Resilience represents a crucial safety paradigm characterizing system risk evolution under specific perturbations, yet its application to microscopic driving safety needs further exploration. Analogous to resilience evolution patterns, generalized driving risk progression exhibits distinct safety decay and recovery phases, thereby establishing theoretical foundations for driving safety resilience analysis. This study develops a resilience-centric framework for quantifying dynamic car-following risk evolution. The car-following pairs were selected from highD natural driving trajectory dataset. Following risk quantification and clustering, 1,676 samples demonstrating complete “safe-dangerous-safe” transition process were identified. Through decomposition of car-following resilience into safety decay and recovery phases, we derived five interpretable resilience features and modeled their functional dependencies on driving variables via an integrated machine learning and SHapley Additive exPlanations (SHAP) analytical framework. Key findings reveal: 1) the CatBoost demonstrated superior fitting performance, achieving a mean absolute percentage error less than 10% across all resilience features; 2) the proposed Low-Rank Polynomial SHAP Fitting (LRP-SF) captured the nonlinear relationships between driving variables and resilience features, quantifying both directional influences and threshold effects; and 3) driver risk perception exhibits phase-dependent variability, with a stimulus-response mechanism governing safety evolution dynamics. The rapid safety deterioration and hazardous states act as triggers for recovery processes. This study further examined resilience threshold validity, car-following variable interactions, and inter-feature correlations. The potential application of LRP-SF in car-following safety control was also anticipated. This study offers methodological advancements for advanced driver assistance systems development and establishes a novel analytical paradigm for driving safety research.
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