  Driver behavior profiling is one of the main issues in the insurance
industries and fleet management, thus being able to classify the driver
behavior with low-cost mobile applications remains in the spotlight of
autonomous driving. However, using mobile sensors may face the challenge of
security, privacy, and trust issues. To overcome those challenges, we propose
to collect data sensors using Carla Simulator available in smartphones
(Accelerometer, Gyroscope, GPS) in order to classify the driver behavior using
speed, acceleration, direction, the 3-axis rotation angles (Yaw, Pitch, Roll)
taking into account the speed limit of the current road and weather conditions
to better identify the risky behavior. Secondly, after fusing inter-axial data
from multiple sensors into a single file, we explore different machine learning
algorithms for time series classification to evaluate which algorithm results
in the highest performance.
