Convolutional Neural Network With Adaptive Regularization to Classify Driving Styles on Smartphones
Driving style evaluation by smartphones depends on the quality of the features extracted from sensors data. Typically, these features are extracted based on experiments, expertness, or heuristics. In more modern approaches, some automatic methods such as convolutional neural network (CNN) are used to extract features including obvious and hidden ones. We also used the CNN on acceleration data collected by smartphones to extract the knowledge regarding driving style, vehicle, environment, and human characteristics. We found that this novel idea was more successful for evaluating the driving style compared with the previous machine learning algorithms. However, we faced over-fitting in the training process of the CNN and to avoid this, we proposed the state-of-the-art learning method applying two adaptive regularization schemes called adaptive dropout and adaptive weight decay. To evaluate these techniques, first, we checked the results on three popular largescale datasets. When we proved the efficiency, we utilized them on two transportation data sets. In transportation-modes dataset, the accuracy was at least 95.8%â??; and regarding the driving-style dataset, the classification accuracy was 95%. Thus, the adaptive regularized CNN is an amazing option for driving style evaluation on smartphones.
Deep learning, driving style evaluation, smartphone sensors, convolutional neural network, adaptive regularization, over-fitting.