Multilevel Sensor Collaboration Merging V2X and Drone Insights for Tunnel-Centric Navigation Frameworks

Abstract

Autonomous navigation in tunnel environments poses unique challenges due to constrained spaces, poor GPS reception, and limited line-of-sight conditions. This paper presents a novel multilevel sensor collaboration framework that integrates vehicle-to-everything (V2X) communication and drone-based sensing to enhance tunnel-centric navigation. By leveraging V2X-enabled infrastructure for real-time data exchange and drones for aerial insights, the proposed framework ensures robust situational awareness and collision-free navigation. The multilevel architecture combines ground-based sensors, vehicular data, and drone imagery, fused using advanced sensor fusion techniques such as Kalman filters and deep learning models. Experimental evaluations demonstrate the system's ability to adapt dynamically to varying tunnel conditions, such as traffic density and environmental obstructions, outperforming traditional standalone navigation systems. This work highlights the synergy between ground and aerial platforms for cooperative navigation and lays the groundwork for next-generation intelligent transportation systems in challenging environments.