A runway exit prediction model with visually explainable machine decisions

• Main Authors: Woo, Chuan Jie, Goh, Sim Kuan, Alam, Sameer, Md Meftahul Ferdaus, Mohamed Ellejmi
• Other Authors: School of Mechanical and Aerospace Engineering
• Format: Conference or Workshop Item
• Language: English
• Published: 2022
• Subjects: Engineering::Aeronautical engineering::Aircraft; Runway Exit Prediction; Explainable AI; Local Interpretable Model-agnostic Explanation; Min-Hash Encoder; Trust in AI
• Online Access: https://hdl.handle.net/10356/160024; https://www.icrat.org/
• Institution: Nanyang Technological University

A growing number of machine learning (ML) enabled tools and prototypes have been developed to assist air traffic controllers (ATCOs) in their decision-making process. These ML tools can facilitate faster and more consistent decisions for traffic monitoring and management. However, many of these...