An Aircraft Collision Avoidance Method Based on Deep Reinforcement Learning

Compared to existing traffic alert and collision avoidance systems (TCAS), the development of the new Airborne Collision Avoidance System X (ACAS X) adopts a model-based optimization approach to enhance airspace safety and operational efficiency. However, limitations such as the generation of massive numerical tables during development and the separation of development and evaluation processes hinder the system's maintenance and further application in avionics systems. Therefore, in this study, we tackle the aircraft collision avoidance problem using deep reinforcement learning methods, which substantially reduce storage requirements and enable self-updating during interaction with the environment, thus streamlining the development process. Our contributions include constructing a simulation environment for aircraft collision avoidance and establishing a reward system. Through three different reinforcement learning methods, we address collision avoidance while considering aircraft scheduling issues. Simulation results demonstrate the effectiveness of reinforcement learning in tackling aircraft collision avoidance and airspace scheduling problems.