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Smartphones consist of different sensors, which provide a platform for dataacquisition in many scientific researches such as driving style identificationsystems. In the present paper, smartphone data are used to evaluate the drivingstyles based on maneuvers analysis. The data obtained for each maneuver is thespeed of the vehicle steering and the vehicle's direct and lateralacceleration. To classify the drivers based on their driving style,machine-learning algorithms can be used on these data. However, these datausually contains more information than it is needed and cause a bad effect onthe learning accuracy. In addition, they may transfer some wrong information tothe learning algorithm. Thus, we used Haar discrete wavelet transformation toremove noise effects. Then, we get the discrete wavelet transformation withfour levels from smartphone sensors data, which include low-to-highfrequencies, respectively. The obtained features vector for each maneuverincludes the raw signal variance as well as the variance of the wavelettransform components. On these vectors, we use the k-nearest neighborsalgorithm for features selection. Then, we use SVM, RBF and MLP neural networkson these features to separate braking and dangerous speed maneuvers from thesafe ones as well as dangerous turning, U-turn and lane-changing maneuvers. Theresults are very interesting.

Smartphone based Driving Style Classification Using Features Made by Discrete Wavelet Transform