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| Content Provider | IET Digital Library |
|---|---|
| Author | Jia, Shuo Hui, Fei Li, Shining Zhao, Xiangmo Khattak, Asad J. |
| 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. |
| Starting Page | 306 |
| Ending Page | 312 |
| Page Count | 7 |
| ISSN | 1751956X |
| Volume Number | 14 |
| e-ISSN | 17519578 |
| Issue Number | Issue 5, May (2020) |
| Alternate Webpage(s) | https://digital-library.theiet.org/content/journals/iet-its/14/5 |
| Alternate Webpage(s) | https://digital-library.theiet.org/content/journals/10.1049/iet-its.2019.0200 |
| Journal | IET Intelligent Transport Systems |
| Publisher Date | 2019-07-05 |
| Access Restriction | Open |
| Rights Holder | © The Institution of Engineering and Technology |
| Subject Keyword | Abnormal Driving Behaviour Recognition Behavioural Sciences Computing Brakes Convolutional Neural Network Driver Driving Behaviour Recognition Data Set Learning in AI Long Short-term Memory Network LSTM-CNN Neural Computing Technique Neural Nets Road Safety Road Traffic Road Traffic Safety Road Vehicle Shallow Learning Social And Behavioural Sciences Computing Statistical Analysis Statistics Traffic Accidents Traffic Engineering Computing |
| Content Type | Text |
| Resource Type | Article |
| Subject | Law Transportation Environmental Science Mechanical Engineering |
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