Object of study: the mechanism for improving vehicle stability on low-friction surfaces within the active-safety control loop. Subject of study: the formation of lateral force in the tire–road interaction and its use for reproducing (accounting for) limit handling modes and adaptive stability control. Because many existing stability-assessment methods rely on simplified wheel/tire rolling models that do not capture the full set of factors governing vehicle dynamics, we develop a framework that explicitly represents the nonlinear nature of tire–road interaction and integrates tire-material properties, pavement microtexture, and dynamic loading into a single mathematical model. The practical significance is in the possibility of embedding the proposed model into ESP/ABS/TCS algorithms for online identification of road-friction parameters and adaptive tuning of intervention thresholds according to current thermoclimatic conditions. At the engineering-design level, the method enables the construction of stability “maps” for various operating scenarios and can be used for tire selection, test-maneuver planning, and calibration of yaw-stability functions. The proposed approach is novel in that it preserves universality while remaining extendable to account for tire-inflation pressure, tread wear, residual tread depth, and non-uniform axle load distribution. The results reveal a sigmoidal temperature sensitivity near 0 °C, a concave-down degradation of stability with increasing wetness, and a pronounced reduction in maximum lateral force capacity in icy scenarios. Comparison with quasi-linear estimates reveals that neglecting the post-peak decay of the lateral force leads to an overestimation of the tire’s limit capability and an inflated stability margin during maneuvers. Overall, enhancing rolling models with explicit nonlinear effects is an effective tool for more accurate reproduction of limit modes and for reducing accident risk under reduced-friction conditions. Application domain: automotive production and automotive service operations.