Optimising driver profiling through behaviour modelling of in-car sensor and global positioning system data

Ahmadi-Assalemi, Gabriela, M. al-Khateeb, Haider, Maple, Carsten, Epiphaniou, Gregory, Hammoudeh, Mohammad ORCID: https://orcid.org/0000-0002-9735-2365, Jahankhani, Hamid and Pillai, Prashant (2021) Optimising driver profiling through behaviour modelling of in-car sensor and global positioning system data. Computers and Electrical Engineering, 91. p. 107047. ISSN 0045-7906

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Abstract

Connected cars have a massive impact on the automotive sector, and whilst this catalyst and disruptor technology introduce threats, it brings opportunities to address existing vehicle-related crimes such as carjacking. Connected cars are fitted with sensors, and capable of sophisticated computational processing which can be used to model and differentiate drivers as means of layered security. We generate a dataset collecting 14 hours of driving in the city of London. The route was 8.1 miles long and included various road conditions such as roundabouts, traffic lights, and several speed zones. We identify and rank the features from the driving segments, classify our sample using Random Forest, and optimise the learning-based model with 98.84% accuracy (95% confidence) given a small 10 seconds driving window size. Differences in driving patterns were uncovered to distinguish between female and male drivers especially through variations in longitudinal acceleration, driving speed, torque and revolutions per minute.

Item Type:	Article
Peer-reviewed:	Yes
Date Deposited:	27 Jul 2020 13:07
Publisher:	Elsevier
Additional Information:	This is an Author Accepted Manuscript of a paper published in Computers and Electrical Engineering, published by and copyright Elsevier.
Divisions:	Faculties > Science and Engineering
Subject terms:	behaviour profiling, classification, connected cars, cybersecurity threat, driver identification, GPS, incident response, machine learning, random forest, Science & Technology, Technology, Computer Science, Hardware & Architecture, Computer Science, Interdisciplinary Applications, Engineering, Electrical & Electronic, Computer Science, Engineering, Driver identification, Behaviour profiling, Classification, Machine learning, Connected cars, Random forest, GPS, Cybersecurity threat, Incident response, THREAT, 0803 Computer Software, 0805 Distributed Computing, 0906 Electrical and Electronic Engineering, Electrical & Electronic Engineering
URI:	https://e-space.mmu.ac.uk/id/eprint/626000
DOI:	https://doi.org/10.1016/j.compeleceng.2021.107047
ISSN	0045-7906
e-ISSN	1879-0755

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