Project Summary: This project proposes the development, deployment, and evaluation of an edge-enabled multi-sensor wildlife detection and driver advisory system to reduce wildlife–vehicle collision risk in rural transportation corridors. The system will integrate roadside optical cameras, thermal cameras, and LiDAR sensors to detect, classify, and localize animals and approaching vehicles in real time. Multimodal sensor fusion will estimate animal position, vehicle speed and trajectory, separation distance, and time-to-collision to support dynamic collision-risk assessment under varying lighting and weather conditions. An edge-based reasoning framework combining artificial intelligence (AI) and rule-based safety logic will process sensor outputs locally to classify collision risk and generate real-time warnings and actionable advisories. Edge computing will enable low-latency operation, improve reliability in remote rural environments, and reduce dependence on continuous cloud connectivity. The system will be deployed and evaluated in collaboration with the National Parks Conservation Association along Interstate 40 in the Pigeon River Gorge. Expected outcomes include a field-tested prototype, validated performance results, and implementation guidance for transportation agencies and rural stakeholders.