Image-based conflict detection with convolutional neural network under weather uncertainty

• Main Authors: Dang, Phuoc Huu, Mohamed Arif Bin Mohamed, Alam, Sameer
• Other Authors: School of Mechanical and Aerospace Engineering
• Format: Conference or Workshop Item
• Language: English
• Published: 2023
• Subjects: Engineering::Aeronautical engineering::Aviation; Engineering::Aeronautical engineering::Air navigation; Conflict Detection; Air Traffic Management
• Online Access: https://hdl.handle.net/10356/169380
• Institution: Nanyang Technological University

Detection of air traffic conflicts in a weather constrained airspace is challenging given the inherent uncertainties and aircraft maneuvers which give rise to new conflict birth-points. Traditional conflict detection tools are untenable in such situations as they primarily rely on flight-plan, aircraft performance characteristics and trajectories projection in short-term (2-4 minutes). This work adopts a convolutional neural network (CNN) model, on radar-like images, for conflict detection task in a constrained airspace. The CNN models are well-known for their learning capabilities when dealing with unstructured data like pixelated images. In this study, historical ADS-B data with weather constrained airspace is input as pixelated images to the CNN model. The learned model was compared with two well-known models for conflict detection (CD). The results demonstrated that the CNN based model was able to predict off-nominal conflict with high accuracy. The CNN model also demonstrated its ability to predict off-nominal conflict early for a given ten-minute look-ahead window. The CNN based model also showed low levels of false alarm signals as compared to other models. Generally speaking, all models showed low probabilities of miss-detection, mostly in the early phase of the 10-minute look-ahead window. This novel approach may serve to develop effective CD algorithms with longer look-ahead time and may aid in early detection of air traffic conflicts in non-nominal scenarios.