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

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 h 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 s 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.

Publication DOI: https://doi.org/10.1016/j.compeleceng.2021.107047
Divisions: College of Business and Social Sciences > Aston Business School > Cyber Security Innovation (CSI) Research Centre
College of Business and Social Sciences > Aston Business School > Operations & Information Management
Additional Information: © 2020 Elsevier. Licensed under the Creative Commons Attribution-NonCommercialNoDerivatives 4.0 International http://creativecommons.org/licenses/by-nc-nd/4.0/.
Uncontrolled Keywords: driver identification,behaviour profiling,classification,machine learning,connected cars,random forest,GPS,cybersecurity threat,incident response
Publication ISSN: 0045-7906
Last Modified: 21 Feb 2024 08:21
Date Deposited: 26 Jan 2023 14:21
Full Text Link:
Related URLs: https://www.sci ... 0653?via%3Dihub (Publisher URL)
PURE Output Type: Article
Published Date: 2021-05
Published Online Date: 2021-03-03
Accepted Date: 2021-02-22
Authors: Ahmadi-Assalemi, Gabriela
Al-Khateeb, Haider (ORCID Profile 0000-0001-8944-123X)
Maple, Carsten
Epiphaniou, Gregory
Hammoudeh, Mohammad
Jahankhani, Hamid
Pillai, Prashant

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