This paper presents a structured framework for real-time quantification of uncertainty measures across di!erent estimation topologies (paths) to ensure reliable estimation under fault conditions. Our multi-stage approach first analyzes multiple redundant estimation pathways to e!ectively isolate fault sources and then reconfigures to the most reliable path based on the theoretically quantified uncertainty measure. Considering all sensor configurations and independent pathways for estimating vehicle states, the resulting structure forms a directed acyclic graph, termed an estimation graph. A reconfigurable estimation scheme is proposed to enhance reliability across diverse fault conditions. The framework leverages a detailed structural analysis of the estimation graph to enhance fault detectability, as shown by detecting the fault by measuring vertical acceleration. By theoretically quantifying fault propagation along each estimation path, the framework enables the real-time selection of the optimal path. The proposed theoretical derivations provide a unified approach to quantifying the e!ects of common soft faults, e.g., bias and excessive noise, by appropriately adapting the influence matrices. Validation using a high-fidelity CarSim model and Monte Carlo simulations confirms the accuracy of the theoretical derivations and demonstrates the framework’s e!ectiveness in localizing fault sources and ensuring reliable estimation under various fault conditions.