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https://www.semanticscholar.org/paper/Using-Support-Vector-Machines-for-Lane-Change-Mandalia-Salvucci/83b60dec53f9c33c53ce9a8c1ead1c4827433d25
Driving is a complex task that requires constant attention, and intelligent transportation systems that support drivers in this task must continually infer driver intentions to produce reasonable, safe responses. In this paper we describe a technique for inferring driver intentions, specifically the intention to change lanes, using support vector machines (SVMs). The technique was applied to ...
https://journals.sagepub.com/doi/abs/10.1177/154193120504902217
In this paper we describe a technique for inferring driver intentions, specifically the intention to change lanes, using support vector machines (SVMs). The technique was applied to experimental data from an instrumented vehicle that included both behavioral data and environmental data.Cited by: 108
https://www.researchgate.net/publication/250199307_Using_Support_Vector_Machines_for_Lane-Change_Detection
Using Support Vector Machines for Lane-Change Detection Article in Human Factors and Ergonomics Society Annual Meeting Proceedings 49(22) · …
https://www.cs.drexel.edu/~salvucci/publications/Mandalia-HFES05.pdf
functioning of SVMs, motivation for using SVMs for lane change detection, and training of lane changes. The next section reports results in terms of the prediction accuracy (true positive rates and false positive rates) and other measures. Brief Overview of Support Vector Machines Support vector machines are based on statistical learning
http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.72.4577
BibTeX @INPROCEEDINGS{Mandalia05usingsupport, author = {Hiren M. Mandalia and Dario D. Salvucci}, title = {Using support vector machines for lane change detection}, booktitle = {In Proceedings of the Human Factors and Ergonomics Society 49th Annual Meeting}, year = {2005}}
https://core.ac.uk/display/24524125
Using support vector machines for lane change detection . By Hiren M. Mandalia and Dario D. Salvucci. Abstract. Driving is a complex task that requires constant attention, and intelligent transportation systems that support drivers in this task must continually infer driver intentions to produce reasonable, safe responses. In this paper we ...Cited by: 108
http://scholarcommons.usf.edu/cgi/viewcontent.cgi?article=5664&context=etd
A Study on Lane-Change Recognition Using Support Vector Machine by Weiping Deng A dissertation submitted in partial fulfillment of the requirements for the degree of Doctor of Philosophy Department of Civil and Environmental Engineering College of Engineering University of South Florida Major Professor: Jian Lu, Ph.D. Chanyoung Lee, Ph.D.
https://journals.sagepub.com/doi/abs/10.1518/001872007x200157
Jun 01, 2007 · Using support vector machines for lane change detection. In Proceedings of the Human Factors and Ergonomics Society 49th Annual Meeting (pp. 1965-1969). Santa Monica, CA: Human Factors and Ergonomics Society. Google ScholarCited by: 98
https://www.researchgate.net/publication/274477938_Lane_Detection_Based_on_Machine_Learning_Algorithm
A lane detection algorithm based on Support Vector Machines (SVM) classifier for marked or unmarked roads is proposed and Catmull Rom splines based lane model combined with a …
http://geodesy.unr.edu/hanspeterplag/library/IGARSS2010/pdfs/1453.pdf
The recasting of this problem in terms of binary classi cation enables the use of more sophisticated machine learning tools than have traditionally been employed for the change detection problem. In this paper, we investigate the use of support vector machines (SVMs) with radial basis kernels for nding anomalous changes.
http://www.robot.t.u-tokyo.ac.jp/~yamashita/paper/B/B174Final.pdf
change the lane, (b) measurement devices in the primary vehicle. Tomar and Verma have suggested the trajectory method using a Support Vector Machine (SVM) and used it as the regression analysis method [13]. However, the method is able to predict only the lane changing trajectory not a lane …
http://geodesy.unr.edu/hanspeterplag/library/IGARSS2010/pdfs/1453.pdf
The recasting of this problem in terms of binary classi cation enables the use of more sophisticated machine learning tools than have traditionally been employed for the change detection problem. In this paper, we investigate the use of support vector machines (SVMs) with radial basis kernels for nding anomalous changes.
