Enhancing vehicle platoon safety assessment: A novel cascading risk measure

Surrogate Safety Measures (SSMs) have demonstrated substantial potential in identifying collision risks and supporting traffic safety evaluation. However, conventional SSMs are primarily designed for individual vehicle–level risk assessment, which limits their ability to capture inter-vehicle risk coupling and the mechanisms of potential risk propagation within a vehicle platoon. To address this limitation, this study proposes a novel Cascading Risk Measure (CRM) to characterize potential cascading risks and enhance platoon-level safety assessment. First, we introduce the concept of critical deceleration, representing the maximum deceleration disturbance from a leading vehicle that a follower can tolerate, to characterize each vehicle's risk state. Building on this, we develop a recursive mathematical framework that captures inter-vehicle coupling and the propagation of cascading risks by establishing recursive relationships between the critical decelerations of successive vehicles. This formulation reveals both the direction and the fundamental manner of risk propagation, thereby offering clear physical interpretability. In addition, the recursive framework ensures that CRM naturally degenerates into its single-vehicle form (denoted as DCRM) when no cascading risk is present, thereby generalizing the single-vehicle view, ensuring full compatibility with conventional SSMs, and providing a coherent and interpretable bridge between vehicle-level and platoon-level risk evaluation. The framework also guarantees complete coverage of cascading risks under arbitrary disturbance-propagation scenarios, ensuring that cascading effects are not omitted by design. Validation results show that, compared with existing SSMs, CRM enables earlier identification of peak platoon risk and generates smoother, more consistent risk profiles by better aligning with the evolution of platoon-level risk. These advantages are quantitatively supported by performance gains of 36.18% in MAE, 17.56% in sMAPE, and 22.22% in RMSSD, as well as an 18.84% increase in Kendall's τ_b relative to the best-performing baseline. CRM also effectively captures the impact of platoon size and vehicle spatial distribution—factors to which conventional SSMs are largely insensitive. These findings highlight CRM's potential for supporting proactive platoon-level risk warning and management.