# Aircraft Collision Avoidance Systems: Technological Challenges and Solutions on the Path to Regulatory Acceptance

arXiv: https://arxiv.org/abs/2510.20916 · HTML: https://mlsanity.com/paper/2510.20916/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 18 of its 111 references that are in the map.
- First posted on arXiv 23 Oct 2025.

## Builds on (most cited first, 18 of 18)

- [Algorithms for Verifying Deep Neural Networks](https://mlsanity.com/paper/1903.06758/) (2019)
- [Deep Neural Network Compression for Aircraft Collision Avoidance Systems](https://mlsanity.com/paper/1810.04240/) (2018)
- [First Three Years of the International Verification of Neural Networks Competition (VNN-COMP)](https://mlsanity.com/paper/2301.05815/) (2023)
- [Adaptive Stress Testing with Reward Augmentation for Autonomous Vehicle Validation](https://mlsanity.com/paper/1908.01046/) (2019)
- [Guaranteeing Safety for Neural Network-Based Aircraft Collision Avoidance Systems](https://mlsanity.com/paper/1912.07084/) (2019)
- [Learning an Urban Air Mobility Encounter Model from Expert Preferences](https://mlsanity.com/paper/1907.05575/) (2019)
- [Verifying Aircraft Collision Avoidance Neural Networks Through Linear Approximations of Safe Regions](https://mlsanity.com/paper/1903.00762/) (2019)
- [Collision Risk and Operational Impact of Speed Change Advisories as Aircraft Collision Avoidance Maneuvers](https://mlsanity.com/paper/2204.14250/) (2022)
- [Collision Avoidance and Geofencing for Fixed-wing Aircraft with Control Barrier Functions](https://mlsanity.com/paper/2403.02508/) (2024)
- [Adaptive Stress Testing of Trajectory Predictions in Flight Management Systems](https://mlsanity.com/paper/2011.02559/) (2020)
- [AVOIDDS: Aircraft Vision-based Intruder Detection Dataset and Simulator](https://mlsanity.com/paper/2306.11203/) (2023)
- [LeRAAT: LLM-Enabled Real-Time Aviation Advisory Tool](https://mlsanity.com/paper/2503.16477/) (2025)
- [ASTPrompter: Preference-Aligned Automated Language Model Red-Teaming to Generate Low-Perplexity Unsafe Prompts](https://mlsanity.com/paper/2407.09447/) (2024)
- [Reward Function Optimization of a Deep Reinforcement Learning Collision Avoidance System](https://mlsanity.com/paper/2212.00855/) (2022)
- [Formal and Practical Elements for the Certification of Machine Learning Systems](https://mlsanity.com/paper/2310.03217/) (2023)
- [Optimized and Trusted Collision Avoidance for Unmanned Aerial Vehicles using Approximate Dynamic Programming (Technical Report)](https://mlsanity.com/paper/1602.04762/) (2016)
- [Formally Verified Next-Generation Airborne Collision Avoidance Games in ACAS X](https://mlsanity.com/paper/2106.02030/) (2021)
- [Efficient Determination of Safety Requirements for Perception Systems](https://mlsanity.com/paper/2307.01371/) (2023)

## Abstract (arXiv)

Aircraft collision avoidance systems is critical to modern aviation. These systems are designed to predict potential collisions between aircraft and recommend appropriate avoidance actions. Creating effective collision avoidance systems requires solutions to a variety of technical challenges related to surveillance, decision making, and validation. These challenges have sparked significant research and development efforts over the past several decades that have resulted in a variety of proposed solutions. This article provides an overview of these challenges and solutions with an emphasis on those that have been put through a rigorous validation process and accepted by regulatory bodies. The challenges posed by the collision avoidance problem are often present in other domains, and aircraft collision avoidance systems can serve as case studies that provide valuable insights for a wide range of safety-critical systems.