Open AccessLong short‐term memory and convolutional neural network for abnormal driving behaviour recognition
Author(s) -
Jia Shuo,
Hui Fei,
Li Shining,
Zhao Xiangmo,
Khattak Asad J.
Publication year - 2020
Publication title -
iet intelligent transport systems
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.579
H-Index - 45
eISSN - 1751-9578
pISSN - 1751-956X
DOI - 10.1049/iet-its.2019.0200
Subject(s) - convolutional neural network , computer science , acceleration , set (abstract data type) , cluster analysis , identification (biology) , artificial intelligence , term (time) , data set , machine learning , pattern recognition (psychology) , real time computing , physics , quantum mechanics , botany , classical mechanics , biology , programming language
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.