Feature Selection for Abnormal Driving Behavior Recognition Based on Variance Distribution of Power Spectral Density
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Date
2020
Journal Title
Journal ISSN
Volume Title
Publisher
IEMEK Journal of Embedded Systems and Applications
Abstract
The detection and recognition of abnormal driving becomes crucial for achieving safety
in Intelligent Transportation Systems (ITS). This paper presents a feature extraction method
based on spectral data to train a neural network model for driving behavior recognition. The
proposed method uses a two stage signal processing approach to derive time-saving and efficient
feature vectors. For the first stage, the feature vector set is obtained by calculating variances
from each frequency bin containing the power spectrum data. The feature set is further reduced
in the second stage where an intersection method is used to select more significant features that
are finally applied for training a neural network model. A stream of live signals are fed to the
trained model which recognizes the abnormal driving behaviors. The driving behaviors considered
in this study are weaving, sudden braking and normal driving. The effectiveness of the proposed
method is demonstrated by comparing with existing methods, which are Particle Swarm
Optimization (PSO) and Convolution Neural Network (CNN). The experiments show that the
proposed approach achieves satisfactory results with less computational complexity.
Description
Keywords
Abnormal driving, Machine learning, Spectrogram, Variance, Smartphone sensor
Citation
Nassuna, H., Kim, J., Eyobu, O. S., & Lee, D. (2020). Feature selection for abnormal driving behavior recognition based on variance distribution of power spectral density. IEMEK Journal of Embedded Systems and Applications, 15(3), 119-127. http://dx.doi.org/10.14372/IEMEK.2020.15.3.119