Improvement and Calibration of Driving Safety Field Model: Resolving Risk Characterization Mismatches

The driving safety field (DSF) model, which comprehensively evaluates driving risks in complex environments by integrating human–vehicle–road factors, serves as a quantitative methodology for assessing dynamic traffic risks. However, it exhibits limitations in certain scenarios where its risk characterization deviates from actual risk variations. To address the challenges, an improved driving safety field (IDSF) model is proposed. The new framework redesigns the calculation of virtual mass, field force, and driving safety index. Parameters in the model were calibrated using accident data and driving simulator experiments. Results demonstrate that the IDSF outperforms conventional time‐to‐collision (TTC) inverse (TTCi) and DSF models. Specifically, in car‐following scenarios, IDSF demonstrates higher correlation ( r ≈0.9) with the TTCi model. In complex environments with high vehicular heterogeneity, compared with the DSF model, the IDSF model exhibits greater stability (80% lower coefficient of variation) and fewer extreme deviations (38% reduction). This study provides a novel theoretical framework for automotive intelligent safety technologies and offers valuable insights for designing more reasonable driving safety algorithms.