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Crash Prediction Using Deep Learning in a Disorienting Spaceflight Analog Balancing Task.

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Area of Science:

  • Human factors in aerospace engineering
  • Neuroscience of spatial orientation
  • Artificial intelligence in autonomous systems

Background:

  • Mars' gravity (0.38g) lacks familiar cues, risking astronaut spatial disorientation.
  • Previous studies simulated spaceflight disorientation using a Multi-Axis Rotation System (MARS).
  • High crash rates were observed in simulated Mars landing scenarios without gravitational cues.

Purpose of the Study:

  • To develop a deep learning model for predicting spacecraft crashes.
  • To identify early indicators of spatial disorientation leading to control errors.
  • To assess the feasibility of AI intervention for preventing landing accidents.

Main Methods:

  • Utilized a Multi-Axis Rotation System (MARS) to simulate reduced gravity disorientation.
  • Trained stacked gated recurrent units (GRU) deep learning model on participant control data.
  • Analyzed joystick inputs and system dynamics to predict crash events.
  • Evaluated model performance using Area Under the Curve (AUC) and false positive/negative rates.

Main Results:

  • Deep learning model predicted crashes 800 ms in advance with 99% AUC.
  • False negatives, linked to disorientation, occurred during unpredictable joystick inputs.
  • Prioritizing false negative reduction increased false positives.
  • AI intervention could prevent 80.7% of crashes if immediate, but only 30.3% with human reaction time.

Conclusions:

  • Deep learning effectively predicts disorientation-induced crashes in simulated Mars landings.
  • AI control offers a potential solution to mitigate landing risks.
  • Human reaction time significantly impacts the effectiveness of automated safety systems.
  • Further research is needed to integrate AI for real-time spacecraft control during critical phases.