This study proposes a deep learning framework for driver identity identification by extracting information from the vehicular controller area network (CAN) bus signals. First, naturalistic driving data of 20 drivers were collected under a fixed testing route with different road types and different traffic conditions. Then, a one-dimensional convolutional neural network was constructed for driver identification, which consists of two convolutional-pooling layers, a fully connected layer, and a SoftMax layer. Model optimisation algorithms were applied to improve accuracy and speed up the training process. Also, the model parameters were optimised by evaluating their influences on the model results. Furthermore, the performance of the proposed algorithm was compared with that of the K-nearest neighbour, support vector machine, multi-layer perceptron, and long short-term memory model. The authors used the ${\bf Macro}\, {\bi F}_1$ M a c r o F 1 score as an evaluation criterion and the identification score of the authors' proposed model reaches 99.10% under 20 testing subjects where the data time window size is one second and the sample data overlap is 80%. The results show that the model's performance is significantly better than the other algorithms, which can effectively identify driver identities with stability and robustness.


    Zugriff

    Zugriff über TIB


    Exportieren, teilen und zitieren



    Driver identification using 1D convolutional neural networks with vehicular CAN signals

    Hu, Hongyu / Liu, Jiarui / Gao, Zhenhai et al. | Wiley | 2020

    Freier Zugriff

    Driver Distraction Identification with an Ensemble of Convolutional Neural Networks

    Hesham M. Eraqi / Yehya Abouelnaga / Mohamed H. Saad et al. | DOAJ | 2019

    Freier Zugriff


    Automated Driver Drowsiness Detection from Single-Channel EEG Signals Using Convolutional Neural Networks and Transfer Learning

    Ghadami, Ali / Mohammadzadeh, Mohammad / Taghimohammadi, Mohammadreza et al. | IEEE | 2022


    On Generalizing Driver Gaze Zone Estimation Using Convolutional Neural Networks

    Vora, Sourabh / Rangesh, Akshay / Trivedi, Mohan M. | British Library Conference Proceedings | 2017