A Prototypical Network with an Attention-based Encoder for Drivers Identification Application

TL;DR

This paper introduces a novel deep learning architecture combining prototypical networks and attention-based encoders for driver identification, achieving high accuracy, faster prediction, and better generalization with limited data and unknown drivers.

Contribution

It proposes a new P-AttEnc model that uses few-shot learning and attention mechanisms to improve driver identification accuracy and efficiency, especially with limited data and unknown drivers.

Findings

Achieves over 99% accuracy in driver identification across datasets.

Reduces model parameters by approximately 87.6%.

Faster prediction times by 44% to 79%.

Abstract

Driver identification has become an area of increasing interest in recent years, especially for data- driven applications, because biometric-based technologies may incur privacy issues. This study proposes a deep learning neural network architecture, an attention-based encoder (AttEnc), which uses an attention mechanism for driver identification and uses fewer model parameters than current methods. Most studies do not address the issue of data shortages for driver identification, and most of them are inflexible when encountering unknown drivers. In this study, an architecture that combines a prototypical network and an attention-based encoder (P-AttEnc) is proposed. It applies few-shot learning to overcome the data shortage issues and to enhance model generalizations. The experiments showed that the attention-based encoder can identify drivers with accuracies of 99.3%, 99.0% and 99.9% in three different datasets and has a prediction time that is 44% to 79% faster because it significantly reduces, on average, 87.6% of the model parameters. P-AttEnc identifies drivers based on few shot data, extracts driver fingerprints to address the issue of data shortages, and is able to classify unknown drivers. The first experiment showed that P-AttEnc can identify drivers with an accuracy of 69.8% in the one-shot scenario. The second experiment showed that P-AttEnc, in the 1-shot scenario, can classify unknown drivers with an average accuracy of 65.7%.