Feature Selection for Abnormal Driving Behavior Recognition Based on Variance Distribution of Power Spectral Density

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dc.contributor.authorNassuna, Hellen
dc.contributor.authorKim, Jaehoon
dc.contributor.authorEyobu, Odongo Steven
dc.contributor.authorLee, Dongik
dc.date.accessioned2023-02-03T16:27:55Z
dc.date.available2023-02-03T16:27:55Z
dc.date.issued2020
dc.description.abstractThe 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.en_US
dc.identifier.citationNassuna, 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.2020.15.3.119en_US
dc.identifier.urihttp://dx.doi.org/10.14372/IEMEK.2020.15.3.119
dc.identifier.urihttps://nru.uncst.go.ug/handle/123456789/7519
dc.language.isoenen_US
dc.publisherIEMEK Journal of Embedded Systems and Applicationsen_US
dc.subjectAbnormal drivingen_US
dc.subjectMachine learningen_US
dc.subjectSpectrogramen_US
dc.subjectVarianceen_US
dc.subjectSmartphone sensoren_US
dc.titleFeature Selection for Abnormal Driving Behavior Recognition Based on Variance Distribution of Power Spectral Densityen_US
dc.typeArticleen_US
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