Sensor Fusion Architecture for V2X Road Condition Monitoring and Drone-Assisted Navigation in Tunnels

Abstract

Sensor fusion plays a pivotal role in enhancing vehicular and drone-based navigation for critical infrastructure, such as tunnels. This paper presents a novel sensor fusion architecture tailored for Vehicle-to-Everything (V2X) communication and drone-assisted road condition monitoring, focusing on tunnel environments. The proposed framework integrates data from multiple sources, including vehicular sensors, road-embedded sensors, and aerial drone-based systems, to achieve real-time situational awareness and adaptive navigation. Key contributions include the development of a robust multi-modal data fusion algorithm, optimized for low-latency V2X communication networks, and a hierarchical decision-making system for interpreting environmental data under challenging conditions such as low visibility and high vehicular density. Additionally, this work explores the use of drones to extend the operational range of monitoring systems in tunnels by identifying hazards, such as obstructions, poor lighting, and structural anomalies, that may impact vehicle safety. Comprehensive simulations and experimental evaluations demonstrate the efficacy of the architecture, showing significant improvements in localization accuracy, hazard detection rates, and network throughput compared to conventional systems. The proposed approach advances the state-of-the-art in smart mobility, offering a scalable and adaptable solution for enhancing safety and efficiency in tunnel navigation scenarios.