Abstract: As autonomous vehicle (AV) deployment expands beyond urban centers, rural agencies need corridor-level infrastructure readiness assessments for mobility planning and operational safety. In this research, we present a data-driven corridor readiness framework using CRITIC (Criteria Importance Through Intercriteria Correlation) to derive objective weights for physical (pavement condition, marking retroreflectivity, surface type) and digital (broadband speed, latency) indicators across 1.18 million North Carolina road segments. Results show spatial variation: western North Carolina weights retroreflectivity more, while primary highways place more weight on digital indicators. CRITIC weights remain stable under Monte Carlo sensitivity analysis. A multiplicative fail-safeness score flags “no-go zones” where any critical deficiency drives readiness to zero, guiding priorities. In Rockingham County, using 196 origin–destination pairs from rural transit data, fail-safe routing increases travel time modestly (8.4% on average; ~2 minutes) while improving safety margins. Overall, the framework supports rural route planning and infrastructure prioritization using available datasets.