Vehicle Driving Behavior Recognition Based on Multi-View Convolutional Neural Network With Joint Data Augmentation

This paper proposes a method for vehicle driving behavior recognition based on a six-axis motion processor. This method uses deep-learning technology to learn the sample data collected by the on-board sensor. To solve the problem of small sample size and easy overfitting, we propose a joint data augmentation (JDA) scheme, and design a new multi-view convolutional neural network model (MV-CNN). The JDA includes the multi-axis weighted fusion algorithm, background noise fusion algorithm, and random cropping algorithm to construct a sample dataset that is more in line with a complex real driving environment. With the CNN model, the direction of information propagation improved, and a new MV-CNN model was developed for the training, learning, and recognition of driving behavior. The performance of MV-CNN is experimentally compared with CNN, recurrent neural networks (RNN), LSTM, CNN+LSTM, and three-dimensional CNN. The results show that MV-CNN can obtain the best recall, precision, and F1-score. At the same time, MV-CNN and JDA have better generalization ability, reduce the training variance and deviation, and increase the stability of the model training process.