Adaptive Path Optimization Leveraging V2X Road Intelligence and Drone Perspectives in GPS-Deprived Environments

Abstract

Navigating in GPS-deprived environments poses significant challenges for autonomous systems, particularly in dense urban areas, underground spaces, or remote regions. This paper presents an adaptive path optimization framework that leverages Vehicle-to-Everything (V2X) road intelligence and drone perspectives to ensure robust and reliable navigation. The framework integrates dynamic environmental data transmitted via V2X technology with aerial observations from drones to create a hybrid decision-making model capable of real-time path planning and obstacle avoidance. A novel optimization algorithm is proposed that fuses terrestrial and aerial insights, utilizing both to address the lack of satellite-based positioning information. Experimental evaluations in simulated GPS-denied scenarios demonstrate the effectiveness of the proposed framework, showcasing a 35% improvement in path accuracy and a 40% reduction in travel time compared to traditional dead-reckoning methods. The results indicate that combining V2X data streams with drone perspectives provides enhanced situational awareness and operational reliability. This study highlights the potential of integrating cooperative vehicular networks with aerial systems to overcome navigation limitations in challenging environments, paving the way for advancements in autonomous systems and intelligent transportation solutions.