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

Gabriela Ahmadi-Assalemi, Haider Al-Khateeb*, Carsten Maple, Gregory Epiphaniou, Mohammad Hammoudeh, Hamid Jahankhani, Prashant Pillai

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review


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.
Original languageEnglish
Article number107047
Number of pages21
JournalComputers and Electrical Engineering
Early online date3 Mar 2021
Publication statusPublished - May 2021

Bibliographical note

© 2020 Elsevier. Licensed under the Creative Commons Attribution-NonCommercialNoDerivatives 4.0 International http://creativecommons.org/licenses/by-nc-nd/4.0/.


  • driver identification
  • behaviour profiling
  • classification
  • machine learning
  • connected cars
  • random forest
  • GPS
  • cybersecurity threat
  • incident response


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