Driver identification using only the CAN-Bus vehicle data through an RCN deep learning approach

Abdennour, Najmeddine; Ouni, Tarek; Amor, Nader Ben

doi:10.1016/j.robot.2020.103707

Cited by 31 publications

(8 citation statements)

References 8 publications

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“…HaiLong Liu [23] used Deep Sparse Autoencoder (DSAE) to extract the hidden features and visualize the driving behavior, where different driving behaviors and driving styles of drivers were represented by converting the features into RGB scale and mapping them on trajectories. N. Abdennour [24] extracted the data from CAN bus and analyses the driving styles through residual convolutional networks (RCN), thereby eliminating the problem of user privacy invasion. In the work reported by Yi Guo [25], the original labels are obtained by voting on multiple clustering methods, and the classification results obtained by three supervised models are then voted on to derive the corresponding driving styles, which combines the advantages of different models and provides more convincing results.…”

Section: Methods Based On Non-visual Driving Datamentioning

confidence: 99%

CNN-LSTM Driving Style Classification Model Based on Driver Operation Time Series Data

et al. 2023

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This paper aims to establish a driving style recognition method that is highly accurate, fast and generalizable, considering the lack of data types in driving style classification task and the low recognition accuracy of widely used unsupervised clustering algorithms and single convolutional neural network methods. First, we propose a method to collect the information on driver's operation time sequence in view of the imperfect driving data, and then extract the driver's style features through convolutional neural network. Then, for the collected temporal data, the Long Short Term Memory networks (LSTM) module is added to encode and transform the driving features, to achieve the driving style classification. The results show that the accuracy of driving style recognition reaches over 93%, while the speed is improved significantly.

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Section: Methods Based On Non-visual Driving Datamentioning

confidence: 99%

CNN-LSTM Driving Style Classification Model Based on Driver Operation Time Series Data

et al. 2023

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“…For advanced driver assistance systems (ADAS), this attribute can be an efficient factor to ensure the security and protection of the vehicle. Additionally, it extends the ADAS capabilities by creating different profiles for the drivers, which helps every driver according to his own driving style and improve the ADAS fidelity [27].…”

Section: Literature Reivewmentioning

confidence: 99%

A machine learning approach for driver identification

Khan

Ali

Haque

et al. 2023

IJEECS

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Driver identification is a momentous field of modern decorated vehicles in the perspective of the controller area network (CAN-Bus). Many conventional systems are used to identify the driver. One step ahead, most of the researchers use sensor data of CAN-Bus but there are some difficulties because of the variation of a protocol of different models of vehicle. We aim to identify the driver through supervised learning algorithms based on driving behavior analysis. To identify the driver, a driver verification technique is proposed that evaluate driving pattern using the measurement of CAN sensor data. In this paper on-board diagnostic (OBD-II) is used to capture the data from CAN-Bus sensor and the sensors are listed under SAE J1979 statement. According to the service of OBD-II drive identification is possible. However, we have gained two types of accuracy on a full data set with 10 drivers and a partial data set with two drivers. The accuracy is good with less number of drivers compared to a higher number of drivers. We have achieved statistically significant results in terms of accuracy in contrast to the baseline algorithm.

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“…With respect to other approaches, in [12] the authors have proposed a machine learning based method to continuously profile the driver. In more recent studies, Deep Neural Networks are successfully used to perform drivers' identification [13], [14].…”

Section: Related Workmentioning

confidence: 99%

An Explainable and Evolving Car Driver Identification System based on Decision Trees

Bernardi

Cimitile

Ducange

et al. 2022

2022 IEEE International Conference on Evolving and Adaptive Intelligent Systems (EAIS)

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Shared mobility represents a more and more widespread model ensuring several advantages for citizens and reducing gas emissions. The birth of car-sharing models drives the necessity to use car monitoring systems able to reduce the possibility that unauthorized people drive a certain car. In this paper, we discuss the architecture of car driver identification systems based on incremental fuzzy decision trees. The main features of the proposed system are i) the explainability, namely the possibility of giving explanations regarding its decisions, provided in terms of linguistic rules, and ii) the possibility of continuously updating the classification model. We show the preliminary results of an experimental campaign in which we compare both fuzzy and non-fuzzy incremental decision trees, both in terms of classification performance and model complexity/explainability.

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Driver identification using only the CAN-Bus vehicle data through an RCN deep learning approach

Cited by 31 publications

References 8 publications

CNN-LSTM Driving Style Classification Model Based on Driver Operation Time Series Data

CNN-LSTM Driving Style Classification Model Based on Driver Operation Time Series Data

A machine learning approach for driver identification

An Explainable and Evolving Car Driver Identification System based on Decision Trees

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