Evolutionary-Optimized Multimodal Driver Risk Assessment Using DE-Enhanced Cross-Attention Fusion of EEG and Telematics

Road safety depends on both a driver’s emotional cognitive state and physical driving behavior, yet most existing systems rely on a single modality, limiting real-world reliability. This paper presents HECANet (Hybrid Evolutionary Cross-Attention Network), a multimodal framework that integrates EEG-based emotional cues and vehicle telematics behavior for robust driver risk assessment. EEG signals are modeled using a PSO-optimized CNN–LSTM to capture spatiotemporal emotional patterns, while telematics data are analyzed using a GA-optimized XGBoost model to identify safe, distracted, and aggressive driving behaviors. A Differential Evolution–optimized cross-attention fusion layer effectively aligns emotional and behavioral features, enabling interpretable emotion–behavior interaction modeling. The fused representation produces a driver safety score and risk probability, with K-Means clustering used to categorize drivers into Safe, Caution, and Risky groups. Experimental results achieve 94.7% accuracy and a 0.94 macro F1-score, demonstrating that joint emotion–behavior modeling significantly enhances driver risk prediction for intelligent transportation and fleet safety applications.