Feature Selection Based on Variance Distribution of Power Spectral Density for Driving Behavior Recognition
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Date
2019
Journal Title
Journal ISSN
Volume Title
Publisher
IEEE
Abstract
Abnormal driving detection and recognition is a
crucial area of research towards achieving safety in intelligent
transportation systems (ITS). In this study, we propose a feature
extraction approach and use the extracted features to train a
deep learning model that is used for abnormal driving behavior
recognition. The proposed approach derives the features based
on variances calculated from each frequency bin containing the
power spectrum data that is generated using the short time
fourier transform. A subset of features is further selected based
on variance similarity of the power spectral data. Similarity is
realized by finding intersecting variance data of different
variance samples obtained from defined data segments of a
given driving behavior class. The driving behaviors considered
are weaving, sudden braking and normal driving. Experiments
were performed using an artificial neural network to test the
efficiency of the proposed feature extraction approach. Results
show that an accuracy of 91.0% can be achieved with
accelerometer data. The accuracy is further improved to 96.1%
by combining accelerometer with gyroscope data.
Description
Keywords
Abnormal driving, Deep learning, Spectrogram, Variance
Citation
Nassuna, H., Eyobu, O. S., Kim, J. H., & Lee, D. (2019, June). Feature selection based on variance distribution of power spectral density for driving behavior recognition. In 2019 14th IEEE Conference on Industrial Electronics and Applications (ICIEA) (pp. 335-338). IEEE.