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, doi: 10.1109/SMC.2016.7844736.

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Title Driver behaviour analysis using topological features
Author(s) Mahmoud, Mostafa Mahmoud Mohammad Hossn
Mohamed, ShadyORCID iD for Mohamed, Shady
Nahavandi, SaeidORCID iD for Nahavandi, Saeid
Nelson, KyleORCID iD for Nelson, Kyle
Hossny, MohammedORCID iD for 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
DOI 10.1109/SMC.2016.7844736
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
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