Colloquium: Ricardo Santana (C&O)

Low-altitude, high-density air traffic is expected to grow in the coming decades with several companies being
certified to initiate Urban Operations for both freight and passenger transport. However, traditional human-
centered Air Traffic Control operations (ATCos) are not scalable to handle the increased demand to maintain
safe separation between aircraft and resolve potential conflicts. Thus, new autonomous solutions to resolve
potential conflicts between aircraft must be developed, allowing humans to supervise and understand machine
actions. We propose a centralized Deep Reinforcement Learning (DRL)-based framework to provide speed
advisories to vehicles, ensuring safe and efficient operations. We used Proximal Policy Optimization (PPO)
to train the intelligent controller and show that our framework is capable of handling challenging merging
point settings. We evaluated our model extensively using a custom OpenAI Gym environment and proved
that it can achieve a 99% success rate in conflict resolution across multiple merging point configurations.

Supervisor: Dr. O.A. Sharpans'kykh