Who is Driving? Event-Driven Driver Identification and Impostor Detection Through Support Vector Machine

Rahim, Mussadiq Abdul; Liu, Jiamou; Zhang, Zijian; Zhu, Liehuang; Li, Xin; Khan, Salabat

doi:10.1109/jsen.2020.2976539

Cited by 20 publications

(14 citation statements)

References 19 publications

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“…Accuracy percentages vary between 60.63 and 100 for the classification and identification of drives in the driver classification studies carried out with different methods [2][3][4][5][6]18,19]. Although the classification percentage is not very high in this study, it is advantageous and different to use the data as raw data without any processing.…”

Section: Weighted Arithmetic Meanmentioning

confidence: 87%

“…The empirical trajectory dataset is utilized to perform the models. Support vector machines have accurate results in the driving classification [5]. Radial Basis Function kernel is proposed monitoring a highly sensitive frame for a large number of drivers.…”

Section: Introductionmentioning

confidence: 99%

“…Classification accuracy may increase up to 90% between 10-24 drives with SVM method. GPS based driving experience data is utilized in many applications [5], [6]. Wireless sensing to collect biometrics of drivers to classify driver behavior is currently studied and machine learning methods are performed [7].…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Identification of Driving Characteristics Using Markov Process Model

Serttaş

Gerek

Hocaoğlu

2020

Eskişehir Technical University Journal of Science and Technology a - Applied Sciences and Engineering

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Considering the role of drivers in traffic, it is an important advantage to know the driver characteristics in advance. Taking this characteristic into consideration, the driver can be warned socially and economically. In this study, taking into account this situation, driving characteristics are removed within certain drivers and the drivers are divided into two classes as calm and aggressive. The data recorded via the smartphone is used directly when classification. By applying Markov process method to the data, the drives are classified with 73% accuracy.

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Section: Weighted Arithmetic Meanmentioning

confidence: 87%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Identification of Driving Characteristics Using Markov Process Model

Serttaş

Gerek

Hocaoğlu

2020

Eskişehir Technical University Journal of Science and Technology a - Applied Sciences and Engineering

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show abstract

“…e problem of RT Traffic signal recognition was addressed in literature [19], which used video to analyze the specific identification indicators of the image to identify and analyze the traffic state. Based on traffic stream and nonlinear theory, literature [20] proposed an expressway traffic state recognition method. An urban road traffic abnormal event management system was designed in the literature [21,22].…”

Section: Related Workmentioning

confidence: 99%

SVM-Based Real-Time Identification Model of Dangerous Traffic Stream State

Huang

2022

Wireless Communications and Mobile Computing

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By comparing and studying the correlation between traffic stream parameters and traffic safety of different highways, the correlations of traffic natural quantity, traffic equivalent, passenger-cargo ratio, car following percentage, congestion degree, and time occupancy rate are obtained. The traffic stream state before the actual accident is used as the criterion to judge the bad traffic stream state. The main parameters are obtained by extracting the parameters from the traffic stream data at the lane level and reducing the dimension of the parameters with the principal component analysis method. Establish a SVM model for RT early warning of traffic stream safety. Compared with other methods, the adaptive parameter selection method can adaptively select parameters according to the training sample set, realize the adaptive ability of the forecast model, and effectively improve the forecast accuracy of traffic stream. This paper studies the risk early warning model of road traffic accidents, which can transform the problem of road traffic safety into active early warning and improve the level of traffic safety. This study provides safety management measures for highway operation departments, which has certain theoretical significance and practical application value.

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“…However, these methods often violate the driver's privacy or enable the drivers to cheat. Nowadays, the modern driver identification systems collect data from in-vehicle sensors [3], GPS [4], inertial sensors [5], or their combination. The related literature have used the following techniques to classify the drivers:…”

Section: Introductionmentioning

confidence: 99%

Discrete Wavelet Transform for Generative Adversarial Network to Identify Drivers Using Gyroscope and Accelerometer Sensors

2022

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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% F1measure that outperforms standard machine learning methods that process features extracted by the statistical, spectral, and temporal approaches.

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Who is Driving? Event-Driven Driver Identification and Impostor Detection Through Support Vector Machine

Cited by 20 publications

References 19 publications

Identification of Driving Characteristics Using Markov Process Model

Identification of Driving Characteristics Using Markov Process Model

SVM-Based Real-Time Identification Model of Dangerous Traffic Stream State

Discrete Wavelet Transform for Generative Adversarial Network to Identify Drivers Using Gyroscope and Accelerometer Sensors

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