Abnormal Driving Detection With Normalized Driving Behavior Data: A Deep Learning Approach

Abnormal driving may cause serious danger to both the driver and the public. Existing detectors of abnormal driving behavior are mainly based on shallow models, which require large quantities of labeled data. The acquisition and labelling of abnormal driving data are, however, difficult, labor-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 method 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 behavior dataset demonstrate that the proposed scheme achieves a superior performance for abnormal driving detection compared to the state-of-the-art.

• Record URL:
  http://dx.doi.org/10.1109/TVT.2020.2993247
• Availability:
  • Find a library where document is available. Order URL: http://worldcat.org/issn/00189545
• Supplemental Notes:
  • Copyright © 2020, IEEE.
• Authors:
  • Maybank, S
  • Zhang, Xiaoqin
  • Maybank, Stephen
• Publication Date: 2020-7

Language

• English

Media Info

• Media Type: Web
• Features: References;
• Pagination: pp 6943-6951
• Serial:
  • IEEE Transactions on Vehicular Technology
  • Volume: 69
  • Issue Number: 7
  • Publisher: Institute of Electrical and Electronics Engineers (IEEE)
  • ISSN: 0018-9545
  • Serial URL: http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=25

Subject/Index Terms

• TRT Terms: Acceleration (Mechanics); Artificial intelligence; Behavior; Data analysis; Detection and identification; Driving; Machine learning
• Subject Areas: Data and Information Technology; Highways; Safety and Human Factors;

Filing Info

• Accession Number: 01753373
• Record Type: Publication
• Files: TRIS
• Created Date: Sep 29 2020 9:58AM