# Optimizing Collision Avoidance in Dense Airspace using Deep Reinforcement Learning

arXiv: https://arxiv.org/abs/1912.10146 · HTML: https://mlsanity.com/paper/1912.10146/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 20 Dec 2019.

## Built on by (most cited first, 6 of 6)

- [A Survey on Reinforcement Learning in Aviation Applications](https://mlsanity.com/paper/2211.02147/) (2022)
- [AAM-Gym: Artificial Intelligence Testbed for Advanced Air Mobility](https://mlsanity.com/paper/2206.04513/) (2022)
- [Obstacle Avoidance for UAS in Continuous Action Space Using Deep Reinforcement Learning](https://mlsanity.com/paper/2111.07037/) (2021)
- [Automating the resolution of flight conflicts: Deep reinforcement learning in service of air traffic controllers](https://mlsanity.com/paper/2206.07403/) (2022)
- [Towards a Standardized Reinforcement Learning Framework for AAM Contingency Management](https://mlsanity.com/paper/2311.10805/) (2023)
- [Tradeoffs When Considering Deep Reinforcement Learning for Contingency Management in Advanced Air Mobility](https://mlsanity.com/paper/2407.00197/) (2024)

## Abstract (arXiv)

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.