Zero-to-Stable Driver Identification: A Non-Intrusive and Scalable Driver Identification Scheme

Rahim, Mussadiq Abdul; Zhu, Liehuang; Li, Xin; Liu, Jiamou; Zhang, Zijian; Qin, Zhan; Khan, Salabat; Gai, Keke

doi:10.1109/tvt.2019.2954529

Cited by 31 publications

(11 citation statements)

References 42 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: 86%

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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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: 86%

“…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%

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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“…In [19], [20], authors note that the error probability of their system is independent of the number of profiles in the profile database. This is, however, only an observation for a small database (only 50-70 profiles) and does not offer an approach to achieving scalability.…”

Section: Related Workmentioning

confidence: 99%

“…We evaluated threshold-based Ver(•, •) for different values of k in k-NN divergence estimator. Figure 4 shows the EER for Ver(•, •) when k in range [1,19], where 19 is obtained by noting that for n = 40 and m = 20 , 19 = min(40, 20) − 1. For smaller k, the calculated EER for Ver(•, •) has higher variance and changes in different measurements.…”

Section: A Experiments To Redesign Dacmentioning

confidence: 99%

Scalable Behavioral Authentication

Islam

Kneppers

2021

IEEE Access

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A Behavioral Authentication (BA) system constructs a behavioral profile for a user and uses it to verify their identity claims. It is primarily used as a second factor in user authentication. A BA system starts with an initial database of user profiles, and uses a verification algorithm to accept or reject a verification request, that consists of a claimed identity and behavioral data by measuring the distance between the profile of the claimed identity and the presented behavioral data. As new users join the system, the size of profile database grows and for a user u, the probability of other users with "close" profiles increases, and this results in an increase in the error probability of verification algorithm. We analyze this problem in distance-based verification systems, and introduce the notion of scalability of BA systems that requires the system error probability to remain (almost) unchanged as the size of the profile database grows. To achieve scalability, we propose personalization of verification algorithm. More specifically, for a user u the verification algorithm considers a set of doppelgangers that consists of users that have "close" profiles to u, and uses the set to form additional verification checks for verification requests against u. We use extensive experiments to validate the above, including the increase in the verification error with the increase in the database size, and implement and evaluate our proposed scalable verification algorithm in reducing this error. To our knowledge, this is the first systematic treatment of scalability of BA systems. Our notion of scalability and the approach to achieving it are general and applicable to other distance-based authentication systems.

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“…The proposed driver identification system was analyzed with 98% performance using visual data from ECU. Rahim et al [ 43 ] proposed a driver identification system with driving patterns using GPS information. It identifies the driver with 96% accuracy using features such as zero-to-stable elapsed time, direction change, stable speed, and overall acceleration.…”

Section: Biometrics Technique Using Ecg Signal For Intelligent Vehmentioning

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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Zero-to-Stable Driver Identification: A Non-Intrusive and Scalable Driver Identification Scheme

Cited by 31 publications

References 42 publications

Identification of Driving Characteristics Using Markov Process Model

Identification of Driving Characteristics Using Markov Process Model

Scalable Behavioral Authentication

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

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