Long short-term memory and convolutional neural network for abnormal driving behaviour recognition
Long short-term memory and convolutional neural network for abnormal driving behaviour recognition
- New search for: Jia, Shuo
- New search for: Hui, Fei
- New search for: Li, Shining
- New search for: Zhao, Xiangmo
- New search for: Khattak, Asad J.
- New search for: Jia, Shuo
- New search for: Hui, Fei
- New search for: Li, Shining
- New search for: Zhao, Xiangmo
- New search for: Khattak, Asad J.
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.
Document information
Long short-term memory and convolutional neural network for abnormal driving behaviour recognition
IET Intelligent Transport Systems ; 14 , 5 ; 306-312
2019-07-24
7 pages
Article (Journal)
Electronic Resource
English
road safety , road vehicles , LSTM-CNN , traffic accidents , statistical analysis , driving behaviour recognition data set , learning (artificial intelligence) , shallow learning , braking , neural nets , abnormal driving behaviour recognition , driver , behavioural sciences computing , convolutional neural network , road traffic , long short-term memory network , traffic engineering computing , road traffic safety
Metadata by IET is licensed under CC BY 3.0
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