An Autonomous Free Airspace En-route Controller using Deep Reinforcement Learning Techniques
This addresses the challenge of complex air traffic control for aviation systems, though it appears incremental as it builds on existing deep learning methods for a specific domain.
The paper tackles the problem of managing increasing air traffic by developing an autonomous air traffic control model using graph-based deep learning, which successfully avoids 100% of collisions and prevents 89.8% of conflicts in realistic scenarios.
Air traffic control is becoming a more and more complex task due to the increasing number of aircraft. Current air traffic control methods are not suitable for managing this increased traffic. Autonomous air traffic control is deemed a promising alternative. In this paper an air traffic control model is presented that guides an arbitrary number of aircraft across a three-dimensional, unstructured airspace while avoiding conflicts and collisions. This is done utilizing the power of graph based deep learning approaches. These approaches offer significant advantages over current approaches to this task, such as invariance to the input ordering of aircraft and the ability to easily cope with a varying number of aircraft. Results acquired using these approaches show that the air traffic control model performs well on realistic traffic densities; it is capable of managing the airspace by avoiding 100% of potential collisions and preventing 89.8% of potential conflicts.