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-inten...

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Bibliographic Details
Published in:IEEE transactions on vehicular technology Vol. 69; no. 7; pp. 6943 - 6951
Main Authors: Hu, Jie, Zhang, Xiaoqin, Maybank, Stephen
Format: Journal Article
Language:English
Published: New York IEEE 01.07.2020
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:
Abnormal driving detection
Acceleration
Adaptation models
Algorithms
Biological system modeling
Data models
Deep learning
Feature extraction
Machine learning
Noise reduction
stacked autoencoder
Vehicles
ISSN:0018-9545, 1939-9359
Online Access:Get full text
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Summary: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.
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ISSN:0018-9545
1939-9359
DOI:10.1109/TVT.2020.2993247