  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.
