  Smartphones consist of different sensors, which provide a platform for data
acquisition in many scientific researches such as driving style identification
systems. In the present paper, smartphone data are used to evaluate the driving
styles based on maneuvers analysis. The data obtained for each maneuver is the
speed of the vehicle steering and the vehicle's direct and lateral
acceleration. To classify the drivers based on their driving style,
machine-learning algorithms can be used on these data. However, these data
usually contains more information than it is needed and cause a bad effect on
the learning accuracy. In addition, they may transfer some wrong information to
the learning algorithm. Thus, we used Haar discrete wavelet transformation to
remove noise effects. Then, we get the discrete wavelet transformation with
four levels from smartphone sensors data, which include low-to-high
frequencies, respectively. The obtained features vector for each maneuver
includes the raw signal variance as well as the variance of the wavelet
transform components. On these vectors, we use the k-nearest neighbors
algorithm for features selection. Then, we use SVM, RBF and MLP neural networks
on these features to separate braking and dangerous speed maneuvers from the
safe ones as well as dangerous turning, U-turn and lane-changing maneuvers. The
results are very interesting.
