# Spacecraft Collision Avoidance Challenge: design and results of a machine learning competition

arXiv: https://arxiv.org/abs/2008.03069 · HTML: https://mlsanity.com/paper/2008.03069/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 7 Aug 2020.

## Built on by (most cited first, 11 of 11)

- [A Spacecraft Dataset for Detection, Segmentation and Parts Recognition](https://mlsanity.com/paper/2106.08186/) (2021)
- [On Margins and Generalisation for Voting Classifiers](https://mlsanity.com/paper/2206.04607/) (2022)
- [Recent Advancements in Deep Learning Applications and Methods for Autonomous Navigation: A Comprehensive Review](https://mlsanity.com/paper/2302.11089/) (2023)
- [Conjunction Data Messages for Space Collision Behave as a Poisson Process](https://mlsanity.com/paper/2303.15074/) (2023)
- [Predicting the Probability of Collision of a Satellite with Space Debris: A Bayesian Machine Learning Approach](https://mlsanity.com/paper/2311.10633/) (2023)
- [A Markov Decision Process Framework for Early Maneuver Decisions in Satellite Collision Avoidance](https://mlsanity.com/paper/2508.05876/) (2025)
- [Probability of Collision of satellites and space debris for short-term encounters: Rederivation and fast-to-compute upper and lower bounds](https://mlsanity.com/paper/2311.08978/) (2023)
- [Treatment of Epistemic Uncertainty in Conjunction Analysis with Dempster-Shafer Theory](https://mlsanity.com/paper/2402.00060/) (2024)
- [Trojan horse hunt in deep forecasting models: Insights from the European Space Agency competition](https://mlsanity.com/paper/2603.20108/) (2026)
- [Deep Tech to Space: Space Data Centers and AI Revolution at the Edge](https://mlsanity.com/paper/2605.19892/) (2026)
- [Satellite Trajectory Optimization via Proximal Policy Optimization for Space Debris Avoidance](https://mlsanity.com/paper/2608.09628/) (2026)

## Abstract (arXiv)

Spacecraft collision avoidance procedures have become an essential part of satellite operations. Complex and constantly updated estimates of the collision risk between orbiting objects inform the various operators who can then plan risk mitigation measures. Such measures could be aided by the development of suitable machine learning models predicting, for example, the evolution of the collision risk in time. In an attempt to study this opportunity, the European Space Agency released, in October 2019, a large curated dataset containing information about close approach events, in the form of Conjunction Data Messages (CDMs), collected from 2015 to 2019. This dataset was used in the Spacecraft Collision Avoidance Challenge, a machine learning competition where participants had to build models to predict the final collision risk between orbiting objects. This paper describes the design and results of the competition and discusses the challenges and lessons learned when applying machine learning methods to this problem domain.