Automating the resolution of flight conflicts: Deep reinforcement learning in service of air traffic controllers

2022/06/15 by George A. Vouros, Vouros, George, Alevizos Bastas +6
Economics, Econometrics and Finance · Engineering · Psychology · #Air Traffic Management and Optimization #Aviation Industry Analysis and Trends #FOS: Computer and information sciences #Human-Automation Interaction and Safety #I.2.1 #I.2.11 #I.2.6 #Machine Learning (cs.LG) #Multiagent Systems (cs.MA)

paper · pdf · doi:10.48550/arxiv.2206.07403

openalex publication_date 2022/06/15 · openalex created_date 2022/11/30 · openalex updated_date 2026/07/28

Abstract

Dense and complex air traffic scenarios require higher levels of automation than those exhibited by tactical conflict detection and resolution (CD&R) tools that air traffic controllers (ATCO) use today. However, the air traffic control (ATC) domain, being safety critical, requires AI systems to which operators are comfortable to relinquishing control, guaranteeing operational integrity and automation adoption. Two major factors towards this goal are quality of solutions, and transparency in decision making. This paper proposes using a graph convolutional reinforcement learning method operating in a multiagent setting where each agent (flight) performs a CD&R task, jointly with other agents. We show that this method can provide high-quality solutions with respect to stakeholders interests (air traffic controllers and airspace users), addressing operational transparency issues.

Related

• Autonomous Air Traffic Controller: A Deep Multi-Agent Reinforcement Learning Approach
• Diffusion-RL Based Air Traffic Conflict Detection and Resolution Method
• Conflict Resolution under Degraded Surveillance in Air Corridors Using Multi-Agent Reinforcement Learning
• Data-driven prediction of Air Traffic Controllers reactions to resolving conflicts
• A Deep Multi-Agent Reinforcement Learning Approach to Autonomous Separation Assurance