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 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.
Driver identification using 1D convolutional neural networks with vehicular CAN signals
IET Intelligent Transport Systems ; 14 , 13 ; 1799-1809
01.12.2020
11 pages
Aufsatz (Zeitschrift)
Elektronische Ressource
Englisch
vehicular CAN signals , driver identification , identification score , driver information systems , road types , sample data overlap , model parameters , road safety , vehicular controller area network bus signals , 1D convolutional neural networks , controller area networks , deep learning framework , optimisation , convolutional neural nets , long short‐term memory model , K‐nearest neighbour , naturalistic driving data , model optimisation algorithms , multilayer perceptrons , SoftMax layer , convolution filters , data time window size , driver identity identification , one‐dimensional convolutional neural network , multilayer perceptron , nearest neighbour methods , learning (artificial intelligence) , fixed testing route , support vector machines , fully connected layer nodes , convolutional‐pooling layers
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