Explanations in Lunar Surface Exploration (ELSE) for Machine Learning Models to Assist in NASA Rover Exploration

Introduction Current interplanetary surface exploration depends on human teleoperation of robots, which helps ensure the rover’s safety, but also limits operations since direct line of sight with communications equipment on Earth is needed. This may limit the amount of the surface of the planetary body the rover can cover over Read more…

ATD-2 Machine Learning Model Validation Framework Design

To realize the full power of machine learning, organizations need to focus on operations and delivery of the predictions as much as the development of the algorithm itself. In the case of NASA and ATD-2, building trust in the machine learning predictions is essential to expanding their use to improve inefficient operations and unused capacity. Air traffic controllers must trust the recommendations presented by them, and validation is essential towards building trust.

Taxi Time Predictions

The taxi-out time predictions help pilots decide when to “single-engine taxi”: taxi most of the way to the runway with only a single-engine turned on, turning on the second engine just a few minutes before take-off. The single-engine taxi decision is typically made by pilots within an hour of push back. Still, our customer asked for predictions up to four hours in advance of the expected push back time.