关键词: 稀疏滤波, 卷积神经网络, 模式识别, 驾驶行为

Abstract: In order to reduce the potential dangers and ensure the drivers safety, this paper proposes an unsupervised feature learning algorithm model for optimal feature distribution—Sparse Filter-Convolution Neural Network, which uses the built-in sensors of smartphone to assist the driver safety. This method utilizes the three-axis acceleration data acquired by smartphone in the running vehicle, and performs the normative joint constraint by sparse filtering to obtain the compact primary characteristic expression. As the input of the neural network, the nonlinear classification is used to identify the driving behavior. The experimental results show that this method has higher recognition rate and robustness to driving behavior, better than traditional neural network, and is very important to evaluate the efficiency of the assisted driving system.

Key words: sparse filter, convolutional neural network, pattern recognition, driving behavior