Study on the risk Assessment of vehicle groups considering driving styles

To address the limited consideration of driving style diversity in group-level risk propagation analysis, a Dynamic Interaction Potential (DIP)-based framework is proposed to capture the risk evolution patterns of vehicles with different driving styles. Vehicle groups are identified by DBSCAN using the vehicle trajectories from HighD. To describe vehicle interactions within groups, DIP is constructed based on motion conflict and behavioral uncertainty. Fluctuation intensity and fluctuation rate are derived from DIP for risk measurement and driving style classification. The roles of different styles in risk propagation are quantified using Network analysis. A comprehensive vehicle group risk index is proposed that incorporates style-specific effects. Risk propagation network analysis reveals the distinct roles of driving styles, identifying aggressive drivers as key risk initiators within vehicle groups. The proposed style-aware risk index (CRAI_Style) demonstrates superior predictive performance compared to baseline indicators. In the one-second prediction window, the ROC-AUC for CRAI_Style is 0.845, followed by its style-agnostic counterpart CRAI (0.745) and traditional conflict-based indicator TTC (0.675). CRAI_Style consistently maintains the highest predictive accuracy when the prediction window extends to three seconds. These findings reveal the differentiated roles of driving styles in risk propagation, where aggressive drivers act as key risk initiators and conservative drivers as risk suppressors, and demonstrate that incorporating this heterogeneity significantly enhances the accuracy and robustness of vehicle group risk assessment.