https://www.sciencedirect.com/science/article/pii/S0924271606001122
The reliability of support vector machines for classifying hyper-spectral images of remote sensing has been proven in various studies. In this paper, we investigate their applicability for land cover change detection. First, SVM-based change detection is presented and performed for mapping urban growth in the Algerian capital.
https://biblioteca.unilasalle.edu.br/docs_online/producao_docente/rute_henrique_da_silva_ferreira/artigos/change_detection.pdf
Change Detection in Multitemporal Remote Sensing Images Using Support Vector Machines and Pixel Relevance 972 An interesting technique for the treatment of change detection is presented in [5] using the concept of mixture pixel. The mixture pixels phenomenon occurs when the same pixel comprises two or more distinct
http://citeseer.ist.psu.edu/showciting?cid=3584402
In particular, we describe an integrated driver model developed in the ACT-R cognitive architecture and demonstrate how this model accounts for the steering profiles, lateral-position profiles, and gaze distributions of human drivers during lane keeping, curve negotiation, and lane changing.
http://adsabs.harvard.edu/abs/2010ESASP.686E.272H
To overcome such drawbacks, much attention has been given lately to algorithms arising from machine learning, such as Support Vector Machines (SVMs). While SVMs have been applied successfully for land cover classifications, the exploitation of this approach for change detection is still in its infancy.
https://link.springer.com/article/10.1007/s10661-016-5494-x
Jul 27, 2016 · Otukei, J. R., & Blaschke, T. (2010). Land cover change assessment using decision trees, support vector machines and maximum likelihood classification algorithms. International Journal of Applied Earth Observation and Geoinformation, 12S, S27–S31. CrossRef Google Scholar
https://www.sciencedirect.com/science/article/pii/S0303243411001565
Supervised change detection in VHR images using contextual information and support vector machines. ... To better understand the role of the spatial-contextual information within the process of supervised change detection, blocks of features and their combinations are tested independently and in growing order. ... Chibani Y.Multiple support ...
https://www.thefreelibrary.com/Driver+lane+change+prediction+using+physiological+measures.-a0476564318
"Using Support Vector Machines for Lane-Change Detection." In Proceedings of the Human Factors and Ergonomics Society Annual Meeting, 49(22), pp. 1965-1969. SAGE Publications, 2005.
http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.391.5575
Patterns extracted from the data between the speaker change points are used as negative examples. The positive and negative examples are used in training a support vector machine for speaker change detection. The trained SVM is used to scan the continuous speech signal of multispeaker data and hypothesize the points of speaker change.
https://digitalcommons.chapman.edu/cgi/viewcontent.cgi?article=1631&context=scs_articles
remote sensing Article Coral Reef Change Detection in Remote Pacific Islands Using Support Vector Machine Classifiers Justin J. Gapper 1, Hesham El-Askary 2,3,4,* , Erik Linstead 3,5 and Thomas Piechota 3 1 Computational and Data Sciences Graduate Program, Schmid College of Science and Technology, Chapman University, Orange, CA 92866, USA
https://www.analyticsvidhya.com/blog/2017/09/understaing-support-vector-machine-example-code/
Sep 13, 2017 · The e1071 package in R is used to create Support Vector Machines with ease. It has helper functions as well as code for the Naive Bayes Classifier. The creation of a support vector machine in R and Python follow similar approaches, let’s take a look now at the following code:
http://adsabs.harvard.edu/abs/2006JPRS...61..125N
Abstract The reliability of support vector machines for classifying hyper-spectral images of remote sensing has been proven in various studies. In this paper, we investigate their applicability for land cover change detection. First, SVM-based change detection is presented and performed for mapping urban growth in the Algerian capital.
https://www.harrisgeospatial.com/docs/SupportVectorMachine.html
Display the input image you will use for SVM classification, along with the ROI file. From the Toolbox, select Classification > Supervised Classification > Support Vector Machine Classification. The Classification Input File dialog appears. Select the input file and perform optional spatial and spectral subsetting, then click OK. The Support ...
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