Abstract

Driver identification is a central 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 the main sensor devices. After extracting features from smartphone-embedded sensors, various machine learning methods can be used to identify the driver. However, the accuracy often degrades as the number of drivers increases. This paper uses a Generative Adversarial Network (GAN) for data augmentation to obtain a driver identification algorithm that maintains excellent performance also when the number of drivers increases. Since GAN diversifies the drivers’ data, it makes it possible to apply the identification algorithm on a larger number of drivers without overfitting. Although GANs are commonly used in image processing for image augmentation, their use for driving signal augmentation is novel. However, GAN’s training on raw driving signals diverges. This challenge is solved by getting the Discrete Wavelet Transform (DWT) on driving signals before feeding to GAN. Our experiments prove the usefulness of GAN model for generating driving signals emanating from 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 utilize features extracted by the statistical, spectral, and temporal approaches.

Highlights

  • A RTIFICIAL intelligence and data mining are two essential paradigms in developing future transportation systems

  • Stacked Generalization Method (SGM): {Support Vector Machine (SVM), Multi-Layer Perceptron (MLP), K-Nearest Neighbor (KNN), Random Forest (RF)} → Logistic Regression (LR) Based on the values of a set of predictor variables, LR predicts the presence of an outcome

  • We compared the performance of the hybrid methods of Generative Adversarial Network (GAN) and most successful classifiers of Fig. 8, Fig. 11: The results of t-SNE projection on the training data together augmented driving data, when the size of augmentation data varies from 0 to 120 minutes

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Summary

INTRODUCTION

A RTIFICIAL intelligence and data mining are two essential paradigms in developing future transportation systems. Thanks to the large amounts of traffic data collected from sensors in automobiles, telecommunications antennas, and smartphones, these paradigms have rapidly transformed the transportation sector. A driver identification problem classifies the drivers based on location or behavior characteristics. It is applicable in such diverse areas as freight transportation, driver control, anti-theft systems, and usagebased insurance systems [1], [2]. These methods often violate the driver’s privacy or enable the drivers to cheat. The modern driver identification systems collect data from in-vehicle sensors [3], GPS [4], inertial sensors [5], or their combination. Since the CAN-bus is available in most modern cars, many researchers used their data for driver identification; see Table I.

48 ECU signals
PROPOSED SYSTEM
Data pre-processing module
Identification module
EXPERIMENTAL RESULTS
Sensitivity analysis on involved axis
Sensitivity analysis on window properties
Analysis on features
Performance of identification algorithms
Performance of Hybrid Model of GAN and SGM
CONCLUSION

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