Driver behaviour analysis using topological features Mahmoud, Mostafa Mahmoud Mohammad Hossn, Mohamed, Shady, Nahavandi, Saeid, Nelson, Kyle and Hossny, Mohammed 2016, Driver behaviour analysis using topological features, in SMC 2016 : Proceedings of the IEEE International Conference on Systems, Man, and Cybernetics, IEEE, Piscataway, N.J., pp. 1-6. Attached Files Name Description MIMEType Size Downloads Title Driver behaviour analysis using topological features Author(s) Mahmoud, Mostafa Mahmoud Mohammad Hossn Mohamed, Shady Nahavandi, Saeid Nelson, Kyle Hossny, Mohammed Conference name Systems, Man, and Cybernetics. IEEE International Conference (2016 : Budapest, Hungary) Conference location Budpest, Hungary Conference dates 9-12 Oct. 2016 Title of proceedings SMC 2016 : Proceedings of the IEEE International Conference on Systems, Man, and Cybernetics Publication date 2016 Conference series Systems, Man, and Cybernetics IEEE International Conference Start page 1 End page 6 Total pages 6 Publisher IEEE Place of publication Piscataway, N.J. Summary Driving behaviour prediction is a challenging problemdue to the nonlinearity of human behaviour. Linear andnonlinear techniques have been used to solve this problem, andthey provide good results presented in the performance of thecurrent autonomous cars. However, they lack the ability to adaptto abruptness that happens because of the human factor. In thispaper, we introduce a method to extract persistent homologybarcode statistics. These statistics are useful as a representativeof the driving process including the human behaviour. Humanfactor identification requires finding features that preserve certainproperties against scalability, deformation, and abruptness.Topological Data Analysis (TDA) using persistent homologyprovides these features for driver behaviour prediction. Wecaptured a driver’s head motion as an experimental behaviouralcue, combined it with captured simulated vehicle data (locationand velocities). Barcodes are extracted using JavaPlex, thenwe extracted descriptive statistics to show the significance ofthese barcode as features for driver behaviour prediction. Thecorrelation between the extracted features shows a promisingstart for a behavioural tracking applications using TDA. ISBN 9781509018970 Language eng Field of Research 099999 Engineering not elsewhere classified Socio Economic Objective 970110 Expanding Knowledge in Technology HERDC Research category E1 Full written paper - refereed ERA Research output type E Conference publication Copyright notice ©2016, IEEE Persistent URL http://hdl.handle.net/10536/DRO/DU:30090976 Document type: Conference Paper Collections: Centre for Intelligent Systems Research Open Access Checking Related Links Link Description Connect to Elements publication management system Go to link with your DU access privileges     Unless expressly stated otherwise, the copyright for items in DRO is owned by the author, with all rights reserved. Versions Version Filter Type Wed, 01 Feb 2017, 10:43:08 EST Thu, 02 Feb 2017, 04:01:02 EST Mon, 20 Mar 2017, 13:27:34 EST Tue, 21 Mar 2017, 05:30:17 EST Sat, 15 Apr 2017, 02:38:50 EST Fri, 19 May 2017, 08:30:34 EST Fri, 19 May 2017, 08:36:47 EST Fri, 19 May 2017, 08:37:37 EST Filtered Full Citation counts:   Cited 0 times in TR Web of Science Cited 0 times in Scopus Search Google Scholar Access Statistics: 10 Abstract Views, 2 File Downloads  -  Detailed Statistics Created: Mon, 20 Mar 2017, 13:27:34 EST

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