An Improved LSTM Model for Behavior Recognition of Intelligent Vehicles

Haipeng Xiao,Miguel Angel Sotelo,Youchun Xu,Yuncheng Zhou,Rendong Wang,Yulin Ma,Zhixiong Li,Bo Cao

doi:10.1109/access.2020.2996203

Abstract

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.

Highlights

In the field of self-driving, the trajectory prediction of dynamic vehicles in surrounding environment is very important for the safety and comfort of the vehicles, and it is the focus of research, and the correct identification of vehicle behavior is the premise of accurate trajectory prediction.Aiming at the classification of vehicle behavior characteristics, a lot of research has been carried out at home and abroad
Edelbrunner and Iossifidis [2] compared the effects of the support vector machine (SVM), feedforward neural network (FNN) and recurrent neural network (RNN) in identifying lane-changing behavior
This paper finds that the traditional Long Short-Term Memory (LSTM) network model cannot produce satisfactory classification results in VOLUME 8, 2020 vehicle behavior recognition

Summary

INTRODUCTION

In the field of self-driving, the trajectory prediction of dynamic vehicles in surrounding environment is very important for the safety and comfort of the vehicles, and it is the focus of research, and the correct identification of vehicle behavior is the premise of accurate trajectory prediction. Considering the strong long-term memory function of LSTM, this paper proposed an improved LSTM model to increase the recognition accuracy of vehicle behaviors and compares it with the MLP method in [4]. The convolutional neural network (CNN) has combined with LSTM He et al [23] used CNN to extract features from images containing characters in text recognition, and fed the features into a LSTM model for sequence labeling, Pareek and Kesavadas [24] propose a novel LSTM-based robot learning from demonstration (LfD) paradigm to mimic a therapist’s assistance behavior. This paper finds that the traditional LSTM network model cannot produce satisfactory classification results in VOLUME 8, 2020 vehicle behavior recognition. In order to improve the traditional LSTM model, this paper proposes an improved LSTM model for vehicle behavior recognition.

METHODS

IMPROVED LSTM NETWORK CLASSIFICATION MODEL

1) EXPERIMENTAL METHOD

Findings

CONCLUSION