Optimizing Collision Avoidance in Dense Airspace using Deep Reinforcement Learning

TL;DR

This paper introduces a deep reinforcement learning-based method to improve collision avoidance systems for aircraft in dense airspace, enhancing safety and efficiency over traditional approaches.

Contribution

It develops a novel deep reinforcement learning framework to optimize collision avoidance in dense airspace, addressing limitations of existing systems.

Findings

The proposed system operates more efficiently than traditional methods.

It maintains high safety levels in dense airspace scenarios.

The approach effectively reduces decision burden on collision avoidance systems.

Abstract

New methodologies will be needed to ensure the airspace remains safe and efficient as traffic densities rise to accommodate new unmanned operations. This paper explores how unmanned free-flight traffic may operate in dense airspace. We develop and analyze autonomous collision avoidance systems for aircraft operating in dense airspace where traditional collision avoidance systems fail. We propose a metric for quantifying the decision burden on a collision avoidance system as well as a metric for measuring the impact of the collision avoidance system on airspace. We use deep reinforcement learning to compute corrections for an existing collision avoidance approach to account for dense airspace. The results show that a corrected collision avoidance system can operate more efficiently than traditional methods in dense airspace while maintaining high levels of safety.