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

arXiv: https://arxiv.org/abs/2510.17250 · HTML: https://mlsanity.com/paper/2510.17250/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 9 of its 28 references that are in the map.
- First posted on arXiv 20 Oct 2025.

## Builds on (most cited first, 9 of 9)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [LSTM Fully Convolutional Networks for Time Series Classification](https://mlsanity.com/paper/1709.05206/) (2017)
- [Attend and Diagnose: Clinical Time Series Analysis using Attention Models](https://mlsanity.com/paper/1711.03905/) (2017)
- [Characterizing Driving Styles with Deep Learning](https://mlsanity.com/paper/1607.03611/) (2016)
- [Autoencoder Regularized Network For Driving Style Representation Learning](https://mlsanity.com/paper/1701.01272/) (2017)
- [Know Your Master: Driver Profiling-based Anti-theft Method](https://mlsanity.com/paper/1704.05223/) (2017)
- [Driver Identification Based on Vehicle Telematics Data using LSTM-Recurrent Neural Network](https://mlsanity.com/paper/1911.08030/) (2019)
- [Driver Identification Using Automobile Sensor Data from a Single Turn](https://mlsanity.com/paper/1708.04636/) (2017)
- [This Car is Mine!: Automobile Theft Countermeasure Leveraging Driver Identification with Generative Adversarial Networks](https://mlsanity.com/paper/1911.09870/) (2019)

## Abstract (arXiv)

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%.