关键词: 深度学习, 驾驶行为识别, 双向长短记忆网络

Abstract:

The identification of abnormal driving behavior plays a vital role in traffic safety, and accurate identification of abnormal driving behavior can significantly improve driving safety. At present, the detection and identification of abnormal driving behaviors, such as rapid acceleration, rapid deceleration, sudden left turn or right turn, etc. In the process of vehicle driving mainly adopts video surveillance or clustering method. In these two methods, the actual effect of the former is restricted by the application scene, while the latter cannot identify the driving behavior of specific individual vehicles. In order to solve the above problems, an extended neural network detection model based on Bi-LSTM and FC is proposed. The model can effectively use the characteristics of sudden change of driving data in time series and improve the recognition accuracy of abnormal driving. The neural network model can effectively use the time series characteristics of driving data and accurately identify the abnormal driving behavior of vehicles. The accuracy rate can reach 98.08%.

Key words: deep learning, driving behavior recognition, Bi-directional Long Short-Term Memory（Bi-LSTM）