Optimising driver profiling through behaviour modelling of in-car sensor and global positioning system data
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Authors
Ahmadi-Assalemi, Gabrielaal-Khateeb, Haider

Maple, Carsten
Epiphaniou, Gregory
Hammoudeh, Mohammad
Jahankhani, Hamid
Pillai, Prashant
Issue Date
2021-03-03
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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.Citation
Ahmadi-Assalemi, G., al-Khateeb, H.M., Maple, C., Epiphaniou, G., Hammoudeh, M., Jahankhani, H. and Pillai, P. (2021) Optimising driver profiling through behaviour modelling of in-car sensor and global positioning system data. Computers and Electrical Engineering, 91, 107047. https://doi.org/10.1016/j.compeleceng.2021.107047Publisher
ElsevierJournal
Computers and Electrical EngineeringAdditional Links
https://doi.org/10.1016/j.compeleceng.2021.107047Type
Journal articleLanguage
enDescription
This is an accepted manuscript of an article published by Elsevier in Computers & Electrical Engineering, available online at: https://doi.org/10.1016/j.compeleceng.2021.107047 The accepted version of the publication may differ from the final published version.ISSN
0045-7906ae974a485f413a2113503eed53cd6c53
10.1016/j.compeleceng.2021.107047
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Except where otherwise noted, this item's license is described as https://creativecommons.org/licenses/by-nc-nd/4.0/