07 - Systems, Subsystems and Equipment
ENHANCED AIRCRAFT TAKEOFF/LANDING SAFETY USING DEEP LEARNING MODEL IN RUNWAY ASSISTANCE SYSTEM
N.L. Ywet¹, A.A. Maw¹, J.W. Han¹, J. Chung, Toronto Metropolitan University, Canada; J.W. Chang, Korea Aerospace University, South Korea; J.-W. Lee¹; ¹Konkuk University, South Korea
This study introduces an innovative Takeoff/Landing Safety Runway Control Assistance System that employs advanced deep learning techniques. The primary objective of this system is to enhance precision and safety during critical flight stages by providing precise guidance through a sophisticated Artificial Intelligence framework. By precisely processing data, the system enhances situational awareness, predicts obstacle trajectories, and facilitates proactive risk avoidance. Leveraging the YOLO and LSTM models further fortifies the safety precautions during takeoff and landing. Moreover, this study emphasizes the importance of building trust in runway control station operations through a thorough review and strategic application of data, optimizing decision-making processes, operational techniques, and overall control center efficiency. The comprehensive system in this study aims to significantly reduce operator stress, ensure safety, enhance service satisfaction, and mitigate risks in hazardous aviation situations.