Driver Identification and Verification From Smartphone Accelerometers Using Deep Neural Networks

This paper addresses driver identification and verification using Deep Learning (DL) on tri-axial accelerometer signals from drivers' smartphones. The proposed driver identification architecture includes ResNet-50 followed by two Stacked Gated Recurrent Units (SGRUs). ResNet provides a deep layer model, thanks to shortcut connections, is able to extract rich features from accelerometers, and GRU layers model the dynamics of drivers' behavior. ResNet-50 pre-trained on image classification has been evaluated testing two approaches to map 1D accelerometer signals into 2D images. Siamese Neural Networks and Triplet Loss Training have been proposed for driver verification. The Siamese architecture is built on the same ResNet-50 + GRU model of driver identification, while the Triplet loss has required obtaining embeddings at journey level. Experimental results have been obtained for a dataset of 25 drivers, performing 20,025 daily life journeys with more than 800 per driver. Driver identification achieved top-1 and top-5 accuracies of 71.89% and 92.02%, respectively, and driver verification a F1 score of 74.09%. These results are competitive with state-of-the-art research that have generally tested smaller databases (in many cases based only on predefined routes), and have relied on information sources other than accelerometers, such as gyroscopes, magnetometers and GPS. Therefore, we believe that the proposed DL architectures are suitable for developing efficient driver monitoring applications based on only energy-efficient smartphone accelerometer signals.