Reference : Revisiting Gaussian Mixture Models for Driver Identification

	Document type :	Scientific congresses, symposiums and conference proceedings : Paper published in a book
	Discipline(s) :	Engineering, computing & technology : Computer science
	Focus Areas :	Security, Reliability and Trust
	To cite this reference:	http://hdl.handle.net/10993/36588

Title :	Revisiting Gaussian Mixture Models for Driver Identification
Language :	English
Author, co-author :	Jafarnejad, Sasan [University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) >]
	Castignani, German [University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > >]
	Engel, Thomas [University of Luxembourg > Faculty of Science, Technology and Communication (FSTC) > Computer Science and Communications Research Unit (CSC)]
Publication date :	Sep-2018
Main document title :	Proceedings of IEEE International Conference on Vehicular Electronics and Safety (ICVES) (ICVES 2018)
Peer reviewed :	Yes
Audience :	International
Event name :	2018 IEEE International Conference on Vehicular Electronics and Safety (ICVES) (ICVES 2018)
Event date :	from 12-09-2018 to 14-09-2018
Event place (city) :	Madrid
Event country :	Spain
Keywords :	[en] driving behavior; spectral analysis; gaussian mixture model; ; machine learning ; driver identification
Abstract :	[en] The increasing penetration of connected vehicles nowadays has enabled driving data collection at a very large scale. Many telematics applications have been also enabled from the analysis of those datasets and the usage of Machine Learning techniques, including driving behavior analysis predictive maintenance of vehicles, modeling of vehicle health and vehicle component usage, among others. In particular, being able to identify the individual behind the steering wheel has many application fields. In the insurance or car-rental market, the fact that more than one driver make use of the vehicle generally triggers extra fees for the contract holder. Moreover being able to identify different drivers enables the automation of comfort settings or personalization of advanced driver assistance (ADAS) technologies. In this paper, we propose a driver identification algorithm based on Gaussian Mixture Models (GMM). We show that only using features extracted from the gas pedal position and steering wheel angle signals we are able to achieve near 100 accuracy in scenarios with up to 67 drivers. In comparison to the state-of-the-art, our proposed methodology has lower complexity, superior accuracy and offers scalability to a larger number of drivers.
Target :	Researchers ; Professionals
Permalink :	http://hdl.handle.net/10993/36588

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