Automobile Driver Fingerprinting: A New Machine Learning Based Authentication Scheme

Xun, Yijie; Li, Jiajia; Kato, Nei; Fang, Yue; Zhang, Yanning

doi:10.1109/tii.2019.2946626

Cited by 107 publications

(27 citation statements)

References 29 publications

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“…e identification accuracy yielded 84.6%. Xun et al [36] used actual vehicles to collect naturalistic driving behavior data, including speed, steering wheel angle, accelerator pedal signal, etc. ey built a model to achieve driver fingerprinting based on a convolutional neural network and support vector domain description and used driver fingerprinting to accurately identify drivers.…”

Section: Introductionmentioning

confidence: 99%

Extraction of Optimal Measurements for Drowsy Driving Detection considering Driver Fingerprinting Differences

Zhang

et al. 2021

Journal of Advanced Transportation

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Contributions of measurements for detecting drowsy driving are determined by calculation parameters, which are directly related to the accuracy of drowsiness detection. The previous studies utilized the same Unified Calculation Parameters (UCPs) to compute each driver’s measurements. However, since each driver has unique driving behavior characteristics, namely, driver fingerprinting, Individual Drivers’ Best Calculation Parameters (IDBCPs) making measurements more discriminative for drowsiness are various. Regardless of the difference in driver fingerprinting among the drivers being tested, using UCPs instead of IDBCPs to compute measurements will limit the drowsiness-detection performance of the measurements and reduce drowsiness-detection accuracies at the individual driver level. Thus, this paper proposed a model to optimize calculation parameters of individual driver’s measurements and to extract individual driver’s measurements that effectively distinguish drowsy driving. Through real vehicle experiments, we collected naturalistic driving data and subjective drowsy levels evaluated by the Karolinska Sleepiness Scale. Eight nonintrusive drowsiness-related measurements were calculated by double-layer sliding time windows. In the proposed model, we firstly applied the Wilcoxon test to analyze differences between measurements of the awake state and drowsy state, and constructed the fitness function reflecting the relationship between the calculation parameters and measurement’s drowsiness-detection performance. Secondly, the genetic algorithms were used to optimize fitness functions to obtain measured IDBCPs. Finally, we selected measurements calculated by IDBCPs that can distinguish drowsy driving to constitute individual drivers’ optimal drowsiness-detection measurement set. To verify the advantages of IDBCPs, the measurements calculated by UCPs and IDBCPs were, respectively, used to build driver-specific drowsiness-detection models: DF_U and DF_I based on the Fisher discriminant algorithm. The mean drowsiness-detection accuracies of DF_U and DF_I were, respectively, 85.25% and 91.06%. It indicated that IDBCPs could enhance measurements’ drowsiness-detection performance and improve the drowsiness-detection accuracies. This paper contributed to the establishment of personalized drowsiness-detection models considering driver fingerprinting differences.

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Section: Introductionmentioning

confidence: 99%

Extraction of Optimal Measurements for Drowsy Driving Detection considering Driver Fingerprinting Differences

Zhang

et al. 2021

Journal of Advanced Transportation

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“…However, both of these techniques can not support effectively Machine-Type communications (MTC) due to many requirements for MTC devices, such as high data rate, lowlatency, low power consumption, high security and network scalability and coverage. The fifth generation (5G) mobile networks are capable of overcoming the limitations of the current technologies, which provides Gigabit data rate and low latency communications to connected devices [7]- [12]. As a result, 5G enabled IoT has attracted a lot of attention in the research community and industry.…”

Section: Introductionmentioning

confidence: 99%

Joint 3D Trajectory Design and Time Allocation for UAV-Enabled Wireless Power Transfer Networks

Feng

Zhao

et al. 2020

IEEE Trans. Veh. Technol.

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This paper considers a rotary-wing unmanned aerial vehicle (UAV)-enabled wireless power transfer system, where a UAV is dispatched as an energy transmitter (ET), transferring radio frequency (RF) signals to a set of energy receivers (ERs) periodically. We aim to maximize the energy harvested at all ERs by jointly optimizing the UAV's three-dimensional (3D) placement, beam pattern and charging time. However, the considered optimization problem taking into account the drone flight altitude and the wireless coverage performance is formulated as a non-convex problem. To tackle this problem, we propose a low-complexity iterative algorithm to decompose the original problem into four sub-problems in order to optimize the variables sequentially. In particular, we first use the sequential unconstrained convex minimization based algorithm to find the globally optimal UAV two-dimensional (2D) position. Subsequently, we can directly obtain the optimal UAV altitude as the objective function of problem is monotonic decreasing with respect to UAV altitude. Then, we propose the multiobjective evolutionary algorithm based on decomposition (MOEA/D) based algorithm to control the phase of antenna array elements, in order to achieve high steering performance of multi-beams. Finally, with the above solved variables, the original problem is reformulated as a single-variable optimization problem where charging time is the optimization variable, and can be solved using the standard convex optimization techniques. Furthermore, we use the branch and bound method to design the UAV trajectory which can be constructed as traveling salesman problem (TSP) to minimize flight distance. Numerical results validate the theoretical findings and demonstrate that significant performance gain in terms of sum received power of ERs can be achieved by the proposed algorithm in UAV-enabled wireless power transfer networks.Index Terms-Multi-beam, trajectory optimization, UAV 3D placement, wireless power transfer.

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“…In order to identify a driver from outside the vehicle, initially, it was attempted to open the door of the vehicle and start the vehicle with physical security systems based on ownership. Later, it was developed into a technology with simple personal customized security systems using the driver’s bio-information that does not risk loss [ 5 ]. Like driver security technology, personalized services are provided by driver recognition technology using the driver’s bio-information inside the vehicle [ 6 ].…”

Section: Introductionmentioning

confidence: 99%

Driver Identification System Using Normalized Electrocardiogram Based on Adaptive Threshold Filter for Intelligent Vehicles

Choi

Lim

Pan

2020

Sensors

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Driver-centered infotainment and telematics services are provided for intelligent vehicles that improve driver convenience. Driver-centered services are performed after identification, and a biometrics system using bio-signals is applied. The electrocardiogram (ECG) signal acquired in the driving environment needs to be normalized because the intensity of noise is strong because the driver’s motion artifact is included. Existing time, frequency, and phase normalization methods have a problem of distorting P, QRS Complexes, and T waves, which are morphological features of an ECG, or normalizing to signals containing noise. In this paper, we propose an adaptive threshold filter-based driver identification system to solve the problem of distortion of the ECG morphological features when normalized and the motion artifact noise of the ECG that causes the identification performance deterioration in the driving environment. The experimental results show that the proposed method improved the average similarity compared to the results without normalization. The identification performance was also improved compared to the results before normalization.

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Automobile Driver Fingerprinting: A New Machine Learning Based Authentication Scheme

Cited by 107 publications

References 29 publications

Extraction of Optimal Measurements for Drowsy Driving Detection considering Driver Fingerprinting Differences

Extraction of Optimal Measurements for Drowsy Driving Detection considering Driver Fingerprinting Differences

Joint 3D Trajectory Design and Time Allocation for UAV-Enabled Wireless Power Transfer Networks

Driver Identification System Using Normalized Electrocardiogram Based on Adaptive Threshold Filter for Intelligent Vehicles

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