Ai-integrated Autonomous Commercial Aircraft Taxiing System

The increasing complexity of airport operations and rising global air traffic demand more efficient, safe, and environmentally sustainable ground movement of aircraft. This research proposes an AI-integrated autonomous taxiing guidance system designed to assist pilots in navigating from gate to runway and vice versa, minimizing reliance on conventional Air Traffic Control (ATC) instructions. Unlike fully autonomous systems, this solution retains human involvement by focusing on real-time route optimization, obstacle detection, and pilot guidance, thereby improving operational efficiency while maintaining pilot oversight. The system leverages a hybrid of technologies, including computer vision, LIDAR,RADAR, and machine learning-based predictive analytics, to recommend optimal taxi routes while detecting and responding to obstacles in real time. Augmented reality (AR) interfaces and 3D mapping enhance situational awareness, especially in low-visibility conditions, while automation of routine communication reduces the cognitive load on both pilots and ATC. The system is designed for integration into existing airport infrastructure, making it scalable across a wide range of airport sizes and configurations. Simulation results demonstrate significant improvements in taxiing efficiency, substantial reductions in fuel consumption, and notable decreases in emission levels, contributing toward sustainable airport operations. This hybrid approach preserves human control while leveraging AI for smarter, safer, and more sustainable decision-making, setting the stage for the future of intelligent ground operations in aviation.