Abstract: According to the Fatality Analysis Reporting System, 43% of all roadway fatalities occur on rural roads, despite only 19% of the US population residing in rural areas. This disproportionate risk largely stems from the unique challenges of rural environments (e.g., higher speeds, volatile weather, variable roadway geometry, frequent wildlife crossings, and limited cellular and communication coverage, etc.). This project aims to address these challenges and improve roadway safety and transportation efficiency in rural corridors by leveraging integrated multimodal sensing, AI-empowered multisource data analytics, customized data, and C2X communication. Key safety applications include AI-driven highway scene understanding (e.g., traffic flow, pavement surface conditions, weather conditions, etc.), curve speed advisory systems, and wildlife (deer) collision avoidance.  The communication framework incorporates both connected vehicle and connected infrastructure components.  The system has been tested in the foothills of South Carolina and a rural mountainous area near Mars Hill North Carolina, demonstrating its capability of successfully warning drivers in target hazardous situations.