An Improved LSTM Model for Behavior Recognition of Intelligent Vehicles

Long Short-Term Memory (LSTM) neural network has been widely used in many applications, but its application in classification of vehicle movement patterns is still limited. In this paper, LSTM is applied to the vehicle behavior recognition problem to identify the left turn, right turn and straight behavior of the vehicle at the intersection. On the basis of the traditional LSTM classification model, this paper transversely merges the input features and then inputs into a LSTM cell to get an improved model. The improved model can make full use of the input information and reduce unnecessary calculations, and the output of a single LSTM cell model can filter out interference information and retain important information, so it has better classification effect and faster training speed. The experimental results show that the proposed improved LSTM network classification model in this paper has a significant improvement in recognition accuracy and training speed compared with the improved model, the accuracy is increased by 1.6%, and the training time is reduced by 3.96 s. In addition, this paper also applies the improved model to regression problems, emotion classification and handwritten digit recognition and all of them have a good improvement effect, which improves the applicability and stability of LSTM in classification problems and provides a new way to deal with classification problems.