Discrete Wavelet Transform for Generative Adversarial Network to
Identify Drivers Using Gyroscope and Accelerometer Sensors
Driver identification is an important research area in intelligent
transportation systems, with applications in commercial freight
transport and usage-based insurance. One way to perform the
identification is to use smartphones as sensor devices. By extracting
features from smartphone-embedded sensors, various machine learning
methods can identify the driver. The identification becomes particularly
challenging when the number of drivers increases. In this situation,
there is often not enough data for successful driver identification.
This paper uses a Generative Adversarial Network (GAN) for data
augmentation to solve the problem of lacking data. Since GAN diversifies
the drivers’ data, it extends the applicability of the driver
identification. Although GANs are commonly used in image processing for
image augmentation, their use for driving signal augmentation is novel.
Our experiments prove their utility in generating driving signals
emanating from the Discrete Wavelet Transform (DWT) on smartphones’
accelerometer and gyroscope signals. After collecting the augmented
data, their histograms along the overlapped windows are fed to machine
learning methods covered by a Stacked Generalization Method (SGM). The
presented hybrid GAN-SGM approach identifies drivers with 97% accuracy,
98% precision, 97% recall, and 97% F1-measure that outperforms
standard machine learning methods that process features extracted by the
statistical, spectral, and temporal approaches.