Context-Aware Quantitative Risk Assessment Machine Learning Model for
  Drivers Distraction
Adebamigbe Fasanmade Ali H. Al-Bayatti Jarrad Neil Morden Fabio Caraffini
Risk mitigation techniques are critical to avoiding accidents associated with
driving behaviour. We provide a novel Multi-Class Driver Distraction Risk
Assessment (MDDRA) model that considers the vehicle, driver, and environmental
data during a journey. MDDRA categorises the driver on a risk matrix as safe,
careless, or dangerous. It offers flexibility in adjusting the parameters and
weights to consider each event on a specific severity level. We collect
real-world data using the Field Operation Test (TeleFOT), covering drivers
using the same routes in the East Midlands, United Kingdom (UK). The results
show that reducing road accidents caused by driver distraction is possible. We
also study the correlation between distraction (driver, vehicle, and
environment) and the classification severity based on a continuous distraction
severity score. Furthermore, we apply machine learning techniques to classify
and predict driver distraction according to severity levels to aid the
transition of control from the driver to the vehicle (vehicle takeover) when a
situation is deemed risky. The Ensemble Bagged Trees algorithm performed best,
with an accuracy of 96.2%.