Distributed Sensor Integration for Drone-V2X Collaboration Aimed at Persistent Connectivity in Constrained Road Networks

Abstract

Distributed sensor arrays harness multi-modal data from unmanned aerial vehicles (UAVs) and roadside units to facilitate collaborative vehicle-to-everything (V2X) communication in constrained transportation networks. Drone-based platforms with embedded imaging, acoustic, and environmental sensors enhance situational awareness for on-road vehicles, enabling proactive congestion management and optimized navigation under suboptimal connectivity conditions. Connectivity challenges on narrow or obstructed corridors are addressed by deploying UAVs as airborne relays that gather road traffic data, meteorological readings, and infrastructure status from multiple vantage points. This data is relayed to ground-based vehicles in near real-time, yielding continuous route planning updates and minimizing disruptions caused by blind spots and signal blockages. The integration of distributed sensor nodes with advanced communication protocols, including millimeter-wave and cellular vehicle-to-everything (C-V2X) technologies, permits more robust pathfinding and distributed decision-making. Machine learning-driven analytics further enrich situational intelligence by fusing information streams from heterogeneous sensors, thereby enabling predictive capacity for event detection such as accidents, extreme weather, or fluctuations in traffic density. This paper explores the architectural dimensions of drone-V2X sensor integration, network protocols for real-time data sharing, resource allocation for seamless connectivity, and a simulated experimental analysis of the approach on constrained road segments. Findings indicate improved service continuity, prompt event detection, and enhanced safety outcomes when drones and ground vehicles engage in tightly coordinated and sensor-augmented interactions.