"The paper shows how a knowledge-based approach can be followed to integrate alarm correlation functionality into traffic monitoring and control systems. To this end, the Alarm Correlation Module (MCA) integrated in the System for Automatic Monitoring of Traffic (SAMOT) is described. According to traffic operator expertise and knowledge, the MCA analyzes, filters and correlates traffic flow anomalies detected by standard video image processing boards. As a consequence of anomaly correlation, the MCA creates adequate image sequences to be shown on an operator's closed-circuit TV and displays adequate messages on variable message panels to keep motorists informed. The MCA knowledge base implements a model of traffic flow concerning the most relevant traffic patterns and taking into account time and space dependence of detected traffic anomalies. The MCA is a successful example of the knowledge-based approach applied to traffic monitoring and control that, after a 6 months trial period, is now functioning on A7 and A10 Italian highways.","PeriodicalId":365722,"journal":{"name":"Proceedings. The IEEE 5th International Conference on Intelligent Transportation Systems","volume":"48 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2002-09-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117267206","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}
{"title":"Fusing optical flow and stereo disparity for object tracking","authors":"T. Dang, C. Hoffmann, C. Stiller","doi":"10.1109/ITSC.2002.1041198","DOIUrl":"https://doi.org/10.1109/ITSC.2002.1041198","url":null,"abstract":"This paper proposes a novel approach to object detection and tracking using video sensors. Two different methods are employed to retrieve depth information from images: stereopsis and depth from motion. The obtained data streams are fused yielding increased reliability and accuracy. A set of image points is tracked over time using an extended Kalman filter. The proposed algorithm clusters points of similar dynamics by analysis of the filter residuals. Experimental results are provided for synthetic as well as for natural image sequences.","PeriodicalId":365722,"journal":{"name":"Proceedings. The IEEE 5th International Conference on Intelligent Transportation Systems","volume":"38 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2002-09-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117279397","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}