Recognition method of abnormal driving behavior using the bidirectional gated recurrent unit and convolutional neural network

Recognition of abnormal driving behavior is an important application area as it can support driving reliability and improve safety. In the last decade, deep learning methods have been presented through fruitful academic research and industrial applications. State-of-the-art deep learning methods are not commonly used for detection of abnor-mal driving behavior based on driving parameter information, and are lacking in terms of recognition accuracy. Based on this, a novel data-driven abnormal driving behaviors method is proposed in this paper by combining a convolutional neural network (CNN) and a Bidirectional gated recurrent unit (BiGRU). In this process, real vehicle driving data, including the extreme acceleration and steering position, are analyzed to establish a dataset of driving behaviors recognition firstly. Then, the datasets are inputted into the CNN-BiGRU algorithm to recognize the abnormal driving behavior where CNN captures non-linear relations from long-term trends of sequences and BiGRU extracts features of time series from driving parameters. The experimental results show that the proposed method offers improved accuracy and robustness in recognizing abnormal driving compared with other existing machine learning methods.(c) 2022 Elsevier B.V. All rights reserved.

Automated summary

Recognition of abnormal driving behavior is important for driving reliability and safety. This paper proposes a novel data-driven method combining a convolutional neural network and a Bidirectional gated recurrent unit to improve accuracy and robustness in recognizing abnormal driving behavior.