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Content Provider | IET Digital Library |
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Author | He, Xin Xu, Li Zhang, Zhe |
Abstract | Driving behaviour analysis is important for both intelligent transportation and public security. The authors propose to characterise driving behaviours by using the phase-space reconstruction (PSR) and the pre-trained convolutional neural network (CNN). PSR is first applied to the raw vehicle test data (VTD) to obtain the reconstructed trajectories. Second, the corresponding feature vectors are acquired by using the pre-trained CNN. Third, the t-distributed stochastic neighbour embedding (t-SNE) algorithm is applied to the feature vectors to validate their characterising ability. Finally, an index is proposed based on the aforementioned feature vectors for quantitative evaluation, i.e. driving style recognition and abnormal driving detection. Simulations are conducted to verify the effectiveness of the proposed scheme. |
Starting Page | 1173 |
Ending Page | 1180 |
Page Count | 8 |
ISSN | 1751956X |
Volume Number | 13 |
e-ISSN | 17519578 |
Issue Number | Issue 7, Jul (2019) |
Alternate Webpage(s) | https://digital-library.theiet.org/content/journals/iet-its/13/7 |
Alternate Webpage(s) | https://digital-library.theiet.org/content/journals/10.1049/iet-its.2018.5499 |
Journal | IET Intelligent Transport Systems |
Publisher Date | 2019-03-18 |
Access Restriction | Open |
Rights Holder | © The Institution of Engineering and Technology |
Subject Keyword | Abnormal Driving Detection Behaviour Analysis Computer Vision And Image Processing Technique Convolutional Neural Nets Convolutional Neural Network Driver Information System Driving Behaviour Characterisation Driving Style Recognition Feature Vector Image Recognition Image Reconstruction Intelligent Transport System Intelligent Transportation Learning in AI Neural Computing Technique Phase-Space Reconstruction Pre-trained CNN Pre-trained Convolution Neural Network PSR Public Security Raw Vehicle Test Data Statistics Stochastic Linearised SCUC T-distributed Stochastic Neighbour Embedding T-SNE Traffic Engineering Computing |
Content Type | Text |
Resource Type | Article |
Subject | Law Transportation Environmental Science Mechanical Engineering |
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