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    Abnormal driving detection with normalized driving behavior data: a deep learning approach

    Hu, J. and Zhang, X. and Maybank, Stephen J. (2020) Abnormal driving detection with normalized driving behavior data: a deep learning approach. IEEE Transactions on Vehicular Technology 69 (7), pp. 6943-6951. ISSN 00189545.

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    Abnormal driving may cause serious danger to both the driver and the public. Existing dectors of abnormal driving behaviour are mainly based on shallow models, which require large quantities of labelled data. The aquisition and labelling of abnormal driving data are, however, difficult, labour-intensive and time-consuming. This situation inspires us to rethink the abnormal driving detection problem and to apply deep architecture models. In this study, we establish a novel deep-learning-based model for abnormal driving detection. A stacked sparse autoencoders model is used to learn generic driving behavior features. The model is trained in a greedy layer-wise fashion. As far as the authors know, this is the first time that a deep learning approach is applied using autoencoders as building blocks to represent driving features for abnormal driving detection. In addition, a model for denoising is added to the algorithm to increase the robustness of feature expression. The dropout technology is introduced into the entire training process to avoid overfitting. Experiments carried out on our self-created driving behaviour dataset demonstrate that the proposed scheme achieves a superior performance for abnormal driving detection compared to the state-of-the-art.


    Item Type: Article
    Additional Information: (c) 2020 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, including reprinting/ republishing this material for advertising or promotional purposes, creating new collective works for resale or redistribution to servers or lists, or reuse of any copyrighted components of this work in other works.
    Keyword(s) / Subject(s): Abnormal driving detection, deep learning, stacked autoencoder
    School: School of Business, Economics & Informatics > Computer Science and Information Systems
    Depositing User: Stephen Maybank
    Date Deposited: 11 May 2020 10:05
    Last Modified: 05 Oct 2022 18:15


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