T-EnFP: An Efficient Transformer Encoder-Based System for Driving Behavior Classification

Recently, Transformer-based architectures have been explored for classifying driving behavior. Although the Transformer effectively employs self-attention for global temporal learning, the presence of redundant modules can detrimentally affect task-specific performance and overall efficiency. In this study, we optimize the method for classification task using Transformer encoder-based network in two steps. First, we propose the feature embedding and encoding modules for time series data. Second, we propose a Time Series Transformer encoder-based system (T-EnFP) that is suitable for and performs time series data classification tasks. We evaluate the proposed approaches on two naturalistic driving behavior datasets, UAH-Drivest and UTDrive dataset. The proposed models achieve 0.97, 0.94 and 0.99 F1 score for different subsets of UAH-Driveset, outperforming the previously proposed Transformer-based models and LSTM-based models. On the UTDrive dataset, the proposed model achieves the best result with a 0.98 F1 score compared to other baseline models.