Long short‐term memory and convolutional neural network for abnormal driving behaviour recognition. Issue 5 (24th July 2019)

• Record Type: Journal Article
• Authors: Jia, Shuo; Hui, Fei; Li, Shining; Zhao, Xiangmo; Khattak, Asad J.
• Is Part Of: IET intelligent transport systems. Volume 14:Issue 5(2020)
• Page Start: 306
• Page End: 312
• Publication Date: 2019-07-24
• Subjects: neural nets -- road safety -- road vehicles -- learning (artificial intelligence) -- road traffic -- traffic engineering computing -- braking -- statistical analysis -- behavioural sciences computing; driving behaviour recognition data set -- statistical analysis -- convolutional neural network -- long short‐term memory network -- shallow learning -- traffic accidents -- abnormal driving behaviour recognition -- LSTM‐CNN -- road traffic safety -- driver; Intelligent transportation systems -- Periodicals; Electronics in transportation -- Periodicals; 388.31205
• DOI: 10.1049/iet-its.2019.0200
• Languages: English
• ISSNs: 1751-956X

Abstract

Abstract : Abnormal driving behaviours, such as rapid acceleration, emergency braking, and rapid lane changing, bring great uncertainty to traffic, and can easily lead to traffic accidents. The accurate identification of abnormal driving behaviour helps to judge the driver's driving style, inform surrounding vehicles, and ensure the road traffic safety. Most of the existing studies use clustering and shallow learning, it is difficult to accurately identify the types of abnormal driving behaviours. Aimed at addressing the difficulty of identifying driving behaviour, this study proposed a recognition model based on a long short‐term memory network and convolutional neural network (LSTM‐CNN). The extreme acceleration and deceleration points are detected through the statistical analysis of real vehicle driving data, and the driving behaviour recognition data set is established. By using the data set to train the model, the LSTM‐CNN can achieve a better result.