A Hybrid Approach for Driving Behavior Recognition: Integration of CNN and Transformer-Encoder with EEG data

Human factors are considered as one of the main causes affecting road traffic safety. Therefore, it is highly necessary to establish a driving behavior model for predicting driver behaviors and states, which can be used for risk monitoring. As an objective indicator that can accurately measure human cognitive and emotional states, electroencephalography (EEG) has attracted widespread attention from researchers due to its suitability and high temporal resolution for driver state and behavior measurement. Our study proposes a novel EEG-based driving behavior recognition algorithm that combines CNN and Transformer-Encoder modules. We deploy CNN to extract spatial and temporal features from EEG signals, capturing local dependencies between different time points. Subsequently, the encoder module of Transformer is utilized to handle long-term dependencies, enhancing the correlation between different time steps and improving classification accuracy. Experimental results demonstrate that our proposed model outperforms the baseline model on a self-constructed driving behavior classification dataset, providing a foundation for further in-depth research into real-world road traffic safety.